Kejun Ying

Chronological age alone cannot capture the heterogeneity in how individuals age1. Robust biomarkers are needed both to evaluate interventions that target the aging process and to stratify individuals by disease risk. Over the past decade, epigenetic clocks have provided quantitative tools for measuring biological age2,3, and mortality-predictive models such as GrimAge4 and PhenoAge5 have extended their clinical relevance.

Abstract Ageing and interventions modulate health and mortality1, yet the underlying molecular mechanisms of this modulation remain unclear. Here we integrate more than 11,000 transcriptomes from more than 25 tissues across 4 mammals (mouse, rat, macaque and human) to develop accurate, interpretable rodent and multi-species biomarkers of chronological age and expected mortality, predicting lifespan-modulating interventions, time to death, chronic diseases and rejuvenation.

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