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

Abstract

Although the two identified clusters may represent progressive obesity-related pathologic processes measured at different stages, other mechanisms in combination could also underpin the identified clusters given no significant age difference between the comparative groups. For instance, clusters may reflect differences in dietary/behavioral patterns or differential rates of metabolic damage.