Personalized glucose-lowering effect of chiglitazar in type 2 diabetes

Chiglitazar (carfloglitazar) is a peroxisome proliferator-activated receptor pan-agonist presenting non-inferior glucose-lowering efficacy with sitagliptin in patients with type 2 diabetes. To delineate the subgroup of patients with greater benefit from chiglitazar, we conducted a machine learning-based post-hoc analysis in two randomized controlled trials. We established a character phenomap based on 13 variables and estimated HbA(1c) decline to the effects of chiglitazar in reference to sitagliptin. Out of 1,069 patients, 63.3% were found to have greater reduction in HbA(1c) levels with chiglitazar, while 36.7% showed greater reduction with sitagliptin. This distinction in treatment response was statistically significant between groups (p(interaction)<0.001). To identify patients who would gain the most glycemic control benefit from chiglitazar, we developed a machine learning model, ML-PANPPAR, which demonstrated robust performance using sex, BMI, HbA(1c), HDL, and fasting insulin. The phenomapping-derived tool successfully identified chiglitazar responders and enabled personalized drug allocation in patients with drug-naïve diabetes.