Serum uric acid-to-HDL-C ratio as an independent predictor of metabolic syndrome in the elderly: diagnostic model optimization and inflammatory mechanism insights

Abstract Objective This study aimed to investigate the independent predictive value of the uric acid-to-HDL-C ratio (UHR) for metabolic syndrome (MS) in the elderly, develop an optimized diagnostic model, and assess whether UHR dynamics reflect inflammatory improvements. Methods A prospective cohort study was conducted including elderly individuals (n = 1064) undergoing health examinations at two hospitals in 2023. MS was diagnosed according to the International Diabetes Federation (IDF, 2005) criteria (central obesity: waist circumference ≥ 90 cm in men, ≥ 80 cm in women). The association between UHR and MS was evaluated using multivariable logistic regression. Predictive performance was assessed by receiver operating characteristic (ROC) analysis, with the optimal cut-off determined by the Youden index. Model robustness was tested by 1000 bootstrap resamplings and net reclassification improvement (NRI)/integrated discrimination improvement (IDI) analyses. Additional analyses included age- and sex-stratified inflammatory profiling (NLRP3, GDF15, SOD) and a quasi-experimental intervention study in participants with UHR ≥ 14.9% (n = 102), who received a 12-week low-purine diet plus daily exercise. Results UHR was identified as an independent risk factor for MS (OR = 1.96, 95% CI: 1.52–2.53, P < 0.001). It also showed significant correlations with key MS components, including waist circumference (r = 0.394) and triglycerides (r = 0.212; both P < 0.001). The AUC of UHR for predicting MS was 0.782 (95% CI: 0.745–0.819), with an optimal cut-off value of 14.9% (sensitivity 75.6%, specificity 72.8%). Incorporating UHR into the Framingham risk model improved the AUC to 0.824 (NRI = 0.24; IDI = 0.07). After intervention, participants with a > 15% reduction in UHR showed marked inflammatory improvement (GDF15 ↓18.0%, NLRP3 ↓27.9%, both P < 0.001). Greater responses were observed in carriers of the SLC2A9 TT genotype (ΔUHR = − 21.3%). Conclusion Conclusion: UHR integrates oxidative stress and lipid metabolism pathways. It is a strong predictor of MS in the elderly (AUC = 0.782) and significantly improves the diagnostic performance of traditional models (ΔAUC = 0.092, P < 0.001). Its low cost (< 5 RMB per test) and dynamic responsiveness (ΔUHR reflecting intervention efficacy) support its utility as a primary screening tool in community settings. However, as the intervention was not a randomized controlled trial, these findings should be interpreted as correlational, and causal relationships require confirmation in large multicenter randomized studies.