Systems Age: a single blood methylation test to quantify aging heterogeneity across 11 physiological systems.

Abstract

Aging occurs at different rates across individuals and physiological systems, but most epigenetic clocks provide a single age estimate, overlooking within-person variation. Here we developed systems-based DNA methylation clocks that measure aging in 11 distinct physiological systems-Heart, Lung, Kidney, Liver, Brain, Immune, Inflammatory, Blood, Musculoskeletal, Hormone and Metabolic-using data from a single blood draw. By integrating supervised and unsupervised machine learning with clinical biomarkers, functional assessments and mortality risk, we derived system-specific scores that outperformed existing global clocks in predicting relevant diseases and aging phenotypes. We also created a composite Systems Age score to capture overall multisystem aging. Clustering individuals based on these scores revealed distinct biological aging subtypes, each associated with unique patterns of health decline and disease risk. This framework enables a more granular and clinically relevant assessment of biological aging and may support personalized approaches to monitor and target system-specific aging processes.