Nature agingNov 4, 2025

A clear system for collecting and assessing aging markers

Longevity & Aging Weekly Brief ↗PubMed ↗DOI ↗

Updated Jun 27, 2026

Abstract

Essence

Biolearn suggests aging biomarkers vary sharply by whether the target is chronological age, mortality, or healthspan.

Evidence

This open-source framework evaluation tested 39 aging biomarkers across more than 20,000 individuals from diverse cohorts, finding chronological-age prediction did not correlate with mortality prediction (R = 0.12, P = 0.67).

Caveat

The results compare existing biomarkers across heterogeneous cohorts and outcomes, so the best marker depends on the specific clinical application rather than a single universal aging measure.

Simplified

Aging biomarkers are essential tools for quantifying biological aging, but systematic validation has been hindered by methodological inconsistencies and fragmented datasets. Here we show that the ability of traditional aging clocks to predict chronological age does not correlate with mortality prediction capacity (R = 0.12, P = 0.67), suggesting that these metrics capture distinct biological processes. We developed Biolearn, an open-source framework enabling standardized evaluation of 39 biomarkers across over 20,000 individuals from diverse cohorts. The Horvath skin and blood clock achieved the highest chronological age accuracy (R = 0.88), while GrimAge2 demonstrated the strongest mortality association (hazard ratio = 2.57) and healthspan prediction (hazard ratio = 2.00). Our systematic evaluation reveals considerable heterogeneity in biomarker performance across different clinical outcomes, with optimal biomarkers varying according to specific application. Biolearn provides unified data processing pipelines with quality control and cell-type deconvolution capabilities, establishing a foundation for reproducible aging research and facilitating development of robust aging biomarkers. 2