A meta-analysis of 11 cohorts encompassing 37,855 individuals aged 17–99 established the first population-based age norms for DunedinPACE, a DNA-methylation-derived epigenetic pace-of-aging biomarker. The study characterized how DunedinPACE changes with chronological age, examined sex differences and nonlinear trajectories, validated findings in longitudinal data, and confirmed that age-normed scores predict clinical outcomes — a critical step toward translating this biomarker from research settings into precision medicine.
DunedinPACE, developed from the Dunedin birth cohort, has emerged as one of the most predictively powerful epigenetic clocks, outperforming earlier measures like Horvath and GrimAge in forecasting morbidity and mortality in several studies. Yet its clinical adoption has stalled because raw scores are difficult to interpret without a reference population — the same problem that limited early use of cholesterol panels before standardized ranges existed. These norms solve that gap, enabling clinicians to tell patients whether their biological aging rate sits above or below that of same-aged peers. Practical implications are substantial: biological age percentiles could guide lifestyle interventions, drug trials could use normed scores as enrichment criteria, and public-health programs could identify accelerated-aging subpopulations. Limitations include predominant reliance on cross-sectional data and uncertain generalizability across ethnicities. As an unreviewed preprint on medRxiv, these norms require independent replication and peer scrutiny before clinical deployment. Still, this represents a genuinely infrastructure-shifting contribution — less a novel discovery than a critical translational bridge between epigenetic research and real-world aging medicine.
