One of the many approaches to building a clock that can determine biological age is to mine the data of the metabolome, the levels of various small molecule metabolites used and generated by cells. Shifts in the metabolome will reflect age-related changes in function and cell behavior. As is the case for all such clocks, finding a good correlation with mortality and morbidity in the data using machine learning approaches doesn't provide any insight into why these relationships exist. That makes it challenging to use such a clock to assess potential interventions to slow or reverse aging, as it is by no means clear that a given clock will usefully reflect the outcome of a given intervention targeting only one or a few of the mechanisms important in aging.
Many biomarkers have been shown to be associated not only with chronological age but also with functional measures of biological age. In human populations, it is difficult to show whether variation in biological age is truly predictive of life expectancy, as such research would require longitudinal studies over many years, or even decades. We followed adult cohorts of 20 Drosophila Genetic Reference Panel (DGRP) strains chosen to represent the breadth of lifespan variation, obtain estimates of lifespan, baseline mortality, and rate of aging, and associate these parameters with age-specific functional traits including fecundity and climbing activity and with age-specific targeted metabolomic profiles.
We show that activity levels and metabolome-wide profiles are strongly associated with age, that numerous individual metabolites show a strong association with lifespan, and that the metabolome provides a biological clock that predicts not only sample age but also future mortality rates and lifespan. This study with 20 genotypes and 87 metabolites, while relatively small in scope, establishes strong proof of principle for the fly as a powerful experimental model to test hypotheses about biomarkers and aging and provides further evidence for the potential value of metabolomic profiles as biomarkers of aging.