Association between uric acid to high-density lipoprotein cholesterol ratio and chronic kidney disease in Chinese patients with type 2 diabetes mellitus: a cross-sectional study

Objectives: To examine the association between uric acid (UA) to high-density lipoprotein cholesterol (HDL-C) ratio (UHR) and chronic kidney disease (CKD) in type 2 diabetes mellitus (T2DM) patients in China. Methods: The investigation stems from a survey conducted in the eastern Chinese province of Zhejiang, spanning from March to November 2018. A multivariable logistic regression model was employed to assess the relationship between UHR and CKD, while restricted cubic spline (RCS) analysis was used to evaluate the dose-response relationship. Receiver operating characteristic (ROC) curve analysis was performed to determine the optimal UHR cut-off value and assess its diagnostic performance for CKD. Model performance was further evaluated using net reclassification improvement (NRI) and integrated discrimination improvement (IDI) metrics. Sensitivity analyses, including propensity score matching (PSM) and k-means clustering, were conducted to enhance the robustness of the findings. Subgroup analyses were performed across various demographic and clinical categories to examine the consistency of the UHR-CKD association. Results: This cross-sectional study included 1,756 Chinese patients with T2DM, among whom 485 (27.62%) were identified with CKD. Multivariable logistic regression analysis revealed a significant positive association between UHR and CKD. Per standard deviation (SD) increase in UHR was associated with a 40% higher odds of CKD (OR = 1.40, 95% CI: 1.23-1.60) after adjusting for potential covariates. When analyzed categorically, participants in the highest UHR tertile (T3) had 1.82-fold higher odds of CKD compared to the lowest tertile (T1) (95% CI: 1.32-2.50). RCS analysis demonstrated a consistent linear dose-response relationship between UHR and CKD across all models (all p for nonlinearity >0.05). ROC curve analysis identified an optimal UHR cut-off value of 12.28 for CKD prediction, with an area under the curve (AUC) of 0.710 (95% CI: 0.683-0.737) in the fully adjusted model. Subgroup analyses confirmed the robustness of the UHR-CKD association across most demographic and clinical variables, except for younger age groups (18-44 and 45-59 years) and smokers. Notably, BMI significantly modified the UHR-CKD relationship, with a nonlinear association observed in individuals with lower BMI (<24 kg/m(2)) and a linear association in those with higher BMI (>= 24 kg/m(2)). Conclusion: This study demonstrates a significant dose-response relationship between the UHR and CKD in Chinese patients with T2DM, highlighting UHR as a promising biomarker for CKD risk assessment. The identified UHR cut-off of 12.28 offers a practical threshold for early renal monitoring and targeted interventions. Future research should explore UHR-targeted therapies and its integration into personalized risk stratification models to improve CKD management in T2DM.