Minimal model assessment of hepatic insulin extraction during an oral test from standard insulin kinetic parameters.
In this article, a first aim was to develop a minimal modeling approach to noninvasively assess hepatic insulin extraction in 204 healthy subjects studied with a standard meal by coupling the already available meal C-peptide minimal model with a new insulin model. The ingredients of this model are posthepatic IDR, which in turn is described in terms of pancreatic ISR and hepatic insulin extraction HE, and a linear monocompartmental model of insulin kinetics. Even if ISR is provided by the C-peptide minimal model, the simultaneous assessment of HE and insulin kinetics is critical, since compensations may arise between parameters describing these two processes. Therefore, as a second aim of this study, a method was developed to predict standard values of insulin kinetic parameters in an individual on the basis of the individual's anthropometric characteristics. The statistical analysis, based on linear regression of insulin kinetic parameters estimated from IM-IVGTT data performed on the same subjects, demonstrated that insulin kinetic parameters can be accurately predicted from age and body surface area. Once kinetic parameters of the new insulin model were fixed to these values, HE profile and indexes during a meal were reliably estimated in each individual, indicating a significant suppression during the meal since the overall index of HE, equal to 60 +/- 1% in the basal state, is reduced to 40 +/- 1% during a meal. However, standard parameters provide an approximation of the individual one; thus, the third aim was to define the impact on estimated indexes of using standard instead of individually estimated values. Our results showed that the 25% uncertainty affecting as an average insulin kinetic parameters of an individual, when they are predicted from age and body surface area, translates into a similar relative uncertainty in the individual's hepatic insulin extraction indexes.
Campioni
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M
Department of Information Engineering, University of Padua, Padua, Italy.
Toffolo
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G
Basu
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R
Rizza
R A
RA
Cobelli
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C
D000465:Algorithms; D001786:Blood Glucose / Q000378:metabolism; D001835:Body Weight / Q000502:physiology; D016208:Databases, Factual; D005260:Female; D005947:Glucose / Q000494:pharmacology; D005951:Glucose Tolerance Test; D006801:Humans; D007004:Hypoglycemic Agents / Q000493:pharmacokinetics*; D007328:Insulin / Q000378:metabolism* / Q000493:pharmacokinetics*; D008099:Liver / Q000378:metabolism*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012015:Reference Standards
D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin; D005947:Glucose