Predictive value of serum uric acid-to-albumin ratio for diabetic kidney disease in patients with type 2 diabetes mellitus: a case-control study.
The aim of this study was to investigate the predictive effects of the serum uric acid-to-albumin ratio (sUAR) on the onset of diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM).
A case-control study was conducted involving 1809 patients with T2DM, including 486 cases with DKD and 1323 cases without DKD. Logistic regression and restricted cubic spline (RCS) analyses were used to analyze the relationship between the serum uric acid-to-albumin ratio (sUAR) and DKD. Receiver operating characteristic (ROC) curve was utilized to evaluate the predictive ability of the models for DKD. Decision curve analysis was performed to assess the clinical net benefit of the predictive models.
Multivariable logistic regression analysis revealed that sUAR was an independent risk factor for DKD (adjusted OR: 1.23, 95% CI: 1.16-1.30, P < 0.05). RCS analysis indicated a non-linear relationship between sUAR and DKD (P non-linear < 0.05). When sUAR exceeded 8.30, the risk of DKD gradually increased with higher sUAR. Additionally, combining sUAR with age, T2DM duration, glycated hemoglobin, systolic blood pressure, triglycerides, and high-density lipoprotein cholesterol significantly improved the predictive accuracy for DKD and the clinical net benefit.
High sUAR levels were the independent risk factor of DKD. Moreover, incorporating sUAR with traditional influencing factors enhanced the predictive value for DKD.
A case-control study was conducted involving 1809 patients with T2DM, including 486 cases with DKD and 1323 cases without DKD. Logistic regression and restricted cubic spline (RCS) analyses were used to analyze the relationship between the serum uric acid-to-albumin ratio (sUAR) and DKD. Receiver operating characteristic (ROC) curve was utilized to evaluate the predictive ability of the models for DKD. Decision curve analysis was performed to assess the clinical net benefit of the predictive models.
Multivariable logistic regression analysis revealed that sUAR was an independent risk factor for DKD (adjusted OR: 1.23, 95% CI: 1.16-1.30, P < 0.05). RCS analysis indicated a non-linear relationship between sUAR and DKD (P non-linear < 0.05). When sUAR exceeded 8.30, the risk of DKD gradually increased with higher sUAR. Additionally, combining sUAR with age, T2DM duration, glycated hemoglobin, systolic blood pressure, triglycerides, and high-density lipoprotein cholesterol significantly improved the predictive accuracy for DKD and the clinical net benefit.
High sUAR levels were the independent risk factor of DKD. Moreover, incorporating sUAR with traditional influencing factors enhanced the predictive value for DKD.