Summary
This study interrogates the mechanistic basis of epigenetic aging by decomposing the accuracy of three widely-used DNA methylation clocks into stochastic and non-stochastic components using realistic simulation models applied to over 22,770 samples across 25 independent cohorts. The authors demonstrate that whilst a substantial fraction (63–90%, depending on the clock) of predictive accuracy can be explained by quasi-random DNA methylation drift, certain biological age accelerations—such as those observed in males or in severe COVID-19 patients—reflect genuinely non-stochastic processes. These findings fundamentally reshape interpretation of epigenetic clocks, suggesting they capture both random and deterministic ageing processes.
UK applicability
The findings are relevant to UK-based ageing research and biomarker development, particularly for clinical applications using epigenetic clocks in NHS settings or longitudinal cohort studies. However, the study is primarily methodological and computational; UK applicability depends on whether UK cohorts were included in the 25 datasets analysed, which is not specified in the abstract.
Key measures
Proportion of epigenetic clock predictive accuracy explained by stochastic DNA methylation change; age acceleration estimates in males (Horvath's clock), severe COVID-19 cases, and smokers (PhenoAge clock)
Outcomes reported
The study quantified the proportion of epigenetic clock accuracy attributable to stochastic DNA methylation changes using simulation models across 22,770 samples from 25 cohorts. Results demonstrated that 66–75% of Horvath's clock accuracy, 90% of Zhang's clock accuracy, and 63% of PhenoAge clock accuracy could be driven by stochastic processes, whilst age acceleration in specific populations reflects non-stochastic biological processes.
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