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Cited by 11Pancreatic function and diabetesDiabetes Management and ResearchDiabetes, Cardiovascular Risks, and Lipoproteins

Modelling the effects of glucagon during glucose tolerance testing

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Kelly, Ross A., Fitches, Molly J., Webb, Steven D., Pop, S. R., Chidlow, Stewart J.·2019·Theoretical Biology and Medical Modelling
DOI 10.1186/s12976-019-0115-3PMC PMC6907263
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Abstract

Abstract Background Glucose tolerance testing is a tool used to estimate glucose effectiveness and insulin sensitivity in diabetic patients. The importance of such tests has prompted the development and utilisation of mathematical models that describe glucose kinetics as a function of insulin activity. The hormone glucagon, also plays a fundamental role in systemic plasma glucose regulation and is secreted reciprocally to insulin, stimulating catabolic glucose utilisation. However, regulation of glucagon secretion by α-cells is impaired in type-1 and type-2 diabetes through pancreatic islet dysfunction. Despite this, inclusion of glucagon activity when modelling the glucose kinetics during glucose tolerance testing is often overlooked. This study presents two mathematical models of a glucose tolerance test that incorporate glucose-insulin-glucagon dynamics. The first model describes a non-linear relationship between glucagon and glucose, whereas the second model assumes a linear relationship. Results Both models are validated against insulin-modified and glucose infusion intravenous glucose tolerance test (IVGTT) data, as well as insulin infusion data, and are capable of estimating patient glucose effectiveness (sG) and insulin sensitivity (sI). Inclusion of glucagon dynamics proves to provide a more detailed representation of the metabolic portrait, enabling estimation of two new diagnostic parameters: glucagon effectiveness (sE) and glucagon sensitivity (δ). Conclusions The models are used to investigate how different degrees of pax‘tient glucagon sensitivity and effectiveness affect the concentration of blood glucose and plasma glucagon during IVGTT and insulin infusion tests, providing a platform from which the role of glucagon dynamics during a glucose tolerance test may be investigated and predicted.

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  • In Kelly et al., estimation of the parameter based on glucagon and insulin data is not reported.

    “In addition, and most importantly, study (30) presents simulations where such parameter was varied in a given interval, whereas the estimation of the parameter based on glucagon and insulin data is not reported, and hence it was not proved that the model may allow estimation of such parameter.”

    From Mathematical Model of Glucagon Kinetics for the Assessment of Insulin-Mediated Glucagon Inhibition During an Oral Glucose Tolerance Test (2021) · cites this paper as [30]

    The paper does vary p11 in simulations, but it also says the ranges for p6–p11 were chosen using their fitted values from an earlier example (Model comparison and predictions > Comparing the LGMM and NLGMM; page not provided). Methods > Model solutions (page not provided) states that unknown parameters were fitted to experimental data. This is contrary to the claim that estimation of the parameter was not reported. (LLM, conf=0.83)

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  • The Kelly et al. model versions include five equations with 11 unknown parameters (nonlinear) and four equations with 10 unknown parameters (linear).

    “In addition, both the model versions (based on nonlinear or linear relationships between glucose, glucagon and insulin) are relatively complex and including several parameters (five equations with 11 unknown parameters, and four equations with 10 unknown parameters, for nonlinear and linear model versions, respectively).”

    From Mathematical Model of Glucagon Kinetics for the Assessment of Insulin-Mediated Glucagon Inhibition During an Oral Glucose Tolerance Test (2021) · cites this paper as [30]

    The Methods > Model solutions passage confirms five equations for the nonlinear model and four for the linear model. It gives 11 and 10 as the maximum numbers of unknown parameters, respectively, not fixed counts as the citing sentence implies. The paper also calls both systems of differential equations nonlinear; “linear” describes the relationships used in the LGMM. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • In Kelly et al., the parameter similar to SGLUCA(t) is assumed constant.

    “However, in study (30) the parameter is assumed constant, thus not considering its possible variations during a test.”

    From Mathematical Model of Glucagon Kinetics for the Assessment of Insulin-Mediated Glucagon Inhibition During an Oral Glucose Tolerance Test (2021) · cites this paper as [30]

  • The Kelly et al. model applies to glucose tolerance tests other than the OGTT, such as the IVGTT.

    “However, that model, which is to some extent an extension of the traditional Minimal Model (50), applies to glucose tolerance tests other than the OGTT, such as the IVGTT (consistently with the field of application of the Minimal Model).”

    From Mathematical Model of Glucagon Kinetics for the Assessment of Insulin-Mediated Glucagon Inhibition During an Oral Glucose Tolerance Test (2021) · cites this paper as [30]

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Authors
Kelly, R. A., Fitches, M. J., Webb, S. D., Pop, S. R., & Chidlow, S. J.
Affiliations
  • Liverpool John Moores University (GB)
  • University of Dundee (GB)
  • University of Chester (GB)
Corresponding author
Kelly, Ross A.
ORCID
Venue
Theoretical Biology and Medical Modelling
Publisher
Springer Science and Business Media LLC
Published
2019-12
Volume
16
Issue
1
Pages
21-21
ISSN
1742-4682 (ISSN-L)
DOI
10.1186/s12976-019-0115-3
PMC
PMC6907263
OpenAlex
W2995160435
Cited by
11
References
29
Citations by year
2026: 1 · 2024: 3 · 2023: 3 · 2022: 1 · 2021: 3
FWCI
0.96
Type
journal article
Language
en
Open access
Licence
Locations
  • Theoretical Biology and Medical Modelling (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Discovery Research Portal (University of Dundee) (acceptedVersion, cc-by)
  • Liverpool John Moores University (acceptedVersion)
  • ChesterRep (University of Chester) (submittedVersion, cc-by)
  • ChesterRep (University of Chester) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
Biotechnology and Biological Sciences Research Council (BB/L502273/2)
Topics
  • Health Sciences › Medicine › Surgery › Pancreatic function and diabetes
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
Keywords
glucagon, glucose tolerance test, glucose kinetics, insulin sensitivity, glucose effectiveness, glucagon sensitivity, mathematical modeling, intravenous glucose tolerance test, insulin infusion, type 1 diabetes, type 2 diabetes, pancreatic islet dysfunction
MeSH terms
Blood Glucose, Computer Simulation, Glucagon, Glucose Tolerance Test, Humans, Hypoglycemia, Insulin Resistance, Models, Biological, Reproducibility of Results, Linear Models

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