From population science to the clinic? Limits of epigenetic clocks as personal biomarkers.

Epigenetic clocks are machine-learning algorithms which use DNA methylation patterns to predict aging-related phenotypes, such as chronological age, composite indicators of health, time-to-death, and the pace of biological aging. These clocks have been instrumental at the population level in revealing how disease risk emerges from behavioral, environmental, and psychosocial factors, and how certain anti-aging interventions may alter those trajectories. Given the success of epigenetic clocks at the population level, it is reasonable to assume they might also hold value as individual-level biomarkers. We contend, however, that fundamental technical and biological properties of these algorithms prohibit their current use at the individual level. Technical concerns include methods of clock construction, sample collection and processing, data preprocessing, and computational implementations. Biological considerations include the nature of DNA methylation and its dynamics, variation across developmental periods, tissue specificity, and sensitivity to environmental/sociodemographic contexts. We show that clocks fail to meet common standards for clinical utility compared with established biomarkers, and that applying epigenetic clocks in individual-level decision making can be uninformative and potentially harmful. Finally, we argue that even if all technical and biological hurdles can be overcome, epigenetic clocks, as we currently understand them, should not be used to make individual-level decisions.