Quantification of the Individual Effect of an Exercise Bout on Insulin Sensitivity: In-Silico Modeling and Linear Regression Combined to Reduce Sampling Protocol Requirements

Exercise modulates metabolism by also acting on insulin sensitivity, being the biological response to insulin stimulation of target tissues. Among the available methods for insulin sensitivity quantification, an option is represented by mathematical-model interpretation of data from dynamic tests (i.e. administration of a glucose challenge). When using such methods, the requirements in terms of data samples in relation to the model characteristics are critical. Thus, starting from a modeling approach previously proposed in the literature to interpret data from oral glucose tolerance test (OGTT), the aim of this study was to provide a simplified procedure for a reliable (model-based) quantification of the effect of an exercise bout on insulin sensitivity without the need to repeat the full test, as the modelling approach otherwise would require. A mathematical model comprising five differential equations was exploited to estimate 13 unknown parameters, with a particular focus on the parameter kxgi, representing insulin sensitivity. The parameter kxgi and other indexes of insulin sensitivity (Matsuda, Cederholm, Stumvoll, OGIS120 and PREDIM) were assessed in a group of ten men who underwent a 75-gram OGTT before and after an exercise bout. Then, Pearson correlation coefficients were calculated between kxgi percentage changes before and after exercise and change in each of the other indexes, revealing Stumvoll as the most strongly correlated (ρ=0.83). Consequently, a linear regression model was set up to estimate kxgi after exercise from changes in the Stumvoll index (β0=-7.1062, β1=14.6745 for coefficients of the linear regression), thus eliminating the need of two full-protocol OGTT tests.