Shedding light on grey noise in diabetes modelling

Mansell, Erin J.; Docherty, Paul; Chase, J. Geoffrey

doi:10.1016/j.bspc.2016.06.007

Cited by 17 publications

(5 citation statements)

References 118 publications

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“…Such factors could be accounted for by introducing time-variant model parameters, which was beyond the page 14 of 34 scope of this work [9,37,38]. Especially, differences in insulin absorption could explain the observed intra-patient variation in steady state insulin concentration despite equal basal rates at all four visits.…”

Section: Discussionmentioning

confidence: 99%

“…New approaches with automatic glucose controlled insulin and glucagon delivery, known as a dual-hormone artificial pancreas (AP), may offer a solution to improve glycemic control [2][3][4][5][6]. To design and tune control algorithms for AP devices prior to in vivo tests, a validated simulation model capturing the dynamics between glucose, insulin and glucagon is needed to perform helpful in silico experiments [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation Using Data From Patients With Type 1 Diabetes

Wendt

Ranjan

Møller

et al. 2017

J Diabetes Sci Technol

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Background: Currently, no consensus exists on a model describing endogenous glucose production (EGP) as a function of glucagon concentrations. Reliable simulations to determine the glucagon dose preventing or treating hypoglycemia or to tune a dual-hormone artificial pancreas control algorithm need a validated glucoregulatory model including the effect of glucagon. Methods: Eight type 1 diabetes (T1D) patients each received a subcutaneous (SC) bolus of insulin on four study days to induce mild hypoglycemia followed by a SC bolus of saline or 100, 200, or 300 µg of glucagon. Blood samples were analyzed for concentrations of glucagon, insulin, and glucose. We fitted pharmacokinetic (PK) models to insulin and glucagon data using maximum likelihood and maximum a posteriori estimation methods. Similarly, we fitted a pharmacodynamic (PD) model to glucose data. The PD model included multiplicative effects of insulin and glucagon on EGP. Bias and precision of PD model test fits were assessed by mean predictive error (MPE) and mean absolute predictive error (MAPE). Results: Assuming constant variables in a subject across nonoutlier visits and using thresholds of ±15% MPE and 20% MAPE, we accepted at least one and at most three PD model test fits in each of the seven subjects. Thus, we successfully validated the PD model by leave-one-out cross-validation in seven out of eight T1D patients. Conclusions: The PD model accurately simulates glucose excursions based on plasma insulin and glucagon concentrations. The reported PK/PD model including equations and fitted parameters allows for in silico experiments that may help improve diabetes treatment involving glucagon for prevention of hypoglycemia.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation Using Data From Patients With Type 1 Diabetes

Wendt

Ranjan

Møller

et al. 2017

J Diabetes Sci Technol

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“…The A 30% and U FCL cases are assessed considering circadian variability of insulin sensitivity. The sensitivity is affected by sinusoidal variations with 24 hours period, random amplitude according to a uniform distribution of ±30% and random phase [60].…”

Section: Circadian Variabilitymentioning

confidence: 99%

Artificial pancreas under stable pulsatile MPC: Improving the closed-loop performance

Abuin

Rivadeneira

Ferramosca

et al. 2020

Journal of Process Control

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“…Moreover, despite the existence of some evidence, there is no absolute consensus about the relationship between BG levels and gastric emptying due to conflicting results from clinical studies [25], except for an apparent normal gastric emptying at normoglycemia [26]. Such a complex mechanism has not been explicitly considered in any mathematical representation of gut transit to this date [27], and thus the model proposed in the present study is not associated with instantaneous BG levels but only the macronutrient content of the mixed meal.…”

Section: Discussionmentioning

confidence: 99%

Mixed Meal Model in Type 1 Diabetes

Noguchi

Furutani

2017

Transactions of ISCIE

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In keeping with the recent recognition of the additive impact of dietary fat on postprandial blood glucose (BG) levels in type 1 diabetes mellitus (T1DM), we include a number of physiologicallyrelevant improvements to a previous conceptual minimal mixed meal model by considering independent signals for gut transit of carbohydrates and dietary fat, and explicit representation of fatty acid spillover in the compartmental representation of non-esterified fatty acids (NEFA) to represent lipid-derived insulin resistance as the complementary increase in triglycerides (TG) and NEFA levels. Simulation results of postprandial plasma lipids and BG excursion compared with clinical data in the literature demonstrate the validity and potential of the minimal mixed meal model proposed.

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Shedding light on grey noise in diabetes modelling

Cited by 17 publications

References 118 publications

Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation Using Data From Patients With Type 1 Diabetes

Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation Using Data From Patients With Type 1 Diabetes

Artificial pancreas under stable pulsatile MPC: Improving the closed-loop performance

Mixed Meal Model in Type 1 Diabetes

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