Metabolomic age (MileAge) predicts health and life span: A comparison of multiple machine learning algorithms.

ABSTRACT: Biological aging clocks produce age estimates that can track with age-related health outcomes. This study aimed to benchmark machine learning algorithms, including regularized regression, kernel-based methods, and ensembles, for developing metabolomic aging clocks from nuclear magnetic resonance spectroscopy data. The UK Biobank data, including 168 plasma metabolites from up to N = 225,212 middle-aged and older adults (mean age, 56.97 years), were used to train and internally validate 17 algorithms. Metabolomic age (MileAge) delta, the difference between metabolite-predicted and chronological age, from a Cubist rule-based regression model showed the strongest associations with health and aging markers. Individuals with an older MileAge were frailer, had shorter telomeres, were more

SUBMITTER: Mutz J 

PROVIDER: S-EPMC11654675 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature