Minimal model analysis with the frequently sampled intravenous glucose tolerance test provides an effective way to measure two important metabolic parameters in vivo under non-steady-state conditions: glucose effectiveness (SG) and insulin sensitivity (SI). Two questions regarding the validity of SG and SI have recently emerged. First, SG from the minimal model is suspected to be overestimated. Second, the occurrence of SI values indistinguishable from zero (“zero-SI”) is not negligible in large clinical studies, and its physiological meaning is uncertain. In this study, we examined the significance of the assumed single-compartment glucose distribution embedded in the minimal model on the estimation of SG and SI. A more accurate two-compartment model was constructed by incorporating insulin action on hepatic glucose output and uptake into a previously validated construction. The two-compartment results were compared with the one-compartment minimal model results. It was shown that the one-compartment assumption contributes to a systematic deviation of SG (slope = 0.54, y-intercept = 0.014 min−1; n = 195 simulations). However, SG from the minimal model was linearly correlated to SG determined from the two-compartment model (r = 0.996). The one-compartment assumption also contributed to the occurrence of zero S1 values for insulin-resistant subjects. A similar linear relationship was found between S1 estimated by both the minimal model and the two-compartment model (slope = 0.58, y-intercept = −0.57 × 10−4 min−1 per μU/ml, r = 0.998). In conclusion, SG and SI from the minimal model are not necessarily equivalent to values emanating from the more accurate two-compartment model. However, the very high correlation between one- and two-compartment results suggests that the minimal model-derived SG and SI are dependable indexes of in vivo glucose effectiveness and insulin sensitivity. Minimal model analysis' advantages of simplicity, minimal invasiveness, reasonable reflection of non-steady-state glucose kinetics, and cost-effectiveness could in many cases outweigh the structural bias introduced by the model simplification.

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