Stochastic Changes are Sufficient to Produce the Behavior of Aging Clocks

That it is possible to produce aging clocks from omics data, that some omics changes map very well to chronological age, biological age, and risk and presence of age-related disease, has been used to argue for aging to be an evolved program. Researchers here use a modeling approach to show that random events, the accumulation of molecular damage, can still produce the outcome observed in aging clocks.

Aging clocks have provided one of the most important recent breakthroughs in the biology of aging, and may provide indicators for the effectiveness of interventions in the aging process and preventive treatments for age-related diseases. The reproducibility of accurate aging clocks has reinvigorated the debate on whether a programmed process underlies aging. Here we show that accumulating stochastic variation in purely simulated data is sufficient to build aging clocks, and that first-generation and second-generation aging clocks are compatible with the accumulation of stochastic variation in DNA methylation or transcriptomic data.

We find that accumulating stochastic variation is sufficient to predict chronological and biological age, indicated by significant prediction differences in smoking, calorie restriction, heterochronic parabiosis, and partial reprogramming. Although our simulations may not explicitly rule out a programmed aging process, our results suggest that stochastically accumulating changes in any set of data that have a ground state at age zero are sufficient for generating aging clocks.


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