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Background <p>Diabetic kidney disease (DKD) represents the leading cause of end-stage renal disease (ESRD) worldwide, characterized by a complex pathophysiology and heterogeneous progression. Accurate prediction of the onset, progression, and adverse outcomes of DKD is critical for early intervention and personalized management.</p> Main body <p>This review systematically summarizes the current research on prediction models in DKD, encompassing both diagnostic and prognostic models. It discusses key methodological considerations in model development and validation, with a specific focus on the application of machine learning (ML) techniques in model construction. Furthermore, this article also evaluates the performance of prediction models based on routine clinical parameters and multimodal models integrating multi-omics, imaging, retinal parameters, and renal pathological features. The primary challenges in clinical translation are analyzed, and future directions for optimizing DKD prediction are proposed.</p> Conclusions <p>In summary, advancing the optimization and clinical translation of DKD prediction models holds significant potential to improve patient care. Future research should focus on addressing the existing challenges, aiming to advance risk-stratified and personalized management and inform future precision medicine approaches in nephrology.</p>

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Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月4日

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  • Yazhi Wang,
  • Jianzhou Wang,
  • Hui Chen

摘要

Background

Diabetic kidney disease (DKD) represents the leading cause of end-stage renal disease (ESRD) worldwide, characterized by a complex pathophysiology and heterogeneous progression. Accurate prediction of the onset, progression, and adverse outcomes of DKD is critical for early intervention and personalized management.

Main body

This review systematically summarizes the current research on prediction models in DKD, encompassing both diagnostic and prognostic models. It discusses key methodological considerations in model development and validation, with a specific focus on the application of machine learning (ML) techniques in model construction. Furthermore, this article also evaluates the performance of prediction models based on routine clinical parameters and multimodal models integrating multi-omics, imaging, retinal parameters, and renal pathological features. The primary challenges in clinical translation are analyzed, and future directions for optimizing DKD prediction are proposed.

Conclusions

In summary, advancing the optimization and clinical translation of DKD prediction models holds significant potential to improve patient care. Future research should focus on addressing the existing challenges, aiming to advance risk-stratified and personalized management and inform future precision medicine approaches in nephrology.