GOAL: Quantitative assessment of hepatic insulin extraction (HE) after an oral glucose challenge, e.g., a meal, is important to understand the regulation of carbohydrate metabolism. The aim of the current study is to develop a model of system for estimating HE. METHODS: Nine different models, of increasing complexity, were tested on data of 204 normal subjects, who underwent a mixed meal tolerance test, with frequent measurement of plasma glucose, insulin, and C-peptide concentrations. All these models included a two-compartment model of C-peptide kinetics, an insulin secretion model, a compartmental model of insulin kinetics (with number of compartments ranging from one to three), and different HE descriptions, depending on plasma glucose and insulin. Model performances were compared on the basis of data fit, precision of parameter estimates, and parsimony criteria. RESULTS: The three-compartment model of insulin kinetics, coupled with HE depending on glucose concentration, showed the best fit and a good ability to precisely estimate the parameters. In addition, the model calculates basal and total indices of HE ( HEb and HEtot, respectively), and provides an index of HE sensitivity to glucose ( SGHE ). CONCLUSION: A new physiologically based HE model has been developed, which allows an improved quantitative description of glucose regulation. SIGNIFICANCE: The use of the new model provides an in-depth description of insulin kinetics, thus enabling a better understanding of a given subject's metabolic state.
GOAL: Quantitative assessment of hepatic insulin extraction (HE) after an oral glucose challenge, e.g., a meal, is important to understand the regulation of carbohydrate metabolism. The aim of the current study is to develop a model of system for estimating HE. METHODS: Nine different models, of increasing complexity, were tested on data of 204 normal subjects, who underwent a mixed meal tolerance test, with frequent measurement of plasma glucose, insulin, and C-peptide concentrations. All these models included a two-compartment model of C-peptide kinetics, an insulin secretion model, a compartmental model of insulin kinetics (with number of compartments ranging from one to three), and different HE descriptions, depending on plasma glucose and insulin. Model performances were compared on the basis of data fit, precision of parameter estimates, and parsimony criteria. RESULTS: The three-compartment model of insulin kinetics, coupled with HE depending on glucose concentration, showed the best fit and a good ability to precisely estimate the parameters. In addition, the model calculates basal and total indices of HE ( HEb and HEtot, respectively), and provides an index of HE sensitivity to glucose ( SGHE ). CONCLUSION: A new physiologically based HE model has been developed, which allows an improved quantitative description of glucose regulation. SIGNIFICANCE: The use of the new model provides an in-depth description of insulin kinetics, thus enabling a better understanding of a given subject's metabolic state.
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