Computational algorithm based on health and lifestyle traits to categorize lifemetabotypes in the NUTRiMDEA cohort.

ABSTRACT: Classifying individuals based on metabotypes and lifestyle phenotypes using exploratory factor analyses, cluster definition, and machine-learning algorithms is promising for precision chronic disease prevention and management. This study analyzed data from the NUTRiMDEA online cohort (baseline: n = 17332 and 62 questions) to develop a clustering tool based on 32 accessible questions using machine-learning strategies. Participants ranged from 18 to over 70 years old, with 64.1% female and 35.5% male. Five clusters were identified, combining metabolic, lifestyle, and personal data: Cluster 1 ("Westernized Millennial", n = 967) included healthy young individuals with fair lifestyle habits; Cluster 2 ("Healthy", n = 10616) consisted of healthy adults; Cluster 3 ("Mediterranean Young Adult", n 

SUBMITTER: Higuera-Gomez A 

PROVIDER: S-EPMC11496800 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature