AutoML analysis in the dataset as a whole delivered a best-performing five-feature biosignature via the Classification Random Forests algorithm, including GCK, IAPP and KCNJ11 methylation and age and BMI, showing very high performance in discriminating T2DM patients from healthy individuals (AUC 0.927). The gene discussed is GCK; the disease is type 2 diabetes mellitus.