Predictors of glycemic control with imeglimin for type 2 diabetes: Results of machine learning analyses using...

Identifying patient characteristics predictive of treatment response is crucial for optimizing type 2 diabetes outcomes. Using data from three phase 2/3 imeglimin trials in Japan, this analysis applied machine learning to determine characteristics associated with HbA1c improvement.

Regression tree and random forest methods identified baseline characteristics predictive of HbA1c improvement. Partial dependence plots (PDP) visualized the relationship between HbA1c change and continuous variables deemed important by Boruta.

For monotherapy, key predictors were baseline HbA1c, hypertension, smoking, type 2 diabetes duration, body mass index (BMI), low-density lipoprotein-cholesterol (LDL-C), metabolic syndrome, and estimated glomerular filtration rate. Nonsmokers with HbA1c ≥8.35% and LDL-C < 3.26 mmol/L at baseline showed the greatest improvement in HbA1c (-1.24%). Random forest analysis and Boruta identified baseline HbA1c, BMI, fatty liver index, smoking, and hypertension as significant predictors of HbA1c improvement. PDPs identified a positive correlation between higher baseline HbA1c, and a negative correlation between BMI and fatty liver index, and HbA1c improvement. For imeglimin add-on to insulin therapy, key predictors were BMI, age, LDL-C, type 2 diabetes duration, systolic blood pressure, and alanine transaminase (ALT). Patients with BMI <25.9, LDL-C < 2.68 mmol/L, and ALT <21 U/L showed the greatest HbA1c improvement (-1.48%). Random forest analysis and Boruta confirmed BMI, age, and LDL-C as significant predictors. PDPs identified a positive correlation between older age, and a negative correlation between higher BMI and LDL-C, and HbA1c improvement.

Machine learning effectively identified baseline characteristics predictive of HbA1c response to imeglimin, supporting the potential for personalized type 2 diabetes treatment strategies.