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