Metabolic Syndrome Beyond Diagnostic Criteria: Population-Scale Integrative Metabolomics Characterization - Talal, Marwa (AUC Knowledge Fountain, 2026)
Background: Metabolic syndrome (MetS) is a complex cluster of interrelated metabolic abnormalities associated with elevated cardiometabolic risk. While diagnosis is based on well-established five clinical criteria, these may overlook early or atypical metabolic alterations. Large-scale metabolomic profiling offers an opportunity to identify biochemical signatures of MetS beyond diagnostic bias and to evaluate their relative importance across different presentations of the syndrome. Methods: Data from 117,147 UK Biobank participants were analyzed in a cross-sectional design. High-throughput NMR quantified 75 circulating metabolites, for. Univariate analyses, MetS subtype stratification, and elastic net models with SHAP interpretation were applied to assess feature importance, predictive performance, and phenotypic heterogeneity. Surrogate indices of insulin resistance and visceral adiposity were also derived. Results: MetS was associated with widespread metabolic alterations across multiple biochemical classes, with the largest effect sizes observed for lipoprotein (LP) subfraction measures, particularly very-low-density lipoprotein (VLDL) and high-density lipoprotein (HDL) subclass concentrations and particle diameters. LP metrics consistently outperformed other metabolite classes in distinguishing MetS from nonMetS, both in univariate and multivariate models, and retained predictive value when considered independently of diagnostic criteria. LP-based models also identified metabolic disturbances in individuals not meeting conventional MetS thresholds, suggesting utility for early or subclinical risk detection. Subtype analyses revealed distinct metabolic patterns across different combinations of diagnostic criteria, highlighting phenotypic heterogeneity within MetS. Conclusions: This large-scale integrative analysis demonstrates that detailed LP subfraction profiling provides strong, independent discriminatory power for MetS classification and may capture early metabolic risk overlooked by standard diagnostic thresholds.