Another Novel Metabolic Clock
There are now scores of published aging clocks built on various omics databases containing data for people at different ages. Many measurable aspects of metabolism and cell biochemistry change with age in sufficiently similar ways across the population to build clocks that reflect biological age, the burden of damage and dysfunction that causes mortality. Prior to the development of modern machine learning techniques, assembling such a clock would have been prohibitively difficult and expensive, but machine learning makes it straightforward enough for any small research group to create a new clock in a relatively short period of time. Thus there are now a great many aging clocks.
At this point the focus should shift to validation of clocks, as the whole point of having a measure of biological age is to be able to use it to rapidly assess the quality of potential rejuvenation therapies. At present no clock can be treated as entirely trustworthy; they do have quirks, and it remains unclear as to how underlying processes of damage, such as accumulation of senescent cells, produce changes in specific clock parameters. Without knowing these relationships, a clock might overestimate or underestimate the effects of a specific therapy on aging.
Metabolites that mark aging are not fully known. We analyze 408 plasma metabolites in Long Life Family Study participants to characterize markers of age, aging, extreme longevity, and mortality. We identify 308 metabolites associated with age, 258 metabolites that change over time, 230 metabolites associated with extreme longevity, and 152 metabolites associated with mortality risk. We replicate many associations in independent studies.
By summarizing the results into 19 signatures, we differentiate between metabolites that may mark aging-associated compensatory mechanisms from metabolites that mark cumulative damage of aging and from metabolites that characterize extreme longevity. We generate and validate a metabolomic clock that predicts biological age. Network analysis of the age-associated metabolites reveals a critical role of essential fatty acids to connect lipids with other metabolic processes. These results characterize many metabolites involved in aging and point to nutrition as a source of intervention for healthy aging therapeutics.