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Association of Variability of Monthly Continuous Glucose Monitoring-Derived Metrics With Diabetic Kidney Disease in

Byeongjae Kang1,2, Rosa Oh3, Taeyoung Kim1

  • 1Medical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea.

Diabetes, Obesity & Metabolism
|July 15, 2026
PubMed

Insights

The standard deviation of monthly time in range (TIR) and time in tight range (TITR) are key indicators for diabetic kidney disease (DKD) in type 1 diabetes (T1D). Machine learning models effectively identify DKD risk using these continuous glucose monitoring metrics.

Area of Science:

  • Endocrinology
  • Nephrology
  • Data Science

Background:

  • Hemoglobin A1c (HbA1c) is standard for glycaemic control but misses glycaemic variability.
  • Continuous glucose monitoring (CGM) offers longitudinal data for improved diabetes management.
  • Diabetic kidney disease (DKD) is a major complication in type 1 diabetes (T1D).

Purpose of the Study:

  • Investigate the discriminatory capacity of longitudinal CGM metrics.
  • Identify CGM metric patterns associated with DKD in T1D.
  • Utilize machine learning (ML) for DKD risk prediction.

Main Methods:

  • Analyzed 1-year CGM data from 282 T1D patients.
  • Defined DKD by persistent albuminuria or reduced eGFR.
  • Developed and compared ML models (LightGBM, XGBoost, Random Forest, Logistic Regression).

Main Results:

  • LightGBM model achieved highest performance (AUROC=0.91).
  • Standard deviation (SD) of monthly time in range (TIR) and time in tight range (TITR) were most influential features.
  • SD of monthly TIR and TITR remained elevated even in early DKD.

Conclusions:

  • SD of monthly TIR and TITR strongly associate with DKD in T1D.
  • CGM metrics offer improved DKD discrimination beyond HbA1c.
  • ML integration of longitudinal CGM data enhances DKD assessment.
Abstract

Keywords:
continuous glucose monitoringdiabetes mellitus type 1diabetic kidney diseasemachine learning

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