Abstract
Biological aging refers to the coordinated deterioration of integrity across multiple systems throughout the body that occurs with advancing chronological age. Whereas chronological aging proceeds at the same rate for everyone, biological aging proceeds faster for some and more slowly for others, possibly explaining differences in disease-free lifespan, called “healthspan.” The geroscience hypothesis posits that interventions to slow biological aging can extend healthspan. Geroprotective interventions show promise in laboratory animals. To advance human translation, methods to quantify biological aging are needed. Proposed methods include algorithms applied to clinical biomarker and genomics data. Algorithms are trained by (1) comparing older persons to younger ones; (2) analyzing a benchmark sample against which deviations can be quantified; or (3) modeling change within individuals over time. Validation comes from prediction of function, frailty, disease, and mortality. We review several published algorithms and compare them in data from the Dunedin Study birth cohort (N=954).