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Biochemistry (Mosc). 2018 Dec;83(12):1517-1523. doi: 10.1134/S000629791812009X.

An Incipient Revolution in the Testing of Anti-aging Strategies.

Author information

1
School of Medicine, Washington University of St. Louis, MO 63130, USA. aging.advice@gmail.com.
2
National Institute of Biological Sciences, Beijing, 102206, China.

Abstract

Recent advances in the technology of "aging clocks" based on DNA methylation suggest that it may soon be possible to measure changes in the rate of human aging over periods as short as a year or two. If this potential is realized, the testing of putative anti-aging interventions will become radically cheaper and faster. This should prompt a re-appraisal of the entire spectrum of methods for evaluating anti-aging technologies in humans and in model systems. In the body of this article, I will argue that (1) testing, not development, is the bottleneck in the flow of knowledge about human anti-aging; (2) single interventions are unlikely to afford major increments in life expectancy in humans; (3) interactions among combinations of known anti-aging interventions are the most important unknown in the field; (4) the daunting number of combinations may be tamed by enrolling large numbers of early adopters who are already using diverse combinations of strategies; (5) the newest methylation clock, called "DNAm PhenoAge" (Levine, M., et al. (2018) Aging (Albany), 10, 573-591) has the potential to tell us which of these people are best succeeding in their quest to slow the aging clock; (6) further optimization of this clock, specialized to the proposed application, is feasible; and (7) multivariate statistics can be used to efficiently identify the best combinations of known interventions that are already being deployed by members of the community which actively seeks to enhance their long-term health. The integration of these ideas leads to a proposal for a human trial crowd-funded largely by the subjects, organized around a web site, as well as standardization of individual record-keeping and an open-source database of methylation results before and after.

PMID:
30878026
DOI:
10.1134/S000629791812009X
[Indexed for MEDLINE]

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