ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Renal Endocrinology
Development and validation of a multivariable prediction model for non-invasive discrimination between diabetic and non-diabetic kidney disease in type 2 diabetes: a clinical nomogram
Lin Li 1
Fuzhe Ma 1
Chaonan Bao 2
Tao Sun 1
Shaojie Fu 1
Zhonggao Xu 1
1. Department of Nephrology, First Hospital of Jilin University, Changchun, China
2. Department of Gastroenterology, First Hospital of Jilin University, Changchun, China
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Abstract
Objective: This study aimed to develop a non-invasive diagnostic model to differentiate diabetic kidney disease (DKD) from non-diabetic kidney disease (NDKD) in type 2 diabetes mellitus (T2DM) patients with renal insufficiency. Methods: We conducted a retrospective, biopsy-based study of diabetic patients with kidney dysfunction between July 2018 and August 2023. Patients were randomly split into training and validation cohorts (7:3). A multivariable logistic regression model based on routinely available, non-invasive clinical variables was developed and internally validated. Discrimination and calibration were evaluated in both cohorts. Results: A total of 507 patients were enrolled: 171 with DKD, 260 with NDKD, and 76 with concurrent DKD and NDKD. A five-variable model incorporating diabetes duration, diabetic retinopathy, systolic blood pressure, fasting plasma glucose, and hemoglobin levels demonstrated good discrimination and acceptable calibration in both datasets. Decision curve analysis suggested the model's potential clinical utility. The model was presented as a nomogram. Conclusions: This nomogram may support non-invasive differential diagnosis between DKD and NDKD in T2DM patients with kidney injury, thereby informing clinical decision-making.
Summary
Keywords
Diabetes Complications, Diabetes Mellitus, Type 2, diagnosis, Kidney Diseases, nomogram
Received
14 January 2026
Accepted
19 February 2026
Copyright
© 2026 Li, Ma, Bao, Sun, Fu and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Zhonggao Xu
Disclaimer
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