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Related Experiment Videos

Developing a machine learning model to predict renal function decline within three years using one-year longitudinal

Mari Kaneda1, Shu Meguro1, Kaiken Kimura2

  • 1Keio University School of Medicine, Tokyo, Japan.

Journal of Diabetes Investigation
|July 1, 2026
PubMed
Summary

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Machine learning accurately predicts kidney function decline in type 2 diabetes mellitus (T2DM) patients. This approach uses routine clinical data to identify high-risk individuals for early intervention, improving diabetic kidney disease (DKD) outcomes.

Area of Science:

  • Nephrology
  • Endocrinology
  • Data Science

Background:

  • Diabetic kidney disease (DKD) is a major complication of type 2 diabetes mellitus (T2DM), leading to dialysis.
  • Predicting renal function decline is crucial for timely intervention.
  • Existing prediction models often require advanced stages of DKD or pre-SGLT2 inhibitor data.

Purpose of the Study:

  • To evaluate machine learning's ability to predict renal outcomes in T2DM patients.
  • To assess renal decline risk using one-year fluctuations in estimated glomerular filtration rate (eGFR).
  • To analyze post-SGLT2 inhibitor data and predict risk at any clinical time point.

Main Methods:

  • Utilized retrospective outpatient T2DM data with mean eGFR ≥45 mL/min/1.73 m².
  • Predicted ≥30% eGFR decline over 3 years using semiannually extracted data.
Keywords:
Diabetic NephropathiesMachine LearningPrognosis

Related Experiment Videos

  • Developed machine learning models incorporating demographic, laboratory, and variability data.

    • Main Results:

    • A baseline model achieved an AUC of 0.77.
    • Incorporating features like proteinuria, HbA1c range, and eGFR variability improved AUC to 0.82.
    • The enhanced model demonstrated strong predictive performance for renal outcomes.

    Conclusions:

    • Machine learning accurately predicts renal outcomes in T2DM patients with preserved renal function.
    • Routinely collected clinical data can facilitate early identification of high-risk patients.
    • Timely therapeutic interventions can be enabled through early risk stratification.