Biological aging markers and polygenic risk scores for mortality prediction: a multicohort study

Chronological age is a strong predictor of mortality but does not fully capture heterogeneity in physiological decline. We evaluated whether clinically accessible biological aging (BA) measures and an aging-related polygenic risk score improve all-cause mortality prediction beyond chronological age in two independent cohorts: Swedish TwinGene (n = 9,617; median follow-up 16.70 years) and UK Biobank (n = 179,504; median follow-up 11.83 years). We studied three biomarker-based biological age estimates (PhenoAge, Klemera–Doubal method, and homeostatic dysregulation), a frailty index, leukocyte telomere length, and multivariate aging polygenic risk scores. To focus on age-independent biological age estimates, we used age-adjusted residuals of biomarker-based biological age estimates in discrimination and prediction analyses. We assessed discrimination using receiver operating characteristic analyses and evaluated multivariable prediction using cross-validated ensemble models. Time-to-event associations were estimated using Cox proportional hazards models. Chronological age showed strong univariate discrimination, with area under the ROC curve (AUC) 0.837 in TwinGene and 0.708 in UK Biobank. Among biological aging measures, PhenoAge residual had the highest discrimination (AUC: 0.874 in TwinGene; AUC: 0.624 in UK Biobank), whereas the polygenic risk score showed near-null discrimination (approximately 0.50 in both cohorts). In cross-validated ensemble prediction, adding biological aging measures to chronological age and covariates substantially improved discrimination in TwinGene (AUC: 0.936) and modestly improved discrimination in UK Biobank (AUC: 0.762), while adding the polygenic risk score without biological aging measures produced minimal change. In multivariable Cox models, PhenoAge residual remained independently associated with mortality in both cohorts, whereas the polygenic risk score was not. Clinically biological aging measures, particularly PhenoAge, improve mortality prediction beyond chronological age, while the evaluated aging polygenic risk score adds little incremental predictive value. Cohort differences highlight the importance of evaluating transportability across populations, biomarker panels, and risk horizons.