Yu-Fei Liu,1,2,* Xiao-Jing Li,2,* Yu-Ting Li,1,2 Xue-Han Liu,2 Hai-Yan Gao,1,2 Tian-Ping Zhang,2 Chun-Mei Yang1,2 

1School of Public Health, Bengbu Medical University, Bengbu, People’s Republic of China; 2The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Tian-Ping Zhang, The First Affiliated Hospital of USTC, 17 Lujiang Road, Hefei, Anhui, 230001, People’s Republic of China, Email zhangtianping@ustc.edu.cn Chun-Mei Yang, School of Public Health, Bengbu Medical University, Bengbu, People’s Republic of China, Email chunmeiyang@ustc.edu.cn

Objective: To develop and validate a nomogram model for predicting the risk of hyperuricemia (HUA) in perimenopausal women.
Methods: In this study, physical examination information of perimenopausal women was collected at the First Affiliated Hospital of University of Science and Technology of China. We utilized the Least Absolute Shrinkage and Selection Operator (Lasso) and binary logistic regression to investigate the risk factors of HUA among perimenopausal women.
Results: We finally collected 5637 patients in this study. Based on the results of Lasso-logistic regression analysis, we incorporated ten different independent variables into the risk prediction model for HUA. The risk prediction model showed good discrimination ability in both the training set (AUC=0.819; 95% CI=0.801~0.838) and validation set (AUC=0.787; 95% CI=0.756~0.818), the calibration curve demonstrates that the model was well-calibrated. In addition, we constructed HUA risk prediction models for perimenopausal women with BMI < 25.0 and BMI ≥ 25.0, respectively. The AUC of the prediction model in the population with BMI < 25.0 was 0.793, and the AUC of the prediction model in the population with BMI ≥ 25.0 was 0.765.
Conclusion: Our study identified several independent risk factors for HUA in perimenopausal women and developed a prediction mode, which might be used to detect the individual conditions and implement the preventive interventions.

Keywords: perimenopausal women, hyperuricemia, prediction