Identification of an integrated mathematical model of standard oral glucose tolerance test for characterization of insulin potentiation in health
- 作者：Roberto Burattini ; r.burattini@univpm.it ; Micaela Morettini
- 关键词：Glucose-insulin system ; Gastric emptying ; Oral glucose absorption ; GIP ; GLP-1 ; Incretin effect ; Parameter estimation
- 刊名：Computer Methods and Programs in Biomedicine
- 出版年：2012
- 期刊代码：68_01692607
- 类别：med
- 出版时间：August, 2012
- 卷：107
- 期：2
- 页码：248-261
- 文件大小：643 K
摘要
Two new formulations, respectively denominated INT_M1 and INT_M2, of an integrated mathematical model to describe the glycemic and insulinemic responses to a 75 g oral glucose tolerance test (OGTT) are proposed and compared. The INT_M1 assumes a single compartment for the intestine and the derivative of a power exponential function for the gastric emptying rate, while, in the INT_M2, a nonlinear three-compartment system model is adopted to produce a more realistic, multiphase gastric emptying rate. Both models were implemented in a Matlab-based, two-step procedure for estimation of seven adjustable coefficients characterizing the gastric emptying rate and the incretin, insulin and glucose kinetics. Model behaviour was tested vs. mean plasma glucagon-like peptide 1 (GLP-1), glucose-dependent insulinotropic polypeptide (GIP), glucose and insulin measurements from two different laboratories, where glycemic profiles observed during a 75 g OGTT were matched in healthy subjects (HC1- and HC2-group, respectively) by means of an isoglycemic intravenous glucose (I-IVG) infusion. Under the hypothesis of an additive effect of GLP-1 and GIP on insulin potentiation, our results demonstrated a substantial equivalence of the two models in matching the data. Model parameter estimates showed to be suitable markers of differences observed in the OGTT and matched I-IVG responses from the HC1-group compared to the HC2-group. Model implementation in our two-step parameter estimation procedure enhances the possibility of a prospective application for individualization of the incretin effect in a single subject, when his/her data are plugged in.