August 5th, 2026
Aging clocks of many varieties have been produced in recent years by applying machine learning techniques to a wide range of biological data that changes with age. This approach yields a tool that is disconnected from our understanding of the mechanisms of aging; links between the forms of cell and tissue damage and dysfunction that drive aging and the measures making up the clocks have yet to be determined. This makes it hard to interpret results, and hard to make practical use of a clock to assess the quality of any given approach to slowing or reversing aging. We have no idea in advance as to whether a given clock will perform well for a given intervention, and finding out is a slow process. The primary approach to this challenge taken by the research community is to produce new clocks at a fair pace, and gather as much data as possible on how the clocks behave, in search of patterns of clock behavior.
The outcome most often used to develop aging biomarkers is age itself, i.e. years lived since birth. However, in humans, relying on years lived as an outcome introduces a range of biases, most prominently confounding of aging with survival; humans in their 70s and beyond are, by definition, successful agers, having outlived most of their peers. The results of machine learning analysis differentiating older from younger people could therefore reflect not only aging-related biological damage, but also resilience.
