Klemera-Doubal biological age method

The Klemera-Doubal method (KDM) is a statistical algorithm that estimates your biological age from clinical biomarkers. It works by minimizing the squared distances between a set of regression lines and your biomarker values, in a multi-dimensional space. First, each biomarker is regressed on chronological age in a reference population. That yields a slope (hᵢ), an intercept (gᵢ), and a root-mean-squared error (sᵢ) for each. These per-biomarker weights are then pooled with a chronological-age anchor (variance sD²) into one KDM-BA score. The key departure from ordinary multiple linear regression (MLR) is the direction: biomarkers are regressed onto age, not age onto biomarkers. That 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, versus 0.827 for chronological age alone, with a hazard ratio of 1.09 per year (95% CI 1.08 to 1.09). Strikingly, chronological age became non-significant once combined with KDM-BA, which the MLR scores did not match. KDM-BA then became the training target in Levine et al. (2018). There, a Gompertz proportional-hazards model turned NHANES III 10-year mortality risk into phenotypic age (PhenoAge), from nine blood biomarkers. That was the clinical precursor to DNAm PhenoAge. Both measures are standard reference algorithms in the BioAge R toolkit (Kwon and Belsky, 2021, GeroScience). Since then, homeostatic-dysregulation scores and machine-learning clocks have beaten KDM in head-to-head mortality tests. So today it works best as a validated, interpretable benchmark, as of 2026.

Last reviewed: June 22, 2026

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