Insulin resistance and uric acid-related metabolic disturbances jointly identify individuals at high risk of advanced cardiovascular-kidney-metabolic syndrome: a population-based study
TL;DR
Higher TyG and SUA levels, particularly their co-occurrence, were associated with prevalent advanced CKM, suggesting these biomarkers may characterize distinct but overlapping metabolic profiles relevant to CKM assessment.
Abstract
Background Cardiovascular-kidney-metabolic (CKM) syndrome encompasses metabolic abnormalities, chronic kidney disease, and cardiovascular disease. The triglyceride–glucose (TyG) index reflects insulin resistance-related glucose–lipid imbalance, whereas serum uric acid (SUA) reflects purine metabolism and renal urate handling. However, their independent and joint associations with advanced CKM remain unclear. Methods We conducted a cross-sectional analysis of 8,939 adults from the Hakka Biobank baseline survey. Advanced CKM (stages 3–4) was defined according to the American Heart Association framework. Multivariable logistic regression examined associations of TyG and SUA with advanced CKM. Restricted cubic splines assessed nonlinearity, and joint associations used the same-dataset ROC-derived TyG threshold of 8.89 and sex-specific hyperuricemia (HUA) thresholds. Sensitivity and subgroup analyses assessed robustness and heterogeneity, while AUC and continuous net reclassification improvement (NRI) evaluated in-sample model performance. Results Among 8,939 participants, 1,164 had advanced CKM. After adjustment, each 1-unit increase in TyG was associated with greater odds of advanced CKM (OR, 1.81; 95% CI, 1.64–2.01), as was each standard deviation increase in SUA (OR, 1.38; 95% CI, 1.28–1.48). TyG showed an approximately linear positive relationship, whereas SUA showed a J-shaped relationship (P for nonlinearity = 0.004), which was more evident in women. Participants with both high TyG and HUA had the greatest odds (OR, 3.01; 95% CI, 2.49–3.64). Results remained consistent across six sensitivity analyses involving medication adjustment, exclusions for renal impairment or metabolic medication use, and menopausal-status adjustment among women. Jointly adding TyG and SUA improved in-sample discrimination (AUC, 0.816 vs. 0.797; p < 0.001) and reclassification (NRI, 0.433; 95% CI, 0.372–0.496). The combined association was stronger in women (P for interaction = 0.028). Conclusion Higher TyG and SUA levels, particularly their co-occurrence, were associated with prevalent advanced CKM. These biomarkers may characterize distinct but overlapping metabolic profiles relevant to CKM assessment. Their potential value for community-based evaluation requires confirmation in prospective studies with external validation; no causal or treatment implications can be inferred from these cross-sectional findings.