What question did this study set out to answer?

The study investigates the uric acid-to-HDL-C ratio as a predictor for metabolic syndrome in the elderly and seeks to optimize a diagnostic model.

March 29, 2026Open Access

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

Puntos clave

The study investigates the uric acid-to-HDL-C ratio as a predictor for metabolic syndrome in the elderly and seeks to optimize a diagnostic model.
Conducted a prospective cohort study with 1064 elderly individuals undergoing health examinations.
Diagnosed metabolic syndrome based on IDF criteria, assessing the association between uric acid-to-HDL-C ratio and metabolic syndrome using logistic regression.
Evaluated predictive performance with ROC analysis and optimal cut-off through the Youden index.
Performed age- and sex-stratified inflammatory profiling and a quasi-experimental intervention with a low-purine diet.
Tested model robustness via bootstrap resampling and NRI/IDI analyses.
Uric acid-to-HDL-C ratio identified as an independent risk factor for metabolic syndrome (OR = 1.96, P < 0.001).
Significant correlations found with waist circumference (r = 0.394) and triglycerides (r = 0.212).
AUC for predicting metabolic syndrome was 0.782 with an optimal cut-off value of 14.9% (sensitivity 75.6%, specificity 72.8%).
Incorporating UHR into the Framingham risk model raised AUC to 0.824.
Participants with > 15% reduction in UHR post-intervention showed significant inflammatory improvements.

Resumen

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. 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. UHR was identified as an independent risk factor for MS (OR = 1.96, 95% CI: 1.52–2.53, P 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: 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.

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