Fight Aging!

It is fair to ignore most studies showing extension of life span in laboratory species conducted much prior to the turn of the century. A majority failed to control for calorie restriction, and thus the (usually small) effects evaporate when more rigorously tested. The way this works is that an intervention makes mice nauseous or otherwise uncomfortable, they eat less as a consequence, and thus live longer solely due to lowered calorie intake. This is on top of the usual estimate that most of all published research results are flawed in some way. That includes animal studies that use too few animals, and thus tend to be prone to statistical happenstance, for example. Small studies with few animals are distressingly common in the study of aging, where funding is typically very restricted. Matters did improve once it was no longer possible to be ignorant of the size of the calorie restriction effect on longevity in short-lived species, as that research gained increasing popularity and interest after the 1990s. But as the open access paper I'll point out here suggests, not improved enough.

I think that part of the problem is that too many people were - and still are - trying to evaluate marginal effects on aging. It is hard to accurately detect and quantify small effects in animal studies. A 10% life span extension observed in a group of twenty mice, as compared to a control group of twenty mice, tells us just about nothing other than perhaps it would be good to seek corroboration in a group five times that size - and this example is around the size of effect for most reported interventions based on adjusting the operation of metabolism to slow aging.

One thing I wish was better understood and discussed in our community of advocates, supporters, and researchers is that size of effect and reliability of effect matter enormously. They are the point of the exercise, and the future of our health depends upon them. Everything shown to result in either small or only intermittently apparent outcomes should be rapidly dropped in favor of the continuing search for truly useful approaches to aging. Senescent cell clearance is a shining example of reliability: it always works; it works on many different aspects of aging; it works to treat many different age-related diseases; in fact it puts just about everything else tried to date to shame. The only item from the camp of metabolic manipulation that is as reliable in animal studies is the use of mTOR inhibitors such as rapamycin - and they are notably less effective when it comes to impact on specific age-related diseases. All in all, far too much time and effort is wasted on hoping that unreliable approaches with small effects are magically hiding something useful.

A Reassessment of Genes Modulating Aging in Mice Using Demographic Measurements of the Rate of Aging

The discovery that single gene manipulations can significantly modulate longevity is arguably the major breakthrough in biogerontology thus far. Genetic manipulations of aging in mice are crucial to gather insights into the underlying mechanisms of aging, to discover pathways modulating longevity and to identify candidate genes for drug discovery. Moreover, the manipulation of the aging process in mammalian models (particularly mice) via genetic manipulation (gene knockouts, overexpression, etc.) is crucial to test mechanistic hypotheses of aging. However, determining if such genetic interventions actually affect the aging process and not some others factor of health is not always straightforward.

For example, should a genetic intervention reduce an organism's resistance to disease, this could conceivably reduce the lifespan of the organism, although the rate of aging would not have been affected. Differentiating between genetic interventions that affect the lifespan of an organism through altered health as opposed to changes in the rate of aging is therefore essential to gain insights on aging, and determine interventions with wide ranging effects.

There are two fundamental methods to determine if a life-extending genetic intervention has altered the rate of aging rather than general health. One can track the onset and progression of age-related ailments and physiological degeneration to determine if there is a shift in the onset and on progression of the ailment. In addition, efforts have been made to quantify aging rates with mathematical models such as the Gompertz law of mortality. From the Gompertz parameters, the mortality rate doubling time (MRDT) can be calculated. The MRDT is the amount of time it takes for the mortality rate to double for a given cohort.

A change in MRDT indicates a change in the demographic rate of aging, which is not a perfect reflection of biological aging but a metric that correlates with physiological deterioration and health. Although some studies have investigated MRDT, many authors still often assume that changes in the lifespan of mice following a genetic intervention directly equates to changes in the rate of aging, leading to the misrepresentation of certain genes as having a causal role in aging, when in reality they do not.

Many studies have reported altered median and/or maximum lifespan as a result of an intervention but lifespan alterations may have a number of causes, including altered age at onset of senescence and age-independent mortality. To address this lack of distinction, we previously used linear regression to fit the Gompertz model to longevity data from published mouse studies, and statistically compared the rates of aging in these cohorts. For example, we showed that caloric restriction increases the MRDT and thus retards the demographic rate of aging. Here, the same methodology was employed to reassess mouse longevity data published since 2005 and to identify which genes are more important in determining the demographic rate of aging.

Overall, only 7 of 54 genes were found to have a statistically significant effect on the demographic rate of aging as expected from longevity manipulations. These results suggest that only a relatively small proportion of interventions reported to affect longevity in mice do so through directly influencing the demographic rate of aging. Surprisingly, 20 of 54 genes had a statistically significant impact on the demographic rate of aging in the opposite direction than would be expected for the published longevity effects. One possible explanation is that many mutations impacted on various parameters affecting longevity in non-linear ways, and indeed we observed that increases in aging independent mortality correlated with a slower demographic aging rate. For instance, Sirt1 deficiency extended lifespan but increased the demographic rate of aging; its effect appeared to be exerted instead by delaying the age of onset of mortality rate escalation. This highlights the complex relationship between lifespan and the demographic rate of aging.

Another caveat of our approach concerns the number of mice used in some of the original studies, which ranged from 10 to 146 animals per cohort. Whilst research reported here has attempted to compensate for this by using the Gompertz equation which allows for small sample sizes, one cannot escape the low statistical power that accompanies such small sample sizes. Interestingly, caloric restriction has been shown to significantly retard the demographic rate of aging, but this was a large study with over 200 animals in total. Therefore, caution must be taken when interpreting some of the results detailed here from studies with small sample sizes. Indeed, we observed that in smaller experimental cohorts subjective decisions in estimating Gompertz parameters can significantly affect the results.

Our main conclusions are: 1) most genetic manipulations of longevity in mice do so by modulating aging-independent mortality; 2) there is substantial variation in the lifespan of controls of the same strain across experiments; 3) studies in which the lifespan of the controls is short have a greater lifespan increase, emphasizing the importance of having adequate control groups; 4) mouse lifespan studies employing small cohorts can yield unreliable results; 5) lifespan-reducing experiments tend to be noisier and more difficult to analyze for demographic parameters than life-extending experiments; 6) a greater aging-independent mortality is usually accompanied by a slower demographic aging rate.