Considerations Regarding Public Use of Longevity Interventions

Abstract

Public attention and interest for longevity interventions are growing. These can include dietary interventions such as intermittent fasting, physical interventions such as various exercise regimens, or through supplementation of nutraceuticals or administration of pharmaceutics. However, it is unlikely that most interventions identified in model organisms will translate to humans, or that every intervention will benefit each person equally. In the worst case, even detrimental health effects may occur. Therefore, identifying longevity interventions using human data and tracking the aging process in people is of paramount importance as we look towards longevity interventions for the public. In this work, we illustrate how to identify candidate longevity interventions using population data in humans, an approach we have recently employed. We consider metformin as a case-study for potential confounders that influence effectiveness of a longevity intervention, such as lifestyle, sex, genetics, age of administration and the microbiome. Indeed, metformin, like most other longevity interventions, may end up only benefitting a subgroup of individuals. Fortunately, technologies have emerged for tracking the rate of 'biological' aging in individuals, which greatly aids in assessing effectiveness. Recently, we have demonstrated that even wearable devices, accessible to everyone, can be used for this purpose. We therefore propose how to use such approaches to test interventions in the general population. In summary, we advocate that 1) not all interventions will be beneficial for each individual and therefore 2) it is imperative that individuals track their own aging rates to assess healthy aging interventions.

Keywords: aging; biological age; chronological age; geroprotectors; interventions; public.

FIGURE 1

Strategy for identifying candidate longevity interventions in humans. Population level data would ideally contain information on treatments that people take, e.g. supplements and drugs, as well as their nutrition patterns and genetics. Calculating biological age of each participant and comparing this to their calendar age would identify which of these factors potentially slow aging (e.g. blue and yellow) compared to interventions which may accelerate aging (e.g. red). Factors that slow aging (blue, yellow) or accelerate aging (red), may comprise nutrients, drugs, exercise routines, other lifestyle factors and/or combinations thereof. These would become candidate therapies to test in a prospective cohort.

FIGURE 2

Population level testing of longevity interventions. In this example, a safe nutraceutical can be given to participants (blue = receiving treatment, grey = receiving placebo). After a predefined period of administration, individuals assess their biological age, compared to their chronological age, calculating deltaAge. This can be used to determine if the drug has a statistical effect at decelerating aging at the population level. In addition, personal data of the participants (e.g., sex, genetics, lifestyle, age, genetics, microbiome) can be used to help decipher which sub populations benefit the most from the treatment (not shown).