Association of Variability of Monthly Continuous Glucose Monitoring-Derived Metrics With Diabetic Kidney Disease in Type 1 Diabetes.
BACKGROUND While HbA1c is the standard for monitoring long-term glycaemic control, it fails to capture glycaemic variability. We investigated the discriminatory capacity of longitudinal continuous glucose monitoring (CGM) metrics and identified CGM metric patterns associated with diabetic kidney disease (DKD) in individuals with type 1 diabetes (T1D) using machine learning (ML). METHODS We analysed cross-sectional data from 282 T1D patients with 1-year consecutive CGM data. DKD was defined by persistent laboratory abnormalities (urine albumin-creatinine ratio ≥ 30 mg/g or estimated glomerular filtration rate < 60 mL/min/1.73 m2) confirmed by at least two measurements within the 1-year period. LightGBM, XGBoost, Random Forest, and Logistic Regression (LR) were developed. Feature importance was assessed using SHAP analysis. RESULTS The LightGBM model achieved the highest performance (AUROC = 0.91 [95% CI, 0.88-0.93], F1 score = 0.65). All tree-based ML models outperformed the LR model. SHAP analysis identified the standard deviation (SD) of monthly time in range (TIR) and time in tight range (TITR) as the most influential features. In contrast, the CV of sensor glucose did not differ significantly between groups (p = 0.416). Even in the early DKD subgroup, the SD of monthly TIR and TITR remained significantly elevated. CONCLUSION The SD of monthly TIR and TITR is strongly associated with DKD in T1D, whereas the CV of sensor glucose is not. ML-based integration of these longitudinal metrics offers improved discrimination of concurrent DKD status beyond conventional glycaemic markers.