A program to estimate insulin resistance based on data from the oral glucose tolerance test

Abstract

Background An instrument to help clinicians to evaluate the oral glucose tolerance test (OGTT) at-a-glance is lacking. Aim To generate a program written in HTML squeezing relevant information from the OGTT with glucose and insulin measurements. Material and Methods We reanalyzed a database comprising 90 subjects. All of them had both an OGTT and a pancreatic suppression test (PST) measuring insulin resistance directly. Thirty-seven of the 90 studied participants were insulin resistant (IR). Receiver operating characteristic (ROC) curves and Bayesian analyses delineated the diagnostic performances of four predictors of insulin resistance: HOMA, QUICKI, ISI-OL (Matsuda-DeFronzo) and I0*G60. We validated a new biochemical predictor, the Percentual Relative Insulin Sensitivity (%RIS), and calculated the Percentual Relative Beta Cell Function (%RBCF). Results The best diagnostic performance of the five predictors were those of the I0*G60 and the %RIS. The poorest diagnostic performances were those of the HOMA and QUICKI. The ISI-OL’s performance was in between. The %RIS of participants with and without IR was 44.4 ± 7.3 and 101.1 ± 8.8, respectively (p < 0.05). The figures for % RBCF were 55.8 ± 11.8 and 90.8 ± 11.6, respectively (p < 0.05). Mathematical modeling of the relationship between these predictors and the Steady State Plasma Glucose Value from the PST was performed. We developed a program with 10 inputs (glucose and insulin values) and several outputs: I0*G60, HOMA, QUICKI, ISI-OL, Insulinogenic Index, Disposition Index, %RBCF, %RIS, and metabolic categorization of the OGTT (ADA 2003). Conclusions The OGTT data permitted us to write successfully an HTML program allowing the user to fully evaluate at-a-glance its metabolic information.