Ying Zhu,* Yiyi Zhang,* Miao Yang, Nie Tang, Limei Liu, Jichuan Wu, Yan Yang

Department of Endocrinology, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Yan Yang, Department of Endocrinology, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, People’s Republic of China, Email yangyan_2012@126.com

Purpose: Diabetic nephropathy (DN), a major complication of diabetes mellitus, significantly impacts global health. Identifying individuals at risk of developing DN is crucial for early intervention and improving patient outcomes. This study aims to develop and validate a machine learning-based predictive model using integrated biomarkers.
Methods: A cross-sectional analysis was conducted on a baseline dataset involving 2184 participants without DN, categorized based on their development of DN over a follow-up period of 36 months: DN (n=1270) and Non-DN (n=914). Various demographic and clinical parameters were analyzed. The findings were validated using an independent dataset comprising 468 participants, with 273 developing DN and 195 remaining as Non-DN over the follow-up period. Machine learning algorithms, alongside traditional descriptive statistics and logistic regression were used for statistical analyses.
Results: Elevated levels of serum creatinine, urea, and reduced eGFR, alongside an increased prevalence of retinopathy and peripheral neuropathy, were prominently observed in those who developed DN. Validation on the independent dataset further confirmed the model’s robustness and consistency. The SVM model demonstrated superior performance in the training set (AUC=0.79, F1-score=0.74) and testing set (AUC=0.83, F1-score=0.82), outperforming other models. Significant predictors of DN included serum creatinine, eGFR, presence of diabetic retinopathy, and peripheral neuropathy.
Conclusion: Integrating machine learning algorithms with clinical and biomarker data at baseline offers a promising avenue for identifying individuals at risk of developing diabetic nephropathy in type 2 diabetes patients over a 36-month period.

Keywords: diabetic nephropathy, prediction, machine learning, biomarkers, risk stratification, type 2 diabetes