2 - TI Pharma

Diabetogenic Effects of Glucocorticoid Drugs:
The Knowns and The Unknowns
Daniël Henri van Raalte

Diabetogenic Effects of Glucocorticoid Drugs:
The Knowns and The Unknowns
The studies described in this thesis were performed within the framework of the Dutch Top
Institute Pharma, project T1-106.
ISBN: 978-94-6191-423-1
Author: Daniël Henri van Raalte
Cover Design: Lotje van Lieshout
Lay-out: Cyriel van Oorschot, Ipskamp drukkers BV
Printed by: Ipskamp Drukkers BV
© D.H. van Raalte, Amsterdam, The Netherlands, 2012.
No part of this thesis may be reproduced, stored or transmitted in any form or by any means,
without prior permission of the author.
Financial support by the Dutch Diabetes Research Foundation and the Dutch Arthritis Foundation
for the publication of this thesis is gratefully acknowledged.
The printing of this thesis was additionally supported by: Boehringer Ingelheim B.V., Eli Lilly
Nederland B.V., GlaxoSmithKline B.V., Novartis Pharma B.V., Posthumus Meyjes fonds, Sanofi
–Aventis B.V., Servier Nederland B.V., Vrije Universiteit.

VRIjE UNIVERSITEIT
Diabetogenic Effects of Glucocorticoid Drugs:
The Knowns and The Unknowns
ACADEMISCH PROEFSCHRIFT
ter verkrijging van de graad Doctor aan
de Vrije Universiteit Amsterdam,
op gezag van de rector magnificus
prof.dr. L.M. Bouter,
in het openbaar te verdedigen
ten overstaan van de promotiecommissie
van de Faculteit der Geneeskunde
op vrijdag 23 november 2012 om 13.45 uur
in de aula van de universiteit,
De Boelelaan 1105
door
Daniël Henri van Raalte
geboren te Amsterdam

Promotor: prof.dr. M. Diamant

Leescommissie: prof.dr. M. Boers
prof.dr. A.K. Groen
prof.dr. R.j. Heine
prof.dr. M.H.H. Kramer
dr. D.M. Ouwens
dr. C. Rustemeijer
prof.dr. H.P. Sauerwein
“I not only use all the brains that I have, but all that I can borrow” –
Thomas Woodrow Wilson

CoNTENT
Part I Introduction
Chapter 1 General introduction and outline of the thesis 11
Chapter 2 Novel insights into glucocorticoid-mediated diabetogenic 23
effects: towards expansion of therapeutic options?
Part II Glucocorticoids and islet-cell fuction
Chapter 3 Prednisolone-induced beta-cell dysfunction is associated with 55
impaired endoplasmic reticulum homeostasis in INS-1E cells.
Chapter 4 Acute and 2-week exposure to prednisolone impair different 75
aspects of beta-cell function in healthy men.
Chapter 5 Islet-cell dysfunction induced by glucocorticoid treatment: 91
role for altered sympathovagal balance?
Chapter 6 Glucocorticoid receptor gene polymorphisms are associated 115
with reduced first-phase glucose-stimulated insulin secretion
and disposition index in women, but not in men.
Chapter 7 GLP-1 receptor agonist treatment prevents glucocorticoid 133
induced glucose intolerance and islet-cell dysfunction in
humans.
Part III Mechanisms of glucocorticoid-induced insulin resistance
Chapter 8 Low-dose glucocorticoid treatment affects multiple aspects
of intermediary metabolism in healthy humans: a randomized
controlled trial.
151
Chapter 9 Glycosphingolipid metabolism and mitochondrial function do
not explain glucocorticoid-induced insulin resistance in
healthy men
175
Chapter 10 Glucocorticoid treatment impairs microvascular function in
healthy males, in association with its adverse effects on
glucose metabolism and blood pressure.
193
Chapter 11 Glucocorticoids alter subcutaneous adipose tissue function
and adipokine secretion in vivo in healthy men
217

Chapter 12 Angiopoietine-like protein 4 is differentially regulated by 231
glucocorticoids and insulin in vitro and in vivo in healthy
humans.
Part IV Metabolic effects of glucocorticoids and interaction with
systemic inflammation
Chapter 13 Metabolic effects of high-dose prednisolone treatment in early
rheumatoid arthritis: diabetogenic effects and inflammation
reduction on the balance.
249
Chapter 14 Glucose tolerance, insulin sensitivity and beta-cell function
in patients with rheumatoid arthritis treated with or without
low-to-medium dose glucocorticoids.
267
Part V Discussion
Chapter 15 Diabetogenic effects of glucocorticoid drugs: the knowns
and the unknowns
289
Nederlandse samenvatting (Dutch summary) 329
Dankwoord (Acknowledgements) 339
Biography 347
List of Publications 351
List of co-author affiliations 357
Color Figures 365

PART
Introduction
I

Chapter 1
General Introduction and Outline of the Thesis
D.H. van Raalte and M.Diamant

Chapter 1 General Introduction
The involvement of hormones secreted by the adrenal cortex in intermediary metabolism was
first established in 1908, when the French investigators Bierry and Malloizel discovered that
adrenalectomized dogs developed lower fasting glucose levels [1]. In 1910, hypoglycemia was
first described in association with Addison’s disease [2]. In the following decades, the critical
role of the adrenal cortex in intermediary metabolism and energy homeostasis was further
characterized [3]. A major advance was made in 1936 with the simultaneous isolation of the
inactive form of the adrenal hormone cortisol, so called cortisone, by the Polish-born, Swiss
chemist Tadeusz Reichstein [4] and the American scientist Edward Calvin Kendall [5]. This
breakthrough enabled further experiments into the various physiological roles of adrenal
cortex hormones. Not only were the metabolic effects of cortisone further explored, cortisone
was also shown to reduce stress responses and traumatic shock in rats [6].
The amount of cortisone that could be isolated from bovine adrenal glands, however, was
small, and the need to produce adrenocorticosteroids through synthetic methods became soon
apparent. This process was fuelled by the US entry into the Second World War, when rumors
circulated that Luftwaffe pilots were taking adrenal extracts to increase their resistance to
oxygen deprivation at high altitudes. Although this rumor was never confirmed, it induced an
all-out quest for large-scale synthesis of active adrenal hormone, which was given even higher
priority than the development of penicillin and anti-malarials [7,8].
In 1946, Lewis Hastings Sarett of Merck Research Laboratories succeeded in synthetically
producing cortisone from desoxycholic acid [9]. By the summer of 1948, sufficient material
was produced to initiate the first studies in humans. In clinical trials conducted in the Mayo
clinic by the rheumatologist Philip Showalter Hench, a collaborator of Kendall, cortisone
treatment for the first time improved the symptoms of Addison’s disease. In addition, Hench
tested cortisone in patients with rheumatoid arthritis, driven by observations that joint
complains were reduced during pregnancy and jaundice, conditions in which sex steroids and
bile acids are increased, substances that were found closely related to the novel discovered
steroid cortisone. Indeed, cortisone treatment induced a spectacular reduction in joint
tenderness and swelling in chronic rheumatoid arthritis patients [10,11]. In the following
years, the use of adrenal cortex hormones formulations was successfully introduced in the
treatment of an increasing number of diseases due to their potent anti-inflammatory and
immunosuppressive effects. Tadeusz Reichstein, Philip Showalter Hench and Edward Calvin
Kendall shared the Nobel Prize for Physiology or Medicine in 1950 ‘for research on the
structure and biological effects of adrenal cortex hormones’. In the same year, cortisone was
officially launched as a pharmacological agent.
12

Figure 1. Overview of the therapeutic and adverse effects induced by glucocorticoid treatment. (See
page 366 for full color figure)
However, with its increasing use, it became quickly apparent that high-dose glucocorticoid
(GC) treatment induced several undesired effects, ranging from salt and water retention
to increased gastric acidity and psychosis. To this end, in the 1950-1960s, newer synthetic
GC compounds were developed, including prednisolone and dexamethasone, which were
designed to induce fewer side effects. Prednisolone, which is characterized by a 1,2 double bond
in the A-ring, was first produced in 1955 by Schering Corporation and was introduced into the
market a decade later under the brand name Meticortelone. When this structure additionally
undergoes 9α-fluorination, 1-dehydrogenation and 16α-methylation, dexamethasone is
yielded and this compound was introduced for clinical use a year later. Although these newer
formulation significantly reduced side effects as compared to cortisone, many important side
effects of GC treatment remain present today (Figure 1). Of these adverse effects, unfavorable
changes in cardiometabolic parameters are prominent and include the development of
glucose intolerance, diabetes, visceral adiposity, dyslipidemia, skeletal muscle atrophy and
hypertension [12]. The mechanisms underlying these so called diabetogenic effects of GCs
regarding glucose, lipid and protein metabolism were studied extensively in the 1960-1970s
13
1
Chapter 1

Chapter 1 General Introduction
and were mainly attributed to GC-induced insulin resistance [13-21]. After this period of
intensive research on the metabolic effects of GCs, research slowed down in this area for
about two decades (Figure 2).
Figure 2. The number of new publications on Pubmed with search terms “glucocorticoids” and “diabetes”
shown per 5-year intervals. After 1975, the amount of research performed on the topic declined
somewhat, but in recent years, the subject has received full attention.
In the last 15 years, however, research into the diabetogenic effects of GCs has been revived,
mainly due to two different reasons. First, it became clear that deregulated (tissue) GC
metabolism may play a role in ‘idiopathic’ obesity and metabolic syndrome, encouraged by
the observation that several features of these conditions strikingly resemble the phenotype of
chronic GC excess. It was postulated by Björntorp that low-grade chronic stress, characterized
by (slightly) increased GC activity, may contribute to visceral obesity, the metabolic syndrome
and cardiovascular disease [22]. In addition, increased tissue cortisol levels, generated from
the inactive metabolite cortisone by enhanced activity of the enzyme 11-beta hydroxysteroid
dehydrogenase (HSD) type 1, were demonstrated in adipose tissue derived from rodent
models of obesity and from obese humans [23]. This makes the hormone 11-beta HSD type 1 an
attractive therapeutic candidate for the treatment of obesity and its metabolic consequences.
As such, a battery of pharmaceutical companies is currently developing compounds that may
reduce tissue cortisol levels by targeting 11-beta HSD type 1. The amount of research done on
this topic has skyrocketed during the last decade (Figure 3).
Second, improved understanding of the mode of action of GCs on the molecular and
cellular level has led to the development of so-called dissociated glucocorticoid receptor
(GR) agonists (Figure 4). In 1985, it was discovered that GCs exert their actions through
14

inding to the intracellular GR, following which this complex migrates to the nucleus, where
it regulates target gene expression [24,25]. Additionally, it became clear that the antiinflammatory
actions and dysmetabolic effects of GCs may be the result of different genomic
actions, i.e. transrepression and transactivation, respectively. This suggests that these effects
may potentially be separated, which forms the basis of these novel GR agonists [25-32].
Several compounds with a dissociated profile are currently under development by different
pharmaceutical companies and have shown promising results in disease and side effect
animal models, where they reduced inflammation without altering glucose levels [33].
Figure 3. The number of new publications on Pubmed with search term “11-beta hydroxysteroid
dehydrogenase” shown per decade. The number of publications on this topic has skyrocketed in the past
2 decades.
Figure 4. The number of new publications on Pubmed with search term “selective glucocorticoid
receptor agonists” shown per decade. In the last decade, information on the development of these novel
compounds has found its way to the public domain.
15
1
Chapter 1

Chapter 1 General Introduction
The work done in this thesis should be seen in light of the development of these dissociated
GR agonists and was conducted within the framework of a relatively novel organization. In
2005, the fifth Dutch Technological Top Institute was founded by the name of Top Institute
Pharma, in which the Dutch government, several pharmaceutical companies and all Dutch
universities were brought together with the goal to develop so called priority medicines. In
our Top Institute Pharma Project consortium (T1.106), we collaborated with NV Organon
(more recently incorporated first by Schering-Plough and later by Merck Sharp & Dohme),
who are currently developing GR agonists with a dissociated profile.
The clinical development of these novel compounds, however, faces important difficulties.
The mechanisms by which prednisolone induces metabolic changes remain largely unknown.
In addition, due to the differences in the type of GC administered, treatment duration, mode
of administration and chosen dose, several contrasting reports exist in the literature. Also,
since many studies were conducted more than 4 decades ago, it may be difficult to compare
recent studies with the results obtained from those early studies due to different research
methods and techniques that are currently being employed. Thus, in order to be able to
further develop the dissociated GR agonists, it is highly important to first obtain detailed
information regarding the metabolic side effects induced by prednisolone treatment and to
select biomarkers that reflect these metabolic side effects in a dose-dependent manner. Only
after these indicators of efficacy and safety have become available, it will be possible to design
compounds with an improved therapeutic index compared to the currently-available classic
GR agonists.
In the present thesis, of which the PANTHEON (Prednisolone peripherAl effects oN glucose
meTabolism, metabolic Hormones, insulin sEnsitivity and secretioN)-trials form the
backbone, we aimed to assess the peripheral metabolic effects of both low-and high-dose GC
treatment in healthy volunteers and patients with rheumatoid arthritis, focusing specifically
on the effects of prednisolone on islet-cell function and insulin sensitivity of various metabolic
pathways.
In chapter 2 of this thesis, an overview of the knowledge regarding the diabetogenic side
effects of GC treatment is presented that was available at the time when our studies were
initiated. Part II describes the effects of GC treatment on pancreatic islet-cell function. In
chapter 3 mechanisms underlying the deleterious effects of GCs on beta-cell function are
addressed in INS-1E cells in vitro, in particular the involvement of endoplasmic reticulum
stress. In chapter 4, the effects of both acute and more prolonged high-dose GC treatment on
beta-cell function are described, using mathematical modeling analysis of glucose, insulin and
C-peptide concentrations obtained from standardized meal challenge tests. In chapter 5, we
16

further explored the effects of both low- and high-dose GC treatment on both alpha- and betacell
function in healthy volunteers using both intravenous hyperglycemic clamp studies and
meal challenge tests. In chapter 6, in order to further delineate the role of GR signaling in betacell
function, we investigated the effects of single nucleotide polymorphisms (SNPs) in the GR
gene on measures of beta-cell function in a cohort of 449 subjects. In chapter 7, we explored
the potential of the novel class of glucose-lowering agents the glucagon-like peptide (GLP)-
1 receptor agonists to prevent the dysmetabolic effects induced by GC treatment in healthy
subjects. Part III of the thesis focuses on GC-induced insulin resistance and the mechanisms
that may contribute to this well-known but only partly understood side effect of GC treatment.
In chapter 8, the effects of both low- and high-dose GC treatment on several aspects of
intermediary metabolism is addressed. Possible mediators of GC-induced insulin resistance in
skeletal muscle, including impaired sphingolipid metabolism and mitochondrial dysfunction,
are investigated in chapter 9. In chapter 10 the possible role of microvascular dysfunction
induced by GC treatment was addressed and in chapter 11 we investigated the effects of GC
treatment on adipose tissue function and adipokine secretion. In chapter 12, the role of the
novel endocrine factor angiopoietin-like protein 4 in GC-induced metabolic alterations was
studied, by combining an in vivo (randomized controlled trial in healthy individuals) and in
vitro (cell culture) approach. In part IV of this thesis, using an oral glucose tolerance test
to assess both insulin secretory response and surrogate markers of insulin resistance, the
diabetogenic effects of GC treatment were investigated in two different rheumatoid arthritis
populations, allowing to study the interaction with systemic inflammation. In chapter 13, the
acute effects of high-dose prednisolone treatment on glucose tolerance, insulin sensitivity and
insulin secretion in patients with early and active rheumatoid arthritis are described, whereas
in chapter 14, the effects of chronic GC treatment on glucose tolerance, insulin sensitivity and
insulin secretion are assessed in patients with long-standing rheumatoid arthritis.
In the general discussion (part V, chapter 15), an overview will be presented of the
pathophysiology of glucose intolerance induced by GC treatment. Additionally, implications
will be discussed for the development of dissociated GR agonists. Finally, since these
compounds are still under development, a proposition will be made how GC-induced diabetes
may best be treated in clinical practice.
17
1
Chapter 1

Chapter 1 General Introduction
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Pharmacol Ther. 2002; 96(1):23-43
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16. Issekutz B, jr., Borkow I. Effect of glucagon and glucose load on glucose kinetics, plasma FFA, and
insulin in dogs treated with methylprednisolone. Metabolism. 1973; 22(1):39-49
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17. Kaplan SA, Shimizu CS. Effects of cortisol on amino acids in skeletal muscle and plasma.
Endocrinology. 1963; 72:267-72
18. Lecocq FR, Mebane D, Madison LL. The acute effect of hydrocortisone on hepatic glucose output and
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molecular mechanisms, and physiological significance. Perspect Biol Med. 1971; 14(2):265-9
20. Ninomiya R, Forbath NF, Hetenyi G, jr. Effect of adrenal steroids on glucose kinetics in normal and
diabetic dogs. Diabetes. 1965; 14(11):729-39
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Biochem j. 1979; 178(1):139-46
22. Björntorp P. The associations between obesity, adipose tissue distribution and disease. Acta Med
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23. Morton NM, Seckl jR. 11beta-hydroxysteroid dehydrogenase type 1 and obesity. Front Horm Res.
2008; 36:146-64
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RM. Primary structure and expression of a function human glucocorticoid receptor cDNA. Nature.
1985; 318(6047):635-41.
25. Rosen j, Miner jN. The search for safer glucocorticoid receptor ligands. Endocr Rev 2005; 26(3):452-
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26. McMaster A, Ray DW. Modelling the glucocorticoid receptor and producing therapeutic agents with
anti-inflammatory effects but reduced side-effects. Exp Physiol. 2007; 92(2):299-309
27. Lowenberg M, Stahn C, Hommes DW, Buttgereit F. Novel insights into mechanisms of glucocorticoid
action and the development of new glucocorticoid receptor ligands. Steroids. 2008; 73(9-10):1025-
9
28. Schacke H, Rehwinkel H, Asadullah K, Cato AC. Insight into the molecular mechanisms of
glucocorticoid receptor action promotes identification of novel ligands with an improved therapeutic
index. Exp Dermatol. 2006; 15(8):565-73
29. Schacke H, Berger M, Rehwinkel H, Asadullah K. Selective glucocorticoid receptor agonists (SEGRAs):
novel ligands with an improved therapeutic index. Mol Cell Endocrinol 2007; 275(1-2):109-17
30. Song IH, Gold R, Straub RH, Burmester GR, Buttgereit F. New glucocorticoids on the horizon: repress,
don’t activate! J Rheumatol. 2005; 32(7):1199-1207
31. Stahn C, Lowenberg M, Hommes DW, Buttgereit F. Molecular mechanisms of glucocorticoid action
and selective glucocorticoid receptor agonists. Mol Cell Endocrinol. 2007; 275(1-2):71-8
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1
Chapter 1

Chapter 1 General Introduction
32. Thompson CF, Quraishi N, Ali A, Mosley RT, Tata jR, Hammond ML, Balkovec jM, Einstein M, Ge L,
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Harris G, Kelly TM, Mazur P, Pandit S, Santoro j, Sitlani A, Wang C, Williamson j, Miller DK, Yamin
TT, Thompson CM, O’Neill EA, Zaller D, Forrest MJ, Carballo-Jane E, Luell S. Novel glucocorticoids
containing a 6,5-bicyclic core fused to a pyrazole ring: synthesis, in vitro profile, molecular modeling
studies, and in vivo experiments. Bioorg Med Chem Lett. 2007; 17(12):3354-61.
33. van Lierop Mj, Alkema W, Laskewitz Aj, Dijkema R, van der Maaden HM, Smit Mj, Conti PGM, jams
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Hofstra CL, Boots AMH, van Duin M, Ingelse BA,Schoonen WGEJ, Grefhorst A, van Dijk T, Kuipers
F, Dokter WHA. A novel non-steroidalselective glucocorticoid receptor modulator with full antiinflammatory
properties andimproved therapeutic index. Manuscript submitted. 2012.

Chapter 2
Novel Insights into Glucocorticoid-Mediated
Diabetogenic Effects: Towards Expansion of
Therapeutic Options?
D.H. van Raalte, D.M. Ouwens and M.Diamant
European Journal of Clinical Investigation. 2009; 39(2):81-93

Chapter 2 Glucocorticoids: metabolic side effects
24
ABSTRACT
At pharmacological concentrations, glucocorticoids (GCs) display potent anti-inflammatory
effects, and are therefore frequently prescribed by physicians to treat a wide variety of
diseases. Despite excellent efficacy, GC therapy is hampered by their notorious metabolic
side-effect profile. Chronic exposure to increased levels of circulating GCs is associated
with central adiposity, dyslipidemia, skeletal muscle wasting, insulin resistance, glucose
intolerance and overt diabetes. Remarkably, many of these side effects of GC treatment
resemble the various components of the metabolic syndrome (MetS), in which indeed subtle
disturbances in the hypothalamic-pituitary-adrenal (HPA) axis and/or increased tissue
sensitivity to GCs have been reported. Recent developments have led to renewed interest
in the mechanisms of GCs diabetogenic effects. First, ‘selective dissociating glucocorticoid
receptor (GR) ligands’, which aim to segregate GCs anti-inflammatory and metabolic
actions, are currently being developed. Second, at present, selective 11β-hydroxysteroid
dehydrogenase type 1 (11β-HSD1) inhibitors, which may reduce local GC concentrations
by inhibiting cortisone to cortisol conversion, are evaluated in clinical trials as a novel
treatment modality for the MetS.
In this review, we provide an update of the current knowledge on the mechanisms that
underlie GC-induced dysmetabolic effects. In particular, recent progress in research into
the role of GCs in the pathogenesis of insulin resistance and beta-cell dysfunction will be
discussed.

1. INTRoDUCTIoN
Glucocorticoid (GC) hormones are secreted by the cortex of the adrenal gland, under control
of the hypothalamic-pituitary-adrenal (HPA) axis. They play essential roles in glucose, lipid
and protein metabolism and contribute to energy homeostasis. In the postabsorptive state,
GCs provide substrate for oxidative metabolism by increasing lipolysis, proteolysis and
hepatic glucose production [1].
At supraphysiological concentrations, GCs display strong anti-inflammatory and
immunosuppressive actions. Hence, GCs are, since decades, the cornerstone in the treatment
of numerous inflammatory conditions. Annually, approximately 10 million new prescriptions
for oral corticosteroids are issued in the United States. Despite their excellent efficacy, GC
use is limited by their side-effect profile, which is dependent of the administered dose and
duration of treatment [2]. Among these side effects are metabolic derangements, including
the development of central adiposity [3], hepatic steatosis [4], dyslipidemia characterized by
increased plasma levels of triglyceride rich lipoproteins (TRL) and nonesterified fatty acids
(NEFA) [5,6], increased breakdown of skeletal muscle mass [7], insulin resistance, glucose
intolerance and overt diabetes in susceptible individuals [2]. In addition, GC-induced beta-cell
dysfunction, which may determine the progression from insulin resistance to hyperglycemia,
has been the topic of extensive research more recently [8,9].
A similar cluster of metabolic abnormalities is also present in subjects with the metabolic
syndrome (MetS) [10]. Although in one study glucose intolerant subjects were shown to
have higher cortisol levels during an oral glucose tolerance test (OGTT) than healthy controls
[11], at present, no clear association was established between excess plasma levels of GCs
and ‘idiopathic’ obesity and/or the MetS [12]. In the last decade, however, the importance of
factors that modulate the biological effects of GCs in target tissues has been recognized [13]. In
this respect, the 11β-hydroxysteroid dehydrogenase (11β-HSD) enzymes, which interconvert
cortisol and its inactive metabolite cortisone, are of particular interest. 11β-HSD type 1, which
is predominantly expressed in liver and adipose tissue, amplifies local GC action by conversion
of cortisone to cortisol, whereas 11β-HSD type 2, which is mainly expressed in the kidney,
reduces GC-induced effects by converting cortisol to cortisone [14]. Indeed, in several studies
in both rodents and humans, 11β-HSD1 expression and activity in subcutaneous adipose
tissue (SCAT) were shown to be positively associated with insulin resistance and obesity [15].
25
2
Chapter 2

Chapter 2 Glucocorticoids: metabolic side effects
Figure 1. Molecular mechanisms of GR action. GCs regulate protein activity both by genomic and
nongenomic pathways. In addition, various modes of transcription regulation by the GR-ligand complex
have been described. Positive regulation of genes (transactivation) is mediated by binding of a ligandactivated
GR homodimer to a GC response element (GRE), which is usually located in the promoter
region of the target gene. An example of such regulation is the gene encoding for the gluconeogenic
enzyme phosphoenolpyruvate carboxykinase (PEPCK). Ligand-activated GR homodimers may also bind
to negative GREs, leading to regression of gene transcription. Moreover, inhibition of target genes may
also be achieved by interaction of GR monomers with other transcription factors via protein-protein
interaction (transrepression). An example of the latter includes binding of GC monomer to the p65
subunit of nuclear factor κB (NF-κB), thereby preventing gene transcription. The transactivation pathway
seems mainly responsible for GC-induced side effects, whereas the transrepression pathway is thought
to primarily induce GC’s anti-inflammatory effects. Abbreviations: DNA: deoxyribonucleic acid; GC:
glucocorticoid; GR: glucocorticoid receptor; GRE: glucocorticoid response element; mRNA: messenger
ribonucleic acid; P: phosphorylated.
1.1 Pharmacological modulation of glucocorticoid action
Due to increased insight into the mechanisms of GC-induced dysmetabolic effects in
recent years, two novel therapeutic classes are currently being explored. First, improved
understanding of the molecular and cellular actions of GCs has lead to the development of
compounds that may display a reduced metabolic side-effect profile compared to classic
glucocorticoid receptor (GR) ligands, while preserving GC-induced anti-inflammatory effects.
These so-called ‘dissociating (GR) activators’ are based on the finding, that the mechanisms by
26

which GCs enhance gene transcription (‘transactivation’) differ from the modes by which GCs
inhibit gene transcription (‘transrepression’) (detailed in Figure 1). GC-induced mechanisms
of transrepression are considered to be responsible for GCs anti-inflammatory actions,
whereas the transactivation pathways are associated with most of GC-induced side effects.
Clearly, new compounds with a ‘dissociated profile’, i.e. that mainly induce transrepression
with reduced transactivation activity, will have an increased therapeutic index compared
to the currently available GCs. Currently, a battery of these ligands are being developed by
pharmaceutical companies [16,17].
Second, since in rodent models of obesity and type 2 diabetes (T2DM) pharmacological
inhibition of 11βHSD1 improved glucose tolerance, insulin sensitivity and lipid profiles [18],
11β-HSD type 1 inhibitors are currently being evaluated in early clinical trials. These agents
are expected to provide a new treatment modality for subjects with the MetS and/or T2DM.
In this review, we will describe the tissue-specific mechanisms underlying the metabolic
derangements associated with GC excess, in particular GC-induced insulin resistance and
beta-cell dysfunction. In addition, the role of GC metabolism in the MetS will be discussed.
2. GLUCoCoRTICoID ACTIoNS oN SKELETAL MUSCLE GLUCoSE
AND PRoTEIN METABoLISM
Skeletal muscle tissue plays a crucial role in glucose metabolism. It is responsible for 80% of
postprandial glucose uptake from the circulation [19] and contains the body‘s largest glycogen
stores. The uptake of glucose in skeletal muscle is dependent on the presence of insulin, and
is therefore referred to as insulin-mediated glucose uptake (IMGU). Since impaired glucose
tolerance is a very consistent finding in clinical studies after GC exposure [9,20,21], it has
been assumed that GC-induced changes in metabolic functions of skeletal muscle tissue
may contribute to the development of GC-induced glucometabolic abnormalities. A number
of potential mechanisms through which GCs decrease IMGU in skeletal muscle tissue have
been identified. GCs may induce insulin resistance by directly interfering with the insulin
signaling cascade in skeletal muscle. In addition, GC-induced changes in protein [22] and
lipid metabolism [23,24] may reduce insulin sensitivity of skeletal muscle tissue. The abovementioned
mechanisms are further detailed below.
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Chapter 2 Glucocorticoids: metabolic side effects
Figure 2. Glucocorticoid-induced effects on glucose and protein metabolism in skeletal muscle
tissue. Insulin stimulates glucose uptake in cells by increasing the translocation of glucose transporter
(GLUT)4 from intracellular vesicles to the cell surface. Upon binding to the transmembrane insulin
receptor (IR), insulin induces GLUT4 migration by activating a cascade of cytosolic proteins, including
insulin receptor substrate (IRS)-1, phosphatidylinositol 3 kinase (PI3-K) and Akt/protein kinase B (PKB),
although the complete cascade remains only partially understood [139]. After glucose is transported
across the plasma membrane, it is further processed by either oxidative or non-oxidative pathways,
the latter being associated with glycogen synthesis. Glycogen synthesis is enhanced by insulin, through
inhibition of glycogen synthase kinase (GSK)-3, which inactivates glycogen synthase (GS) [139].
GCs interfere at several steps in insulin signalling cascade, resulting in reduced glucose uptake and
glycogen synthesis. In addition, blunted insulin signalling contributes to GC-induced protein catabolism.
Abbreviations: 4E-BP1: eIF4E-binding protein 1; GLUT4: glucose transporter 4; GS: glycogen synthase;
GSK-3: glycogen synthase kinase-3; IFG-1: insulin-like growth factor-1; IR: insulin receptor; IRS-1: insulin
receptor substrate-1; mTOR: mammalian target of rapamycin; MuRF-1: Muscle Ring Finger-1; PI3-K:
phosphatidylinositol-3 kinase; PKB: protein kinase B; PP-1: protein phosphatase-1; S6K1: protein S6
kinase 1.
2.1 Glucocorticoids directly inhibit insulin signaling in skeletal muscle
GCs impair IMGU by directly interfering with several components of the insulin signaling
cascade (Figure 2), as was extensively demonstrated both in vitro and in vivo in animals
[25-29]. Since studies into the effects of GCs on the expression of the insulin receptor (IR) in
skeletal muscle have rendered contradictory results [1,28], the markedly reduced IMGU seen
28

after GC-treatment has been proposed as a postreceptor defect [21,30]. Accordingly, in both
L6 skeletal muscle cells and in rat skeletal muscle, dexamethasone treatment decreased the
expression and phosphorylation of insulin receptor substrate (IRS)-1, phosphatidylinositol
3-kinase (PI3-K) and protein kinase B (PKB)/Akt [25,26,28]. In rats, this was indeed shown to
result in reduced glucose transporter (GLUT4) migration to the cell surface [29]. In addition
to reducing glucose uptake, GCs decreased glycogen synthesis rate in Wistar rats by lowering
PKB/Akt and glycogen synthase kinase (GSK)-3 phosphorylation [27].
To date, few data exist regarding the effects of GCs on insulin signaling in humans. In healthy
volunteers, treated for 5 days with 4 mg dexamethasone daily [31], and in patients who were
exposed to long-term, high-dose GCs following renal transplantation [32], reduced glycogen
synthesis rates with concomitant decreased glycogen synthase (GS) concentration and
activity in skeletal muscle biopsies were reported. Based on studies in cell lines and animals,
a schematic overview of the insulin signaling pathway in skeletal muscle and the proposed
sites of modulating action by GCs is depicted in Figure 2.
2.2 Glucocorticoid-induced protein catabolism is linked to insulin resistance
Marked skeletal muscle atrophy is a key feature of prolonged exposure to increased plasma
levels of GCs, as seen in Cushing’s syndrome and during steroid therapy [7]. Also following
short-term high-dose GC treatment, enhanced protein degradation, as measured by stable
isotope dilution methodology, with concomitant elevating circulating amino acid levels, was
reported [33]. Amino acids interfere with insulin signaling by inhibiting insulin-stimulated
IRS phosphorylation and activation of PI3-K in cultured hepatoma cells and myocytes [34],
and were shown to reduce IMGU and glycogen synthesis in humans [22]. Thus, the atrophyrelated
decrease in total muscle area as well as the elevated circulating amino acid levels
contribute to reduced IMGU in GC excess state.
GCs reduce skeletal muscle mass both by decreasing the rate of protein synthesis and by
increasing the rate of protein breakdown [35]. GCs inhibit protein synthesis by reducing
transport of amino acids into the muscle and by abolishing the anabolic effects of insulin and
insulin-like growth factor (IGF)-1. Specifically, the activation of two key proteins in the protein
synthesis machinery, eIF4E-binding protein 1 (4E-BP1) and ribosomal protein S6 kinase 1
(S6K1), is reduced through inhibition of PKB/Akt and mammalian target of rapamycin
(mTOR) phosphorylation. GCs induce muscle proteolysis by activating a number of proteolytic
systems. Most notably, various proteins of the ubiquitin-proteasome system are activated by
GCs, such as Muscle Ring Finger (MuRF)-1 and Atrogin-1. The transcriptional factors FOXO 1
and 3, the gene expression of which are upregulated by GCs, are thought to play a pivotal role
in this pathway [35] (Figure 2).
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Chapter 2 Glucocorticoids: metabolic side effects
2.3 Glucocorticoid-induced dyslipidemia reduces insulin sensitivity
GCs increase whole-body lipolysis [36], resulting in increased plasma levels of NEFA and TG
[5,6] (further discussed under the section ‘GCs enhance whole-body lipolysis’). Augmented
plasma levels of NEFA may enhance the accumulation of intramyocellular lipids (IMCL),
such as fatty acyl CoA, diacylglycerol (DAG) and ceramide, which reduce glucose uptake and
disposal [37]. It was proposed by Randle in 1963 [38], that intracellular lipids decrease IMGU
by competing with glucose for oxidation, but more recent studies have demonstrated that
IMCL rather reduce IMGU by interfering with insulin signaling. Thus, intracellular lipids may
activate various serine kinases, such as c-Jun amino-terminal kinase (JNK) and IkB kinase-β
(IKK-β), which phosphorylate serine sites on IRS-1, resulting in decreased insulin signaling
[24].
To summarize, GCs reduce insulin sensitivity and consequently IMGU in skeletal muscle
tissue, not only by directly perturbing insulin signaling, but also secondary to unfavourable
changes in protein and lipid metabolism.
3. GLUCoCoRTICoIDS AND HEPATIC GLUCoSE AND LIPID
METABoLISM
The liver plays a key role in controlling glucose and lipid homeostasis, within a complex
regulatory network of hormonal, autonomic nervous and metabolic stimuli. In the fasting
state, the liver maintains euglycemia by producing glucose through both gluconeogenesis
(GNG) and glycogenolysis (GGL). Insulin, in the presence of abundant glucose, is the most
important hormone that suppresses endogenous glucose production (EGP) [39], whereas GCs
and glucagon increase hepatic glucose output.
3.1 Glucocorticoids increase endogenous glucose production
Excess plasma levels of GCs may increase EGP both directly and indirectly, the latter by
antagonizing insulin’s metabolic actions [1,40]. GC-induced hepatic insulin resistance
results in impaired suppression of hepatic glucose production by insulin, especially in the
postprandial state, as was extensively demonstrated in healthy subjects following shortterm
GC exposure [21,41,42]. In addition, GCs directly increase EGP by activating a large
number of genes involved in hepatic carbohydrate metabolism [43]. Accordingly, GCs were
shown to augment EGP in the fasting state in healthy humans [21,42,44], although this could
not be confirmed in other studies [41,45-48]. Possibly the use of different GC compounds
(cortisol versus dexamethasone), different modes of administration (intravenous versus
30

oral), the vulnerability of the population exposed [44] and the different stable isotope dilution
protocols that were employed to quantify EGP, may explain these seemingly contrasting
results. GC-induced increments in EGP in the basal state seem to be driven by increased GNG,
through a number of proposed mechanisms. First, GCs were shown to induce the expression
of rate limiting enzymes of GNG, including phosphoenolpyruvate carboxykinase (PEPCK)
and glucose-6-phosphatase (G6Pase), both in vitro and in vivo in rats [49-51]. Indeed, the
PEPCK gene contains a glucocorticoid response element (GRE) in its promoter region and is
considered a key player in GC-induced hyperglycemia [43]. Second, long-term GC-exposure
promotes breakdown of protein and fat stores [52], thus increasing the supply of substrates
for GNG, such as alanine and glycerol, to the liver [53]. As such, increased proteolysis was
shown to contribute to augmented GNG in obese nondiabetic subjects, in whom enhanced
skeletal muscle GC sensitivity has been reported [54], as compared to lean controls [55].
Third, GCs facilitate metabolite transport across the mitochondrial membranes in rat liver,
which enhances GNG [1,53]. Fourth, GCs might potentiate the effects of other glucoregulatory
hormones, such as glucagon and epinephrine, thus further enhancing EGP [52,56]. Finally,
a role for the nuclear receptor peroxisome proliferator-activated receptor (PPAR)-alpha
in GC-induced hepatic insulin resistance and hyperglycemia has recently been confirmed
[57,58]. PPAR-α, which is predominantly expressed in the liver and is the receptor for
the hypolipidemic fibrates, acts as a fatty acid sensor and is a major regulator of energy
homeostasis by promoting fatty acid oxidation, GNG and ketogenesis [59]. It was elegantly
demonstrated in PPAR-α knock out mice that PPAR-α expression is necessary for GC-induced
increases in EGP [57], and that the autonomous nervous system plays a key role [58].
The importance of the hepatic GC-GR axis in regulating EGP was additionally demonstrated
in transgenic mice with a liver-specific inactivation of the GR. These mice developed
hypoglycemia after prolonged fasting due to impaired activity of gluconeogenic enzymes [60].
3.2. Glucocorticoids alter hepatic lipid metabolism
In addition to regulating hepatic carbohydrate metabolism, insulin plays a key role in
hepatic lipid metabolism. In the postprandial state, portal insulin stimulates lipogenesis and
lipoprotein synthesis, whereas it suppresses very low-density lipoprotein (VLDL) secretion
[61]. GCs induce hepatic insulin resistance, both by direct interference with insulin signaling
and indirectly, by elevating plasma NEFA and TG supply to the liver [61]. GC-induced changes
in hepatic lipid metabolism may induce hepatic steatosis and dyslipidemia in humans,
although the currently available data are not conclusive.
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Chapter 2 Glucocorticoids: metabolic side effects
Both in vitro and in vivo in rats, GC treatment was shown to induce accumulation of
intrahepatic lipids [62,63], both through increased TG synthesis and through decreased fatty
acid oxidation [63]. In line with these results, mice with a liver-specific disruption of the GR
had diminished hepatic TG levels [64]. In humans, one study indeed reported a prevalence
of hepatic steatosis of up to 20% in patients with Cushing’s disease [65], although additional
reports linking fatty liver to GC excess are limited to case reports in present literature [66,67].
In addition to changing hepatic fat content, GCs may also augment hepatic VLDL secretion,
resulting in increased plasma levels of TG. In studies in rodents, systemic GC excess induced
an atherogenic plasma lipid profile [63]. In patients with Cushing’s disease, enhanced VLDL
secretion by the liver was reported, which was normalized after correction of cortisol levels
[5]. In addition, short-term treatment with prednisolone also increased VLDL concentration
in healthy subjects [68], although the published results are not unequivocal [69].
To summarize, increased levels of GCs in the liver contribute to GC-related dysmetabolic
effects, including insulin resistance, hyperglycemia and dyslipidemia. The effects of GCs on
the development of hepatic steatosis in humans warrants further investigation.
4. GLUCoCoRTICoIDS, BoDy FAT DISTRIBUTIoN AND ADIPoSE
TISSUE BIoLoGy
4.1 Glucocorticoids increase body fat content and alter body fat distribution
GCs play a critical role both in the regulation of adipose tissue metabolism and in the
differentiation of pre-adipocytes into mature adipocytes [70]. Excess plasma levels of
GCs, as seen in Cushing’s disease or during prolonged GC therapy, increase body fat mass.
Remarkably, GCs specifically enhance fat deposition in the visceral compartment, while
reducing peripheral fat stores [70]. It has been raised that, in peripheral fat tissue, GCs induce
hormone-sensitive lipase (HSL) and reduce intravascular lipoprotein lipase (LPL) activity,
resulting in augmented fat mobilization. In contrast, in visceral adipose tissue (VAT), GCs may
enhance pre-adipocyte differentiation, LPL activity and TG synthesis, leading to increased
adipose tissue mass [70]. However, the molecular mechanisms underlying the distinct
regulation of central and peripheral adipose tissue depots by GCs remain currently largely
unknown. Off note, recent research demonstrating the role of endogenous GC dysmetabolism
in the development of central adiposity in ‘idiopathic’ obesity and the MetS will be addressed
in a separate section.
Inasmuch as increased VAT is well associated with risk for metabolic and cardiovascular
disease [71], the GC-related increase in VAT may indeed translate into clinical disease [72].
32

4.2. Glucocorticoids modulate adipose tissue biology
4.2.1. Glucocorticoids alter the secretion of adipokines
In addition to changing adipose tissue distribution, GCs also affect adipose tissue biology.
In the past decades, adipose tissue has been identified as an endocrine organ, secreting
numerous metabolically active hormones and peptides, collectively referred to as adipokines
[73]. GCs were shown to change the secretion of a number of these adipokines in vitro and in
vivo in rodents [74]. Whether the expression of the encoding genes are directly altered by GCs,
or whether the changed expression may be secondary to GC-induced adipose tissue insulin
resistance [75-77], is currently not clear. In 3T3-L1 adipocytes and in white adipose tissue
from dexamethasone-treated mice, GCs increased mRNA and protein expression levels of
resistin, an adipocytokine that impairs glucose tolerance. Furthermore, in 3T3-L1 and human
adipocytes, dexamethasone decreased the expression of adiponectin, which is associated with
enhanced insulin sensitivity. Conversely, GCs suppressed the pro-inflammatory adipokines
interleukin (IL)-6 and tumor necrosis factor (TNF)-α in vitro, making these proteins unlikely
candidates for mediating GC-induced insulin resistance. Finally, also in vitro, GCs stimulated
leptin expression and secretion [78].
In humans, both a two and five-day treatment with dexamethasone had no effects on plasma
levels of adiponectin, resistin, TNF-α or leptin in healthy subjects, while insulin sensitivity was
markedly reduced [79,80]. In addition, surgical treatment of Cushing’s disease did not alter
serum adiponectin levels, despite a decrease in cortisol plasma levels and increase in insulin
sensitivity [81]. Whether the expression levels of these adipokines were modulated in adipose
tissue, was not determined. In contrast, a seven-day treatment with the GC prednisolone did
increase the expression of leptin in a group of overweight, postmenopausal women [82].
4.2.2 Glucocorticoids enhance whole-body lipolysis
In addition to modifying the secretion of various adipokines, GCs also alter lipid metabolism
in adipose tissue. GC-induced insulin resistance in adipose tissue may contribute, but GCs
also directly alter the expression of key proteins in lipid metabolism. Thus, GCs were shown
to induce the expression and activity of HSL, the enzyme responsible for the hydrolysis of TG
in adipocytes [83]. Indeed, acute cortisol infusion in healthy humans lead to increased wholebody
lipolysis in several studies, as measured with stable isotopes dilution techniques [36,84-
86]. Although it remains to be determined which adipose tissue compartment particularly
contributes to increased NEFA fluxes, one of these studies reported a decreased FFA efflux
from subcutaneous abdominal tissue concurrently with increased whole-body lipolysis [85],
an observation compatible with the adipose tissue distribution that is seen in patients with
Cushing’s syndrome. In addition, GCs were shown to augment LPL activity in the presence
33
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Chapter 2 Glucocorticoids: metabolic side effects
of insulin [87]. Increased LPL activity and intravascular lipolysis, stimulates the uptake of
NEFA and glycerol into adipose tissue. It has been proposed that GCs regulate LPL activity in
a site-specific manner, i.e. increase LPL activity in VAT, but reduce LPL activity in peripheral
fat stores [6,88,89], thus contributing to the Cushing phenotype. Robust evidence for this
hypothesis, however, is lacking in humans.
5. GLUCoCoRTICoIDS AND INSULIN SECRETIoN
The pancreatic beta cell plays a crucial role in glucose metabolism, and in the past decades,
beta-cell dysfunction has been acknowledged as the key defect underlying the development of
T2DM [90]. Beta cells can adapt to changes in insulin sensitivity by varying insulin secretion,
thus maintaining euglycemia. During prolonged periods of reduced insulin sensitivity, e.g. in
conditions such as pregnancy and obesity, beta cells may additionally meet this increased
demand by expanding their volume through both hypertrophy and hyperplasia [91]. From the
above, it is clear that if beta-cell function is assessed by measuring insulin secretion, this value
needs to be corrected for prevailing glucose levels and insulin sensitivity.
Since GCs induce occasional hyperglycemia, it was speculated that GCs, in addition to inducing
insulin resistance, might exert an inhibitory effect on beta cells. From present literature it
is apparent that the effects of GCs on beta-cell function are highly dependent of duration of
exposure, dosage, and susceptibility of the population exposed.
5.1 Glucocorticoids impair insulin secretion in vitro
Several studies have assessed the effects of GCs on insulin secretion in vitro. Rodent-derived
islets decreased insulin release in response to various GC compounds, both following acute
exposure (minutes) [92,93] and following more prolonged exposure (hours to days) [8,94-
99]. GCs may impair the pathways of both nonmetabolizable and metabolizable insulin
secretagogues, most notably glucose.
Glucose uptake and its oxidation in mitochondria of beta cells results in an increase in the
adenosine triphosphate (ATP)/ adenosine monophosphate (ADP) ratio, closure of ATPsensitive
potassium channels, plasma membrane depolarization, increased cytoplasmatic
calcium concentrations, and finally exocytosis of insulin-containing granules, through
yet incompletely understood pathways. In addition, calcium fluxes activate a number
of potentiating signal pathways, including protein kinase A (PKA) en protein kinase C
(PKC), which amplify insulin secretion [100]. GCs were shown to impair beta-cell glucose
34

metabolism by reducing the expression levels of GLUT2 [95,101] and glucokinase (GK) [102],
and to increase futile glucose cycling by enhancing G6Pase activity [102,103]. Through these
mechanisms, GCs may reduce glucose uptake and phosphorylation, and thereby decrease ATP
synthesis and calcium influx.
Figure 3. Insulin secretory process in pancreatic beta cells and the proposed modes of interference
by glucocorticoids. GCs induce beta-cell dysfunction by inhibiting several pathways. Most notably, GCs
impair beta-cell glucose uptake and oxidation, decrease protein kinase A and C activation, and reduce
calcium fluxes by permitting repolarizing potassium currents. Abbreviations: AC: adenyl cyclase; Ach:
acetylcholine; ATP: adenosine triphosphate; cAMP: cyclic adenosine monophosphate; DAG: diacylglycerol;
G6P: glucose-6-phosphatase; Gi : G-coupled inhibitory protein; GC: glucocorticoid; GK: glucokinase;
GLUT2: glucose transporter 2; HK: hexokinase; IP3: inositol triphosphate; K 1,5: voltage-dependent K
v
channel; PIP2: phosphatidylinositol biphosphate; PKA: protein kinase A; PKC: protein kinase C; PLC:
phospholipase C; SGK-1: serum- and glucocorticoid inducible kinase-1.
Other than impairing β-oxidation of glucose in beta-cells, additional GC-induced effects
may contribute to beta-cell dysfunction. Dexamethasone was shown to augment inward,
repolarizing potassium currents by upregulating K ion channels, through activation of the
v
serum- and glucocorticoid inducible kinase (SGK)-1 [98]. These repolarizing currents may
limit calcium influx and insulin secretion. Furthermore, GCs inhibited more downstream steps
in the insulin secretory pathway. In isolated rat islets, dexamethasone decreased the activation
35
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Chapter 2 Glucocorticoids: metabolic side effects
of PKC through inhibition of the DAG-phospholipase C (PLC) pathway [99]. Moreover, GCs
increased the expression of a adrenergic receptors [99,104], leading to reduced cyclic
2
adenosine monophosphate (cAMP) levels, PKA activity and subsequent decreased insulin
release. These distal sites of interference by GCs in the insulin secretory process may explain
the wide range of non-glucose insulin secretagogues that are also inhibited by GCs, including
ketoisocaproate [8,93], the amino acid arginine [8], the sulfonylurea drugs tolbutamide [8]
and gliclazide [93], and the phorbolester PMA, which activates PKC [93,99]. In line with these
results, cAMP, a potent activator of PKA, was shown to prevent GC-induced effects on insulin
secretion [8]. In addition to perturbing the process of insulin secretion, GCs may also decrease
insulin biosynthesis by reducing the ATP/ADP ratio [96,105] and may reduce beta-cell mass
by inducing apoptosis [106].
To summarize, GCs interfere with the signaling pathways of various insulin secretagogues,
but the exact mechanisms through which this occurs, remain largely unknown, despite major
research efforts. Figure 3 shows a schematic overview of the here described insulin secretion
process and the proposed modes of interference by GCs.
5.2 Glucocorticoids effects on insulin secretion ex vivo and in vivo in rodents
The effects of GCs on insulin secretion have also been assessed ex vivo and in vivo in rodents,
however, establishing direct effects of GCs on beta cells under these conditions may be more
challenging, since systemic metabolic consequences of GC treatment (e.g. changes in levels
of glucose, NEFA and other metabolites) will interfere with GC-mediated changes in beta-cell
function as well.
Hydrocortisone treatment acutely inhibited insulin secretion in mice, resulting in hyperglycemia
following an intravenous glucose challenge [107]. However, pancreatic islets isolated from
healthy rats after more prolonged treatment with dexamethasone or hydrocortisone (several
days), showed both unchanged and increased glucose-stimulated insulin secretion (GSIS)
[101,108], most likely to compensate for GC-induced insulin resistance. The increases in GSIS
were accompanied by expansion of beta-cell volume [101,109]. However, if dexamethasone
treatment induced fasting hyperglycemia in these animals, GSIS was impaired [101]. Similar
results were obtained in rodent models of obesity and insulin resistance. In Zucker fatty rats
(fa/fa) [101,110] and ob/ob mice [103] dexamethasone readily induced hyperglycemia and
markedly reduced or completely abrogated GSIS. In rats with streptozocin-induced diabetes,
dexamethasone further increased fasting hyperglycemia and diminished GSIS [111].
Transgenic mice overexpressing the GR in beta cells under control of the insulin promoter
were glucose intolerant, due to blunted insulin secretion as compared to wildtype mice [112].
36

At older age, these mice developed manifest diabetes, which was attributed to progressive
loss of beta-cell function due to higher expression of a -adrenergic receptors, while insulin
2
sensitivity remained intact [113].
5.3 Assessment of glucocorticoid-induced beta-cell dysfunction in humans
5.3.1 Acute inhibitory effects of glucocorticoids on insulin secretion in vivo
Both cortisol infusion [47] and acute treatment with high-doses of oral prednisolone
[114,115] acutely impaired insulin secretion in healthy volunteers. In these studies, fasting
insulin levels remained unchanged following GC treatment, despite the increments in fasting
glucose. Furthermore, insulin release during glucose infusion [114] and during a meal test
[115] were decreased, suggesting an acute inhibitory effect on the beta-cell.
5.3.2 Glucocorticoids increase insulin secretion after short-term treatment
More prolonged exposure (2 to 5 days) of healthy subjects to high doses of dexamethasone
or prednisolone, resulted in fasting hyperinsulinemia and increased insulin secretion
during hyperglycemic clamp studies or intravenous glucose tolerance tests [9,20,44,116-
118]. In these studies, healthy subjects were able to compensate for GC-induced insulin
resistance. Consequently, the product of insulin secretion and insulin sensitivity, also called
the disposition index, remained constant. However, in susceptible populations, including
normoglycemic individuals with a reduced insulin sensitivity [9] or a low GSIS [44,117]
before treatment with GCs, healthy, first-degree relatives of patients with T2DM [20] and
obese women [119], this compensation failed, resulting in hyperglycemia. These studies were
limited by the fact that beta-cell function was assessed by intravenous glucose tolerance tests.
Although the hyperglycemic clamp is regarded as the gold standard for the quantification
of insulin secretion, it represents a less physiological condition relative to tests using orally
administered insulin secretagogues, since insulin secretion also depends on, among-others,
non-glucose metabolic stimuli, incretins, and neuronal inputs [120]. Recently, new methods
have been validated to determine various aspects of beta-cell function from a frequentlysampled
OGTT or from standardized mixed-meal tests, by using modeling analysis [121].
Using this approach, GCs were recently shown to induce beta-cell dysfunction in completely
healthy males, indicating that the full scope of GC-induced effects on beta-cell function may as
yet not be fully detailed [115].
5.3.3 Chronic effects of glucocorticoid-treatment on beta-cell function
The most important limitation of most studies performed in humans, however, is the short
exposure (maximally up to 14 days) to GCs. In daily practice, many patients are treated with
37
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Chapter 2 Glucocorticoids: metabolic side effects
GCs for prolonged time periods, even years. However, due to ethical issues, it is not viable to
chronically expose healthy volunteers to GCs in order to study the long-term effects on betacell
function. Conversely, it is not feasible to study the prolonged effects of GCs on beta-cell
function in patients using GCs in clinical practice. Since the indication for GC treatment often
comprises diseases with a state of systemic inflammation, inflammation-induced beta-cell
effects will interfere with GC-modulation of beta-cell function [122].
As chronic GC-treatment is associated with the development of diabetes, GCs most likely exert
inhibitory effects on the beta cell after prolonged exposure, although strong (direct) evidence
is currently lacking. As described previously, GC-induced pro-apoptotic effects and reductions
in insulin biosynthesis may contribute to beta-cell failure in time. In addition, chronic GCtreatment
may induce beta-cell failure indirectly through elevation of plasma concentrations
of TG and NEFA [5,6], which contribute to beta-cell dysfunction by mechanisms related
to lipotoxicity, a process which is characterised by accumulation of NEFA in the pancreas,
resulting in beta-cell dysfunction [123].
Taken together, GCs acutely inhibit insulin secretion, both in vitro and in vivo. Prolonged
exposure however, induces hyperinsulinemia, due to compensation for GC-induced insulin
resistance. Chronic exposure likely results in progressive loss of beta-cell function in
susceptible individuals.
6. ENDoGENoUS GLUCoCoRTICoID METABoLISM IN THE
METABoLIC SyNDRoME
Since conditions with systemic GC excess shear great similarities with the MetS, the role
of (tissue-specific) GC dysmetabolism in the latter condition has been the topic of intense
research in the past decade. Most notably, the 11β-HSD enzymes and their relation with the
development of central adiposity and hepatic steatosis have been investigated [12,14,70,89].
6.1 Altered 11β-HSD activity in adipose tissue induces central adiposity
In several studies in rodent obesity models, strong evidence for disturbed GC metabolism by
an altered activity of 11β-HSD1 was observed. Transgenic mice selectively overexpressing
11β-HSD1 in white adipose tissue under control of the AP2 promoter, thereby increasing local
cortisol concentrations, developed visceral obesity, dyslipidemia and insulin resistance [124].
In contrast, 11β-HSD1 (-/-) mice gained significantly less weight in response to a high-fat diet
and preferentially stored adipose tissue in peripheral fat depots [125]. In addition, transgenic
38

mice with adipose tissue-specific 11β-HSD2 overexpression, resisted weight gain on high-fat
diet, and displayed improved insulin sensitivity compared to wildtype mice [15]. These data
support the concept that reducing GC activity in adipose tissue has favourable effects on body
composition and metabolic profile. Translating these promising data to humans, however, has
proven to be difficult. In obese subjects, increased 11β-HSD1mRNA and activity were shown
in SCAT [126,127], rather than in VAT [128], and increased cortisol metabolite excretion was
associated with total body fat content, but not specifically with VAT area [67].
6.2 Glucocorticoid dysmetabolism in the liver is linked with hepatic steatosis
Hepatic steatosis represents a pathophysiological hallmark of the MetS, and altered hepatic
cortisol metabolism has been linked to this condition [40]. Again, particularly the enzyme
11β-HSD1 has received much attention. In transgenic mice, it was elegantly demonstrated that
liver-specific overexpression of 11β-HSD1 induced fatty liver by increasing the lipogenesis/
lipid oxidation ratio, without increasing abdominal fat depots [129]. However, studies in
humans linking 11β-HSD1 activity to hepatic fat content were less successful [67].
In conclusion, involvement of GC metabolism in obesity and its metabolic consequences
seems evident, although translating data from animal studies in humans has proven to be
difficult. Nevertheless, given its important role in GC metabolism and disturbed activity of
this enzyme in other insulin resistant states, such as polycystic ovary syndrome [130], HIVassociated
lipodystrophy [131] or cortisone reductase deficiency [132], 11β-HSD1 remains a
promising target for novel therapeutics, as will be discussed below.
7. NoVEL THERAPEUTICS RELATED To GLUCoCoRTICoID
METABoLISM AND ACTIoN
Advances in research regarding GC metabolism and mode of action, has led to the development
of two novel classes of agents, albeit it for different indications, i.e. the dissociated GR agonists
and the 11β-HSD1 inhibitors.
7.1 Dissociated glucocorticoid receptor agonists
This new class of synthetic GR ligands is designed to specifically induce GC-induced
transrepression pathways, while minimizing transactivation activity. As described earlier,
and as demonstrated in figure 1, these compounds are thus expected to display the antiinflammatory
properties of classic GR agonists, such as prednisolone and dexamethasone,
but without inducing dysmetabolic side effects [16,17]. Both pharmaceutical companies and
university groups have made great efforts to identify dissociated GR ligands with an increased
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Chapter 2 Glucocorticoids: metabolic side effects
therapeutic index (reviewed in [133]).
The first compounds, which still had a steroidal structure, showed good dissociation in
vitro, however, their beneficial profile was not sustained in vivo. Since then, several non-
steroidal GR-ligands have been identified. A number of these compounds, such as AL-438, ZK
216348 and BI115, indeed demonstrated an increased therapeutic index in vivo in different
disease- and side effect animal models. These GR ligands were shown to be as efficient as
prednisolone and dexamethasone to suppress inflammation, but they did not elevate blood
glucose levels [133]. Although all dissociated GR ligands are currently still at the preclinical
level of development, and many aspects of the molecular pathways that underlie GC’s actions
remain to be elucidated, it is evident that these compounds may become of great value for
patients who require treatment with the classic GR agonists. It will be exciting to see whether
the promising results derived from in vitro assays and animal models will hold in studies in
humans that are expected to be conducted in the coming years.
7.2 11β-HSD type 1 inhibitors
Given the role of increased GC activity in liver and adipose tissue in the development of
obesity and the MetS, 11β-HSD1 inhibition has been proposed as an interesting strategy for
the treatment of these conditions. Indeed, in obese rodents, pharmacological inhibition of
11β-HSD1 improved glucose tolerance, insulin sensitivity and lipid profiles [15]. In humans,
the first studies were performed with carbenoxolone, a nonselective 11β-HSD1 inhibitor.
Although carbenoxolone decreased hepatic glucose production during a hyperinsulinemiceuglycemic
clamp, it did not augment glucose disposal [70]. Several new classes of selective
11β-HSD1 inhibitors are currently in development, including arylsulfonamidothiazoles [134]
and perhydroquinolylbenzamides [135]. Although these agents were shown to lower blood
glucose concentrations by increasing hepatic insulin sensitivity, clamp studies in obese mice
demonstrated no beneficial effects on glucose disposal [136]. Most recently, new compounds
with benzamide [137] and thiazole [138] structures have been published. The important
challenge now is to assess whether these compounds (and other future 11β-HSD1 inhibitors),
in addition to improving hepatic insulin sensitivity, exert beneficial effects on adipose tissue
and whole-body insulin sensitivity in humans. Despite the limitations of the current available
11β-HSD1 inhibitors, 11β-HSD1 remains an attractive target for the development of novel
drugs that may be added to the present arsenal of therapeutics in the combat against the MetS
pandemic.
Funding: This paper was written within the framework of project T1-106 of the Dutch Top
Institute Pharma.
40

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50

PART
Glucocorticoids and islet-cell fuction
II

Chapter 3
Prednisolone-Induced Beta-Cell Dysfunction
is Associated with Impaired Endoplasmic
Reticulum Homeostasis in INS-1E Cells
M.M.L. Linssen, D.H. van Raalte, E.J.M. Toonen, W. Alkema, G.C. van
der Zon, W.H. Dokter, M. Diamant, B. Guigas and D. M. Ouwens
Cellular Signalling. 2011;23(11):1708-15

Chapter 3 Glucocorticoids induce beta-cell ER stress
56
ABSTRACT
Glucocorticoids (GCs), such as prednisolone, are widely prescribed anti-inflammatory
drugs, but their use may induce glucose intolerance and diabetes. GC-induced betacell
dysfunction contributes to these diabetogenic effects through mechanisms that
remain to be elucidated. In this study, we hypothesized that activation of the unfolded
protein response (UPR) following endoplasmic reticulum (ER) stress could be one of the
underlying mechanisms involved in GC-induced beta-cell dysfunction. We report here that
prednisolone did not affect basal insulin release, but time-dependently inhibited glucosestimulated
insulin secretion in INS-1E cells. Prednisolone treatment also decreased both
PDX1 and insulin expression, leading to a marked reduction in cellular insulin content.
These prednisolone-induced detrimental effects were found to be prevented by prior
treatment with the glucocorticoid receptor (GR) antagonist RU486 and associated with
activation of two of the three branches of the UPR. Indeed, prednisolone induced a GRmediated
activation of both ATF6 (activating transcription factor 6) and IRE1 (inositolrequiring
kinase 1)/XBP1 (X-box-binding protein 1) pathways, but was found to reduce
the phosphorylation of PERK (protein kinase RNA-like ER kinase) and its downstream
substrate eIF2α (eukaryotic initiation factor 2α). These modulations of ER stress pathways
were accompanied by upregulation of calpain 10 and increased cleaved caspase 3,
indicating that long-term exposure to prednisolone ultimately promotes apoptosis. Taken
together, our data suggest that the inhibition of insulin biosynthesis by prednisolone in the
insulin-secreting INS-1E cells results, at least in part, from a GR-mediated impairment in ER
homeostasis, which may lead to apoptotic cell death.

INTRoDUCTIoN
Glucocorticoids (GCs), such as prednisolone, are potent and widely prescribed antiinflammatory
drugs. However, their use is frequently associated with the development of
adverse metabolic effects, and may lead to the development of steroid diabetes in up to 20-
50% of patients [1, 2]. GC-induced hyperglycemia has been classically attributed to progressive
development of insulin resistance in peripheral tissues [1, 2], but recent studies also point
toward involvement of beta-cell dysfunction [3]. A clinical study performed in healthy
males recently showed that both acute and chronic treatment with prednisolone attenuated
pancreatic insulin secretion during standardized meal tests by impairing multiple aspects
of beta-cell function [3]. In vitro studies have also shown that GCs lower glucose-stimulated
insulin secretion (GSIS) in beta-cell lines and isolated pancreatic islets by various putative
mechanisms involving either downregulation of GLUT2, inhibition of phospholipase C/
protein kinase C signaling, modulation of the voltage-gated K(+) channels activity, alterations
in intracellular Ca2+ homeostasis and/or decreased calcium efficacy on the insulin secretory
process (see [2] for recent review). In addition, GCs may also reduce pancreatic beta-cell mass
and affect insulin biosynthesis, at least in part by inducing apoptotic cell death [4-6].
Defects in endoplasmic reticulum (ER) homeostasis have been proposed to underlie beta-cell
dysfunction and impairment in insulin secretion in patients with type 2 diabetes, Wollcot-
Rallison and Wolfram syndrome [7-9]. Three proteins associated with the ER-membrane,
activating transcription factor 6 (ATF6), inositol-requiring enzyme 1α (IRE1α), and PKRlike
eukaryotic initiation factor 2α kinase (PERK) monitor the activity of ER [8]. In absence
of stress, these sensors of ER homeostasis are held in an inactive state through interaction
with the ER-chaperone immunoglobulin binding protein (BIP) [8]. Conditions that affect ER
homeostasis in beta cells, such as increased unfolded/misfolded proteins, Ca2+ depletion or
enhanced insulin biosynthesis, result in release of BIP from the sensor proteins and activation
of the unfolded protein response (UPR) [7, 8].
Activation of ATF6, the first branch of the UPR, requires its translocation from the ER to
the Golgi apparatus where ATF6 is cleaved into an active nuclear transcription factor that
can regulate the expression of ER genes, such as X-box binding protein-1 (XBP1) [8]. The
second pathway involved in the UPR leads to activation of the endoribonuclease IRE1α and
subsequent cleavage of XBP1 mRNA into XBP1s, an alternative spliced form. XBS1s alone, or
in conjunction with ATF6, regulates the synthesis of ER chaperones, and proteins involved in
both ER biogenesis and ER-associated protein degradation (ERAD) [7, 8]. Finally, activation of
PERK, which constitutes the third branch of the UPR, occurs through autophosphorylation of
57
3
Chapter 3

Chapter 3 Glucocorticoids induce beta-cell ER stress
its kinase domain and results in phosphorylation of its substrate eukaryotic initiation factor 2α
(eIF2α). This leads to inhibition of global protein synthesis in addition to increased synthesis
of ATF4 through alternative translation [10]. ATF4 does not only regulate the expression of
ER-chaperones in order to restore ER homeostasis, but can also increase the expression of
genes involved in apoptosis, like C/EBP-homologous protein (CHOP) and tribbles homolog
3 (TRIB3) [8]. Mutations that functionally impair the activity of the PERK/eIF2α-pathway
cause neonatal diabetes in humans (Wollcot-Rallison syndrome) and mouse models [11-13],
further illustrating the importance of this pathway for beta cell function.
Given the critical role of the UPR in beta-cell homeostasis, the aim of this study was to
determine whether ER stress could be one of the underlying mechanisms involved in
prednisolone-induced beta-cell dysfunction.
MATERIAL AND METHoDS
Cell culture: Rat insulinoma-derived insulin-secreting INS-1E cells (kind gift from Pr. P.
Maechler, [14]) were cultured in a humidified atmosphere containing 5% CO in RPMI 1640
2
medium (Invitrogen, Breda, The Netherlands) supplemented with 10 mM HEPES, pH7.4,
5% (v/v) heat-inactivated fetal calf serum, 2 mM glutamine, 1 mM sodium pyruvate, 50 mM
2-mercaptoethanol, 100 units/ml penicillin, and 100 mg/ml streptomycin (Invitrogen, Breda,
The Netherlands) as described previously [15]. For experiments, INS-1E cells were seeded
at 2x105 cells/1 ml in Falcon 24-well plates (insulin secretion), or 9x105 cells/2 ml in Falcon
6-well plates (western blotting, gene expression), and used 4 days later, with one medium
change on day 3. When indicated, prednisolone, the glucocorticoid receptor antagonist RU486
(mifepristone) and/or their vehicle (DMSO, 0.5% v/v) were added to the medium.
Insulin secretion: Insulin secretion in response to glucose was performed as described
earlier [15]. Briefly, cells were incubated with prednisolone or vehicle (0.5% DMSO) for
various time points. Then, the cells were maintained for 2 h in glucose-free culture medium
containing prednisolone or vehicle (0.5% DMSO), followed by two washes and a 30 min at
37oC incubation in glucose-free Krebs–Ringer bicarbonate HEPES buffer (KRBH: 135 mM
NaCl, 3.6 mM KCl, 5 mM NaHCO3, 0.5 mM NaH2PO4, 0.5 mM MgCl2, 1.5 mM CaCl2, 0.1%
BSA and 10 mM HEPES, pH 7.4) in the presence or absence of DMSO or prednisolone.
Next, cells were washed with glucose-free KRBH and incubated for 30 min in KRBH with
or without prednisolone and secretagogues, as indicated. Then, plates were placed on ice
and the supernatants were collected for determination of insulin secretion. Cellular insulin
58

content was measured in acid–ethanol extracts [15]. Insulin concentrations were measured
in supernatants and acid–ethanol extracts using a rat/mouse ELISA kit (Millipore, Nuclilab,
Ede, The Netherlands).
Protein analysis: INS-1E cells were incubated with vehicle or prednisolone as indicated.
Then, cells were washed twice with phosphate buffered saline (PBS) and lysed in a buffer
containing 10% (w/v) glycerol, 3% (w/v) SDS and 100 mM Tris–HCl (pH 6.8). Lysates were
immediately boiled for 5 min and centrifugated (13,200 rpm; 2 min). Protein content of the
supernatant was determined using a bicinchoninic acid protein assay kit (Pierce, Rockford,
USA). Proteins were separated by SDS-PAGE followed by transfer to a polyvinylidene
difluoride (PVDF) membrane. Membranes were blocked for 1 h at room temperature in TBST
buffer (10 mM Tris–HCl (pH 7.4), 150 mM NaCl, and 0.1% (v/v) Tween 20 containing 5%
(w/v) fat free milk) and incubated overnight with primary antibodies (see Supplementary
Table 1). The membranes were then washed in TBST buffer and incubated with horseradish
peroxidase-conjugated secondary antibodies for 1 h at room temperature. After washing,
blots were developed using enhanced chemiluminescence and quantified by densitometry
analysis using Image j software (NIH, Bethesda, USA).
Gene expression studies: INS-1E cells were lysed in RLT-buffer (Qiagen, Hilden, Germany)
for isolation of total RNA using the RNeasy system including on-column DNAse I treatment
(Qiagen, Hilden, Germany). For PCR analysis, total RNA (1.5 µg) was reverse transcribed using
a Superscript first strand synthesis kit (Invitrogen, Breda, The Netherlands) and quantitative
real-time PCR was performed with SYBR Green on a StepOne Plus Real-time PCR system
(Applied Biosystems, Foster City, CA, USA). All the primers sets used were designed for
spanning an exon (if any) and have an efficiency of ~100+/-5% (Supplementary Table 2). Every
sample was analyzed in duplicate and mRNA expression was normalized to hypoxanthineguanine
phosphoribosyltransferase (HPRT) mRNA content, and expressed as arbitrary
units. For whole genome expression profiling, total RNA (200 ng) was amplified, labeled and
fragmented using the GeneChip 3’ IVT Express Kit (Affymetrix, Santa Clara, CA) according
to the manufacturer’s instructions. Fragmented amplified RNA (10 µg) was applied to the
Rat Genome 230 2.0 Array and hybridized for 16 hours at 45°C in a GeneChip Hybridization
Oven 640 (Affymetrix). Following hybridization, the arrays were washed and stained with a
GeneChip Fluidics Station 450 (Affymetrix) using the Affymetrix Hybridization Wash Stain
(HWS) kit. The arrays were laser scanned with a GeneChip Scanner 3000 7G (Affymetrix,
Santa Clara, CA). Data was saved as raw image file and quantified using Affymetrix GeneChip
Command Console v 1.0 (Affymetrix).
59
3
Chapter 3

Chapter 3 Glucocorticoids induce beta-cell ER stress
Data analysis: All data are presented as means ± SEM. Statistical analysis was performed in
SPSS version 17.0 (SPSS Inc., Chicago, IL, USA). Differences among groups were determined
using independent t-tests or by ANOVA followed by Bonferroni correction for multiple
comparisons. P2 and p-value

Figure 2. Effects of prednisolone on PDX1 and insulin expression in INS-1E cells. INS-1E cells were
incubated with vehicle (0.5% v/v DMSO) or 700 nM prednisolone for the indicated times. A. Representative
immunoblots for PDX1 and insulin protein expression are shown. Insulin receptor β-subunit (IRβ)
expression was used as loading control. B-C. Densitometric quantification of immunoblots was performed
and the results expressed in % of those from vehicle (DMSO)-treated cells at the same time-point. D-F.
PDX1, insulin 1 and 2 mRNA expressions were determined using real time PCR, normalized for HPRT
expression using the ∆Ct-method and expressed in arbitrary units. All the results are expressed as means
± SEM (n=4). *, p

Chapter 3 Glucocorticoids induce beta-cell ER stress
with the reduction in insulin content measured by ELISA, the insulin protein level was also
significantly reduced 12 h following prednisolone addition when compared to vehicle-treated
cells (Fig. 2a/c). The reductions in PDX1 and insulin protein expression induced by
prednisolone were preceded by rapid decline in PDX1, insulin 1 and insulin 2 mRNA levels,
which already became significant 1 h after addition of the drug (Fig. 2d-f). All these
prednisolone-induced decrease in PDX1 and insulin mRNA and protein levels were prevented
by prior treatment with RU486 (Fig. 3).
Figure 4. Effects of prednisolone and RU486 on ATF6-mediated XBP1 mRNA expression in INS-
1E cells. INS-1E cells were incubated with vehicle (0.5% v/v DMSO) or 700 nM prednisolone for the
indicated times (A) or for 20h in the presence (black bars) or absence (open bars) of 1 µM of RU486 as
indicated (B). A-B. XBP1 mRNA expression was determined using real time PCR, normalized for HPRT
expression using the ∆Ct-method and expressed in arbitrary units. All the results are expressed as means
± SEM (n=4). *, p

Figure 5. Effects of prednisolone and RU486 on IRE1α/XBP1s pathway in INS-1E cells. INS-1E
cells were incubated with vehicle (0.5% v/v DMSO) or 700 nM prednisolone for the indicated times
(A,C,E) or for 20h in the presence (black bars) or absence (open bars) of 1 µM of RU486 (B,D,F). A,E.
Time-dependent effects of prednisolone were determined on IRE1α and XBP1s by immunoblots and
densitometric quantifications. The results are expressed in % of those from vehicle (DMSO)-treated
cells at the same time-point. C. Time-dependent effect of prednisolone on XBP1s mRNA expression was
determined using real time PCR, normalized for HPRT expression using the ∆Ct-method and expressed in
arbitrary unit. G. Representative immunoblots for IRE1α, XBP1s and IRβ protein expression are shown.
B,F. Densitometric quantifications of IRE1α and XBP1s immunoblots were performed and the results
expressed in arbitrary units. D. XBP1s mRNA expression was determined and expressed in arbitrary
units. All the results are expressed as means ± SEM (n=4). *, p

Chapter 3 Glucocorticoids induce beta-cell ER stress
reduced 8-12 h following the addition of prednisolone (Fig. 6a-b). This prednisolone-induced
dephosphorylation of PERK and eIF2α was prevented by pre-treatment with RU486 (Fig. 6ce).
The prednisolone-induced inactivation of the PERK/eIF2α pathway was accompanied by
decrease in mRNA levels of CHOP and TRIB3 downstream target genes (data not shown).
Figure 6. Effects of prednisolone and RU486 on PERK/eIF2α pathway in INS-1E cells. INS-1E cells
were incubated with vehicle (0.5% v/v DMSO) or 700 nM prednisolone for the indicated times (A,C) or for
20h in the presence (black bars) or absence (open bars) of 1 µM of RU486 (B,D,E). A,C. Time-dependent
effects of prednisolone were determined on pPERK-Thr980, peIF2α-Ser51 and IRβ by immunoblots and
densitometric quantifications. The results are expressed in % of those from vehicle (DMSO)-treated
cells at the same time-point. E. Representative immunoblots for pPERK-Thr980, peIF2α-Ser51 and IRβ
protein expression are shown. B,D. Densitometric quantifications of pPERK-Thr980 and peIF2α-Ser51
immunoblots were performed and the results expressed in arbitrary units. All the results are expressed
as means ± SEM (n=4). *, p

Prednisolone increases expression of distal markers of ER-stress pathways and induces
apoptosis: Prednisolone did not affect mRNA or protein expression of the ER-chaperone BIP
(data not shown). However, using microarray profiling analysis, we found that prednisolone
induced a time-dependent upregulation of the ER-associated protein degradation (ERAD)
components EDEM1 and MAN1A1 (Fig. 7a-b). Furthermore, we also observed a significant
increase in calpain 10 mRNA expression within 4 h after the addition of prednisolone to INS-
1E cells (Fig. 7c). This protease is involved in the ER-stress-induced apoptosis via delayed
activation of the caspases 3 and 12 [17]. Accordingly, a significant GR-mediated increase in
cleaved caspase-3 was found in INS-1E cells treated for 20h with prednisolone (Fig. 7d-e).
Figure 7. Effects of prednisolone on distal markers of ER stress and apoptosis in INS-1E cells. INS-
1E cells were incubated with vehicle (0.5% v/v DMSO) or 700 nM prednisolone for the indicated times
(A-D) or for 20h in the presence (black bars) or absence (open bars) of 1 µM of RU486 (E-F). A-C. Timedependent
effect of prednisolone on EDEM1, MAN1A1 and calpain 10 mRNA expression was determined
using Affymetrix profiling. D. Time-dependent effect of prednisolone was determined on cleaved caspase
3 by immunoblots and densitometric quantification was performed. The results are expressed in % of
those from vehicle (DMSO)-treated cells at the same time-point. E. Representative immunoblots for
cleaved caspase 3 and IRβ protein expression are shown. F. Densitometric quantification of cleaved
caspase 3 immunoblots was performed and the results expressed in arbitrary units. All the results are
expressed as means ± SEM (n=4). *, p

Chapter 3 Glucocorticoids induce beta-cell ER stress
of prednisolone on beta-cell dysfunction are tightly associated with atypical GR-mediated
modulation of the ER stress-induced UPR, as illustrated by concomitant activation of ATF6
and IRE1α and inactivation of PERK/eIF2α signaling pathways.
In response to perturbations of ER homeostasis in beta cells, a coordinated program is
activated leading to upregulation of genes enhancing the ER protein processing capacity,
such as protein folding, trafficking, and degradation [7-9]. ATF6-dependent transcriptional
induction is regulated by ER stress-induced trafficking of ATF6 from the ER to the Golgi where
the protein is cleaved by proteases to release a cytosolic active fragment. Although we were
not able to detect an effect of prednisolone on ATF6 cleavage, the GR-mediated upregulation
of the well-characterized ATF6 downstream target gene XBP1 suggests that this pathway
is activated. Of note, prednisolone had no significant effect on XBP1 protein expression
(data not shown), suggesting that the XBP1 mRNA is rapidly cleaved into XBP1s by IRE1α.
Interestingly, nutrient or chemical ER stress-mediated activation of ATF6 has been shown
to impair insulin expression and secretion in INS-1E cells, at least in part via suppression of
PDX1 expression [18]. In addition to mitigating ER stress by increasing the transcription of
key proteins involved in the regulation of ER folding capacity, IRE1α also lowers ER protein
load by degrading mRNA of ER-targeted proteins [19]. In beta cells, IRE1α was shown to
be directly involved in the degradation of insulin mRNA in response to ER stress [20-22].
Furthermore, accumulation of the transcription factor XBP1s has also been linked to betacell
dysfunction by reducing both insulin and PDX1 expression, and promoting apoptotic
beta-cell death [23]. An additional explanation for the rapid decrease in insulin mRNA level
induced by prednisolone could be the presence of a GC responsive element in the promoter
region of the gene, which acts as a negative regulatory site [24, 25]. However, although this
could contribute to the rapid downregulation of insulin expression, it can not explain the
similar rapid decline in PDX1 mRNA observed. Interestingly, in our experimental conditions,
the prednisolone-induced reduction of PDX1 and insulin mRNA levels preceded the onset of
the ER-stress response. Therefore, it is possible that the ER-stress response is triggered by
an increased demand for insulin synthesis caused by the initial decline in insulin and PDX1
mRNA levels. Furthermore, PDX1 deficiency also enhances beta-cell susceptibility to ER
stress-associated apoptosis [26].
Another cellular strategy to alleviate ER stress is the reduction in protein influx by reducing
protein translation, a process which is classically mediated by activation of PERK and
subsequent phosphorylation of its downstream target eIF2α [7-9]. One of our most puzzling
results is that prednisolone, by contrast to its activating effect on ATF6 and IRE1α pathways,
decreases the phosphorylation of PERK and eIF2α, suggesting a GR-mediated increase in the
66

activity of protein phosphatase(s) involved in the dephosphorylation of these key proteins.
Although we did not find any change in protein phosphate 1 expression (data not shown),
which has been implicated in the dephosphorylation of eIF2α [7-9], we cannot exclude more
subtle enzymatic regulation in response to prednisolone and/or involvement of upstream
phosphatases targeting PERK. It should however be mentioned that the exact mechanism(s)
by which the PERK/eIF2α pathway is regulated during ER stress is still not entirely clear.
For example, similar counter-intuitive observations were recently made in activated B cells
where phosphorylation of the PERK/eIF2α pathway was reduced during ER stress and did
not correlate with cellular protein synthesis rate [27]. In addition, loss of PERK function has
also been reported not to affect protein synthesis but rather to impair ER-to-Golgi protein
trafficking and proteasomal degradation [28].
In relation to signaling pathways attenuating protein translation, crosstalk between GR and the
nutritional sensor mammalian of rapamycin (mTOR) has been recently highlighted in muscle,
identifying Regulated in Development and DNA damage responses 1 (REDD1), an upstream
negative regulator of mTOR, as a direct GR-targeted gene [29]. Interestingly, in our conditions,
prednisolone induced significant upregulation of REDD1 and concomitantly reduced mTORC1
activity in INS-1E cells (MML/DMO, unpublished data), suggesting that modulation of this
pathway could contribute to the GR-mediated reduction in insulin biosynthesis by decreasing
global protein synthesis. Further experiments are still required to clarify this point.
One of the limitations of our findings could eventually be the concentration of prednisolone
used in the present study (700 nM), which is ~3-4 fold higher than the plasma therapeutic level
reported in humans treated with the drug [30]. However, significant effects of prednisolone
on insulin secretion/biosynthesis and ER stress signaling pathways are already present
at lower concentrations (data not shown). To note, similar time- and dose-dependent GRmediated
effects were also observed in INS-1E cells treated with dexamethasone, another GC
compound (MML/ DMO, unpublished data).
In a recent clinical study, we have reported that treatment with the glucagon-like peptide-1
(GLP-1) receptor agonist exenatide (exendin-4) prevented prednisolone-induced beta-cell
dysfunction in healthy men [31]. Although the underlying molecular mechanism(s) remain(s)
to be elucidated, it is striking that exendin-4 was previously shown to decrease geneticallyor
pharmacologically-induced ER stress and to improve beta-cell function and survival in
mice, isolated islets and INS-1E cells [32, 33]. In addition, exendin-4 prevented GC-induced
apoptosis in INS-1E cells [6]. Taken together, it is therefore tempting to suggest that the
beneficial effect of exenatide evidenced on beta cell functions in humans could be ascribed,
67
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Chapter 3

Chapter 3 Glucocorticoids induce beta-cell ER stress
at least in part, to a reduction in prednisolone-induced pancreatic ER stress [31]. Further in
vitro studies are required to investigate whether exendin-4 is able to prevent prednisoloneinduced
ER stress and beta-cell dysfunction.
In conclusion, we report here that prednisolone increases ER stress in INS-1E cells, promoting
activation of selective UPR signaling pathways which leads to decreased insulin expression.
Taken together, our data suggest that the inhibition of GSIS could be ascribed, at least in
part, to a prednisolone-induced decrease in insulin biosynthesis resulting from GR-mediated
impairment in ER homeostasis and apoptotic cell death.
Funding: This work was supported by Dutch TI Pharma grants (project T1-106, to ML, DHR,
EjT, WHD, MD and DMO; project T2-105, to BG and DMO), the Federal Ministry of Health, the
Ministry of Innovation, Science, Research and Technology of the German State of North-Rhine
Westphalia (DMO), and the EU European Cooperation in the field of Scientific and Technical
Research (COST) Action BM0602 (DMO).
68

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Chapter 3 Glucocorticoids induce beta-cell ER stress
SUPPLEMENTARy FILES
Supplementary Table 1. List of Antibodies
Primary antibody Supplier Reference Dilution
cleaved caspase 3 R&D systems Mab835 1:1000
Insulin Santa Cruz Biotechnology sc-9168 1:1000
Insulin receptor β-subunit Santa Cruz Biotechnology sc-711 1:1000
IRE1α Cell Signaling Technology 3294 1:1000
PDX1 Santa Cruz Biotechnology sc-14664 1:1000
peIF2α-S51 Cell Signaling Technology 9721 1:1000
pPERK-T980 Santa Cruz Biotechnology 32577 1:1000
XBP1s Biolegend 619502 1:1000
XBP1t Abcam ab37151 1:1000
Supplementary Table 2. Primer sequences
Gene Accession no Forward primer Reverse primer
HPRT NM012583 5’-ggtccttttcaccagcaagct-3’ 5’-tgacactggcaaaacaatgca-3’
Insulin 1 NM019129 5’-gcagaagcgtggcattgtg-3’ 5’-ggtggactcsagttgcagtagttctc-3’
Insulin 2 NM019130 5’-gctggccctgctcatcct-3’ 5’-ccaccaagtgagaaccacaaag-3’
PDX1 NM022852 5’-ggtatagccagcgagatgct-3’ 5’-tcagttgggagcctgattct-3’
XBP1s NM001004210 5’-gagtccgcagcaggtg-3’ 5’-gcgtcagaatccatggga-3’
XBP1t NM001004210 5’-gagcagcaagtggtggattt-3’ 5’-tctcaatcacaagcccatga-3’
72

Chapter 4
Acute and 2-Week Exposure to Prednisolone
Impair Different Aspects of Beta-Cell Function
in Healthy Men
D.H. van Raalte, V. Nofrate, M.C. Bunck, T. van Iersel, J. Elassaiss
Schaap, U.K Nässander, R. J. Heine, A. Mari, W.H.A. Dokter and M.
Diamant
Eur J Endocrinol. 2010;162(4):729-35

Chapter 4 Glucocorticoids impair beta-cell function in humans
76
ABSTRACT
objective. Glucocorticoids (GCs), such as prednisolone, are associated with adverse
metabolic effects, including glucose intolerance and diabetes. In contrast to the well-known
GC-induced insulin resistance, effects of GCs on beta-cell function are less well established.
We assessed the acute and short-term effects of prednisolone treatment on beta-cell
function in healthy men.
Research Design and Methods. A randomized, double blind, placebo-controlled trial,
consisting of two protocols was conducted. In protocol 1 (n=6), placebo and a single dose
of 75 mg prednisolone were administered. In protocol 2 (n=23), participants received 30
mg prednisolone daily or placebo for 15 days. Both empirical and model-based parameters
of beta-cell function were calculated from glucose, insulin and C-peptide concentrations
obtained during standardized meal tests before and during prednisolone treatment
(protocols 1 and 2), and one day after cessation of treatment (protocol 2).
Results: 75 mg prednisolone acutely increased the area under the postprandial glucose
curve (AUC ) (P=0.005) and inhibited several parameters of beta-cell function, including
gluc
AUC /AUC ratio (P=0.004), insulinogenic index (P=0.007), glucose sensitivity (P=0.02)
c-pep gluc
and potentiation factor ratio (PFR) (P=0.04). A 15-day treatment with prednisolone
increased AUC (P

Glucocorticoids (GCs), such as prednisolone, are very efficacious and frequently prescribed
anti-inflammatory drugs. Unfortunately, supraphysiological levels of GCs induce adverse
metabolic effects, including glucose intolerance and diabetes [1]. Steroid diabetes may
develop in up to 20-50% of patients with excessive plasma GC levels [2]. GCs are well known
to reduce insulin sensitivity, resulting in increased hepatic glucose production and decreased
peripheral glucose disposal [3]. The role of beta-cell dysfunction in GC-related diabetogenic
effects is less clear. GCs impaired insulin secretion in rodent-derived islets in vitro [4]. In
vivo in both rodents [5] and humans [6], a single day of GC administration impaired insulin
secretion, resulting in hyperglycemia. More prolonged exposure to GCs, on the other hand,
induced fasting hyperinsulinemia and increased insulin secretion in both wild type rodents
[7] and healthy humans [8-12], most likely to compensate for impaired insulin sensitivity. In
rodent models of obesity [13, 14] and in susceptible humans, however, this compensation
failed. These ‘at risk’ populations included normoglycemic individuals with reduced insulin
sensitivity or low glucose-stimulated insulin secretion (GSIS) before GC treatment [9, 11, 12]
and normoglycemic, first-degree relatives of patients with type 2 diabetes mellitus [10]. It
was concluded that GCs may only induce beta-cell dysfunction in vulnerable populations.
The above-mentioned studies, however, have a limitation. Beta-cell function was assessed
by tests using intravenous glucose loads, such as the intravenous glucose tolerance test
(IVGTT) or the hyperglycemic clamp. As the magnitude of the insulin response under normal
conditions also depends on other factors, such as non-glucose substrates [15, 16], incretins
[17] and neurotransmitters [18], the hyperglycemic clamp may represent a less physiological
condition relative to tests using orally administered insulin secretagogues.
More recently, various parameters of beta-cell function have been calculated by modeling
glucose and C-peptide plasma concentrations during standardized meal tests [19]. This
approach enables the assessment of various aspects of beta-cell function under daily life
conditions and also allows to evaluate the separate roles of insulin secretion and insulin
sensitivity on glucose control in a single test [19].
The aim of the present study was to assess the effects of both acute and short-term exposure
to a widely used GC, i.e. prednisolone, on various aspects of beta-cell function in healthy men.
77
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Chapter 4

Chapter 4 Glucocorticoids impair beta-cell function in humans
RESEARCH DESIGN AND METHoDS
Study design: The study was a single-center, double blind, randomized, placebo-controlled
study, consisting of two distinct parts.
Protocol 1: Acute Study: In order to assess the acute effects of prednisolone treatment, eligible
participants (n=6) ingested a placebo tablet on day 0 at 08:00 AM and a 75 mg prednisolone
capsule at 08:00 AM on day 1. No study medication was given on day 2. Standardized meal
tests were performed on days 0, 1 and 2 at 10:00 AM.
Protocol 2: Two-week study: The effects of short-term treatment with prednisolone on beta-
cell function were assessed in different subjects. Participants (n=23) were randomly assigned
to a treatment with either 30 mg prednisolone once daily (n=12) or placebo (n=11) for a period
of 15 days (medication was taken in the morning). Standardized meal tests were performed
at day 0 and at day 15 at 10.00 AM. Placebo was administered as a subject-blinded treatment
on day 0 at 08:00 AM (baseline). On day 15, study medication was also administered at 08:00
AM.
Study medication: Prednisolone tablets were obtained from Pfizer AB (Sollentuna, Sweden),
placebo tablets were provided by Schering-Plough (Oss, The Netherlands). The tablets were
encapsulated in order to allow the treatment to be blinded.
Study population: Both protocols enrolled healthy male volunteers (age range 20 and
45 years; body mass index (BMI) 22-30 kg/m 2 ). Health status was confirmed by medical
history taking, physical and laboratory examinations, and ECG and vital signs recordings.
Furthermore, normal glucose metabolism was verified by a 75 g 2-hour oral glucose tolerance
test (OGTT). Participants were excluded if they had a clinically relevant history or presence
of a medical disorder known to affect the investigational parameters, if they were taking
medication, except for incidental analgesics, or if they had a first-degree relative with type 2
diabetes mellitus.
Study assessments: Screening assessments were performed within a three-week period prior
to inclusion. Subjects were admitted to the clinical research unit at Xendo Drug Development
(Groningen, The Netherlands) at 10:00 PM. The following day at 10:00 AM, subjects
consumed a standardized 4-hour meal test (35 g proteins, 39 g fat and 75 g carbohydrates)
after an overnight fast of minimal 12 hours. Samples for determination of glucose, insulin
and C-peptide were obtained at times 0, 5, 10, 20, 30, 60, 90, 120, 180, and 240 min, with the
78

meal beginning immediately after the time 0 sample and consumed within 15 min. Insulin
and C-peptide were measured by Xendo Drug Development using immuno assays (Mercodia,
Uppsala, Sweden).
Data analysis: Area under the 4-hr postprandial glucose (AUC ) and C-peptide (AUC gluc c-
) curves were determined by using the trapezoidal rule. Measures of insulin sensitivity,
pep
including homeostatic model assessment (HOMA-IR) [20] and oral glucose insulin sensitivity
(OGIS) [21] were calculated. Empirical measures of beta-cell function, including AUC / c-pep
AUC ratio and the insulinogenic index (IGI) (insulin -insulin )/(gluc -gluc ) [22]
gluc t=30 t=0 t=30 t=0
were calculated.
Modeling analysis of beta-cell function: Pancreatic beta-cell function was assessed with a
model that describes the relationship between insulin secretion and glucose concentration,
which has been described in detail previously [19]. The model expresses insulin secretion (in
pmol/min per m2 of body surface area) as the sum of two components. The first component
describes the dose-response relation between insulin secretion and glucose concentrations
during the meal test. Three parameters are obtained from this dose-response relation. First,
the sensitivity of the beta cell to changes in plasma glucose levels, called glucose sensitivity.
It is derived from the mean slope of the dose-response curve. Second, the fasting secretory
tone is calculated from the dose-response curve. This represents fasting insulin secretion
rates at a fixed glucose concentration of 4.5 mM (approximately the mean fasting glucose
concentration in the groups). The third parameter is a potentiation factor, which may
account for several potentiating signals to the beta cell (e.g. non-glucose metabolites, incretin
hormones and neural factors) or amplifying pathways within the beta cell [23], although its
exact physiological basis warrants further investigation. The excursion of the potentiation
factor was quantified using a ratio between mean values at times 160-180 and 0–20 min, and
is called the potentiation factor ratio (PFR). The second component of the model describes
the insulin response to the rate of change of glucose concentration. This component is termed
rate sensitivity, which is related to early insulin release [19].
The model parameters were estimated from glucose and C-peptide concentration by
regularized least squares, as described previously. Regularization involves the choice of
smoothing factors that were selected to obtain glucose and C-peptide model residuals with
standard deviations close to the expected measurement error (1% for glucose and 5% for
C-peptide). Insulin secretion rates (ISR) were calculated from the model every 5 min [24].
Estimation of the individual model parameters was performed blinded to the randomization
of patients to treatment.
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Chapter 4 Glucocorticoids impair beta-cell function in humans
Statistical analysis: Data are presented as mean values ± standard error of the mean (SEM)
or, in case of skewed distribution, as median (interquartile range). In the acute study, outcome
variables were log-transformed and differences were tested by analysis of variance (ANOVA)
with Bonferroni post-hoc test. For the two-week study, between-group treatment effects were
tested with Mann-Whitney U. All statistical analyses were run on SPSS for Windows version
15.0 (SPSS, Chicago, IL, USA). A P < 0.05 was considered statistically significant.
Ethics and good clinical practice: All participants provided written informed consent. The
study was approved by an independent ethics committee and the study was conducted in
accordance with the Declaration of Helsinki, using good clinical practice.
RESULTS
Acute study
Fasting metabolic parameters and glucose- and C-peptide profiles during meal tests:
Six healthy participants were included in this protocol (age: 24.3 ±1.5 years, BMI: 24.2 ± 0.9
kg/m2 ). High single-dose prednisolone treatment did not change fasting plasma glucose (FPG)
or insulin (FPI), but decreased OGIS (P=0.009) (Table 1). During the meal test, prednisolone
augmented AUC (P=0.005), while C-peptide secretion remained unchanged (Figure
gluc
1, panels A and B; Table 1). One day following discontinuation of prednisolone treatment,
glucose levels during the meal test normalized, but AUC was increased (P=0.002).
c-pep
Figure 1. 75 mg Prednisolone acutely increased postprandial glucose concentrations (panel A), which
was not accompanied by increased AUCc-pep (panel B). After discontinuation of prednisolone treatment,
glucose concentrations normalized during the meal test, but C-peptide levels were increased. Solid line
with black squares represents day 0 (placebo), dotted line with black circles denotes day 1 (prednisolone
75 mg), and intersected line with white circles is day 2 (no treatment). Date are mean ± SEM.
80

Table 1 (Acute study). Subject characteristics following administration of placebo (day 0) and 75 mg
prednisolone (day 1), and after cessation of treatment (day 2). Values are median (interquartile range).
P1 indicates day 0 versus day 1; P2 denotes day 1 versus day 2.
Fasting plasma glucose
(mmol/l)
Fasting plasma insulin
(pmol/l)
HOMA-IR
(no dimension)
OGIS
(ml/min.m2 )
AUC gluc
(mmol/L.240min)
AUC cpep
(nmol/L.240min)
Insulinogenic Index
(no dimension)
AUC /AUC x 1000
ISR gluc
(no dimension)
P1 P2
Day 0 4.6 (4.1-4.8) NS 0.037
Day 1 4.8 (4.3-5.2)
Day 2 4.2 (3.8-4.3)
Day 0 22 (21-27) NS NS
Day 1 26 (22-31)
Day 2 29 (22-33)
Day 0 0.5 (0.5-0.6) NS NS
Day 1 0.6 (0.5-0.7)
Day 2 0.6 (0.5-0.7)
Day 0 459 (424-509) 0.009 0.003
Day 1 387 (341-418)
Day 2 490 (470-503)
Day 0 1210 (966-1295) 0.005 0.017
Day 1 1565 (1393-1778)
Day 2 1264 (1033-1309)
Day 0 37 (32-41) NS 0.003
Day 1 34 (30-40)
Day 2 50 (48-57)
Day 0 173 (131-303) 0.007 0.04
Day 1 74 (55-94)
Day 2 174 (96-217)
Day 0 30 (26-36) 0.004

Chapter 4 Glucocorticoids impair beta-cell function in humans
Figure 2. 75 mg Prednisolone reduced all model-derived parameters of beta-cell function, of which
glucose sensitivity and potentiation factor ratio reached statistical significance. Graphs are Box-and-
Whisker plots with median and interquartile range. Panel A: glucose sensitivity of the beta cell; panel
B: basal secretory tone; panel C: potentiation factor ratio; panel D: rate sensitivity. * P

Figure 3. A two-week treatment with prednisolone increased area under the postprandial glucose curve
(panel A: placebo; panel B: prednisolone), despite increased C-peptide levels (panel C: placebo; panel D:
prednisolone). Solid line with black squares represents day 0, dotted line with white squares represents
day 15. Date are mean ± SEM.
Figure 4. A 15-day treatment with prednisolone decreased the basal secretory tone and potentiation
factor ratio (panel B and C). Glucose sensitivity and rate sensitivity were not affected (panel A and D).
Graphs are Box-and-Whisker plots with median and interquartile range. * P

Chapter 4 Glucocorticoids impair beta-cell function in humans
Table 2 (Two-week study). Subject characteristics before and during 15 day-treatment with 30 mg
prednisolone daily (n =12) or placebo (n =11). Values are median (interquartile range).
Fasting plasma glucose
(mmol/l)
Fasting plasma insulin
(pmol/l)
HOMA-IR
(no dimension)
OGIS
(ml/min.m2 )
AUC gluc
(mmol/L.240min)
AUC cpep
(nmol/L.240min)
Insulinogenic Index
(no dimension)
AUC /AUC x 1000
ISR gluc
(no dimension)
84
Pre-treatment On-treatment P
PLB 4.6 (4.5-4.9) 4.4 (3.9-4.8) 0.023
30 4.3 (3.9-5.2) 5.0 (4.4-5.1)
PLB 22 (21-36) 27 (22-37) NS
30 28 (22-39) 57 (39-68)
PLB 0.5 (0.4-0.8) 0.6 (0.5-0.7) 0.06
30 0.6 (0.5-0.8) 1.2 (0.8-1.5)
PLB 463 (441-524) 496 (444-517) 0.031
30 457 (427-511) 404 (374-415)
PLB 1173 (1038-1241) 1147 (1097-1224) 0.001
30 1224 (1070-1297) 1466 (1389-1532)
PLB 292 (223-316) 260 (213-288) 0.05
30 278 (256-364) 334 (320-391)
PLB 127 (117-160) 115 (87-182) NS
30 139 (78-163) 155 (79-161)
PLB 34 (28-43) 31 (25-37) NS
30 34 (30-38) 31 (30-38)
Abbreviations. 30: prednisolone 30 mg daily; AUCgluc: area under postprandial glucose curve; AUCc-pep:
area under postprandial C-peptide curve; HOMA-IR: homeostatic model assessment insulin resistance;
IQR: interquartile range; OGIS: oral glucose insulin sensitivity; PLB: placebo.
Comparisons between subjects of both protocols: No statistical significant differences
were measured between the subjects of the acute and two-week protocol, regarding age, BMI,
physical activity and metabolic parameters at baseline.
Experience of prednisolone-related symptoms: Subjects treated with prednisolone did
not report increased physical complaints as compared to placebo. The completion rate of
both studies was 100%, none of the participants withdrew due to side effects of prednisolone
exposure.
DISCUSSIoN
GCs are well known to perturb glucose metabolism in humans, which most often is attributed
to reduction of insulin sensitivity. It is far less clear to what extent beta-cell dysfunction
contributes to the diabetogenic effects of GC therapy. In this study, we demonstrate that

prednisolone, as the most widely prescribed oral GC worldwide, in addition to reducing
insulin sensitivity, impairs beta-cell function in healthy men, both following acute and twoweek
exposure. In contrast to previous studies, we assessed the effects of GCs on beta-cell
function under daily life conditions, i.e. during standardized meal tests [19].
A single dose of 75 mg prednisolone acutely increased AUC gluc , while C-peptide secretion
failed to respond (Figure 1, panels A and B). These data confirm and expand previous studies
assessing the acute effects of GCs in both rodents [5] and humans [6], in which a single-day
treatment with hydrocortisone [5] or prednisolone [6] prevented adequate response of beta
cells to hyperglycemia. In line with these observations, Plat and colleagues demonstrated that
elevation of morning cortisol levels acutely impairs insulin secretion [25]. In our study, all
measured beta-cell function parameters declined by 25-50%, including measures for earlyand
late phase insulin secretion. In vitro studies in rodent islet cells have revealed several
mechanisms by which GCs acutely interfere with insulin secretory pathways. First, GCs reduce
the uptake and oxidation of several metabolites including glucose. Moreover, GCs augment
outward potassium currents, which, by reducing cell membrane depolarization, limit calcium
influx. In addition, GCs may reduce insulin secretion by decreasing the efficacy of calcium on
the secretory process. Finally, GCs were shown to reduce insulin secretion induced by the
parasympathetic nervous system [4, 26].
On day 2, beta-cell function appeared to have recovered from the acute effects of prednisolone,
since fasting insulin secretion and insulin secretion during the standardized meal test were
increased. This may indicate delayed compensation for the insulin resistance on day 1, but
the increased insulin secretion could also serve to correct subtle disturbances in glucose
homeostasis, although surrogate markers for insulin sensitivity were not decreased on day 2.
In contrast to the acute study, subjects treated with prednisolone 30 mg daily for 15 days,
increased C-peptide secretion during prednisolone treatment (Figure 3). Despite this
enhanced secretion, however, fasting glucose and postprandial AUC exceeded baseline
gluc
levels, indicating a relative hypoinsulinemia and accordingly a decline in beta-cell function.
This relative hypoinsulinemia under fasting conditions becomes evident in our beta-cell
model parameter ‘insulin secretory tone at a glucose level of 4.5 mM’, in which ISR are directly
related to glucose plasma concentrations. This parameter was significantly reduced following
15-days of prednisolone exposure. In addition to insufficient basal secretory tone, PFR was
also reduced by a two-week prednisolone treatment.
Previous studies in which subjects were exposed for multiple days to GCs, also reported
increased insulin secretion, as assessed by hyperglycemic clamps [8, 9, 11, 12] and IVGTTs
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Chapter 4 Glucocorticoids impair beta-cell function in humans
[10]. However, the studies that accounted for GC-induced reductions in insulin sensitivity
by calculating the disposition index [11] or by using minimal model analysis [10], reported
adequate compensation of insulin resistance in healthy subjects through sufficiently
augmented insulin secretion. Only subjects with (subtle) glucometabolic abnormalities prior
to GC treatment were unable to fully compensate for GC-induced insulin resistance [9-12].
The most important difference between the above-mentioned studies and our study is that we
used an oral stimulation test to assess various aspects of beta-cell function. Our experimental
design may be more physiological compared to tests using intravenous glucose, since the
former comprises the contribution of multiple factors, including incretins [17], non-glucose
metabolic stimuli, such as non-esterified fatty acids (NEFA) [15] or amino acids [16], and the
autonomic nervous system [18], all of which together account for a substantial proportion
of the normal meal-related insulin response. These non-glucose insulin secretagogues are
included as the ‘potentiation factor’ in our beta-cell model, which was significantly impaired
by prednisolone, both following acute and short-term exposure. Our findings illustrate that
the harmful effects of GCs on beta-cell function may only become fully apparent when using
an oral stimulation test, which more comprehensively tests the role of the intestinal-islet axis
on glucose homeostasis. To identify the specific non-glucose stimuli for insulin secretion that
are impaired by GC treatment will require additional investigation.
It is important to note that the meal-induced insulin response during prednisolone treatment
was markedly different in the acute protocol as compared to the two-week study. We propose
that GCs induce an acute inhibitory effect on beta-cell function, as extensively demonstrated
in both in vitro and in vivo experiments, but that beta-cell function partly recovers following
more prolonged exposure. In the latter situation, GC-induced insulin resistance may oppose
the direct effect of GCs on the beta cell by enhancing insulin secretion. It should however
also be stressed that the dosages used in the two protocols were different, which could have
contributed to the observed difference in insulin responses. It is well known that the effects of
GCs on glucose metabolism are highly dependent on the administered dose [27].
We conclude that GCs impair several aspects of beta-cell function, both following acute and
short-term treatment, in healthy normoglycemic men, when measured under physiological,
daily life conditions. These data indicate that GC-induced beta-cell dysfunction, in addition to
insulin resistance, contributes to the development of steroid diabetes.
Funding: This paper was written within the framework of project T1-106 of the Dutch Top
Institute Pharma.
86

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88

Chapter 5
Islet-cell Dysfunction Induced by Glucocorticoid
Treatment: Potential Role for Altered
Sympathovagal Balance?
D.H. van Raalte, K.A.A. Kwa, R.E. van Genugten, M.E. Tushuizen,
J.J. Holst, C.F. Deacon, J.M. Karemaker, R.J. Heine, A. Mari and M.
Diamant.
Metabolism, in revision

Chapter 5 Glucocorticoids, islet-cell function & ANS
92
ABSTRACT
objectives: To assess the effects of low- and high-dose glucocorticoid (GC) treatment on
pancreatic islet-cell function in healthy males and to study the potential influence of altered
autonomous nervous system (ANS) balance and meal-related incretin responses.
Research design and methods: In a randomized, placebo-controlled, double-blind,
dose-response intervention study, 32 males (age: 21±2 years; BMI: 21.9±1.7 kg/m2 ) were
allocated to prednisolone 7.5 mg once daily (n=12), prednisolone 30 mg once daily (n=12),
or placebo (n=8) for two weeks using block randomization. Main outcome measures were:
1) C-peptide secretion during a hyperglycemic clamp with additional arginine stimulation
2) fasting and postprandial glucagon levels 3) parasympathetic activity and sympathovagal
balance and 4) postprandial incretin responses.
Results: Prednisolone treatment did not significantly affect first- and second-phase
glucose-stimulated C-peptide secretion, however, C-peptide secretion following arginine
stimulation on top of hyperglycemia (ASI-iAUC ) was dose-dependently reduced: -2.8
CP
(-5.2;0.2) and -3.1 (-8.8; -1.0) nmol/L.min for prednisolone 7.5 mg and prednisolone 30
mg, respectively (P=0.035 vs. placebo). Fasting glucagon levels and postprandial glucagon
levels were only increased by prednisolone 30 mg. Measures of the ANS and incretin
responses were not significantly altered by treatment. However, changes in parasympathic
activity associated with fasting plasma glucose levels (r=-0.407; P=0.03) and tended
to associate with fasting glucagon concentrations (r=-0.337; P=0.07). The change in
sympathovagal balance was inversely related to ASI-iAUC (r=-0.365; P=0.05). No side
CP
effects of prednisolone treatment were reported.
Conclusions: Impairments in islet-cell function contribute to prednisolone-induced
glucose intolerance in healthy men. Altered sympathovagal balance may contribute to these
effects.
Clinical Trial Registration Number: ISRCTN78149983

Glucocorticoids (GCs) are the cornerstone in the treatment of numerous diseases due to their
potent anti-inflammatory and immunosuppressive actions [1]. However, pharmacological GC
levels also induce adverse effects on glucose metabolism [1, 2]. In population-based studies,
GC therapy was associated with incident diabetes [3]. Classically, the association between GCs
and diabetes has been attributed to GC-induced insulin resistance [4].
The extent to which pancreatic islet-cell dysfunction, and particularly beta-cell dysfunction,
contributes to the diabetogenic effects of GCs is less well-known. In vitro, GCs were shown
to decrease insulin secretion and insulin synthesis [2]. In addition to reducing glucosestimulated
insulin secretion (GSIS), GCs impaired the in vitro effects of nonmetabolizable
insulin secretagogues, including arginine and acetylcholine, suggesting that the site of action
of GCs is in the end of the insulin secretory process [5]. Transgenic mice with a beta-cell
specific overexpression of the glucocorticoid receptor (GR) develop diabetes due to beta-cell
failure, in the presence of increased α-2 adrenergic activity [6].
In humans, a single high-dose of prednisolone was shown to impair both first-phase glucosestimulated
and arginine-stimulated C-peptide secretion during a hyperglycemic clamp [7] and
glucose sensitivity of the beta cell during a meal challenge test [8]. (Sub)acute, GC-exposure
(a 2 day-treatment), however, generated seemingly opposing results. A number of studies
reported increased insulin secretion during a hyperglycemic clamp or intravenous glucose
tolerance test following this treatment duration with high-dose GCs [9-13]. This most likely
can be attributed to reduced insulin sensitivity. Only one of these studies measured insulin
sensitivity with the hyperinsulinemic-euglycemic clamp [11], allowing adjustment prevailing
insulin sensitivity. This study indicated impaired compensation in several subjects.
In addition to beta-cell function, GCs may also affect pancreatic alpha-cell function. Two
studies showed increased glucagon secretion during high-dose GC treatment [14, 15].
However, there are no data available regarding the effects of more prolonged GC treatment on
islet-cell function in humans. Also, the dose-dependency of these effects is largely unknown.
Finally, mechanisms that could contribute to GC-induced effects on islet cell function in
humans have, to our knowledge, not been investigated. Given the preclinical data [5, 6],
alterations in the autonomic nervous system (ANS) balance could be implicated in GC-induced
islet effects. Whereas parasympathetic branches of the ANS are well-known to stimulate
insulin secretion via acetylcholine signaling [16], sympathetic fibers decrease insulin release
via catecholamine-related pathways [17] and stimulate glucagon release [18]. Also, it is at
present unclear whether the incretin hormones, important regulators of postprandial isletcell
function, may be involved in GC-induced islet effects.
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Chapter 5 Glucocorticoids, islet-cell function & ANS
Therefore, in the present study, we assessed the effects of a prolonged treatment, i.e. two
weeks, with low or high-dose prednisolone, the most commonly prescribed GC, on islet-cell
function in healthy normoglycemic males and cardiovascular ANS balance and meal-related
incretin responses.
RESEARCH DESIGN AND METHoDS
Participants: Thirty-two healthy Caucasian males were recruited by local advertisement.
Inclusion criteria included: age 18-35 years, body mass index (BMI) 20.0-25.0 kg/m2 , good
physical health (determined by medical history, physical examination and screening blood
tests) and normoglycemia as defined by fasting plasma glucose (FPG) < 5.6 mmol/L and
2-h glucose < 7.8 mmol/L following a 75g oral glucose tolerance test (OGTT), performed at
screening visit. Exclusion criteria were the presence of any disease, use of any medication,
first-degree relative with type 2 diabetes, smoking, shift work, a history of GC use, excessive
sport activities (i.e. > two times/week) and recent changes in weight or physical activity.
The study was approved by an independent ethics committee and the study was conducted
in accordance with the Declaration of Helsinki. All participants provided written informed
consent before participation.
Study design: The study was a randomized, placebo-controlled, double blind, doseresponse
intervention study. Following assessment of eligibility and baseline measurements,
participants were randomized to receive either prednisolone 30 mg once daily (n=12),
prednisolone 7.5 mg once daily (n=12) or placebo (n=8) treatment for a period of 14 days using
block randomization, as carried out by the Department of Experimental Pharmacology of the
VU University Medical Center. An outline of the study design is presented in Supplementary
Figure 1. At baseline and day 13 of treatment, insulin sensitivity and beta-cell function were
measured in a combined euglycemic-hyperglycemic clamp procedure (Supplementary Figure
2A). At baseline and day 14 of treatment, a standardized consecutive meal challenge test was
performed and cardiovascular ANS function was measured in the fasted state (Supplementary
Figure 2B). All measurements were conducted following a 12-h overnight fast with the
subjects in the semi-supine position. Subjects refrained from drinking alcohol for a period of
24h before the study days and did not perform strenuous exercise for a period of 48h before
the study days. During all visits, including a follow-up visit at day 7 of treatment, safety and
tolerability were assessed. A patient flow diagram is shown in Supplementary Figure 3.
94

Hyperinsulinemic-euglycemic clamp and hyperglycemic clamp: After an overnight fast,
participants were admitted to the clinical research unit at 7.30 AM. An indwelling cannula
was inserted into an antecubital vein for infusion of glucose and insulin. To obtain arterialized
venous blood samples, a retrograde cannula was inserted in a contralateral wrist vein and
the hand placed in a heated box, maintained at 50°C. A primed, continuous (40 mU/m2 .min)
insulin infusion (Actrapid; Novo Nordisk, Bagsvaerd, Denmark) was given for 120 min;
plasma glucose was kept at 5 mmol/L by a variable infusion of 20% glucose, as described
previously [19]. The hyperglycemic clamp was started 60 min after cessation of exogenous
insulin infusion. Plasma glucose concentration was then raised to 10 mmol/L by a body
weight-adjusted intravenous bolus of 20% glucose and a variable 20% glucose infusion was
adjusted to maintain the targeted glucose level. After 80 min hyperglycemia, an intravenous
bolus of 5 g arginine (dissolved in 50 mL NaCl) was given over 45 seconds, and the glucose
level was maintained at 10 mmol/L for an additional 30 min (Supplementary Figure 2A).
Standardized consecutive meal challenge: After an overnight fast, participants were
admitted to the clinical research unit at 7.30 AM. An indwelling cannula was inserted to allow
blood sampling during the test. Two consecutive identical meals were served as breakfast
at 09.00 AM and as lunch at 1.00 PM, each containing 905 kcal (50 g fat, 75 g carbohydrates,
35 g protein). Samples for determination of glucose, insulin, C-peptide, glucagon, glucagonlike
peptide (GLP)-1 and glucose-dependent insulinotropic polypeptide (GIP) were obtained
at times 0, 5, 10, 20, 30, 60, 90, 120, 150, 180, 210 and 240 min following each meal, with
the meal beginning immediately after the time 0 sample and being consumed within 15 min
(Supplementary Figure 2B).
Heart rate variability: Prior to consumption of the first meal, continuous finger arterial
blood pressure (BP) and heart rate (HR) were recorded (Portapres, FMS, Amsterdam, The
Netherlands) in the supine position for 30 min on a beat-to-beat basis as described previously
[20]. From the arterial pressure signal, interbeat interval was derived (Beatscope software1.1,
FMS, Amsterdam, The Netherlands). Power spectral analysis was assessed by discrete Fourier
transform as described previously [21]. The low-frequency band (LF; 0.04-0.15 Hz) and the
high frequency band (HF; 0.15-0.4 Hz) were selected. LF and HF bands were expressed in
normalized units (LF and HF , respectively), where HF represents parasympathetic
norm norm norm
activity. In addition, the LF/HF ratio was computed as a measure of sympathovagal balance
[21].
Study medication: Prednisolone tablets were purchased from Pfizer AB (Sollentuna,
Sweden) and placebo tablets were obtained from Xendo Drug Development (Groningen, The
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Chapter 5 Glucocorticoids, islet-cell function & ANS
Netherlands). Tablets were capsulated in order to allow the treatment to be blinded [8]. Study
medication was taken at 08.00 AM during the two-week treatment except for day 13 and
day 14, when it was ingested at 06.00 AM. Patients kept a diary in which the exact time of
medication intake during the study was registered.
Analytical determinations: Blood glucose concentrations were measured using an YSI 2300
STAT Plus analyzer (YSI, Yellow Springs, OH). Insulin and C-peptide levels were determined
using an immunometric assay (Advia, Centaur, Siemens Medical Solutions Diagnostics, USA).
Glucagon concentrations were determined by radioimmuno assay (Linco Research, St. Louis,
USA). Total GLP-1 levels were measured using a C-terminally directed radioimmunoassay
(antiserum no. 89390) as described previously [22]. Total GIP was analyzed using a newly
developed assay, employing a C-terminally directed antiserum (code no 80867) raised in
rabbits immunized with a C-terminal fragment of GIP [GIP (28-42)] conjugated to keyhole
limpet haemocyanin via its N-terminus. This assay has broadly the same specificity and
characteristics as the previously published assay using antiserum R65 [23], recognizing
equally both intact GIP (1-42) and the primary metabolite, GIP (3-42).
Data analyses: From the hyperglycemic clamp the beta-cell function parameters first-phase
(min 0-10) and second-phase (min 10-80) incremental area under the C-peptide curve
(iAUC ), as well as arginine-stimulated C-peptide secretion (ASI- iAUC ) (min 80-110) were
CP CP
calculated using the trapezoid method (nmol/L.min). From the hyperinsulinemic-euglycemic
clamp whole-body insulin sensitivity was quantified by the M-value (mg/kg.min), calculated
between min 90-120 during steady-state insulin concentrations as described previously [19].
During the double meal challenge absolute area under the curves (AUCs) for glucose, insulin,
C-peptide, glucagon, GLP-1 and GIP were calculated using the trapezoid method.
Beta-cell function during the meal challenge test: Beta-cell function during the standardized
double meal challenge test was assessed by mathematical modeling which was described in
detail previously [24]. The model describes the relationship between insulin secretion and
glucose concentration as the sum of two components. The first component represents the
dependence of insulin secretion on absolute glucose concentrations at any time point and
is characterized by a dose-response function relating the two variables. The characteristic
parameter of the dose response is its mean slope, denoted here as glucose sensitivity. The
dose response is modulated by both glucose-mediated and non-glucose-mediated factors
(i.e. non-glucose substrates, gastrointestinal hormones, and neurotransmitters), which are
collectively modeled as a potentiation factor. The excursion of the potentiation factor was
quantified using a ratio between mean values at times 160-180 min and 0–20 min, and is
96

called the potentiation factor ratio (PFR). In addition, the fasting secretory tone is calculated
from the dose-response curve as insulin secretion at the glucose concentration of 4.5 mmol/L.
The second component of the model describes the insulin response to the rate of change of
glucose concentration. This component is termed rate sensitivity, which is related to early
insulin release [24].
Statistical analyses: Data are presented as mean values ± standard deviation (SD), or as
median (interquartile range) in case of skewed distribution. Non-parametric analysis was
chosen due to uneven sample size over the groups, the relatively small N, and unequal variances
that were observed for some parameters. Between-group comparisons of baseline values
were performed using Kruskal-Wallis test. For treatment-induced effects, absolute changes
from baseline were calculated (on-treatment value minus pre-treatment value) and were
compared by Kruskal-Wallis test with trend analysis (‘the Jonckheere-Terpstra test’). Only in
case of a significant finding, prednisolone 7.5 mg and prednisolone 30 mg were compared
against placebo by posthoc testing, using the Mann-Whitney U test. To correct for multiple
testing, Bonferroni correction was applied. Correlations between the various parameters
were assessed with Pearson’s correlations. All statistical analyses were run on SPSS version
15 for Windows (Chicago, IL, USA). A P < 0.05 was considered statistically significant.
RESULTS
Anthropometric characteristics: No significant differences in subject characteristics were
observed among the groups at baseline (Table 1). Body weight was not altered by prednisolonetreatment
irrespective of the dose (Supplementary Table 1). Systolic blood pressure (6±1.2
mmHg increase; P=0.006), but not diastolic blood pressure was raised by prednisolone 30
mg. Prednisolone 7.5 mg did not affect blood pressure (Supplementary Table 1).
Fasting glucose and hormone levels: A dose-dependent rise in fasting plasma glucose
levels was observed by prednisolone treatment relative to placebo (P=0.04) (Table 2).
Fasting plasma insulin levels were significantly increased (P=0.008) in the prednisolone 30
mg arm. The Prednisolone-induced increase in fasting insulin levels was due to increased
basal secretion, not altered clearance, since fasting C-peptide levels were similarly enhanced
(P=0.001) (Table 2).
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Chapter 5 Glucocorticoids, islet-cell function & ANS
Table 1. Subject characteristics at inclusion
Placebo Prednisolone
7.5 mg 30 mg P
N 8 12 12
Age (y) 21±3 21±2 21±2 0.670
Weight (kg) 75±8 75±6 73±10 0.886
Height (cm) 186±7 184±6 185±5 0.718
BMI (kg m-2 ) 21.6±1.5 22.1±1.5 21.4±2.2 0.656
Systolic blood pressure (mmHg) 126±7 124±9 120±9 0.284
Diastolic blood pressure (mmHg) 79±8 79±6 78±10 0.872
Fasting plasma glucose (mmol/l) 4.4±0.3 4.4±0.2 4.5±0.2 0.852
2-hr glucose OGTT (mmol/l) 3.7±1.0 3.9±0.9 3.6±0.8 0.782
Data are mean±SD. Significance was tested by Kruskal-Wallis. No statistically significant differences were
observed between the groups at baseline. 2-hr glucose OGTT denotes plasma glucose concentrations 2 h
after ingestion of 75 g glucose during an oral glucose tolerance test.
Table 2. Fasting metabolic parameters before and on day 14 of treatment with placebo, prednisolone 7.5
mg once daily and prednisolone 30 mg once daily.
Fasting plasma glucose
(mmol/l)
Fasting plasma insulin
(pmol/l)
Fasting C-peptide
(nmol/l)
Fasting glucagon
(pmol/l)
98
Pre-treatment On-treatment P1 P2 P3
PLB 4.4±0.3 4.5±0.3
7.5 4.4±0.2 4.7±0.3
0.04 0.418 0.09
30 4.5±0.2 4.9±0.4
PLB 31 (28-40) 33 (28-39)
7.5 36 (29-47) 36 (27-65) 0.001 1.0 0.008
30 32 (26-44) 56 (41-72)
PLB 0.4±0.1 0.4±0.1
7.5 0.4±0.1 0.5±0.2
0.001 0.918 0.002
30 0.4±0.1 0.6±0.2
PLB 12±4 10±3
7.5 12±3 13±3
0.002 0.152 0.014
30 12±3 15±3
Data are mean±SD or median (interquartile range). Between-group changes from baseline were tested
by Kruskal-Wallis with trend analysis (P1). In case of a significant finding, posthoc testing (by Mann-
Whitney U) was done (P2 and P3). Treatment groups: PLB=placebo; 7.5=prednisolone 7.5 mg daily;
30=prednisolone 30 mg daily.
Hyperglycemic clamp: Prednisolone treatment tended to increase first- and second-phase
glucose-stimulated C-peptide secretion (Figure 1A-B). In multivariate analysis however,
this trend was no longer observed when the M-value was added in the model (1st-phase

iAUC: β=0.126; P=0.638; R 2 =0.119 and 2 nd -phase iAUC: β=0.051; P=0.847; R 2 =0.152).
C-peptide secretion following arginine stimulation on top of hyperglycemia (ASI-iAUC CP )
was significantly and dose-dependently reduced by prednisolone treatment: -2.7 (-5.2;-0.3)
nmol/L.min for prednisolone 7.5 mg and -3.0 (-7.6;-0.2) ) nmol/L.min for prednisolone 30 mg
(P=0.035) (Figure 1C). In multivariate analysis, this relation was independent of changes in
insulin sensitivity. The results from the hyperglycemic clamp were similar when insulin iAUCs
were calculated (data not shown).
Euglycemic clamp: Prednisolone treatment dose-dependently decreased insulin sensitivity
(M-value) as compared to placebo: mean differences: -2.1±0.8 mg kg-1 min-1 for prednisolone 7.5
mg and -4.5±0.7 mg kg-1 min-1 for prednisolone 30 mg (Figure 2). Insulin levels reached steady
state during min 90-120 of the euglycemic clamp at 490 ± 77 pmol/L prior to treatment, and
were not altered during the on-treatment clamps (Supplementary Table 2). Adjustment of the
M-value by insulin levels during the steady-state part of the clamp (M/I) did not affect the results.
Figure 1. The effects of prednisolone treatment on first-phase- (A) and second-phase (B) glucosestimulated,
and arginine-stimulated C-peptide secretion (C) during the hyperglycemic clamp.
Prednisolone 30 mg tended to increase first-and second-phase C-peptide secretion. However, both
dosages of prednisolone significantly decreased C-peptide secretion following arginine administration on
top of hyperglycemia. Box-and-Whisker plots (min-max) are shown. White bars: before treatment; black
bar: on treatment. Between-group changes from baseline were tested by Kruskal-Wallis test with trend
analysis (indicated by top line). Posthoc tests were done by Mann-Whitney U with Bonferroni correction
for multiple testing (indicated by line with brackets). *P

Chapter 5 Glucocorticoids, islet-cell function & ANS
sensitivity (Figure 4A-B). PFR was significantly decreased by prednisolone 30 mg, but not by
prednisolone 7.5 mg (Figure 4C). Basal insulin secretion at a fixed glucose level of 4.5 mmol/L
tended to decrease following prednisolone treatment (P=0.052) (Figure 4D).
Figure 2. Prednisolone treatment dose-dependently decreased insulin sensitivity during the
hyperinsulinemic-euglycemic clamp. Box-and-Whisker plots with the absolute change from baseline are
shown. Between-group changes from baseline were tested by Kruskal-Wallis test with trend analysis
(indicated by top line). Posthoc tests were done by Mann-Whitney U with Bonferroni correction for
multiple testing (indicated by line with brackets). ***P

Figure 3. Postprandial responses at baseline and at 14 day of intervention. Panel A: Glucose concentrations.
Panel B: Insulin concentrations. Panel C: C-peptide concentrations. Panel D: Glucagon-like peptide (GLP)-
1 concentrations. Panel E: Glucose-dependent insulinotropic polypeptide (GIP) concentrations. Panel F:
Glucagon concentrations. Straight line with black squares denotes pre-treatment. Dotted line with open
circles represents on-treatment. Box-and-Whisker plots with absolute change in area under the curve
(AUC) from baseline are shown. Between-group changes from baseline were tested by Kruskal-Wallis test
with trend analysis (indicated by top line). Posthoc tests were done by Mann-Whitney U with Bonferroni
correction for multiple testing (indicated by line with brackets). ***P

Chapter 5 Glucocorticoids, islet-cell function & ANS
Figure 4. Results from the modeling analysis of beta-cell function from glucose and C-peptide levels
during 2 consecutive standardized meals given as breakfast (t=0) and as lunch (t=240 min). Glucose
sensitivity of the beta cell (A) and rate sensitivity (B) were not altered by study medication. Potentiation
factor ratio between T=160-180 min and T=0-20 min was significantly decreased by prednisolone 30 mg
(C). Finally, prednisolone treatment non-significantly reduced fasting insulin secretion rates at a fixed
glucose level of 4.5 mmol/L. Box-and-Whisker plots (min-max) are shown. White bars: before treatment;
black bar: on treatment. Between-group changes from baseline were tested by Kruskal-Wallis test with
trend analysis (indicated by top line). Posthoc tests were done by Mann-Whitney U with Bonferroni
correction for multiple testing (indicated by line with brackets). **P

Safety and tolerability: One subject in the prednisolone 30 mg group complained of
sleeplessness, which was mild and transient of nature. Otherwise, no side effects were
reported in any of the treatment arms.
DISCUSSIoN
The principal findings of the present study are that a two-week treatment with high-dose
prednisolone impaired various parameters of pancreatic islet-cell function, and that
altered ANS balance may be involved in these changes, although ANS balance itself was
not significantly altered. Furthermore in this study, we observed a clear dose-dependency
of prednisolone-induced effects. However, probably due to lack of power, the effects of lowdose
prednisolone treatment did not reach statistical significance. At increased levels, GCs
induce glucose intolerance and diabetes in susceptible individuals, which has classically been
attributed to GC-induced insulin resistance (4). Indeed, our study confirmed the presence
of GC-induced insulin resistance: prednisolone treatment dose-dependently reduced clampmeasured
M-value.
Figure 6. The relation between treatment-induced changes in insulin sensitivity and 1st phase glucosestimulated
C-peptide secretion for placebo (A), prednisolone 7.5 mg daily (B) and prednisolone 30 mg
daily (C). As demonstrated in (C), during treatment with prednisolone 30 mg, C-peptide secretion is
enhanced to compensate for reduced insulin sensitivity. Black squares: pre-treatment. Open circles: ontreatment.
Grey triangle: mean pre-treatment. Open triangle: mean on-treatment.
The effects of short-term GC treatment on islet-cell function, and particularly beta-cell
function, have been under debate. The observation that 2 days GC exposure induced fasting
hyperinsulinemia and increased insulin secretion in response to oral and intravenous glucose
loads, may suggest a lowering of insulin sensitivity by GCs [9-13]. In the present study, we
similarly observed elevated fasting insulin levels, postprandial hyperinsulinemia and a
tendency towards increased first- and second-phase glucose-stimulated C-peptide secretion.
Using multivariate analysis, we demonstrated in this study that increased 1st and 2nd phase
103
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Chapter 5 Glucocorticoids, islet-cell function & ANS
C-peptide secretion was driven by treatment-related reduction in insulin sensitivity, showing
adequate compensation for prednisolone-induced insulin resistance (Figure 6), and did not
indicate improved beta-cell function per se. Interestingly, prednisolone treatment reduced
arginine-stimulated C-peptide secretion, a measure of insulin secretory capacity, in a dosedependent
matter.
A similar pattern was observed during the meal challenge test. Modeling analysis of glucose
and C-peptide concentrations, revealed decreased glucose-adjusted insulin secretion rates
following prednisolone treatment, also at fasting glucose levels (Supplementary Figure 4;
Figure 4D for insulin secretion at 4.5 mmol/L glucose). While prednisolone treatment did
not affect glucose sensitivity of the beta-cell or rate sensitivity, prednisolone 30 mg markedly
decreased the potentiation factor ratio, a finding that confirms data from a previous study
published by our group [8]. This represents insulin secretion that is not primarily related
to plasma glucose levels and may include secretion induced by non-glucose secretagogues,
incretin hormones, and neuronal factors [25, 26].
From both the hyperglycemic clamp test and the meal test we conclude that prednisolone
induces beta-cell dysfunction, although this effect is detectable on specific beta-cell function
parameters that seem to involve potentiation phenomena. Both arginine-induced secretion
at 10mmol/L glucose [27] and potentiation factor ratio [24, 26] during the meal are
dependent on potentiation. On the other hand, the enhancement of first- and second phase
insulin secretion as well as postprandial insulin release is mainly mediated by an increase in
glucose levels and a compensatory response to prednisolone-induced insulin resistance. In
addition to impairments in beta-cell function, we observed GC-induced increased fasting and
postprandial glucagon levels during high-dose prednisolone treatment. Thus, prednisolone
treatment altered islet-cell functional balance.
Additionally, we evaluated mechanisms possibly underlying the effects of GCs on islet-cell
function. First, we evaluated the role of the ANS system. Whereas catecholamines released
by the sympathetic nervous system inhibit insulin secretion via a -adrenergic receptor
2
(AR) signaling and stimulate glucagon release, acetylcholine released by parasympathetic
nerves stimulates insulin release via protein kinase C-related pathways [2]. In addition,
parasympathetic nerve fibers increase islet blood flow, thus facilitating increased insulin
secretion [28]. In beta-cell lines, GCs decreased the efficacy of acetylcholine to release
insulin [5], and also upregulated expression and signaling of a -ARs [29]. Transgenic mice
2
with beta-cell specific overexpression of the GR were shown to develop diabetes through
beta-cell failure consequential to increased a -AR expression. We observed a tendency
2
104

towards withdrawal of vagal activity following high-dose prednisolone treatment, which
was negatively associated with fasting glucose and glucagon levels. In addition, alterations
in sympathovagal balance, expressed as LF/HF ratio, were negatively related to changes in
C-peptide secretion in response to arginine. The latter finding is partly in line with a previous
study, in which interruption of cholinergic transmission by trimethaphan impaired argininestimulated
insulin secretion during treatment with dexamethasone [30]. However, in that
acute study, a 2-day treatment with high-dose dexamethasone, increased arginine-stimulated
secretion, whereas we observed a decline in this beta-cell function parameter. Differences in
treatment duration and study population (in that study, highly insulin sensitivity participants,
as determined by hyperinsulinemic-euglycemic clamp prior to inclusion, were studied) may
have contributed to these seemingly opposing results. In addition, dexamethasone is a pure
GR agonist, whereas prednisolone also activates the mineralocorticoid receptor. Various GC
compounds, depending on their receptor specificity, may have a different effect on measures
of cardiovascular autonomic function and variables such as blood pressure and heart rate,
making the compounds difficult to compare [31, 32].
Second, we measured incretin responses following prednisolone treatment. The incretin
hormones GLP-1 and GIP substantially contribute to glucose tolerance and we hypothesized
that prednisolone treatment would reduce incretin levels [33]. However, we did not observe
declined plasma levels of GLP-1 and GIP following prednisolone treatment, confirming
a recently published study, in which high-dose prednisolone treatment combined with a
hypercaloric diet and physical inactivity did not decrease postprandial levels of GLP-1 and
GIP [14]. However, in the same study, prednisolone treatment was shown to reduce the
insulinotropic effects of GLP-1 and GIP. Thus, an impaired incretin effect at the level of the
beta cell may in fact contribute to GC-induced hyperglycemia. Future in vitro studies will need
to address this hypothesis.
The observations done in the present study may have important implications for the treatment
of GC-induced glucose intolerance and diabetes. Our data indicate that pharmacological
measures that aim to improve pancreatic islet-cell function may particularly be effective in
restoring glucose tolerance during prednisolone treatment. In line with this hypothesis, we
have recently shown that the acute effects of high-dose prednisolone treatment on glucose
tolerance and islet-cell function in healthy humans could be prevented by concomitant
treatment with the GLP-1 receptor agonist exenatide [7]. Further studies in relevant
populations should explore the full potential of incretin-based therapies to prevent GCinduced
glucose intolerance.
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Chapter 5 Glucocorticoids, islet-cell function & ANS
We conclude that, in addition to reducing insulin sensitivity, prolonged prednisolone treatment
dose-dependently impairs islet-cell function in healthy males. Our data furthermore suggest
that changes in ANS balance may contribute to these GC-related changes.
Funding: This paper was written within the framework of the Dutch Top Institute Pharma
T1.106. Drs. Renate van Genugten is supported by the EFSD/MSD Clinical Research
Programme 2008. The authors thank dr. M.W. Heymans of the department of Epidemiology
and Biostatistics of the VU University Medical Center for his help with the statistical analyses.
106

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29. Hamamdzic D, Duzic E, Sherlock jD, Lanier SM. Regulation of alpha 2-adrenergic receptor expression
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30. Ahren B. Evidence that autonomic mechanisms contribute to the adaptive increase in insulin secretion
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31. Dodt C, Keyser B, Molle M, Fehm HL, Elam M. Acute suppression of muscle sympathetic nerve activity
by hydrocortisone in humans. Hypertension. 2000;35(3):758-63.
32. Dodt C, Wallin G, Fehm HL, Elam M. The stress hormone adrenocorticotropin enhances sympathetic
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33. Zhang R, Packard BA, Tauchi M, D’Alessio DA, Herman JP. Glucocorticoid regulation of preproglucagon
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109
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Chapter 5 Glucocorticoids, islet-cell function & ANS
SUPPLEMENTARy FIGURES
Supplementary Figure 1. Study Design.
Day
110
-14
S
Baseline
PLB, PRED 7.5 mg or PRED 30 mg QD
-2 -1 1
7
13 14
C M T
V C M
Offtreatment
S: Screening M: Meal Test C: Clamp Test T: start treatment V: Follow-up visits
Supplementary Figure 2A. Combined Hyperinsulinemic-Euglycemic and Hyperglycemic Clamp
Procedure.
Insulin 40 mU/m 2.min
Variable glucose 20% infusion
Plasma Glucose: 5.0 mM 10.0 mM
0 90 120 150 0 10
80 110
M-value -30
Time (min)
Glucose bolus
Supplementary Figure 2B. Double Standardized Meal Challenge Test.
- 60
HRV
studies
Time (min)
5g Arginine
0 60 120 180 240 300 360 420 480
Meal
75 g carbs
50 g fat
34 g protein
Meal
75 g carbs
50 g fat
34 g protein
28
V

Supplementary Figure 3. Patient flow diagram. 100% of the randomized participants completed the
study.
Supplementary Figure 4. Dose-response curve for insulin secretion and glucose levels during the meal
challenge test. The dose-response curve is shifted downwards by both PRED dosages. Panel A: Placebo.
Panel B: Prednisolone 7.5 mg daily. Panel C: Prednisolone 30 mg daily. Straight line with black squares
denotes pre-treatment. Dotted line with open circles represents on-treatment.
111
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Chapter 5 Glucocorticoids, islet-cell function & ANS
Supplementary Table 1. Body weight and blood pressure before and during two-week treatment with
placebo, prednisolone 7.5 mg once daily, or prednisolone 30 mg once daily.
Weight
(kg)
BMI
(kg/m2 )
Systolic Blood Pressure
(mmHg)
Diastolic Blood Pressure
(mmHg)
112
Pre-treatment On-treatment P1 P2 P3
PLB 75±8 76±7
7.5 75±6 75±6
0.539 NA NA
30 73±10 74±10
PLB 21.6±1.5 21.7±1.3
7.5 22.1±1.5 22.1±1.7 0.614 NA NA
30 21.4±2.2 21.5±2.4
PLB 126±7 123±9
7.5 124±9 124±11
0.002 0.710 0.006
30 120±9 124±8
PLB 79±8 76±7
7.5 79±6 76±6
0.042 1.0 0.164
30 78±10 80±9
Mean±SD or median (interquartile range) are provided. Between-group changes from baseline were
tested by Kruskal-Wallis with trend analysis (P1). In case of a significant finding, posthoc testing (by
Mann-Whitney U) was done (P2 and P3). Treatment groups: PLB=placebo; 7.5=prednisolone 7.5 mg
daily; 30=prednisolone 30 mg daily.
Supplementary Table 2. Clamp insulin levels before and during two-week treatment with placebo,
prednisolone 7.5 mg once daily, or prednisolone 30 mg once daily.
Clamp Insulin levels
(pmol/l)
Pre-treatment On-treatment P
PLB 441±81 412±49
7.5 488±61 453±69
NS
30 504±78 460±81
Mean±SD or median (interquartile range) are provided. Between-group changes from baseline were
tested by Kruskal-Wallis with trend analysis (P). Treatment groups: PLB=placebo; 7.5=prednisolone 7.5
mg daily; 30=prednisolone 30 mg daily.

Chapter 6
Glucocorticoid Receptor Gene Polymorphisms
are Associated with Reduced First-phase
Glucose-Stimulated Insulin Secretion and
Disposition Index in Women, but not in Men.
D.H. van Raalte*, N. van Leeuwen*, A.M. Simonis-Bik, G. Nijpels,
T.W. van Haeften, S.A. Schafer, D.I. Boomsma, M.H.H. Kramer,
R.J. Heine, J.A. Maassen, H. Staiger, F. Machicao, H.U. Häring, P.E.
Slagboom, G. Willemsen, E.J. de Geus, J.M. Dekker, A. Fritsche, E.
M. Eekhoff, M. Diamant, L.M. ‘t Hart.
*Both authors contributed equally
Diabetic Medicine. 2012; 29(8):e211-e216

Chapter 6 Glucocorticoid receptor SNPs & beta-cell function
ABSTRACT
Background and aim: Glucocorticoids (GCs) are efficacious anti-inflammatory agents,
but in susceptible individuals, these drugs may induce glucose intolerance and diabetes by
affecting beta-cell function and insulin sensitivity. We assessed whether polymorphisms in
the glucocorticoid receptor (GR) gene NR3C1 associate with measures of beta-cell function
and insulin sensitivity derived from hyperglycemic clamps in subjects with normal or
impaired glucose tolerance.
Methods and study design: A cross-sectional cohort study was conducted in four academic
medical centers in the Netherlands and Germany. Four hundred and forty-nine volunteers
(188 males; 261 females) were recruited with normal glucose tolerance (n=261) and
impaired glucose tolerance (n=188). From 2-h hyperglycemic clamps, first- and secondphase
glucose-stimulated insulin secretion (GSIS )as well as insulin sensitivity index and
disposition index were calculated. All participants were genotyped for the functional
NR3C1 polymorphisms N363S (rs6195), BclI (rs41423247), ER22/23EK (rs6189/6190),
9β A/G (rs6198) and ThtIIII (rs10052957). Associations between these polymorphisms
and beta-cell function parameters were assessed.
Results: In women, but not in men, the N363S polymorphism was associated with reduced
disposition index (P=1.06 10-4 ). Also only in women, the ER22/23EK polymorphism was
associated with reduced first-phase GSIS (P=0.011) and disposition index (P=0.003). The
other SNPs were not associated with beta-cell function. Finally, none of the polymorphisms
was related to insulin sensitivity.
Conclusion: The N363S and ER22/23EK polymorphisms of the NR3C1 gene are negatively
associated with parameters of beta-cell function in women, but not in men.
116

Excess glucocorticoid (GC) levels induce glucose intolerance [1, 2] and are associated with
incident diabetes [3]. In addition to GC-induced insulin resistance [4], GC-induced beta-cell
dysfunction is a hallmark of GC-associated adverse metabolic effects [2, 5-7]. GCs exert many
effects by binding to its cytosolic glucocorticoid receptor (GR), following which the ligandactivated
GR translocates to the nucleus where it regulates target gene transcriptional activity.
Considerable variability exists in the sensitivity to GCs across individuals, a phenomenon that
was linked to functional polymorphisms in the GR gene (NR3C1) [8-10]. As such, the NR3C1
variants ER22/23EK (two Single Nucleotide Polymorphisms (SNPs) that are in complete
linkage disequilibrium) [11] and 9β A/G [12] may induce relative GC resistance, whereas the
NR3C1 gene variants BclI C/G [13] and N363S [14] are associated with enhanced GC sensitivity.
The effects of the TthIIII polymorphism are currently less clear [15]. Importantly, several of
these SNPs have been linked to metabolic parameters. In some studies, GC resistance was
associated with insulin sensitivity, increased lean body mass and reduced waist circumference
[11, 16, 17]. In contrast, GC sensitivity may be associated with a less favorable metabolic
profile [18, 19]. Importantly, gender-specific effects have frequently been observed [8-10,
17, 19], e.g. the ER22/23EK variant was associated with beneficial body composition, muscle
strength and metabolic profile in men, but not in women [16].
It is currently unknown whether these NR3C1 gene polymorphisms affect beta-cell function.
Interestingly, mice overexpressing the GR in beta cells become diabetic because of beta-cell
failure [20]. We hypothesized that alterations in GC sensitivity due to SNPs in the NR3C1
gene could relate to beta-cell function. This hypothesis was addressed for the first time in the
present study, where 449 subjects were genotyped for NR3C1 polymorphisms and beta-cell
function was measured by the gold-standard hyperglycemic clamp.
Research design and methods
Cohorts: 449 Caucasian subjects were recruited from three independent studies from the
Netherlands and one from Germany [21-25]. Characteristics and inclusion criteria of the
separate cohorts are provided in supplemental Table 1 and 2.
Hyperglycemic clamp procedure: All participants underwent a hyperglycemic clamp at 10
mmol/l glucose for at least two hours as described previously [21, 22, 24, 25]. First-phase
glucose-stimulated insulin secretion (GSIS) was computed as the sum of the insulin levels
during the first 10 minutes of the clamp. Second-phase GSIS was determined as the mean
insulin level during the last 40 min of the second hour of the clamp (80-120 min). The insulin
sensitivity index (ISI) was defined as the glucose infusion rate (M, mmol/min.kg) divided by
the plasma insulin concentration (I, pmol/l) during the last 40 min of the clamp (M/I, mmol/
117
6
Chapter 6

Chapter 6 Glucocorticoid receptor SNPs & beta-cell function
min.kg.pmol/l), which was shown to correlate well with insulin sensitivity measured by the
hyperinsulinemic-euglycemic clamp [26]. Insulin secretion adjusted for insulin sensitivity
was expressed as the disposition index (DI), calculated by multiplying first-phase GSIS and
ISI [27].
Genotyping: Based on the available literature, 5 SNPs were genotyped: TthIIII (rs10052957),
ER22/23EK (rs6189/6190), N363S (rs6195), BclI site (rs41423247) and 9β A/G (rs6198),
using the Sequenom platform (Sequenom, San Diego, California, USA). The genotyping success
rate was above 98% for all SNPs and samples measured in duplicate (~5%) were in complete
concordance. The SNPs did not deviate from Hardy-Weinberg equilibrium (HWE, Haploview
program (MIT, Harvard Broad Institute, Cambridge, MA, USA)). Individual haplotypes were
constructed using SNPHAP (http://www-gene.cimr.cam.ac.uk/clayton/software/snphap.
txt).
Statistics: The effect of the SNPs on beta-cell function was examined with linear regression
assuming an additive model. To take into account the family relatedness, empirical standard
errors were used (using the generalized estimating equations). For the monozygotic twins we
computed the mean of the beta-cell measures and included only these mean measures in the
analysis. The data from the non-identical twins were both used. The analyses of both first- and
second-phase GSIS were adjusted for age, BMI, study centre, glucose tolerance status (NGT/
IGT) and ISI. For the analysis of ISI and DI, ISI was removed from the covariates. All outcomes
were log transformed prior to analysis. Due to the fact that NR3C1 polymorphisms have
previously been shown to display gender-specific effects, male and females participants were
analyzed separately [8-10]. All data are given as estimated mean (95%CI). After Bonferroni
correction for multiple testing, results were regarded significant at a level of P

Figure 1. Schematic overview of the glucocorticoid receptor (GR) gene, NR3C1, polymorphisms and
haplotypes. The location of each SNP in the NR3C1 gene is indicated by a black arrow. Minor allele
frequencies of the SNPs are indicated below the SNP name. Haplotype alleles are indicated with black
lines containing the nucleic acid for each SNP. Haplotype allele frequencies are displayed left of the
haplotype allele and haplotype alleles are numbered in order of decreasing frequency.
Associations with beta-cell function: The N363S variant was associated with reduced
disposition index (P=1.06 10-4 ) and showed a trend towards reduced first-phase insulin
secretion in women (Table 1). Also in women only, the ER22/23EK polymorphism was
associated with reduced first-phase GSIS (P=0.011) and disposition index (P=0.003). No
other associations were observed between NR3C1 SNPs and measures of beta-cell function,
nor in men, nor in women. Similar results were obtained for the associations between NR3C1
haplotypes and beta-cell function parameters (Supplementary Table 3). The results of the
analyses were not different when NGT and IGT subjects were analyzed separately (data not
shown).
Associations with insulin sensitivity: None of the NR3C1 SNPs or haplotypes was significantly
associated with insulin sensitivity (Table 1 and Supplementary Table 3 respectively).
119
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Chapter 6

Chapter 6 Glucocorticoid receptor SNPs & beta-cell function
Table 1. Insulin response according to NR3C1 SNP in women and men.
120
Women Men
DI
(mmol/min.
kg)
ISI
(mmol/min.
kg.pmol/l)
2 nd -phase
GSIS (pmol/l)
1 st -phase
GSIS (pmol/l)
n
DI
(mmol/min.
kg)
ISI
(mmol/min.
kg.pmol/l)
Second-phase
GSIS (pmol/l)
First-phase
GSIS (pmol/l)
Genotype n
BclI (rs41423247)
CC 110 713 (656-766) 244 (225-264) 0.14 (0.12-0.15) 100 (91-109) 74 730 (643-828 262 (239-288) 0.15 (0.13-0.17) 107 (94-123)
CG 98 773 (712-839) 248 (229-269) 0.14 (0.13-0.16) 109 (100-118) 82 665 (594-745) 236 (217-257) 0.16 (0.14-0.18) 103 (91-117)
GG 30 837 (688-1017) 270 (233-312) 0.15 (0.12-0.19) 116(95-143) 13 710 (518-972) 258 (195-341) 0.12(0.08-0.17) 70 (70-119)
β (SE) 0.035 (0.020) 0.018 (0.017) 0.018 (0.024) 0.035 (0.021) -0.021 (0.029) -0.023 (0.023) -0.013 (0.031) -0.027 (0.028)
P 0.084 0.292 0.448 0.100 0.462 0.314 0.683 0.349
9β
(rs6198)
AA 156 733 (685-784) 244 (229-260) 0.15 (0.13-0.16) 104 (98-112) 111 687 (622-759) 250 (231-271) 0.15 (0.13-0.17) 102 (92-114)
AG 76 804 (723-893) 256 (233-281) 0.13 (0.11-0.15) 108 (96-121) 51 679 (577-798) 244 (216-275) 0.15 (0.13-0.18) 103 (87-122)
GG 8 758 (503-1144) 313 (210-468) 0.11 (0.07-0.17) 91 (59-122) 7 930 (722- 240 (207-280) 0.17 (0.11-0.27) 142 (94-216)
1197)
β (SE) 0.028 (0.026) 0.033 (0.024) -0.051 (0.030) -0.003 (0.028) 0.022 (0.030) -0.010 (0.020) 0.011 (0.034) 0.030 (0.034)
P 0.280 0.174 0.085 0.911 0.457 0.628 0.753 0.386
TthIIII (rs10052957)
CC 117 740 (686-798) 244 (226-264) 0.14(0.13-0.16) 104 (97-113) 71 698 (619-787) 217 (239-286) 0.15 (0.13-0.17) 102 (94-123)
CT 99 787 (719-862) 260 (240-283) 0.14 (0.12-0.16) 109 (98-120) 77 686 (607-776) 243 (219-268) 0.15 (0.13-0.17) 103 (91-117)
TT 23 715 (569-897) 228(193-271) 0.14 (0.10-0.16) 97 (78-121) 19 719 (553-934) 218 (184-259) 0.16 (0.12-0.22) 106 (70-119)
β (SE) 0.005 (0.021) 0.001 (0.018) -0.018 (0.024) -0.004 (0.022) 0.002 (0.027) -0.037 (0.019) 0.011 (0.030) 0.007 (0.028)
P 0.808 0.938 0.466 0.865 0.949 0.055 0.707 0.798

N363S (rs6195)
AA 222 766 (722-812) 248 (235-262) 0.14 (0.13-0.15) 108 (101-115) 161 696 (637-760) 249 (232-267) 0.15 (0.13-0.17) 103 (94-114)
AG 17 613 (515-731) 263 (225-307) 0.14 (0.09-0.14) 74 (62-88) 8 658 (553-783) 229 (191-274) 0.20 (0.14-0.29) 118 (91-154)
β (SE) -0.096 (0.041) 0.026 (0.036) -0.111 (0.055) -0.165 (0.043) -0.024 (0.039) -0.037 (0.041) 0.130 (0.085) 0.058 (0.058)
P 0.020 0.476 0.043 0.0001 0.534 0.375 0.125 0.327
ER22/23EK (rs6189/rs6190)
GG/GG 231 764 (722-808) 248 (235-262) 0.14 (0.13-0.15) 106 (100-113) 155 690 (632-754) 249 (232-266) 0.15 (0.14-0.17) 104 (94-114)
AA/GG 9 518 (386-694) 294 (205-420) 0.10 (0.08-0.14) 68 (51-90) 14 751 (595-947) 240 (210-274) 0.14 (0.10-0.18) 106 (78-144)
β (SE) -0.169 (0.066) 0.073 (0.080) -0.131 (0.064) -0.197 (0.065) 0.037 (0.052) -0.015 (0.030) -0.047 (0.068) 0.011 (0.069)
P 0.011 0.360 0.040 0.003 0.485 0.618 0.491 0.874
Data are estimated means (95% CI), the β (SE) is the β (SE) of the log transformed variable. Only one participant was homozygous for the N363S variant; the subject was
added to the heterozygous group. For the ER22/23EK SNP, non-carriers are depicted as GG/GG. Heterozygotes are AA/GG. No homozygotes were identified in this cohort.
All variables were log transformed before analysis. P values were computed for additive models using linear generalized estimating equations, which takes into account the
family relatedness when computing the standard errors. First- and second-phase GSIS were adjusted for study center, family relatedness, glucose tolerance status, age, BMI
and ISI. ISI and DI were adjusted for study center, family relatedness, glucose tolerance status, age and BMI. Significant findings are marked in bold. Abbreviations: GSIS:
glucose-stimulated insulin secretion; ISI: insulin sensitivity index; DI: disposition index.
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DISCUSSIoN
In four cohorts of NGT and IGT subjects, we found that the NR3C1 SNPs N363S and ER22/23EK
were associated with reduced beta-cell function parameters in women. Both first-phase
GSIS and disposition index, which denotes the adaptation of beta cells to prevailing insulin
sensitivity, were influenced. As expected, the corresponding NR3C1 haplotypes containing
these polymorphisms provided identical results. The N363S SNP enhances GC sensitivity by
increasing gene transcription [14]. Indeed, in various studies a link was established between
the N363S SNP and characteristics of a Cushingoid phenotype, including increased BMI and
waist circumference, dyslipidemia and augmented fasting insulin levels, indicating reduced
insulin sensitivity [10, 19]. The ER22/23EK SNP on the other hand, demonstrated reduced GR
activation in vitro, and relative GC resistance in vivo [11, 16]. As such, in men, the ER22/23EK
was associated with a beneficial metabolic phenotype, including increased muscle mass
and strength, lower LDL cholesterol and insulin levels [11, 16]. On the other hand, female
carriers of the ER22/23EK SNP were at increased risk to develop cardiovascular disease
[28]. In another cohort, carriers of the ER22/23EK had higher HbA1c levels as compared
to noncarriers [29], thus raising doubt on the hypothesis that this SNP may induce a more
favorable metabolic profile, especially in women.
More recently, impaired GSIS was shown to be another hallmark of GC-induced adverse
metabolic effects both in vitro and in vivo in humans, where several measures of beta-cell
function were impaired [2, 5-7]. Furthermore, mice overexpressing the GR specifically in
beta-cells developed diabetes through beta-cell failure [20]. Our present data support the
concept that GCs impair beta-cell function.
Interestingly, the associations between SNPs in the NR3C1 gene and beta-cell function
parameters were only observed in women, not in men. As outlined above, genderspecific
effects of NR3C1 gene variants have been observed in various studies for various
anthropometric and metabolic variables [8-10, 16, 17, 19]. Additionally, gender-related
hormonal factors are known to affect beta-cell function [30]. As such, premenopausal women
and women receiving estrogen replacement therapy have reduced prevalence of diabetes,
which has been attributed to the beta-cell protective effects of estrogens [30]. Furthermore,
the male sex hormone testosterone may also affect beta-cell function [31]. The effects of NR3C1
polymorphisms on beta-cell function may therefore interact differently with sex hormones.
An important limitation of the present study is the relatively small number of participants
that were included, although this cohort is the largest to undergo a hyperglycemic clamp in
the context of genetic analysis currently available in the literature. We cannot rule out the
122

possibility to have missed subtle effects of other NR3C1 polymorphisms on beta-cell function
variables. Another limitation is the fact that the SNPs did not tag the whole NR3C1 gene,
therefore we cannot exclude that the associations found were caused by another untested
SNP, although given the extensive literature on the SNPs function, this seems unlikely. We fully
subscribe the need for replication of these data, although such replication is nontrivial because
the hyperglycemic clamp methodology is demanding for both researchers and participants.
In conclusion, this is the first report to show that the N363S and ER22/23EK NR3C1 gene
variants are associated with reduced first-phase GSIS and DI in women, but not in men. This
may point to a differential effect of genetically determined variation in GR activity in women
as compared to men in the adaptation of (first-phase) insulin secretion to insulin sensitivity.
Funding: This paper was written within the framework of project T1-106 of the Dutch Top
Institute Pharma. The work in this study was additionally financially supported by the Dutch
Diabetes Research Foundation grant 2006.00.060.
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Chapter 6 Glucocorticoid receptor SNPs & beta-cell function
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H, Stumvoll M. The prevalent Gly1057Asp polymorphism in the insulin receptor substrate-2 gene is
not associated with impaired insulin secretion. j Clin Endocrinol Metab. 2001;86(10):4822-5.
22. Ruige jB, Dekker jM, Nijpels G, Popp-Snijders C, Stehouwer CD, Kostense Pj, Bouter LM, Heine Rj.
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settings. Twin Res Hum Genet. 2008;11(6):597-602.
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relatives of type 2 diabetic subjects. Assessments using hyperglycemic glucose clamps and
oral glucose tolerance tests. Diabetes Care. 1998;21(2):278-82.
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Disturbances in beta-cell function in impaired fasting glycemia. Diabetes. 2002;51 Suppl 1:S265-70.
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26. Mitrakou A, Vuorinen-Markkola H, Raptis G, Toft I, Mokan M, Strumph P, Pimenta W, Veneman T,
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SUPPLEMENTAL FILES
Supplementary Figure 1
Pairwise Linkage Disequilibrium (LD) values between the SNP quantified using r 2 in the Haploview
program are indicated. Black color indicates full linkage disequilibrium, lower shades of gray indicate
lower LD and white color indicates no LD.
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Chapter 6 Glucocorticoid receptor SNPs & beta-cell function
Supplemental Table 1. Characteristics of the cohort.
Hoorn*1 Utrecht*2,3 NTR*4 Germany*5
128
IGT NGT IGT NGT IGT NGT IGT
n 137 64 12 116 7 81 32
Sex (male/female) 64/73 15/49 4/8 58/58 0/7 33/48 14/18
Age (years) 60.5±8.6 45.9±6.4 49.5±7.7 31.5±6.5 31.2±3.2 36.7±12.6 45.9±13.3
BMI (kg m -2 ) 28.1±4.0 25.8±3.8 26.7±4.1 24.2±3.5 24.5±3.3 25.0±5.8 26.8±4.5
FPG (mmol/l) 6.3±0.7 4.6±0.4 5.1±0.4 4.6±0.4 4.6±0.6 5.0±0.6 5.5±0.7
FPI (pmol/l) 62 (46-91) 30 (24-42) 66 (42-78) 34 (27-51) 39 (29-60) 40 (30-60) 58 (39-95)
587 (378-895) 885 (644-1217) 678 (461-909) 814 (589-1162) 795 (693-1210) 667 (522-1148) 524 (416-922)
First-phase GSIS
(pmol/l)
255 (176-354) 260 (191-365) 251 (186-307) 218 (162-358) 217 (210-434) 229 (147-343) 196 (145-294)
Second-phase GSIS
(pmol/l)
0.11 (0.07-0.17) 0.19 (0.13-0.29) 0.11 (0.08-0.26) 0.23 (0.15-0.32) 0.12 (0.11-0.18) 0.15 (0.09-0.22) 0.11 (0.06-0.15)
ISI (mmol/min.
kg.pmol/l)
DI (mmol/min.kg) 65 (42-92) 172 (103-238) 72 (55-128) 180 (140-234) 138 (82-151) 109 (71-166) 62 (45-80)
Data are mean ± SD, median (interquartile range) or n. *Original population from which the cohort originated [1-5]. FPG: fasting plasma glucose; FPI: fasting plasma
insulin; GSIS: glucose-stimulated insulin secretion; ISI: insulin sensitivity index; DI: disposition index.
1. Ruige jB, Dekker jM, Nijpels G, Popp-Snijders C, Stehouwer CD, Kostense Pj, et al. Hyperproinsulinaemia in impaired glucose tolerance is associated with a
delayed insulin response to glucose. Diabetologia. 1999 Feb;42(2):177-80.
2. van Haeften TW, Dubbeldam S, Zonderland ML, Erkelens DW. Insulin secretion in normal glucose-tolerant relatives of type 2 diabetic subjects. Assessments
using hyperglycemic glucose clamps and oral glucose tolerance tests. Diabetes Care. 1998 Feb;21(2):278-82.
3. van Haeften TW, Pimenta W, Mitrakou A, Korytkowski M, jenssen T, Yki-jarvinen H, et al. Disturbances in beta-cell function in impaired fasting glycemia.
Diabetes. 2002 Feb;51 Suppl 1:S265-70.
4. Simonis-Bik AM, Eekhoff EM, Diamant M, Boomsma DI, Heine Rj, Dekker jM, et al. The heritability of HbA1c and fasting blood glucose in different measurement
settings. Twin Res Hum Genet. 2008 Dec;11(6):597-602.
5. Fritsche A, Madaus A, Renn W, Tschritter O, Teigeler A, Weisser M, et al. The prevalent Gly1057Asp polymorphism in the insulin receptor substrate-2 gene is
not associated with impaired insulin secretion. j Clin Endocrinol Metab. 2001 Oct;86(10):4822-5.

Supplemental Table 2. Major inclusion criteria of the study cohort.
Cohort Reference Ethnicity Age Relatedness Glucometabolic State FDR T2DM
Hoorn [1] Caucasian 45-74 Unrelated IGT No
Utrecht [2] Caucasian NA Unrelated NGT Yes
[3] Caucasian NA Unrelated NGT/IGT No
NTR [4] Caucasian 20-50 Same sex twins and siblings NGT and IGT No
Tubingen [5] Caucasian NA Unrelated NGT and IGT No
Glucometabolic state was determined following an OGTT. FDR T2DM: first degree relative of patient with type 2 diabetes mellitus; IGT: impaired glucose tolerance;
NA: not applicable; NTR: Netherlands Twin Registry; NGT: normal glucose tolerance.
1. Ruige jB, Dekker jM, Nijpels G, Popp-Snijders C, Stehouwer CD, Kostense Pj, et al. Hyperproinsulinaemia in impaired glucose tolerance is associated with a
delayed insulin response to glucose. Diabetologia. 1999 Feb;42(2):177-80.
2. van Haeften TW, Dubbeldam S, Zonderland ML, Erkelens DW. Insulin secretion in normal glucose-tolerant relatives of type 2 diabetic subjects. Assessments
using hyperglycemic glucose clamps and oral glucose tolerance tests. Diabetes Care. 1998 Feb;21(2):278-82.
3. van Haeften TW, Pimenta W, Mitrakou A, Korytkowski M, jenssen T, Yki-jarvinen H, et al. Disturbances in beta-cell function in impaired fasting glycemia.
Diabetes. 2002 Feb;51 Suppl 1:S265-70.
4. Simonis-Bik AM, Eekhoff EM, Diamant M, Boomsma DI, Heine Rj, Dekker jM, et al. The heritability of HbA1c and fasting blood glucose in different measurement
settings. Twin Res Hum Genet. 2008 Dec;11(6):597-602.
5. Fritsche A, Madaus A, Renn W, Tschritter O, Teigeler A, Weisser M, et al. The prevalent Gly1057Asp polymorphism in the insulin receptor substrate-2 gene is
not associated with impaired insulin secretion. j Clin Endocrinol Metab. 2001 Oct;86(10):4822-5.
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Chapter 6 Glucocorticoid receptor SNPs & beta-cell function
130
Supplemental Table 3. Insulin response according to NR3C1 haplotype in women and men.
Women Men
DI
(mmol/min.
kg)
ISI
(mmol/min.
kg.pmol/l)
2 nd -phase
GSIS (pmol/l)
1 st -phase
GSIS (pmol/l)
n
DI
(mmol/min.
kg)
ISI
(mmol/min.
kg.pmol/l)
Second-phase
GSIS (pmol/l)
First-phase
GSIS (pmol/l)
Haplotype n
Haplotype 1
0 80 802 (721-892) 269 (245-294) 0.13 (0.11-0.15) 105 (94-118) 44 651 (552-768) 227 (200-257) 0.15 (0.12-0.18) 98 (86-116)
1 108 754 (693-821) 246 (227-267) 0.14 (0.13-0.16) 107 (98-116) 92 719 (641-807) 254 (233-276) 0.15 (0.14-0.17) 108 (96-123)
2 52 694 (625-772) 229 (205-257) 0.15 (0.13-0.16) 102 (91-115) 33 677 (570-804) 260 (226-300) 0.14 (0.12-0.17) 99 (83-119)
P 0.06 0.03 0.18 0.75 0.68 0.12 0.83 0.86
Haplotype 2
0 148 716 (665-771) 240 (224-256) 0.14 (0.13-0.15) 100 (93-108) 114 706 (636-783) 245 (225-267) 0.15 (0.14-0.17) 106 (95-119)
1 81 811 (745-883) 263 (240-288) 0.14 (0.13-0.16) 112 (102-123) 53 660 (569-767) 245 (220-274) 0.15 (0.13-0.18) 101 (86-119)
2 11 937 (687-1278) 292 (238-357) 0.14 (0.09-0.21) 122 (92-163) 2 923 (763-1115) 418 (362-482) 0.06 (0.05-0.07) 79 (59-106)
P 0.02 0.03 0.74 0.04 0.65 0.54 0.71 0.45
Haplotype 3
0 163 730 (684-780) 247 (232-264) 0.14 (0.13-0.15) 104 (97-111) 126 698 (634-768) 250 (232-269) 0.15 (0.13-0.17) 104 (93-116)
1 73 807 (720-904) 252 (229-278) 0.13 (0.12-0.15) 108 (95-122) 37 654 (537-797) 246 (212-284) 0.16 (0.13-0.19) 102 (83-125)
2 4 889 (731-1081) 306 (183-512) 0.12 (0.06-0.21) 109 (72-165) 6 835 (689-1012) 232 (200-270) 0.16 (0.09-0.26) 117 (88-156)
P 0.08 0.51 0.30 0.58 0.98 0.63 0.70 0.87
Haplotype 4
0 189 756 (711-804) 253 (238-268) 0.14 (0.13-0.15) 104 (97-111) 124 711 (645-784) 258 (241-278) 0.15 (0.13-0.17) 106 (95-118)
1 47 781 (681-895) 241 (218-268) 0.15 (0.13-0.17) 113 (98-130) 40 633 (535-748) 215 (188-246) 0.16 (0.13-0.19) 99 (84-116)
2 4 488 (312-762) 198 (131-300) 0.14 (0.09-0.20) 70 (45-109) 5 772 (541-1101) 257 (186-355) 0.13 (0.07-0.25) 98 (66-145)
P 0.58 0.23 0.37 0.96 0.45 0.06 0.95 0.44

Haplotype 5
0 223 767 (724-813) 248 (235-263) 0.14 (0.13-0.15) 108 (101-115) 161 696 (637-760) 249 (232-267) 0.15 (0.13-0.17) 103 (94-114)
1 17 614 (515-760) 264 (226-308) 0.11 (0.09-0.14) 74 (62-88) 8 658 (553-783) 229 (191-274) 0.20 (0.14-0.29) 118 (91-154)
P 0.02 0.47 0.04 0.0001 0.53 0.38 0.13 0.33
Haplotype 6
0 231 764 (722-808) 248 (235-262) 0.14 (0.13-0.15) 106 (100-113) 155 690 (632-754) 249 (232-266) 0.15 (0.14-0.17) 104 (94-114)
1 9 518 (386-694) 294 (205-420) 0.10 (0.08-0.14) 68 (51-90) 14 751 (595-947) 240 (210-274) 0.14 (0.10-0.17) 106 (78-144)
P 0.011 0.36 0.04 0.003 0.49 0.62 0.49 0.87
For the haplotypes dummy variables indicating 0, 1 and 2 copies of each haplotype allele are shown. Data are estimated means (95% CI) unless otherwise
indicated. All variables were log transformed before analysis. P values were computed for additive models using linear generalized estimating equations,
which takes into account the family relatedness when computing the standard errors. First- and second-phase GSIS were adjusted for study center, family
relatedness, glucose tolerance status, age, BMI and ISI. ISI and DI were adjusted for study center, family relatedness, glucose tolerance status, age and BMI.
Significant findings are marked in bold. Abbreviations: GSIS: glucose-stimulated insulin secretion; ISI: insulin sensitivity index; DI: disposition index.
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Chapter 7
Glucagon-Like Peptide-1 Receptor Agonist
Treatment Prevents Glucocorticoid-
Induced Glucose Intolerance and Islet-Cell
Dysfunction in Humans
D.H. van Raalte*, R.E. van Genugten*, M.M.L. Linssen, D.M.
Ouwens, M. Diamant
*Both authors contributed equally
Diabetes Care. 2011; 34(2):412-7

Chapter 7 GLP-1 prevents glucocorticoid-related metabolic effects
ABSTRACT
objective: Glucocorticoids (GCs) are regarded as diabetogenic since they impair insulin
sensitivity and islet-cell function. In this study we assessed whether treatment with the
glucagon-like peptide receptor agonist (GLP-1 RA) exenatide could prevent GC-induced
glucose intolerance.
Research design and methods: A randomized, placebo-controlled, double blind, crossover
study in 8 healthy males (age: 23.5 (20.0-28.3) years; BMI: 26.4 (24.3-28.0) kg/m 2 ) was
conducted. Participants received 3 therapeutic regimens for 2 consecutive days, i.e. (1) 80
mg oral prednisolone once daily and intravenous (IV.) exenatide infusion (PRED+EXE),
(2) 80 mg oral prednisolone once daily and IV. saline infusion (PRED+SAL), and (3) oral
placebo-prednisolone once daily and IV. saline infusion (PLB+SAL). On day 1, glucose
tolerance was assessed during a meal challenge test. On day 2, participants underwent a
clamp procedure to measure insulin secretion and insulin sensitivity.
Results: PRED+SAL treatment increased postprandial glucose levels (vs. PLB+SAL, P=
0.012), which was prevented by concomitant exenatide (vs. PLB+SAL, P=NS). Exenatide
reduced prednisolone-induced hyperglucagonemia during the meal challenge (P=0.018)
and decreased gastric emptying (vs. PRED+SAL, P=0.028; vs. PLB+SAL, P=0.046). PRED+SAL
decreased first-phase glucose-stimulated and arginine-stimulated C-peptide secretion (vs.
PLB+SAL, P=0.017 and P=0.05 respectively), while PRED+EXE improved first-phase and
second-phase glucose-stimulated, as well as arginine-stimulated C-peptide secretion (vs.
PLB+SAL; P=0.017, 0.012 and 0.093 respectively).
Conclusions: The GLP-1 RA exenatide prevented prednisolone-induced glucose intolerance
and islet-cell dysfunction in healthy humans. Incretin-based therapies should be explored
as a potential strategy to prevent steroid diabetes.
Clinical Trial Registration Number: NCT00744224
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Glucocorticoids (GCs) are diabetogenic agents since they reduce insulin sensitivity [1], impair
alpha-cell function [2], and, according to more recent findings, impair beta-cell function
[3, 4]. As such, chronic use of GCs was associated with odds ratios between 1.4 and 2.3 to
develop diabetes [5-7]. Loss of glycemic control during GC use is particularly due to impaired
postprandial glucose metabolism, while fasting plasma glucose levels are usually only mildly
elevated [4, 5]. Although the exact prevalence of steroid-related diabetes is unknown, the
widespread use of GCs indicates that it may represent a major clinical problem worldwide.
When initiating GC therapy in current clinical practice, preventive pharmacological measures
are taken to prevent some of the GC-related side-effects, most notably osteoporosis and peptic
ulcer disease [8, 9]. Interestingly, in spite of the above-mentioned highly-prevalent occurrence
of steroid-diabetes, to date, no strategies are undertaken to prevent the adverse metabolic
effects of GC treatment. Previous studies showed that metformin and the thiazolidinedione
(TZD) pioglitazone were unable to mitigate the effects of GCs on glucose tolerance [10], while
the TZD troglitazone prevented GC-induced hyperglycemia by enhancing GC clearance [10,
11]. Due to liver toxicity, however, troglitazone is no longer available for treatment in humans.
The gut hormone glucagon-like peptide (GLP)-1 and synthetic dipeptidyl-peptidase (DPP)-
4 resistant GLP-1 receptor agonists (GLP-1 RA), such as exenatide, lower blood glucose by,
glucose-dependently, enhancing insulin secretion and production and inhibiting glucagon
secretion, and by slowing down gastric emptying [12]. One year of exenatide treatment
was shown to improve clamp-measured beta-cell function in patients with type 2 diabetes
mellitus (T2DM) [13]. Recently, the GLP-1 RA exendin-4 was shown to prevent GC-induced
beta-cell apoptosis in vitro ([14]. In a single patient with Cushing’s disease, GLP-1 infusion
was as effective in lowering blood glucose levels as compared to patients with ‘typical’ T2DM
[15]. Finally, GLP-1 infusion effectively reduced stress hyperglycemia in patients undergoing
coronary artery bypass graft (CABG) [16].
Given these beneficial effects of GLP-1 RA treatment, and the pathophysiologic defects
underlying GC-induced glucose intolerance and diabetes, we aimed to assess whether IV.
infusion of the GLP-1 RA exenatide could prevent the acute adverse effects of prednisolone
treatment on glucose metabolism, islet-cell function and insulin sensitivity in healthy
normoglycemic individuals.
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Chapter 7 GLP-1 prevents glucocorticoid-related metabolic effects
RESEARCH DESIGN AND METHoDS
Participants: Eight healthy males were recruited via local advertisements. Inclusion criteria
included age = 18-35 years, body mass index (BMI) = 22.0-28.0 kg/m 2 , good physical
health (determined by medical history, physical examination and screening blood tests)
and normoglycemia as defined by fasting plasma glucose < 5.6 mmol/L and 2-hour glucose
< 7.8 mmol/L following a 75g oral glucose tolerance test (OGTT), performed at screening
visit. Exclusion criteria were the presence of any disease, use of any medication, first-degree
relative with type 2 diabetes, smoking, shift work, a history of GC use and recent changes
in weight or physical activity. The study was approved by an independent ethics committee
and the study was conducted in accordance with the Declaration of Helsinki. All participants
provided written informed consent before participation.
Experimental design: The study was a randomized, placebo-controlled, double blind
crossover study. Following assessment of eligibility, participants received for two consecutive
days, in random order: (1) 80 mg oral prednisolone once daily and IV. exenatide infusion
(PRED+EXE), (2) 80 mg oral prednisolone once daily and IV. saline infusion (PRED+SAL),
and (3) oral placebo-prednisolone once daily and IV. saline infusion (PLB+SAL). On day 1,
a standardized meal challenge was performed (Supplemental Figure 1A) and on day 2,
participants underwent a combined clamp procedure, starting with a hyperinsulinemiceuglycemic
clamp, followed by a hyperglycemic clamp with subsequent arginine stimulation
(Supplemental Figure 1B) [13]. The three 2-day treatment blocks were separated by at least
4 weeks. Participants were instructed to refrain from intense physical activity 2 days before
each treatment block.
The primary endpoint was the 4-hour glucose area under the curve (AUC G ) during the meal
challenge test. From previous studies, we expected an increase in AUC G of 400 mmol/L•240min
following prednisolone treatment [4]. A sample size of 8 would provide 80% power to detect
a significant reduction of 50% by exenatide of prednisolone-induced augmentation of AUC . G
Secondary endpoints included first-phase and second-phase C-peptide incremental area
under the curve (iAUC ), and arginine-stimulated C-peptide secretion (ASI- iAUC ) during
CP CP
the hyperglycemic clamp.
Standardized meal challenge: On day 1 of each treatment block, participants underwent a
standardized meal challenge test following an overnight fast of minimally 10 hours. The meal
contained 905 kcal (50 g fat, 75 g carbohydrates, 35 g protein) and 1g liquid acetaminophen
to estimate gastric emptying rates. Samples for determination of glucose, insulin, C-peptide,
136

glucagon, acetaminophen and exenatide were obtained at times -120, -60, -30, 0, 10, 20, 30,
60, 90, 120, 150, 180 and 240 min, with the meal beginning immediately after the time 0
sample and consumed within 15 min. Eighty mg oral prednisolone or placebo was ingested
2 hours before meal consumption, and IV. exenatide or saline infusion started 60 min before
the meal at an infusion rate of 40 ng/min for 30 min, and was decreased to 20 ng/min for the
remainder of the test (Supplemental Figure 1A).
Hyperinsulinemic-euglycemic clamp and hyperglycemic clamp: On day 2 of each block, a
combined hyperinsulinemic-euglycemic and hyperglycemic clamp procedure was done. After
an overnight fast, an indwelling cannula was inserted into an antecubital vein for infusion of
glucose and insulin. To obtain arterialized venous blood samples, a retrograde cannula was
inserted in a contralateral wrist vein and maintained in a heated box at 50°C. Insulin was
infused at a rate of 40 mU/m2•min for 120 min, plasma glucose was kept at 5 mM by a variable
infusion of 20% glucose. The hyperglycemic clamp was started 90 min after cessation of
exogenous insulin infusion. Plasma glucose concentration was then raised to 10 mM by a body
weight-adjusted intravenous bolus of 20% glucose and a variable 20% glucose infusion was
adjusted to maintain the targeted glucose level. After 80 min hyperglycemia, an intravenous
bolus of 5 g arginine (dissolved in 50 mL) was given over 45 seconds, and the glucose level was
maintained at 10 mM for 30 min. Eighty mg oral prednisolone or placebo was administered 2
hours before initiation of hyperinsulinemic-euglycemic clamp. IV. exenatide or saline infusion
was started 60 min before the start of the hyperglycemic clamp at an infusion rate of 40 ng/
min for 30 min, and was decreased to 20 ng/min for the rest of the hyperglycemic clamp
(Supplemental Figure 1B). Note that exenatide was not infused during the hyperinsulinemiceuglycemic
clamp, since, in a pilot study, exenatide strongly induced insulin secretion at a
glucose level of 5 mM. The hyperinsulinemic-euglycemic clamp was performed in order to be
able to calculate the disposition index (see below).
Study medication: Prednisolone tablets were purchased from Pfizer AB (Sollentuna,
Sweden) and placebo tablets were obtained from Xendo Drug Development (Groningen,
The Netherlands). Tablets were capsulated in order to allow the treatment to be blinded [4].
Commercially available exenatide was purchased (Pharmacy VU University Medical Center)
and diluted in saline containing 1% human serum albumin, forming a colorless solution. IV.
administration of exenatide was chosen in order to be able to gain steady-state exenatide
levels within a short period of time. The plasma exenatide target level was 100 pg/ml, which
demonstrated good efficacy and tolerability during previous infusion experiments [17].
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Analytical determinations: Blood glucose concentrations were measured using an YSI 2300
STAT Plus analyzer (YSI, Yellow Springs, OH). Insulin and C-peptide levels were determined
using an immunometric assay (Advia, Centaur, Siemens Medical Solutions Diagnostics, USA).
Glucagon (Linco Research, St. Louis, USA) and acetaminophen (Abbott Laboratories, IL, USA)
concentrations were determined by radioimmuno assay. Exenatide levels were determined
by an immunoenzymetric assay as described previously (Amylin, San Diego, CA) (17).
Body fat percentage was estimated by bioelectrical impedance analysis (BF-906, Maltron
International, UK).
Data analyses: Absolute area under the curves (AUCs) for glucose, insulin, C-peptide,
glucagon and acetaminophen were calculated during the 4-hr meal challenge using the
trapezoid method. The Matsuda whole-body insulin sensitivity index was calculated from
the meal challenge. Whole-body insulin sensitivity as obtained from the hyperinsulinemiceuglycemic
clamp was quantified by the M-value, calculated between min 90-120 during
steady-state insulin concentrations. iAUC for the first-phase (min. 0-10), second-phase (min.
CP
10-80) and ASI-iAUC (min. 80-110) were calculated during the hyperglycemic clamp using
CP
the trapezoid method. The disposition index (DI) from the clamp tests was calculated by the
C-peptide iAUC multiplied by the M-value.
Statistical analyses: Data are presented as mean values ± standard error of the mean (SEM)
or, in case of skewed distribution, as median (interquartile range). Between-block differences
were tested non-parametrically with Friedman’s Test and, in case of a significant result,
further analyzed using Wilcoxon Signed Ranks Test. All statistical analyses were run on SPSS
(SPSS, Chicago, IL, USA). A P < 0.05 was considered statistically significant.
RESULTS
Subject characteristics: 8 healthy Caucasian males were included (median (interquartile
range)): age = 23.5 (20.0-28.3) years; BMI = 25.8 (23.2-27.7) kg/m2 ; waist = 91 (82-95) cm;
body fat = 21 (15-26)%; fasting plasma glucose = 5.0 (4.8-5.3) mmol/L; triglycerides = 1.1
(0.7-1.5) mmol/L; systolic blood pressure = 119 (117-125) mmHg; diastolic blood pressure
= 77 (72-82) mmHg.
Standardized meal challenge: PRED+SAL treatment increased AUC G as compared to
PLB+SAL (P=0.012), which was prevented by concomitant exenatide administration (Table
1, Figure 1A). AUC for insulin (AUC ) was not decreased by PRED+SAL, although AUC for
I
138

C-peptide (AUC CP ) tended to be lower (P=0.07). PRED+EXE significantly decreased both AUC I
and AUC CP . (Table 1, Figure 1B+C). PRED+SAL non-significantly increased glucagon secretion
compared to PLB+SAL (P=0.09), which was mitigated by exenatide treatment (Figure 1D).
Exenatide significantly decreased AUC for acetaminophen (AUC ) compared to both
ACET
placebo and prednisolone, compatible with its gastric emptying slowing effects. The Matsuda
whole-body insulin sensitivity index increased during exenatide treatment both vs. placebo
and prednisolone (Table 1).
Table 1. Results from the standardized meal challenge.
AUC glucose
(mmol/L•240min)
AUC insulin
(nmol/L•240min)
AUC c-peptide
(nmol/L•240min)
AUC glucagon
(pmol/L•240min)
AUC acetominophen
(mg/L•240min)
Matsuda index
(No dimension)
PLB+SAL 1199 (1043-1248)
PRED+SAL 1335 (1254-1501)
PRED+EXE 1247 (1156-1260)
PLB+SAL 66.2 (35.2-81.0)
PRED+SAL 52.5 (27.2-70.2)
PRED+EXE 32.2 (22.8-40.1)
PLB+SAL 354 (212-382)
PRED+SAL 270 (191-328)
PRED+EXE 203 (184-221)
PLB+SAL 2845 (1941-2965)
PRED+SAL 3085 (2859-3633)
PRED+EXE 2232 (1850-3149)
PLB+SAL 1272 (1002-1485)
PRED+SAL 1449 (1243-1542)
PRED+EXE 940 (801-1039)
PLB+SAL 22.6 (13.0-39.4)
PRED+SAL 20.4 (16.1-41.2)
PRED+EXE 36.8 (26.9-50.0)
P1 P2 P3
0.012 NS 0.025
NS 0.017 0.017
0.069 0.017 0.017
0.091 NS 0.018
NS 0.028 0.046
NS 0.025 0.012
Abbreviations: AUC: area under the curve; EXE: exenatide; PLB: placebo; PRED: prednisolone; SAL: saline.
P1 indicates PLB+SAL vs PRED+SAL. P2 indicates PLB+SAL vs PRED+EXE. P3 indicates PRED+SAL vs
PRED+EXE.
Combined clamp procedure:
C-peptide secretion, hyperglycemic clamp: Prednisolone decreased first-phase iAUC and CP
ASI-iAUC (vs. PLB+SAL; P=0.017 and P=0.05 respectively), but did not affect second-phase
CP
iAUC (Table 2). Exenatide restored prednisolone-induced reductions in first-phase iAUC CP CP
and ASI-iAUC and significantly improved C-peptide secretion during the entire clamp, both
CP
compared to PRED+SAL and PLB+SAL (Table 2, Figure 2A). Insulin iAUC results were not
different from the C-peptide iAUC results (Figure 2B).
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Chapter 7 GLP-1 prevents glucocorticoid-related metabolic effects
Table 2. Results from the hyperglycemic clamp
1 st iAUC CP
(nmol•min/L)
2 nd iAUC CP
(nmol•min/L)
1 st +2 nd iAUC CP
(nmol•min/L)
ASI-iAUC CP
(nmol•min/L)
140
PLB+SAL 6 (4-8)
PRED+SAL 4 (2-6)
PRED+EXE 10 (8-13)
PLB+SAL 30 (17-48)
PRED+SAL 26 (18-53)
PRED+EXE 111 (63-117)
PLB+SAL 83 (79-107)
PRED+SAL 71 (41-100)
PRED+EXE 201 (160-249)
PLB+SAL 26 (24-34)
PRED+SAL 18 (17-29)
PRED+EXE 37 (28-43)
P1 P2 P3
0.017 0.017 0.012
0.779 0.012 0.012
0.208 0.012 0.012
0.05 0.093 0.012
Abbreviations: ASI-iAUC CP : arginine-stimulated incremental area under the curve for C-peptide; iAUC CP :
incremental area under the curve for C-peptide. EXE: exenatide; PLB: placebo; PRED: prednisolone; SAL:
saline. P1 indicates PLB+SAL vs PRED+SAL. P2 indicates PLB+SAL vs PRED+EXE. P3 indicates PRED+SAL
vs PRED+EXE.
Figure 1. The effect of prednisolone with or without concomitant exenatide infusion on plasma
glucose (A), insulin (B), C-peptide (C) and glucagon (D) levels during the meal challenge.
Prednisolone increased AUC , which was prevented by exenatide (A), despite lower insulin and C-peptide
G
levels (B+C). Exenatide infusion reduced postprandial glucagon levels as compared Prednisolone (D).
Mean ± SEM are shown. Black solid line with closed squares: PLB+SAL; Grey intersected line with closed
circles: PRED+SAL; Black dotted line with open circles: PRED+EXE.

Figure 2. The effect of prednisolone with or without concomitant exenatide infusion on C-peptide
(A) and insulin (B) concentrations during the hyperglycemic clamp. PRED+SAL decreased first-phase
(min 0-10) and arginine-stimulated C-peptide secretion (min 80-110), which were completely restored
and improved by concomitant exenatide administration (Black solid line with closed squares: PLB+SAL;
Grey intersected line with closed circles: PRED+SAL; Black dotted line with open circles: PRED+EXE).
Panel C depicts prednisolone-induced changes in M-value during the euglycemic clamp. Prednisolone
reduced combined first- and second-phase (min 0-80) disposition index (DI) (panel D) and argininestimulated
(min 80-110) DI (panel E), which was restored and improved by exenatide (Box-and-Whisker
plots (min-max) are shown).
Insulin sensitivity, euglycemic clamp: Insulin levels reached steady-state during min 90-
120 of the euglycemic clamp, averaging 431 ± 62 (PLB+SAL), 418 ± 73 (PRED+SAL) and
422 ± 57 pM (PRED+EXE). Prednisolone acutely decreased the M-value obtained from the
euglycemic clamp by 20% (P=0.018) (Figure 2C). Adjustment of the M-value by insulin levels
during the steady-state part of the clamp (M/I) did not affect the results (data not shown).
Note that the effects of exenatide on whole-body insulin sensitivity were not assessed during
the euglycemic clamp, exenatide was administered during the hyperglycemic clamp only.
Disposition index: PRED+SAL decreased the DI from T = 0-80 min of the hyperglycemic
clamp (combined first- and second-phase; P=0.012) and the DI from T = 80-110 min of the
hyperglycemic clamp (arginine stimulation; P=0.012). The prednisolone-induced decrease in
DI was fully restored by concomitant exenatide infusion, and exenatide significantly improved
the DI as compared to PLB+SAL from T = 0-80 min (Figure 2D+E).
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Exenatide plasma levels and adverse effects: Mean exenatide plasma levels equaled 65 ± 4
pg/mL between T = 0 and T = 240 of the meal challenge (Supplemental Figure 2A) and 80 ±
4 pg/mL between T = -30 and T = 110 of the hyperglycemic clamp (Supplemental Figure 2B).
No adverse effects of either prednisolone or exenatide treatment were experienced by the
participants during the meal or clamp procedure.
DISCUSSIoN
GCs are known to impair glucose metabolism by inducing insulin resistance and, more
recently, beta-cell dysfunction [1, 4]. This study is the first to demonstrate that treatment
with the GLP-1RA exenatide prevents prednisolone-induced glucose intolerance as assessed
by a standardized meal challenge test. During the hyperglycemic clamp, exenatide infusion
restored prednisolone-induced impairment of beta-cell function variables, and even
significantly improved a number of these variables relative to the control situation. In contrast
to the findings observed during the clamp procedure, exenatide treatment given during the
meal challenge, improved glucose tolerance but resulted in decreased insulin plasma levels.
This observation is in line with a previous study in healthy individuals in which subcutaneous
exenatide treatment reduced postprandial glucose excursions despite significantly lower
insulin levels [18]. The glucose-lowering effects of exenatide were attributed to decreased
glucagon secretion and gastric emptying and, due to its glucose-dependent mode of action,
exenatide did not further stimulate insulin secretion in the presence of normoglycemia
[18]. In our study, we similarly found reduced postprandial glucagon secretion and gastric
emptying following exenatide treatment. Studies employing stable isotope techniques have
demonstrated that exenatide may also reduce hepatic glucose output and increase wholebody
glucose disposal in the postprandial state, independent of its more established effects
on islet hormone secretion and gastric emptying [19, 20]. In our study, exenatide improved
whole-body insulin sensitivity during the meal challenge as estimated by the Matsuda index,
however, reduced glucose appearance due to decreased gastric emptying seemed primarily
responsible for improving glucose tolerance. We did not assess the effects of exenatide on
whole-body insulin sensitivity during the hyperinsulinemic-euglycemic clamp for previous
mentioned reasons.
In this proof-of-principle study, both treatment regimens were administered for a short
period of time: i.e. for two consecutive days per study block. Although the acute metabolic
effects of both prednisolone and exenatide may to some extent differ from their effects
following prolonged administration, it provides a good model to study the effects of each of
142

oth drugs as well as their interaction. IV. exenatide infusion was able to prevent the acute
adverse effects of prednisolone on glucose tolerance, and additional benefits from exenatide
treatment may be expected when both compounds are administered for a more prolonged
time period. Chronic GC use is associated with increased appetite, significant weight gain,
increased visceral fat mass, altered secretion of adipocytokines and dyslipidemia [1], all of
which contribute to the adverse effects of GCs on glucose metabolism. In clinical studies
in T2DM patients, chronic exenatide treatment was shown to reduce appetite, resulting in
substantial weight loss, decrease in truncal fat mass, and increased secretion of adiponectin
[13, 21, 22]. Also, exenatide improved postprandial dyslipidemia [23]. The strong reduction
of postprandial glucose levels by exenatide, rather than a pronounced effect on fasting plasma
glucose [13], matches the profile of GC-induced hyperglycemia, which is predominantly
present during the day [5].
During the hyperglycemic clamp experiments, pharmacological concentrations of exenatide
were able to restore prednisolone-induced changes in beta-cell function, including firstphase
and ASI C-peptide secretion and DI calculated for the entire hyperglycemic clamp. GC
exposure was demonstrated to impair various pathways in the beta cell in vitro. These include
both steps in the uptake and metabolism of glucose, but GCs also affected distal pathways in
the insulin exocytosis process, resulting in impaired insulin secretion in response to different
secretagogues [1, 3]. Since exenatide was able to restore insulin secretion, one may speculate
that GCs do not block the pathways mediating GLP-1 action on beta cells. However, it was very
recently reported that a two-week treatment with oral prednisolone reduced the insulinotropic
effects of endogenous GLP-1 and glucose-dependent insulinotropic polypeptide (GIP) [24].
A limitation of our study is that we treated healthy subjects with GCs, while GCs are
prescribed to treat acute and chronic inflammatory, as well as autoimmune diseases. Chronic
inflammation is also associated with whole-body insulin resistance and beta-cell dysfunction,
as was recently reported in patients with rheumatoid arthritis [25]. Therefore, the complex
interrelationship between inflammation, GC and GLP-1RA treatment needs do be studied
prospectively in relevant patient populations.
The plasma levels of exenatide reached with our infusion protocol were lower than
those usually obtained following subcutaneous injection of exenatide 10 mg BID, i.e.
the recommended dose for the current treatment in T2DM. Although good efficacy was
demonstrated by current plasma exenatide levels, the full potential of GLP-1 RA treatment
to prevent prednisolone-induced glucose intolerance may be fully unveiled in clinical studies
administering exenatide at the usual dose.
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Chapter 7 GLP-1 prevents glucocorticoid-related metabolic effects
Taken together, this study provides evidence that the GLP-1 RA exenatide may prevent
prednisolone-induced glucose intolerance and restore islet-cell functional balance. Longterm
studies in relevant populations should explore the potential of GLP-1 RA treatment as a
novel strategy to prevent steroid diabetes.
Acknowledgments and Funding: The authors express their gratitude to Mark Fineman
(Amylin Pharmaceuticals) for the determination of exenatide plasma levels. Drs. Daniël
van Raalte and Drs. Margot Linssen are supported by the Dutch Top Institute Pharma (TIP)
grant T1-106. Drs. Renate van Genugten is supported by the EFSD/MSD Clinical Research
Programme 2008. Dr. D. Margriet Ouwens is supported by the Ministry of Innovation, Science,
Research and Technology of the German state of North-Rhine Westphalia, and the EU European
Cooperation in the field of Scientific and Technical Research (COST) Action BM0602.
144

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plasma glucose in subjects with type 2 diabetes. j Clin Endocrinol Metab. 2003;88(7):3082-9.
19. Cervera A, Wajcberg E, Sriwijitkamol A, Fernandez M, Zuo P, Triplitt C, Musi N, DeFronzo RA,
Cersosimo E. Mechanism of action of exenatide to reduce postprandial hyperglycemia in type 2
diabetes. Am j Physiol Endocrinol Metab. 2008;294(5):E846-52.
20. Zheng D, Ionut V, Mooradian V, Stefanovski D, Bergman RN. Exenatide sensitizes insulin-mediated
whole-body glucose disposal and promotes uptake of exogenous glucose by the liver. Diabetes.
2009;58(2):352-9.
21. Bunck MC, Diamant M, Eliasson B, Corner A, Shaginian RM, Heine Rj, Taskinen MR, Yki-jarvinen H,
Smith U. Exenatide affects circulating cardiovascular risk biomarkers independently of changes in
body composition. Diabetes Care. 2010;33(8):1734-7.
22. Klonoff DC, Buse jB, Nielsen LL, Guan X, Bowlus CL, Holcombe jH, Wintle ME, Maggs DG. Exenatide
effects on diabetes, obesity, cardiovascular risk factors and hepatic biomarkers in patients with type
2 diabetes treated for at least 3 years. Curr Med Res Opin. 2008;24(1):275-86.
23. Bunck MC, Corner A, Eliasson B, Heine Rj, Shaginian RM, Wu Y, Yan P, Smith U, Yki-jarvinen H, Diamant
M, Taskinen MR. One-year treatment with exenatide vs. insulin glargine: effects on postprandial
glycemia, lipid profiles, and oxidative stress. Atherosclerosis. 2010;212(1):223-9.
24. Hansen KB, Vilsboll T, Bagger jI, Holst jj, Knop FK. Reduced glucose tolerance and insulin resistance
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Arthritis Rheum. 2006;54(9):2765-75.
146

SUPPLEMENTAL FIGURES
Supplemental Figure 1. Overview of the meal challenge test (A) and the combined hyperinsulinemiceuglycemic
and hyperglycemic clamp procedure (B). Note that exenatide was infused during the
hyperglycemic clamp, but not during the euglycemic clamp. Abbreviations: EXE: exenatide; PRED:
prednisolone.
Supplemental Figure 2. Exenatide plasma levels equaled 65 ± 4 pg/mL between T = 0 and T = 240 of the
meal challenge (A) and 80 ± 4 pg/mL between T = -30 and T = 110 of the hyperglycemic clamp (B). Mean
± SEM are depicted.
147
7
Chapter 7

PART
Mechanisms of glucocorticoid-induced insulin
resistance
III

Chapter 8
Low-dose Glucocorticoid Treatment
Affects Multiple Aspects of Intermediary
Metabolism in Healthy Humans: a
Randomized Controlled Trial
D.H. van Raalte*, M. Brands*, N.J. van der Zijl, M.H. Muskiet, P.
J.W. Pouwels , M.T. Ackermans, H.P. Sauerwein, M.J. Serlie and
M.Diamant
*Both authors contributed equally
Diabetologia. 2011; 54(8):2102-12

Chapter 8 Glucocorticoids affect intermediary metabolism
ABSTRACT
objectives: To assess whether low-dose glucocorticoid (GC) treatment induces adverse
metabolic effects, as is evident for high GC doses.
Methods: In a randomized, placebo-controlled, double blind, dose-response intervention
study, 32 healthy males (age: 22±3 years; BMI 22.4±1.7 kg/m2 ) were allocated to
prednisolone 7.5 mg once daily (n=12), prednisolone 30 mg once daily (n=12), or placebo
(n=8) for two weeks using block randomization. Mean outcomes measures were glucose-,
lipid- and protein metabolism, as measured by stable isotopes, before and at 2 weeks of
treatment, in the fasted state and during a two-step hyperinsulinemic clamp.
Results: Prednisolone, as compared to placebo, dose-dependently and significantly
increased fasting plasma glucose levels, whereas only prednisolone 30 mg increased
fasting insulin levels (29±15 pmol/l). Prednisolone 7.5 mg and prednisolone 30 mg
decreased the ability of insulin to suppress endogenous glucose production (by 17±6%
and 46±7%, respectively, vs. placebo). Peripheral glucose uptake was not reduced by
prednisolone 7.5 mg, but was decreased by prednisolone 30 mg by 34±6% (P

Glucocorticoids (GCs) are the cornerstone in the treatment of numerous chronic autoimmune
and inflammatory diseases, including rheumatoid arthritis [1], polymyalgia rheumatica
[2] and giant cell arteritis [3], systemic lupus erythematosus [4] and inflammatory bowel
diseases [5]. GC treatment is accompanied by significant metabolic adverse effects, including
insulin resistance, glucose intolerance and diabetes, visceral adiposity, dyslipidemia and
skeletal muscle atrophy [6]. The development of these adverse metabolic effects is dependent
on the administered dose [7], and has mainly been studied with high doses. As such, 36
mg prednisolone equivalent daily impaired glucose tolerance by reducing hepatic [8] and
peripheral insulin sensitivity [9]. Furthermore, 30 mg prednisolone daily stimulated adipose
tissue, but not whole-body, lipolysis [10], and induced dyslipidemia [11]. In addition, 60 mg
prednisolone daily was shown to stimulate breakdown of skeletal muscle tissue [12]. Finally,
chronic exposure to markedly increased endogenous GC levels, as shown in patients with
Cushing’s syndrome, unfavorably affected body fat distribution, by promoting visceral fat
accumulation and hepatic steatosis [13].
However, most patients treated with GCs for prolonged time periods, receive, following
an induction dose, GC dosages in the lower range: ≤ 7.5 mg prednisolone equivalent daily.
Although the efficacy of these doses on disease activity in various conditions is wellestablished
[1-4;7], the extent to which these low dosages induce adverse metabolic effects
is unclear. In large retrospective case-control studies, it was demonstrated that low-dose GC
treatment increased the odds for the initiation of blood-glucose lowering therapy by 63%
[14] and increased the risk to develop diabetes [15]. In contrast, in clinical trials with chronic
use of low-dose GC treatment, incident diabetes is reported less frequently [16]. The main
limitation of these data is that no distinction can be made between disease-induced and agerelated
abnormalities on the one hand, and the metabolic abnormalities induced by low-dose
prednisolone treatment on the other. To date, no studies have assessed the effects of low-dose
GC treatment on the 3 most important pathways of intermediary metabolism. In addition,
previous studies addressing the metabolic adverse effects of GC treatment have only studied
the (hyper)acute effects and typically administered a single dose.
Thus, in the present study, we studied the effects of a two-week, low-dose prednisolone
treatment (7.5 mg daily) on glucose-, lipid-, and protein metabolism using stable isotopes
in healthy males, and included a typical induction dosage of prednisolone (i.e. 30 mg daily),
which is known to impair intermediary metabolism, and a matching placebo treatment.
Through combined infusion of tracers for glucose and protein metabolism, as well as lipolysis,
we assessed differences in the sensitivity of metabolic fluxes to the dose-dependent effects of
GC treatment.
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Chapter 8

Chapter 8 Glucocorticoids affect intermediary metabolism
METHoDS
Participants: Thirty-two healthy white males were recruited via local advertisements. All
participants were in good health as confirmed by medical history, physical examination,
screening blood tests and a 75-g 2-hr oral glucose tolerance test (OGTT), performed at
screening visit. Inclusion criteria included age: 18-35 years, body mass index (BMI): 20-25 kg/
m2 , and normoglycemia as defined by fasting plasma glucose (FPG) < 5.6 mmol/l and 2-hour
glucose < 7.8 mmol/l during OGTT. Exclusion criteria were the presence of any disease, use of
any medication, first-degree relative with type 2 diabetes, smoking, shift work, a history of GC
use, excessive sport activities (i.e. more often than two times/week) and changes in weight in
the 3 months prior to study participation. The study was approved by an independent ethics
committee and the study was conducted in accordance with the Declaration of Helsinki. All
participants provided written informed consent before participation.
Study design: The study was a randomized, double blind, placebo-controlled, doseresponse
intervention study. Following assessment of eligibility and baseline measurements,
participants were randomized to receive either prednisolone 7.5 mg once daily (n=12), or
prednisolone 30 mg once daily (n=12), or placebo (n=8) treatment for a period of 14 days
using block randomization, as carried out by the department of experimental pharmacology of
the VU University Medical Center. On day -2 and day 13 of treatment, body composition, body
fat distribution and liver fat content were quantified. On day -1 and on day 14 of treatment,
glucose kinetics, lipolysis and proteolysis were measured in the basal state and during a twostep
hyperinsulinemic-euglycemic clamp using stable isotopes (Supplementary Figure 1a,b).
All measurements were conducted following an 12-h overnight fast with the subjects in the
semi-supine position. Subjects refrained from drinking alcohol for a period of 24h before the
study days and did not perform strenuous exercise for a period of 48h before the study days.
During all visits, including a follow-up visit at day 7 of treatment, safety and tolerability were
assessed. A patient flow diagram is shown in Supplementary Figure 2.
Experimental protocol – body composition/body fat distribution: Body composition
was measured by Dual Energy X-ray Absorptiometry (DEXA) scans (Delphi A, Hologic,
Waltham, MA). Magnetic resonance imaging (MRI), for determination of visceral (VAT) and
subcutaneous (SAT) adipose tissue area at the level of L3-L4, and proton-magnetic resonance
spectroscopy ( 1H-MRS), to quantify liver fat content, were performed using a 1.5 T MRI
scanner (Sonata; Siemens, Erlangen, Germany), as described previously [17;18]. All magnetic
resonance examinations (DVR) and quantification of abdominal fat compartments (MHM)
were done by a single experienced investigator.
154

Experimental protocol – clamp: After an overnight fast of 12 hours, subjects were admitted
at the clinical research unit at 0730 AM. An indwelling cannula was inserted into an antecubital
vein for infusion of stable-isotope tracers, glucose and insulin. To obtain arterialized venous
blood samples, a retrograde cannula was inserted in a contralateral wrist vein and maintained
in a thermoregulated box at 50°C. 0.9% NaCl was infused to keep the sampling line patent.
[6,6-2H ]Glucose, [1,1,2,3,3,- 2 2H ]glycerol and L-[1- 5 13C]valine were used as tracers (> 99%
enriched; Cambridge Isotopes, Andover, MA) to study glucose kinetics, lipolysis and valine
turnover respectively. At T = 0 h (0800 AM), blood samples were drawn for determination of
background enrichments. Then, a primed, continuous infusion of isotopes was started: [6,6-
2H2 ]glucose (prime: 11 mmol/kg; continuous: 0.11 mmol/kg.min), [1,1,2,3,3,-2H ]glycerol
5
(prime: 1.6 mmol/kg; continuous: 0.11 mmol/kg.min), and L-[1-13C]valine (prime: 13.7
mmol/kg; continuous: 0.153 mmol,kg.min) and continued until the end of the clamp. After
a 2-h equilibrium period (14 h of fasting), 3 blood samples were drawn for determination
of basal glucose concentrations, isotope enrichments, glucoregulatory hormones and
nonesterified fatty acids (NEFA). Thereafter, a 2-step hyperinsulinemic-euglycemic clamp
was started: step 1 included an infusion of insulin at a rate of 20 mU/m2 .min (Actrapid 100
IU/mL; Novo Nordisk Farma BV, Alphen aan den Rijn, Netherlands) to assess hepatic insulin
sensitivity. Glucose 20% was started to maintain a plasma glucose concentration of 5 mmol/l.
[6,6-2H ]Glucose was added to the glucose solution to achieve glucose enrichments of 1% in
2
order to approximate the values for enrichment reached in plasma and thereby minimizing
changes in isotopic enrichment due to changes in the infusion rate of exogenous glucose.
Plasma glucose concentrations were measured every 5 min at bedside. After 2-h of insulin
infusion, 5 blood samples were drawn at 5min intervals for the measurement of glucose
concentrations and isotopic enrichments. Another blood sample was drawn for measurement
of glucoregulatory hormones and NEFA. Hereafter, insulin infusion was increased to a rate of
60 mU/m2 .min (step 2) to assess peripheral insulin sensitivity. After 2 h of insulin infusion,
blood sampling for glucose, isotope enrichments, glucoregulatory hormones and NEFA was
repeated (Supplementary Figure 1b).
Indirect calorimetry: Oxygen consumption (VO 2 ) and carbon dioxide production (VCO 2 )
were measured continuously during the final 20 min of both the basal state and during step
2 of the hyperinsulinemic-euglycemic clamp by indirect calorimetry using a ventilated hood
system (Vmax model 2900; Sensormedics, Anaheim, CA). The VO and VCO measurements
2 2
during the last 10 minutes were used for further calculations.
Study medication: Prednisolone tablets were purchased from Pfizer AB (Sollentuna, Sweden)
and matching placebo tablets were obtained from Xendo Drug Development (Groningen, The
155
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Chapter 8

Chapter 8 Glucocorticoids affect intermediary metabolism
Netherlands). The tablets were encapsulated in order to allow the treatment to be blinded,
as described previously [19]. Study medication was taken at 08.00 AM during the two-week
treatment except for day 13 and day 14, when it was ingested at 06.00 AM. Patients kept a
diary in which the exact time of medication intake during the study was registered.
Glucose, lipid, and valine measurements: Plasma glucose concentrations were measured
with the glucose oxidase method using a Biosen C-line plus glucose analyzer (EKF Diagnostics,
Barleben/Magedeburg, Germany). Plasma NEFA concentrations were measured with an
enzymatic colorimetric method (NEFA-C test kit; Wako Chemicals GmbH, Neuss, Germany)
with an intra-assay variation of 1%, inter-assay variation of 4-15% and detection limit of
0.02 mmol/l. [6,6-2H ]Glucose, [1,1,2,3,3,- 2 2H ]glycerol and L-[1- 5 13C]valine enrichment were
measured with gas chromatography-mass spectroymetry (GC-MS) as described previously
[20-22]. Briefly, temperature programmed GC with splitinjection (Model 6890 Agilent
Technologies, Palo Alto, USA) was coupled to a mass selctive detector (Model 5973 Agilent
technologies, Palo Alto USA) in the electron impact (EI) ionization mode for glucose and
valine analysis and in the positive chemical ionisation mode for glycerol. Exact GC and MS
parameters are given in ref 20-22.
Glucoregulatory hormones: Insulin was determined on an Immulite 2000 system
(Diagnostic Products Corporation, Los Angeles, CA) with a chemiluminescent immunometric
assay, intra-assay variation of 3-6%, an inter-assay variation of 4-6%, and a detection limit of
15 pmol/l. Glucagon was determined with the Linco 125I radioimmunoassay (St Charles, MO)
with an intra-assay variation of 9-10%, an inter-assay variation of 5-7%, and a detection limit
of 15 ng/l.
Calculations: Endogenous glucose production (EGP) and the peripheral uptake of glucose
(Rate of disappearance, Rd) were calculated using modified versions of the Steele equations
for the non-steady state and were expressed as mmol/kg.min as described previously
[23;24]. Lipolysis (glycerol turnover) and proteolysis (valine turnover) were computed using
formulas for steady state kinetics adapted for stable isotopes [20;22] and were expressed as
mmol/kg.min. The equations are provided in Supplementary Figure 3. The abbreviated Weir
equation was used to calculate the 24-hour energy expenditure. Glucose oxidation and fatty
acid oxidation rates were derived from oxygen consumption and carbon oxide production
as reported previously [25]. Glucose oxidation during insulin infusion was addditionally
expressed as percentage of glucose Rd.
156

Statistics:
Data are presented as mean values ± standard deviation (SD), or as median (interquartile
range) in case of skewed distribution. Absolute changes from baseline (on treatment value
minus pre treatment value) were compared between the groups using the Kruskal-Wallis test.
Non-parametric analysis was chosen due to the relatively small number of participants and the
uneven group sizes. Only in case of a significant finding, prednisolone 7.5 mg and prednisolone
30 mg were compared against placebo by posthoc testing, using the Mann-Whitney U test. To
correct for multiple testing, Bonferroni correction was applied by multiplying the obtained
p-value from the Mann-Whitney U test by the numbers of comparisons (i.e. two) that were
carried out. All statistical analyses were run on SPSS for Windows version 15.0 (SPSS, Chicago,
IL, USA). A p

Chapter 8 Glucocorticoids affect intermediary metabolism
twith prednisolone 7.5 mg (Table 2). Pre-treatment, glucose oxidation increased to to 20.0
(18.3-23.7) mmol/kg.min (9±5% of Rd) during hyperinsulinemia (P

Lipid Metabolism:
Lipolysis in the fasted state tended to be decreased by prednisolone treatment (P=0.062),
which was driven by the prednisolone 30 mg arm (P=0.09) (Figure 2a). Insulin-mediated
suppression of lipolysis was markedly and dose-dependently impaired by prednisolone during
hyperinsulinemic conditions (Figure 2b,c). Similarly to changes in lipolysis, basal NEFA levels
were decreased by prednisolone treatment, which seemed to be driven by prednisolone 30
mg (Table 2). During step 1 of insulin infusion (20 mU m2 min-1 ), insulin-mediated suppression
of plasma NEFA levels was dose-dependently decreased by prednisolone treatment
(Supplementary Table 2). During the second step of insulin infusion (60 mU m2 min-1 ), NEFA
levels were reduced to detection level in the placebo group and prednisolone 7.5 mg group,
but remained detectable in participants in the prednisolone 30 mg arm (Supplementary Table
2). Fasting triacylglycerol (TG) levels were increased by prednisolone 30 mg only (Table 3).
In the total study population, pre-treatment, fasting fatty acid oxidation rate was 1.5 (1.3-
1.8) mmol/kg.min and decreased during insulin infusion to 0.4 (0.2-0.7) mmol kg-1 min-1 (p

Chapter 8 Glucocorticoids affect intermediary metabolism
Table 2. Resting energy expenditure and glucose- and fatty acid oxidation in the fasted state and
during insulin infusion before and during two-week treatment with placebo, prednisolone 7.5 mg
daily or prednisolone 30 mg daily.
Basal REE
(kJ/kg.day)
REE during clamp
(kJ/kg.day)
Basal glucose oxidation
(mmol/kg.min)
Glucose oxidation clamp
(mmol/kg.min)
Glucose oxidation clamp
(% of Rd)
Basal fatty acid oxidation
(mmol/kg.min)
Fatty acid oxidation clamp
(mmol/kg.min)
160
Pre-treatment On-treatment P1 P2 P3
PLB 95 (86-110) 89 (86-98)
7.5 98 (91-107) 99 (92-109) 0.139 NA NA
30 106 (91-114) 107 (98-110)
PLB 102 (95-114) 100 (95-111)
7.5 105 (97-110) 106 (99-111) 0.352 NA NA
30 111 (96-111) 109 (96-111)
PLB 7.7 (4.9-9.0) 5.5 (4.2-8.0)
7.5 5.8 (3.0-8.5) 5.8 (3.2-12.6) 0.006 0.430 0.002
30 5.1 (3.0-7.7) 11.2 (7.7-14.6)
PLB 20.3 (18.8-22.2) 19.0 (15.8-25.2)
7.5 19.6 (17.5-24.0) 16.1 (11.8-20.1) 0.133 NA NA
30 19.4 (15.5-25.4) 20.0 (17.4-22.8)
PLB 11.4 (7.8-16.1) 9.0 (5.8-15.9)
7.5 9.4 (4.9-12.0) 9.3 (4.8-13.6) < 0.001 0.728 < 0.001
30 8.5 (4.9-9.8) 25.1 (15.8-41.3)
PLB 1.4 (1.2-1.5) 1.3 (1.2-1.8)
7.5 1.4 (1.2-1.6) 1.6 (1.1-1.7) 0.02 0.640 0.022
30 1.7 (1.5-1.9) 1.1 (1.0-1.5)
PLB 0.4 (0.1-0.6) 0.5 (0.1-0.7)
7.5 0.5 (0.1-0.7) 0.7 (0.3-1.0) 0.083 NA NA
30 0.5 (0.2-0.8) 0.5 (0.2-0.7)
Median (interquartile range) are provided. Between-group changes from baseline were tested by by
Kruskal-Wallis (P1). In case of a significant finding, posthoc testing (by Mann-Whitney U) was done (P2
and P3). Abbreviations: Rd: rate of disappearance; REE: resting energy expenditure. Treatment groups:
PLB=placebo; 7.5=prednisolone 7.5 mg daily; 30=prednisolone 30 mg daily.
Glucoregulatory hormones:
Prednisolone 7.5 mg did not change fasting plasma insulin levels or glucagon
levels. Prednisolone 30 mg, in contrast, induced both fasting hyperinsulinemia and
hyperglucagonemia (Table 3). Before treatment, insulin levels in the entire study population
during the clamp were 171 (153-183) pmol/l (step 1) and 533 (485-564) pmol/l (step 2).
These levels remained unchanged during the on-treatment clamps, although there was a
tendency towards increased insulin levels in the placebo group (Supplementary Table 2).
Glucagon levels remained increased during step 1, but not step 2, of the hyperinsulinemic
clamp during prednisolone 30 mg treatment (Supplementary Table 2).

Figure 3. The effect of prednisolone treatment on protein metabolism. a Valine turnover in the basal state,
was not affected by prednisolone treatment. b Overall, valine turnover was increased during step 1 of
the hyperinsulinemic-euglycemic clamp by prednisolone treatment. This seemed driven by prednisolone
30 mg in post-hoc analysis, but failed to reach statistical significance. c A similar pattern was observed
during step 2 of the clamp, where overall prednisolone treatment increased valine turnover, however
in post-hoc testing, no significance was reached for either dose as compared to placebo. Data represent
mean ± SEM. Black bars, before treatment; white bars, day 14 of treatment. Between-group changes from
baseline were tested by Kruskal-Wallis (indicated by top line). Posthoc tests were done by Mann-Whitney
U with Bonferroni correction for multiple testing (indicated by line with brackets). *P

Chapter 8 Glucocorticoids affect intermediary metabolism
Safety and Tolerability
One subject in the prednisolone group complained of gastric discomfort which was mild of
nature and did not require any intervention. Otherwise, no treatment-related side effects
were reported in any of the groups.
DISCUSSIoN
Low-dose GC treatment is chronically prescribed to a great number of patients, but to date it is
uncertain whether low-dose GC treatment induces adverse metabolic effects, as is evident for
higher GC doses. The present study is the first to demonstrate that low-dose GC therapy, that
is just above the normal daily cortisol replacement dose for adrenal insufficiency, significantly
impairs the effects of insulin on glucose and lipid metabolism, but not on whole-body protein
metabolism. Using a relatively long exposure time, i.e. two weeks, we report that treatment
with prednisolone 7.5 mg once daily impaired the ability of insulin to suppress EGP, wholebody
lipolysis and plasma NEFA levels. These effects were present after only two weeks of
treatment and occurred in the absence of changes in body weight, LBM, liver fat content and
body fat distribution. Our data, demonstrating clear low-dose prednisolone-induced hepatic
and adipose tissue insulin resistance, provide supportive mechanisms for observations made
in large retrospective, case-control studies that reported increased risk to develop diabetes
[15] and increased need to initiate blood-glucose lowering therapy [14] during or following
low-dose GC therapy.
In prospective studies, the reported incidence of diabetes at lower GC doses has been more
modest, although there was considerable variation depending on the patient group exposed
[16]. The numbers reported in these clinical trials may underestimate the actual incidence of
diabetes. Although not well specified in the different studies, most studies relied on fasting
glucose measurements to monitor changes in glucose tolerance. Our data demonstrate a
very mild increase in fasting glucose and unchanged fasting insulin levels, but show evident
changes in metabolic fluxes under insulin-stimulated conditions during treatment with
low-dose prednisolone. This observation is in line with previous studies reporting large
postprandial glucose excursions with only modest changes in fasting glucose levels [19;26].
These data imply that, in order to properly assess the effects of GCs on glucose metabolism,
measurements under stimulated conditions should be performed, such as an OGTT or a
glucose day curve.
162

By including an additional treatment arm, we were able to study dose-dependent effects of
prednisolone treatment. Prednisolone 30 mg, an induction dosage often used in the clinic for
short-term treatment, impaired the effects of insulin on EGP, glucose disposal and lipolysis
to a greater extent than prednisolone 7.5 mg. In addition, prednisolone 30 mg increased
proteolysis under hyperinsulinemic conditions, increased glucagon levels and augmented
fasting TG levels. Surprisingly, prednisolone 30 mg increased both basal glucose oxidation
(absolute levels) and glucose oxidation during the clamp (expressed as % of Rd). Increased
basal glucose oxidation has, to our knowledge, not been reported previously following GC
treatment. We hypothesize that prednisolone treatment induced metabolic inflexibility.
This phenomenon refers to the inability to adjust fuel oxidation to substrate availability.
As such, impaired fat oxidation in the fasted state was extensively shown in obese, insulin
resistant and type 2 diabetic individuals [27]. In addition, following high-dose prednisolone
treatment, fasting plasma glucose levels were, albeit it marginally, increased, which could
have contributed to augmented glucose oxidation in the basal state. The finding that GC
treatment increased glucose oxidation under hyperinsulinemic conditions, has been reported
previously in nondiabetic individuals who were exposed to dexamethasone treatment
[28]. GCs are well to known to impair the insulin-stimulated glycogen synthesis pathway
[29] and therefore, increased glucose oxidation was proposed to serve as a mechanisms to
compensate for reduced glycogen synthesis, in the presence of higher glucose levels within
the skeletal muscle [28]. Our data similarly indicate preferential glucose oxidation compared
to glycogen synthesis pathways, since absolute glucose oxidation rates were not altered
despite lower glucose uptake. Glucose oxidation as percentage of Rd was thus increased.
Finally, prednisolone 30 mg tended to decrease basal lipolysis and NEFA levels as compared
to placebo. In acute studies, cortisol infusion was shown to increase lipolysis in the fasted
state. In these studies, hyperinsulinemia was prevented by performing a pancreatic clamp
with insulin infusion at basal levels [30;31]. In studies addressing the effects of chronic GC
treatment, typically no changes were observed in whole-body lipolysis in the fasted state [32],
and fasting insulin levels were increased [33-35]. In our study we found a tendency towards
decreased lipolysis. Since lipolysis is more sensitive to small changes in insulin concentration
then EGP [36;37], and plasma NEFA levels are closely correlated to changes in lipolysis,
we hypothesize that decreased basal lipolysis and NEFA levels in individuals treated with
prednisolon 30 mg daily may be due to the observed prednisolone-induced 80% increase
in fasting plasma insulin levels. The negative correlation between the treatment-induced
change in fasting plasma insulin and fasting NEFA levels (r=-0.564; p=0.001) supports our
hypothesis. Another hypothesis is that prednisolone 30 mg decreased the sympathetic tone
to adipose tissue resulting in lower rates of basal lipolysis [38].
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Chapter 8 Glucocorticoids affect intermediary metabolism
The mechanisms underlying prednisolone-induced insulin resistance on the various metabolic
fluxes have not been elucidated. GCs were shown to interfere with insulin signaling in vitro and
in vivo in rodents in liver [39], adipose tissue [40] and skeletal muscle[41]. On the other hand,
GCs may also directly activate genes involved in the regulation of various metabolic fluxes. As
such, GCs were shown to induce phosphoenolpyruvate carboxykinase, the rate controlling
enzyme in hepatic glucose production [42]. In addition, hormone-sensitive lipase, a major
regulator of adipose tissue lipolysis was demonstrated to be regulated by GCs [43]. Moreover,
GCs directly activated muscle ring finger-1 and Atrogin-1, proteins involved in skeletal muscle
proteolysis [44]. Finally, GCs impaired glucose transport into skeletal muscle independent of
changes in insulin signaling [6].
In the present study we exposed healthy volunteers to different doses of prednisolone,
allowing us to specifically measure the metabolic effects of prednisolone. In clinical practice,
prednisolone is used to treat patients with chronic inflammatory diseases. Since systemic
inflammation also impairs insulin sensitivity [45], the effects of GCs on various fluxes, given
their combined anti-inflammatory and metabolic effects, could be more complex in this
population.
One potential, yet inevitable limitation of our experimental design when addressing the effects
of real-life oral prednisolone treatment, as compared to previously reported, more artificial
cortisol infusion studies, is that our clamp tests were performed under non-steady state
prednisolone levels. Since prednisolone has a T of 4-6 hours, the final part of the clamp may
1/2
have been performed under lower prednisolone levels. However, plasma prednisolone levels
may be a poor marker of its pharmacodynamics, since many effects exerted by prednisolone
are genomic of nature, which take a number of hours to become effected. In addition, our
participants were treated for two weeks, in contrast to the acute studies that have been mostly
conducted up to now, and therefore, differences caused by the acute effects of prednisolone
treatment may be very limited.
We conclude that GC treatment, even at a low, so-called maintenance dose that is prescribed
to large numbers of patients for prolonged periods of time, impairs both glucose metabolism
and lipolysis by impairing insulin’s metabolic actions on the liver and adipose tissue. The
results of this study uncover the mechanisms by which GC dosages just above cortisol
replacement levels, especially when used chronically, may impair glucose tolerance in
susceptible patients. Physicians treating patients with low-dose GCs should be aware of the
induction of metabolic disturbances and should not solely rely on fasting measurements. In
addition, our study indicates that insulin-sensitizing therapies could be considered when
164

treating patients with GC therapy. As such, the thiazolidinedione troglitazone was shown to
prevent GC-induced glucose intolerance in healthy humans [46]. Troglitazone, however, is
no longer available for use in humans, and therefore a search for other options to alleviate
GC-induced insulin resistance seems justified. In this regard, the current development of
the so called dissociated glucocorticoid receptor agonists, which aim to separate the antiinflammatory
and dysmetabolic effects of GCs, may be highly interesting [47].
Acknowledgements
This paper was written within the framework of project T1-106 of the Dutch Top Institute
Pharma.
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44. Schakman O, Gilson H, Thissen jP. Mechanisms of glucocorticoid-induced myopathy. j Endocrinol.
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metabolic effects of glucocorticoids in humans: effects on glucose tolerance, insulin sensitivity,
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Chapter 8 Glucocorticoids affect intermediary metabolism
SUPPLEMENTARy MATERIAL
Supplementary Figure 1. Study Design (a) and overview of the 2-step hyperinsulinemic-euglycemic
clamp protocol (b).
Day -14 -2 -1 0
7
13 14
S T1 T2 M
V T1 T2
170
a
Baseline
PLB, PRED 7.5 mg or PRED 30 mg QD
Offtreatment
S: Screening T1: MRI/DEXA scan T2: Clamp M: start study medication V: Follow-up visits
b
Insulin 60 mU/m2 Insulin 20 mU/m .min
Glucose 5.0 mM
2 .min
Variable Glucose 20% enriched with 6,6 –2H 2 Glucose
6,6 –2H 2 Glucose bolus + Continuous infusion
1-13C 1 -Valine bolus + continuous infusion
1,1,2,3,3-2H 5 -Glycerol bolus + continuous infusion
0 120 Time (min)
240
360
Supplementary Figure 2. Patient flow diagram. 100% of the randomised participants completed the
study
28
V

Supplementary Figure 3. Kinetic equations for the steady-state (a) and for the non-steady state (b).
Where: R a = rate of appearance; TTR tracer = tracer/tracee ratio of the tracer infused (=1); I tracer = tracer
infusion rate; TTR p = tracer/tracee ratio in plasma; TTR exo = tracer/tracee ratio of exogenous glucose
infusion; I exo : infusion rate of exogenous glucose; R d = rate of disappearance; R a (t)= average R a between t=1
and t=2; TTR p (t)= average TTR p between t=1 and t=2; pV= distribution volume; C unl (t)= average plasma
concentration tracee between t=1 and t=2; dTTR p (t)/dt = change in plasma TTR p between t=1 and t=2;
R d (t) = average R d between t=1 and t=2; dC unl (t)/dt = change in tracee concentration between t=1 and t=2;
EP(t) = average endogenous production between t=1 and t=2; I unl = tracee infusion rate.
171
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Chapter 8 Glucocorticoids affect intermediary metabolism
Supplementary Table 1. Parameters of body composition and body fat distribution before and during
two-week treatment with placebo, prednisolone 7.5 mg or prednisolone 30 mg.
BMI
(kg/m2 )
LBM
(%)
Fat mass
(%)
Liver fat content
(%)
SAT area
(cm2 )
VAT area
(cm2 )
172
Pre-treatment On-treatment P
PLB 23.0±2.3 22.8±2.2
7.5 22.3±2.0 22.3±1.8
0.449
30 22.6±1.1 22.6±1.1
PLB 78.6±7.9 80.2±10.0
7.5 82.1±3.9 81.8±3.7
0.275
30 80.7±2.5 80.9±2.0
PLB 16.9±4.7 17.1±4.6
7.5 15.9±2.8 16.1±3.7
0.503
30 14.8±3.5 14.7±3.2
PLB 1.3 (0.3-2.5) 1.5 (0.4-3.5)
7.5 0.8 (0.4-2.1) 1.3 (0.7-2.4)
0.537
30 1.0 (0.5-1.6) 1.6 (1-1.8)
PLB 131 (91-202) 130 (102-201)
7.5 127 (94-154) 130 (89-160)
0.447
30 129 (114-151) 132 (114-147)
PLB 82 (48-107) 71 (48-112)
7.5 72 (69-99) 86 (66-93)
0.291
30 77 (67-88) 80 (72-88)
Mean±SD or median (interquartile range) are provided. Between-group changes from baseline were
tested by Kruskal-Wallis (P). No significnant differences were observed during treatment. Abbreviations:
BMI: body mass index; LBM: lean body mass; SAT: subcutaneous adipose tissue; VAT: visceral adipose
tissue. Treatment groups: PLB=placebo; 7.5=prednisolone 7.5 mg daily; 30=prednisolone 30 mg daily.

Supplementary Table 2. Metabolic parameters during the two-step hyperinsulinemic-euglycemic clamp
before and during two-week treatment with placebo, prednisolone 7.5 mg or prednisolone 30 mg.
Glucose step 1
(mmol/l)
Glucose step 2
(mmol/l)
Insulin step 1
(pmol/l)
Insulin step 2
(pmol/l)
Glucagon step 1
(pmol/l)
Glucagon step 2
(pmol/l)
NEFA step 1
(mmol/l)
NEFA step 2
(mmol/l)
Pre-treatment On-treatment P1 P2 P3
PLB 5.0±0.1 5.0±0.2
7.5 4.9±0.1 4.9±0.2
0.389 NA NA
30 5.0±0.2 5.0±0.1
PLB 5.1±0.2 5.1±0.2
7.5 5.1±0.2 5.0±0.1
0.860 NA NA
30 5.0±0.2 5.0±0.2
PLB 172±36 179±35
7.5 171±26 161±32
0.398 NA NA
30 165±33 151±39
PLB 529±74 571±102
7.5 531±73 522±81
0.055 NA NA
30 497±86 481±105
PLB 41±8 36±5
7.5 48±6 52±13
0.016 0.232 0.02
30 38±9 58±20
PLB 31±8 32±7
7.5 35±8 41±12
0.156 NA NA
30 32±8 44±15
PLB 0.03 (0.02-0.04) 0.03 (0.02-0.04)
7.5 0.03 (0.02-0.05) 0.05 (0.04-0.1) < 0.0001 0.034 0.0002
30 0.02 (0.02-0.03) 0.12 (0.09-0.16)
PLB

Chapter 9
Glycosphingolipid Metabolism and
Mitochondrial Function do not Explain
Glucocorticoid-Induced Insulin Resistance
in Healthy Men
D.H. van Raalte*, M. Brands*, M. João Ferraz, H.P. Sauerwein,
A.J.Verhoeven, J.F.G.Aerts, M.Diamant and M.J. Serlie
*Both authors contributed equally
Submitted

Chapter 9 Glucocorticoids, mitochondrial function & glycosphingolipid metabolism
Abstract
objective: Glucorticoids (GCs) are well known to induce skeletal muscle insulin resistance,
however, the mechanisms underlying this phenomenon are presently incompletely
understood. In this study we explored the involvement of changes in muscle ceramide and
ganglioside GM3 levels, and the potential role of mitochondrial dysfunction in GC-induced
insulin resistance in healthy men.
Research design and methods: In a randomized, placebo-controlled, double blind, dose-
response intervention study, 32 healthy men (age: 22±3 years; BMI 22.4±1.7 kg/m 2 ) were
allocated to prednisolone 7.5 mg once daily (n=12), prednisolone 30 mg once daily (n=12),
or placebo (n=8) for two weeks. At baseline and on day 14 of treatment, insulin sensitivity
was measured by hyperinsulinemic-euglycemic clamp. In skeletal muscle biopsies, levels of
ceramide and GM3 were measured, and mitochondrial function was determined by ex vivo
respirometry.
Results: Prednisolone treatment dose-dependently impaired skeletal muscle insulin
sensitivity. Muscle ceramide and GM3 concentrations were not affected by low- or highdose
prednisolone treatment (P>0.05 for both). In addition, mitochondrial respiratory
fluxes were not affected by prednisolone treatment (all P>0.05).
Conclusion: No apparent role for altered muscle ceramide and GM3 levels, or mitochondrial
dysfunction was observed in GC-induced insulin resistance in healthy males. Further studies
are warranted to investigate the mechanisms by which GCs impair insulin sensitivity in
skeletal muscle.
176

INTRoDUCTIoN
Glucocorticoids (GCs) are important anti-inflammatory and immunosuppressive agents that
are clinically used in a broad spectrum of disease entities [1, 2]. However, GCs also induce
metabolic side effects, which limit their use. Patients exposed to excess GCs levels develop
weight gain, in specific central adiposity, breakdown of skeletal muscle mass [3], dyslipidemia
characterized by increased nonesterified fatty acid (NEFA) levels [4], hepatic and peripheral
insulin resistance [5], glucose intolerance, and overt diabetes [6-8]. Recently we reported on
the metabolic effects of a two-week treatment with prednisolone 7.5 mg daily and prednisolone
30 mg daily and described a dose-dependent decrease of insulin sensitivity with concurrent
elevated fasting triglyceride levels and increased NEFA levels during hyperinsulinemia. This
effect occurred in the absence of significant changes in body weight, lean body mass, liver fat
content and body fat distribution [5].
Despite extensive research, the mechanisms by which GCs induce skeletal muscle insulin
resistance remain at present to be elucidated. It was demonstrated in rodents that GCs impair
insulin signaling. As such, GC treatment in rats was shown to reduce the phosphorylation
of insulin receptor substrate (IRS)-1, phosphatidylinositol 3-kinase (PI3-K) and Akt/protein
kinase B (PKB). This impairment in insulin signaling induced a reduction in transportation
of glucose transporter (GLUT)-4 to the plasma membrane and reduced activation of glycogen
synthase kinase (GSK)-3, resulting in impaired glucose uptake and glycogen synthesis
respectively [9-13]. More recently, decreased phosphorylation of insulin signaling proteins
[14] and glycogen synthase kinase [15] was also shown in skeletal muscle of healthy humans
treated with GCs. However, pathophysiological mechanisms that underlie GC-induced defects
in insulin signaling have not yet been elucidated.
GC treatment increases lipolysis and plasma NEFA concentrations. This may endorse processes
commonly referred to as lipotoxicity, which is induced through several mechanisms, amongst
others by formation and accumulation of intracellular lipid metabolites [16]. Two of these
metabolites are ceramide and the downstream ganglioside GM3. Muscle ceramide levels
seem to be increased in patients with type 2 diabetes mellitus (T2DM) [17] and correlate in
some, but not all studies, with insulin sensitivity [17-21]. Ceramide interferes with insulin
signaling by reducing insulin-mediated phosphorylation of Akt/PKB [22-24]. Interestingly, it
was shown that impairment of ceramide synthesis in rats prevented dexamethasone-induced
insulin resistance in skeletal muscle [25]. GM3 has also been shown to be involved in insulin
sensitivity in mice [26]. Gangliosides reside within the plasma membrane and are able to
modulate insulin signaling at the level of the insulin receptor [27-31].
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Chapter 9 Glucocorticoids, mitochondrial function & glycosphingolipid metabolism
Intracellular lipid accumulation and peripheral insulin resistance have also been associated
with decreased mitochondrial function [32]. Interestingly, chronic GC treatment in patients
causes myopathy, characterized by increased proteolysis, oxidative stress and mitochondrial
dysfunction [33]. Whether decreased mitochondrial function plays a role in GC-induced
insulin resistance has not been elucidated.
In the present study we investigated whether GC-induced peripheral insulin resistance
could be explained by either accumulation of intramyocellular ceramide and GM3 levels or
mitochondrial dysfunction in healthy men.
METHoDS
Participants: Thirty-two healthy normoglycemic Caucasian men were recruited via local
advertisements. All participants were in good health as confirmed by medical history,
physical examination, screening blood tests and a 75-g 2-hr oral glucose tolerance test
(OGTT), performed at screening visit. Inclusion criteria were: age between 18-35 years and
body mass index (BMI) ≤ 25 kg/m2 . Exclusion criteria were any previous or current illness,
use of any medication, first-degree relative with T2DM, smoking, shift work and a history of
GC use. In addition, participants who exercised more than two times per week were excluded.
Participants were instructed not to change their physical activity or diet during the study. To
achieve homogeneity of the study group, a physical fitness test was performed as described
previously [34]. Participants with a maximal oxygen uptake (VO2 max) between 45 and
60 ml/kg.min were included. The study was approved by an independent ethics committee
and the study was conducted in accordance with the Declaration of Helsinki. All participants
provided written informed consent before participation.
Study design: Details on study design were reported previously [5]. Briefly, in a randomized,
placebo-controlled, double-blind, dose-response intervention trial, 32 healthy volunteers
were allocated to a treatment with prednisolone 7.5 mg once daily (n=12), prednisolone 30
mg once daily (n=12) or placebo (n=8) for a period of two weeks using block randomization
as carried out by the department of Experimental Pharmacology of the VU University Medical
Center. Before and at day 14 of treatment, peripheral insulin sensitivity was measured
during a hyperinsulinemic-euglycemic clamp using a stable glucose isotope. At the end
of the clamp, biopsies from the vastus lateralis muscle were collected for measurement of
glycosphingolipids and mitochondrial function. During all visits, including a follow-up visit at
day 7 of treatment, safety and tolerability were assessed.
178

Experimental protocol: The design of the hyperinsulinemic-euglycemic clamp was described
in detail elsewhere [5]. In short, following an overnight fast and following measurement of
the background enrichment of glucose, a primed, continuous infusion of [6,6-2H ]glucose
2
(prime: 11 mmol/kg; continuous: 0.11 mmol/kg.min) was started and continued until the
end of the clamp. After a total of 4 hours of insulin infusion, blood samples were drawn for
determination of glucose concentrations, isotope enrichment, and additional parameters of
interest. The first 2 hours, insulin was infused at a rate of 20 mU/m2 .min, thereafter, insulin
was infused at a rate of 60 mU/ m2 .min for 2 hours. Plasma glucose was kept at 5 mmol/l by
a variable infusion of glucose 20% solution enriched with [6,6-2H ]glucose. Peripheral uptake
2
of glucose (rate of disappearance, R ) was calculated during the final 20 min of the clamp
d
using a modified version of the Steele equation as described [5].
Muscle biopsies: Immediately after the last blood sample was drawn and with continuation
of glucose and insulin infusion, a muscle biopsy was taken from the vastus lateralis muscle
under local anesthesia with Lidocaine 20% (Fresenius Kabi; Den Bosch, the Netherlands).
Biopsies were obtained using an automatic biopsy instrument (Pro-Mag I 2.5; Medical Device
Technologies Inc, Gainesville, USA). This procedure yields muscle biopsies of 10-15 mg each.
Two muscle biopsies were collected in a test tube on ice with 10 ml of ice cold BIOPS solution
containing 10 mmol/L Ca-EGTA buffer, 0.1 µmol/L free calcium, 20 mmol/L imidazole, 20
mmol/L Taurine, 50 mmol/L K-Mes, 0.5 mmol/L DTT, 6.65 mmol/L MgCl , 5.77 mmol/L ATP,
2
15 mmol/L phosphocreatinine, pH 7.1 and were used to measure mitochondrial respiration
after permeabilisation (see below). Additional muscle biopsies were washed with 0.9% NaCl
fortified with 10mmol/L Na-Hepes to reduce blood contamination, snap-frozen in liquid
nitrogen and stored at -80˚C until further analysis.
Study medication: Prednisolone tablets were purchased from Pfizer (Sollentuna, Sweden)
and matching placebo tablets were obtained from Xendo Drug Development (Groningen, the
Netherlands). The tablets were encapsulated in order to allow the treatment to be blinded,
as described previously [35]. Study medication was taken at 08:00 hours during the 2-week
treatment period except on days 13 and 14, when it was ingested at 06:00 hours. Patients kept
a diary in which the exact time of medication intake during the study was registered.
Laboratory analysis: Plasma glucose concentrations were measured with the glucose oxidase
method using a Biosen C-line plus glucose analyzer (EKF Diagnostics, Barleben/Magedeburg,
Germany). Insulin was determined on an Immulite 2000 system (Diagnostic Products
Corporation, Los Angeles, CA) with a chemiluminescent immunometric assay. Plasma NEFA
concentrations were measured with an enzymatic colorimetric method (NEFA-C test kit;
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Chapter 9 Glucocorticoids, mitochondrial function & glycosphingolipid metabolism
Wako Chemicals GmbH, Neuss, Germany). [6,6- 2 H 2 ] glucose enrichment was measured with
gas chromatography-mass spectrometry (GC-MS) as described in detail elsewhere [36].
Muscle glycosphingolipids: Ceramide concentrations were measured as described
previously [37]. Briefly, 100 μL of muscle homogenate was extracted with 600 μL of CHCl 3 /
MeOH 1/2 (vol/vol). The extract was centrifuged for 10 min at 13,200 rpm and the pellet
discarded. 460 μL of CHCl /MQ-H O 1/1.3 (vol/vol) was added, mixed, and centrifuged for 3
3 2
min at 13,200 rpm to separate the phases. The lower phase was collected and the upper phase
re-extracted with 400 μL of CHCl . Ceramide and glycosphingolipid content (glucosylceramide
3
(GlCer), lactosylceramide (Lac-cer) and globocylceramide (GB3)) were determined as
previously described [37] with slight modifications. The combined lower phases were dried
under N flow, taken up in 500 μL of freshly prepared 0.1M NaOH in MeOH, and deacetylated
2
in a microwave (SAM-155, CEM corp.) for 60 min. 50 μL of this solution was derivatized with
25 μL o-phtaldehyde (OPA) reagent. The OPA-derivatized lipids were separated by HPLC and
quantified as previously described, using C17 sphinganine as an Internal Standard (Avanti
Polar Lipids, Alabama, USA) [37]. All measurements were done in duplicate from two different
biopsies. Biopsies with a GlCer content above 5 nmol/g, and a GlCer/ceramide ratio above 0.5
were excluded for further analysis, because this was interpreted as blood contamination.
Muscle GM3: The ganglioside GM3 was detected by analysis of the acidic glycolipid fraction
obtained by Bligh-Dyer extraction, as previously described [38]. Gangliosides were desalted
on a disposable SPE C18 column (Bakerbond, Mallinckrodt Baker Inc., Phillipsburg, Nj, USA),
eluted in 1 mL MeOH and concentrated in an Eppendorf concentrator 5301 at 45 °C. The
samples were digested with ceramide glycanase (Recombinant endoglycoceramidase II,
Takara Bio Inc., Otsu, Shiga, japan) at 37 °C for 18 h in order to release the oligosaccharides
from glycosphingolipids. The enzyme was used according to the manufacturer’s instructions.
Oligosaccharides were then fluorescently labeled at their reducing end with 80 μL anthranilic
acid (2-aminobenzoic acid) labeling mixture, at 80 °C, 300 rpm for 45 min. The excess labeling
was removed by gravity flow using Discovery DPA-6S columns and the samples were eluted
with 600 μL of water. Purified 2-AA-labeled oligosaccharides were separated by normalphase
HPLC and quantified as previously described [38], using monosialoganglioside-GM1
(Sigma, St Louis, Mo, USA) as an internal standard for muscles measurements and Gt1b as an
internal standard for plasma measurements.
Mitochondrial function: Mitochondrial respiration was measured ex-vivo with high
resolution respirometry using polarographic oxygen sensors in a two-chamber Oxygraph
(OROBOROS ® Instruments, Innsbruck, Austria) as described before [34]. To evaluate
180

oxidative phosphorylation (OXPHOS) capacity, oxygen consumption was measured during
subsequent addition of different substrates. First malate (2 mmol/L) was added to obtain
state 2 respiration, followed by glutamate (20 mmol/L) as a substrate for complex 1. Then,
an excess of ADP (2 mmol/L) was introduced to measure state 3 respiration. Cytochrome C
(10 mmol/L) was subsequently added to check the integrity of the OXPHOS chain. Thereafter
succinate (10 mmol/L) was added as substrate for complex 2. Additionally, aurovertin was
introduced. Aurovertin inhibits the respiratory chain by binding to ATP synthesis. The oxygen
consumption after the addition of aurovertin (1umol/L) therefore indicates the amount of
leakage under stimulated conditions. Maximal oxygen flux rates were measured with the
chemical uncoupler carbonylcyanide-4-(trifluoromethoxy)-phenylhydrazone (FCCP). FCCP
was titrated (0.25 µmol/L per addition) until no further stimulation of respiration could
be detected. Rotenone (0.1 ummol/L) was subsequently included as inhibitor of complex 1.
Finally, antimycin A (2.5 mmol/L) was added which blocks the respiratory chain at complex 3.
Mitochondrial DNA: Mitochondrial DNA (mtDNA) copy number reflects the tissue
concentration of mitochondria. MtDNA copy number was determined as the ratio of NADH
dehydrogenase subunit 1 (ND1) to lipoprotein lipase (LPL) (mtDNA/nuclear DNA) as
described previously [39].
Statistics: Data are presented as median and range. Absolute changes from baseline (on
treatment value minus pre treatment value) were compared between the groups using the
Kruskal-Wallis test. Non-parametric analysis was chosen due to the relatively small number
of participants and the uneven group sizes. Only in case of a significant finding, prednisolone
7.5 mg and prednisolone 30 mg were compared against placebo by posthoc testing, using
the Mann-Whitney U test. Correction for multiple testing was performed by Bonferroni.
Correlations were made using the non parametric Spearman’s rank test. All statistical
analyses were run on SPSS for Windows version 15.0 (SPSS, Chicago, IL, USA). A P

Chapter 9 Glucocorticoids, mitochondrial function & glycosphingolipid metabolism
Table 1. Subject characteristics at inclusion
182
Placebo Prednisolone
7.5 mg 30 mg P
N 8 12 12
Age (y) 22±3 22±2 22±3 0.740
Weight (kg) 76.9±8.7 74.8±7.2 77.0±5.4 0.250
BMI (kg m -2 ) 22.7±2.1 22.2±1.9 22.4±1.2 0.906
Lean body mass (%) 78.6±7.9 82.1±3.9 80.7±2.5 0.731
Fat mass (%) 16.9±4.7 15.9±2.8 14.8±3.5 0.531
Fasting plasma glucose (mmol/l) 5.1±0.3 5.0±0.2 5.0±0.4 0.899
2-hr glucose OGTT (mmol/l) 4.2±0.8 4.7±0.9 4.2±1.1 0.407
VO2 max (ml.kg.min) 49.9 (44.6-58.2) 53.8 (45.0-60.3) 48.6 (45.4-55.2) 0.159
Data are mean ± SD or median (interquartile range). Significance was tested by Kruskal-Wallis. No
differences were observed between the groups at baseline [5].
Table 2. Muscle glycosphingolipid concentrations before and during two-week treatment with
placebo, prednisolone 7.5 mg or prednisolone 30 mg.
Ceramide
pmol/mg musle
GlCeramide
pmol/mg musle
Lac-Ceramide
pmol/mg musle
GB3
pmol/mg musle
GM3
pmol/mg musle
Pre-treatment On-treatment P1 P2 P3
PLB 20.3 (14.4-37.9) 21.6 (14.0-33.7)
7.5 19.4 (15.0-27.0) 19.6 (6.3-26.3) 0.954 NA NA
30 19.4 (13.5-35.9) 21.9 (17.4-25.7)
PLB 1.03 (0.47-4.34) 1.21 (0.29-1.91)
7.5 1.18 (0.53-2.67) 1.12 (0.54-3.68) 0.558 NA NA
30 1.58 (0.53-2.57) 1.44 (0.87-3.61)
PLB 12.9 (10.2-30.5) 10.6 (5.0-12.9)
7.5 12.6 (7.2-23.6) 12.2 (3.2-16.0) 0.038 0.155 0.013
30 12.5 (6.2-24.0) 17.3 (10.4-22.7)
PLB 5.1 (2.4-13.5) 5.3 (1.6-10.4)
7.5 4.9 (2.2-14.0) 4.2 (3.4-8.0)
0.899 NA NA
30 6.1 (2.2-18.2) 5.9 (3.8-11.8)
PLB 9.3 (5.8-13.3) 9.2 (6.2-16.8)
7.5 10.8 (3.6-29.9) 11.8 (3.3-30.4) 0.310 NA NA
30 10.4 (6.8-53.2) 12.5 (3.6-55.0)
Data are median (range). Bonferroni was applied to correct for multiple testing. Between-group changes
from baseline were tested by Kruskal-Wallis (P1). P2: Placebo vs. prednisolone 7.5 mg and P3: placebo
vs. prednisolone 30 mg (post hoc testing by Mann-Whitney U in the case of a significant finding with
Kruskal-Wallis). 7.5: prednisolone 7.5 mg; 30: prednisolone 30 mg; GB3: globocylceramide; GlCeramide:
glucosylceramide; Lac-Ceramide: lactosylceramide; PLB: placebo.

Table 3. Mitochondrial respiration rates corrected for mitochondrial density before and during
two-week treatment with placebo, prednisolone 7.5 mg or prednisolone 30 mg.
Malate
pmol/mg.sec.mtDNA
Glutamate
pmol/mg.sec.mtDNA
ADP
pmol/mg.sec.mtDNA
Cytochrome C
pmol/mg.sec.mtDNA
Succinate
pmol/mg.sec.mtDNA
Aurovertin
pmol/mg.sec.mtDNA
FCCP
pmol/mg.sec.mtDNA
Rotenone
pmol/mg.sec.mtDNA
Antimycin A
pmol/mg.sec.mtDNA
Pre-treatment On-treatment P
PLB 1.6 (1.0-3.2) 1.4 (0.6-4.2)
7.5 1.4 (0.6-3.5) 1.3 (0.8-4.2)
NS
30 1.6 (1.0-3.7) 2.2 (1.3-5.8)
PLB 2.1 (0.8-3.3) 1.8 (0.8-4.4)
7.5 2.2 (1.0-3.5) 2.6 (1.1-5.9)
NS
30 2.2 (1.2-3.9) 2.4 (1.7-7.0)
PLB 23.0 (8.3-25.5) 15.9 (6.6-25.2)
7.5 17.6 (9.4-31.0) 17.1 (10.4-57.4)
NS
30 21.8 (12.8-38.0) 20.0 (13.4-50.5)
PLB 22.2 (8.3-29.4) 16.1 (7.6-22.6)
7.5 20.9 (10.0-30.0) 18.4 (10.5-58.2)
NS
30 21.9 (12.5-38.5) 19.2 (14.5-33.8)
PLB 32.7 (12.8-41.4) 28.7 (14.8-35.2)
7.5 30.7 (15.1-40.0) 27.4 (15.4-79.6)
NS
30 31.4 (17.7-43.6) 28.4 (21.7-54.3)
PLB 7.7 (2.9-9.7) 6.8 (3.4-11.6)
7.5 7.3 (3.4-12.3) 7.6 (3.6-21.8)
NS
30 7.6 (4.2-9.8) 8.7 (6.1-13.9)
PLB 57.0 (17.7-61.3) 39.9 (17.6-74.0)
7.5 44.2 (22.2-82.4) 38.8 (22.0-124.8)
NS
30 51.3 (30.6-66.9) 43.8 (19.5-130.0)
PLB 26.3 (10.0-30.7) 22.6 (11.1-38.1)
7.5 20.3 (11.2-40.0) 20.3 (12.1-71.3)
NS
30 25.0 (14.2-45.0) 23.0 (10.7-62.0)
PLB 1.8 (0.7-3.1) 1.6 (0.6-4.9)
7.5 1.8 (0.7-6.3) 1.4 (0.4-4.0)
NS
30 1.8 (0.4-2.9) 1.9 (0.8-4.0)
Data are median (range). Between-group changes from baseline were tested by Kruskal-Wallis. No
significant differences were observed between the groups. Abbreviations 7.5: prednisolone 7.5 mg; 30:
prednisolone 30 mg; FCCP: carbonylcyanide-4-(trifluoromethoxy)-phenylhydrazone. PLB: placebo.
Peripheral insulin sensitivity: As described previously [5], peripheral glucose uptake was
non-significantly reduced by prednisolone 7.5 mg 5 (Rd= 61.05 [47.85-76.8] vs. 60 .19 [43.92-
7.09] mmol/kg.min ; P= 0.090), but decreased significantly after treatment with prednisolone
30 mg by 34±6% (Rd = 67.35 [50.87-93.53] vs. 42.42 [28.83-60.40]; P < 0.001).
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Chapter 9 Glucocorticoids, mitochondrial function & glycosphingolipid metabolism
Muscle glycosphingolipids and GM3: As compared to placebo, muscle ceramide
concentrations were not altered by prednisolone treatment (Table 2). Concentrations of the
sphingolipids GlCer and GB3 were also not affected by prednisolone treatment (Table 2). Due
to suspected blood contamination, the GlCer data from two participants in the prednisolone
7.5 mg group and one participant in the prednisolone 30 mg group were excluded from
analysis. Muscle concentrations of Lac-Cer showed a significance increase during high-dose
prednisolone treatment (Table 2). There was no relation between the change in Lac-Cer levels
and treatment-induced changes in insulin sensitivity. Muscle GM3 concentrations were not
significantly altered from baseline by prednisolone treatment (Table 2).
Mitochondrial function: Mitochondrial respiration rates, corrected for mitochondrial
density, were not altered by prednisolone treatment (Table 3). Uncorrected mitochondrial
respiration rates as well as mitochondrial density were also not changed by prednisolone
treatment (data not shown).
DISCUSSIoN
In the present study, we demonstrate that GC-induced insulin resistance cannot be explained
by an increase in muscle ceramide concentrations or the downstream ganglioside GM3. In
addition, muscle mitochondrial function was not changed during two-week treatment with
low or high-dose prednisolone, while insulin sensitivity was dose-dependently decreased.
This suggests that other mechanisms than mitochondrial dysfunction and accumulation of
glycolipids and GM3 are responsible for the induction of GC-induced insulin resistance in
young healthy men.
Both decreased affinity of the insulin receptor and impaired insulin signaling may importantly
contribute to reduced insulin sensitivity during GC treatment [40, 41]. Furthermore, GCs
may also directly inhibit the glucose transport system, by affecting GLUT4 subcellular
trafficking [12]. In this respect, we were interested to assess the effects of GC treatment on
muscle ceramide since this intermediate in lipid metabolism was shown to inhibit insulin
signaling by impairing PKB/Akt activation [22-24]. But although ceramide was shown to play
a crucial role in GC-induced insulin resistance in rodents [25], we could not detect an effect
of GC treatment on muscle ceramide levels in humans. In addition, the more downstream
glycolipids GlCer and GB were also not affected by treatment with study medication. We did
observe a significant increase in the glycolipid Lac-Cer during prednisolone 30 mg treatment
as compared to placebo, however, as the increase in Lac-Cer was not significant within the
184

prednisolone 30 mg group, and the change in Lac-Cer concentrations did not correlate with
changes in Rd, this does not suggest a causal relation with the induction of insulin resistance.
Finally, the ganglioside GM3, which is known to be involved in insulin sensitivity [26], was not
changed by prednisolone treatment.
Thus, we conclude that changes in ceramide, other glycosphingolipids and the ganglioside
GM3 do not seem to play an important role in the impairment of in insulin sensitivity induced
by short-term GC treatment in healthy men. We cannot rule out that other lipids may have
negatively affected insulin sensitivity. Diacylglycerol and several unsaturated fatty acids that
were not measured in the present study due to limited amount of available skeletal muscle
tissue, have been shown in previous studies in obese subjects and patients with T2DM to be
associated with impaired insulin sensitivity [42].
In addition to alterations in myocellular glycoshingolipids, we investigated a potential role for
muscle mitochondrial dysfunction in GC-induced insulin resistance. Decreased mitochondrial
capacity has been related to reduced metabolic flexibility, and may play an important role in
the pathogenesis of lipid accumulation and insulin resistance [32, 42]. Interestingly, chronic
GC treatment is well known to induce myopathy. Patients treated with GCs during 1 year
for different indications were shown to have increased lactate production during exercise
and decreased muscle strength. In addition, analysis of individual mitochondrial respiratory
chain complex (I–IV) activities in these patients showed that complex 1 was significantly
decreased and production of reactive oxygen species was significantly increased, indication
mitochondrial dysfunction [33]. In our study, however, no differences in muscle mitochondrial
respiration rates were observed during prednisolone treatment. Our finding of preserved
mitochondrial capacity despite the occurrence of insulin resistance is in agreement with a
study conducted by Short et al who showed that 6 days treatment with 0.5 mg/kg prednisolone
in healthy men did not affect oxidative enzyme activity or mitochondrial ATP production [43].
The difference results of these studies may be explained by several factors, including the
treatment duration (1 year vs. 1-2 weeks) and the population studied (healthy volunteers
vs. patients). In addition, in the clinic, it is common to administer GCs to the patients with
most active disease, and therefore the association between GC treatment and skeletal muscle
mitochondrial dysfunction may have been caused by disease activity (i.e. bias by indication).
Moreover, no correction for mitochondrial density was done in the chronic study in GC-treated
patients [33]. Furthermore, the good exercise capacity of our participants at baseline may
have contributed to the absence of prednisolone-induced changes in mitochondrial function.
Finally, it should be mentioned that an additional difficulty is that currently no gold-standard
measure exists to assess mitochondrial function. Ex vivo measurement of mitochondrial
185
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Chapter 9 Glucocorticoids, mitochondrial function & glycosphingolipid metabolism
function has the advantage to measure intrinsic mitochondrial capacity and function of the
different oxidative phosphorylation complexes, but cytosolic factors that might influence
oxidative phosphorylation are not accounted for.
Several other mechanisms have been suggested that may contribute to GC-induced insulin
resistance. GCs were shown to induce the acute phase response in skeletal muscle tissue
which could reduce insulin sensitivity [44]. In addition, changes in amino acid metabolism
are associated with impaired insulin sensitivity [45]. Since GC treatment induces skeletal
muscle proteolysis [5], this could be a contributing factor, especially during prolonged GC
treatment. Moreover, the endoplasmic reticulum (ER) has been identified as a major regulator
of metabolic homeostasis and inflammatory responses and ER stress was shown to have
unbeneficial effects on insulin sensitivity [46]. Whereas GCs were recently shown to induce
ER stress in beta cells [47], it is uncertain whether GCs may also alter ER function in skeletal
muscle tissue. Finally, GCs may impair insulin sensitivity by impairing the delivery of insulin
and glucose to skeletal muscle by reducing capillary recruitment through the induction of
vascular insulin resistance [14].
In conclusion, short-term treatment with GCs induces insulin resistance in lean men. This
reduction in insulin sensitivity was not related to a change in muscle ceramide, GM3 or
mitochondrial function. Which specific mechanisms are involved in the induction of insulin
resistance by GC treatment remains to be elucidated.
Acknowledgements
The authors thank M. van der Plas for performing the VO2 max measurements. Furthermore,
P. Schrauwen and E. Kornips are acknowledged for the measurement of MT-DNA.
Funding
This paper was written within the framework of project T1-106 of the Dutch Top Institute
Pharma.
186

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47. Linssen MM, van Raalte DH, Toonen Ej, Alkema W, van der Zon GC, Dokter WH, Diamant M, Guigas B,
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190

Chapter 10
Glucocorticoid Treatment Impairs
Microvascular Function in Healthy Males,
in Association with its Adverse Effects on
Glucose Metabolism and Blood Pressure
D.H. van Raalte, M. Diamant, D.M. Ouwens, R.G. IJzerman, M.M.L.
Linssen, B. Guigas, E.C. Eringa, E.H. Serné
Manuscript in Preparation

Chapter 10 Glucocorticoids and microvascular function
ABSTRACT
Glucocorticoids (GCs) are anti-inflammatory agents but also induce side effects, including
insulin resistance, glucose intolerance and hypertension. In this study, we investigated the
role of microvascular function in the development of these adverse effects. In a randomized,
placebo-controlled, double-blind, dose-response intervention study, 32 healthy males (age:
21±2 years; BMI: 21.9±1.7 kg/m2 ) were allocated to prednisolone 30 mg once daily (n=12),
prednisolone 7.5 mg once daily (n=12), or placebo (n=8) for two weeks. Compared to placebo,
prednisolone treatment dose-dependently decreased capillary recruitment. Additionally,
prednisolone treatment dose-dependently impaired insulin sensitivity and glucose
tolerance, but only prednisolone 30 mg increased systolic blood pressure. Prednisoloneinduced
changes in insulin-stimulated capillary recruitment were associated with insulin
sensitivity (r=+0.76), postprandial glucose levels (r=-0.52) and systolic blood pressure
(r=-0.62). To further investigate underlying mechanisms, male Wistar rats were treated
with prednisolone or saline for 7 days. Prednisolone treatment attenuated the vasodilatory
actions of insulin in muscle resistance arteries by impairing insulin signaling through the
Akt pathway. We conclude that prednisolone dose-dependently impaired insulin-stimulated
capillary recruitment by inducing vascular insulin resistance through direct interference
with vascular insulin signaling. We propose that GC-induced impairments of microvascular
function may contribute to the adverse effects of GC treatment on glucose metabolism.
Clinical Trial Registration Number: ISRTCN 78149983
194

Glucocorticoids (GCs) represent the most important and frequently used class of antiinflammatory
drugs, but their use is hampered by an increased risk of serious side effects,
particularly hypertension, insulin resistance, reduced glucose tolerance and overt diabetes
in susceptible individuals [1-5]. Although the exact mechanisms underlying these adverse
effects remain to be identified, GC-induced insulin resistance may underlie part of these side
effects [6]. Insulin resistance is characterized by the diminished ability of insulin to initiate
intracellular signaling, primarily in liver, adipose tissue and skeletal muscle [7]. Impaired
insulin signaling in these tissues results in reduced glucose uptake, insufficient suppression of
hepatic glucose output, compensatory hyperinsulinemia and dyslipidemia, which collectively
constitute the so-called metabolic syndrome [7].
Insulin action is not restricted to metabolically active tissues. Recently, it has become clear
that vascular tissue, and particularly endothelial cells, represents an important physiological
target for insulin and a significant regulator of overall insulin-stimulated glucose uptake [8-
10]. Insulin promotes its own access to muscle interstitial space by increasing blood flow and
by recruiting capillaries to expand the endothelial transporting surface available for nutrient
exchange. In addition, the local actions of insulin on (micro)vascular tissue may be of relevance
to the pathogenesis of hypertension, an important feature of the metabolic syndrome [8,
10-12]. Insulin exerts a vasodilatory action by promoting nitric oxide (NO) release from
the endothelial cells. This involves the phosphorylation of endothelial NO synthase (eNOS),
which on its turn is mediated by the phosphatidylinositol 3-kinase (PI3K)/Akt pathway [13].
Concomitantly, insulin activates extracellular signal-regulated kinase (ERK)1/2 signaling
in endothelial cells, which results in enhanced expression of the vasoconstrictor regulator
endothelin-1 [10, 13]. In healthy subjects, the vasodilatory signal prevails, but if activation
of the PI3K/Akt/eNOS pathway is impaired, as observed in insulin resistant states, insulinregulated
vasodilatation becomes compromised. In this manner, vascular insulin resistance
may contribute to the development of hypertension and impaired whole-body insulinstimulated
glucose uptake [14].
It is presently unclear whether vascular insulin resistance could be involved in the unfavorable
effects of GCs on glucose metabolism and blood pressure. Thus, in the present study, we
investigated whether the metabolic effects of the GC prednisolone, given at both a clinically
relevant low- and high dosage for 2 weeks, would concomitantly induce vascular insulin
resistance in healthy humans. Subsequently, we assessed associations among prednisoloneinduced
vascular insulin resistance and changes in blood pressure, whole-body insulinmediated
glucose uptake and glucose tolerance. In order to detail underlying mechanisms,
vasoreactivity and Akt-signaling and MAPK pathway signaling in the endothelium were
examined in cremaster arterioles of Wistar rats treated with prednisolone.
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METHoDS
Clinical study
Participants: Thirty-two healthy Caucasian subjects were recruited by local advertisement.
Inclusion criteria were: age between 18-35 years, body mass index (BMI) between 20.0-
25.0 kg/m2 , good physical health (determined by medical history, physical examination and
screening blood tests) and normoglycemia as defined by fasting plasma glucose (FPG) < 5.6
mmol/L and 2-hour glucose < 7.8 mmol/L following a 75 g oral glucose tolerance test (OGTT),
performed at screening visit. Exclusion criteria were any previous or current illness, use of
any medication, first-degree relative with type 2 diabetes, smoking, shift work, a history of
GC use, excessive sports activities (i.e. more often than two times/week) and recent changes
in weight or physical activity. The study was approved by an independent ethics committee
and the study was conducted in accordance with the Declaration of Helsinki. All participants
provided written informed consent before participation.
Study design: The study was a randomized, placebo-controlled, double blind, doseresponse
intervention study. Following assessment of eligibility and baseline measurements,
participants were randomized to receive either prednisolone 30 mg once daily (n=12),
prednisolone 7.5 mg once daily (n=12), or placebo treatment (n=8) for a period of 14 days using
block randomization, as carried out by the Department of Experimental Pharmacology of the
VU University Medical Center. Prior to treatment and on day 13 of treatment, microvascular
function was measured by capillary microscopy in the fasted state and during insulin infusion.
Insulin sensitivity was determined by hyperinsulinemic-euglycemic clamp. Prior to treatment
and on day 14 of treatment, a standardized consecutive meal challenge test was performed
to assess glucose tolerance. All measurements were conducted in a temperature-controlled
room (23.4±0.4°C) starting at 0730 AM, following a 12-h overnight fast with the subjects in
the supine position. Subjects refrained from drinking alcohol for a period of 24h before the
study day and did not perform strenuous exercise for a period of 48h before each study day.
Baseline measurements were obtained following 30 min of rest and acclimatization. During
all visits, including a follow-up visit at day 7 of treatment, safety and tolerability were assessed
(Supplementary Figure 1). A patient flow diagram is shown in Supplementary Figure 2.
Capillary microscopy: Nail fold capillary studies were performed in the fasted state (T=-
120 min) and during hyperinsulinemia (T=120 min) as described previously [15] by a single
experienced investigator (Figure 1). Baseline capillary density was defined as the number
of continuously erythrocyte-perfused capillaries per square millimeter of nail fold skin.
Postocclusive reactive hyperemia after 4 min of arterial occlusion was used to assess functional
196

capillary recruitment. Capillary recruitment was calculated by dividing the increase in
perfused capillary density during postocclusive reactive hyperemia by the baseline perfused
capillary density. In addition, venous congestion to expose a maximal number of capillaries
was done. All procedures were performed twice, and the mean of both measurements was
used for analyses. The day-to-day coefficient of variation of functional capillary recruitment
was 15.9±8.0 % as determined in 10 healthy individuals on 2 separate days. Skin temperature
was monitored during the tests. One subject in the prednisolone 7.5 arm could not be analyzed
due to insufficient quality of microscopic images during treatment with study medication and
was left out of the analyses.
Blood pressure: Systolic and diastolic blood pressure were manually measured (Welch Allyn,
Delft, Netherlands) by a single experienced investigator following the 30 min acclimatization
period. The average of three consecutive blood pressure measurements with 5-min interval
was used for further analyses.
Figure 1. Design of the experimental protocol. A two-hour hyperinsulinemic-euglycemic clamp
was performed for 180 min. Microcirculation: nailfold capillary microscopy; BP: blood pressure
measurements; biopsy: percutaneous skeletal muscle biopsy. Arrows indicate measurement of plasma
insulin levels.
Hyperinsulinemic-euglycemic clamp: Whole-body insulin sensitivity was assessed by
hyperinsulinemic-euglycemic clamp method as described previously [16]. Briefly, a primed,
continuous (40 mU/m.min) insulin infusion (Actrapid 100 IU/mL; Novo Nordisk, Bagsvaerd,
Denmark) was given for 120 min; plasma glucose was kept at 5 mmol/l by adjusting the
rate of a 20% glucose infusion based on plasma glucose measurements performed at 5-min
intervals (Figure 1). Whole-body insulin sensitivity was quantified by the M-value (mg/
kg.min), calculated between min 90-120 during steady-state insulin concentrations as
described previously [16].
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Standardized meal test: Glucose tolerance was assessed following two consecutive identical
meals that were served as breakfast at 09.00 AM and as lunch at 1.00 PM. The meals contained
905 kcal (50 g fat, 75 g carbohydrates, 35 g protein) each. Samples for determination of
glucose were obtained at 30 min intervals, with the meal beginning immediately after the
time 0 sample and consumed within 15 min. Area under the postprandial glucose curve
(AUC ) was calculated by the trapezoid method. Insulin sensitivity in the postprandial state
G
was calculated as the oral glucose sensitivity index (OGIS) [17].
Insulin signaling in skeletal muscle biopsies: Since skeletal muscle tissue represents the
major site of glucose clearance in the postprandial state, characterized by a classic intracellular
insulin signaling cascade, we assessed the effects of prednisolone on key proteins in this
cascade in skeletal muscle tissue. To this end, a percutaneous skeletal muscle biopsy was
taken from the vastus lateralis muscle using a Bergstrom needle [18] under local anesthesia
with lidocaine 1% (B. Braun, Melsungen, Germany) before and after 30 min of insulin infusion.
Muscle biopsies were washed with sterile NaCl 0.9% to reduce blood contamination and were
thereafter snap-frozen in liquid nitrogen and stored at -80°C for further analysis. Western
blotting of proteins involved in insulin signaling, including phosphorylation of Akt and its
downstream substrate proline-rich Akt substrate of 40 kDa (PRAS40) was done as described
previously [19]. Antibodies for PRAS40, Phospho-Akt-Ser473 and Tubulin were obtained from
Cell Signaling Technology (Boston, MA), for total Akt from Millipore Corporation (Billerica,
MA), for the insulin receptor from Santa Cruz Biotechnology Inc (Santa Cruz, CA) and for
Phospho-PRAS40-Thr246 from Biosource (Carlsbad, CA).
Study medication: Prednisolone tablets were purchased from Pfizer AB (Sollentuna,
Sweden) and placebo tablets were obtained from Xendo Drug Development (Groningen, The
Netherlands). Tablets were capsulated in order to allow the treatment to be blinded [20].
Study medication was taken at 08.00 AM during the two-week treatment except for day 13
and day 14, when it was ingested at 06.00 AM. Patients kept a diary in which the exact time of
medication intake during the study was registered.
Biochemical methods: Blood glucose concentrations were measured using an YSI 2300
STAT Plus analyzer (YSI, Yellow Springs, OH). Insulin levels were determined using an
immunometric assay (Advia, Centaur, Siemens Medical Solutions Diagnostics, USA).
Statistical analyses: Data are presented as mean values ± standard deviation (SD), or as
median (interquartile range) in case of skewed distribution. Absolute changes from baseline
were calculated (on treatment value minus pre treatment value) for all parameters. Nonparametric
analysis was chosen due to uneven sample size in the different groups and the
198

elatively small numbers in each group. Baseline values were compared between the groups
by Kruskal-Wallis. For treatment-induced effects, Kruskal-Wallis with trend analysis was
performed (‘the Jonckheere-Terpstra test’). Only in case of a significant finding, prednisolone
7.5 mg and prednisolone 30 mg were compared against placebo by posthoc testing, using
the Mann-Whitney U test. To correct for multiple testing, Bonferroni correction was applied.
Correlations between the various parameters were assessed with Spearman correlations. All
statistical analyses were run using SPSS version 18.0 for Mac OS X (Chicago, IL, USA). A P <
0.05 was considered statistically significant.
Animal Study
Animals and treatments: The investigation conformed to the Guide for the Care and Use
of Laboratory Animals published by the US National Institutes of Health. The local ethics
committee for animal experiments approved the procedures. Male Wistar rats (300-350
g; Charles-River, Maastricht, the Netherlands) were treated with prednisolone (8 mg/kg
per day; n=6) or saline (n=6) by intraperitoneal injection for 7 days. Two hours after the
final injection, the rats were sacrificed by isoflurane overdose and resistance arteries of the
cremaster muscles were isolated as described previously [21].
Vasoreactivity experiments: After dissection, a first order cremaster arteriole was placed
in a pressure myograph. Only vessels that developed substantial spontaneous constriction
(>40% of the passive diameter) to pressure (65 mmHg) and then showed a clear vasodilator
response (>10% of the diameter) to the endothelium-dependent vasodilator acetylcholine
(0.1 mm; Sigma) were used [21]. Acute effects of insulin (Novo Nordisk, Alphen a/d Rijn, the
Netherlands) on the diameter were studied at three different concentrations: 0.02, 0.2 and 2
nmol/l, and diameter changes at each concentration were recorded for 30 min.
Western blots: Segments of cremaster arteries from the same rat were exposed to insulin
or solvent for 15 min at 34°C as described [22]. The protein lysates were stained with a
specific antibody against phosphorylated Akt, total Akt, phosphorylated ERK1/2 and total
ERK1/2 (Cell Signaling Technology, Boston, MA) and visualized with a chemiluminescence
kit (Amersham). Differences in phosphorylated protein were adjusted for differences in the
corresponding total protein staining or actin.
Statistical analyses: Steady-state responses are reported as mean changes in diameter from
baseline (in percent) ± SEM. The baseline diameter was defined as the arterial diameter just
before addition of the first concentration of insulin. Western blot data were expressed as
relative to unstimulated controls, assigning a value of 1 to the control. Differences between
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Chapter 10 Glucocorticoids and microvascular function
the various parameters were assessed by the Mann-Whitney U test. A P < 0.05 was considered
statistically significant.
RESULTS
Clinical study
Baseline characteristics and anthropometrics: Prior to treatment, all treatment groups
had comparable anthropometrics, blood pressure and glucose tolerance (Table 1). Treatment
with study medication did not alter body weight (Table 2).
Table 1. Baseline characteristics
Placebo Prednisolone
7.5 mg 30 mg P
N 8 12 12
Age (y) 21±3 21±2 21±2 0.670
Weight (kg) 75±8 75±6 73±10 0.886
Height (cm) 186±7 184±6 185±5 0.718
BMI (kg/m2 ) 21.6±1.5 22.1±1.5 21.4±2.2 0.656
Systolic blood pressure (mmHg) 126±7 124±9 120±9 0.284
Diastolic blood pressure (mmHg) 79±8 79±6 78±10 0.872
Fasting plasma glucose (mmol/l) 4.4±0.3 4.4±0.2 4.5±0.2 0.852
2-hr glucose OGTT (mmol/l) 3.7±1.0 3.9±0.9 3.6±0.8 0.782
Mean±SD are provided. Significance was tested by Kruskal-Wallis. No statistically significant differences
were observed between the groups at baseline. 2-hr glucose OGTT denotes plasma glucose concentrations
2 h after ingestion of 75 g glucose during an oral glucose tolerance test.
Prednisolone impaired capillary recruitment: In the fasted state prior to insulin infusion,
prednisolone treatment did not affect baseline capillary density, capillary recruitment or
maximal number of capillaries following venous congestion (Figure 2A; Table 3). Insulin
infusion increased capillary recruitment by 13±4% (P

Table 2. Metabolic parameters and blood pressure before and during treatment.
Weight
(kg)
Systolic blood pressure
(mmHg)
Diastolic blood pressure
(mmHg)
Fasting plasma glucose
(mmol/l)
Fasting plasma insulin
(pmol/l)
Area under glucose curve
(mmol/l.8hr)
OGIS
(ml/min.m2 )
M-value
(mg/kg.min)
Clamp glucose levels
(mmol/l)
Clamp insulin levels
(pmol/l)
Pre-treatment On-treatment P1 P2 P3
PLB 75±8 76±7
7.5 75±6 75±6
0.539 NA NA
30 73±10 74±10
PLB 126±7 123±9
7.5 124±9 124±11
0.002 0.710 0.006
30 120±9 124±8
PLB 79±8 76±7
7.5 79±6 76±6
0.042 1.0 0.164
30 78±10 80±9
PLB 4.4±0.3 4.5±0.3
7.5 4.4±0.2 4.7±0.3
0.04 0.418 0.09
30 4.5±0.2 4.9±0.4
PLB 31 (28-40) 33 (28-39)
7.5 36 (29-47) 36 (27-65) 0.001 1.0 0.008
30 32 (26-44) 56 (41-72)
PLB 2197±193 2182±92
7.5 2250±121 2492±127

Chapter 10 Glucocorticoids and microvascular function
Figure 2. The effects of prednisolone (PRED) on capillary recruitment. Capillary recruitment in the
fasted state was not impaired by either prednisolone dosage (A), however, prednisolone treatment dosedependently
impaired insulin-stimulated microvascular function (B). Data represent absolute change
from baseline; mean ± SEM are provided. White bars: placebo; grey bars: prednisolone 7.5 mg daily; black
bars: prednisolone 30 mg daily. Between-group changes from baseline were tested by Kruskal-Wallis
with trend analysis (indicated by top line). Posthoc tests were done by Mann-Whitney U with Bonferroni
correction for multiple testing (indicated by line with brackets). ***p

Table 3. Capillary density before and during treatment.
Baseline density fasting
(n/mm2 )
Peak density fasting
(n/mm2 )
Capillary recruitment
fasting
(%)
Venous occlusion
(n/mm2 )
Baseline density
hyperinsulinemia
(n/mm2 )
Peak density
hyperinsulinemia
(n/mm2 )
Capillary recruitment
hyperinsulinemia
(%)
Pre-treatment On-treatment P1 P2 P3
PLB 37.6±3.9 40.9±5.1
7.5 39.4±2.9 40.2±4.3 0.688 NA NA
30 40.1±6.3 41.8±6.6
PLB 47.6±5.4 52.1±6.0
7.5 51.3±5.6 54.3±7.1 0.189 NA NA
30 52.0±8.9 54.8±9.8
PLB 26.6±2.8 26.4±8.4
7.5 30.0±5.8 30.8±7.7
1.0 NA NA
30 29.7±6.6 28.5±8.5
PLB 51.1±6.1 54.5±6.4
7.5 59.6±7.8 60.0±7.6 0.742 NA NA
30 57.0±7.9 60.3±10.6
PLB 37.1±2.1 41.4±6.5
7.5 39.3±4.0 41.0±5.6 0.701 NA NA
30 39.1±6.5 41.7±7.4
PLB 51.3±2.2 60.8±8.1
7.5 57.1±8.0 57.9±8.4 0.011 0.03 0.038
30 55.1±9.7 54.4±10.3
PLB 38.7±4.2 44.8±9.8
7.5 44.9±8.2 41.5±12.9 0.001 0.18 0.002
30 41.2±5.2 30.6±8.2
Data are mean±SD or median (interquartile range). Between-group changes from baseline were tested by
Kruskal-Wallis with trend analysis (P1). In case of a significant finding, posthoc testing (by Mann-Whitney
U) was done (P2 to compare prednisolone 7.5 mg vs. placebo and P3 to compare prednisolone 30 mg vs.
placebo, respectively). Treatment groups: 7.5=prednisolone 7.5 mg daily; 30=prednisolone 30 mg daily.
Abbrevations: NA: not applicable.
Prednisolone dose-dependently impaired glucose metabolism and insulin sensitivity:
Prednisolone 30 mg, but not prednisolone 7.5 mg increased FPG levels relative to placebo
(P=0.04), despite fasting hyperinsulinemia (P=0.008) (Table 2). As compared to placebo,
glucose tolerance during the standardized meal test was dose-dependently reduced by
prednisolone treatment: glucose levels, measured as area under the curve (AUC), increased by
11±5% and 27±9%, for prednisolone 7.5 mg and Prednisolone 30 mg respectively (Table 2).
Clamp-measured whole-body insulin sensitivity was decreased by prednisolone treatment as
compared to placebo in a dose-dependent manner: mean differences: -2.1±0.8 mg/kg.min for
prednisolone 7.5 mg and -4.5±0.7 mg/kg.min for prednisolone 30 mg. Similarly, postprandial
insulin sensitivity, calculated by OGIS, was dose-dependently reduced by prednisolone
treatment as compared to placebo (Table 2).
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Chapter 10 Glucocorticoids and microvascular function
Figure 4. Associations among prednisolone (PRED)-induced changes in insulin-stimulated
microvascular function, and PRED-induced changes in metabolic and vascular parameters,
including fasting plasma glucose (A), area under the postpandrial glucose curve (B), insulin-stimulated
glucose disposal (C), postprandial insulin sensitivity (D) and systolic blood pressure (E). Correlations
were assessed with Spearman correlations.
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Prednisolone and insulin signaling in skeletal muscle biopsies: As compared to
placebo, prednisolone treatment did not result in significant reduction in phosphorylation
of Akt (Figure 3A) and PRAS40 (Figure 3B) before and during insulin infusion. Of note, when
within-group changes were assessed, the characteristic insulin-mediated increase in PRASphosphorylation
as observed prior to treatment, was no longer present following high-dose
prednisolone treatment (Figure 4B), indicating reduced insulin-stimulated phosphorylation.
Associations among insulin-stimulated capillary, metabolic parameters and blood
pressure: Prednisolone-induced reductions in insulin-stimulated capillary recruitment were
negatively associated with changes in fasting (P

Chapter 10 Glucocorticoids and microvascular function
reduced the insulin-stimulated increase in Akt phosphorylation (Figure 5B). Basal and
insulin-stimulated phosphorylation of ERK1/2 were not affected by prednisolone treatment
(Figure 5C).
Figure 5. The effects of prednisolone treatment on isolated rat cremaster resistance arterioles.
Prednisolone treatment impaired the vasoactive actions of insulin ex vivo (A). Prednisolone impaired
insulin-stimulated phosphorylation of Akt (B), but did not affect phosphorylation of ERK1/2 (C). A
representative blot is shown in (D). Data indicate percentual change from baseline for vasoreactivity
experiments (A) and the ratio between phosphorylated and total protein content in arbitrary units for
Western Blotting experiments (B) and (C); mean ± SEM are provided. White bars: fasting state; black
bars: insulin infusion. Between-group changes from baseline were tested by Mann-Whitney-U test.
**p

DISCUSSIoN
This is the first study describing a dose-dependent impairment of insulin-stimulated
microvascular function following GC treatment in healthy individuals, as a possible
mechanism contributing to the concomitantly occurring decrease in insulin sensitivity and
glucose tolerance and elevation of blood pressure. These findings were substantiated by the
demonstration of reductions in insulin-stimulated vasodilatation through impaired insulinmediated
phosphorylation of Akt, but unchanged MAPK pathway signaling in rat resistance
arteries. These findings are consistent with a role for vascular insulin resistance in the
development of GC-related adverse metabolic effects.
As observed previously [6, 20], a two-week treatment with prednisolone dose-dependently
reduced whole-body insulin sensitivity and glucose tolerance, while high-dose prednisolone
treatment significantly increased systolic blood pressure. An additional confirmatory finding
of the present study is the identification of the vasoactive properties of insulin, as insulin
increased capillary recruitment by 13% in these healthy subjects. A novel finding was that
prednisolone dose-dependently impaired these vasodilatory actions of insulin in vivo, while
prednisolone treatment did not affect microvascular function in the fasted state. It has been
reported previously that GCs particularly impair metabolism under stimulated conditions, i.e.
during hyperinsulinemic-euglycemic clamps or in the postprandial state. Accordingly, we have
recently shown that prednisolone dose-dependently impaired metabolic fluxes during insulin
infusion, but not in the fasted state when prednisolone induced fasting hyperinsulinemia [6].
Moreover, fasting glucose levels are often only mildly elevated by GC treatment, whereas
during the day significant hyperglycemia develops, especially in the postprandial period [20,
23]. This seems also the case for the vascular effects of prednisolone, because in the present
study we did not observe prednisolone-induced changes in capillary recruitment in the fasted
state, when prednisolone augmented fasting insulin levels. Collectively, the data demonstrated
that prednisolone induced vascular insulin resistance.
Subsequently, we analyzed whether vascular insulin resistance was related to the
prednisolone-induced impairment of glucose metabolism and blood pressure rise. Indeed,
prednisolone-induced impairment in capillary recruitment was significantly associated
with insulin-stimulated glucose disposal during the hyperinsulinemic-euglycemic clamp, as
well as to postprandial glucose levels and postprandial insulin sensitivity, suggesting that
capillary recruitment may also be an important factor contributing to postprandial glucose
metabolism. In line with this finding, Keske and colleagues recently demonstrated using
contrast-enhanced ultrasound that in obese, insulin resistant subjects, capillary recruitment
in the postprandial state was impaired [24].
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Additionally, the approximately 6 mmHg increase in systolic blood pressure induced by highdose
prednisolone treatment was related to the decrease in insulin-stimulated capillary
recruitment. Previously, various mechanisms have been proposed to underlie the GC-induced
increase blood pressure, including salt and water retention, altered sympathovagal balance,
increased responsiveness to pressor stimuli such as catecholamines and angiotensin II and
reduced NO availability [25]. We suggest that GC-induced vascular insulin resistance with
impaired insulin signaling via PI3K/Akt to eNOS may be an alternative mechanism to explain
the increase in blood pressure observed during GC treatment. A similar concept has been
proposed for the development of hypertension in obesity and other insulin resistant states
[10-12]. However, the contribution of insulin signaling to eNOS in the regulation of blood
pressure in different states of insulin resistance is not unequivocal [12].
Although it seems very likely that prednisolone treatment also directly affects insulin
signaling in skeletal muscle tissue, we did not detect altered insulin signaling as measured
by phosphorylated Akt and its substrate PRAS40 in skeletal muscle biopsies obtained in the
healthy participants, when compared to placebo. In rat skeletal muscle, it has been shown
previously that the GC dexamethasone impaired various components of the insulin signaling
cascade, including decreased phosphorylation of IRS-1, PI-3K and Akt, resulting in impaired
translocation of glucose transporter (GLUT) 4 to the plasma membrane [26-30]. These
seemingly discrepant findings may not be easily explained. Since we did find statistically
significant changes when within-group comparisons were made, we may have been limited by
the relatively small size of the study. Furthermore, since proteins remain in a phosphorylated
state for a limited time period, the timing of biopsy collection, i.e. at 30 min following onset of
hyperinsulinemia, may have influenced our findings. Taken together, our data are compatible
with an important role for prednisolone-induced vascular insulin resistance in the reduction
in glucose uptake by skeletal muscle during hyperinsulinemia and in the postprandial state
following prednisolone treatment.
Since muscle tissue is the main peripheral site of insulin-mediated glucose uptake and
vascular resistance, it would have been more straightforward to measure insulin-mediated
microvascular recruitment in muscle instead of skin. However, the human skin is the only site
available in humans to directly and to non-invasively assess capillary density and recruitment
of capillaries. Moreover, the effects of obesity and free fatty acids on insulin-mediated
microvascular recruitment in muscle [31, 32] can be reproduced in human skin [15, 33],
suggesting that the vascular responses observed in skin reflect those in muscle. In a recent
study, we could demonstrate concurrent insulin-mediated microvascular recruitment in skin
and skeletal muscle. Both were mutually related and strongly associated with whole-body
208

glucose uptake suggesting that the cutaneous microcirculation is a representative vascular
bed to examine insulin’s actions on the microcirculation [34].
In order to study underlying mechanisms of prednisolone-induced vascular insulin
resistance, Wistar rats were additionally treated with prednisolone. Also in rats, insulin
dose-dependently increased the diameter of isolated cremaster resistance arterioles,
which was impaired by prednisolone treatment on all insulin levels. At the molecular level,
prednisolone attenuated the vascular effects of insulin by reducing phosphorylation of the
Akt pathway. Phosphorylation of the ERK1/2 pathway was not affected. Thus, by reducing
Akt phosphorylation, prednisolone impaired insulin’s vasodilatory actions. Unfortunately, the
amount of eNOS protein in one cremaster resistance artery is not sufficient to measure eNOS
phosphorylation, but Akt phosphorylation has been shown to be a reliable marker of eNOS
phosphorylation in this model [21, 35].
Interestingly, prednisolone treatment specifically attenuated the vasodilatory effects
of insulin, since endothelium-dependent vasodilation as assessed by acetylcholine
administration remained intact. This may indicate that specifically vascular insulin signaling
is disturbed, whereas eNOS function is intact. Nevertheless, acetylcholine may also induce
vasodilation by additional mechanisms than NO production, i.e. by releasing prostanoids and
other endothelium-derived hyperpolarizing factors [36].
In conclusion, prednisolone treatment in healthy volunteers dose-dependently impaired
insulin-stimulated capillary recruitment. In addition, prednisolone-induced changes in
insulin-stimulated capillary recruitment were strongly related to unfavorable metabolic
effects induced by prednisolone treatment, including impaired glucose tolerance, decreased
insulin sensitivity as well as increased systolic blood pressure. Importantly, a substantial part
of the decrease in insulin-stimulated glucose disposal could be explained by prednisoloneinduced
microvascular dysfunction. In isolated rat cremaster resistance arteries, we found
evidence that prednisolone specifically impaired insulin-induced vasodilation by attenuating
insulin-stimulated phosphorylation of the Akt pathway. Thus, we propose that vascular insulin
resistance may contribute to the cardiovascular side effects associated with GC treatment.
Funding: This paper was written within the framework of project T1-106 of the Dutch Top
Institute Pharma. Dr E.H. Serné is financially supported by a fellowship from The Netherlands
Heart Foundation (grant no. 2010T041).
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SUPPLEMENTARy MATERIAL
Supplemental Figure 1. Study Design.
Baseline PLB, PRED 7.5 mg or PRED 30 mg once daily Off-treatment
Day S -2 -1 1 7
13 14
T1 T2 M
V T1 T2
S: Screening T1: Meal Tolerance Test T2: Euglycemic M: study medication V: Follow up
Clamp + Capillary
Visit
Supplemental Figure 2. Patient flow diagram. 100% of the randomized participants completed the
study.
28
V
213
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Chapter 10 Glucocorticoids and microvascular function
Supplemental Figure 3. Prednisolone treatment did not affect endothelium-dependent vasodilation
as assessed by acetylcholine administration. A dose-response curve is presented. Data are mean ± SD.
Significance was tested by Mann-Whitney U test. Prednisolone-treated rats, n=6; Saline-treated rates,
n=6. Ach: acetylcholine.
Supplemental Table 1. General characteristics of isolated cremaster arterioles following a 7-day
treatment with prednisolone or saline.
General Characteristics Prednisolone 8 mg/kg Saline P
A. Passive Diameter (mm) 184±14 173±8 0.180
B. Basal Diameter (mm) 75±25 83±19 0.485
C. Basal Tone (%) 60±11 52±12 0.310
D. Diameter change after Ach (%) 85±16 65±28 0.310
Data are mean ± SD. Passive diameter was measured after isolation of cremaster arterioles. The basal
diameter was measured at a pressure of 65 mmHg and 37°C. Basal arterial tone was calculated by
{[(A-B)/A]*100}. Significance was tested by Mann-Whitney U test. Prednisolone-treated rats, n=6; Salinetreated
rates, n=6. Ach: acetylcholine.
214

Chapter 11
Glucocorticoids alter Subcutaneous
Adipose Tissue Function and Adipokine
Levels in vivo in Healthy Men
D.H. van Raalte, S. Lehr, H. Mueller, D.M. Ouwens and M Diamant.
Manuscript in Preparation

Chapter 11 Glucocorticoids, adipose tissue & adipokines
ABSTRACT
Introduction: Glucocorticoid (GC) treatment is associated with diabetogenic side effects.
However, the effects on adipose tissue function are currently not completely clear. Evidence
for insulin resistance and increased lipolysis was observed in vitro. In the present study
we assessed the effects of low- and high-dose prednisolone treatment on adipose tissue
function and circulating levels of adipokines in vivo in healthy men.
Research design and methods: Healthy men (n=24; age 21.1±2.1 years; BMI 21.9±1.7
kg/m2 ) were allocated to a two-week treatment with prednisolone 7.5 mg or 30 mg daily
for two weeks. At baseline and on day 14 of treatment, insulin sensitivity was measured by
hyperinsulinemic-euglycemic clamp and plasma levels of adiponectin, resistin and leptin
were determined. An abdominal subcutaneous adipose tissue (SAT) biopsy was taken
in the fasted state and during insulin infusion before and after the intervention to study
insulin signaling (protein kinase B (PKB/Akt) and proline-rich Akt substrate (PRAS)-40
phosphorylation). In addition, adipose tissue protein expression of adipogenesis markers
(peroxisome-proliferator activated receptor (PPAR)-γ and Kruppel-like factor (KLF)-
4), lipolytic enzymes (adipose triglyceride lipase (ATGL) and hormone-sensitive lipase
(HSL)) and adiponectin gene expression was measured. Treatment-induced changes were
compared to baseline using paired statistical tests.
Results: Prednisolone 30 mg increased fasting glucose levels (P

Glucocorticoids (GCs) are important regulators of metabolic homeostasis, but have
diabetogenic effects, in particular when present in supraphysiological concentrations [1].
Although the adverse metabolic effects of elevated GC levels on glucose [2] and protein
metabolism [3] are reasonably well defined, the effects on lipid metabolism are less clear [4].
In addition, the involvement of a number of organs in the development of the diabetogenic
effects of GC treatment, including pancreatic islet cells, skeletal muscle and liver are
understood to a greater extent than the role of adipose tissue. However, several observations
indicate that GCs may exert untoward effects on adipose tissue.
As such, chronic GC excess in Cushing’s syndrome or during GC treatment induces fat
deposition in the visceral compartment [5] and promotes liver fat accumulation [6], with
wasting of subcutaneous adipose tissue depots. This increment in visceral adipose tissue
(VAT) by GCs may be induced by several factors, including increased intake of high-caloric
“comfort food” [7], increased lipoprotein lipase (LPL) activity particularly in VAT [8, 9],
increased VAT adipogenesis through stimulation of differentiation of pre-adipocytes into
mature adipocytes [10] and possibly by enhancing de novo lipogenesis [4, 11]. GC-induced
increase in VAT is very relevant since VAT is well known to be associated with an unfavorable
metabolic profile as opposed to subcutaneous adipose tissue (SAT) [12].
In contrast to their lipogenic actions, GCs are catabolic hormones and, as such, have lipolytic
properties [4, 11]. In most performed in vitro studies, GCs increased lipolysis [4] by enhancing
the activity of key lipolytic enzymes including adipose triglyceride lipase (ATGL) and hormone
sensitive lipase (HSL) [13, 14]. It should be noted that differences were observed between
adipose tissue depots with increased lipolysis in VAT as compared to SAT [15]. In addition,
antilipolytic effects of GC treatment have also been observed in vitro [4]. In line with most
of the in vitro observations, acute GC administration was shown to increase fasting wholebody
lipolysis in healthy humans [16, 17], however, during more prolonged GC exposure,
this effect was no longer present, most likely due to compensatory fasting hyperinsulinemia
[18-20]. Indeed, we could recently show that a two-week treatment with prednisolone 7.5
mg did not increase, while prednisolone 30 mg daily even reduced basal lipolysis in healthy
men [21]. However, in the same study, prednisolone treatment dose-dependently impaired
insulin-stimulated suppression of lipolysis and thus increased plasma nonesterified fatty
acids (NEFA) during hyperinsulinemia [21]. It is noteworthy that these effects occurred in
the absence of significant changes in body weight, body composition, body fat distribution
and liver fat content. Increased lipolysis and consequently enhanced plasma NEFA levels
are detrimental since they impair glucose metabolism by reducing muscle and liver insulin
sensitivity [22].
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Chapter 11 Glucocorticoids, adipose tissue & adipokines
Finally, another mechanism by which GC-induced changes in adipose tissue may exert its
diabetogenic effects is via alteration of the secretion of various adipokines, as was shown
in in vitro studies [23]. An important limitation of the above-mentioned studies that have
addressed the effects of GCs on adipose tissue function is that they were mostly carried out in
vitro or in vivo in rodents, and no data are available from in vivo studies in humans. Therefore,
in the present study, we assessed changes in SAT function and plasma levels of adipokines
following a two-week treatment with low- or high dose prednisolone treatment in healthy
men.
RESEARCH DESIGN AND METHoDS
Participants: Twenty-four healthy normoglycemic Caucasian men (age 18-35 years, BMI
20-25 kg/m2 ) were recruited. Health status was confirmed by history, physical examination,
screening blood tests and 75-g 2-h OGTT during the screening visit. Exclusion criteria
were the presence of any disease, use of any medication, first-degree relative with type 2
diabetes, smoking, shift work, a history of glucocorticoid use, sport activities > twice/
week and changes in weight in the 3 months prior to study participation. The study was
approved by an independent ethics committee and the study was conducted in accordance
with the Declaration of Helsinki. All participants provided written informed consent before
participation.
Study design: Participants were randomly assigned to a 14-day treatment with prednisolone
7.5 mg once daily or prednisolone 30 mg once daily. At baseline and after two weeks of
treatment, fasting blood samples were obtained, insulin sensitivity was measured by
hyperinsulinemic-euglycemic clamp and subcutaneous adipose tissue biopsies were obtained
both in the fasted state and following 45-min insulin infusion. Subjects were asked to refrain
from drinking alcohol and to perform no strenuous exercise for a period of 48h before the
study days.
Hyperinsulinemic-euglycemic clamp: A two-hour hyperinsulinemic-euglycemic clamp
at 5.0 mM was performed to assess insulin sensitivity as described [24]. The mean glucose
infusion rate during steady state (last 30 min of the clamp) was used to assess insulin
sensitivity.
Adipose tissue biopsy: An abdominal subcutaneous adipose tissue biopsy was collected
6-8 cm lateral from the umbilicus under local anesthesia (2% lidocaine) by needle biopsy.
220

Adipose tissue was washed with sterile saline and was snap frozen in liquid nitrogen and
stored at -80°C until analysis.
Biochemical analyses: Blood glucose concentrations were measured using an YSI 2300
STAT Plus analyzer (YSI, Yellow Springs, OH). Insulin levels were determined using an
immunometric assay (Advia Centaur; Siemens Medical Solutions Diagnostics, Deerfield,
IL). A single-plex human magnetic adiponectin and a three-plex human leptin, visfatin, and
resistin assay (all from Biorad, Hercules, CA, USA) were used to determine serum levels of
adiponectin, leptin, visfatin, and resistin using a Bioplex 200 suspension array system and
Bioplex Pro II wash station (Biorad, Hercules, CA, USA) respectively. Adipokine concentrations
were calculated from the appropriate optimized standard curves using Bio-Plex Manager
software version 6.0 (Biorad). Intra- and interassay coefficients of variation for the Bioplex
assays were both

Chapter 11 Glucocorticoids, adipose tissue & adipokines
study design, no formal power analysis was done. Calculations were done using SPSS 18.0 for
Mac (Chicago, IL, USA). P

Figure 1. Effects of glucocorticoids on adipose tissue function. Prednisolone 30 mg, but not
prednisolone 7.5 mg treatment significantly impaired insulin signaling as measured by insulin-stimulated
phosphorylation of Akt and proline-rich Akt substrate (PRAS)-40 (panel A+B). Both prednisolone
dosages reduced the expression of adipogenesis markers peroxisome proliferator-activated receptor
(PPAR)-γ and krüppel-like factor (KLF)-4 (panel C+D). Adipose triglyceride lipase (ATGL) expression was
reduced by prednisolone 7.5 mg and 30 mg in the fasted state, but no differences were observed during
insulin infusion (panel E). Neither fasting hormone sensitive lipase (HSL) expression (panel F) nor HSL
phosphorylation by insulin was affected by prednisolone treatment (panel G). Adiponectin expression
was reduced by both prednisolone dosages (panel H). Mean±SD is shown. For panel A,B and G: white
bar: pre treatment, fasting; black bar: pre treatment, during insulin infusion; bar with block-pattern:
post treatment, fasting; grey bar: post treatment, during insulin For panel C, D, E, F and H: white bar:
pretreatment, black bar: post treatment. # P=0.07;*P< 0.05;**P0.05).
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Chapter 11 Glucocorticoids, adipose tissue & adipokines
PRED7.5 mg did not alter the plasma levels of any of these adipokines (P>0.05) (Table 1). No
associations were observed between prednisolone-induced changes in insulin sensitivity and
changes in circulating levels of adipokines (data not shown).
Adipose tissue insulin signaling: Insulin infusion stimulated phosphorylation of PKB/
Akt and its substrate PRAS40 as compared to the fasted state. This effect was reduced by
prednisolone 30 mg treatment (Figure 1A+B). Although a trend was observed for the low
dose group, this effect failed to reach statistical significance (Figure 1A+B).
Adipose tissue markers of adipogenesis: Both low-and high dose prednisolone treatment
decreased the expression of markers of adipose tissue differentiation/adipogenesis PPAR-γ
and KLF-4 (Figure 1C+D).
Adipose tissue lipolytic enzymes: Both prednisolone 7.5 mg and prednisolone 30 mg
reduced the ATGL expression (Figure 1E), but did not alter HSL protein expression (Figure
1F). Insulin tended to lower HSL phosphorylation (P=0.06), which was not altered by low- or
high-dose prednisolone treatment (Figure 1G).
Adipose tissue adiponectin expression: Both prednisolone 7.5 mg and prednisolone 30
mg reduced the expression of adiponectin (P

Impaired insulin action in adipose tissue may adversely affect both glucose and lipid
metabolism. Indeed, an intact insulin signaling pathway is of pivotal importance for insulinstimulated
glucose uptake through recruitment of the glucose transporter (GLUT)-4 to
the plasma membrane enabling glucose transport into the cell [27]. Although the overall
contribution of glucose uptake by adipose tissue in the postprandial state is thought to be
relatively modest [28], it does contribute to glucose tolerance as was elegantly demonstrated
in adipose tissue-specific glut4-/- mice [29]. These mice developed hyperglycemia and were
characterized by liver and skeletal muscle insulin resistance, most likely due to altered
secretion of adipokines [29]. In our study, prednisolone 30 mg altered the secretion of various
adipokines towards a more diabetogenic profile. Thus, plasma concentrations of adiponectin,
which are generally positively associated with insulin sensitivity, showed a tendency toward
reduced levels following high-dose prednisolone treatment, whereas adiponectin protein
expression in SAT was decreased by both prednisolone dosages. The adipokines resistin
and leptin, which are usually increased in insulin resistant states were both increased by
prednisolone 30 mg. Although increased leptin levels has been shown to inhibit food intake
in the CNS, leptin also acts as pro-inflammatory factor in adipose tissue by promoting the
release of pro-inflammatory adipokines. Similarly, resistin has also been demonstrated to
promote circulating pro-inflammatory factors derived from adipose tissue [30].
In addition to promoting glucose uptake in adipose tissue, insulin is a key hormone in the
regulation of lipid metabolism, amongst others by inhibiting adipose tissue lipolysis and
thus decreasing plasma NEFA levels [31]. In contrast to the effects of insulin, GCs increase
fasting lipolysis rates when administered acutely [16, 17]. During more prolonged GC
exposure, however, this lipolytic effect is no longer observed most likely due to compensatory
hyperinsulinemia [18-21]. In our recent study, high-dose prednisolone treatment even
lowered fasting lipolysis [21]. In line with these findings, we observed decreased fasting ATGL
expression, the rate-limiting enzyme in adipose tissue lipolysis, during both low- and highdose
prednisolone treatments, while fasting HSL expression was unchanged.
As stated previously, GCs impaired the suppressive effects of insulin on whole-body lipolysis
[21]. Since both ATGL expression and phosphorylation of HSL during insulin infusion were
unaltered by prednisolone treatment, it is unclear through which mechanisms this effect
occurs. Possibly, the inhibiting effects of insulin on protein kinase A and perilipin expression, a
lipid droplet-associating protein, are reduced by prednisolone treatment, thereby promoting
triglyceride lipolysis [13]. An alternative hypothesis is that the increment in whole-body
lipolysis is mostly derived from VAT, from which no biopsy could be obtained in the present
225
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Chapter 11 Glucocorticoids, adipose tissue & adipokines
study design. Importantly, increased lipolysis levels impair glucose homeostasis by reducing
muscle and liver insulin sensitivity [22].
In addition to changing adipose tissue function, GCs are known to alter adipose tissue
distribution during more prolonged treatment by augmenting central fat deposition with
wasting of peripheral fat depots. Although two weeks is a relatively short treatment period,
and no significant alterations were shown in body fat distribution in a previous study [21], we
observed decreased markers of adipogenesis in SAT, compatible with the observed phenotype
after more prolonged GC exposure. The GC-induced reduction in SAT with concomitant
increase in VAT is clinically relevant since VAT is well known to be associated with an untoward
metabolic profile as opposed to SAT and is associated with increased cardiovascular risk [12].
Importantly, a number of effects were already observed during treatment with prednisolone
7.5 mg daily, a low dose commonly used in clinical practice for prolonged periods. Many
patients treated with GCs in clinical practice suffer from systemic inflammatory conditions,
which may also be associated with impaired adipose tissue function [32]. Therefore the
effects of GC treatment on adipose tissue function may be more complex since GCs reduce
systemic inflammation and require further investigation.
In conclusion, this study demonstrates for the first time that prednisolone treatment impaired
SAT function by impairing insulin signaling and reducing adipogenesis in vivo in healthy
humans. In addition, plasma levels of adiponectin, leptin and resistin were changed to a more
diabetogenic profile. Further studies in humans are warranted to study the mechanisms that
may underly the effects of GCs on adipose tissue function.
Funding: This paper was written within the framework of project T1-106 of the Dutch Top
Institute Pharma.
226

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Chapter 11
11

Chapter 12
Angiopoietin-Like Protein 4 is Differentially
Regulated by Glucocorticoids and Insulin in
vitro and in vivo in Healthy Humans
D.H. van Raalte*, M. Brands*, M.J. Serlie, K.Mudde, R.Stienstra,
H.P. Sauerwein, S. Kersten and M. Diamant
*Both authors contributed equally
Expl Clin Endocrinol Diabetes. 2012, in press

Chapter 12 Angptl4 in glucocorticoid-induced diabetogenic effects
ABSTRACT
objective: Angiopoietin-like protein 4 (Angptl4) is a circulating inhibitor of plasma
triglyceride clearance via inhibition of lipoprotein lipase. The aim of the present study was
to examine the regulation of Angptl4 by glucocorticoids and insulin in vivo in humans, since
these factors regulate Angptl4 expression in vitro.
Research Design and Methods: In a randomized, placebo-controlled, double-blind, doseresponse
intervention study, 32 healthy males (age: 22±3 years; BMI 22.4±1.7 kg m-2 ) were
allocated to prednisolone 30 mg once daily (n=12), prednisolone 7.5 mg once daily (n=12),
or placebo (n=8) for two weeks. Angptl4 levels and lipid metabolism were measured before
and at 2 weeks of treatment, in the fasted state and during a two-step hyperinsulinemic
clamp. Additionally, human hepatoma cells were treated with dexamethasone and/or
insulin.
Results: Compared to placebo, prednisolone treatment tended to lower fasting Angptl4
levels (P=0.073), raised fasting insulin levels (P=0.0004) and decreased fasting nonesterified
fatty acid concentrations (NEFA) (P=0.017). Insulin infusion reduced Angptl4 levels by 6%
(plasma insulin~200 pmol/l, P=0.006) and 22% (plasma insulin~600 pmol/l, P

The regulation of circulating lipids is tightly controlled. An important endocrine factor involved
in the regulation of circulating lipid levels is angiopoietin-like protein (Angptl) 4. Angptl4 is a
50kD protein secreted by a variety of tissues including liver, white adipose tissue and skeletal
muscle [1-3]. Studies in various animal models have shown that Angptl4 increases plasma
triglyceride levels [4-6], by inhibiting plasma lipoprotein lipase (LPL) activity [7]. Accordingly,
carriers of a rare dysfunctional variant of the Angptl4 gene exhibit low plasma TG levels [8].
Besides raising plasma TG, Angptl4 increases plasma non-esterified fatty acids (NEFA) by
stimulating white adipose tissue lipolysis [6, 9].
Expression of Angptl4 is stimulated by fatty acids in several mouse tissues via activation of
the peroxisome-proliferator activated receptors α, γ and δ [3, 10-12]. Consistent with a role of
PPARs in regulating Angptl4 production in humans, synthetic agonists of PPARα and PPARγ
raise plasma Angptl4 levels in human subjects [3, 4, 13].
Insulin, on the other hand, may suppress Angptl4 levels. In 3T3-L1 adipocytes, insulin
treatment reduced Angptl4 expression, an effect that was attenuated by pharmacological
inhibition of the insulin-signaling cascade and by tumor necrosis factor-α treatment [14].
Recently, a possible role for glucocorticoids (GCs) in the regulation of Angptl4 expression was
suggested. Dexamethasone treatment was found to increase Angptl4 mRNA levels in primary
hepatocytes and adipocytes, and in mouse liver and white adipose tissue. A glucocorticoid
response element (GRE) was identified in the 3′-untranslated region of the Angptl4 gene,
suggesting that Angptl4 represents a direct GC target gene [15]. Interestingly, Angptl4-/- mice
were less susceptible to hypertriglyceridemia and hepatic steatosis following dexamethasone
treatment [15]. Whether GCs are important regulators of Angptl4 levels in humans is presently
unclear.
More recently, we conducted a study in which we observed that short term treatment with both
low- and high-dose prednisolone in healthy men affected glucose metabolism and lipolysis,
resulting in impaired insulin-stimulated suppression of endogenous glucose production
and whole-body lipolysis [16]. Given the recent discovery of the involvement of Angptl4 in
glucocorticoid-induced metabolic deregulation, we additionally determined Angptl4 plasma
concentrations in these individuals before and after prednisolone treatment in samples that
were collected during the study. We hypothesized that prednisolone would increase Angptl4
plasma concentrations and that changes in Angptl4 concentrations would be related to the
observed prednisolone-induced alterations in lipid metabolism. Our experimental design
also permitted to assess the effect of hyperinsulinemia on plasma Angptl4 concentrations.
We hypothesized that insulin infusion would decrease Angptl4 concentrations and that
233
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12

Chapter 12 Angptl4 in glucocorticoid-induced diabetogenic effects
prednisolone would attenuate this effect by inducing insulin resistance. The study was
supported by in vitro experiments using human hepatoma cells.
RESEARCH DESIGN AND METHoDS
Clinical study
Participants: Thirty-two healthy normoglycemic males were included, following a screening
visit during which history was taken and a physical examination, screening blood tests and
an oral glucose tolerance test were performed. Inclusion criteria were: age between 18-35
years and body mass index (BMI) ≤ 25 kg/m2 . Exclusion criteria were any previous or current
illness, use of any medication, first-degree relative with type 2 diabetes, smoking, shift work, a
history of glucocorticoid use, excessive sport activities (i.e. more often than two times/week)
and recent changes in weight. Participants were recruited via local advertisement and mostly
concerned students.
Study design: Details on study design were reported previously [16]. Briefly, in a randomized,
placebo-controlled, double-blind, dose-response intervention trial, 32 healthy volunteers
were allocated to a treatment with prednisolone 30 mg once daily (n=12), prednisolone 7.5
mg once daily (n=12) or placebo (n=8) for a period of two weeks using block randomization
as carried out by the department of Experimental Pharmacology of the VU University
Medical Center. Before and at day 14 of treatment, Angptl4 levels, glucose metabolism and
total triglyceride lipolysis were measured in the fasted state and during a hyperinsulinemiceuglycemic
clamp using stable isotopes. The study protocol was approved by a local ethics
committee and was performed in accordance to the principles described in the declaration
of Helsinki. All participating subjects gave their written informed consent prior to screening.
Experimental protocol: Design of the hyperinsulinemic-euglycemic clamp with stable
isotopes is described in detail elsewhere [16]. In short, following an overnight fast and
following measurement of background enrichments, a primed, continuous infusion of isotopes
was started: [6,6-2H ]glucose (prime: 11 mmol/kg; continuous: 0.11 mmol/kg.min) and
2
[1,1,2,3,3,-2H ]glycerol (prime: 1.6 mmol/kg; continuous: 0.11 mmol/kg.min) and continued
5
until the end of the clamp. After a 2-h equilibrium period, blood samples were drawn for
determination of basal glucose concentrations, isotope enrichments, glucoregulatory
hormones, NEFA and Angptl4. Thereafter, a 2-step hyperinsulinemic-euglycemic clamp was
started: step 1 included an infusion of insulin at a rate of 20 mU/m2 .min for 2 hours to assess
hepatic insulin sensitivity; during step 2, insulin was infused at a rate of 60 mU/m2 .min for
234

2 hours to assess peripheral insulin sensitivity. Plasma glucose was kept at 5 mmol/l by a
variable infusion of glucose 20% solution enriched with [6,6-2H ]glucose. After 2 hours of each
2
insulin infusion, blood samples were drawn for the measurement of glucose concentrations,
isotopic enrichments, glucoregulatory hormones, NEFA and Angptl4 (Figure 1).
Figure 1. Design of the two-step hyperinsulinemic clamp using stable isotopes.
Laboratory Analyses: Angptl4 was measured by ELISA as described previously [3]. Briefly,
96-well plates were coated with anti-human Angptl4 polyclonal goat IgG antibody (AF3485,
R&D Systems) and incubated overnight at 4°C. Plates were washed extensively between each
step. After blocking, 100 μl of 20-fold diluted human plasma was applied, followed by 2 h
incubation at room temperature. A standard curve was prepared using recombinant human
Angptl4 (3485-AN, R&D Systems) at 0.3 to 2.1 ng/well. Next, 100 μl of diluted biotinylated
anti-human Angptl4 polyclonal goat IgG antibody (BAF3485, R&D Systems) was added for
2 h, followed by addition of streptavidin-conjugated horseradish peroxidase for 20 min, and
tetramethyl benzidine substrate reagent for 6 min. The reaction was stopped by addition of
50 μl of 10% H2SO4, and the absorbance was measured at 450 nm. The intra-assay coefficient
of variation (CV) for the assay was determined at 7%. Plasma glucose concentrations were
measured with the glucose oxidase method using a Biosen C-line plus glucose analyzer (EKF
Diagnostics, Barleben/Magedeburg, Germany). Insulin was determined on an Immulite
2000 system (Diagnostic Products Corporation, Los Angeles, CA) with a chemiluminescent
immunometric assay. Plasma NEFA concentrations were measured with an enzymatic
colorimetric method (NEFA-C test kit; Wako Chemicals GmbH, Neuss, Germany). [6,6-2H ] 2
Glucose and [1,1,2,3,3,-2H ]glycerol enrichment were measured with gas chromatography-
5
mass spectrometry (GC-MS) as described in detail elsewhere [17, 18].
Calculations: Endogenous glucose production (EGP) and the peripheral uptake of glucose
(Rate of disappearance, Rd) were calculated using modified versions of the Steele equations
for the non-steady state [19, 20]. Lipolysis (glycerol turnover) was computed using formulas
for steady state kinetics adapted for stable isotopes [17].
235
Chapter 12
12

Chapter 12 Angptl4 in glucocorticoid-induced diabetogenic effects
Statistical analyses: Data are presented as mean values ± standard deviation (SD), or
as median (interquartile range) in case of skewed distribution. To assess the effects of
prednisolone treatment on various parameters, absolute changes from baseline (on treatment
value minus pre treatment value) were computed for each treatment arm. Significance was
tested using the Kruskal-Wallis test. Non-parametric analysis was chosen due to the relatively
small number of participants and the uneven group sizes (P1 value in Table 2). Only in case
of a significant finding, prednisolone 7.5 mg and prednisolone 30 mg were compared against
placebo by posthoc testing, using the Mann-Whitney U test (P2 and P3 respectively in Table
2). To correct for multiple testing, Bonferroni correction was applied. The effects of insulin
infusion on plasma Angptl4 levels prior to PRED treatment was tested with the paired t-test.
Correlations between Angptl4 levels and parameters of glucose and lipid metabolism were
calculated by regression analysis adjusting for age, BMI [21] and insulin concentrations.
Since the measurement of Angptl4 was not a primary endpoint of the original study, no formal
power calculation was performed for this parameter. However, our sample size determined
for the primary endpoints of the original protocol would provide a power of 82% with an
estimated PRED-induced increase of 3 ng/mL in plasma Angptl4 plasma concentrations,
a standard deviation of 30% and alpha set at 0.05, which we considered sufficient. The
estimated changes were based on other interventions that altered Angptl4 concentrations,
including fenofibrate treatment and caloric restriction [3]. All statistical analyses were run
on SPSS 18.0 for Mac OS X (SPSS, Chicago, IL, USA). All statistical tests were conducted at a
significance level of 0.05 (two-sided).
In vitro study
Cell culture: HepG2 cells were cultured in DMEM containing 10% fetal calf serum, 100U/
mL penicillin and 100 µg/mL streptomycin. Cells were plated in 12-well plates at density
of 250000 cells per well. Medium was switched to DMEM without serum one hour prior to
hormone incubations. Cells were treated with dexamethasone (Sigma, Zwijndrecht, NL) and/
or insulin at the indicated concentrations for 24 or 48 hours. Both Angptl4 mRNA expression
and protein levels in the medium were measured. Differences were evaluated using Student’s
t-test.
RESULTS
Baseline characteristics and anthropometrics: Baseline characteristics of the participants
are presented in Table 1. No significant differences were observed between the groups at
baseline. BMI was not changed by either treatment regimen (Table 2) [16].
236

Table 1. Subject characteristics at inclusion.
Placebo Prednisolone
7.5 mg 30 mg P
N 8 12 12
Age (y) 22±3 22±2 22±3 0.740
Weight (kg) 76.9±8.7 74.8±7.2 77.0±5.4 0.250
Height (cm) 184±5.6 183±7.6 186±4 0.641
BMI (kg m-2 ) 22.7±2.1 22.2±1.9 22.4±1.2 0.906
Lean body mass (%) 78.6±7.9 82.1±3.9 80.7±2.5 0.731
Fat mass (%) 16.9±4.7 15.9±2.8 14.8±3.5 0.531
Systolic blood pressure (mmHg) 118±11 125±9 127±9 0.172
Diastolic blood pressure (mmHg) 74±14 72±14 79±13 0.392
Fasting plasma glucose (mmol/l) 5.1±0.3 5.0±0.2 5.0±0.4 0.899
2-hr glucose OGTT (mmol/l) 4.2±0.8 4.7±0.9 4.2±1.1 0.407
Mean±SD are provided. Abbreviations: BMI: body mass index; 2-hr glucose OGTT: plasma glucose
concentrations 2 h after ingestion of 75 g glucose during an oral glucose tolerance test.
Figure 2. Plasma Angptl4 levels before and at 2 weeks treatment with study medication. Angptl4 levels
in the fasted state are depicted for placebo (A), prednisolone 7.5 mg daily (B) and prednisolone 30 mg
daily (C). Median changes from baseline are provided in D (Box-and-Whisker plots; min-max). Prior
to treatment, insulin infusion dose-dependently decreased Angptl4 levels (E). Fasting values were set
at 100% (white bar), Angptl4 levels during step 1 of the clamp (200 pmol/l) are depicted in the grey
bar as % of baseline, and Angptl4 levels during step 2 of the clamp (600 pmol/l) are depicted in the
black bar as % of baseline. The effects of prednisolone treatment on Angptl4 concentrations during
insulin infusion are shown in panel F and G. Panel F shows Angptl4 concentrations during low-dose
insulin infusion, expressed as percentage change from fasting concentrations. White bar represents pretreatment
values and black bar post-treatment values. Fasting values were set at 100%. Panel G shows
Angptl4 concentrations during high-dose insulin infusion, expressed as percentage change from fasting
concentrations. White bar represents pre-treatment values and black bar post-treatment values. Fasting
values were set at 100%. Median and SD are provided in panel (E-G). † p=0.073,*p

Chapter 12 Angptl4 in glucocorticoid-induced diabetogenic effects
Angptl4 levels: Prior to treatment, median Angptl4 levels were 10.8 (6.4-14.0) ng/mL. As
observed previously [3], Angptl4 levels showed considerable inter-individual variability
(Figure 2A-C). At baseline, plasma Angptl4 levels were not related to fasting glucose, insulin,
NEFA and triglyceride levels (data not shown). High-dose prednisolone treatment tended to
reduce plasma Angptl4 levels in the fasted state (Figure 2A-D) (P=0.073). Insulin infusion
dose-dependently decreased plasma Angptl4 levels to 94% and 78% of fasting concentrations
at insulin levels of 171 (153-183) pmol/l and 534 (485-564) pmol/l, respectively (Figure
2E). During low-dose insulin infusion (step 1 of the clamp), Angptl4 levels, expressed as %
of fasting values, were not altered by either dosages of prednisolone (Figure 2F). However,
during high-dose insulin infusion (step 2 of the clamp), insulin-mediated suppression of
Angptl4 levels was blunted by prednisolone treatment (P=0.032) (Figure 2G). This effect
seemed driven by the prednisolone 30 mg arm, but did not reach statistical significance in
posthoc testing (data not shown). In supplemental Table 1, the absolute values of plasma
Angptl4 levels during insulin infusion are depicted.
Prednisolone effects on glucose and lipid metabolism: As published previously [16],
prednisolone treatment dose-dependently impaired glucose and lipid metabolism primarily
by interfering with the metabolic actions of insulin. As such, insulin-stimulated suppression
of EGP and lipolysis and insulin-stimulated glucose disposal were decreased in a dosedependent
manner. In addition, prednisolone 30 mg, but not prednisolone 7.5 mg, increased
fasting glucose, insulin and triglyceride levels (Table 2).
Associations between prednisolone-induced metabolic changes and Angptl4 levels:
Since NEFA are potent activators of Angptl4 expression and plasma NEFAs were shown to
positively correlate with plasma Angptl4 levels in large cohort studies [22], the reduction in
plasma Angptl4 levels by prednisolone and insulin may be a consequence of changes in plasma
NEFA levels, which were lowered by prednisolone and insulin at baseline and during the
clamp, respectively. Indeed, prednisolone-induced changes in fasting NEFA levels correlated
positively with prednisolone-induced alterations in fasting Angptl4 levels (β=0.444; P=0.011;
R2 =0.329). Changes in plasma NEFA levels by insulin infusion, however, were not related to
alterations in Angptl4 levels. Prednisolone-induced changes in plasma Angptl4 levels in the
fasted state were not related to changes in fasting glucose, insulin or triglyceride levels (data
not shown). Furthermore, prednisolone-induced changes in Angptl4 levels during low-and
high-dose insulin infusion were not associated with changes in whole-body lipolysis and Rd
(data not shown).
238

Table 2. Parameters of glucose metabolism and lipolysis before and during treatment with prednisolone 30 mg once daily, prednisolone 7.5 mg once
daily or placebo for a period of two weeks.
Placebo Prednisolone 7.5 mg Prednisolone 30 mg P1 P2 P3
Baseline On-treatment Baseline On-treatment Baseline On-treatment
BMI (kg m-2 ) 23.0±2.3 22.8±2.2 22.3±2.0 22.3±1.8 22.6±1.1 22.6±1.1 0.449 NA NA
FPG (mmol/l) 5.0±0.2 4.8±0.3 4.9±0.2 5.1±0.3 4.7±0.2 5.4±0.3 < 0.0001 0.06 < 0.001
FPI (pmol/l) 30 (16-51) 28 (

Chapter 12 Angptl4 in glucocorticoid-induced diabetogenic effects
Figure 3. Insulin and dexamethasone differentially regulate Angptl4 mRNA and secretion in hepatocytes
in vitro. Human HepG2 hepatoma cells were treated with indicated concentrations of insulin (A)
or dexamethasone (B). Relative changes in Angptl4 mRNA expression (left panel) and the absolute
concentration of Angptl4 in the culture medium (right panel) were measured. C) Human HepG2 hepatoma
cells were treated with indicated concentrations of dexamethasone in the presence or absence of insulin.
Means and SD are provided. *p

is under control of GCs and insulin in healthy humans. Accordingly, it can be hypothesized that
specific metabolic actions of GCs and insulin may be partially accounted for via regulation of
Angptl4.
Recently, it was shown that the Angptl4 gene contains a GRE in its 3’-untranslated region, and
is a direct transcriptional target of GCs in cultured hepatocytes and adipocytes, as well as in
mouse liver and white adipose tissue [15]. Upregulation of Angptl4 mRNA by GCs seemingly
conflicts with data showing that cortisol markedly stimulates LPL activity in human adipose
tissue in vitro [23]. Interestingly, Angptl4-/- mice were more resistant to dexamethasoneinduced
hypertriglyceridemia compared to their wild type littermates, leading the authors
to suggest a role for Angptl4 in the metabolic effects of dexamethasone treatment [15]. Using
human HepG2 cells, we confirmed that dexamethasone increases Angptl4 mRNA expression
as well as protein secretion.
Surprisingly, in the in vivo part of this study, rather than raising fasting Angptl4 levels,
high-dose prednisolone showed a tendency towards lowering Angptl4 levels. It is unlikely
that this reduction is consequential to a direct effect of prednisolone treatment. High-dose
prednisolone treatment induced several other metabolic effects that could have modulated
fasting Angptl4 concentrations. Importantly, prednisolone 30 mg daily induced insulin
resistance, fasting hyperinsulinemia and decreased fasting NEFA concentrations. Since
plasma NEFA concentrations augment Angptl4 concentrations both in vitro and in vivo [1-3],
decreased fasting NEFA concentrations could have therefore led to a net reduction of plasma
Angptl4 concentrations. In support of this notion, in regression analysis, prednisoloneinduced
changes in NEFA independently predicted concomitant changes in Angptl4,
whereas prednisolone treatment was not a significant predictor in this model. We show
that insulin decreases Angptl4 concentrations and the observed increase in fasting insulin
could also have contributed to the net reduction in fasting Angptl4 concentrations following
glucocorticoid treatment. In addition, other, yet unknown, regulators that were not measured
in the present study could have modulated fasting Angptl4 concentrations upon treatment
with prednisolone. It should be emphasized that many aspects of the regulation of Angptl4
concentrations in humans are presently unclear.
Furthermore, we did not find a significant relationship between prednisolone-induced
changes in Angptl4 levels and changes in adipose tissue lipolysis and triglyceride levels, both
in the fasting state and during insulin infusion, suggesting that Angptl4 may not play a role in
the metabolic effects of glucocorticoid treatment.
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12

Chapter 12 Angptl4 in glucocorticoid-induced diabetogenic effects
In addition to regulation by NEFA and GCs, Angptl4 is governed by insulin. In our study,
insulin infusion dose-dependently reduced plasma Angptl4 levels. Since insulin infusion
also decreased NEFA levels, it is impossible to conclude whether the decrease in Angptl4 is
due to a direct effect of insulin on Angptl4 levels and/or an indirect effect through reduction
of circulating NEFA. Again using HepG2 cells, it was shown that insulin dose-dependently
reduced expression and secretion of Angptl4, pointing to a direct effect of insulin. Previously,
insulin was already shown to inhibit Angptl4 expression in 3T3-L1 adipocytes [14]. The
mechanism by which insulin regulates Angptl4 remains presently unclear.
Suppression of Angptl4 production in adipocytes and hepatocytes, which likely represent the
primary sites of Angptl4 production in humans [13], may in part explain the stimulatory effect
of insulin on adipose LPL and on postprandial clearance of triglyceride-rich lipoproteins [24].
Conversely, during fasting, the lack of insulin-mediated suppression of Angptl4 production
may explain the decreased LPL activity [25]. Studies are underway using Angptl4-/- mice to
verify the role of Angptl4 in regulation of LPL activity in the feeding-fasting cycle.
The attenuating effect of high-dose prednisolone on the insulin-mediated decrease in Angptl4
levels suggests an interaction between insulin and GCs in regulation of Angptl4 in vivo.
Similarly, in HepG2 cells, dexamethasone prevented the reduction in Angptl4 secretion by
insulin. Considering the low NEFA levels during insulin infusion and given the fact that the
interaction was also observed in vitro, the attenuating effect of GCs is likely independent of
changes in NEFA levels. Conceivably, GCs may interfere with insulin signaling and thereby
reduce the effects of insulin. However, we did not observe an association between changes
in Angptl4 levels and changes in insulin sensitivity, including EGP and Rd, suggesting
independence of changes in insulin signaling.
We conclude that in addition to regulation by NEFAs, plasma Angptl4 is regulated by GCs
and insulin, albeit in opposing ways. Whereas GCs enhance Angptl4 expression and secretion,
both are reduced by insulin. Treatment with GCs attenuated the insulin-mediated decrease in
Angptl4. Determination of the direct independent effect of these hormones on Angptl4 levels
in humans is complicated by their interdependence. Finally, our data suggest that Angtpl4
may not be an important regulator in GC-mediated changes in lipid metabolism in humans
but may contribute to insulin-mediated changes in lipid metabolism.
Funding: This paper was written within the framework of project T1-106 of the Dutch Top
Institute Pharma.
242

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2002;277(14):11927-32.
244

SUPPLEMENTAL MATERIALS
Supplemental Table 1. Effect of study medication on absolute plasma Angptl4 levels in the fasted state and during insulin infusion.
Placebo prednisolone 7.5 mg prednisolone 30 mg
Baseline On-treatment Baseline On-treatment Baseline On-treatment
9.5 (8.0-23.6) 11.8 (8.0-18.5) 9.0 (6.9-13.2) 10.8 (6.3-13.4) 8.6 (5.4-19.8) 6.6 (5.9-16.0)
9.1 (7.5-19.2) 10.3 (8.2-17.1) 8.7 (7.0-12.6) 12.0 (7.9-13.5) 9.3 (4.9-17.9) 6.6 (5.1-17.5)
7.8 (5.9-16.9) 8.7 (7.2-16.1) 7.3 (5.8-11.1) 9.2 (7.1-11.6) 5.9 (3.8-14.2) 7.4 (6.2-18.5)
Angptl4 fasting
(ng/mL)
Angptl4 clamp 1
(ng/mL)
Angptl4 clamp 2
(ng/mL)
245
Chapter 12
12

PART
Metabolic Effects of Glucocorticoids and
Interaction with Systemic Inflammation
IV

Chapter 13
Metabolic Effects of High-Dose Prednisolone
Treatment In Early Rheumatoid Arthritis
Balance Between Diabetogenic Effects and Inflammation
Reduction
D. den Uyl, D.H. van Raalte, M.T. Nurmohamed, W.F. Lems, J.W.J.
Bijlsma, J.N. Hoes, B.A.C. Dijkmans and M. Diamant
Arthritis Rheum. 2012;64(3):936-46

Chapter 13 Glucocorticoids in acute rheumatoid arthritis
ABSTRACT
objective: To investigate the dose-related effects of glucocorticoid (GC) treatment on
glucose tolerance, beta-cell function and insulin sensitivity in patients with early active
rheumatoid arthritis (RA).
Methods: A randomized controlled, single-blind trial was conducted in 41 patients with
early active RA. At the beginning of the trial patients had not been treated for their RA and
were randomized to receive prednisolone 60mg daily or prednisolone 30mg daily. Before
and at the end of 1 week of treatment, a frequently-sampled oral glucose tolerance test
was performed. The area under glucose curve (AUC ) was calculated. In addition, beta-cell
G
function and insulin sensitivity parameters were computed.
Results. Patients (age 55.5 ± 14.8 years and 54.2 ± 12.6 years in the prednisolone 60mg
daily and prednisolone 30mg daily groups, respectively; body mass index 24.5 ± 4.1 kg/m2 and 25.4 ± 4.2 kg/m2 , respectively) had active disease at baseline (Disease Activity Score in
44 joints 4.1 ± 0.7 and 4.0 ± 0.8, respectively; C-reactive protein (CRP): 14 (6-34) mg/liter
and 19 (3-39) mg/liter respectively). In addition, 56% of the patients had impaired glucose
tolerance, and 7% were found to have previously unrecognized type 2 diabetes mellitus
(T2DM). Associations of the AUC with erythrocyte sedimentation rate (β=2.430 [95%
G
confidence interval 0.179-4.681], P=0.04) and with CRP (β=2.358 [95% confidence interval
0.210-4.506], P=0.03) were demonstrated. Treatment with prednisolone at both dosages
reduced CRP levels significantly. The incidence of T2DM increased to 24% (P

INTRoDUCTIoN
Patients with rheumatoid arthritis (RA) are at increased risk of developing cardiovascular
disease (CVD) [1]. Defects in glucose metabolism may contribute to this increased CVD risk [2].
Two main determinants of glucose metabolism are insulin sensitivity and beta-cell function.
Insulin sensitivity refers to the ability of insulin to reduce plasma glucose by stimulating
its uptake in insulin-sensitive tissues and by suppressing its production in the liver. Betacell
function is commonly referred to as the ability of the pancreatic beta cell to secrete the
appropriate amount of insulin to maintain euglycemia. These processes are closely related: if
insulin sensitivity decreases, a healthy beta cell will respond by augmenting insulin secretion,
to prevent hyperglycemia [3]. In RA patients, impaired insulin sensitivity, as assessed in the
fasting state using the homeostatic model assessment of insulin resistance (HOMA-IR), has
been reported, and this was associated with measures of disease activity and plasma levels
of inflammatory proteins, including C-reactive protein (CRP), tumor necrosis factor α and
interleukin-6 [4, 5].
Glucocorticoids (GCs) are commonly used to treat RA [6]. However, the metabolic effects of GCs
in RA patients are uncertain. On the one hand, short-term clinical trials in healthy individuals
have shown that GCs deteriorate glucose metabolism by reducing hepatic and peripheral
insulin sensitivity [7] and by impairing beta-cell function [8]. In population-based studies,
GC use was associated, in a cumulative dose-dependent manner, with incident diabetes [9]
and need for blood-glucose lowering treatment [10]. In cross-sectional studies in RA patients,
GC exposure was related to impaired fasting insulin sensitivity [11], and tended to predict
development of type 2 diabetes (T2DM) [12, 13]. On the other hand, the use of GCs in chronic
inflammatory conditions may improve glucose tolerance via anti-inflammatory and disease
modifying effects, as has been demonstrated in a number of short-term studies [14, 15].
Previous studies of the effects of GC treatment on parameters of beta-cell function and insulin
sensitivity in RA patients relied solely on measures obtained in the fasting state (i.e., HOMA
of beta-cell function (HOMA-B) and HOMA-IR, respectively) and yielded conflicting results
(for review, see [2]). As mentioned above, since insulin sensitivity and insulin secretion are
interrelated, the use of these HOMA formulas, both of which are based on fasting plasma
glucose and insulin levels, may not be appropriate for discerning changes in insulin sensitivity
and changes in insulin secretion [16]. In addition, fasting parameters provide no information
of the stimulated state. From dynamic tests, such as the frequently-sampled oral glucose
tolerance test (OGTT), indices of glucose-stimulated beta-cell function and postload insulin
sensitivity may be calculated, providing more detailed information on glucose metabolism.
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Chapter 13 Glucocorticoids in acute rheumatoid arthritis
Therefore, in the present randomized clinical study, we used frequently-sampled OGTTs
to assess the effects of 2 different doses prednisolone, i.e., 60 mg daily and 30 mg daily,
administered for 1 week, on glucose tolerance, beta-cell function and insulin sensitivity in
recently diagnosed untreated RA patients.
RESEARCH DESIGN AND METHoDS
Participants: Patients with early RA were recruited between March 2008 and july 2010 in
three clinics in Amsterdam and Hoorn, the Netherlands. Inclusion criteria were: RA (according
to the revised American College of Rheumatology classification criteria [17]), age ≥ 18 years,
disease duration < 2 years and currently active disease. The latter was defined as the presence
of 6 or more painful and swollen joints, an erythrocyte sedimentation rate (ESR) of ≥ 28
mm/h and/or a global health score of ≥ 20mm (on a visual analogue scale 0-100). Exclusion
criteria were: previous treatment with GCs, DMARDs (other than hydroxychloroquine (HCQ)),
known T2DM, heart failure (i.e. New York Heart Association class 3 and -4 [18]), uncontrolled
hypertension, ALAT/ASAT > 3 times normal values, reduced renal function (serum creatinine
> 150 micromol), contra-indications for GC use, indications of probable tuberculosis and
pregnancy or wish to conceive during the study. Concomitant treatment with nonsteroidal
anti-inflammatory drugs (NSAIDs) during the study was permitted. The study was approved
by an independent ethics committee and was conducted in accordance with the Declaration of
Helsinki. All participants provided written informed consent before participation.
Study design: This study was a randomized, active-controlled, single blind, multicenter trial
and a substudy of a larger study comparing two treatment schedules for the treatment of
early RA (www.controlled-trials.com; ISRCTN55552928). Following assessment of eligibility,
participants were randomized to receive either prednisolone 60 mg once daily (n=21)
or prednisolone 30mg once daily (n=20). On day 0 and day 7, a frequently-sampled OGTT
was performed to measure glucose tolerance, parameters of beta-cell function and insulin
sensitivity. In addition, CRP levels were determined on both study days. On day 7, study
medication was taken 2 hours before the start of the OGTT [19].
Screening visit: At baseline, demographics were recorded and body weight, body mass
index (BMI), waist circumference and blood pressure were determined. Disease activity was
measured by the 44-joint Disease Activity Score (DAS44 [20]). In addition, screening blood
tests were performed, including measurement of ESR, CRP, rheumatoid factor (RF) and anticyclic
citrullinated peptide (anti-CCP).
252

oral glucose tolerance test: The patients visited the research unit at 08.00 AM following an
overnight fast of 10 hours. To obtain venous blood samples, a cannula was inserted in a vein
within the fold of the elbow or in a wrist vein. Patients ingested 75 gram glucose solution,
following which blood was withdrawn for the determination of plasma glucose, insulin and
C-peptide levels at time points 0, 10, 20, 30, 60, 90 and 120 min. C-peptide is secreted by the
pancreatic beta-cell at an equimolar rate with insulin, but is cleared from the circulation by
urinary excretion, whereas insulin clearance is also strongly determined by the liver. Since
insulin clearance may vary considerably between subjects [21], plasma C-peptide levels may
provide more accurate information on beta-cell function than determination of peripheral
insulin levels.
Laboratory measurements: Plasma glucose concentrations were determined using a
hexokinase method (Gluco-quant, Roche Diagnostics). Insulin and C-peptide levels were
determined using an immunometric assay (ADVIA Centaur immunoassay system, Siemens
Medical Solutions Diagnostics). CRP concentration was determined by Immuno turbidometric
method (Roche diagnostics). Anti-CCP levels were determined by second-generation anti-CCP
enzyme-linked immunosorbent assay (ELISA;Axis Shield). The anti-CCP test was performed
according to manufactures instructions with a cut-off level for positivity set at 5 Arbitrary
Units/ml. IgM-RF was measured with an in-house ELISA. The cut-of level was set at 30 IU,
determined on the basis of receiver operator characteristics curves as described previously
[22].
Data analysis: Glucose tolerance state was assessed according to the American Diabetes
Association guidelines [23]. Normal glucose metabolism was defined as fasting plasma
glucose < 5.6 mmol/L and a glucose level < 7.8 mmol/L 2 h following a 75 g OGTT. Impaired
glucose metabolism was defined as FPG between 5.6 – 6.9 mmol/L, or a 2 h OGTT glucose value
between 7.8 mmol/L and 11.0 mmol/L. T2DM was diagnosed with a FPG > 6.9 mmol/L or a
2 h OGTT glucose value >11.0 mmol/L [23]. Area under the 2 h glucose (AUC ), insulin (AUC )
G I
and C-peptide (AUC ) curves were determined by the trapezoidal rule. Insulin sensitivity
CP
during the OGTT was estimated by oral glucose insulin sensitivity (OGIS) index [24] and by
homeostatic model assessment (HOMA-IR) in the fasted state [25]. We calculated empirical
measures of beta-cell function, including total secretion C-peptide secretion divided by
glucose levels (AUC /AUC ratio) and the insulinogenic index (IGI), i.e., (insulin -insulin )/
CP G t=30 t=0
(gluc -gluc ), a marker for early phase insulin secretion. Fasting beta-cell function was
t=30 t=0
measured by HOMA-B [25].
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13

Chapter 13 Glucocorticoids in acute rheumatoid arthritis
Statistical analysis: Data are presented as mean ± SD or when distribution was skewed, as the
median and interquartile range (IQR). Differences between the two prednisolone dose groups
were analyzed using the independent-samples t-test or non-parametric Mann-Whitney U test,
as appropriate. Within-group changes were tested using the paired-samples t-test or the nonparametric
Wilcoxon’s signed rank test. The effect of treatment on glucose tolerance state
was analyzed by the Chi-square test. Multivariate regression analysis was used to evaluate the
relation between disease characteristics and activity, and metabolic parameters at baseline,
with correction for age, gender and BMI. Similarly, determinants of prednisolone-induced
changes on AUC , beta-cell function and insulin sensitivity were assessed in multivariate
G
linear regression analyses, with correction for age, gender and BMI. Multivariate logistic
regression analyses were used to associate disease characteristics with progression of
glucose state, also with correction for age, gender and BMI. Pooled analysis was performed
since treatment group was not an effect modifier in any of the tested parameters (this was
evaluated by adding treatment group as an interaction term using multivariate regression
analysis, P-value > 0.1). Correlation analyses included calculation of 95% confidence intervals
(%95 CIs). All statistical analyses were run on SPSS for Windows version 15.0 (SPSS, Chicago,
IL, USA). A P < 0.05 was considered statistically significant.
RESULTS
Characteristics of the study patients: Forty-one consecutive subjects with recent-onset
RA were enrolled. Baseline characteristics are shown in Table 1. Patients were middle-aged
and were not obese. The majority was female, and the median disease duration was 21
weeks. Based on the OGTT at screening, T2DM was diagnosed in 3 patients (7%), and 23
patients (56%) were classified as having impaired glucose metabolism. None of the baseline
characteristics differed significantly between the 2 prednisolone dose groups (Table 1).
Inflammation profiles: Patients had active disease as indicated by DAS44, ESR and CRP values
(Table 1). In multivariate analysis, ESR and CRP plasma levels were positively correlated with
AUC (for ESR, β=2.430 [95% CI 0.179-4.681], P=0.04; for CRP, β=2.358 [95% CI 0.210-4.506],
G
P=0.03). Correcting for ethnicity, NSAID or HCQ usage did not alter the associations (data
not shown). During treatment with prednisolone 60 m daily and with 30 mg daily, plasma
CRP levels decreased to a similar extent, i.e. by 84% (to a median of 2.5 (2.0-2.7) mg/liter;
P

Table 1 Baseline characteristics of the RA study population.
Prednisolone 60 mg
(N=21)
Prednisolone 30 mg
(N=20)
Age (years) 55.5 ±14.8 54.2 ±12.6
Female (%) 62 60
Nonwhite ethnicity (N) 2 0
Symptom duration (weeks) 15 (10-30) 24 (12-52)
DAS44 (no dimension) 4.1±0.7 4.0 ±0.8
ESR (mm/hour) 28 (18-63) 21 (12-44)
CRP (mg/liter) 14 (6-34) 19 (3-39)
RF positive (%) 71 50
Anti-CCP positive (%) 81 55
BMI (kg/m 2 ) 24.5 ±4.1 25.4 ±4.2
Waist circumference (cm) 89 ±15 92 ±13
Current smoking (%) 19 25
Impaired glucose tolerance (N;(%)) 12 (57) 11 (55)
Type 2 diabetes mellitus no (%) 3 (14) 0 (0)
Prior HCQ treatment (N) 1 0
Concurrent NSAID treatment (N) 21 19
Data are mean±SD or median (interquartile range). There were no statistically significant differences
between the 2 treatment groups, as assessed by independent t-test, Mann-Whitney U test, or chi-square
test. RA= rheumatoid arthritis; IQR= interquartile range; DAS44= disease activity score 44 joint count;
ESR= erythrocyte sedimentation rate; CRP= C-reactive protein; RF= rheumatoid factor; anti-CCP= anticyclic
citrullinated peptide; BMI= body mass index; HCQ= hydrochloroquine; NSAID= nonsteroidal antiinflammatory
drug.
Figure 1. oral glucose tolerance test results before treatment (squares connected by solid lines)
and on day 7 of treatment with prednisolone 60 mg daily (circles connected by dashed lines). A,
Glucose concentrations. B, Insulin concentrations. C, C-peptide concentrations. No significant changes
were induced by treatment with prednisolone 60 mg/day. Values are the mean ± SD.
255
Chapter 13
13

Chapter 13 Glucocorticoids in acute rheumatoid arthritis
Table 2. Parameters of beta-cell function and insulin sensitivity during the oral glucose tolerance
test, before and after 7 days prednisolone treatment.
Fasting glucose
(mmol/l)
Fasting insulin
(pmol/l)
Fasting C-peptide
(nmol/l)
AUC G
(mmol/L.120min)
AUC I
(nmol/L.120min)
AUC CP
(nmol/L.120min)
IGI
(no dimension)
AUC CP /AUC G
(no dimensions)
OGIS
(ml/min.m 2 )
HOMA-IR
(no dimension)
HOMA-B
(%)
256
Prednisolone 60 mg/day Prednisolone 30 mg/day
Pre- On-treatment P† Pre- On-treatment P†
treatmenttreatment
5.5±0.7 5.3±0.9 NS 5.6±0.5 5.3±0.6 NS
43 (34-69) 53 (37-77) NS 36 (30-113) 68 (44-129) 0.004
0.5 (0.4-0.7) 0.8 (0.7-1.0) 0.001 0.5 (0.4-0.9) 0.9 (0.6-1.1) 0.001
1051±237 1077±341 NS 1050±172 1031±260 NS
48 (35-74) 46 (32-83) NS 50 (28-95) 63 (33-90) NS
304±92 355±141 NS 339±172 389±180 NS
73 (46-161) 95 (37-284) 0.02 95 (58-148) 127 (65-236) 0.04
0.3 (0.2-0.4) 0.3 (0.2-0.5) NS 0.3 (0.2-0.4) 0.4 (0.2-0.5) 0.02
366 ±64.2 365 ±71.4 NS 358±68 363±68 NS
1.0 (0.7-1.5) 1.1 (0.8-1.6) NS 0.8 (0.7-2.5) 1.5 (1.0-2.8) 0.005
84 (60-113) 104 (76-124) NS 75 (61-122) 119 (77-169) 0.001
Data are mean±SD or median (interquartile range). There were no statistically significant differences
between the 2 treatment groups in the degree of change, as assessed by independent t-test or Mann-
Whitney U test. NS= not significant; AUC = area under the C-peptide curve; AUC = area under the
CP G
glucose curve; AUC = area under the insulin curve; IGI= insulinogenic index; OGIS= oral glucose insulin
I
sensitivity index; HOMA-IR= homeostatic model assessment of insulin resistance; HOMA-B= HOMA of
beta-cell function. † By paired-samples t-test or Wilcoxon’s signed rank test.
Glucose profiles and glucose tolerance: Fasting glucose levels were not altered following
prednisolone treatment (Table 2). Similarly, AUC was not changed following treatment with
G
prednisolone at either 60 mg/day or 30 mg/day (Figure 1A and 2A, Table 2); however, at
the individual level, there was a large variation in response to prednisolone treatment. Seven
patients with impaired glucose metabolism at baseline were classified as having T2DM after
prednisolone treatment, resulting in an increase in the number with T2DM from 3 (7%)
to 10 (24%) (P

(4 in the 30 mg/day group and 5 in the 60 mg/day group). The number of patients who were
first classified as having T2DM was equally divided between the 2 groups (data not shown).
Patients were classified as progressor if the glucose state deteriorated with prednisolone
treatment and as nonprogressor if the glucose state remained unaltered or showed
improvement. Characteristics of the progressor and nonprogressor groups are shown in
Table 3. Disease duration was significantly associated with deterioration of the glucose state
(OR=1.068 [95% CI 1.017-1.122], p=0.009).
Table 3. Disease and anthropometric characteristics of glucometabolic state progressors and
nonprogressors.
Progressors
(n=13)
Nonprogressors
(n=28)
Age (years) 57 ±10.8 53.8 ±14.8
Female (%) 46 68
Non white ethnicity (N) 7 4
Symptom duration (weeks) 36 (12-74)† 16 (8-24)
DAS44 (no dimension) 4.0 ±0.8 4.0 ±0.7
ESR (mm/hour) 33 (16-55) 27 (13-47)
CRP (mg/l) 17 (9-27) 12 (4-39)
RF positive (%) 54 64
Anti-CCP positive (%) 54 75
BMI (kg/m2 ) 25.0 ±4.5 24.9 ±3.9
Waist circumference (cm) 93 ±13 89 ±14
Current smoker (%) 23 25
Prior HCQ treatment (N) 0 1
Concurrent NSAIDs treatment (N) 13 27
Fasting glucose (mmol/l) 5.5 ±0.5 5.5 ±0.7
Fasting insulin (pmol/l) 43 (33-89) 36 (29-74)
Fasting C-peptide (nmol/l) 0.5 (0.3-0.8) 0.6 (0.4-0.8)
HOMA-IR (No dimension) 1.0 (0.7-2.0) 0.8 (0.6-1.8)
HOMA-B (No dimension) 81 (64-120) 82 (60-114)
Data are mean±SD or median (interquartile range). HOMA-IR= homeostatic model assessment of insulin
resistance; HOMA-B= HOMA of the beta cell function. † P=0.04 versus nonprogressors by Mann-Whitney
U test.
Insulin and C-peptide responses during the oGTT: Fasting insulin levels were similar
in the 2 groups prior to treatment. AUC and AUC were not different between the groups
I CP
at baseline (Table 2). Fasting insulin levels were increased following treatment with
prednisolone 30 mg/day, but not with prednisolone 60 mg/day, whereas fasting C-peptide
257
Chapter 13
13

Chapter 13 Glucocorticoids in acute rheumatoid arthritis
levels were increased significantly following prednisolone treatment at either dosage (Table
2). Treatment with prednisolone at either dosage did not significantly affect postload insulin
and C-peptide concentrations (Figure 1B-C, Figure 2B-C, Table 2).
Figure 2: oral glucose tolerance test results before treatment (squares connected by solid lines)
and on day 7 of treatment with prednisolone 30mg daily (circles connected by dashed lines). A,
Glucose concentrations. B, Insulin concentrations. C, C-peptide concentrations. No significant changes
were induced by treatment with prednisolone 30 mg/day. Values are the mean ± SD.
Parameters of beta-cell function: The IGI, as an index for early insulin secretion, increased
during both prednisolone 60 mg/day (P=0.02) and prednisolone 30 mg/day (P=0.04) (Table
2). The ratio of C-peptide levels to glucose levels during the entire OGTT was enhanced
significantly by prednisolone 30 mg/dasy and a near-significant enhancement was observed
with prednisolone 60 mg/day (P=0.07) (Table 2). The amount of increase in parameters of
beta-cell function did not differ between the treatment groups (Table 2).
Insulin sensitivity: Fasting insulin sensitivity (HOMA-IR) was not changed by prednisolone
60 mg/day, but was decreased by prednisolone 30 mg/day (P=0.005). Insulin sensitivity
during the OGTT (OGIS) was not changed by treatment with prednisolone at either 60 mg/
day or 30 mg/day. The degree of change in parameters of insulin sensitivity did not differ
between treatment groups (Table 2).
Determinants of prednisolone-induced changes: Multivariate analysis revealed that
disease duration was positively associated with prednisolone-induced changes in the AUCG (b=3.626 [95% CI 1.077-6.174], P=0.007). Correcting for ethnicity, NSAID or HCQ usage
did not alter this relation (data not shown). No associations of disease characteristics with
changes in IGI or AUC /AUC ratio were identified (data not shown).
CP G
258

DISCUSSIoN
The main findings of the present study are that glucose metabolism is frequently impaired
in patients with early and active RA, probably, at least partly related to inflammation, and
that short-term treatment with high-dose prednisolone does not further deteriorate glycemic
control, as determined in the fasted state and following an oral glucose load. There was,
however, large variability among subjects, with some patients showing improvement, and
others showing deterioration in glucose tolerance. Overall, an increase in patients classified as
having T2DM was observed following 1-week prednisolone treatment. Interestingly, though,
in 9 patients with impaired glucose metabolism at baseline, glucose metabolism reverted to
normal after short-term GC exposure.
The results of this study are clinically important, since GCs are frequently used as
immunosuppressive and anti-inflammatory drugs in the treatment of RA and other
inflammatory rheumatic diseases, such as systemic lupus erythematosus (SLE), polymyalgia
rheumatica, polymyositis and Wegener’s disease, showing powerful effects on disease activity
and radiological progression. However, despite compelling evidence showing the effectiveness
of high-dose GCs in the treatment of early and active RA, rheumatologists are reluctant to
prescribe comparably high dosages, due to fear of serious prednisolone-associated adverse
events, including the development of hyperglycemia and diabetes [26].
Treatment with prednisolone 30 mg daily increased fasting insulin levels in the present study,
but this was not found after treatment with prednisolone 60 mg. Insulin levels are known
to have higher between and within-subject variability than C-peptide levels, as shown in
reproducibility studies [27], which could have contributed to this unexpected finding. Fasting
C-peptide levels, which indicate fasting insulin secretion and are known to be less affected
by high variability, increased significantly in both groups. Furthermore, since clearance of
C-peptide is primarily by the kidney and hepatic clearance is negligible [21], the differences
in fasting insulin levels might be due to altered hepatic clearance of insulin.
In a previous study of healthy individuals performed by our group, high-dose prednisolone
treatment impaired both beta-cell function and insulin sensitivity and deteriorated glucose
tolerance [8]. However, approximately half of the present population, i.e. RA patients with
active disease, had glucometabolic disturbances at baseline: 56% were classified as having
impaired glucose metabolism and 7% fulfilled the criteria of overt T2DM (an increased
percentage as compared to the normal population). This finding is in accordance with the
results of earlier studies in patients with active chronic RA, who showed impaired handling of
intravenously administered glucose compared with matched controls [15, 28].
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Chapter 13 Glucocorticoids in acute rheumatoid arthritis
Impaired glucose tolerance in RA patients has been mainly attributed to the pro-inflammatory
state. Indeed, in the present study, we demonstrated for the first time that baseline ESR
and plasma CRP levels were positively associated with postload glucose levels. Preclinical
studies suggest that inflammation may impair glucose tolerance both by reducing insulin
sensitivity and by impairing beta-cell function. Several pro-inflammatory cytokines, including
interleukin-6 and tumor necrosis factor α, are known to interfere with the insulin-receptor
signaling cascade resulting in impaired glucose uptake in insulin sensitive tissues [29, 30].
The acute-phase protein CRP was similarly shown to induce insulin resistance by impairment
of insulin signaling in an animal model [31]. Importantly, pro-inflammatory cytokines have
recently been shown to induce beta-cell dysfunction [32]. In addition, chronic inflammation
may reduce beta-cell mass by stimulating apoptosis of beta-cells [33].
While on average no significant effects on fasting and postload glucose were observed, an
increased number of patients met the criteria for T2DM after 1-week treatment with highdose
GCs (with percentage increasing from 7% to 24%). Most of these patients (7 of 10)
had impaired glucose metabolism prior to treatment. Interestingly, 9 patients with impaired
glucose metabolism at baseline were classified as having normal glucose metabolism after
prednisolone treatment, with the numbers equally distributed between the 2 treatment
groups. Although our study was not designed to identify predictors of deterioration of
the glucometabolic state, we found that patients who progressed from normal glucose
metabolism to impaired glucose metabolism or T2DM, or from impaired glucose metabolism
to type 2 DM, had significantly longer disease duration. It is important to note that the use of
the OGTT is hampered by its high within-subject variability [34]. Accordingly, this study does
not allow to distinguish the relative contributions to the observed changes in classification of
the glucometabolic state upon retesting that were attributed to prednisolone treatment per
se, versus the within-subject variability of the OGTT method.
Patients with longest disease duration in the present study not only had the highest
postload glucose levels after treatment, but were also at higher risk of progression in their
glucometabolic classification. These results confirm the findings from preclinical studies
which showed that prolonged inflammation has a significant negative impact on beta-cell
function [32]. Although the disease duration in all patients in our study is relatively short,
plasma CRP levels have been shown to be increased years before the first clinical symptoms of
RA [35], and a cumulative effect of systemic inflammation can be expected. While we observed
that short-term treatment with high-dose prednisolone does not further deteriorate overall
glycemic control, an additional effect of GCs might further disrupt already impaired beta
cells in patients with long disease duration. These observations suggest that treatment in
260

RA patients should be started early in the course of the disease, not only to slow down the
radiological progression of the disease [36], but also to limit the development of comorbid
conditions such as metabolic and cardiovascular disease.
Another potential explanation for the present findings is the possible contribution of
interindividual responses to GC treatment, due to genetic variation in the glucocorticoid
receptor (GR) [37]. Given these differential metabolic responses to high dose prednisolone,
further characterization of progressors versus nonprogressors might better identify
susceptible subjects. In clinical practice, regularly follow-up measurement of plasma glucose
levels and glycosylated hemoglobin levels during GC treatment is recommended.
It was of interest that no dose-dependent effects of prednisolone with regard to the effects on
glucose tolerance, beta-cell function and insulin sensitivity were demonstrated in the present
study. Results of previous studies indicated that the effects of GCs on glucose metabolism were
dose-dependent [10, 11]. However, both dosages of prednisolone used in the present study
are considered to be high [38], and although precise data on a dose-dependent occupation of
the GR is lacking, we hypothesize that similar receptor saturation might explain the fact that
we showed no differences between the 2 treatment groups.
This study has a number of limitations. First, it provides no information regarding the longterm
effects of high-dose GCs on glucose metabolism. However, in clinical practice, the GC
dosage is rapidly tapered and patients are at low maintenance dose levels within 2 months.
Whether the effects shown in this study will be sustained in the long term needs to be
determined.
Second, we used surrogate measures for beta-cell function and insulin sensitivity, as
derived from the OGTT. The ‘gold’ standards for measurement of beta-cell function and
insulin sensitivity are the hyperglycemic clamp and the hyperinsulinemic-euglycemic
clamp procedures, respectively. These tests are rather laborious and were considered too
demanding in this population. In addition, the measures derived from the OGTT as presented
in the manuscript are well validated against the clamp methods [24] and suitable for followup
measurements. Another advantage of the OGTT is that glucose tolerance and parameters
of both beta-cell function and insulin sensitivity can be derived from a single test.
Finally, we measured whole-body insulin sensitivity by HOMA-IR and OGIS, which does not
allow to tease out the various influences of prednisolone on different tissues. Using stable
isotopes, it was shown that prednisolone treatment may induce insulin resistance to a
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Chapter 13 Glucocorticoids in acute rheumatoid arthritis
different extent in the various insulin-sensitive tissues, including liver, skeletal muscle and
adipose tissue [39]. In our study we were unable to address this topic.
We conclude that short-term treatment with prednisolone at 60mg daily or 30mg daily
did not cause deterioration of the glucometabolic state in patients with active RA. Our data
suggest that there is a balance in the diabetogenic effects and anti-inflammatory effects of GC
treatment in early active RA patients, making short-term exposure to high-dose prednisolone
a safe treatment option for most patients, from a metabolic viewpoint. Due to individual
differences, however, regular glucose monitoring is recommended.
Acknowledgements
This research was performed within the framework of project T1-106 of the Dutch Top
Institute Pharma.
262

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Chapter 14
Glucose Tolerance, Insulin Sensitivity and
Beta-Cell Function in Rheumatoid Arthritis
Patients Treated With or Without Low-to-
Medium Dose Glucocorticoids.
J.N. Hoes, M.C. van der Goes, D.H. van Raalte, N.J. van der Zijl, D.
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Ann Rheum Dis. 2011;70(11):1887-94

Chapter 14 Glucocorticoids in chronic rheumatoid arthritis
ABSTRACT
objective: To compare glucose tolerance and parameters of insulin sensitivity and betacell
function between chronic glucocorticoid (GC)-using and GC-naive RA patients.
Research design and methods: Frequently-sampled 75-g oral glucose tolerance tests were
performed in 58 chronic GC-using and 82 GC-naive RA patients with established disease,
known type 2 diabetes mellitus (T2DM) precluded participation, and 50 control subjects
of comparable age with normal glucose tolerance. The associations between cumulative
GC dose and disease characteristics and glucose tolerance state, insulin sensitivity and
beta-cell function were tested using multivariate linear and logistic regression models,
correcting for patient characteristics.
Results: Glucose tolerance state, insulin sensitivity and beta-cell function did not differ
between both RA populations; we detected de novo T2DM in 11% and impaired glucose
metabolism in 35% of RA patients. Within RA patients, cumulative GC dose was associated
with T2DM, which seemed mostly driven by the effects of cumulative GC dose on insulin
resistance; however, the association decreased when corrected for current disease activity.
RA patients had decreased insulin sensitivity and impaired beta-cell function compared to
controls, and multivariate regression analyses showed a negative association between the
presence of RA and insulin sensitivity.
Conclusions: GC-using and GC-naive RA patients had comparable metabolic parameters,
and had decreased insulin sensitivity and beta-cell function as compared to healthy controls.
Although cumulative GC dose was shown to have a negative impact on glucose tolerance
state and insulin sensitivity, confounding by indication remains the main challenge in this
cross-sectional analysis.
268

INTRoDUCTIoN
Rheumatoid arthritis (RA) patients are at increased risk to develop cardiovascular disease,
comparable to the risks observed in subjects with type 2 diabetes mellitus (T2DM) [1].
Additional impairment in glucose metabolism may contribute significantly to the accelerated
atherogenesis in RA patients [2]. Fasting and postprandial glucose metabolism are
determined by beta-cell function (insulin production) and by the peripheral effects of insulin
(insulin sensitivity) aimed to increase glucose uptake in skeletal muscle and to decrease
glucose production by the liver. Glucocorticoids (GCs) impair hepatic and peripheral insulin
sensitivity and induce beta-cell dysfunction, however, the precise underlying mechanisms
are still being investigated [3, 4]. Previously, RA patients were shown to have impaired
fasting insulin sensitivity (homeostatic model assessment (HOMA)-IR) and fasting beta-cell
function (HOMA-B), which correlated with disease activity and markers of inflammation [5-
7]. Consequently, prevalent diabetes was estimated to be up to 15-19% in RA patients [8, 9],
an increased number as compared to the estimated T2DM prevalence of 4-8% in middle-aged
men and women in the general population [10].
The role of GCs in glucose intolerance in RA patients has been one of paradox. On the one
hand, in animal models and in short-term clinical trials in healthy subjects, GCs were shown
to deteriorate glucose metabolism by impairing hepatic and peripheral insulin sensitivity
and by inducing beta-cell dysfunction [11]. In retrospective, population-based studies, GC
therapy was associated with incident diabetes [12], and the need for blood-glucose lowering
treatment in a cumulative dose-dependent way [13]. In retrospective studies in RA patients,
GC exposure was shown to correlate with insulin resistance [14] and to predict diabetes
[15]. On the other hand, the use of GCs in chronic inflammatory states may improve glucose
tolerance by their anti-inflammatory effects, as was demonstrated in a number of short-term
studies using GC-treatment [16, 17]; this was also shown in a study using methotrexate [18].
In addition, confounding by indication should be kept in mind when evaluating the relation
between GC use and glucose tolerance in RA patients in observational studies. This is the
possibility that patients with higher cumulative inflammation (i.e. higher disease activity),
resulting in a priori increased insulin resistance, were more likely to be given high-dose GCs
than those with less inflammation (disease) activity. Thus, the impact of GC-treatment on
glucose metabolism in RA patients requires further clarification.
Previous studies that have addressed the effects of GCs on glucose tolerance, insulin sensitivity
and beta-cell function in RA patients included a small number of patients [16], or relied solely
on fasting parameters, i.e. HOMA-IR and HOMA-B [14]. As mentioned above, in as much as
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Chapter 14 Glucocorticoids in chronic rheumatoid arthritis
insulin sensitivity and insulin secretion are interrelated, the use of the HOMA formulas, both
of which utilize the same fasting variables, i.e. fasting plasma insulin and glucose, may not be
appropriate to discern changes in insulin sensitivity from those in insulin secretion [19]. For
instance, if two subjects have the same fasting glucose level, but, one persons achieves this
with higher fasting insulin levels, this person is more insulin resistant (thus has lower insulin
sensitivity), which is expressed by a higher HOMA-IR score. Similarly, from fasting glucose and
insulin levels, a HOMA-B score is calculated which gives an impression on beta-cell function.
Although these model-derived indices are well-validated, they provide no information about
the stimulated, postload state [19]. From dynamic tests, such as the frequently-sampled oral
glucose tolerance test (OGTT), indices of postload insulin sensitivity and glucose-stimulated
beta-cell function may be calculated, in order to provide more detailed information on glucose
metabolism [20].
Therefore, in the present study, we compared glucose tolerance and (fasting and dynamic)
parameters of insulin sensitivity and beta-cell function from frequently-sampled OGTTs in a
large group of chronic GC-using RA patients versus GC-naive RA patients. Furthermore, we
included a control group of comparable age to create a perspective of our OGTT findings in RA
patients, and to assess the association of RA per se on measures of insulin sensitivity and betacell
function in subjects with normal glucose tolerance. Finally, we assessed the association
between cumulative GC-dose and disease characteristics with these metabolic parameters.
METHoDS
Population: RA patients with established disease, i.e. defined as having a disease duration
of more than 2 years, were recruited in 5 rheumatology clinics in the region of Utrecht,
the Netherlands. Patients were either both current and chronic GC-users (RA+GC), which
indicated GC treatment for at least 3 months, or they were GC usage naïve (RA-GC). Known
T2DM (defined as receiving treatment) was an exclusion criterion. We included a control
group (controls) with normal glucose tolerance and without first-degree relatives with T2DM,
consisting of individuals who had undergone an OGTT for screening purposes for other studies
at the Diabetes Center of the VU University Medical Center in Amsterdam. Accordingly, this
group consisted of relatively overweight predominantly male individuals. We did not match
the control population to the RA population, since our main focus was on studying the effects
of GCs in RA patients (particularly compared to GC-naive RA patients). The healthy control
group in our study served to give a perspective on the values obtained in the RA patients. An
270

independent ethics committee approved the study and all subjects provided written informed
consent before participation; the protocol was according to the ‘Declaration of Helsinki’.
Protocol: Participants visited the clinic following an overnight fast of minimally 10 hours.
A physical examination, including recording of length, weight and waist circumference
was performed and fasting blood tests were acquired in all patients. In the RA patients,
in addition, the disease activity score (DAS28 and DAS28-CRP) [21, 22] was calculated; in
addition, DMARD-history taking, anti-citrullinated protein antibodies (ACPA) laboratory
measurement, and X-rays of hands and feet (to detect erosive damage) were performed.
Finally, all participants underwent a 2-hr 75 g OGTT. Blood samples for determination of
glucose, insulin and C-peptide were collected at times 0, 10, 20, 30, 60, 90, and 120 minutes,
starting immediately after the ingestion of the 75 g glucose solution. Since insulin clearance
may vary considerably between subjects [23], plasma C-peptide levels may provide additional
information on beta-cell function.
Analytical determinations: Plasma glucose was measured using a chemical technique on
a DXC-800 analyser (Beckman Coulter, Los Angeles, USA). Plasma insulin was measured
using an immunometric technique on an Immulite 1000 Analyzer (Siemens Medical
Solutions Diagnostics, Los Angeles, USA). Plasma C-peptide was measured using an
electrochemiluminescence immunoassay on the Modular E170 (Roche Diagnostics GmbH,
D-68298 Mannheim, Germany).
Data analysis: Normal glucose tolerance was defined as fasting plasma glucose (FPG) < 6.1
and a 2-hour glucose value < 7.8 mM; impaired glucose metabolism (IGM) as FPG between 6.1
and 7.1 mM or a 2-hour glucose value between 7.8 mM and 11.1 mM; T2DM as FPG > 7.1 mM
or a 2-hour glucose value > 11.1 mM. Area under the 2-hour glucose (AUC ), insulin (AUC ),
gluc ins
and C-peptide (AUC ) curves were determined using the trapezoidal rule. Insulin sensitivity
c-pep
in the fasted state was computed by HOMA-IR [24]. Estimated metabolic clearance rate
(MCR /Stumpvoll Index) and oral glucose insulin sensitivity (OGIS) were used to estimate
est
postload insulin sensitivity [25]. Various measures of beta-cell function were calculated:
HOMA-B was derived from fasting measures [24]. Dynamic measures of beta-cell function
were derived from OGTT data and included: AUC /AUC ratio over the 2-hour period
c-pep gluc
and the insulinogenic index (IGI): insulin -insulin )/(gluc -gluc ), as measure for early
t=30 t=0 t=30 t=0
insulin secretion [26]. The oral disposition index (DI) was calculated by multiplying IGI and
OGIS, to adjust insulin secretion for insulin sensitivity. Insulin clearance was calculated by
dividing AUC and AUC [23].
c-pep ins
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14

Chapter 14 Glucocorticoids in chronic rheumatoid arthritis
Statistical analysis:
Comparison of parameters of glucose tolerance state, insulin sensitivity and beta-cell function
of RA+-GC groups and controls:
Data were presented as mean values ± SD or as median (interquartile range) in case of nonnormal
distribution. Intergroup differences in continuous outcomes were tested by ANOVA,
and with the Kruskal-Wallis tests in case of non-normal distribution. Differences between
groups in dichotomous outcomes were tested with the chi-square test. Post-hoc Bonferroni
correction was applied in case of multiple testing by multiplying the p-value times 2 (3 groups
minus 1).
Associations between patient and disease characteristics and parameters of glucose tolerance
state, insulin sensitivity and beta-cell function:
Associations between known determinants of insulin sensitivity and beta-cell function (age,
waist circumference, BMI, and insulin clearance) and gender, and, within the RA populations,
cumulative and daily GC-dose and disease activity (DAS28 and its components, DMARD-use,
hand or feet erosions on X-ray, ACPA) and parameters of insulin sensitivity and beta-cell
function were investigated with linear regression analysis; associations of the above factors
with IGM and T2DM were investigated with logistic regression analyses. Non-normally
distributed variables were log-transformed when used in multivariate linear regression
analysis. In the multivariate analyses with OGTT-outcomes as dependent variable where the
RA populations were compared with the controls, RA patients with previously unknown
T2DM, IGM or with 1st degree relatives with T2DM were excluded (i.e. fitting the exclusion
criteria of controls). SPSS for Mac version 16.0 (SPSS, Chicago, IL, USA) was used for all
statistical analyses. A p-value

Glucose tolerance state: The prevalence of previously unknown T2DM was comparable
between the two RA groups (Table 1). If those RA-patients who were excluded because of
known T2DM (n=27) were included, then the prevalence of T2DM would be 19% (RA-GC 18%
vs. RA+GC 21%, P=0.9). Within the RA groups, both cumulative and daily prednisolone dose
was associated with incident T2DM in univariate analyses (odds ratio cumulative dose (g):
1.04; P=0.002; daily dose (mg): 1.13; P=0.048). This association sustained after adjusting for
disease activity and patient characteristics (DAS28, ACPA, erosions, DMARD history, disease
duration, age, BMI, waist circumference, and gender; odds ratio cumulative dose (g): 1.04;
P= 0.03; daily dose (g): 1.15; P=0.13), whereas it was decreased and less significant (a trend)
after adjustment for current disease activity alone and patient characteristics (DAS28, age,
BMI, waist circumference, and gender; odds ratio cumulative GC-dose (g): 1.02; P= 0.08; daily
dose (g): 1.11; P=0.3).
Metabolic responses during oGTT: Glucose levels during the OGTT were not different
between the RA groups, whereas AUC gluc was higher in RA patients as compared to controls,
which was mostly driven by higher glucose levels during the final 90 minutes of the test (Figure
1A). Insulin levels were comparable between all groups (Figure 1B); however, C-peptide
secretion was higher in the RA groups as compared to controls (Figure 1C), with no difference
between the RA groups, indicating increased insulin clearance in RA patients as compared to
controls (data not shown). Insulin clearance was decreased in RA+GC as compared to RA-GC
(data not shown).
Figure 1. Glucose and insulin levels during the oral glucose tolerance test (oGTT). Mean (±SD) for
glucose and C-peptide levels during the OGTT for 1) control subjects, 2) glucocorticoid (GC)-naive RA
patients and 3) GC-using RA patients are shown. *P

Chapter 14 Glucocorticoids in chronic rheumatoid arthritis
Table 1. Baseline characteristics.
vs. RA- vs.
Controls RA-GCs RA+GCs GCs RA+GCs
N 50 82 58 -
Age (years) 56 ± 8 57 ± 12 59 ± 12 1.0 0.4
274
P-value Controls*
Female (%) 38 71 71

Data represent means ± standard deviation or median (interquartile range) when data was not normally
distributed. Intergroup differences in continuous outcomes were tested by ANOVA, and with both the
Kruskal-Wallace and Mann-Whitney tests in case of non-normal distribution. Differences between groups
in dichotomous outcomes were tested with the chi-square test. Post-hoc Bonferroni correction was
applied in case of multiple testing (p-value times 2).
$ p-Value controls is the intergroup difference tested by ANOVA, Mann-Whitney or chi-square test with the
Bonferroni post-hoc test. p Values of the RA-GC RA+GC difference was not depicted; the only significant/
trend differences were male HDL P=0.07; dyslipidaemia P=0.009.
* Increased waist circumference was defined as > 102 cm in male and > 88 cm in female. Hypertension
was defined as ≥ 140 mmHg systolic or 90 mmHg diastolic pressure.
** Diabetes was defined as either fasting plasma glucose ≥ 7.1, or ≥ 11 at 120 min of OGTT; Impaired
glucose metabolism was defined as either fasting plasma glucose (< 7.1 and > 6.1), or impaired glucose
tolerance (7.8 at 120 min of OGTT).
*** Dyslipidaemia was defined as Triglycerides > 1.7 mmol/l and/or HDL-cholesterol < 0.9 mmol/l (male)
and < 1 mmol/l (female). Hypercholesterolemia was defined as Total cholesterol > 5 mmol/l and/or LDLcholesterol
> 3 mmol/l.
ANOVA: analysis of variance; BMI, body mass index; CCP: cyclic citrullinated peptide; DAS28, Disease
activation score using 28 joints; DBP, diastolic blood pressure; DMARD: disease-modifying antirheumatic
drug; ESR, erythrocyte sedimentation rate; FPG: fasting plasma glucose; GC: glucocorticoid; HDL:
high-density lipoprotein; IGM, impaired glucose metabolism; LDL: low-density lipoprotein; OGTT:
oral glucose tolerance test; RA: rheumatoid arthritis; RA+GCs, rheumatoid arthritis patients, currently
using glucocorticoids; RA–GCs, rheumatoid arthritis patients, glucocorticoid naïve; SBP, systolic blood
pressure;VAS, visual analogue scale.
Parameters of insulin sensitivity: Parameters of both fasting (Figure 2A) and postload
insulin sensitivity (Figure 2 B+C) were comparable between the RA groups. In multivariate
linear regression analyses (correcting for age, gender, BMI, waist circumference, and disease
activity) the presence of RA, waist circumference and cumulative GC dose were independent
predictors of HOMA-IR (Table 2). MCRest was negatively associated with DAS28, ESR, age,
BMI and waist circumference (Table 2); a similar pattern was observed for OGIS (data not
shown). The healthy control group was more insulin sensitive in the fasted state, but had
similar postload insulin sensitivity as compared to the RA groups.
Figure 2. Parameters of insulin sensitivity. Mean (±SD) for insulin sensitivity indices homeostatic
model assessment of insulin resistance (HOMA-IR; fasting index), metabolic clearance rate (MCR ) and
est
oral glucose insulin sensitivity (OGIS) are shown. **P

Chapter 14 Glucocorticoids in chronic rheumatoid arthritis
Table 2. Association of risk factors and disease characteristics with HoMA-IR and MCRest
Patient characteristics Beta (95% CI) Beta (95% CI) Beta (95% CI)
(RA-patients (RA-patients only (RA-patients only
(n=56), controls
(n=50))
(n=140))
(n=140))
Three multivariate regression analysis models with HOMA-IR as dependent variable
RA-GC* 0.6 (0.08, 1.0)
RA+GC* 1.0 (0.6, 1.5)
Waist circumference 0.02 (-0.01, 0.04) 0.02 (-0.003, 0.05) 0.03 (0.0002, 0.06)
Age 0.002 (-0.02, 0.02) 0.008 (-0.007, 0.02) 0.006 (-0.01, 0.02)
BMI 0.03 (-0.05, 0.1) 0.05 (-0.2, 0.1) 0.03 (-0.04, 0.1)
Female gender 0.2 (-0.2, 0.6) -0.1 (-0.5, 0.3) -0.06 (-0.5, 0.4)
Cumulative GC-dose (g)** 0.01 (0.003, 0.02) 0.01 (0.003, 0.02)
Daily GC-dose** (mg) 0.01 (-0.04, 0.05) 0.005 (-0.04, 0.05)
DAS28*** 0.1 (-0.04, 0.3)
Any erosions of the hands or feet -0.3 (-0.7, 0.2)
Past DMARDs (n)**** -0.06 (-0.2, 0.05)
ACPA 0.2 (-0.7, 0.2)
Disease duration (years) 0.02 (-0.008, 0.04)
Three multivariate regression analysis models with MCR as dependent variable
est
RA-GC* 0.4 (-0.5, 1.3)
RA+GC* 0.4 (-0.5, 1.2)
Waist -0.02 (-0.07, 0.03) -0.06 (-0.1, 0.01) -0.09 (-0.2, -0.02)
Age -0.03 (-0.06, 0.005) -0.07 (-0.1, -0.03) -0.07 (-0.1, -0.03)
BMI -0.3 (-0.4, -0.1) -0.2 (-0.4, -0.06) -0.1 (-0.3, -0.06)
Female gender -0.8 (-1.6, 0.08) -0.08 (-1.3, 1.1) -0.5 (-1.7, 0.7)
Cumulative GC-dose (g)** 0.002 (-0.01, 0.04) 0.01 (-0.01, 0.04)
Daily GC-dose** (mg) -0.07 (-0.19, 0.05) -0.04 (-0.2, 0.08)
DAS28*** -0.5 (-0.9, -0.1)
Any erosions of the hands or feet 0.8 (-0.3, 2.0)
Past DMARDs (n)**** 0.2 (-0.06, 0.5)
ACPA -0.7 (-1.8, 0.4)
Disease duration (years) 0.03 (-0.04, 0.1)
Three multivariate models, correcting for age, BMI, and female gender were used to test
associations with insulin sensitivity parameters HOMA-IR and MCRest: (1) tested RA populations
(as compared to healthy controls), (2) tested cumulative and daily GC-dose, and (3) tested
disease activity (DAS28 and its components, DMARD-use, hand or feet erosions on X-ray, ACPA).
Significant associations are depicted in bold.
* RA-GC and RA+GC populations are tested with the healthy control group used as the reference population;
only RA patients with normal glucose tolerance during OGTT and without 1st degree relatives with type 2
diabetes mellitus were included in the model for the comparison with healthy controls.
** Cumulative GC-dose, daily GC-dose were separately tested in models of only the RA population.
*** Disease parameters (DAS28, DAS28CRP, CRP, ESR) were separately tested in the model of only the RA
population; only DAS28 is depicted, whereas ESR was also significantly associated with MCRest in this
model.
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**** Number of DMARDs (both synthetic and biological; not glucocorticoids) that were used by a patient
in the past.
ACPA: anti-citrullinated protein antibodies; BMI, body mass index; CRP: C-reactive protein; DAS28,
disease activation score measured by questioning general health physical examination of 28 joints and
ESR or CRP; DMARD: disease-modifying antirheumatic drug; ESR, erythrocyte sedimentation rate; GC,
glucocorticoid; HOMA-IR, homeostatic model assessment of insulin resistance; MCRest, insulin sensitivity
index by Stumvoll; OGTT: oral glucose tolerance test; RA: rheumatoid arthritis.
Parameters of beta-cell function: HOMA-B was higher in RA+GC as compared to RA-GC
(Figure 3A), while all dynamic measures of beta-cell function were comparable between the
RA groups (Figure 3B-3D). Positive associations between the presence of RA and cumulative
GC-use with HOMA-B were found (corrected for age, gender, BMI, waist circumference, and
disease activity; data not shown). Age and both RA-GC and RA+GC were negatively associated
with IGI (corrected for age, gender, BMI, waist circumference; data not shown), whereas no
patient characteristics correlated with the disposition index (table 3). As compared to healthy
controls, RA patients had higher basal C-peptide secretion (higher HOMA-B), but impaired
early insulin secretion, also when corrected for insulin sensitivity (Figure 3B+C). The total
amount of C-peptide secreted during the entire OGTT relative to glucose levels, was higher in
RA patients than healthy controls (Figure 3D).
Figure 3. Parameters of beta-cell function. Mean (±SD) for beta cell indices homeostatic model
assessment of beta-cell function (HOMA-B; fasting index), insulinogenic index (IGI), disposition index
(DI), and area under the C-peptide curve divided by area under the glucose curve (AUC /AUC ratio)
c-pep gluc
are shown. . *P

Chapter 14 Glucocorticoids in chronic rheumatoid arthritis
Table 3. Associations of risk factors and disease characteristics with the disposition index.
Patient
Beta (95% CI)
Beta (95% CI)
Beta (95% CI)
Characteristics (RA-patients (n=56), (RA-patients only (RA-patients only
controls (n=50)) (n=140))
(n=140))
Three multivariate regression analysis models with DI as dependent variable
RA-GC* -42489 (-96745, 11767)
RA+GC* -23040 (-75891, 29810)
Waist circumference -1778 (-4895, 1338) 315 (-1831, 2462) 274 (-2009, 2557)
Age -3295 (-5219, -1370) -1280 (-2502, -57) -1100 (-2385, 186)
278
BMI 6205 (-1930, 14340) -2771 (-8193, 2650) -3070 (-8893, 2752)
Female gender -8287 (-52574, 36000) 25773 (-10358, 61904) 31014 (-6984, 69013)
Cumulative GC-dose
-482 (-1307, 343) -284 (-1150, 582)
(g)**
Daily GC-dose** -885 (-4669, 2899) 140 (-3731, 4010)
DAS28*** -4759 (-16525, 7007)
Any erosions of the
-14480 (-50730, 21770)
hands or feet
Past DMARDs (n)
662 (-7572, 8896)
****
ACPA 24336 (-9300, 57973)
Disease duration
-1392 (-3447, 664)
(years)
Three multivariate models, correcting for age, BMI, and female gender were used to test associations with
the beta-cell parameter disposition index: (1) tested RA populations (as compared to healthy controls),
(2) tested cumulative and daily GC-dose, and (3) tested disease activity (DAS28 and its components,
DMARD-use, hand or feet erosions on X-ray, ACPA).
Significant associations are depicted in bold.
* RA-GC and RA+GC populations are tested against the control subject population; only RA patients with
normal glucose tolerance during OGTT and without 1st degree relatives with type 2 diabetes mellitus
were included for the comparison with control subjects. RA+GC was significantly associated with DI when
these analyses were performed with log-transformed DI.
** Cumulative GC-dose, daily GC-dose were separately tested in models of only the RA population.
*** Disease parameters (DAS28, DAS28CRP, CRP, ESR) were separately tested in the model of only the RA
population; only DAS28 and significant correlations are depicted.
**** Number of DMARDs (both synthetic and biological; not glucocorticoids) that were used by a patient
in the past.
ACPA: anti-citrullinated protein antibodies; BMI, body mass index; CRP: C-reactive protein; DAS28,
disease activation score measured by questioning general health physical examination of 28 joints and
ESR or CRP; DI: disposition index; DMARD: disease-modifying antirheumatic drug; ESR, erythrocyte
sedimentation rate; GC, glucocorticoid; RA: rheumatoid arthritis; RA+GCs, rheumatoid arthritis patients
currently using glucocorticoids; RA–GCs, glucocorticoid naive rheumatoid arthritis patients.

Effect modification: As compared to RA patients, controls had a higher percentage of male
gender, and had a higher body mass index (BMI) and waist circumference; in addition, RA+GC
had higher disease activity as compared to RA-GC patients (Table 1). These factors were
studied for effect modification using interaction-terms in the below-mentioned regression
models, and were shown not to modify the effects of patient and disease characteristics on
glucose metabolism outcomes in the multivariate models (data not shown).
DISCUSSIoN
In this study of RA patients with established disease, chronic GC-users and GC-naive patients
had similar insulin sensitivity and beta-cell function parameters; however high cumulative
or daily GC-dose was associated with T2DM. In addition, in all RA patients IGM and T2DM
were frequently diagnosed, suggesting that glucose intolerance remains an underestimated
problem in RA. As compared to a healthy control group, RA patients had impaired insulin
sensitivity and beta-cell dysfunction, explaining their impaired metabolic state.
To our knowledge, this is the first study that has examined glucose metabolism in a relatively
large sample of RA patients with established disease in such detail. Few studies investigated
glucose tolerance state in RA: One study showed an increased prevalence of T2DM when
compared to age-matched controls [27]; another study reported 19% diabetes prevalence
as part of a longitudinal medical record cohort on cardiovascular risk [8]. Both studies relied
on self-reported T2DM and did not perform glucose measurements. Because of the OGTT
measurements of glucose at 0 and 120 minutes we were now able to register 11% T2DM
prevalence in RA patients with established disease without known T2DM, and in addition,
identify high-risk patients, by detecting 35% prevalence of IGM. This shows that glucose
intolerance is a considerable and underestimated problem in RA patients with established
disease, and might (partially) explain their increased cardiovascular risk [1].
The subject of insulin resistance in RA has been addressed in recent years, but was only
evaluated by fasting measure HOMA-IR [2, 5, 6]. Unlike these studies, we were able to
show a negative association of DAS28 with insulin sensitivity after correction for potential
confounders and other risk factors, which could have been due to the fact that we also used
stimulated measures of insulin sensitivity and because our sample size was larger.
Another important finding of our study was impaired beta-cell function in RA patients as
compared to controls, as was shown by decreased dynamic parameters IGI and DI, also after
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Chapter 14 Glucocorticoids in chronic rheumatoid arthritis
correction for age, BMI and waist circumference (in case of IGI). This indicates impaired insulin
secretion during the early phase after glucose stimulation. So far, a limited number of other
studies have reflected upon beta-cell function in RA, and only used the fasting state measure
HOMA-B. In one retrospective study HOMA-B was decreased in RA patients with a higher level
of inflammation when compared to RA patients with lower level of inflammation [6], which
seems in line with our findings of impaired beta-cell function in RA patients when compared
to the control population. In our current analysis, HOMA-B was higher in RA patients, which
indicates increased basal C-peptide secretion. This seems contradictory next to the decrease
in beta-cell function parameters obtained in the stimulated state. However, HOMA-B should
always be interpreted in the context of prevailing insulin resistance [19]. In our study, RA+GC
participants had higher HOMA-IR values, i.e. they were more insulin resistant. In order to
maintain fasting glucose levels within the normal range, more insulin was secreted in the
fasted state, which resulted in a higher HOMA-B score. However, this higher HOMA-B score
does not imply improved beta-cell function, but merely indicates the potential to compensate
for reduced insulin sensitivity in the fasted state.
In addition, we addressed the specific role of (cumulative or daily) GC-dose and disease
characteristics within RA patients and found strong indications that RA+GC patients were
less glucose tolerant in a dose-dependent manner: Although no relation was shown between
(cumulative or daily) GC dose and dynamic parameters of insulin sensitivity and beta-cell
function, cumulative- and daily GC dose were associated with previously unknown T2DM and
negatively affected fasting insulin sensitivity (HOMA-IR); independent of age, gender, BMI,
waist circumference, and disease activity. In addition, RA+GC patients had a decreased insulin
clearance, indicating hepatic insulin resistance, which is explained by the steeper insulin curve
in the first part of the OGTT. Our results are in line with one other retrospective study of nondiabetic
RA patients [14]; that study analysed a successive group of RA patients and showed
that ever having taken oral prednisone and/or high doses of pulsed GCs was independently
associated with decreased insulin sensitivity, independent of BMI.
We acknowledge some limitations in our study design; one is the difference with regards
to anthropometrics between controls (with normal glucose tolerance) and RA patients, i.e.
controls were primarily recruited for other studies at the Diabetes Center of the VU University
Medical Center and therefore consisted of more males and had a relatively high BMI, and
lower insulin clearance. Compared to these control subjects RA patients were more insulin
resistant and had impaired beta-cell function. Although the use of this control population,
as compared to more lean, insulin sensitive controls, may be suboptimal, it is very likely
that the impact of RA and the associated pro-inflammatory state on glucose metabolism as
280

described here, may even be underestimated. Besides, in multivariate analyses we corrected
for these anthropometrics, and furthermore the control subjects served mainly to create a
perspective for the insulin resistance and beta-cell parameters of RA patients. Another point
is confounding by indication that could have caused the effects of GCs on glucose metabolism,
since cumulative GC-use could be a proxy for long-term disease activity, which itself influences
glucose metabolism. This was shown also in our study by a decrease of the regression
coefficient (beta) for the association between cumulative or daily GC-dose and T2DM when
disease activity (DAS28) was added to the multivariate regression model.
Concluding, because of (1) the stimulated state measurement of glucose metabolism
parameters, (2) the size of our population, and (3) the contrast with control subjects, we were
able to draw firm conclusions on the prevalence of glucose tolerance abnormalities in RA
patients with established disease and to confirm the relation between RA (activity) and insulin
resistance and beta-cell dysfunction. Chronic GC-use was associated with metabolic toxicity
in a dose dependent way, but this association was difficult to assess due to confounding by
indication.
Until more clarity is given on the issue of glucose intolerance in GC-using RA patients, it remains
important to keep the use of GCs of short duration and lowest possible dose, as is advised
by the EULAR recommendations on RA treatment [28], and on systemic GC use [29]. The
question how harmful long-term GCs are with regards to diabetogenic effects in established
RA patients needs further assessment in longitudinal (randomized) trials. These trials should
measure glucose metabolism with stimulated state measures, and study whether GCs exert
direct metabolic toxicity or secondary to other GC-related phenomena, such as abdominal fat
and adipocytokines, which are known mediators of metabolic toxicity in RA [30].
Funding: This paper was written within the framework of project T1-106 of the Dutch Top
Institute Pharma.
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PART
Discussion
V

Chapter 15
General Discussion:
“Diabetogenic Effects of Glucocorticoid
Drugs: The Knowns and The Unknowns”
D.H. van Raalte and M. Diamant
Manuscript in Preparation

Chapter 15 General Discussion
Glucocorticoids: efficacious anti-inflammatory agents limited by their side effects
For decades, glucocorticoids (GCs) are the most commonly prescribed anti-inflammatory and
immunosuppressive agents. GCs reduce the activity of the innate immune system by decreasing
phagocytic activity and by attenuating the production of pro-inflammatory mediators [1].
Additionally, GCs affect the acquired immune system by impairing a variety of T-cell functions
[1]. Because of these combined immunomodulatory actions, GCs are the cornerstone in the
treatment of numerous inflammatory diseases, including rheumatic diseases (rheumatoid
arthritis (RA) [2] and polymyalgia rheumatica [3]), systemic lupus erythematosus [4], small
and large vessel arteritis [5], inflammatory bowel disease [6], chronic obstructive pulmonary
disease [7] and various other conditions of inflammatory origin. Already in 1949, the beneficial
effects of systemic cortisone treatment, the first available GC, on some of these conditions
were recognized and the published results were received with great enthusiasm. Pioneers in
the discovery of adrenal gland hormones and their clinical effects, Tadeusz Reichstein, Philip
Showalter Hench and Edward Calvin Kendall were awarded the Nobel Prize in 1950.
In the following decade, however, the initial enthusiasm for GC treatment was tampered when
its detrimental side effect profile became increasingly apparent. The earliest side effects
reported, included salt and water retention, increased gastric acidity and psychosis [8]. Soon the
full scope of side effects induced by GC treatment became evident and is now known to include
weight gain, fluid retention, hypertension, impaired glucose metabolism, diabetes, skeletal
muscle atrophy, osteoporosis, gastric ulcer disease, glaucoma, neuropsychiatric symptoms and
increased risk to develop cardiovascular disease. And this summary of GC-related side effects
may even not be complete [9, 10]. Despite the formulation of newer synthetic compounds,
such as prednisolone and dexamethasone, the side-effect profile remained largely unchanged.
This unfavorable side effect profile of systemic GC treatment has several important clinical
consequences. First, physicians are often forced to lower GC dosages compromising
efficacy of treatment of several disease entities. Secondly, resistance among physicians
to prescribe and among patients to take GCs due to their infamous side effect profile
is a well-characterized phenomenon, resulting in inadequate treatment of several
disease entities [11]. Finally, in clinical practice, additional therapies are often initiated
to mitigate the side effects induced by GC treatment, e.g. bisphosphonates to prevent
osteoporosis [12] and proton pump inhibitors to prevent gastric ulcer disease [13].
Glucocorticoids diabetogenic effects: focus of this thesis
In this thesis, we have focused on the unfavorable effects of GCs on glucose metabolism, and to
a lesser extent on lipid and protein metabolism. Additionally, we have performed pioneering
studies to further detail the underlying mechanisms of the metabolic side effects. Specifically,
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the effects of GCs on insulin production (beta-cell function) and insulin sensitivity were
assessed. We have concentrated on these glucometabolic side effects of GC treatment since
GC-induced glucose intolerance and overt diabetes are frequently occurring adverse events
of GC treatment [14-16]. However, these effects, in particular the mechanisms contributing
to their development, are still only partly understood. Additionally, no guidelines currently
exist how to treat or prevent this adverse effect, as has been the case for some of the forementioned
side effects of systemic GC treatment.
Glucocorticoids diabetogenic effects: importance of understanding the pathophysiology
Understanding both the mechanisms of GC action and the mechanisms underlying GCs
diabetogenic effects is relevant for several reasons. First, at a molecular level, increased
insight to the (nuclear) actions of GCs has led to the current development of dissociated
glucocorticoid receptor (GR) agonists or selective GR modulators (as extensively reviewed
in chapter 2). These compounds are designed to specifically induce transrepression, but
not transactivation, thus aiming to reduce metabolic side effects with unchanged antiinflammatory
capacities, potentially leading to an enhanced therapeutic index [17-19]. At
present, several pharmaceutical companies are developing such compounds, including Merck
Sharp Dome (former NV Organon), our partner in this Top Institute Pharma consortium
(see general introduction). In recent studies, they were able to show that their compound
ORG214007-0 (C H N OS) was able to reduce inflammation both in vitro and in vivo in
24 23 5
collagen-induced arthritis mice to the same extent as prednisolone, while demonstrating
reduced transactivation. As such, the expression of key enzymes in gluconeogenesis
phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase (GPPase) was
decreased, resulting in unchanged fasting glucose plasma levels in mice treated with
ORG214007-0, while prednisolone induced hyperglycemia [20]. However, to enable further
development of these compounds for use in humans, insight into the mechanisms of GCinduced
side effects is necessary. More specifically, there is need for the identification of
biomarkers that in a dose-dependent matter reflect or predict the occurrence of side effects of
the classic GR agonists, in order to compare these adverse effects with novel GR modulators.
Secondly, increased understanding of the mechanisms underlying GCs diabetogenic effects
may improve strategies to treat or even prevent these glucometabolic abnormalities, since
the novel dissociated GR activators are not likely to become available for clinical use within a
short period of time. Thus, identification of the organs and pathways involved may provide an
evidence base to develop therapeutic and preventive strategies in the context of chronic GC
treatment and its associated diabetogenic side effects. Additionally, these data will be critical
to expand our knowledge that can be harnessed for the development of targeted dissociated
GR agonists.
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Studies conducted in this thesis
In order to study the mechanisms underlying the adverse metabolic effects of GCs, a number
of studies were performed in this thesis. Most of the data in this thesis were obtained from the
PANTHEON (I + II) studies: the PeripherAl effects of prednisolone oN glucose meTabolism,
metabolic Hormones, insulin sEnsitivity and insulin secretioN in healthy young males: two
randomized, placebo-controlled, double blind, dose-response intervention studies. In both
PANTHEON-I and -II study, 32 healthy male volunteers were randomized to a two-week
treatment with prednisolone 30 mg daily (so called induction dosage; n=12), prednisolone
7.5 mg daily (so called maintenance dosage; n=12) or placebo (n=8) (chapters 5, 8, 9, 10,
11 and 12). These dosages were chosen since they represent the commonly prescribed highand
low dosage in clinical practice. In both studies, participants were treated for a period
of two weeks in order to enable studying past the acute effects of prednisolone treatment.
However, for ethical reasons, it was not feasible to administer healthy volunteers high-dose
prednisolone for a more prolonged period of time.
In the PANTHEON studies, the dose-dependent effects of GCs on glucose tolerance was
assessed by standardized meal challenge tests (chapter 5). We studied the effects of GC
treatment on insulin sensitivity of liver, skeletal muscle and adipose tissue (chapter 5 and
8) by using hyperinsulinemic-euglycemic clamps with stable isotopes. To gain insight into
mechanisms of GC-induced skeletal muscle insulin resistance, insulin signaling was studied
in skeletal muscle biopsies before and during GC treatment (chapter 10). Additionally, the
potential role for both accumulation of glycosphingolipids and the ganglioside GM-3, and
mitochondrial dysfunction in GC-induced skeletal muscle insulin resistance was addressed in
chapter 9. The contribution of microvascular dysfunction to the GC-induced attenuation of
insulin-stimulated glucose uptake in skeletal muscle is reported in chapter 10.
To characterize the effects of GC on adipose tissue function at the molecular level, subcutaneous
adipose tissue biopsies were collected before and during prednisolone treatment and were
analyzed for insulin signaling, lipolytic enzymes, markers of differentiation and secretion
of adipocytokines (chapter 11). In addition, the role of the recently discovered protein
angiopoietin-like protein (Angptl)-4 in GC-induced changes in lipid metabolism particularly
in relation to insulin resistance was studied in chapter 12.
In addition to the effects of GCs on insulin sensitivity, the effects of GCs on beta-cell function
was assessed, both following acute (chapters 4 and 7) and more chronic exposure (chapter
5). In chapter 3, we report that GC-induced beta-cell dysfunction is characterized by
endoplasmic reticulum (ER) stress resulting in impaired insulin production and secretion and
beta-cell apoptosis.
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In chapter 7, we have described a proof-of-concept study demonstrating the potential
of glucagon-like peptide-1 receptor agonists (GLP-1 RA) to prevent GC-induced glucose
intolerance and beta-cell dysfunction. In this study, called the PREDEX (PREdnisoloneinduced
beta-cell Dysfunction prevented by EXenatide) study 8 healthy men were treated
with a single gift of prednisolone 80 mg (or placebo) and received the GLP-1 RA exenatide (or
saline) intravenously.
Finally, we investigated the effects of GCs on glucose tolerance, insulin sensitivity and insulin
secretion in patients with RA. In these studies, the metabolic effects of chronic inflammation
and reduction hereof by prednisolone treatment could be added into the equation. To this
extent, a single blind, randomized controlled trial in forty-one patients with early active RA
was conducted with two high dosages of prednisolone (30 mg daily and 60 mg daily) dosed for
a single week, as a sub study of the COBRA-light trial (Combinatie Behandeling Reumatoide
Artritis) (chapter 13). Off note, the COBRA-light study was designed to compare two different
treatment schedules for early RA (COBRA schedule according to BeSt compared to a modified
COBRA schedule (‘light’) with lower prednisolone dosages).
Moreover, a cross-sectional study in chronic RA patients was performed. In this study, glucose
metabolism was compared among RA patients chronically treated with GCs and RA patients
that had never used GCs, allowing studying the metabolic effects of GC treatment while
correcting for the influence of the underlying disease (chapter 14).
In the following section, the key organs involved in insulin action and insulin secretion that
are affected by GC treatment are summarized in the section below, based both on the results
obtained from our studies and on previous data or data recently obtained by other research
groups.
Glucocorticoids diabetogenic effects: involved organs and pathways
1. Liver
The liver is a key regulator of metabolism, within a complex regulatory network of hormonal,
autonomic nervous and metabolic stimuli. Under fasting conditions, the liver maintains
euglycemia by producing glucose through both gluconeogenesis and glycogenolysis. Insulin,
which is secreted in response to a meal or carbohydrate load is the most important hormone
that suppresses endogenous glucose production (EGP). On the other hands, the so-called
contra-regulatory hormones cortisol and glucagon increase EGP under hypoglycemic
conditions. The endogenous hormone cortisol as well as exogenously administered GCs act
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Figure 1. Currently known mechanisms by which glucocorticoids (GCs) induce hyperglycemia in
healthy humans. GCs impair beta-cell function by inducing endoplasmic reticulum (ER) stress and by
reducing calcium signaling in beta cells, resulting in impaired insulin secretion. GCs furthermore impair
the effects of a number of non-glucose insulin secretagogues, including arginine, acetylcholine and the gut
hormones glucagon-like peptide (GLP)-1 and glucose-dependent insulinotropic peptide (GIP). In addition,
GC impair alpha-cell function, since glucagon levels are inappropriately elevated both under fasting
conditions and in the postprandial state. Increased glucagon levels may stimulate endogenous glucose
production (EGP) by the liver. In addition, GCs induce liver insulin resistance, thus further contributing
to GC-induced increment of EGP, especially in the hyperinsulinemic state. GCs also induce adipose tissue
insulin resistance, resulting in impaired glucose uptake and increased lipolysis in the postprandial
period. Moreover, plasma levels of adipokines are altered towards a more diabetogenic profile. Finally,
GCs impair glucose uptake in skeletal muscle by impairing insulin signaling. This reduction in insulinstimulated
glucose uptake may in part be explained by impaired insulin-stimulated capillary recruitment,
since GCs also induce vascular insulin resistance. By these combined actions, GCs induce hyperglycemia
in healthy men. (See page 367 for full color figure)
partly under influence of the autonomic nervous system (ANS) that innervates the adrenal
gland. As such, in a number of studies [21-23], but not in all [24-27], short-term GC treatment
was shown to augment fasting EGP in healthy volunteers. Indeed, we confirmed and expanded
these findings by showing a relatively small increase in fasting EGP following a two-week
treatment with prednisolone 30 mg daily as measured by hyperinsulinemic-euglycemic
clamp with labeled glucose as described in chapter 8 [28]. Low-dose prednisolone did not
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affect EGP in the fasted state. As reviewed in chapter 2, GCs may increase EGP and particular
gluconeogenesis by several mechanisms. First, GCs increase the expression and activity of
rate limiting enzymes of gluconeogenesis including PEPCK and G6Pase [29-31]. Indeed, the
PEPCK gene contains a glucocorticoid response element (GRE) in its promoter region and is
considered a key player in GC-induced hyperglycemia [32], but several other genes in liver
glucose metabolism were identified as GC target genes in gene expression profiling studies
[33]. Interestingly, it was recently demonstrated that other nuclear receptors, including
peroxisome-proliferator activated receptor (PPAR)-alpha and liver X receptors (LXRs),
important regulators of lipid and cholesterol metabolism respectively, are potentiating
factors in GC-induction of PEPCK and G6Pase, since PPAR and LXR knock-out mice models
were shown to be in part resistant to the metabolic effects of GC treatment by reducing the
activation of these enzymes [34, 35]. In addition, GC treatment promotes breakdown of
protein and fat stores [36], thus providing increased supply of substrates for gluconeogenesis,
such as alanine and glycerol, to the liver [37, 38]. As such, we could demonstrate in chapter
8 that during hyperinsulinemic conditions, high-dose prednisolone treatment impaired
the insulin-mediated suppression of lipolysis and proteolysis, whereas low-dose treatment
only increased lipolysis [28]. Finally, GCs may potentiate the effect of other glucoregulatory
hormones, such as glucagon and epinephrine, thus further enhancing fasting EGP [35, 39].
The strongest effects of GCs on liver glucose metabolism in our studies, however, were
observed under hyperinsulinemic conditions. In healthy volunteers, insulin infusion at 200
mU/m2 .min decreased EGP by approximately 80%. This insulin-effect was markedly and
dose-dependently reduced by prednisolone treatment, by 17±6 and 46±7 % respectively
(chapter 8) [28]. The observation that glucocorticoids blunt the suppressive effects of insulin
on EGP was shown previously during a 24-hr high-dose continuous cortisol infusion [21, 22].
We could demonstrate for the first time that this effect is already present at prednisolone
dosages just above physiological levels (7.5 mg daily). The mechanisms underlying GCinduced
liver insulin resistance are at present not clarified. In rats, dexamethasone was
shown to impair insulin-induced phosphorylation of insulin receptor substrate (IRS)-1 and
phosphatidylinositol 3-kinase (PI-3K) in liver cells indicating impaired insulin signaling [40].
We conclude that the liver is a key player in the diabetogenic effects induced by GC treatment
through increased EGP, and more particular, by inappropriate suppression of glucose
production in the hyperinsulinemic state (Figure 1). Thus, several experimental approaches
have been undertaken to further characterize the involved mechanisms and to assess strategies
to reduce these hepatic metabolic effects of excess GC levels. First, treatment of rats with a
liver-selective GR antagonist resulted in reduced fasting plasma glucose levels and decreased
EGP during a hyperinsulinemic-euglycemic clamp [41]. In addition, also in rats, activation of
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AMP-activated protein kinase (AMPK) was shown to suppress GC-induced hyperglycemia by
reducing PEPCK and G6Pase expression [42]. Interestingly, the most commonly prescribed
blood-glucose lowering agent metformin has been shown to exert its effects by activating
AMPK, which potentially renders this drug capable to prevent or counteract GC-induced
diabetogenic effects [43].
2. Skeletal Muscle
Skeletal muscle tissue is the most important site for insulin-stimulated glucose uptake in the
postprandial period and plays a crucial role in glucose metabolism [44]. Already in studies
performed several decades ago, GCs were shown to reduce insulin-stimulated glucose disposal
in a dose-dependent matter [21, 45, 46], which was confirmed in our studies (chapters 5, 9,10
and 11). In agreement with other studies [47, 48] we could demonstrate that particularly
nonoxidative glucose disposal (reflecting glycogen synthesis) is impaired by GC treatment,
whereas glucose oxidation rates remained intact (chapter 9) [28]. Despite the fact that GCinduced
skeletal muscle insulin resistance is a well-known observation, the pathways in
skeletal muscle tissue responsible for this impairment presently remain incompletely detailed.
From rodent studies, it has become evident that insulin signaling is impaired by GC treatment.
As such, the phosphorylation of several proteins in the insulin-signaling cascade including
IRS-1, PI-3K, Akt and glycogen synthase kinase (GSK) was shown to be reduced [40, 49-52].
We could for the first time confirm this finding in humans, where high-dose prednisolone
treatment impaired insulin-induced phosphorylation of its downstream targets in skeletal
muscle (chapter 10) [53]. However, to date, it is unclear which molecular mechanisms are
targeted by GC treatment that could interfere with insulin signaling.
It has been postulated that GC-induced changes in protein metabolism may be involved. Indeed,
GC treatment is frequently associated with skeletal muscle wasting due to proteolysis [54-59].
GCs not only reduce skeletal muscle mass, but may also increase plasma levels of amino acids
which were shown to impair insulin signaling in skeletal muscle [60, 61]. As shown in chapter
8, and in line with previous reports [57], we observed that high-dose prednisolone treatment
impaired the suppressive effects of insulin on whole-body valine turnover, but that it did not
affect whole-body valine turnover under fasting conditions. Given the small effect size that
was observed and the absence of a correlation with measures of glucose metabolism, we feel
that altered amino acid metabolism is unlikely to be responsible for the dramatic reduction in
insulin-stimulated glucose uptake following short-term GC treatment.
Additionally, GC-induced changes in lipid metabolism could play a role in skeletal muscle
insulin resistance. Glucorticoids were shown to impair the suppressive effects of insulin
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on whole-body lipolysis, resulting in increased levels of nonesterified fatty acids (NEFA)
(chapter 8) [28]. NEFA spillover may enhance the accumulation of intramyocellular lipids
(IMCL) [62], including triacylglycerol, but also more toxic intermediates such as fatty acyl
CoA, diacylglycerol, sphingolipids such as ceramide and gangliosides such as GM-3, which
have been shown to impair insulin signal transduction and have been associated with reduced
skeletal muscle insulin sensitivity [63-66]. We were particularly interested in the role of
sphingolipids, since attenuation of ceramide synthesis either by pharmacological treatment
with myriocin or through genetic ablation of dihydroceramide desaturase (des1) was shown
to prevent GC-induced insulin resistance in rodents [67]. However, in the presence of marked
insulin resistance, skeletal muscle concentrations of ceramide, glucosylceramide and GM-3
were not affected by prednisolone treatment (chapter 9) [28]. An explanation for this
discrepant finding as compared to the rodent study may possibly be explained by the different
tissue examined. Although GC treatment was shown to attenuate insulin-mediated glucose
uptake in skeletal muscle, increased ceramide levels were particularly demonstrated in liver,
and not in skeletal muscle. In addition, several other factors, including different treatment
duration and differences in lipid metabolism between rodents and humans, may contribute
to the observed difference.
Recently, impaired mitochondrial function has received much interest given its association with
reduced peripheral insulin sensitivity in subjects with T2DM [68]. It was hypothesized that
impaired mitochondrial function results in accumulation of toxic lipid intermediates, which
affect insulin signaling and glucose metabolism. Since GCs induce myopathy, we hypothesized
that mitochondrial dysfunction could be involved in GC-induced insulin resistance. Thus, we
measured ex vivo mitochondrial respirometry capacity in human skeletal muscle, adjusted
for mitochondrial copy number, before and after 14-days prednisolone treatment. However,
despite marked insulin resistance, mitochondrial function was not altered by prednisolone
treatment (chapter 9). It should be noted that several measures of mitochondrial function
exist in literature, where the gold standard measure remains to be established [68]. Therefore,
we cannot completely rule out a potential role of mitochondrial dysfunction in GC-induced
insulin resistance [69, 70].
Other factors may contribute to GC-induced impairment in insulin signaling and insulinstimulated
glucose disposal. First, GCs were shown, despite its general anti-inflammatory
properties, to induce the acute phase response in skeletal muscle tissue, which could reduce
insulin sensitivity [71]. Moreover, the endoplasmic reticulum (ER) has been identified as a
major regulator of metabolic homeostasis and inflammatory responses and ER stress was
shown to have unbeneficial effects on insulin sensitivity [72]. Whereas GCs were recently
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shown to induce ER stress in beta cells [73], it is uncertain whether GCs may also alter ER
function in skeletal muscle tissue. Finally, GCs may impair insulin sensitivity by impairing the
delivery of insulin and glucose to skeletal muscle by reducing capillary recruitment through
the induction of vascular insulin resistance as detailed below [53].
We conclude that skeletal muscle tissue is a key player in GC-mediated insulin resistance
resulting in a dose-dependent reduction in insulin-stimulated glucose uptake (Figure 1).
However, a single causative pathway could not be identified in our studies.
3. Adipose tissue
Although the adverse metabolic effects of elevated GC levels on glucose [74] and protein
metabolism [75] are reasonably well defined, the effects on lipid metabolism are presently
less clear [76]. In addition, the involvement of a number of organs in the development of GCs
diabetogenic effects, including pancreatic islet cells, skeletal muscle and liver are understood
to a greater extent than the role of adipose tissue [9]. However, several observations indicate
that GCs exert unfavorable effects on adipose tissue and lipid metabolism.
As such, chronic GC excess in Cushing’s syndrome or during GC treatment increases fat
deposition in the visceral compartment [77] and promotes liver fat accumulation [78], at the
cost of subcutaneous fat deposition. This increment in visceral adipose tissue (VAT) by GCs
may be induced by several factors, including increased intake of high-caloric “comfort food”
[79], increased lipoprotein lipase (LPL) activity particularly in VAT [80, 81], increased VAT
adipogenesis through stimulation of differentiation of pre-adipocytes into mature adipocytes
[82] and possibly enhanced de novo lipogenesis [83]. Although we observed no significant
changes in body weight, body composition, body fat distribution and liver fat content following
two-week treatment with low- or high-dose prednisolone (chapter 8) [28], in subcutaneous
adipose tissue (SAT) biopsies decreased protein expression of adipogenesis markers
(peroxisome-proliferator activated receptor (PPAR)-γ and Kruppel-like factor (KLF)-4) was
observed (chapter 11). Although our study did not allow to examine VAT, on extrapolation,
these findings are in line with and may ultimately lead to the classical body fat distribution
observed following chronic GC treatment as indicated above. The GC-induced reduction in
SAT with concomitant increase in VAT is clinically relevant since VAT is well known to be
associated with an untoward metabolic profile as opposed to SAT and is associated with
increased cardiovascular risk [84].
In addition to altering adipose tissue distribution, GCs may also affect adipose tissue function.
We were able to show that GC treatment impaired insulin signaling in SAT in vivo in healthy
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humans. The insulin-stimulated phosphorylation of well-known insulin target proteins
Akt/PKB and the downstream target PRAS-40 was impaired by high-dose prednisolone
treatment (chapter 11). These data confirm and expand in vitro and in vivo rodent studies,
where GC treatment impaired insulin signaling [85]. Impaired insulin action in adipose tissue
may adversely affect both glucose and lipid metabolism. Indeed, an intact insulin-signaling
pathway is of pivotal importance for insulin-stimulated glucose uptake through recruitment
of the glucose transporter (GLUT)-4 to the plasma membrane enabling glucose transport
into the cell [86]. Although the overall contribution of glucose uptake by adipose tissue in
the postprandial state is thought to be relatively modest [44], it does contribute to glucose
tolerance as was elegantly demonstrated in adipose tissue-specific glut4-/- mice [87]. These
mice developed hyperglycemia and were characterized by liver and skeletal muscle insulin
resistance, most likely due to altered secretion of adipokines [87]. In our study, prednisolone
30 mg altered the circulating levels of various adipokines towards a more diabetogenic
profile, as was previously oberved in in vitro studies [88]. Thus, plasma concentrations of
adiponectin, which are generally positively associated with insulin sensitivity, were reduced
following high-dose prednisolone treatment, whereas adiponectin protein expression in SAT
was decreased by both prednisolone dosages. The adipokines resistin and leptin, which are
usually negatively associated with insulin sensitivity, were both increased by prednisolone
30 mg (chapter 11). Although increased leptin levels have been shown to inhibit food intake
in the central nervous system (CNS), leptin also acts as pro-inflammatory factor in adipose
tissue by promoting the release of pro-inflammatory adipokines. Similarly, resistin has also
been demonstrated to promote circulating pro-inflammatory adipokine levels [89].
In addition to promoting glucose uptake in adipose tissue, insulin is a key hormone in the
regulation of lipid metabolism, amongst others by inhibiting adipose tissue lipolysis and thus
decreasing plasma NEFA levels [90]. In contrast, GCs were shown, despite their lipogenic
actions in VAT, to acutely increase fasting lipolysis rates in a number of in vitro studies [76].
GC-induced induction of lipolysis could be explained by increased activity of key lipolytic
enzymes adipose triglyceride lipase (ATGL) [91] and hormone sensitive lipase (HSL)
[92] and possibly by augmented beta-adrenergic signaling following GC treatment [76]. It
should be noted that differences were observed between various adipose tissue depots
where GC-induced augmentation in lipolysis rates was particularly observed in VAT and less
pronounced in SAT [93]. In addition, antilipolytic effects of GC treatment have also observed
in vitro [76]. In line with most of these in vitro observations, acute GC administration was
shown to increase fasting whole-body lipolysis in healthy humans [83, 94, 95]. During more
prolonged GC exposure, however, this lipolytic effect was no longer observed most likely
due to compensatory hyperinsulinemia [25, 96, 97]. In our study, high-dose prednisolone
treatment even lowered fasting lipolysis (chapter 8). In line with these findings, we observed
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decreased fasting ATGL expression, the rate-limiting enzyme in adipose tissue lipolysis,
during both low- and high-dose prednisolone treatments, while fasting HSL expression was
unchanged (chapter 11).
In the same study, however, we observed for the first time that prednisolone treatment dosedependently
impaired insulin-stimulated suppression of lipolysis and increased plasma NEFA
levels during hyperinsulinemia, indicating adipose tissue insulin resistance. Since both ATGL
expression and phosphorylation of HSL in SAT during insulin infusion were unaltered by
prednisolone treatment, it is unclear through which mechanisms this effect occurs. Possibly,
the inhibiting effects of insulin on protein kinase A and perilipin expression, a lipid dropletassociating
protein, are reduced by prednisolone treatment, thereby promoting triglyceride
lipolysis. An alternative hypothesis is that the increment in whole-body lipolysis is mostly
derived from VAT, from which no biopsies could be obtained in our study design. Increased
lipolysis and the ensuing consequently enhanced plasma NEFA levels have been associated
with impaired glucose metabolism via their inhibiting effects on muscle and liver insulin
signaling [98].
In conclusion, based on our findings in healthy volunteers, GCs diabetogenic effects also
involve actions on adipose tissue, i.e. induction of adipose tissue insulin resistance, resulting
increased lipolysis, as well as induction of untoward plasma adipokine levels, and therefore it
is likely that adipose tissue is involved in GCs diabetogenic effects.
4. Pancreatic beta cells
The pancreatic beta-cell plays a crucial role in glucose metabolism and in the past decades,
beta-cell dysfunction has been acknowledged as the key defect underlying the development
of T2DM [99]. Under physiological conditions, insulin secretion is directly related to insulin
sensitivity through a hyperbolic relation [100]. The product of these parameters, known as
the disposition index, remains constant. Thus, when the workload on the beta cell increases
(by factors such as obesity, insulin resistance or low-grade inflammation), healthy beta cells
can adapt by augmenting insulin secretion to meet this increased demand, thus maintaining
euglycemia [101]. Before addressing the various aspects of beta-cell function that are altered
by GC treatment, it is important to state that beta-cell function cannot be quantified by one
single measure. Fasting measures include homeostatic model assessment of beta-cell function
(HOMA-B) [102] and pro-insulin/insulin ratio (PI/I ratio), the latter giving an impression of
insulin processing in the beta cell [103]. Dynamic parameters, including the insulinogenic
index (IGI) and the total C-peptide response in relation to prevalent glucose levels (AUC / CPEP
AUC ) may be obtained from oral and intravenous glucose challenge tests. In addition,
GLUC
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mathematical models have been developed to calculate various parameters of beta-cell
function from dynamic tests. In the model developed by Mari, a dose-response curve relating
insulin secretion and glucose levels is calculated, from which glucose sensitivity of the beta
cell is derived [104]. This dose-response curve is modulated by two additional factors. First,
the insulin response to the rate of change of glucose concentrations is calculated as rate
sensitivity. Secondly, the effects of non-glucose insulin secretagogues, including non-glucose
substrates, gastrointestinal hormones and neurotransmitters are expressed as a potentiation
factor [104]. The gold standard of beta-cell function measurements is the disposition index,
where insulin secretion is adjusted for insulin sensitivity, both determined by clamp method.
Thus, insulin secretion is assessed by the hyperglycemic clamp, whereas insulin sensitivity is
obtained from the hyperinsulinemic-euglycemic clamp [105]. Arginine may additionally be
added during the hyperglycemic clamp to obtain maximal secretory capacity at this of level of
hyperglycemia [106].
Direct effects of glucocorticoids on beta-cell function: in vitro studies
As reviewed extensively in chapter 2, GCs were shown to directly impair insulin secretion in
vitro in insulinoma cell lines and in rodent-derived islets [9]. GCs reduced glucose-stimulated
insulin secretion (GSIS) by reducing glucose uptake and oxidation, resulting in reduced
ATP synthesis and calcium influx. However, GCs also impaired more distal pathways in the
insulin secretory process. As such, glucorticoid treatment reduced the activity of protein
kinase A (PKA) and protein kinase C (PKC), two key enzymes involved in insulin exocytosis.
These distal sites of interference by GCs in the insulin secretory process may explain the
wide range of non-glucose insulin secretagogues that are also inhibited by GC treatment,
including the PKA ligand ketoisocaproate, the amino acid arginine, the sulfonylurea drugs
tolbutamide and gliclazide and the phorbolester PMA, which activates PKC [9]. Interestingly,
GCs may also impair insulin secretion by disturbing autonomous nervous system (ANS)
function. As such, GCs inhibited the phospholipase C (PLC)-PKC pathway, which is used by
acetylcholine to induce insulin secretion [107]. In addition, GCs increased the expression of
α2 adrenergic receptors, thus enhancing sympathetic activity, leading to reduced PKA activity
and subsequent decreased insulin release [108, 109].
In addition to reducing insulin release, GCs may display other detrimental effects on beta-cell
function by several different mechanisms. First, GC treatment was shown to reduce insulin
biosynthesis by reducing ATP/ADP ratio and pancreatic and duodenal homeobox (PDX)-1
expression [73, 110, 111]. Second, in vitro studies have indicated that GCs induce beta-cell
apoptosis, which may ultimately result in reduced beta-cell mass [112].
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Interestingly, endoplasmic reticulum (ER) stress may be an important factor in these GCinduced
beta-cell effects [73]. The ER is a highly dynamic cell organelle, which is responsible for
the synthesis of all secreted proteins. In the ER, proteins are translated, folded and assessed for
quality before they are released. In the beta cell, (pro)-insulin is the most abundant produced
protein, and in the glycemia-stimulated state, it may account for up to 50% of total protein
production. Sustained increased demand for insulin due to e.g. hyperglycemia, however,
may impose burden or ‘stress’ on the ER. ER stress is characterized by an accumulation of
misfolded proteins inside the organelle. In response, the so-called unfolded protein response
(UPR) is initiated, which aims to restore ER homeostasis by decreasing ER protein load and by
increasing folding capacity. When the UPR fails to alleviate ER stress, however, the UPR triggers
apoptosis [113]. We observed that prednisolone induced ER stress and initiated the UPR as
demonstrated by increased expression of activating transcription factor (ATF) 6 and inositolrequiring
enzyme (IRE)-1/X-box binding protein (XBP)-1 pathways. These modulations of
ER stress pathways were accompanied by upregulation of calpain 10 and increased cleaved
caspase 3, confirming that exposure to prednisolone may promote apoptosis (chapter 3)
[73].
Acute effects of glucocorticoids on beta-cell function in humans
Nearly four decades ago, the acute effects of GCs on glucose metabolism were studied for the
first time. Cortisol infusion induced fasting hyperglycemia in the abscence of an appropriate
insulin response [114], suggesting impaired beta-cell function. Moreover, 24-hr treatment
with prednisolone 60 mg was shown to impair insulin secretion during low-dose glucose
infusion [115]. We could confirm and extend these acute effects of prednisolone treatment
on glucose metabolism and insulin secretion. Prednisolone 80 mg acutely impaired firstphase
GSIS, arginine-induced insulin secretion and beta-cell disposition index as measured
by hyperglycemic clamps in healthy males (chapter 7). In addition, treatment with a similar
prednisolone dosage acutely increased postprandial glucose levels by impairing various betacell
function parameters. These included empirical measures of beta-cell function such as
IGI and AUC /AUC , but also model-derived parameters including glucose sensivity and
CPEP gluc
the potentiation factor (chapter 4). Thus, high-dose GC treatment acutely impairs beta-cell
function resulting in postprandial glucose excursions.
Subacute glucocorticoid treatment in healthy humans
A number of studies have addressed the subacute effects of GCs on beta-cell function, typically
with treatment duration of 3 days. We have extended these studies by prolonging the treatment
duration to a two-week period as this more closely mimics clinical treatment schedules and
by studying dose-dependency (chapter 5 and 6). The difficulty of studying the subacute
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effects of GCs on insulin secretion, however, is the fact that GCs also induce insulin resistance,
which influences the response of the beta cell. As such, short-term treatment with high doses
of dexamethasone or prednisolone in healthy subjects resulted in increased fasting insulin
levels [23, 45, 46, 116-122] and increased insulin secretion during oral glucose tolerance tests
(OGTT) [23, 118, 119], standardized meal tolerance tests [121, 122], intravenous glucose
tolerance tests (IVGTT) [45, 120] and hyperglycemic clamp studies [23, 46, 116, 117, 121].
This increased beta-cell response does not indicate improved beta-cell function, but likely
compensation for GC-induced insulin resistance. As such, we observed that the increase
in first- and second-phase GSIS was no longer significant when we adjusted for insulin
sensitivity (M-value obtained by hyperinsulinemic-euglycemic clamp), i.e. that the disposition
index remained unchanged following prednisolone treatment. Other studies calculating
different variants of the disposition index, similarly reported adequate compensation in
healthy volunteers. However, in susceptible populations, including normoglycemic insulin
resistant individuals [46] or low GSIS [23, 117] prior to treatment with GCs, healthy, firstdegree
relatives of patients with T2DM [45] and obese women [123], GSIS was not enhanced
to such extent to compensate for GC-induced impairment of insulin sensitivity, and thus, the
disposition index decreased following GC treatment.
In addition to first- and second-phase GSIS, we additionally studied other parameters of
beta-cell function during the two-week treatment. On top of the hyperglycemic clamp, 5 g of
arginine was administered to assess maximal insulin secretion capacity at the level of 10 mM
glucose. Interestingly, arginine-stimulated insulin secretion was dose-dependently reduced
by prednisolone treatment. In addition, various aspects of beta-cell function were measured
during standardized meal tests using the model developed by Mari as described. We observed
that prednisolone reduced fasting insulin secretion rates when adjusted for prevailing glucose
levels. In addition, the potentiation factor, which comprises insulin secretion induced by nonglucose
stimuli, was reduced by both low- and high-dose prednisolone treatment (Figure 1).
From both the combined hyperglycemic clamp and the meal test data we conclude that
GC treatment induces beta-cell dysfunction in healthy individuals. This effect is detectable
on specific beta-cell function parameters that seem to involve potentiation phenomena
[104, 106, 124]. On the other hand, the enhancement of first- and second phase GSIS as
well as postprandial insulin release is mainly mediated by an increase in glucose levels
and a compensatory response to GC-induced insulin resistance. Although it appeared that
prednisolone already at a low dose impaired several aspects of beta-cell function, our study
was limited in its power to detect these changes.
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Mechanisms of glucocorticoid-induced beta-cell function
The mechanisms underlying GC-induced beta-cell dysfunction following short-term
treatment presently remain unclear. It is unlikely that chronic processes such as glucotoxicity,
lipotoxicity, deposition of amyloid or loss of beta-cell volume due to extensive apoptosis
are involved. Given GC-induced insulin resistance, ER stress, on the other hand, could be a
mechanism limiting the production of a bioactive insulin molecule. In addition we found
evidence that impaired sympathovagal balance may contribute to beta-cell dysfunction. As
detailed previously, whereas the parasympathetic nervous system (PNS; neurotransmitter:
acetylcholine) promotes insulin secretion, this is inhibited by the sympathetic nervous
system (SNS; neurotransmitter: adrenaline). In vitro, dexamethasone was to reduce insulin
secretion by increasing α2 adrenergic receptors [108, 109] and by impairing acetylcholineinduced
insulin secretion [125]. In our healthy participants, we observed a tendency towards
increased SNS over PNS activity as assessed by cardiovascular ANS measurements derived from
continuous beat-to-beat finger blood pressure recordings, which was associated with fasting
glucose levels and arginine-stimulated insulin secretion (chapter 5). Further experimental
data are needed to clarify the role of ANS in prednisolone-induced beta-cell effects.
Chronic glucorticoid treatment in healthy humans
At present there are no data available from studies addressing the chronic effects of GCs
on beta-cell function in healthy individuals, due to the obvious ethical reasons. However,
the chronic effects of GCs on beta-cell function have been addressed in patients with RA, as
detailed below. However, it is difficult to compare these data to the results obtained in healthy
volunteers, since chronic inflammation also compromises both beta-cell function[126] and
insulin sensitivity [127].
Glucocorticoid receptor polymorphisms and beta-cell function
GCs exert many effects by binding to its cytosolic GR, following which the ligand-activated
GR translocates to the nucleus where it regulates target gene transcriptional activity [128-
130]. Functional single nucleotide polymorphisms (SNPs) in the GR gene (NR3C1), which
demonstrated altered GC sensitivity in vitro, were linked to various metabolic parameters
in vivo [128-130]. Since pancreatic GR overexpression in mice resulted in the development
of beta-cell dysfunction and diabetes [131], we hypothesized that alterations in the GR gene
could be related to measures of beta-cell function. We addressed this hypothesis in 449
subjects with normal- or impaired glucose tolerance, who underwent a hyperglycemic clamp.
We observed that the N363S and ER22/23EK polymorphisms were negatively associated
with first-phase GSIS and disposition index in women, but not in men [132]. The N363S
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SNP displays increased GC sensitivity in vitro [133] and is associated with features of a
Cushingoid phenotype including increased BMI, waist-to-hip ratio, dyslipidemia and fasting
hyperinsulinemia [130, 134]. The ER22/23EK SNP on the other hand, demonstrated reduced
GR activation in vitro, and relative GC resistance in vivo. As such, in men, the ER22/23EK
was associated with a beneficial metabolic phenotype, including increased muscle mass
and strength, lower LDL cholesterol and insulin levels [135, 136]. On the other hand, female
carriers of the ER22/23EK SNP were at increased risk to develop cardiovascular disease
(CVD) [137]. In another cohort, carriers of the ER22/23EK had higher HbA1c levels as
compared to noncarriers, thus raising doubt on the hypothesis that this SNP may induce a
more favourable metabolic profile, especially in women [138]. Although we only observed
the negative association between these NR3C1 SNPs and beta-cell function parameters in
women, the outcome of this study strengthens the evidence for a key role of pancreatic betacell
dysfunction in the development of GCs diabetogenic effects.
5. Pancreatic Alpha Cells
By secreting glucagon, the pancreatic alpha cell has an important role in glucose metabolism
[139]. As previously mentioned, glucagon stimulates hepatic glucose production by
promoting glycogenolysis and gluconeogenesis [140]. In many patients with T2DM, glucagon
levels are increased in the fasted state and are incompletely suppressed in the postprandial
state. Thus, elevated glucagon levels were shown to contribute importantly to both fasting
and postprandial hyperglycemia [141].
Already in 1971, GCs were shown to augment glucagon levels. This was demonstrated both
in healthy persons treated with dexamethasone 2 mg daily for 3 days and in patients with
Cushing’s syndrome. In both groups, fasting glucagon levels were increased and glucagon
concentrations were incompletely suppressed following ingestion of a protein meal or
following alanine infusion [142]. After this single study, the effects of GCs on glucagon levels
were not studied for another 4 decades. Here, we studied the dose-dependent effects of GCs
on glucagon levels and observed that a two-week treatment with low-dose prednisolone
treatment (7.5 mg daily) did not alter fasting or postprandial glucagon levels, however,
that high-dose prednisolone treatment (30 mg daily) administered for this time period
increased fasting glucagon levels and impaired the suppression of glucagon levels following
a standardized meal test and during insulin infusion (chapter 5 and 8) [28, 121]. These
effects of GC treatment were already observed in the acute setting, since a single gift of 80 mg
prednisolone increased postprandial glucagon levels, as described in chapter 7 [143]. Similar
results were obtained by Hansen and colleagues, who observed increased glucagon levels
following a 12-day combined treatment with prednisolone 37.5 mg daily, physical inactivity
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and a hypercaloric diet. Glucagon levels were increased in the fasted state and incompletely
suppressed during an OGTT and isoglycemic intravenous glucose infusion [118, 119]. We
conclude that both fasting and postprandial hyperglucagonemia are present in GC-induced
hyperglucagonemia (Figure 1).
6. The Gut
The incretin hormones, glucagon-like peptide-1 (GLP-1) and glucose-dependent
insulinotrophic polypeptide (GIP) are hormones secreted by the gut that are released
following nutrient ingestion [144]. GLP-1 lowers (postprandial) blood glucose through
several mechanisms, including stimulation of meal-related insulin response and suppression
of glucagon secretion in a glucose-dependent manner. In addition, GLP-1 slows down gastric
emptying, promotes satiety, decreases appetite and reduces body weight. In human beta
cells and in vivo in animals and in humans, exogenous GLP-1 administration, in the presence
of elevated blood glucose, acutely induces insulin-secretion while more prolonged GLP-1
exposure may result in increased insulin production. Interestingly, in rodents, chronic GLP-
1 treatment was shown to increase beta-cell mass by promoting beta-cell regeneration and
proliferation, and inhibiting apoptosis [145, 146]. In addition, GLP-1 treatment may protect
beta cells against several toxic processes, including ER stress [147].
When a similar level of glycemia is induced by glucose ingested by the oral versus the
intravenous route in healthy individuals, the oral glucose yields an insulin response that is
approximately 50-70% higher than the intravenous administered glucose. In patients with
T2DM, this so-called incretin effect is substantially diminished [148]. Although post-load GLP-
1 secretion was found to be reduced in some studies, but not in others, the reduced incretin
effect in T2DM seems rather attributed to a decreased sensitivity of beta cells to endogenous
GLP-1 and GIP [149]. In T2DM patients, a vast body of research [144] indicates that an
impaired incretin effect contributes substantially to (postprandial) hyperglycemia.
Up to recently, the effects of GCs on both GLP-1 and GIP secretion and their insulinotropic
actions have been little addressed. At the onset of our studies, we speculated that GC
treatment would reduce secretion of GLP-1 and GIP based on data obtained in rodents, where
GC treatment resulted in reduced mRNA stability of the preproglucagon gene, a precursor
of GLP-1 [150]. However, during our standardized meal tests, GLP-1 and GIP levels were
not reduced by low- or high-dose prednisolone treatment (chapter 5) [23]. In contrast, a
trend towards increased levels of GIP was observed in the prednisolone 30 mg daily group.
A similar observation was made in two recent studies by another research group, who found
that high-dose prednisolone in combination with physical inactivity and high calorie diet did
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not affect GLP-1 secretion but increased GIP levels during an OGTT and liquid meal test [118,
119]. Interestingly, the insulinotropic effects of GLP-1 and/or GIP were shown to be reduced,
since the incretin effect was significantly decreased [118]. Indeed, the potentiation of GSIS
by incretin hormones was shown to be reduced in a recent study [151]. It is unclear whether
this impaired incretin effect is a specific defect induced by GC treatment or whether it may
be secondary to general GC-induced beta-cell dysfunction, as detailed previously. Thus, an
impaired gut-islet axis characterized by impaired insulinotropic effects of GLP-1 and GIP is
present in GC-induced hyperglycemia (Figure 1).
7. Microcirculation
As discussed to extent previously, GCs dose-dependently induce insulin resistance at the
level of liver, adipose tissue and skeletal muscle, resulting in impaired suppression of EGP
and reduced insulin-stimulated glucose uptake [28]. However, insulin action is not restricted
to these metabolically active tissues. It is now evident that vascular tissue, and particularly
endothelial cells, represents an important physiological target for insulin. Insulin exerts
a vasodilatory action by promoting nitric oxide (NO) release from the endothelial cells.
This involves the phosphorylation of endothelial NO synthase (eNOS), which on its turn
is mediated by the PI3K/Akt pathway [152]. By increasing blood flow and by recruiting
capillaries to expand the endothelial transporting surface available for nutrient exchange,
the vascular actions of insulin significantly contribute to overall insulin-stimulated glucose
uptake [153-155]. In addition, the local actions of insulin on (micro)vascular tissue may be
important in the regulation of blood pressure and the pathogenesis of hypertension [7, 156,
157]. Prior to our experiments (chapter 10), the effects of GCs on microvascular function, in
particular in the presence of insulin, in vivo in humans had not been investigated. In addition,
it was unclear whether impairments in microvascular function could contribute to GCinduced
hyperglycemia. An indication that GCs may impair microvascular function came from
observations in resistance arterioles isolated from C57BL/6j mice. Treatment of these mice
with dexamethasone suppressed endothelial eNOS protein levels and impaired endotheliumdependent
vasodilation [158].
We assessed the effects of a two-week treatment with low- and high-dose prednisolone
on microvascular function, as measured by capillary microscopy, in healthy humans both
in the fasted state and during insulin infusion. Prednisolone dose-dependently impaired
capillary recruitment during insulin infusion, but not in the fasted state, when prednisolone
induced fasting hyperglycemia. Moreover, prednisolone-induced impairment in insulinstimulated
capillary recruitment was related to insulin-stimulated glucose disposal
during the hyperinsulinemic-euglycemic clamp, as well as to postprandial glucose levels
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and postprandial insulin sensitivity, suggesting that capillary recruitment may also be an
important factor contributing to postprandial glucose metabolism. In addition, the decrease
in insulin-stimulated capillary recruitment was associated with the prednisolone-induced
increase in systolic blood pressure. In isolated cremaster resistance arteries of prednisolonetreated
male Wistar rats, the vasodilatory actions of insulin were similarly attenuated.
At the molecular level, prednisolone impaired the vascular effects of insulin by reducing
phosphorylation of the Akt pathway, thus providing a mechanism for the vascular insulin
resistance observed in vivo [53]. In summary, prednisolone impairs the vasodilatory actions
of insulin at the level of the microcirculation by attenuating insulin signaling through the Akt
pathway, resulting in impaired glucose disposal, impaired glucose tolerance and increments
in systolic blood pressure. Thus, impaired microvascular function may be regarded among
the mechanisms by which GC act to exert their diabetogenic effects. Figure 1 summarizes the
adverse effects of GCs on glucose metabolism.
8. other potential glucocorticoid-target organs
A number of organs that were not addressed in the sections above may also be involved in
GC-associated adverse metabolic effects. The brain most likely represents a very important
contributor to GC-induced hyperglycemia, but this area was not studied in our conducted trials,
which focused on the peripheral effects of GC treatment. Indeed, GRs are expressed in various
part of the brains, including the hypothalamus [159]. In rodents, intracerebroventricular
infusion of dexamethasone increased food intake [160] and decreased peripheral glucose
uptake [161]. In addition, it was recently shown that dexamethasone administration in the
arcuate nucleus of the hypothalamus resulted in hepatic insulin resistance through activation
of sympathetic pathways to the liver [162].
In addition, the kidney has been shown to be play a role in glucose homeostasis. Glucose is
freely filtered by the glomerulus and is reabsorbed in the proximal tubule mainly by the sodium
glucose transport protein (SGTL)2. Increased SGTL2 activity, particularly in the presence of
hyperglycemia, stimulates tubular glucose absorption and may sustain hyperglycemia [163].
Interestingly, in a mouse sepsis model, GCs were shown to decrease glucose excretion by
upregulating SGLT2 [163]. The role of the kidney in GC-induced hyperglycemia in humans,
however, remains to be established.
Finally, GCs were shown to alter cardiac glucose and lipid metabolism, possibly by altering
cardiac insulin sensitivity. Dexamethasone treatment was shown to reduce glucose oxidation
in myocytes in rodents. In addition, GC treatment increased NEFA uptake and oxidation, but
was also shown to promote myocardial NEFA overload [164]. Importantly, the latter has been
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linked to the development of cardiomyopathy [164]. Although the effects of GCs on cardiac
substrate metabolism could in part explain the GC-associated increased risk to develop heart
failure or cardiovascular disease [165], it is presently unclear whether changes in cardiac
metabolism may contribute to the systemic diabetogenic effects of GC treatment. Although
the brain, kidneys and heart seem important GC target organs, they were beyond the scope
of our studies.
Glucocorticoids diabetogenic effects and interaction with inflammation:
studies in rheumatoid arthritis patients
Addressing glucocorticoids metabolic effects in RA patients is interesting for several reasons.
First, in RA patients GCs are used on large scale [2], thus rendering these patients into a
very clinically relevant population. In addition, in RA patients, it is possible to examine the
interaction of GC treatment and systemic inflammation, of which the latter also affects both
beta-cell function [126] and insulin sensitivity [127]. Moreover, some RA patients are treated
chronically with GCs, thus providing data that extend the short-term studies conducted in
healthy individuals.
Irrespective of treatment given, RA patients are at increased risk to develop impaired
glucose metabolism and eventually T2DM likely due to chronic inflammation [166]. As such,
correlations were found between insulin resistance, glucose tolerance and various markers of
systemic inflammation in several studies [167-171]. The metabolic effects GC treatment in RA
population, however, are a topic of extensive discussion.
Short-term intervention studies
Reduction of inflammation by GC treatment was shown to improve glucose tolerance in
two short-term studies [172, 173]. We similarly studied the short-term effects of high-dose
(60 mg or 30 mg once daily administered for 1 week) prednisolone treatment in recentlydiagnosed,
drug-naive RA patients with active disease (chapter 13) [170]. Glucose tolerance,
beta-cell function and insulin sensitivity were measured during frequently-sampled OGTTs.
Following treatment, we observed no deterioration of glucose tolerance in the entire group,
however, there was considerable between-subjects variability. The incidence of T2DM
increased from 3% to 10%, but a significant number of patients also improved their glucose
tolerance state. The duration of disease was the most important predictor to determine
whether patients progressed to T2DM or improved their glucometabolic state. Fasting insulin
sensitivity (HOMA-IR) was reduced by treatment, but this was compensated for by enhanced
beta-cell response during the OGTT. To conclude, it remains unclear whether the reduction of
inflammation may completely off set the adverse metabolic effects induced by GCs, however,
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short-term treatment in RA patients does not seem to be complicated by marked glucose
intolerance as is characterized in healthy subjects, although inter-individual variability is
considerable (chapter 13) (Figure 2).
Figure 2. Hypothetic representation of the balance between systemic inflammation severity and
(high)-dose glucocorticoid (GC) treatment in relation to glucose tolerance. In healthy volunteers, the
diabetogenic effects of GCs are well described. However, in patients with inflammation, GCs may improve
glucose tolerance by reducing systemic inflammation. In patients treated with GCs for conditions that
are usually characterized by low-grade systemic inflammation, such as chronic obstructive pulmonary
disease (COPD), the diabetogenic effects of GC treatment may dominate its anti-inflammatory actions
with respect to glucose metabolism. However, in patients with high-grade inflammation, such as recently
diagnosed rheumatoid arthritis (RA) patients, GCs anti-inflammatory actions may outweigh their
diabetogenic effects. (See page 367 for full color figure)
Cross-sectional studies in chronic rheumatoid arthritis patients
In a number of small cross-sectional studies, chronic GC exposure was related to impaired
fasting insulin sensitivity as measured by HOMA-IR [174] and tended to predict T2DM,
although in these studies the presence of a chronic disease (i.e. RA) affecting these
parameteres was poorly addressed [175, 176]. In order to further characterize the effects of
GCs in chronic RA patients, we recruited a large cohort of chronic RA patients treated with
GCs for over 3 months (RA+GC; n=58) and assessed glucose tolerance, beta-cell function
and insulin sensitivity by frequently-sampled OGTTs. This group was compared to chronic
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RA patients who were GC-naïve (RA-GC; n=82) (chapter 14). We observed no differences in
glucose tolerance and static or dynamic measures of beta-cell function and insulin sensitivity
between groups, however, cumulative GC dosage was independently associated with HOMA-
IR and T2DM prevalence, also after adjusting for disease activity and patient characteristics.
However, in this cross-sectional study, confounding by indication should be kept in mind. This
indicates that cumulative GC use might be a proxy for long-term disease activity, which itself
influences glucose metabolism, since GCs are mostly given to the patients with refractory
disease activity [171]. We concluded that chronic GC use may be related to T2DM as compared
to other RA medications, however, since these findings were not substantiated in randomized
controlled trials, no definitive conclusions can be drawn.
From the studies in RA patients it is evident that the metabolic effects of GCs are much less
pronounced than in healthy controls, as GC may simultaneously alleviate the diabetogenic
effects of the inflammatory disease. The amount of systemic inflammation when GC therapy
is initiated may be an important determinant of the net effects of GC treatment on glucose
tolerance (Figure 2).
Glucocorticoids diabetogenic effects: implications for the development of dissociated
glucocorticoid receptor agonists
A number of lessons may be appreciated from the above-summarized studies for the current
development of dissociated GR agonists. First, it is clear that various organ systems and
different pathways contribute to the diabetogenic effects of GCs. This should be kept in mind
when assessing the metabolic effects of novel GR modulators. In addition, we have shown that
several unfavorable effects (i.e. increased postprandial glucose levels, impaired suppression
of EGP and lipolysis by insulin) may already develop during treatment with dosages as low
as 7.5 mg prednisolone daily (chapters 5 and 8) [28, 121] (Table 1), which were previously
considered to be ‘safe’. Thus, ideally, the novel GR agonists should display anti-inflammatory
properties similar to high-dose prednisolone treatment, but should have a side-effect profile
of an equivalent prednisolone dose lower than 7.5 mg daily. Finally, since many metabolic
adverse effects become apparent in the postprandial state or during hyperinsulinemia
(detailed below), the metabolic effects of classic GR agonists and dissociated GR agonists,
when compared in future head-to-head studies in humans, should be assessed using dynamic
tests such as OGTTs and clamp tests, since fasting measures may underestimate the GCinduced
effects.
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Table 1. Adverse metabolic effects induced by low-dose glucocorticoid treatment in healthy men
as observed in our studies.
Glucose levels
312
Elevated fasting plasma glucose levels*
Postprandial hyperglycemia
Potentially involved mechanisms
Liver Impaired suppression of EGP by insulin
Skeletal muscle Reduced insulin-stimulated glucose uptake*
Adipose tissue Impaired suppression of lipolysis by insulin
Increased NEFA levels during hyperinsulinemia
Decreased markers of adipogenesis in SAT
Decreased adiponectin expression in SAT
Beta cells Reduced glucose-adjusted basal insulin secretion*
Impaired potentiation of insulin secretion by non-glucose stimuli*
Postprandial midday hyperinsulinemia
* Indicates trend. EGP: endogenous glucose production; NEFA: non-esterified fatty acids; SAT:
subcutaneous adipose tissue.
Glucocorticoids diabetogenic effects: from mechanism to treatment?
Despite the vast number of documented observations clearly showing that glucocorticoids
induce glucose intolerance and diabetes in the clinic [14-16], remarkably, there are no studies
that have investigated how GC-induced diabetes may best be treated, or preferentially, could be
prevented. It has become increasingly clear that the most pronounced effects of GC treatment
are in the postprandial period. We could demonstrate increased postprandial glucose
levels, increased EGP, lipolysis and proteolysis, and impaired capillary recruitment during
hyperinsulinemic conditions, whereas all these parameters were not affected in the fasted
state when prednisolone treatment induced fasting hyperinsulinemia (chapters 4,5,7,8
and 10) [28, 53, 121, 122, 143]. In line with these data, patients with Cushing’s syndrome
have predominantly increased postprandial glucose levels as opposed to near normal fasting
glucose levels [177]. And more recently, similar observations have also been observed in
clinical studies in patients treated with medium-to-high dose GCs [16, 178, 179]. Patients
with chronic obstructive pulmonary disease (COPD) that were treated with GCs at 08.00 in
the morning displayed significant glucose elevation resulting in afternoon and evening, but
not overnight, hyperglycemia [179]. In another study, this phenomenon was explained by an
early inhibition of insulin secretion followed by decreased insulin action later during the day.
All metabolic parameters were completely restored by the next morning [178].

These observations have important implications for treatment of GC-induced hyperglycemia.
Glucose-lowering therapy should be predominantly directed at the time period between
midday and midnight, and caution should be exercised with the use of long-acting basal
insulin, because it may precipitate nocturnal hypoglycemia when the effect of prednisolone
wanes. Given this specific period of time, short-acting insulin analogues seem a reasonable
choice to treat the typical pattern of hyperglycemia.
In this thesis, we have also explored the potential of GLP-1 based treatment to treat GCinduced
hyperglycemia [143]. Since GLP-1 treatment stimulates insulin secretion [180, 181],
reduces glucagon secretion [182] and improves postprandial insulin sensitivity [183, 184], it
addresses at least three important pathophysiological features of GC-induced hyperglycemia.
Indeed, in a proof-of-concept study in healthy volunteers, infusion of the GLP-1 receptor
agonist (GLP-1 RA) exenatide prevented glucose intolerance and islet-cell dysfunction
induced by acute prednisolone treatment (chapter 7) [143]. In addition, in the long term,
GLP-1 RA reduce appetite [185], stimulate weight loss [186], decrease visceral fat tissue
[187] and hepatic steatosis [188], improve postprandial lipid profiles [189] and favorably
change adipose tissue biology [187], effects that could counteract the features associated with
chronic GC treatment. Currently, studies are ongoing in which the effects of more prolonged
treatment with the dipeptidyl-peptidase (DPP)-4 inhibitor sitagliptin on glucose metabolism
are assessed in men with the metabolic syndrome concomitantly treated with high-dose
prednisolone [190]. It will be interesting to see whether also in this population, incretinbased
therapies may be useful in the treatment of GC-induced hyperglycemia.
Thus, expanding knowledge of the pathophysiology of the diabetogenic effects of GC treatment
should in due time result in a more tailored therapy to treat the associated hyperglycemia.
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327
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15

Nederlandse
Samenvatting
Diabetogene effecten van glucocorticoïde
medicatie: bekende en onbekende aspecten
Dutch summary

Nederlandse samenvatting
ontdekking van het bijnierschorshormoon cortisol
Cortisol is een hormoon dat in ons lichaam wordt geproduceerd in de schors (ook wel cortex
genoemd) van de bijnieren. De bijnieren zijn organen die, zoals de naam al aangeeft, in de
buurt van de nieren liggen. Cortisol wordt vanwege zijn structuur een steroïd hormoon
genoemd (er zijn nog vele andere steroïde hormonen in ons lichaam), en omdat snel duidelijk
werd dat cortisol een belangrijke rol speelt in de glucose stofwisseling werd het geclassifeerd
als glucocorticosteroïd hormoon. Dit werd later in de naamgeving verkort tot glucocorticoïd
hormoon.
Al begin 20ste eeuw werd duidelijk dat dieren en mensen die geen of onvoldoende actieve
bijnieren hebben verschillende problemen krijgen, zoals te lage bloedsuikers, te lage
bloeddruk en klachten van algehele zwakte, tezamen ook wel de ziekte van Addison genoemd.
Uit dierproeven uitgevoerd rond 1930 werd tevens duidelijk dat stoffen uit de bijnieren een
belangrijke rol spelen bij stressreacties van het lichaam. Het was toen echter nog onduidelijk
welke stof hiervoor precies verantwoordelijk was. Hierin kwam verandering toen de inactieve
vorm van cortisol (cortison) in 1936 voor het eerst werd geïsoleerd uit de bijnieren van
runderen door Poolse chemicus Tadeusz Reichstein en door de Amerikaan wetenschapper
Edward Calvin Kendall. Hierdoor werd het mogelijk de effecten van cortison nauwkeurig te
bestuderen (het menselijk lichaam zet cortison om in cortisol, waardoor het actief wordt).
Cortisol zelf werd een tiental jaren later ook geïsoleerd.
ontwikkeling van synthetische bijnierschorshormonen tot medicijnen
Hoewel de isolatie van cortison een grote stap vooruit was om de werking van het hormoon
precies te kunnen onderzoeken, werd meteen een grote beperking duidelijk: de hoeveelheid
die geïsoleerd kon worden uit runderen was zeer beperkt. Daarom werd gezocht naar een
methode om cortison synthetisch te produceren. Dit proces werd versneld door Amerikaanse
deelname aan de tweede wereldoorlog. Er gingen hardnekkige, maar nooit bewezen geruchten
dat Duitse Luftwaffe piloten bijnierextracten gebruikten om zo beter bestand te zijn tegen
zuurstof tekort ten gevolge van vliegen op grote hoogte. Ondanks enorme inspanningen werd
het eerste synthetische cortison vlak na afloop van de oorlog geproduceerd.
In 1948 konden met het nieuw geproduceerd cortison de eerste studies in mensen uitgevoerd
worden. Reumatoloog Philip Showalter Hench (een collega van Kendall) toonde aan dat
cortison inderdaad het bijnierschors hormoon was dat de ziekte van Addison kon verhelpen
en daarmee werd de kritieke rol van cortisol in de regulatie van bloeddruk en glucose
stofwisseling bevestigd. Maar cortison behandeling bleek meer gunstige effecten te hebben:
het gaf een dramatische afname van gewrichtsklachten in patiënten met reumatoïde artritis en
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het bleek ook heel effectief bij de behandeling van andere auto-immuunziekten (aandoeningen
waarbij het lichaam eigen weefsels aanvalt en doet ontsteken). Het wondermiddel cortison
werd in 1950 officieel gelanceerd als medicijn en in hetzelfde jaar kregen Reichstein, Kendall
en Hench de Nobelprijs voor de Geneeskunde voor hun onderzoek naar de structuur en
werking van bijnierschorshormonen.
Glucocorticoïden: een zorgwekkend bijwerking profiel
Na een eerste periode van enorm enthousiasme werd helaas snel duidelijk dat cortison
therapie ging met forse bijwerkingen. Hiertoe behoorden onder andere de ontwikkeling
van overgewicht, suikerziekte, hoge bloeddruk, versnelde bot- en spierafbraak, verhoogde
kans op infecties, maagzweren, depressie of psychose, huidafwijkingen, verhoogde
kans op hart- en vaatziekten en staar. En deze lijst is zeker nog niet compleet. Met als
doel het bijwerkingenprofiel te verbeteren werden nieuwe synthetische glucocorticoïd
hormonen gemaakt, zoals prednisolon (=prednison) (1965) en dexamethason (1966).
Hoewel deze middelen minder bijwerkingen vertoonden ten opzichte van cortison, bleven
teleurstellenderwijs de meesten hiervan bestaan, zeker in hoge dosering.
Dit heeft hedendaags nog steeds consequenties voor het gebruik van glucocorticoïden bij
patiënten. je bent als arts door de bijwerkingen gedwongen snel de dosis te verlagen wat
kan leiden tot onderbehandeling van de onderliggende ziekte. Verder hebben patiënten vaak
weerstand prednisolon te gebruiken vanwege zijn beruchte bijwerkingen (het wordt vaak
‘een paardenmiddel’ genoemd). Tenslotte moeten artsen vaak extra medicatie starten om
bijwerkingen te voorkomen of te bestrijden, zoals middelen die botontkalking remmen en het
maagslijmvlies beschermen.
Nadelige effecten van glucocorticoïden op de glucose stofwisseling
Al snel na de introductie werd bekend dat behandeling met glucocorticoïden belangrijke
nadelige effecten heeft op de glucose stofwisseling. Schattingen zijn dat bij 30% van de
patiënten die langdurig glucocorticoïden gebruiken suikerziekte optreedt. Ondanks dat deze
bijwerkingen al relatief lang bekend zijn, zijn de mechanismen hierachter minder duidelijk.
In de jaren ‘60-‘70 werd bekend dat glucocorticoïden de werking van het hormoon insuline
verminderen (‘insuline resistentie’). Ook zijn er op dit moment geen duidelijke richtlijnen hoe
suikerziekte veroorzaakt door glucocorticoïden het best kan worden behandeld of kan worden
voorkomen. Na de jaren ‘70 raakte het onderzoek naar de bijwerkingen van glucocorticoïden
op de stofwisseling wat uit beeld.
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Nederlandse samenvatting
Hierin kwam de laatste 10 jaar verandering door de ontwikkeling van nieuwe glucocorticoïd
medicijnen, die wel krachtig ontsteking kunnen remmen, maar waarschijnlijk minder
bijwerkingen hebben op de stofwisseling. Deze medicijnen in ontwikkeling worden
zogenaamde “gedissocieerde glucocorticoïd receptor agonisten” genoemd. Om de
ontwikkeling van deze nieuwe medicatie verder mogelijk te maken is echter meer kennis
nodig van de bijwerkingen van de huidige glucocorticoïden in mensen, zodat in een latere
fase de middelen met elkaar vergeleken kunnen worden in studies. Daarom hebben we in
mijn proefschrift de metabole bijwerkingen van glucocorticoïden bestudeerd en biomarkers
geprobeerd te vinden die het bijwerkingenprofiel zo goed mogelijk weergeven. We hebben
de effecten van het glucocorticoïd prednisolon bestudeerd, omdat dit wereldwijd het meest
wordt voorgeschreven en gekozen voor een hoge (inductie) dosis (30 mg per dag) en een
zogenaamde onderhoudsdosering (7,5 mg per dag).
In het eerste deel van mijn proefschrift beschrijven wij de introductie van glucocorticoïden
als medicatie in de jaren ’50 (hoofdstuk 1) en de stand van zaken omtrent de kennis van
metabole bijwerkingen van glucocorticoïden toen ons onderzoek startte (hoofdstuk 2).
Hierin wordt duidelijk dat er in de vroegere artikelen veel nadruk werd gelegd op het ontstaan
van insuline resistentie in spier, lever en vetweefsel door glucocorticoïd behandeling.
In deel 2 van mijn proefschrift laten we zien dat verstoring van de alvleesklierfunctie een
belangrijke rol speelt bij het ontstaan van de glucocorticoïd-geïnduceerde bijwerkingen op
de stofwisseling.
In hoofdstuk 3 tonen we dat de bèta cellen in de eilandjes van Langerhans onvoldoende
insuline kunnen produceren doordat de functie van het endoplasmatisch reticulum, de
eiwitfabriek van de cellen, door glucocorticoïd behandeling wordt verstoord. Ook vonden
we dat behandeling van bèta cellen met prednisolon leidt tot celdood, wat de functie van de
eilandjes van Langerhans verder belemmert.
In hoofdstuk 4 worden de effecten van prednisolon op bèta-cel functie in gezonde mannen
beschreven. We vonden voor het eerst dat bèta-cel functie specifiek na het eten van een
maaltijd verstoord is, zowel na acute als meer langdurige prednisolon therapie. Hiervoor
maakten we gebruik van mathematische modeling analyse van glucose en C-peptide curves
na de maaltijd voor en na prednisolon behandeling.
In hoofdstuk 5 hebben we dit concept verder onderzocht. We vonden opnieuw dat bèta-
cel functie na de maaltijd verstoord wordt, maar ook dat de maximale secretoire capaciteit
van de bèta cel, gemeten tijdens intraveneuze glucose en arginine toediening, verlaagd wordt
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door prednisolon behandeling. In dit hoofdstuk beschrijven we tevens dat de functie van
de alpha cellen, die eveneens gelegen zijn binnen de eilandjes van Langerhans, ook door
prednisolon behandeling wordt verstoord. Alpha cellen scheiden namelijk te veel van het
hormoon glucagon uit, met name tijdens de maaltijden, hetgeen leidt tot overmatige glucose
productie door de lever. Zowel de negatieve veranderingen in alpha- als bèta-cel functie
leken gerelateerd aan ongunstige wijzigingen in de balans tussen het parasympathische en
sympathische gedeelte van het autonome zenuwstelsel, die insuline en glucagon secretie in
de eilandjes reguleert.
In hoofdstuk 6 beschrijven we dat mensen die genetische veranderingen in het DNA hebben
dat de glucocorticoïd receptor codeert, verminderde hoeveelheid insuline uitscheiden bij
een glucose stimulus (dus verminderde functie van de bèta cel hebben). Hoewel dit een
associatieve studie betreft in tegenstelling tot de eerdere interventie studies, benadrukt
dit hoofdstuk onze mening dat verstoorde alvleesklierfunctie, gekenmerkt door relatief te
weinig insuline productie, een cruciale rol speelt bij het ontstaan van hoge bloedsuikers door
prednisolon behandeling.
Door het toenemende inzicht in de verstoring van de alvleesklierfunctie door prednisolon
hebben we een additionele studie geïniteerd om te kijken of we de negatieve effecten van
prednisolon konden voorkomen door gelijktijdige behandeling met de glucagon-achtige
peptide-1 (GLP-1) receptor agonist exenatide. Dit middel is onlangs geregistreerd voor de
behandeling van suikerziekte omdat het de productie van insuline stimuleert en de aanmaak
van glucagon remt, met name in de periode na de maaltijden. Behandeling met exenatide
voorkwam inderdaad de effecten van prednisolon op de suikerstofwisseling door glucagon
productie te remmen en insuline productie te stimuleren; dit hebben wij beschreven in het
laatste hoofdstuk van deel 2, te weten hoofdstuk 7. Omdat thans geen goede richtlijnen
bestaan over welke middelen het beste gebruikt kunnen worden om suikerziekte veroorzaakt
door glucocorticoïd therapie te behandelen, postuleren wij dat GLP-1 receptor agonisten zoals
exenatide een geschikte kandidaat zouden kunnen zijn. Momenteel worden de beschermende
effecten van zogenaamde DPP-4 remmers, medicatie die het GLP-1 in het lichaam verhoogt,
op de suikerstofwisseling onderzocht tijdens prednisolon gebruik.
Insuline resistentie op het niveau van de lever, skeletspier en vetweefsel is een belangrijke
oorzaak voor de nadelige effecten van glucocorticoïden op de stofwisseling, zoals reeds
decennia bekend is. Echter, de wijze waarop dit gebeurt is nog steeds voor een groot deel
onduidelijk. In deel 3 hebben we mechanistische studies ondernomen om nader te kijken
naar onderliggende oorzaken.
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Nederlandse samenvatting
In hoofdstuk 8, bevestigen we dat hoge dosering insuline resistentie geeft in de skeletspier,
lever en vetweefsel, echter wij tonen aan dat, met name gedurende hogere insuline
concentraties in het bloed, een lage dosering prednisolon, tot voor kort veilig geacht, ook de
insuline gevoeligheid vermindert van het vetweefsel en de lever, wat op langere termijn zou
kunnen leiden tot verstoring van bloed glucose waarden en vetten in het bloed.
In hoofdstuk 9 zoeken we naar de onderliggende mechanismen van prednisolon
geïnduceerde insuline resistentie in de skeletspier. We hypothetiseerden dat prednisolon
schadelijke vetten in spier zou doen ophopen (zoals ceramides), echter dit bleek niet het
geval. Ook veronderstelden we dat mitochondriën, de energiecentrale van de cel, minder
goed functioneerden met als gevolg minder glucose opname in de skeletspier, maar de
mitochondriële functie bleef intact.
Een “orgaan” waarvan wij voor het eerst vonden dat het een rol speelt bij de metabole
bijwerkingen van prednisolon is de microcirculatie, de kleinste vaatjes in ons lichaam
waar energie uitwisseling plaats vindt. Tijdens hoge insuline concentraties in het bloed,
het belangrijkste moment van glucose opname door het lichaam, verstoort prednisolon de
functie van de kleinste bloedvaten leidend tot minder glucose opname (hoofdstuk 10). Dus
concludeerden wij dat prednisolon behandeling de kleinste bloedvaatjes resistent maakt
voor de effecten van insuline, leidend tot minder glucose uptake, met name in de skeletspier.
Ook de stijging in bloeddruk die we vonden na prednisolon behandeling was gecorreleerd
met afwijkende werking van de kleinste bloedvaatjes waarvan bekend is dat ze de bloeddruk
reguleren door de vaatweerstand te bepalen.
In hoofdstuk 11 hebben we naar de effecten van prednisolon op het subcutane vetweefsel
gekeken. We vonden voor het eerst in mensen dat er een afgenomen activatie is tijdens
insuline toediening van belangrijke eiwitten betrokken bij glucose opname in het vetweefsel.
Ook scheidt het vetweefsel ongunstige hormonen uit die elders in het lichaam de stofwisseling
kunnen ontregelen.
In het laatste hoofdstuk van deel 3, hoofdstuk 12, hebben we specifiek gegeken naar het eiwit
Angiopoietine-like protein 4, kortweg Angptl4. Angptl4 is op belangrijke wijze betrokken in
onze vetstofwisseling. Interessant genoeg werd onlangs aangetoond dat muizen die geen
Angptl4 hebben, minder bijwerkingen ondervonden van prednisolon behandeling. Daarom
besloten we dit eiwit in onze deelnemers te meten voor en na behandeling. Hoewel de
vetstofwisseling bij iedere deelnemer ongunstig werd veranderd door prednisolon, het eiwit
Angptl4 leek hier geen rol van belang bij te spelen. Wel verstoort prednisolon de regulatie van
Angptl4 door insuline.
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In deel 4 van mijn proefschrift beschrijven we de effecten van prednisolon bij patiënten met
reumatoide artritis (RA). Dit hebben wij gedaan omdat RA patienten vaak glucocorticoïden
krijgen voor hun ziekte en daarmee vormen ze dus een zeer relevante onderzoekspopulatie.
Verder biedt het de mogelijkheid om de effecten van glucocorticoïden op de stofwisseling
in mensen met systemische ontsteking te meten. Systemische ontsteking zelf namelijk is
ook ongunstig voor de stofwisseling omdat het insuline resistentie geeft en ook de werking
van de bèta cel vermindert, daarom zouden glucocorticoïden zelfs een gunstig effect op de
stofwisseling kunnen hebben door inflammatie te verminderen. Bij patiënten die onlangs
met RA zijn gediagnosticeerd en hoge ontstekingswaarden in het bloed hebben, bleken
de negatieve effecten van glucocorticoïden op de suikerstofwisseling inderdaad minder
uitgesproken, waarschijnlijk door vermindering van systemische ontsteking. Dit laten we
zien in hoofdstuk 13. In mensen met langer bestaande RA is glucocorticoïd therapie echter
geassocieerd met het voorkomen van suikerziekte, zoals beschreven in hoofdstuk 14. Het
moeilijke echter is dat de meest zieke mensen vaker glucocorticoïden krijgen, derhalve is het
lastig om in te schatten of de ziekte zelf, of juist de therapie, heeft geleid tot suikerziekte.
Tenslotte worden in het vijfde deel alle resultaten van het proefschrift samengevat in een
overzichtsartikel (hoofdstuk 15) dat een volledige update tracht te geven over de wijze
waarop glucocorticoïden de stofwisseling verstoren en de kans op suikerziekte sterk
verhogen.
De belangrijke conclusies in mijn proefschrift zijn dat: 1. tot voor kort veilig geachte lage
doseringen prednisolon (7,5 mg equivalent) niet geheel veilig zijn uit een metabool perspectief,
2. meest uitgesproken bijwerkingen van prednisolon niet in de nuchtere toestand optreden
maar na de maaltijd typisch in de middag en avond, wanneer er hoge insuline concentraties
zijn en 3. dysfunctie van de alpha en bèta cellen in de eilandjes van Langerhans op belangrijke
wijze bijdragen aan de metabole bijwerkingen van glucocorticoïd behandeling.
Deze conclusies hebben enkele belangrijke consequenties voor de ontwikkeling van de
zogenaamde gedissocieerde glucocorticoïd receptor agonisten en de behandeling van
suikerziekte veroorzaakt door prednisolon. Bij de ontwikkeling van de nieuwe gedissocieerde
glucocorticoïd receptor agonisten moet er goed gekeken worden naar de postprandiale fase,
omdat in de nuchtere toestand belangrijke negatieve effecten van prednisolon therapie worden
gemist en derhalve het bijwerkingenprofiel kan worden onderschat. Ook is het noodzaak
een compound te ontwikkelen met een bijwerkingen profiel minder dan prednisolon 7,5 mg
per dag equivalent. Verder kunnen deze nieuwe inzichten leiden tot betere behandeling van
suikerziekte ontstaan door glucocorticoïd therapie in de kliniek. Men zou met name de hoge
335
Nederlandse samenvatting

Nederlandse samenvatting
bloed glucose spiegels in de middag en avond moeten behandelen met kortwerkende insuline
preparaten. Thans worden veel patienten nog met langwerkende preparaten behandeld
waarbij er in de ochtend te lage en potentieel levensbedreigende glucose concentraties
kunnen ontstaan. Tevens is het vertrouwen op nuchtere bloed glucose waarden niet mogelijk
vanwege dit specifieke patroon van te hoge bloedsuiker concentraties later op de dag en
avond.Verdere studies zijn echter nodig die prospectief onderzoeken hoe de bijwerkingen
van prednisolon op de suikerstofwisseling het beste in de kliniek kunnen worden behandeld
of liever nog kunnen worden voorkomen.
336

Dankwoord
Acknowledgements

Dankwoord
Een van de weinige absolute waarheden in ieder proefschrift is de zinsnede “promoveren
doe je niet alleen”, en aangezien dit proefschrift niet anders is wil ik graag een aantal mensen
bedanken.
Allereerst alle deelnemers van de Pantheon-I, -II, en PREDEX studies. Zonder jullie was dit
proefschrift niet tot stand gekomen. Ook dank voor alle gezelligheid tijdens de ongeveer 2
duizend uur die de clamps en maaltijdtesten in totaal in beslag namen. Ik kan me niet meer
herinneren hoeveel GB’s aan muziek en films we hebben gedraaid en uitgewisseld!
Dan natuurlijk mijn promotor, Prof. Diamant. Beste Michaela, er zijn weinig personen die zo
moeilijk in een kort stukje zijn te karakteriseren als jij. Enorm gedreven, perfectionistisch tot
op het bot, veelzijdige ideeëngenerator, warm, betrokken en humorvol. En niet te vergeten
behoorlijk mesjogge. je hebt altijd goed aangevoeld op welke manier we met ons onderzoek
op zinnige wijze aan het veld konden bijdragen. Ik heb altijd het vertrouwen gehad dat
wanneer ik jou ergens van kon overtuigen, het daarna overal stand zou kunnen houden. Voor
een promotie per se ben je bij jou eigenlijk aan het verkeerde adres; in 4 tot 5 jaar word je de
mogelijkheid geboden jezelf te ontwikkelen tot een onafhankelijk onderzoeker. Dank voor al
het vertrouwen en de geboden ruimte tot groei en ontwikkeling afgelopen jaren.
Graag wil ik de leden van de leescommissie bedanken.
Geachte Prof. Heine, beste Rob. Ik heb door je vertrek naar Eli Lilly in de VS maar een jaar
intensief met je samengewerkt; - ik heb die periode als zeer plezierig ervaren. Daarna bleef je,
ondanks een paar duizend km afstand, altijd zeer geïnteresseerd en betrokken waar nodig, en
was je op een of andere manier in staat mijn mailtjes nog sneller te beantwoorden dan toen je
nog in het VUmc werkte. Niet onbelangrijk, al bij mijn eerste sollicitatiegesprek stelde je me
gerust hoe te functioneren in een omgeving met zo veel dominante vrouwen... Dank!
Geachte Prof. Sauerwein, beste Hans. Op een NVDO congres in de buurt van Arnhem
bespraken we om 02.30 in de bar voor het eerst de mogelijkheid om d.m.v. stabiele isotopen
metabole fluxen te meten voor en na prednisolon behandeling. Een aantal weken later was het
Pantheon-II protocol een feit. Ik heb enorm veel geleerd van uw precieze methodologische
benadering, heldere formulering van probleemstellingen en oneindige lijkende kennis van de
stofwisseling en biochemie. Het was zeer leerzaam om een tijd bij de metabole groep op F5
mee te mogen kijken! In 1 adem wil ik hierbij graag ook Dr. Serlie bedanken. Beste Mireille,
hartelijk dank voor je hulp en begeleiding vanaf het allereerste begin bij het opzetten van het
protocol tot aan het corrigeren van de rebuttals van onze papers!
340

Geachte Dr. Ouwens, beste Margriet. We hebben een turbulente periode binnen TIP meegemaakt
na jouw vertrek naar Dusseldorf. jouw oneindig enthousiasme werkt aanstekelijk en ik wil je
bedanken voor alle samenwerking binnen de bioptstudies, waar ik je altijd kon benaderen
voor allerhande ideeën en plannen.
Geachte dr. Rustemeijer, beste Cees, veel dank voor het beoordelen van mijn proefschrift.
Tevens wil ik je bedanken voor de gastvrijheid met betrekking tot de onderzoekskamer in het
Amstelland ziekenhuis, hoewel mijn studies daar uiteindelijk niet zijn uitgevoerd.
Geachte Prof. Dr. Kramer, beste Mark, dank voor het kritisch beoordelen van mijn proefschrift.
Ik ben blij onder jouw begeleiding de opleiding tot internist te volgen!
Geachte Prof. Groen en Boers, veel dank voor het kritisch lezen van mijn proefschrift.
Verder gaat veel dank uit naar mijn TI Pharma collega’s met wie we het translationele project
tot uitvoer hebben gebracht.
LUMC. Margot, dank voor je hulp bij alle bioptstudies. Ik hoop dat je op een gegeven moment
in staat bent om je onderzoeken alsnog te voltooien. Dear Bruno, thanks for stepping in into
TIP and for your help with the analysis of the skeletal muscle biopsies. Geachte prof. Maassen,
beste Ton, hartelijk dank voor je input gedurende het project!
Ook uit Leiden, maar niet van TIP: Nienke en Leen. Samen hebben we de glucocorticoid
receptor data geanalyseerd en opgeschreven. Veel dank voor het begeleiden van een dokter
bij het uitvoeren en beschrijven van genetische analyses!
UMCG. Beste Anke, dank voor je voor de gezelligheid tijdens alle TIP meetings! Ik bewonder
dat je je proefschrift hebt weten te af te ronden na zo’n moeilijke periode. Veel succes met je
opleiding bij de klinische chemie in Leeuwarden! Theo, veel dank voor je suggesties tijdens
onze besprekingen.
Organon/Schering-Plough/Merck. Beste Wim, als PI van het overkoepelende project had jij de
niet eenvoudige taak iedereen in het gareel te houden. Ik vond het leerzaam binnen de keuken
van een farmaceutische gigant mee te mogen kijken en ik waardeer enorm dat je ondanks
uiterst roerige tijden je altijd 100% hebt ingezet om wetenschappelijke belangen te laten
prevaleren. Veel dank ook naar Ulla en jeroen bij het opschrijven van de Organon maaltijd
studies en Erik Toonen voor de input tijdens de TIP vergaderingen.
341
Dankwoord

Dankwoord
Tot slot wil ik mijn reumatologie collega’s binnen TIP bedanken voor de prettige samenwerking.
VUmc. Debby, het was fijn met je samenwerken met mijn kamer midden tussen de
reumatologen in! Het eindresultaat mag er zijn! Veel succes met de afronding van jouw boekje
en de start van je opleiding! Ook Marieke, veel succes met de afronding van jouw COBRA-Light
data. Verder gaat mijn dank uit naar Prof. Lems, dr. Nurmohamed en Prof. Dijkmans voor het
mogelijk maken van de OGTTs binnen het toch al uitgebreide COBRA-Light protocol.
UMCU. Beste jos, met de MacBookjes gewired hebben we ons door de ingewikkelde statistische
analyses van het Utrecht cohort gewerkt, was mooi! Succes met de laatste loodjes van je
vooropleiding, we zien elkaar bij de volgende COIG... Marlies veel succes met het afronden van
jouw promotie. Geachte Prof. Bijlsma, beste Hans, veel dank voor uw interesse en waardevolle
suggesties gedurende de TIP meetings.
Bij het uitvoeren van mijn onderzoek is de hulp van de research nurses onmisbaar geweest.
Lieve Sandra, jeanette, Ingrid en Mariëlle. Ik kan oprecht zeggen dat het zonder jullie
niet gelukt zou zijn. Enorm veel dank. Ook jennifer Benit, Ans en Nicolette, veel dank bij
alle logistieke hulp op de research unit, het heeft het uitvoeren van mijn onderzoek veel
eenvoudiger gemaakt! Ook Lilian en Louise, dank voor alle hulp over de afgelopen jaren!
Furthermore, I would like to thank Dr. Mari. Dear Andrea, thanks for all your help and
guidance in the different beta-cell projects that we did. The week that I spent in your lab in
Padua (together with the dinner on your roof terrace and being witness of your qualities as a
tour guide in different Padua churches) is something that I consider as of one the highlights of
my PhD period. I have learned a great deal from your analytical mind and technical approach
to interpret data. I hope that we can continue our collaboration in the future.
Dr. Pouwels, beste Petra, dank je wel voor het mogelijk maken van de MRI metingen zowel qua
ontwikkeling als uitwerking.
Ook wil ik enkele mensen van de afdeling radiologie zelf bedanken. Vooral Ton Schweigmann
(nogmaals sorry aan je vrouw dat we haar soms om 06.30 wakker belden bij onbegrijpelijke
MRI storingen) en Astrid, zonder jou was het onmogelijk geweest de DEXA scans zo punctueel
georganiseerd te krijgen!
Erik Serné en Ed Eringa, dank voor de samenwerking op het microcirculatie manuscript.
342

Dr. Kersten, beste Sander, veel dank voor de samenwerking op onze Angptl4 paper!
In het AMC, dr. Ackermans, beste Mariëtte, dank voor je hulp bij het design van de isotopen
infusie protocollen en voor het feit dat de verrijkingsgetallen altijd zo spoedig bepaald werden
in je lab!
Ook in het AMC, Prof. Karemaker, beste john, veel dank voor uw hulp bij de analyses van de
portapres data (en uw gastvrijheid naar Kelly)!
Daarnaast wil ik mijn directe collega’s uit het diabetes centrum en de research afdeling
Interne Geneeskunde bedanken.
Allereerst Nynke. Samen zijn we in de zomer van 2006 begonnen en we hebben van begin tot
eind ons hele promotie traject gedeeld. Ik heb enorm veel aan je te danken gehad gedurende
de jaren. je sociale vaardigheden alsmede organisatorisch talent zijn onnavolgbaar; het heeft
voor mij flink wat deuren open gezet in het VUmc. Ook waardeerde ik onze gesprekken over
wat buiten ons werk in ons leven speelde en hadden we gezellige etentjes met Boudewijn en
Lisette. Fijn dat je mijn paranymf wilt zijn!
Mijn andere roomie, Renate. Wat fijn dat jij het glucocorticoiden stokje hebt overgenomen!
Never a dull moment met jou op de kamer, dance moves, weekend verhalen over wielrennen
in de Limburgse heuvels en natuurlijk je messcherpe retoriek, met name na wat alcoholische
consumpties! Succes met het afronden van jouw GC proefschrift!
Bunckie, bedankt voor de mooie tijden, met name alle congressen all over the world die we
hebben bezocht. What happens op de via pescarotto stays...
Tus, enorm veel dank voor alle interesse in en het creatief meedenken met mijn protocol in
de opstartfase. Als nestor voor jonge onderzoekers werd de overstap vanuit het AMC naar het
VUmc makkelijk!
Remco, ik heb de gezelligheid in de Meander gewaardeerd, gesprekken over wetenschap
tijdens tennis spelletjes op de computer! En met name je gastvrijheid in Zuid-Afrika
voor Lisette en mij. Game driven in Kruger en kamperen en braaien in Tsenzde onder de
sterrenhemel; het kon slechter.
Verder wil ik graag mijn andere collega’s bedanken voor de leuke tijd: Larissa, Weena, Luuk,
Diane, Mariska, Roger, Michiel, Rick, Liselotte (ik hou van je humor), Eelco, Annemarie,
343
Dankwoord

Dankwoord
Carolien, Mirjam en jennifer. Verder Kelly en Marcel: het was ontzettend leuk om met zulke
gedreven studenten samen te werken!
Myrte, we hebben heel wat uren geclampt in de metabole kamer! Je inzet, precisie en werklust
zijn een voorbeeld voor velen; samen hebben we ondanks alle strubbelingen tussen de twee
Amsterdamse academische centra de studie tot een succesvol einde weten te brengen!
Ik wil graag mijn vrienden bedanken voor alle leuke dingen buiten het werk, hoewel de
meesten niet alleen geneeskunde hebben gestudeerd, maar ook nog eens gepromoveerd zijn
en de gesprekken derhalve toch ook nog genoeg over ons werk gaan... Ons contact, de laatste
2 jaar verrijkt met de komst van alle kids, waardeer ik enorm. De jaarlijkse wintersport,
de wekelijkse zaalvoetbalwedstrijden in de middenmoot van de 4de klasse KNVB, de
fietsweekenden waar de km’s steeds langer lijken te worden en dezelfde heuvels steeds steiler,
en natuurlijk alle etentjes en borrels, zijn altijd erg gezellig!! Danny, top dat je mijn paranimf
wil zijn! Jonathan, dank voor alle mooie happenings along the way en een gaaf stagejaar in
Vancouver in onze studententijd!
Schoonouders Frits en Lieke, stand-in oppas bij noodgevallen, klussen in ons huis, (race)
fietsen tussen Amsterdam en Soesterberg, niets is jullie teveel. Dank voor jullie liefdevolle
interesse en betrokkenheid afgelopen jaren! En tante Antoinette dank voor je fameuze
verwenweekendjes in Raalte voor het hele gezin!
Lieve Sander en joost, ik zou mijn jeugd zonder jullie als broers mij niet kunnen voorstellen.
Oneindig veel hockeyen en voetballen zowel binnens- als buitenshuis en dat we af en toe wat
bij elkaar braken hoorde er een beetje bij...
Lieve Pap en Mam, enorm bedankt voor alle support en stimulatie van jongs af aan om
mezelf te ontplooien. jullie afzonderlijke steun is onvoorwaardelijk en lijkt oneindig. Een
meer vastere bodem had ik niet kunnen wensen. jan en Marianne, ik hoef geloof ik niet te
benadrukken wat voor enorme aanwinst jullie voor de familie zijn!!
Tenslotte jij lieve Lisette! Na alle verre reizen die we met z’n 2-en hebben gemaakt, zijn we
nu met kleine Sam neergedaald in de Watergraafsmeer. Het kost jou weinig moeite om me te
laten zien dat er veel belangrijkere en leukere dingen in het leven zijn dan werk. je zorgt altijd
voor voldoende “jus” in ons leven...
Lobby you!
344

Biography

Biography
Daniël Henri van Raalte werd geboren op 8 juli 1980 in het Amsterdamse Slotervaart
Ziekenhuis. Na het afronden van de Amstelveense basisschool Martin Luther King School, ging
hij naar het Vossius Gymnasium in Amsterdam. In het jaar na zijn eindexamen werkte hij bij
belasting- en accounting specialist KPMG en sorteerde hij fruit in een Israelische kibbutz, en
werd zo klaargestoomd voor de opleiding Geneeskunde aan de Universiteit van Amsterdam.
Hij behaalde zijn propedeutisch- (2000), doctoraal- (2004) en artsexamen (2006) cum laude.
Als student deed hij onderzoek bij de afdeling Vasculaire Geneeskunde van het Academisch
Medisch Centrum (AMC) onder supervisie van Prof. Dr. E.S. Stroes en Dr. j.A. Kuivenhoven.
Dit werd gevolgd door een stage bij het Atherosclerosis Specialty Laboratory/Faculty of
Pharmaceutical Sciences van de University of British Columbia (UBC) in Vancouver, Canada
onder begeleiding van Prof. Dr. P.H. Pritchard, Prof. Dr. K.M. Wasan en Dr. M. Li. Gedurende zijn
studie speelde hij hockey bij Pinoké heren 1 en 2 en bij de Vancouver Hawks HC, en was hij
chauffeur voor AFC Ajax in de Champions League in het seizoen 2003-2004. In 2006 startte
hij als promovendus bij het Diabetes Centrum van het Vrije Universiteit Medisch Centrum in
Amsterdam onder begeleiding van Prof. Dr. M. Diamant waarvan de meeste resultaten in dit
proefschrift zijn beschreven. Hij ontving in 2010 een Keystone Fellowship. Tevens werd een
beursaanvraag door de European Foundation for the Study of Diabetes (EFSD) gehonoreerd,
waardoor het onderzoek naar de effecten van glucocorticoïden op de suikerstofwisseling
momenteel wordt voortgezet. Sinds mei 2011 is hij in opleiding tot internist in het Haarlemse
Kennemer Gasthuis onder supervisie van Prof. Dr. R.W. ten Kate en Prof. Dr. M.H.H. Kramer.
Daniël woont samen met Lisette Klip en ze hebben een zoon genaamd Sam.
348

Daniël Henri van Raalte was born on july 8 th , 1980 in Amsterdam. After finishing primary
education at the Martin Luther King School in Amstelveen, he went on to the Vossius Gymnasium
in Amsterdam. In the year following his final exams, he worked at the large tax and auditor
specialist KPMG and sorted fruits in an Israeli Kibbutz, and was thus properly prepared to
start his medical training at the University of Amsterdam. He completed his first year (2000),
master’s (2004) and MD (2006) examinations cum laude. As a student, he participated in
research projects at the department of Vascular Medicine of the Academic Medical Center
(AMC) supervised by Prof. Dr. E.S. Stroes and Dr. j.A. Kuivenhoven. This was followed by a
scientific internship at the Atherosclerosis Specialty Laboratory/Faculty of Pharmaceutical
Sciences of the University of British Columbia (UBC) in Vancouver, Canada under guidance of
Prof. Dr. P.H. Pritchard, Prof. Dr. K.M. Wasan and Dr. M. Li. During his studies, he played field
hockey at a competitive level for Pinoké and Vancouver Hawks HC, and was driver for soccer
club AFC Ajax in the 2003-2004 Champions League season. In 2006 he started his PhD at the
Diabetes Center of the VU University Medical Center in Amsterdam under supervision of Prof.
Dr. M. Diamant. Most of the research performed during this period is presented in this thesis.
For his research, he received a Keystone Fellowship in 2010. In addition, he co-authored a
grant awarded by the European Foundation for the Study of Diabetes (EFSD), enabling
continuation of research into the effects of glucocorticoids on carbohydrate metabolism. In
May 2011, he started his residency in Internal Medicine at the Kennemer Hospital, Haarlem,
under supervision of Prof. Dr. R.W. ten Kate and Prof. Dr. M.H.H. Kramer.
Daniël lives together with Lisette Klip and they have a son named Samuel.
349
Biography

List of
Publications

List of Publications
1. van Genugten RE, van Raalte DH, Diamant M. DPP-4 inhibition restores glucocorticoid-
352
induced pancreatic beta-cell dysfunction in human metabolic syndrome. Submitted
2. van Genugten RE, Serne EH, Heymans MW, van Raalte DH, Diamant M. Postprandial
microvascular function deteriorates along with gradual worsening of insulin sensitivity
and glucose tolerance in men with metabolic syndrome and type 2 diabetes. Submitted.
3. van Genugten RE, Möller-Goede DL, van Raalte DH, Diamant M. Extra-pancreatic effects
of incretin-based therapies: potential benefit for cardiovascular risk management in
type 2 diabetes. Submitted.
4. van Raalte DH, Diamant M, Ouwens DM, Ijzerman RG, Linssen MML, Guigas B, Eringa
EC, Serné EH. Glucocorticoid treatment impairs microvascular function in healthy
males, in association with its adverse effects on glucose metabolism and blood pressure.
Submitted.
5. Brands M, van Raalte DH, joão Ferraz M, Sauerwein HP, Verhoeven Aj, Aerts jM,
Diamant M, Serlie Mj. Glycosphingolipid metabolism and mitochondrial Function do not
explain glucocorticoid-induced insulin resistance in healthy men. Submitted.
6. van Raalte DH, Kwa KAA, van Genugten RE, Tushuizen ME, Holst jj, Deacon CF,
Karemaker jM, Heine Rj, Mari, A, Diamant M. Islet-cell dysfunction induced by
glucocorticoid treatment: potential role for altered sympathovagal balance? Metabolism,
in revision.
7. van Raalte DH, Brands M, Serlie Mj, Mudde K, Stienstra R, Sauerwein HP, Kersten S,
Diamant M. Angiopoietine-like protein 4 is differentially regulated by glucocorticoids
and insulin in vitro and in vivo in healthy humans. Exp Clin Endocrinol Diabetes. In press.
8. van Raalte DH, Den Uyl D, Nurmohamed MT, Diamant M, Lems WF. Glucocorticoid
treatment and hyperglycemia. Arthritis Rheum. 2012. july 25 Epub ahead of print.
9. van Raalte DH, Diamant M. Goals of treatment in type 2 diabetes: beta-cell
preservation. In: Evidence-based management of diabetes. TFM Publishing Ltd. 2012.
10. van Raalte DH, van Leeuwen N, Simonis-Bik AM, Nijpels G, van Haeften TW, Boomsma
DI, Kramer MHH, Heine Rj, Maassen jA, Slagboom PE, Willemsen G, de Geus Ej, Dekker
jM, Eekhoff EM, Diamant M, ‘t Hart LM. Glucocorticoid gene receptor polymorphisms
are associated with reduced first-phase glucose-stimulated insulin secretion and
disposition index in women, but not in men. Diabet Med. 2012.

11. Den Uyl D, van Raalte DH, Nurmohamed MT, Lems WF, Bijlsma jW, Hoes jN, Dijkmans
BA, Diamant M. Metabolic effects of high-dose prednisolone treatment in early
rheumatoid arthritis: diabetogenic effects and inflammation reduction on the balance.
Arthritis Rheum. 2012. 64(3):639-646.
12. Hoes jN, van der Goes MC, van Raalte DH, van der Zijl Nj, den Uyl D, Lems WF, Lafeber
FP, jacobs jW, Welsing PM, Diamant M, Bijlsma jW. Glucose tolerance, insulin sensitivity
and beta-cell function in patients with rheumatoid arthritis treated with or without
low-to-medium dose glucocorticoids. Ann Rheum Dis. 2011. 70(11):1887-94.
13. van Raalte DH, Diamant M. Glucolipotoxicity and beta-Cells in type 2 diabetes: target
for durable therapy? Diab Clin Res Pract. 2011. Suppl 1:S37-46.
14. van Genugten RE, van Raalte DH, Diamant M. Dipeptidyl peptidase-4 inhibitors and
preservation of pancreatic islet-cell dysfunction: a critical appraisal of the evidence.
Diabetes Obes Metab. 2012. 14(2):101-11.
15. Linssen MM, van Raalte DH, Toonen Ej, Alkema W, van der Zon GC, Dokter WH,
Diamant M, Guigas B, Ouwens DM. Prednisolone-induced beta-cell dysfunction is
associated with impaired endoplasmic reticulum homeostasis in INS-1E cells. Cell
Signal. 2011. 23(11):1708-15.
16. van Raalte DH, Brands M, van der Zijl Nj, Muskiet MH, Pouwels Pj, Ackermans MT,
Sauerwein HP, Serlie Mj, Diamant M. Low-dose glucocorticoid treatment affects multiple
aspects of intermediary metabolism in healty humans: a randomised controlled trial.
Diabetologia. 2011. 54(8):2103-12.
17. van Raalte DH, van Genugten RE, Linssen MM, Ouwens DM, Diamant M. Glucagonlike
peptide-1 receptor agonist treatment prevents glucocorticoid-induced glucose
intolerance and islet-cell dysfunction in humans. Diabetes Care. 2011. 34(2):412-7.
18. van der Zijl Nj, Goossens GH, Moors CC, van Raalte DH, Muskiet MH, Pouwels Pj, Blaak
EE, Diamant M. Ectopic fat storage in the pancreas, liver and abdominal fat depots:
impact on beta-cell function in individuals with impaired glucose metabolism. J Clin
Endocrinol Metab. 2011. 96(2):459-67.
19. van Raalte DH, van der Zijl Nj, Diamant M. Pancreatic steatosis in humans: cause or
marker of lipotoxicity? Curr Opin Clin Nutr Metab Care. 2010. 13(4):478-85.
353
List of Publications

List of Publications
20. van Raalte DH, Nofrate V, Bunck MC, van Iersel T, Elassais Schaap j, Nässander UK,
354
Heine Rj, Mari A, Dokter WHA, Diamant M. Acute and 2-week exposure to prednisolone
impair different aspects of beta-cell function in healthy men. Eur J Endocrinol. 2010.
162(4):729-35.
21. van Genugten RE, van Raalte DH, Diamant M. Does glucagon-like peptide-1 receptor
agonist therapy add value in the treatment of type 2 diabetes? Focus on exenatide. Diab
Clin Res Pract. 2009.86 Suppl 1:S26-34.
22. van Raalte DH, Ouwens DM, Diamant M. Novel insights into glucocorticoid-mediated
diabetogenic effects: towards expansion of therapeutic options? Eur J Clin Invest. 2009.
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List of
co-author
affiliations

List of co-author affiliations
Andrea Mari, Institute of Biomedical Engineering, National Research Council, Padova Italy
Andreas Fritsche, Department of Internal Medicine, Eberhard-Karls University of Tübingen,
Tübingen, Germany
Annemarie M. Simonis-Bik, Diabetes Center/Department of Internal Medicine, VU
University Medical Center, Amsterdam, The Netherlands
Arthur J. Verhoeven, Department of Medical Biochemistry, Academic Medical Center,
Amsterdam, The Netherlands.
Ben A.C Dijkmans, Department of Rheumatology, VU University Medical Center, Amsterdam,
The Netherlands
Bruno Guigas, Department of Molecular Cell Biology, Leiden University Medical Center,
Leiden, The Netherlands
Carolyn F. Deacon, Department of Biomedical Sciences, The Panum Institute, University of
Copenhagen, Copenhagen, Denmark
Debby den Uyl, Department of Rheumatology, VU University Medical Center, Amsterdam, The
Netherlands
D. Margriet ouwens, Institute of Clinical Biochemistry and Pathobiochemistry, German
Diabetes Center, Düsseldorf, Germany
Doret I. Boomsma, Biological Psychology, VU University, Amsterdam, The Netherlands
Eco J. de Geus, Biological Psychology, VU University, Amsterdam, The Netherlands
E. Marelise Eekhoff, Diabetes Center/Department of Internal Medicine, VU University
Medical Center, Amsterdam, The Netherlands
Erik J.M. Toonen, Department of Internal Medicine, Radboud University Nijmegen Medical
Center, Nijmegen, The Netherlands
Erik H. Serné, Department of Internal Medicine, VU University Medical Center, Amsterdam,
The Netherlands
Etto C. Eringa, Laboratory for Physiology, Institute for Cardiovascular Research, VU University
Medical Center, Amsterdam, The Netherlands
Fausto Machicao, Department of Internal Medicine, Eberhard-Karls University of Tübingen,
Tübingen, Germany
358

Floris P.G.J. Lafeber, Department of Rheumatology and Clinical Immunology, University
Medical Center Utrecht, Utrecht, The Netherlands
Gerard C. van der Zon, Department of Molecular Cell Biology, Leiden University Medical
Center, Leiden, The Netherlands
Giel Nijpels, The EMGO Institute for Health and Care Research, VU University Medical Center,
Amsterdam, the Netherlands
Gonneke Willemsen, Biological Psychology, VU University, Amsterdam, The Netherlands
Hans P. Sauerwein, Department of Endocrinology and Metabolism, Academic Medical Center,
Amsterdam, The Netherlands.
Hans-U. Häring, Department of Internal Medicine, Eberhard-Karls University of Tübingen,
Tübingen, Germany
Harald Staiger, Department of Internal Medicine, Eberhard-Karls University of Tübingen,
Tübingen, Germany
Heidi Mueller, Institute of Clinical Biochemistry and Pathobiochemistry, German Diabetes
Center, Düsseldorf, Germany
Jacqueline M. Dekker, The EMGO Institute for Health and Care Research, VU University
Medical Center, Amsterdam, the Netherlands
Jens J. Holst, Department of Biomedical Sciences, The Panum Institute, University of
Copenhagen, Copenhagen, Denmark
Jeroen Elaissaiss-Schaap, Department of Early Clinical Research and Experimental Medicine,
MSD Research Laboratories, Oss, The Netherlands
Johannes F.G. Aerts, Department of Medical Biochemistry, Academic Medical Center,
Amsterdam, The Netherlands.
Johannes W.G. Jacobs, Department of Rheumatology and Clinical Immunology, University
Medical Center Utrecht, Utrecht, The Netherlands
Johannes W.J. Bijlsma, Department of Rheumatology and Clinical Immunology, University
Medical Center Utrecht, Utrecht, The Netherlands
John M. Karemaker, Department of Physiology, Academic Medical Center, University of
Amsterdam, Amsterdam, The Netherlands
359
List of co-author affiliations

List of co-author affiliations
Jos N. Hoes, Department of Rheumatology and Clinical Immunology, University
Medical Center Utrecht, Utrecht, The Netherlands
Karin Mudde, Nutrition, Metabolism and Genomics Group, Division of Human Nutrition,
Wageningen University, Wageningen, The Netherlands.
Kelly A.A. Kwa, Diabetes Center/Department of Internal Medicine, VU University Medical
Center, Amsterdam, The Netherlands
Maarten E. Tushuizen, Diabetes Center/Department of Internal Medicine, VU University
Medical Center, Amsterdam, The Netherlands
Marcel H. Muskiet, Diabetes Center/Department of Internal Medicine, VU University Medical
Center, Amsterdam, The Netherlands
Margot M.L. Linssen, Department of Molecular Cell Biology, Leiden University Medical
Center, Leiden, The Netherlands
Mariëtte T. Ackermans, Department of Clinical Chemistry, Laboratory of Endocrinology,
Academic Medical Centre, Amsterdam, The Netherlands
Maria Joao Ferrez, Department of Medical Biochemistry, Academic Medical Center,
Amsterdam, The Netherlands.
Mark H.H. Kramer, Department of Internal Medicine, VU University Medical Center,
Amsterdam, The Netherlands
Marlies C. van der Goes, Department of Rheumatology and Clinical Immunology, University
Medical Center Utrecht, Utrecht, The Netherlands
Mathijs C. Bunck, Diabetes Center/Department of Internal Medicine, VU University Medical
Center, Amsterdam, The Netherlands
Michaela Diamant, Diabetes Center/Department of Internal Medicine, VU University Medical
Center, Amsterdam, The Netherlands
Mike T. Nurmohamed, Department of Rheumatology, VU University Medical Center,
Amsterdam, The Netherlands
Mireille J. Serlie, Department of Endocrinology and Metabolism, Academic Medical Center,
Amsterdam, The Netherlands.
360

Myrte Brands, Department of Endocrinology and Metabolism, Academic Medical Center,
Amsterdam, The Netherlands
Leen M. ‘t Hart, Section Molecular Epidemiology, Department of Medical Statistics and
Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.
Nienke van Leeuwen, Department of Molecular Cell Biology, Leiden University Medical
Center, Leiden, The Netherlands.
Nynke J. van der Zijl, Diabetes Center/Department of Internal Medicine, VU University
Medical Center, Amsterdam, The Netherlands
Paco M.J. Welsing, Department of Rheumatology and Clinical Immunology, University
Medical Center Utrecht, Utrecht, The Netherlands
P. Eline Slagboom, Section Molecular Epidemiology, Department of Medical Statistics and
Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.
Petra J.W. Pouwels, Department of Physics and Medical Technology, VU University Medical
Center, Amsterdam, The Netherlands
Renate E. van Genugten, Diabetes Center/Department of Internal Medicine, VU University
Medical Center, Amsterdam, The Netherlands
Richard G. IJzerman, Department of Internal Medicine, VU University Medical Center,
Amsterdam, The Netherlands
Rinke Stienstra, Nutrition, Metabolism and Genomics Group, Division of Human Nutrition,
Wageningen University, Wageningen, The Netherlands.
Rob J. Heine, Diabetes Center/Department of Internal Medicine, VU University Medical
Center, Amsterdam, The Netherlands
Sander Kersten, Nutrition, Metabolism and Genomics Group, Division of Human Nutrition,
Wageningen University, Wageningen, The Netherlands.
Stefan Lehr, Institute of Clinical Biochemistry and Pathobiochemistry, German Diabetes
Center, Dusseldorf, Germany
Silke A. Schäfer, Department of Internal Medicine, Eberhard-Karls University of Tübingen,
Tübingen, Germany
Thijs van Iersel, Xendo Drug Development, Groningen, The Netherlands
361
List of co-author affiliations

List of co-author affiliations
Timon W. van Haeften, Department of Internal Medicine, Utrecht University Medical Center,
Utrecht, the Netherlands
Ton J.A. Maassen, Leiden University Medical Center, Leiden
Ulla K. Nässander, Department of Early Clinical Research and Experimental Medicine, MSD
Research Laboratories, Oss, The Netherlands
Valentina Nofrate, Institute of Biomedical Engineering, National Research Council, Padova,
Italy
Willem F. Lems, Department of Rheumatology, VU University Medical Center, Amsterdam,
The Netherlands
Wim H. Dokter, Department of Immune Therapeutics, MSD Research Laboratories, Oss, The
Netherlands
Wynand Alkema, Department of Molecular Design and Informatics, MSD Research
Laboratories, Oss, The Netherlands
362

Color Figures

Color Figures
Chapter 1 Figure 1. Overview of the therapeutic and adverse effects induced by glucocorticoid treatment.
366

Chapter 15 Figure 1. Currently known mechanisms by which glucocorticoids (GCs) induce hyperglycemia
in healthy humans.
Chapter 15 Figure 2. Hypothetic representation of the balance between systemic inflammation severity
and (high)-dose glucocorticoid (GC) treatment in relation to glucose tolerance.
367
Color Figures