Objective: Prandial insulin dosing is an empirical practice associated frequently with poor reproducibility in postprandial glucose response. Based on continuous glucose monitoring (CGM), a method for prandial insulin administration (iBolus) is presented and evaluated for people with type 1 diabetes using CSII therapy. Subjects and Methods: An individual patient's model for a 5-h postprandial period was obtained from 6-day ambulatory CGM and used for iBolus calculation in 12 patients with type 1 diabetes. In a double-blind, crossover study each patient underwent four meal tests with 40 g or 100 g of carbohydrates (CHOs), both on two occasions. For each meal, the iBolus or the traditional bolus (tBolus) was given before mealtime (t 0 ) in a randomized order. We measured the postprandial glycemic response as the area under the curve of plasma glucose (AUC-PG 0-5h ) and variability as the individual coefficient of variation (CV) of AUC-PG 0-5h . The contribution of the insulin-to-CHO ratio, CHO, plasma glucose at t 0 (PG t0 ), and insulin dose to AUC-PG 0-5h and its CV was also investigated. Results: AUC-PG 0-5h was similar with either bolus for 40-g (iBolus vs. tBolus, 585.5 -127.5 vs. 689.2 -180.7 mg/dL$h) or 100-g (752.1 -237.7 vs. 760.0 -263.2 mg/dL$h) CHO meals. A multiple regression analysis revealed a significant model only for the tBolus, with PG t0 being the best predictor of AUC-PG 0-5h explaining approximately 50% of the glycemic response. Observed variability was greater with the iBolus (CV, 16.7 -15.3% vs. 10.1 -12.5%) but independent of the factors studied. Conclusions: A CGM-based algorithm for calculation of prandial insulin is feasible, although it does not reduce unpredictability of individual glycemic responses. Causes of variability need to be identified and analyzed for further optimization of postprandial glycemic control.

Obtaining an individual model parametrisation based on ambulatory continuous glucose monitoring in patients with type 1 diabetes is a key challenge for the development of an artificial pancreas. Although multiple model structures have been proposed in the literature, parameter identification from individual glycaemic profiles remains a difficult task. In this work, an optimal experimental design study is carried out, based on Fisher Information Matrix techniques, for improving identification of individual glycaemic parameters. Simple experiment conditions are proposed to find out the most appropriate and timely insulin bolus according to different meals. Based on a qualitative interpretation of the results, a clinical protocol is proposed for model identification based on ambulatory data provided by patients (continuous glucose monitoring, insulin pump and patient's logbook). Clinical studies are currently being performed in order to validate the proposed experiments. Preliminary results are shown and discussed.

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