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

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 < 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: 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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Journal
BMC Geriatrics
Published
2026-03-27
DOI
https://doi.org/10.1186/s12877-026-07315-y
Primary Topic
Gout, Hyperuricemia, Uric Acid
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Zhaodong Sun, Zhigang Wu, Bin Hu, Yuan Jin et al.
BMC Geriatrics
Gout, Hyperuricemia, Uric Acid
article

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

Zhaodong Sun, Zhigang Wu, Bin Hu, Yuan Jin, Jiaping Wang, Jin Yang, Runfeng Sun, Na Wang
article en

Abstract

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 < 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: 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.

BMC Geriatrics
Hohai University (CN), The First People’s Hospital of Lianyungang (CN)
Gender equality
Openalex Percentile: Top 3%
Gout, Hyperuricemia, Uric Acid
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