Treatment of type 1 diabetes consists of maintaining postprandial normoglycemia using the correct prandial insulin dose according to food intake. Nonetheless, it is hardly achieved in practice, which results in several diabetes-related complications. In this study we present a feedforward plus feedback blood glucose control system that considers the glycemic index of foods. It consists of a preprandial insulin bolus whose optimal bolus dose and timing are stated as a minimization problem, which is followed by a postprandial closed-loop control based on model predictive control. Simulation results show that, for a representative carbohydrate intake of 50 g, the present control system is able to maintain postprandial glycemia below 140 mg/dL while preventing postprandial hypoglycemia as well.

The authors propose a mathematical model of glucose-insulin metabolism in type 1 diabetes based on Bergman and Shimoda insulin models, which are adjusted to represent diabetic state and improve the accuracy of subcutaneous insulin absorption, respectively. The authors also propose a model of digestion and absorption from carbohydrates based on the glycemic index (GI) of foods and carbohydrate bioavailability concepts that provide a glucose-equivalent representation of the impact of carbohydrates on blood glucose levels. Comparison with clinical data demonstrates that the proposed model is able to represent postprandial blood glucose excursion for carbohydrates with varying GI values.

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In this study we introduce an extension of a previously developed model of glucose-insulin metabolism in type 1 diabetes (T1D) from carbohydrates that includes the effect of dietary fat on postprandial glycemia. We include two compartments that represent plasma triglyceride and nonesterified fatty acid (NEFA) concentration, in addition to a mathematical representation of delayed gastric emptying and insulin resistance, which are the most well-known effects of dietary fat metabolism. Simulation results show that postprandial glucose as well as lipid levels in our model approximates clinical data from T1D patients.

An increasing number of closed-loop blood glucose (BG) control algorithms have been developed in recent years with the arti cial pancreas as the ultimate goal, although tight postprandial BG control remains an elusive goal. In this report, the authors propose a novel semi closed-loop BG control algorithm with meal announcement, which involves computation of the optimal continuous subcutaneous insulin infusion for a speci c meal 60 min prior to mealtime. It utilizes a mathematical model of glucose-insulin metabolism to predict the impact of carbohydrates on postprandial BG levels based on carbohydrate intake and glycemic index (GI) value. The optimal pre-meal insulin is infused until mealtime, after which the control algorithm switches to model predictive control (MPC) to stabilize postprandial glycemia at the target value of 100 mg/dL (5.55 mmol/L). In silico results for four representative foods with GI values spanning a wide range show that in the case of exact patient-model match with precise information of carbohydrate composition and mealtime, postprandial BG levels can be maintained between 86-134 mg/dL (4.78-7.44 mmol/L) and 86-152 mg/dL (4.78-8.44 mmol/L) for 50 g and 100 g of carbohydrates, respectively. With consideration of intra-patient variability and meal-related uncertainties regarding the estimated carbohydrate amount and start of meal consumption, the BG control range is 75-159 mg/dL (4.17-8.83 mmol/L) with no critical hypoglycemic episodes.