The Mitochondrial Free Radical Theory of Aging - Supernova: Pliki

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The Mitochondrial Free Radical Theory of Aging
17.1. Demographic Challenges
Demographics is, without doubt, one of the most valuable areas of gerontology today.
Policy-makers throughout the world must plan many years ahead in order to ensure that
economic stability is maintained, and they can only do this if they have a reasonably accurate
idea of, among other things, how much money will be required to support those who are
unable to support themselves. The largest sector of society to which this applies is the elderly.
It is by no means easy to predict, even roughly, how many old-age pensioners there will be in
a given country ten or thirty years from now, and guesses based on intuition have tended to
be wrong. Therefore, policy-makers rely heavily on the calculations of specialists who have
developed sophisticated statistical techniques for giving reliable predictions.
One of the earliest students of demographics was Benjamin Gompertz, a 19th century
actuary. His most lasting contribution to the field 1 was his observation that there was, in the
human societies that he studied, an exponential relationship between age and mortality
rate. (The mortality rate of a group of individuals is simply the proportion of that group
which will die within the next year.) Thus, if one identifies a large group of people who were
55 seventy years ago, and their age at death is plotted, it is found that (say) 5% died within
eight years, then 10% of the remainder within the next eight years, then 20% of the remainder
within the next eight years, and so on; a hypothetical, exact Gompertz distribution is shown
in Figure 17.1. (The “of the remainder” aspect is important—it is why the graph in Figure
17.1c has a “tail” while that in Figure 17.1b does not.) The magic number eight, above, is
called the mortality rate doubling time, or MRDT. This geometric progression obviously
reaches 100% at a finite age, which implies that humans have a maximum lifespan potential
that is exceeded only by a very small number of “outliers.”* The accuracy of the Gompertz
relation is impressive and has not diminished since his time; attempts are still being made to
explain it in mechanistic terms. 2 Moreover, the MRDT of human societies appears not to
have increased measurably during the past century, despite the unarguably immense advances
in health care. For these reasons, many demographers base extensive and detailed predictions
on the assumption that the MRDT will also not increase significantly in the foreseeable
future.
All predictions of this sort are, by definition, based on extrapolation. I have been
challenged by distinguished demographers in the past on the basis that a dramatic increase
in life expectancy within a few decades is “nearly mathematically impossible,” by which is
meant that the required rate of increase of lifespan would be, say, an order of magnitude
greater than it has ever been in our history. The underlying presumption is that, intuitively,
this is fantastically unlikely.
However, it is easy to think of technological advances that have transcended this sort of
logic. One clear example is the history of intercontinental travel. In 1900, one could have
looked at the rate at which ocean-going liners were getting faster and made reasonable
predictions for how long it would take in 1950 to travel from London to New York. Those
predictions would not have been in the region of a few hours, because they would not have
taken into account the advent of aeroplanes. Similarly, any argument about longevity that is
based on extrapolation from past trends does not take into account the likely advent of gene
therapy as a routine treatment and the possibility that it may have a similarly dramatic
impact on the shape of demographers’ graphs.
* Study of why these outliers exist, in the face of the Gompertz distribution's remarkable accuracy for the rest
of the population, is rightly a flourishing research topic. 3 They are not the"tail" of Figure 17.1c, which depicts
a hypothetical, precise Gompertz distribution. But here I want to focus on the population in general, so rare
exceptions are of relatively minor relevance.