Mathematical modeling of the glucagon challenge test

Saeed, Masroor,; Dongen, Marloes G J van; Álvarez-Jiménez, Ricardo; Burggraaf, Koos; Peletier, L. A.; Peletier, Mark A.

doi:10.1007/s10928-019-09655-2

Cited by 10 publications

(8 citation statements)

References 23 publications

(36 reference statements)

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“…As previously outlined, some mathematical models were developed with the aim to investigate glucagon secretion at cellular level (12)(13)(14)(15)(16)(17)(18)(19)(20)(21), whereas other models, more similarly to ours, were developed for whole-body analyses, but mainly for simulation purposes rather than for clinical applications (22)(23)(24)(25). Other studies presented models of glucagon kinetics for possible clinical applications, but they were focused on the analysis of glucagon administered exogenously, or for the analysis of the not common glucagon challenge test (26)(27)(28)(29). The study having more aspects in common with ours is that of Kelly et al (30).…”

Section: Discussionmentioning

confidence: 99%

“…One study analyzed the kinetics of glucagon administered exogenously ( 26 ), without however accounting for the interplay with insulin or glucose; another study performed similar analyses for the case of therapy based on glucagon (plus insulin) infusion ( 27 ). Some other studies developed models for the analysis of the glucagon challenge test, which is however not widely used ( 28 , 29 ). The study ( 30 ) had purposes more similar to those of our study, but the developed model analyzed glucagon kinetics during an intravenous glucose tolerance test (IVGTT), or an insulin-infusion test.…”

Section: Introductionmentioning

confidence: 99%

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Mathematical Model of Glucagon Kinetics for the Assessment of Insulin-Mediated Glucagon Inhibition During an Oral Glucose Tolerance Test

et al. 2021

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Glucagon is secreted from the pancreatic alpha cells and plays an important role in the maintenance of glucose homeostasis, by interacting with insulin. The plasma glucose levels determine whether glucagon secretion or insulin secretion is activated or inhibited. Despite its relevance, some aspects of glucagon secretion and kinetics remain unclear. To gain insight into this, we aimed to develop a mathematical model of the glucagon kinetics during an oral glucose tolerance test, which is sufficiently simple to be used in the clinical practice. The proposed model included two first-order differential equations -one describing glucagon and the other describing C-peptide in a compartment remote from plasma - and yielded a parameter of possible clinical relevance (i.e., SGLUCA(t), glucagon-inhibition sensitivity to glucose-induced insulin secretion). Model was validated on mean glucagon data derived from the scientific literature, yielding values for SGLUCA(t) ranging from -15.03 to 2.75 (ng of glucagon·nmol of C-peptide-1). A further validation on a total of 100 virtual subjects provided reliable results (mean residuals between -1.5 and 1.5 ng·L-1) and a negative significant linear correlation (r = -0.74, p < 0.0001, 95% CI: -0.82 – -0.64) between SGLUCA(t) and the ratio between the areas under the curve of suprabasal remote C-peptide and glucagon. Model reliability was also proven by the ability to capture different patterns in glucagon kinetics. In conclusion, the proposed model reliably reproduces glucagon kinetics and is characterized by sufficient simplicity to be possibly used in the clinical practice, for the estimation in the single individual of some glucagon-related parameters.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mathematical Model of Glucagon Kinetics for the Assessment of Insulin-Mediated Glucagon Inhibition During an Oral Glucose Tolerance Test

et al. 2021

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“…These models use a large number of variables and parameters, and describe a multitude of biophysical processes, rather than the resulting control strategy itself. For instance, the model recently proposed by Masroor et al 7 comprises 5 dynamical equations and over 25 parameters. The use of such models is limited by the "curse of dimensionality", i.e., the catastrophic growth of the number combinations of parameter values to explore when attempting to reproduce measured data.…”

Section: Introductionmentioning

confidence: 99%

Homeostasis as a proportional–integral control system

Morra¹,

Palanica²,

Fossat³

2020

npj Digit. Med.

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According to medical guidelines, the distinction between "healthy" and "unhealthy" patients is commonly based on single, discrete values taken at an isolated point in time (e.g., blood pressure or core temperature). Perhaps a more robust and insightful diagnosis can be obtained by studying the functional interdependence of such indicators and the homeostasis that controls them. This requires quasi-continuous measurements and a procedure to map the data onto a parsimonious control model with a degree of universality. The current research illustrates this approach using glucose homeostasis as a target. Data were obtained from 41 healthy subjects wearing over-the-counter glucose monitors, and projected onto a simple proportional-integral (PI) controller, widely used in engineering applications. The indicators quantifying the control function are clustered for the great majority of subjects, while a few outliers exhibit less responsive homeostasis. Practical implications for healthcare and education are further discussed.

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“…These models use a large number of variables and parameters, and describe a multitude of biophysical processes, rather than the resulting control strategy itself. For instance, the model recently proposed by Masroor et al [6] comprises 5 dynamical equations and over 25 parameters. The use of such models is limited by the curse of dimensionality, i.e., the catastrophic growth of the number combinations of parameter values to explore when attempting to reproduce measured data.…”

Section: Introductionmentioning

confidence: 99%

I'm a doctor, not a mathematician! Homeostasis as a proportional-integral control system

Morra¹,

Palanica²,

Fossat³

2019

Preprint

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The distinction between "healthy" and "unhealthy" patients is commonly based on single, discrete values taken at an isolated point in time (e.g., blood pressure or core temperature). Perhaps a more robust and insightful diagnosis can be obtained by studying the functional interdependence of such indicators and the homeostasis that controls them. This requires quasi-continuous measurements and a procedure to map the data onto a parsimonious control model with a degree of universality. The current research illustrates this approach using glucose homeostasis as a target. Data were obtained from 41 healthy subjects wearing over-the-counter glucose monitors, and projected onto a simple proportionalintegral (PI) controller, widely used in engineering applications. The indicators quantifying the control function are clustered for the great majority of subjects, while a few outliers exhibit less efficient homeostasis. Practical implications for healthcare and education are further discussed.

show abstract

Mathematical modeling of the glucagon challenge test

Cited by 10 publications

References 23 publications

Mathematical Model of Glucagon Kinetics for the Assessment of Insulin-Mediated Glucagon Inhibition During an Oral Glucose Tolerance Test

Mathematical Model of Glucagon Kinetics for the Assessment of Insulin-Mediated Glucagon Inhibition During an Oral Glucose Tolerance Test

Homeostasis as a proportional–integral control system

I'm a doctor, not a mathematician! Homeostasis as a proportional-integral control system

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