A Composite Model of Glucagon-Glucose Dynamics for In Silico Testing of Bihormonal Glucose Controllers
Open accessAbstract
Background: The utility of simulation environments in the development of an artificial pancreas for type 1 diabetes mellitus (T1DM) management is well established. The availability of a simulator that incorporates glucagon as a counterregulatory hormone to insulin would allow more efficient design of bihormonal glucose controllers. Existing models of the glucose regulatory system that incorporates glucagon action are difficult to identify without using tracer data. In this article, we present a novel model of glucagon-glucose dynamics that can be easily identified with standard clinical research data. Methods: The minimal model of plasma glucose and insulin kinetics was extended to account for the action of glucagon on net endogenous glucose production by incorporating a new compartment. An existing subcutaneous insulin absorption model was used to account for subcutaneous insulin delivery. The same model of insulin pharmacokinetics was employed to model the pharmacokinetics of subcutaneous glucagon absorption. Finally, we incorporated an existing gastrointestinal absorption model to account for meal intake. Data from a closed-loop artificial pancreas study using a bihormonal controller on T1DM subjects were employed to identify the composite model. To test the validity of the proposed model, a bihormonal controller was designed using the identified model. Results: Model parameters were identified with good precision, and an excellent fitting of the model with the experimental data was achieved. The proposed model allowed the design of a bihormonal controller and demonstrated its ability to improve glycemic control over a single-hormone controller. Conclusions: A novel composite model, which can be easily identified with standard clinical data, is able to account for the effect of exogenous insulin and glucagon infusion on glucose dynamics. This model represents another step toward the development of a bihormonal artificial pancreas.
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In these experimental conditions, this model and the other models of the literature are still suboptimal because they do not account for the glucagon evanescent effect visible in the data.
“Nonetheless, in our experimental conditions, this [11] and the other models of the literature [10, 12–15] are still suboptimal since they do not account for the glucagon evanescent effect visible in the data (Fig. 1, Panel D).”
From Modeling the effect of glucagon on endogenous glucose production in healthy individuals under meal-like conditions (2025) · cites this paper as [12]
The Methods (page unavailable) identify the cited work as a glucagon–glucose model and describe how it represents glucagon action, supporting the identification of “this [11].” The paper does not assess the citing authors’ experimental conditions or say that its model fails to capture the evanescent effect visible in their data; that limitation is the citing authors’ own assessment. (LLM, conf=0.76)
Experimental — accuracy not yet measuredMost existing mathematical models of postprandial endogenous glucose production suppression use plasma glucose and not endogenous glucose production as the variable to fit.
“However, most of them use plasma glucose and not EGP as a variable to fit.”
From Modeling the effect of glucagon on endogenous glucose production in healthy individuals under meal-like conditions (2025) · cites this paper as [12]
The Methods (page unavailable) state that this paper fitted its model by minimizing the error between measured and predicted plasma glucose, and describe earlier models used to estimate blood glucose. They do not establish that most existing models of postprandial EGP suppression fit plasma glucose rather than EGP. (LLM, conf=0.83)
Experimental — accuracy not yet measuredSome mathematical models exist in the literature describing postprandial endogenous glucose production suppression as a function of plasma glucose, insulin, and glucagon.
“Some mathematical models exist in the literature describing the postprandial EGP suppression as a function of plasma glucose, insulin [10, 11], and glucagon [12–15].”
From Modeling the effect of glucagon on endogenous glucose production in healthy individuals under meal-like conditions (2025) · cites this paper as [12]
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- Authors
- Herrero, P., Georgiou, P., Oliver, N., Reddy, M., Johnston, D., & Toumazou, C.
- Affiliations
- Imperial College London (GB)
- Imperial College Healthcare NHS Trust (GB)
- Corresponding author
- Herrero, Pau
- Venue
- Journal of Diabetes Science and Technology
- Publisher
- SAGE Publications
- Published
- 2013-07
- Volume
- 7
- Issue
- 4
- Pages
- 941-951
- ISSN
- 1932-2968 (ISSN-L)
- PMC
- PMC3879758
- OpenAlex
- W1830314513
- Cited by
- 55
- References
- 27
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 5 · 2023: 4 · 2022: 3 · 2020: 1 · 2019: 9 · 2018: 5 · 2017: 4 · 2016: 5 · 2015: 11 · 2014: 3 · 2013: 1
- FWCI
- 5.24
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Diabetes Science and Technology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (100921)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Surgery › Pancreatic function and diabetes
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Keywords
- type 1 diabetes, artificial pancreas, endogenous glucose production, subcutaneous insulin absorption, closed-loop glucose control, glycemic control, model parameter identification
- MeSH terms
- Algorithms, Blood Glucose, Computer Simulation, Diabetes Mellitus, Type 1, Glucagon, Humans, Insulin, Insulin Infusion Systems, Models, Biological, Blood Glucose Self-Monitoring, Computational Biology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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