We all want to live longer, but I find most discussions of available interventions unsatisfying because they tend to ignore any consideration of whether the intervention is too expensive (as the joke about veganism/caloric restriction goes, you may live longer but will you want to?), and be simultaneously too gullible (nutrition research; looking at any health parameter which improves) and too narrow-minded (I don’t care if an intervention has p>0.05 if that still means a 90% probability of benefit).

What I would like to see is a practical guide, taking a decision theory perspective, which follows a procedure something like the following, in taking interventions which:

reduce “all-cause mortality”, ACM is the end-point of choice for life-extension because it is both closest to what we care about (additional life) and it is the hardest of all end-points - it is difficult to cheat, miscount, or not notice, and by being simple, consistent, and unambiguous, avoids many failure modes such as subgroup hacking or dropping outliers or tweaking covariates (either a larger fraction of the controls are dead or not). It also can typically be extracted from most studies, even if they otherwise fail to report much of the data. From a benefit point of view, average reduction in all-cause mortality is excellent for ignoring any zero-sum tradeoffs: it’s no good if reported benefits are only because the doctors writing up a trial kept slicing deaths by different categorizations of causes until they finally found a p<0.05 or if the gains only appear by excluding some deaths as ‘irrelevant’ or if the gains in one cause of death comes at the expense of another cause (is it really helpful to avoid death by heart attack if the intervention just means you die of cancer instead?). For example, one primate study of caloric restriction found benefits only if it excluded a bunch of deaths in the CR group and defined them as not age-related, ignoring “monkeys who died while taking blood samples under anesthesia, from injuries or from infections, such as gastritis and endometriosis.” (Even in the followup paper several years later, the all-cause mortality reduction is much smaller than the “age-related” subcategory.) From a life-extension perspective, it is irrelevant if an intervention reduces some kinds of diseases while making one much more prone to dying from routine causes such as injuries or minor operations under anesthesia (especially since, in modern societies, one is guaranteed to be operated on at some point) - it either reduces all-cause mortality or it doesn’t. as estimated in randomized trials, Correlation is not causation and correlates only rarely turn out to be usefully causal. With aging, the situation is especially severe since aging is the exponential increase in mortality with time as all bodily systems gradually begin to fail; one cannot simply take a correlation of some chemical with aging or mortality and expect an intervention on it to have more than a minute chance of making a difference, since almost every chemical or substance or biomarker will change with age (eg papers on magnesium levels often note that surprisingly, blood levels of magnesium don’t change substantially with age). Worse, over more than a century of concerted effort and tinkering with a bewildering variety of thousands of interventions from injecting embryos to consuming testicles (or crushing one’s own) to yogurt to yoga, it can safely be said that (almost?) all tried interventions have failed, and the prior odds are strongly against any new one. on healthy adult human populations Studies of interventions in the sick are also unhelpful, as (hopefully!) the benefits observed may come from their particular disorder being treated. Animal studies are also unhelpful as results cross-species are highly unreliable and model organisms may age in entirely different manner from humans, who are unusually long-lived as it is. One challenge for this criteria is studies done in elderly populations: because death rates are so high, they offer higher power to detect reductions in mortality; but because that death rate is pushed so high due to the aging process, they will tend to have many chronic conditions and diseases as well. Do we ignore studies recruiting from, for example, 70-90yos who all inherently have health problems? I would say it’s probably better to err on the side of inclusion here as long as subjects were not selected for any particular health problem. for which there are enough trials to do a random-effects/multilevel model, so one can extract a posterior predictive interval of the benefit, It’s important to include heterogeneity from population to population as part of the uncertainty. One of the subtleties often missed in dealing with modeling is that a 95% CI around a parameter is not a CI around the outcome or result. letting one compare mortality reduction against intervention cost, For making decisions, the costs of the intervention must be taken into account. A taxing exercise regimen may be proven to increase longevity, but is that of any value of the regimen takes away much more of your life than it gives back? It may be that no matter whether our certainty is 99% or 99.99% that it works, we would never choose to embark on that regimen. On the other hand, for a cheap enough intervention, we may be willing to accept substantially lower probability: baby aspirin, for example, costs $7 & <10 seconds each morning / <60 minutes a year and we might be willing to use it if our final probability of any benefit is a mere 50%. then yielding an expected value of the intervention (and if <$0, perhaps further analysis about whether it has a reasonable chance of ever becoming profitable and what the Value of Information might be of further trials)

If an intervention passes all these criteria, we can be highly confident that we will gain life, rather than lose it. (At least, considering just 1 intervention at a time. There may be overlap/redundancy/“interaction”, perhaps because of tapping into the same causal mechanisms, leading to canceling out or even backfiring; eg metformin/exercise.)

Of course, these criteria are stringent: we can, for example, exclude almost everything which might appear in an issue of Life Extension Magazine for being too small, conducted in animals, conducted in sick patients, not being a randomized experiment at all, or reporting benefits only on an extremely specific biological endpoint like some cholesterol-related metric you’ve never heard of. Medical journals don’t offer rich pickings either, as they understandably tend to focus on experiments done in sick people rather than healthy people (apparently there’s more funding for the former - who knew?). And when a randomized experiment is done in healthy adult humans, the experiment may either be too small or have run for too short a time, since annual mortality rates for many participant groups are 1% or less; for any sort of effect to show up, you want (very expensive, very rare) studies which enroll at least a thousand subjects for a decade or more. It would not be surprising if we must intone the ritual ending to all Cochrane Reviews, “there is insufficient evidence for a recommendation”.

Fortunately, I do know of a few plausible candidate interventions. For example, baby aspirin has been tested in what must be dozens of trials at this point (generally turning in relative risk/RRs like 0.90 or 0.95 in the meta-analyses), is almost too cheap to bother including the cost, and has minimal side-effects (rarely, internal bleeding); it would not be surprising if a cost-benefit indicated the best decision is taking baby aspirin.

Unfortunately, no one has done this sort of thing before, so for my own use, while not an expert on any of the interventions or on health economics in general, I thought I might try to sketch out some such analyses.