Combining elemental analysis of toenails and machine learning techniques as a non-invasive diagnostic tool for the robust classification of type-2 diabetes.

•A toenail-based non-invasive method for diagnosing type-2 diabetes was developed.•Al, Cs, Ni, V, Zn in toenails were significantly different for diabetes patients.•Toenail concentrations of 22 elements were used for machine learning modeling.•A random forest model correctly classified 7 out of 9 samples, with AUC = 0.90.