Data-driven glycolipid network phenotypes reveal a graded risk spectrum for cardiometabolic multimorbidity: a prospective study in middle-aged and older Chinese adults

BACKGROUND: Glycolipid dysregulation is a core driver of cardiometabolic multimorbidity (CMM), yet its role as an integrated network in CMM pathogenesis remains unexplored. OBJECTIVE: To explore the association between individual differential glycolipid covariance networks (IDGCNs)—pairwise glycolipid correlation matrix from six glycolipid biomarkers—and incident CMM. METHODS: We utilized 9-year longitudinal data (2011–2020) from 12,267 middle-aged and older Chinese adults. IDGCNs were constructed from six glycolipid biomarkers-fasting plasma glucose, glycated hemoglobin A1c, total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C)-and quantified as 6 × 6 partial-correlation matrices, with 15 off-diagonal elements transformed into feature vectors. K-means clustering (k = 3) identified network phenotypes. Fine-Gray subdistribution hazards regression models were used to estimate hazard ratios (HRs) for incident CMM across clusters. The effects of glycolipid-coupling mixtures were evaluated using quantile-based g-computation. The incremental predictive utility was assessed using the C-statistic, net reclassification improvement, integrated discrimination improvement, and likelihood-ratio test. RESULTS: During a median follow-up of 9 years, 2,040 participants developed CMM. Relative to the normal group, multivariable-adjusted HRs (95%CI) for incident CMM escalated across clusters: Cluster 1 = 2.321 (1.400–3.850), Cluster 2 = 5.385 (3.155–9.192), and Cluster 3 = 5.845 (3.027–11.284). HDL-related couplings were protective, while glucose–LDL coupling was deleterious factors. The addition of the glycolipid-coupling mixture to conventional risk factors improved predictive metrics significantly. CONCLUSIONS: Glycolipid network topology independently predicts incident CMM, supporting a shift from single-biomarker thresholds to network-based risk phenotyping.