Abstract: Objective To identify the risk factors for hyperuricemia and construct a risk prediction model for patients with Type 2 Diabetes Mellitus (T2DM), thereby enabling the identification of high-risk individuals within this patient population. Methods This study was based on retrospective data from 2 329 patients with T2DM admitted to the Chinese PLA General Hospital between December 5, 2016, and December 13, 2021. Univariate analysis was initially employed to describe and explore factors associated with hyperuricemia. Subsequently, a random forest algorithm was utilized for preliminary variable screening, followed by logistic regression analysis to determine statistically significant independent predictors. These predictors were then used to construct the risk prediction model. The predictive efficacy of the model was evaluated using the Receiver Operating Characteristic (ROC) curve. Results The prevalence of hyperuricemia among the cohort of patients with T2DM was 20.35%. Following the initial screening with the random forest algorithm, 20 variables were selected from the initial dataset. Logistic regression analysis further identified five key independent variables: Blood Urea (BU), Triglycerides (TG), Body Mass Index (BMI), Glycated Hemoglobin (HbA1c), and Total Protein (TP). A logistic regression model was subsequently constructed, yielding the following risk prediction formula for hyperuricemia in patients with T2DM: logit(P)=-4.936-0.159×HbA1c(%)+0.062×BMI+0.196×TG(mmol/L)+0.155×BU(mmol/L)+0.023×TP(g/L). The Area Under the ROC Curve (AUC) was 0.765 (95% CI: 0.739-0.791) in the training set and 0.699 (95% CI: 0.632-0.766) in the testing set. Conclusion Lower levels of HbA1c and elevated levels of BMI, TG, BU, and TP are significant risk factors for the development of hyperuricemia in patients with T2DM. The prediction model, developed based on these five variables, demonstrates a competent capacity for accurately identifying individuals at high risk for hyperuricemia within the T2DM patient population.

Key words: Type 2 diabetes mellitus, Hyperuricemia, Risk prediction model