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<p>Diabetic kidney disease (DKD) is a common type 2 diabetes mellitus (T2DM) complication, often leading to end-stage kidney disease. Predicting kidney function decline may enable timely interventions to slow disease progression. We developed machine learning (ML) models to predict future estimated glomerular filtration rate (eGFR) values at specific annual time points among patients with T2DM, utilizing only baseline characteristics obtained from routine medical check-ups. We followed 974 patients for a median of 5.3 years across multiple centers. Fifty-four baseline clinical features and a time-point variable were included in three ML algorithms (Light Gradient Boosting Machine, Random Forest, and Support Vector Machine [SVM]). Their predictive performance was compared with each other and with a benchmark multiple linear regression (MLR) model, evaluated using the coefficient of determination (R<sup>2</sup>) and root mean squared error, with 95% confidence intervals (95% CIs). The SVM model showed the best prediction performance across a wide eGFR range (R<sup>2</sup> 0.67 [95% CI 0.62–0.72]), maintaining moderate or better performance for up to 6 years, whereas the MLR model struggled to predict beyond 4 years. These findings suggest that SVM-based prediction using baseline data and time-point information could support long-term management of DKD progression in patients with T2DM.</p>

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Predicting future kidney function in type 2 diabetes mellitus using machine learning and baseline health information

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月27日

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  • Hiroyuki Unoki-Kubota,
  • Kentaro Nakajima,
  • Yosuke Shimizu,
  • Shu Tamano,
  • Hiroshi Kajio,
  • Ryotaro Bouchi,
  • Shigeo Yamashita,
  • Yuka Fukazawa,
  • Naoto Seki,
  • Michihiro Matsumoto,
  • Yukari Uemura,
  • Daisuke Yoneoka,
  • Kohjiro Ueki,
  • Mitsuhiko Noda,
  • Yasushi Kaburagi

摘要

Diabetic kidney disease (DKD) is a common type 2 diabetes mellitus (T2DM) complication, often leading to end-stage kidney disease. Predicting kidney function decline may enable timely interventions to slow disease progression. We developed machine learning (ML) models to predict future estimated glomerular filtration rate (eGFR) values at specific annual time points among patients with T2DM, utilizing only baseline characteristics obtained from routine medical check-ups. We followed 974 patients for a median of 5.3 years across multiple centers. Fifty-four baseline clinical features and a time-point variable were included in three ML algorithms (Light Gradient Boosting Machine, Random Forest, and Support Vector Machine [SVM]). Their predictive performance was compared with each other and with a benchmark multiple linear regression (MLR) model, evaluated using the coefficient of determination (R2) and root mean squared error, with 95% confidence intervals (95% CIs). The SVM model showed the best prediction performance across a wide eGFR range (R2 0.67 [95% CI 0.62–0.72]), maintaining moderate or better performance for up to 6 years, whereas the MLR model struggled to predict beyond 4 years. These findings suggest that SVM-based prediction using baseline data and time-point information could support long-term management of DKD progression in patients with T2DM.