Integrated gut microbiome and serum lipidomics reveals microbial-lipid interactions for predicting incident metabolic syndrome: a nested case-control study.

BACKGROUND: Metabolic syndrome (MetS) is a multifactorial disorder characterized by obesity, dyslipidemia, hypertension, and insulin resistance. Although gut microbiota and lipid metabolism are both known to influence MetS development, their interactions remain incompletely characterized. METHODS: We conducted an exploratory nested case-control study within a prospective health examination cohort. We selected 100 participants (50 incident MetS cases and 50 matched controls) based on age, sex, and baseline MetS components. Gut microbial profiles were characterized by metagenomic sequencing, and serum lipid metabolites were measured using high-resolution mass spectrometry. Multi-omics integration was performed using correlation-based feature fusion. We constructed a support vector machine (SVM) model, optimized with recursive feature elimination (RFE) and five-fold cross-validation, to predict the incidence risk of MetS. RESULTS: MetS participants differed from controls in gut microbial composition, metabolic pathway activities, and lipidomic profiles. Circos analysis revealed positive associations between Blautia and sphingomyelins and negative associations between Bacteroides and triglycerides. The integrated model combining microbiota and lipidomic features demonstrated strong discrimination in the training set (AUC = 0.995, 95% CI: 0.987-0.999) and acceptable performance in the validation set (AUC = 0.722, 95% CI: 0.525-0.919). CONCLUSION: Integration of baseline gut microbiota and lipidomic data revealed specific pre-disease microbial-lipid signatures, including positive Blautia-sphingomyelin and negative Bacteroides-triglyceride associations. A multi-omics model improved prediction of incident MetS over single-omics models, supporting the potential of microbiota-metabolite panels for early risk detection.