Limits...
Modelling endogenous insulin concentration in type 2 diabetes during closed-loop insulin delivery.

Ruan Y, Thabit H, Wilinska ME, Hovorka R - Biomed Eng Online (2015)

Bottom Line: The selected model successfully described endogenous insulin concentration over 24 h in both study periods and provided plausible parameter estimates.Model-derived results were in concordance with a clinical finding which revealed increased posthepatic endogenous insulin concentration during the control study period (P < 0.05).The modelling results indicated that the excess amount of insulin can be attributed to the glucose-independent effect as the glucose-dependent effect was similar between visits (P > 0.05).

View Article: PubMed Central - PubMed

Affiliation: Department of Paediatrics, University of Cambridge, Cambridge, UK. yr233@cam.ac.uk.

ABSTRACT

Background: Closed-loop insulin delivery is an emerging treatment for type 1 diabetes (T1D) evaluated clinically and using computer simulations during pre-clinical testing. Efforts to make closed-loop systems available to people with type 2 diabetes (T2D) calls for the development of a new type of simulators to accommodate differences between T1D and T2D. Presented here is the development of a model of posthepatic endogenous insulin concentration, a component omitted in T1D simulators but key for simulating T2D physiology.

Methods: We evaluated six competing models to describe the time course of endogenous insulin concentration as a function of the plasma glucose concentration and time. The models were fitted to data collected in insulin-naive subjects with T2D who underwent two 24-h visits and were treated, in a random order, by either closed-loop insulin delivery or glucose-lowering oral agents. The model parameters were estimated using a Bayesian approach, as implemented in the WinBUGS software. Model selection criteria were used to identify the best model describing our clinical data.

Results: The selected model successfully described endogenous insulin concentration over 24 h in both study periods and provided plausible parameter estimates. Model-derived results were in concordance with a clinical finding which revealed increased posthepatic endogenous insulin concentration during the control study period (P < 0.05). The modelling results indicated that the excess amount of insulin can be attributed to the glucose-independent effect as the glucose-dependent effect was similar between visits (P > 0.05).

Conclusions: A model to describe endogenous insulin concentration in T2D including components of posthepatic glucose-dependent and glucose-independent insulin secretion was identified and validated. The model is suitable to be incorporated in a simulation environment for evaluating closed-loop insulin delivery in T2D.

No MeSH data available.


Related in: MedlinePlus

Schematic representation of the six competing models. The models are represented with the (A) glucose-dependent and (B) glucose-independent parameters.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4359432&req=5

Fig1: Schematic representation of the six competing models. The models are represented with the (A) glucose-dependent and (B) glucose-independent parameters.

Mentions: We developed six models of increasing complexity to describe endogenous plasma insulin concentration [IENDO(t)] as a function of plasma glucose concentration [G(t)] and time. A schematic representation of the six competing models is shown in Figure 1. We incorporate the glucose-dependent model components (in Model 1 to 6) and additionally the glucose-independent components (in Model 4 to 6). Models’ mathematical formulations are provided in Additional file 1.Figure 1


Modelling endogenous insulin concentration in type 2 diabetes during closed-loop insulin delivery.

Ruan Y, Thabit H, Wilinska ME, Hovorka R - Biomed Eng Online (2015)

Schematic representation of the six competing models. The models are represented with the (A) glucose-dependent and (B) glucose-independent parameters.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4359432&req=5

Fig1: Schematic representation of the six competing models. The models are represented with the (A) glucose-dependent and (B) glucose-independent parameters.
Mentions: We developed six models of increasing complexity to describe endogenous plasma insulin concentration [IENDO(t)] as a function of plasma glucose concentration [G(t)] and time. A schematic representation of the six competing models is shown in Figure 1. We incorporate the glucose-dependent model components (in Model 1 to 6) and additionally the glucose-independent components (in Model 4 to 6). Models’ mathematical formulations are provided in Additional file 1.Figure 1

Bottom Line: The selected model successfully described endogenous insulin concentration over 24 h in both study periods and provided plausible parameter estimates.Model-derived results were in concordance with a clinical finding which revealed increased posthepatic endogenous insulin concentration during the control study period (P < 0.05).The modelling results indicated that the excess amount of insulin can be attributed to the glucose-independent effect as the glucose-dependent effect was similar between visits (P > 0.05).

View Article: PubMed Central - PubMed

Affiliation: Department of Paediatrics, University of Cambridge, Cambridge, UK. yr233@cam.ac.uk.

ABSTRACT

Background: Closed-loop insulin delivery is an emerging treatment for type 1 diabetes (T1D) evaluated clinically and using computer simulations during pre-clinical testing. Efforts to make closed-loop systems available to people with type 2 diabetes (T2D) calls for the development of a new type of simulators to accommodate differences between T1D and T2D. Presented here is the development of a model of posthepatic endogenous insulin concentration, a component omitted in T1D simulators but key for simulating T2D physiology.

Methods: We evaluated six competing models to describe the time course of endogenous insulin concentration as a function of the plasma glucose concentration and time. The models were fitted to data collected in insulin-naive subjects with T2D who underwent two 24-h visits and were treated, in a random order, by either closed-loop insulin delivery or glucose-lowering oral agents. The model parameters were estimated using a Bayesian approach, as implemented in the WinBUGS software. Model selection criteria were used to identify the best model describing our clinical data.

Results: The selected model successfully described endogenous insulin concentration over 24 h in both study periods and provided plausible parameter estimates. Model-derived results were in concordance with a clinical finding which revealed increased posthepatic endogenous insulin concentration during the control study period (P < 0.05). The modelling results indicated that the excess amount of insulin can be attributed to the glucose-independent effect as the glucose-dependent effect was similar between visits (P > 0.05).

Conclusions: A model to describe endogenous insulin concentration in T2D including components of posthepatic glucose-dependent and glucose-independent insulin secretion was identified and validated. The model is suitable to be incorporated in a simulation environment for evaluating closed-loop insulin delivery in T2D.

No MeSH data available.


Related in: MedlinePlus