Modeling Glucagon Action in Patients with Type 1 Diabetes

A. Emami, Joseph El Youssef, Remi Rabasa-Lhoret, Joelle Pineau, Jessica Castle, Ahmad Haidar

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

The dual-hormone artificial pancreas is an emerging technology to treat type 1 diabetes (T1D). It consists of a glucose sensor, infusion pumps, and a dosing algorithm that directs hormonal delivery. Preclinical optimization of dosing algorithms using computer simulations has the potential to accelerate the pace of development for this technology. However, current simulation environments consider glucose regulation models that either do not include glucagon action submodels or include submodels that were proposed without comparison to other candidate models. We consider here nine candidate models of glucagon action featuring a number of possible characteristics: Insulin-independent glucagon action, insulin/glucagon ratio effect on hepatic glucose production, insulin-dependent suppression of glucagon action, and the effect of rate of change of glucagon. To assess the models, we use measurements of plasma insulin, plasma glucagon, and endogenous glucose production collected from experiments involving eight subjects with T1D who receive four subcutaneous glucagon boluses. We estimate each model's parameters using a Bayesian approach, and the models are contrasted based on the deviance information criterion. The model achieving the best fit features insulin-dependent suppression of glucagon action and incorporates effects of both glucagon levels and its rate of change.

Original languageEnglish (US)
Article number7517341
Pages (from-to)1163-1171
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Volume21
Issue number4
DOIs
StatePublished - Jul 1 2017

Keywords

  • Artificial pancreas
  • Bayesian estimation
  • closed-loop systems
  • computer simulation
  • glucagon action

ASJC Scopus subject areas

  • Biotechnology
  • Medicine(all)
  • Computer Science Applications
  • Health Information Management
  • Electrical and Electronic Engineering

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