
This study used a multi-omics approach to identify distinct cardiometabolic profiles in individuals with obesity (BMI ≥ 30 kg/m²) within the Multi-Ethnic Study of Atherosclerosis (MESA) cohort. By employing machine learning-based clustering on proteomics and metabolomics data from 243 participants, two distinct subpopulations were identified: iCluster1 and iCluster2. iCluster2 showed higher BMI, fasting glucose, and inflammation levels, while iCluster1 had elevated total cholesterol and HDL cholesterol. Pathways linked to cell growth and energy expenditure were associated with iCluster1, while inflammatory and insulin resistance pathways were prominent in iCluster2. These clusters may represent different stages or mechanisms of obesity-related disease progression, suggesting potential for tailored cardiometabolic interventions.
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