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In this work, we test the performance of Peak glucose concentration ($A$) andaverage of glucose removal rates ($\alpha$), as normoglycemia and dysglycemiaindices on a population monitored at the Mexico General Hospital between theyears 2017 - 2019. A total of 1911 volunteer patients at the Mexico GeneralHospital are considered. 1282 female patients age ranging from 17 to 80 yearsold, and 629 male patients age ranging from 18 to 79 years old. For eachvolunteer, OGTT data is gathered and indices are estimated in Ackerman's model.A binary separation of normoglycemic and disglycemic patients using a SupportVector Machine with a linear kernel is carried out. Classification indices aresuccessful for 83\%. Population clusters on diabetic conditions and progressionfrom Normoglycemic to T2DM may be concluded. The classification indices, $A$and $\alpha$ may be regarded as patient's indices and used to detect diabetesrisk. Also, criteria for the applicability of glucose-insulin regulation modelsare introduced. The performance of Ackerman's model is shown.

A validation study of normoglycemia and dysglycemia indices as a diabetes risk model