Mathematical modeling of glucose, insulin and c-peptide during the OGTT in pre-diabetic subjects: A DIRECT study
Christiane Dings (1), Nina Scherer (1), Jan Freijer (2), Valerie Nock (2) and Thorsten Lehr (1) for the DIRECT consortium
(1) Clinical Pharmacy, Saarland University, Saarbruecken, Germany, (2) Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
Objectives: Development of a mechanistic model of glucose, insulin and c-peptide levels during an oral glucose tolerance test (OGTT) in pre-diabetic subjects with high risk of developing type 2 diabetes mellitus (T2DM).
Methods: Data from the Diabetes Research on Patient Stratification (DIRECT) study was used[1]. The subjects in the study were pre-diabetic, which was defined by the inclusion criteria of HbA1c <6.5% and fasting plasma glucose <10 mM without antidiabetic treatment, and a high risk of developing T2DM according to the DETECT2 risk algorithm[2]. All subjects underwent an OGTT. Glucose, insulin and c-peptide concentrations were measured before and 0, 15, 30, 45, 60, 90 and 120 min after oral glucose intake. Modeling and simulation was performed using non-linear mixed-effects methods implemented in the software NONMEM (version 7.3.0). Stochastic simulations were performed for model evaluation.
Results: The dataset included 2281 subjects and 54562 data points. Glucose, insulin and c-peptide concentrations were described simultaneously using one compartment turn-over models with zero-order synthesis or release rates (0.15 g/(l*h), 48.7 pM/h and 331 pM/h, respectively) and first-order utilization or degradation rates. Baseline levels of glucose, insulin and c-peptide were estimated as 1.01 g/l, 47.6 pM and 759 pM, respectively. Oral glucose uptake was described using a transit model with a first-order absorption rate constant (3.86 h-1) and one transit compartment. The influence of insulin levels on the glucose utilization as well as the effect of glucose levels on the release of insulin and c-peptide were modelled using exponential effect models (effect rate constants of 0.7, 3.4 and 3.2, respectively). Further, the incretin effect was implemented as an additional release of insulin and c-peptide with linear dependency of the amount of glucose in the transit compartment. The precision of parameter estimates was excellent (residual standard error <12%).
Conclusion: An OGTT model simultaneously describing the changes in glucose, insulin and c-peptide levels was successfully developed for pre-diabetic subjects. The inclusion of both c-peptide and insulin release enabled the distinction between changes in beta-cell function and first pass clearance of insulin, which improved the characterization of the individual glycemic condition. In future application this model is planned to be used to observe and predict disease progression of T2DM.
References:
[1] Koivula RW et al. Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes: Rationale and design of the epidemiological studies within the IMI DIRECT Consortium. Diabetologia (2014) 57(6):1132–42.
[2] Alssema M et al. The Evaluation of Screening and Early Detection Strategies for Type 2 Diabetes and Impaired Glucose Tolerance (DETECT-2) update of the Finnish diabetes risk score for prediction of incident type 2 diabetes. Diabetologia (2011) 54(5):1004–1012.