Abstract: Objective: To construct and evaluate a clinical prediction model for type 2 diabetes patients with hyperuricemia and
explore its independent risk factors. Methods: Retrospective analysis was conducted on the medical records of 553 type 2 diabetes
patients admitted to the department of endocrinology of Zhaotong Hospital of Traditional Chinese Medicine from January 2020 to
December 2021. The patients were divided into hyperuricemia group and non-hyperuricemia group according to whether hyperuricemia
was complicated or not. LASSO regression was used to select predictive variables and construct a Logistic regression model to identify
independent risk factors for type 2 diabetes patients with hyperuricemia. The model was visualized by drawing a nomogram, and the
predictive efficacy of the model was evaluated by the degree of differentiation, calibration and clinical applicability. Results: There
were statistically significant differences （P<0.05） between the two groups of patients in body mass index, concurrent diabetic
nephropathy, standardized hypoglycemic therapy, total cholesterol, triglycerides, serum creatinine, blood urea nitrogen, insulin
resistance index, dampness-heat obstructing the spleen syndrome, and qi-yin deficiency syndrome. Eight predictive variables were
selected by LASSO regression for multicollinearity diagnosis, and the prediction model was constructed after the qi-yin deficiency
syndrome was deleted and multi-factor Logistic regression was performed. The results showed that seven predictive variables were
independent risk factors for type 2 diabetes patients with hyperuricemia （P<0.05）, and the model had good differentiation, calibration
and clinical applicability. Conclusion: Body mass index, concurrent diabetic nephropathy, standardized hypoglycemic therapy, total
cholesterol, triglycerides, insulin resistance index, and dampness-heat obstructing the spleen syndrome are independent risk factors for
type 2 diabetes patients with hyperuricemia. The clinical prediction model based on these factors can provide a reliable basis for the
clinical prevention and treatment of hyperuricemia in patients with type 2 diabetes.

Key words: type 2 diabetes, hyperuricemia, clinical prediction model, nomogram