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Machine learning‐based clustering identifies obesity subgroups with differential multi‐omics profiles and metabolic patterns

1 Nov 2024

Abstract excerpt

OBJECTIVE: Individuals living with obesity are differentially susceptible to cardiometabolic diseases. We hypothesized that an integrative multi-omics approach might improve identification of subgroups of individuals with obesity who have distinct cardiometabolic disease patterns. METHODS: ), leveraging data from 243 individuals in the Multi-Ethnic Study of Atherosclerosis (MESA) cohort. Omics that contributed to...

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PMID: 39497627
DOI: 10.1002/oby.24137

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