A GC-MS-Based Urinary Metabolomic Profiling to Identify Potential Biomarkers of Metabolic Syndrome

Metabolic syndrome (MetS) is a multifactorial condition characterized by central obesity, dyslipidemia, hypertension, and insulin resistance, increasing the risk of cardiovascular disease and type 2 diabetes. Despite its clinical significance, current diagnostic methods rely on invasive blood-based assessments. This study investigates the potential of urinary metabolomics as a noninvasive alternative for MetS diagnosis. Using gas chromatography-mass spectrometry (GC-MS), we analyzed urinary metabolites from 127 individuals classified into Normal, Borderline (BL), and MetS groups based on clinical diagnostic criteria. A total of 80 metabolites were identified, and partial least-squares discriminant analysis (PLS-DA) revealed distinct metabolic profiles between groups. Key metabolites, including glucuronate, galacturonic acid, and cystine, showed significant associations with MetS and its diagnostic components. Pathway analysis indicated metabolic perturbations primarily in carbohydrate, amino acids, and fatty acid metabolism. Furthermore, receiver operating characteristic (ROC) curve analysis demonstrated that a selected panel of urinary metabolites improved MetS classification accuracy. These findings suggest that urinary metabolomics profiling can provide novel biomarkers for MetS, offering a promising approach for noninvasive screening and early detection.