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Modelling Day-to-Day Variability of Glucose-Insulin Regulation over 12-Week Home Use of Closed-Loop Insulin Delivery.

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Ruan, Yue 
Wilinska, Malgorzata E 
Thabit, Hood 


Parameters of physiological models of glucose-insulin regulation in type 1 diabetes have previously been estimated using data collected over short periods of time and lack the quantification of day-to-day variability. We developed a new hierarchical model to relate subcutaneous insulin delivery and carbohydrate intake to continuous glucose monitoring over 12 weeks while describing day-to-day variability. Sensor glucose data sampled every 10 min, insulin aspart delivery and meal intake were analyzed from 8 adults with type 1 diabetes (male/female 5/3, age 39.9±9.5 years, BMI 25.4±4.4 kg/m2, HbA1c 8.4±0.6%) who underwent a 12-week home study of closed-loop insulin delivery. A compartment model comprised five linear differential equations; model parameters were estimated using the Markov chain Monte Carlo approach within a hierarchical Bayesian model framework. Physiologically plausible a posteriori distributions of model parameters including insulin sensitivity, time-to-peak insulin action, time-to-peak gut absorption, and carbohydrate bioavailability, and good model fit were observed. Day-to-day variability of model parameters was estimated in the range of 38 to 79% for insulin sensitivity and 27 to 48% for time-to-peak of insulin action. In conclusion, a linear Bayesian hierarchical approach is feasible to describe a 12-week glucose-insulin relationship using conventional clinical data. The model may facilitate in silico testing to aid the development of closed-loop insulin delivery systems.



Artificial pancreas, bayesian parameter estimation, hierarchical model, simulation, type 1 diabetes (T1D)

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IEEE Trans Biomed Eng

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Institute of Electrical and Electronics Engineers (IEEE)
Wellcome Trust (100574/Z/12/Z)
European Commission (247138)
Juvenile Diabetes Research Foundation Ltd (JDRF) (22-2011-668)
This work was supported in part by the European Community Framework Programme 7 FP7-ICT- 2009–4 under Grant 247138, in part by JDRF (22– 2011–668), the National Institute for Health Research Cambridge Biomedical Research Centre, and Wellcome Strategic Award (100574/Z/12/Z).