Machine learning and data-driven inverse modeling of metabolomics unveil key process of active aging

Abstract

AbstractPhysical inactivity and a weak fitness status have become a global health concern. Metabolomics, as an integrative systematic approach, might link to individual’s fitness at the molecular level. In this study, we performed blood samples metabolomics analysis of a cohort of elderly people with different treatments. By defining two groups of fitness and corresponding metabolites profiles, we tested several machine learning classification approaches to identify key metabolite biomarkers, which showed robustly aspartate as a dominant negative marker of fitness. Following, the metabolomics data of the two groups were analyzed by a novel approach for metabolic network interaction termed COVRECON. Where we identified the enzyme AST as the most important metabolic regulation between the fit and the less fit groups. Routine blood tests in these two cohorts validated significant differences in AST and ALT. In summary, we combine machine learning classification and COVRECON to identify metabolomics biomarkers and causal processes for fitness of elderly people.