Open access
Translating cellular aging clocks into disease risk prediction
Medicine
Abstract
Ding et al. mapped over 7,000 plasma proteins to more than 40 cell types and developed machine learning aging clocks across 60,000 individuals, demonstrating that cell-type-specific biological aging is heterogeneous, measurable from blood alone, and powerfully predictive of neurodegenerative disease, cancer, and mortality up to 15 years before clinical onset.1