Global metabolic profiling to model biological processes of aging in twins.

ABSTRACT: Aging is intimately linked to system-wide metabolic changes that can be captured in blood. Understanding biological processes of aging in humans could help maintain a healthy aging trajectory and promote longevity. We performed untargeted plasma metabolomics quantifying 770 metabolites on a cross-sectional cohort of 268 healthy individuals including 125 twin pairs covering human lifespan (from 6 months to 82 years). Unsupervised clustering of metabolic profiles revealed 6 main aging trajectories throughout life that were associated with key metabolic pathways such as progestin steroids, xanthine metabolism, and long-chain fatty acids. A random forest (RF) model was successful to predict age in adult subjects (≥16 years) using 52 metabolites (R²  = .97). Another RF model selected

SUBMITTER: Bunning BJ 

PROVIDER: S-EPMC6974708 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature