Integrating dietary patterns and metabolomics with machine learning for metabolic syndrome risk assessment in a rural population

This study aimed to identify dietary patterns and metabolomic signatures associated with metabolic syndrome (MetS) and develop machine learning models integrating both dietary and metabolomic data for risk assessment. In this cross-sectional study of 2810 adults, four dietary patterns derived using principal component analysis were associated with MetS. Serum metabolomic profiling was conducted in 400 participants using liquid chromatography-tandem mass spectrometry. Metabolic signatures of the meat and traditional Chinese patterns remained positively associated with MetS, whereas the rice and tuber pattern metabolic signature remained inversely associated. Metabolomic analysis revealed 71 differential metabolites associated with MetS, including elevated monoacylglycerols and decreased metabolites such as 4-Hydroxyisoleucine. A metabolomic risk score based on key metabolites showed a strong association with MetS (OR = 23.55, 95% CI: 11.77-47.12). Integrating dietary patterns with MetS-specific biomarkers significantly improved risk assessment models (AUC: 0.759-0.820) compared to using dietary patterns and pattern-associated metabolites (AUC: 0.640-0.709), with the Support Vector Machine (SVM) model performing optimally (AUC: 0.820). Specific dietary patterns and their associated metabolomic signatures are significantly associated with MetS. Integrating dietary and metabolomic data markedly improves the SVM model's risk assessment accuracy for MetS risk, offering a robust approach for precision prevention in high-risk populations.