Dongmei Zhou,1,* Lingyu Shao,2,* Libo Yang,3,* Yongkang Chen,1,* Yue Zhang,4,* Feng Yue,3 Weipeng Gu,3 Shuyi Li,1 Shuyan Li,2 Jing Wei3 

1Department of Rheumatology and Immunology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, People’s Republic of China; 2School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, People’s Republic of China; 3Department of Endocrinology, The Affiliated Taian City Central Hospital of Qingdao University, Taian, Shandong, People’s Republic of China; 4Department of Endocrinology, Xuzhou New Health Hospital, Xuzhou, Jiangsu, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Jing Wei, Department of Endocrinology, The Affiliated Taian City Central Hospital of Qingdao University, Taian, Shandong, 271000, People’s Republic of China, Email drw996223@163.com Dongmei Zhou, Department of Rheumatology and Immunology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221000, People’s Republic of China, Email zdm@xzhmu.edu.cn

Objective: Distinguishing diabetic nephropathy (DN) from non-diabetic renal disease (NDRD) remains challenging. This study developed and validated a machine learning model for differential diagnosis of DN and NDRD.
Methods: We included 100 type 2 diabetes mellitus (T2DM) patients with proteinuria from four Xuzhou hospitals (2013– 2021), divided into DN (n=50) and NDRD (n=50) groups based on renal biopsy. Clinical data were used to build a predictive model. External validation was performed on 55 patients from The Affiliated Taian City Central Hospital of Qingdao University (2019– 2023). Models were constructed using Python’s scikit-learn library (v1.4.2), with feature selection via Recursive Feature Elimination (RFE).
Results: Compared to NDRD, DN patients had lower TG/Cys-c ratio [1.45 (0.75, 1.99) vs 2.78 (1.81, 4.48)], higher systolic blood pressure (156.80 ± 20.14 vs 137.66 ± 17.67), longer diabetes duration [78 (24, 120) vs 18 (6, 48) months], higher diabetic retinopathy prevalence (60% vs 40%), higher HbA1c [7.98 (6.50, 10.40) vs 7.10 (6.70, 7.90)], and lower hemoglobin (115.66 ± 22.20 vs 135.64 ± 18.59). The logistic regression (LR) model, incorporating TG/Cys-c ratio, SBP, diabetes duration, DR, HbA1c, and Hb, achieved an AUC of 0.9305, accuracy of 0.8333, sensitivity of 0.8283, and specificity of 0.8701. External validation showed an AUC of 0.9642, accuracy of 0.9455, sensitivity of 0.9615, and specificity of 0.9310. We named this method PDN (Prediction of Diabetic Nephropathy) and developed an online platform: http://cppdd.cn/service/PDN.
Conclusion: This machine learning-based method effectively differentiates DN from NDRD, aiding clinicians in diagnosis and treatment planning.

Keywords: diabetic nephropathy, non-diabetic renal disease, discriminant model, machine learning, logistic Regression