Integrative Multi-Omics Reveal the Molecular Architecture and Systemic Burden of Metabolic Syndrome in a Brazilian Cohort

Metabolic Syndrome (MetS) includes a complex cluster of metabolic disorders that significantly increases the risk for cardiovascular disease and Type 2 Diabetes Mellitus. This study aimed to elucidate the molecular architecture and systemic burden of MetS by integrating high-resolution plasma proteomics, metabolomics, and lipidomics. A cohort of 60 adult volunteers (n = 37 controls; n = 23 MetS) from Brazil was subjected to comprehensive clinical evaluation and multi-omics profiling. Clinical analysis identified HOMA-IR and triglycerides as primary predictors of MetS. FDR was used for multiple testing correction for all data. Untargeted proteomics identified 589 proteins, with 26 showing significant differential abundance, including the upregulated Intercellular Adhesion Molecule-1 (ICAM-1) and Vanin-1 and the depleted protective Apolipoprotein D (ApoD) in MetS. Metabolomic analysis resulted in the identification of 410 metabolites, with 27 distinct features, revealing accumulation of branched-chain and aromatic amino acids, alongside a significant reduction in the signaling molecule 3-hydroxybutyrate, in the case group. Lipidomic profiling identified 1241 lipids, with 41 differentially abundant, highlighting the increase in Phosphatidylinositol (PI 40:5) and Phosphatidylcholine (PC) classes and the depletion of Sphingomyelins (SM). Integrative analysis demonstrated that MetS comprises a synchronized and multisystemic molecular disruption rather than a collection of independent risk factors. The homeostatic breakdown is characterized by the depletion of the body’s antioxidant and anti-inflammatory defense, and the elevation of pro-inflammatory mediators like ICAM-1 and Vanin-1, the accumulation of lipotoxic drivers and a significant disruption in amino acid catabolism (BCAA) that overactivates the mTORC1 pathway. These findings provide a multifactorial metabolic dysregulation, offering putative signatures for future diagnoses and risk stratification.