Unsupervised clustering of biochemical markers reveals health profiles associated with function and survival in active aging.

ABSTRACT: This study explores the relationships between biochemical phenotypes identified using machine learning, and key health outcomes, including body composition, physical function, and mortality risk. Data were collected from 536 physically active Spanish participants aged over 65 years (76.5% women) enrolled in the EXERNET cohort (2017-2018), with a 6-year mortality follow-up. Principal component analysis, and hierarchical and k-means clustering was used to identify distinct biochemical profiles. Associations between clusters and health outcomes were assessed using analysis of covariance and Cox proportional hazards models. Three distinct clusters emerged: 'Healthy', characterized by biochemical values within the normal range and used as the reference group; 'Metabolic', marked by dysregulated

SUBMITTER: Gonzalez-Martos R 

PROVIDER: S-EPMC12368133 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature