A Machine Learning Model for Predicting Diabetic Nephropathy Based on TG/Cys-C Ratio and Five Clinical Indicators

Aug 2, 2026

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Machine learning is leveraged to address the clinical challenge of distinguishing diabetic nephropathy (DN) from non-diabetic renal disease (NDRD) in type 2 diabetes patients with proteinuria. A model was developed using 100 biopsy-confirmed cases from four Xuzhou hospitals and externally validated on 55 patients from a separate center. Selected features included TG/Cys-C ratio, systolic blood pressure, diabetes duration, diabetic retinopathy, HbA1c, and hemoglobin. Performance was strong, with AUCs of 0.9305 (internal) and 0.9642 (external), supporting clinical diagnostic and treatment planning.

LanguageEnglish

CategoryResearch & Report

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Pages13

Reading Time33 min

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Objective

Methods

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Model development and validation

Conclusion

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Diabetes, Metabolic Syndrome and Obesity downloaded from [https://www.dovepress.com/](https://www.dovepress.com/) For personal use only. Diabetes, Metabolic Syndrome and Obesity Open Access Full Text Article ORIGINAL RESEARCH A Machine Learning Model for Predicting Diabetic Nephropathy Based on TG/Cys-C Ratio and Five Clinical Indicators Dongmei Zhou 1 , *, Lingyu Shao2 , *, Libo Yang 3 , *, Yongkang Chen 1 , *, Yue Zhang4 , *, Feng Yue 3 , Weipeng Gu 3 , Shuyi Li 1 , Shuyan Li2 , Jing Wei 3 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](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](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](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 | | --- | | Introduction
Diabetes mellitus (DM) is one of the most important health problems in the world. The number of people with diabetes is estimated to be 537 million in 2021 and is expected to increase to 784 million by 2045.1 Diabetic nephropathy is one of the most common microvascular complications in DM patients. Diabetic nephropathy is the main cause of ESRD, and about 30–50% of End Stage Renal Disease (ESRD) worldwide is caused by DN.2 However, proteinuria in T2DM patients is not necessarily DN, and may also be NDRD. The typical pathological manifestations of DN include thickening of the glomerular basem

FAQs

What problem does the study address?

Distinguishing diabetic nephropathy (DN) from non-diabetic renal disease (NDRD) remains difficult in type 2 diabetes patients with proteinuria, and misclassification can affect treatment decisions.

How was the machine learning model built and validated?

The model used clinical data from 100 type 2 diabetes patients with proteinuria, split into DN and NDRD groups by renal biopsy, and was externally validated on 55 additional patients from another hospital.

Which clinical indicators were used in the final model?

The logistic regression model incorporated TG/Cys-C ratio, systolic blood pressure, diabetes duration, diabetic retinopathy, HbA1c, and hemoglobin.

Aug 2, 2026

Upload by Levi