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Review
. 2025 May;26(5):350-368.
doi: 10.1038/s41576-024-00807-w. Epub 2025 Jan 13.

Epigenetic ageing clocks: statistical methods and emerging computational challenges

Andrew E Teschendorff  1 , Steve Horvath  2
Affiliations
Review

Epigenetic ageing clocks: statistical methods and emerging computational challenges

Andrew E Teschendorff et al. Nat Rev Genet. 2025 May.

Abstract

Over the past decade, epigenetic clocks have emerged as powerful machine learning tools, not only to estimate chronological and biological age but also to assess the efficacy of anti-ageing, cellular rejuvenation and disease-preventive interventions. However, many computational and statistical challenges remain that limit our understanding, interpretation and application of epigenetic clocks. Here, we review these computational challenges, focusing on interpretation, cell-type heterogeneity and emerging single-cell methods, aiming to provide guidelines for the rigorous construction of interpretable epigenetic clocks at cell-type and single-cell resolution.

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Conflict of interest statement

Competing interests: S.H. and his team developed the first epigenetic clocks for human saliva, for all tissues (pan-tissue clock), for human mortality risk prediction (PhenoAge, GrimAge), and pan-mammalian clocks. The Regents of the University of California are the sole owner of patents and patent applications directed at epigenetic biomarkers for which S.H. is a named inventor; S.H. is a founder and paid consultant of the non-profit Epigenetic Clock Development Foundation that licenses these patents. S.H. is a principal investigator at the Altos Labs, Cambridge Institute of Science, a biomedical company that works on biological rejuvenation. A.E.T. declares no competing interests.

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