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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

Median weighted residuals obtained with the six competing models. Weighted residuals are difference between model predictions and data divided by the standard deviation. The error bars represent the interquartile range (n = 11). Data collected from the closed-loop experiments were used.
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Fig2: Median weighted residuals obtained with the six competing models. Weighted residuals are difference between model predictions and data divided by the standard deviation. The error bars represent the interquartile range (n = 11). Data collected from the closed-loop experiments were used.

Mentions: The weighted residuals of Model 3 (Figure 2) show postprandial underestimation of plasma insulin level indicating that underlying factors other than glucose stimulation influence the posthepatic insulin secretion. Thus, in Model 4, a two-segment piecewise linear function representing additional posthepatic insulin secretion after breakfast Iadd,b(t) is added. This additional flux assumes value 0 at t = 0, peaks at t=tpeak,b = 30 min and falls gradually to 0 at t=tend. The peak value Ipeak,b (mU/min) is estimated.Figure 2


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)

Median weighted residuals obtained with the six competing models. Weighted residuals are difference between model predictions and data divided by the standard deviation. The error bars represent the interquartile range (n = 11). Data collected from the closed-loop experiments were used.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig2: Median weighted residuals obtained with the six competing models. Weighted residuals are difference between model predictions and data divided by the standard deviation. The error bars represent the interquartile range (n = 11). Data collected from the closed-loop experiments were used.
Mentions: The weighted residuals of Model 3 (Figure 2) show postprandial underestimation of plasma insulin level indicating that underlying factors other than glucose stimulation influence the posthepatic insulin secretion. Thus, in Model 4, a two-segment piecewise linear function representing additional posthepatic insulin secretion after breakfast Iadd,b(t) is added. This additional flux assumes value 0 at t = 0, peaks at t=tpeak,b = 30 min and falls gradually to 0 at t=tend. The peak value Ipeak,b (mU/min) is estimated.Figure 2

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