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

Abstract

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: We performed machine learning-based, integrative unsupervised clustering to identify proteomics- and metabolomics-defined subpopulations of individuals living with obesity (BMI ≥ 30 kg/m²), leveraging data from 243 individuals in the Multi-Ethnic Study of Atherosclerosis (MESA) cohort. Omics that contributed to the observed clusters were functionally characterized. We performed multivariate regression to assess whether the individuals in each cluster demonstrated differential patterns of cardiometabolic traits.

Results: We identified two distinct clusters (iCluster1 and 2). iCluster2 had significantly higher average BMI values, fasting blood glucose, and inflammation. iCluster1 was associated with higher levels of total cholesterol and high-density lipoprotein cholesterol. Pathways mediating cell growth, lipogenesis, and energy expenditures were positively associated with iCluster1. Inflammatory response and insulin resistance pathways were positively associated with iCluster2.

Conclusions: 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.

Figure 1.

Graphical illustration of the study design.

Figure 2.

Enriched Kyoto Encyclopedia of Genes and Genomes (KEGG)-pathways and metabolite-metabolite interaction networks in identified obesity clusters. A). KEGG pathways enriched in identified obesity clusters, B). Genes encoding highly enriched proteins associated with obesity subgroups that underpin enriched KEGG pathways in each obesity cluster, C). metabolite-metabolite interaction network in obesity subgroup iCluster1, and D). metabolite-metabolite interaction network in obesity subgroup iCluster2.

Figure 3.

Biological processes with negative enrichment score (i.e., associated with iCluster1) were associated with largely organ development, proteins transportation, DNA replication, and cell proliferation (orange color). In contrast, processes associated with iCluster2 were mainly involved in regulation of cell death (apoptosis), antigen presentation, activation of lymphocytes, response to chemokines, cellular defense, and response to tumor necrosing factors (light blue color).

Figure 4.

Proposed hypotheses on potential mechanisms underpinning identified obesity clusters.