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

TL;DR

This study uses urine samples and GC–MS to find potential noninvasive biomarkers for metabolic syndrome, offering a new diagnostic approach.

Contribution

The study introduces urinary metabolomic profiling as a noninvasive method for identifying biomarkers of metabolic syndrome.

Findings

80 metabolites were identified, with distinct profiles between normal, borderline, and MetS groups.

Key metabolites like glucuronate, galacturonic acid, and cystine were significantly associated with MetS.

Urinary metabolites improved classification accuracy for MetS diagnosis.

Abstract

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.

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