J Korean Med Sci.  2014 Mar;29(3):378-385. 10.3346/jkms.2014.29.3.378.

Simulation of Oral Glucose Tolerance Tests and the Corresponding Isoglycemic Intravenous Glucose Infusion Studies for Calculation of the Incretin Effect

Affiliations
  • 1Interdisciplinary Program for Bioengineering, Graduate School, Seoul National University, Seoul, Korea.
  • 2Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
  • 3Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, Korea. sungwan@snu.ac.kr
  • 4Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
  • 5Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea.
  • 6Department of Biomedical Engineering, Seoul National University Hospital, Seoul, Korea.

Abstract

The incretin effect, which is a unique stimulus of insulin secretion in response to oral ingestion of nutrients, is calculated by the difference in insulin secretory responses from an oral glucose tolerance test (OGTT) and a corresponding isoglycemic intravenous glucose infusion (IIGI) study. The OGTT model of this study, which is individualized by fitting the glucose profiles during an OGTT, was developed to predict the glucose profile during an IIGI study in the same subject. Also, the model predicts the insulin and incretin profiles during both studies. The incretin effect, estimated by simulation, was compared with that measured by physiologic studies from eight human subjects with normal glucose tolerance, and the result exhibited a good correlation (r > 0.8); the incretin effect from the simulation was 56.5% +/- 10.6% while the one from the measured data was 52.5% +/- 19.6%. In conclusion, the parameters of the OGTT model have been successfully estimated to predict the profiles of both OGTTs and IIGI studies. Therefore, with glucose data from the OGTT alone, this model could control and predict the physiologic responses, including insulin secretion during OGTTs and IIGI studies, which could eventually eliminate the need for complex and cumbersome IIGI studies in incretin research.

Keyword

Incretins; Oral Glucose Tolerance Test (OGTT); Isoglycemic Intravenous Glucose Infusions (IIGI); Individualized Model

MeSH Terms

Administration, Oral
Adult
Area Under Curve
Blood Glucose/analysis
*Computer Simulation
Female
Glucose/metabolism/pharmacology
Glucose Tolerance Test
Humans
Incretins/*blood
Insulin/blood
Liver/drug effects
Middle Aged
*Models, Theoretical
ROC Curve
Blood Glucose
Glucose
Incretins
Insulin

Figure

  • Fig. 1 A schematic overview of the process of parameter estimation for the model and validation between measured data and simulation results. OGTT, oral glucose tolerance test; IIGI, isoglycemic intravenous glucose infusions; G, glucose; I, insulin; GIP, glucose-dependent insulinotropic polypeptide; iAUC, incremental area under the curves.

  • Fig. 2 Plasma glucose concentration profiles in subject #7. The solid lines are from the simulation (blue, 75 g OGTT; red, IIGI study; green, 4 glucose infusion rates). The dotted lines are from the measured data (sky blue [•], OGTT; purple [○], IIGI study).

  • Fig. 3 Plasma insulin concentration profiles in subject #7. The solid lines are from the simulation (blue, 75 g OGTT; red, IIGI study). The dotted lines are from the measured data (sky blue [•], OGTT; purple [○], IIGI study).

  • Fig. 4 Plasma incretin (GIP) concentration profiles in subject #7. The solid lines are from the simulation (blue, 75 g OGTT; red, IIGI study). The dotted lines are from the measured data (sky blue [•], OGTT; purple [○], IIGI study).

  • Fig. 5 The IIGI simulation for all OGTT models with the same intravenous glucose infusion rates for subject #7. The solid lines with different colors show the OGTT model of eight subjects (s1-8). The black dashed line shows the measured data for subject #7.


Cited by  1 articles

Incretin and Pancreatic β-Cell Function in Patients with Type 2 Diabetes
Chang Ho Ahn, Tae Jung Oh, Se Hee Min, Young Min Cho
Endocrinol Metab. 2023;38(1):1-9.    doi: 10.3803/EnM.2023.103.


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