Klemera-Doubal biological age method

The Klemera-Doubal method (KDM) is a statistical algorithm that estimates biological age from clinical biomarkers by minimizing the sum of squared distances between m regression lines and m biomarker values in multidimensional space. Each biomarker is first regressed on chronological age in a reference population, yielding slope hᵢ, intercept gᵢ, and root mean squared error sᵢ; these per-biomarker weights are pooled with a chronological-age anchor (variance sD²) into a single KDM-BA score. The key architectural departure from multiple linear regression (MLR) is that biomarkers regress onto age rather than age onto biomarkers, which reduces error propagation and collinearity. Levine (2013, J Gerontol A) tested five biological age algorithms in 9,389 NHANES III participants followed 18 years (1,843 deaths): KDM-BA reached an AUC of 0.851 vs 0.827 for chronological age alone, hazard ratio 1.09 per year (95% CI 1.08–1.09); chronological age became non-significant when combined with KDM-BA, a property MLR scores did not match. KDM-BA then served as the training target in Levine et al. (2018): a Gompertz proportional-hazards model converted NHANES III 10-year mortality risk into phenotypic age (PhenoAge) from nine blood biomarkers, the clinical precursor step to DNAm PhenoAge. Both measures are standard reference algorithms in the BioAge R toolkit (Kwon and Belsky, 2021, GeroScience). Homeostatic dysregulation scores and machine-learning clocks have since outperformed KDM in head-to-head mortality comparisons, positioning it as a validated, interpretable benchmark as of 2026.