Mathematical modelling of a glucose-insulin system for type 2 diabetic patients in Chad

Authors

  • Adam Hassan Adoum Department of Mathematics, University Adam Barka d'Abeche, Faculty of Sciences and Techniques (FAST), Chad https://orcid.org/0000-0003-2670-2572
  • Mahamat Saleh Daoussa Haggar Laboratory of Modeling, Mathematics, Computer Science, Applications and Simulation (L2MIAS), Department of Mathematics, Faculty of Exact and Applied Sciences, University of N'djamena, Chad https://orcid.org/0000-0002-0863-2235
  • Jean Marie Ntaganda Department of Mathematics, College of Science and Technology, School of Science, University of Rwanda, Rwanda https://orcid.org/0000-0003-2464-2377

DOI:

https://doi.org/10.53391/mmnsa.2022.020

Abstract

In this paper, we focus on modelling the glucose-insulin system for the purpose of estimating glucagon, insulin, and glucose in the liver in the internal organs of the human body. A three-compartmental mathematical model is proposed. The model parameters are estimated using a nonlinear inverse optimization problem and data collected in Chad. In order to identify insulin and glucose in the liver for type 2 diabetic patients, the Sampling Importance Resampling (SIR) particle filtering algorithm is used and implemented through discretization of the developed mathematical model. The proposed mathematical model allows further investigation of the dynamic behavior of hepatic glucose, insulin, and glucagon in internal organs for type 2 diabetic patients. During periods of hyperglycemia (i.e., after meal ingestion), whereas insulin secretion is increased, glucagon secretion is reduced. The results are in agreement with empirical and clinical data and they are clinically consistent with physiological responses.

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Published

2022-12-30
CITATION METRICS
DOI: 10.53391/mmnsa.2022.020

How to Cite

Adoum, A. H., Haggar, M. S. D., & Ntaganda, J. M. (2022). Mathematical modelling of a glucose-insulin system for type 2 diabetic patients in Chad. Mathematical Modelling and Numerical Simulation With Applications, 2(4), 244–251. https://doi.org/10.53391/mmnsa.2022.020

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Research Articles