Du N, Li WX, Qiu CY, Wang J. Risk stratification model for diabetic kidney disease progression based on metabolic and inflammatory biomarkers. World J Diabetes 2026; 17(8): 121173 [DOI: 10.4239/wjd.121173]
Corresponding Author of This Article
Jun Wang, MD, Department of Nephrology, Chongqing University Three Gorges Hospital, No. 165 Xincheng Road, Wanzhou District, Chongqing 404010, China. 18290529622@163.com
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Endocrinology & Metabolism
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research-article
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Du N, Li WX, Qiu CY, Wang J. Risk stratification model for diabetic kidney disease progression based on metabolic and inflammatory biomarkers. World J Diabetes 2026; 17(8): 121173 [DOI: 10.4239/wjd.121173]
World J Diabetes. Aug 15, 2026; 17(8): 121173 Published online Aug 15, 2026. doi: 10.4239/wjd.121173
Risk stratification model for diabetic kidney disease progression based on metabolic and inflammatory biomarkers
Ni Du, Wen-Xia Li, Chuang-Ye Qiu, Jun Wang
Ni Du, Jun Wang, Department of Nephrology, Chongqing University Three Gorges Hospital, Chongqing 404010, China
Wen-Xia Li, Department of Endocrinology, Jiangmen Central Hospital, Jiangmen 529000, Guangdong Province, China
Chuang-Ye Qiu, Department of Nephrology, Jiangmen Central Hospital, Jiangmen 529000, Guangdong Province, China
Co-first authors: Ni Du and Wen-Xia Li.
Co-corresponding authors: Chuang-Ye Qiu and Jun Wang.
Author contributions: Du N and Li WX contributed equally to study design, data collection, and manuscript drafting as co-first authors; Qiu CY and Wang J supervised the study, provided critical revisions, and approved the final manuscript as co-corresponding authors; all authors read and approved the published version.
AI contribution statement: No AI tools were used at any stage of manuscript preparation. The authors assume full responsibility for the integrity and scientific validity of this work.
Supported by the Chongqing Wanzhou District Science and Health Joint Medical Research Project, No. wzwjw-kw2024015; and Science and Technology Plan Project in the Medical and Health Field of Jiangmen City, No. 2022YL01040.
Institutional review board statement: This study was approved by the Ethics Committee of Chongqing University Three Gorges Hospital (No. 2026-33) in accordance with the Declaration of Helsinki.
Informed consent statement: Written informed consent was waived by the Ethics Committee of Chongqing University Three Gorges Hospital (No. 2026-33) owing to the retrospective design and the exclusive use of anonymized clinical data.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
STROBE statement: The authors have read the STROBE Statement – checklist of items, and the manuscript was prepared and revised according to the STROBE Statement – checklist of items.
Data sharing statement: De-identified data are available from the corresponding author (18290529622@163.com) upon reasonable request and subject to institutional approval.
Corresponding author: Jun Wang, MD, Department of Nephrology, Chongqing University Three Gorges Hospital, No. 165 Xincheng Road, Wanzhou District, Chongqing 404010, China. 18290529622@163.com
Received: March 20, 2026 Revised: June 5, 2026 Accepted: July 14, 2026 Published online: August 15, 2026 Processing time: 138 Days and 13.9 Hours
Abstract
BACKGROUND
Diabetic kidney disease (DKD), a leading global cause of kidney failure, is driven by a complex interplay of metabolic, inflammatory, and hemodynamic factors. While estimated glomerular filtration rate (eGFR) and albuminuria remain diagnostic cornerstones, they incompletely capture this multifactorial pathophysiology. Emerging metabolic indices and inflammatory markers show strong prognostic value, yet a practical tool integrating these novel biomarkers with standard parameters for clinical risk stratification is lacking.
AIM
To develop and validate a comprehensive, readily applicable risk stratification model for predicting DKD progression in patients with type 2 diabetes. The model integrates easily accessible metabolic and inflammatory biomarkers with standard renal parameters to enable earlier identification of high-risk patients and facilitate personalized management.
METHODS
In this single-center retrospective cohort study, we included 458 patients with type 2 diabetes hospitalized between March 2018 and December 2022. DKD progression was defined as a composite endpoint (≥ 30% eGFR decline, progression to macroalbuminuria, renal replacement therapy, or renal death). Baseline metabolic and inflammatory biomarkers were comprehensively assessed. Machine learning algorithms were employed to develop and internally validate a risk prediction nomogram, the performance of which was further evaluated in an independent external cohort (n = 215).
RESULTS
Among 458 patients followed for a median of 36 months, 142 (31.0%) experienced DKD progression. A nomogram incorporating eight independent risk predictors (e.g., baseline eGFR < 60 mL/minute/1.73 m², hazard ratio = 3.87; urinary albumin-to-creatinine ratio ≥ 300 mg/g, hazard ratio = 4.15) was developed, demonstrating excellent discrimination (C-index: 0.843) and calibration. The model stratified patients into low-risk, intermediate-risk, and high-risk groups with markedly distinct 3-year progression rates (8.7%, 28.4%, and 62.3%, respectively; log-rank P < 0.001). Critically, it significantly outperformed existing prediction models (Kidney Failure Risk Equation, Kidney Disease: Improving Global Outcomes, Chronic Kidney Disease Progression Score) in both discrimination and net reclassification (net reclassification improvement 23.6%-29.3%, all P < 0.001). Its performance was consistent across key subgroups (all interaction P > 0.10). External validation in an independent cohort (n = 215) confirmed robust generalizability, with a C-index of 0.826 and well-matched calibration across 1-3 years.
CONCLUSION
This study successfully identify a comprehensive risk stratification model by integration of metabolic and inflammatory biomarkers that accurately predicts DKD progression in patients with type 2 diabetes mellitus. The novel integrated model outperforms traditional risk scores and is a ready reference for personalized risk assessment and targeted intervention strategies in clinical practice.
Core Tip: This study proposed an innovative risk stratification model for diabetes kidney disease progression by combining metabolic and inflammatory biomarkers with renal parameters. A nomogram-based model was a good predictive tool and stratified patients into three groups: (1) Low-risk for progression; (2) Moderate-risk for progression; and (3) High-risk for progression. This readily applicable clinical model could help identify patients eligible for early intervention, tailoring treatment schemes based on their individual risk of progression and therefore reduce the burden of end-stage renal disease in high prevalence diabetes populations.
