Evaluation of a Novel Continuous Glucose Monitoring-Based Method for Mealtime Insulin Dosing—the iBolus—in Subjects with Type 1 Diabetes Using Continuous Subcutaneous Insulin Infusion Therapy: A Randomized Controlled Trial

Abstract

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.

Real-time estimation of plasma insulin concentration from continuous glucose monitor measurements

“…Insulin estimations have been compared using real data from 12 insulin pump patients with Type 1 diabetes who underwent four mixed meal studies (9 women; 41.8 ± 7.3 years old; diabetes duration 20.2 ± 10.3 years; body mass index: 25.1 ± 2.8 kg/m 2 ) (Rossetti, Ampudia-Blasco, et al 2012). The performance of insulin estimations has been evaluated by a comparison with plasma insulin measurements, computing RMSE (2) and MARD (3).…”

“…This study is much closer to the artificial pancreas real performance than previous studies, as it is applied to diabetic human patients in postprandial state (not IVGTTs) with glucose measurements from a SC glucose monitor. We have evaluated different approaches in an in-silico study with 100 Type 1 diabetes patients during 25 hours, and using clinical data from 12 insulin pump patients with Type 1 diabetes who underwent four mixed meal studies (Rossetti, Ampudia-Blasco, et al 2012).…”

Novel Insulin Delivery Profiles for Mixed Meals for Sensor-Augmented Pump and Closed-Loop Artificial Pancreas Therapy for Type 1 Diabetes Mellitus

“…22,23 Although combining such a meal library with a controller within the loop were explored, 23 meal compensation beyond basal bolus therapies has been limited. Insulin advisory software, 4,24 called "bolus 958 Journal of Diabetes Science and Technology 8 (5) wizards," 25 were tested under clinical conditions, but these merely facilitate the derivation of bolus amounts and still fundamentally depend on impulse deliveries. Current insulin delivery profiles are limited to single boluses (instantaneous insulin dose) and/or dual waves (instantaneous dose with a prolonged wave-like delivery).…”

Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System

“…Due to this difference in dynamics of insulin action and carbohydrate (CHO) absorption, attempts to avoid hyperglycemic peaks are usually accompanied by hypoglycemic excursions [10]. Several approaches have been tested to overcome such issue [11][12][13][14], but while an ultrarapid insulin analogue is not available [15], postprandial control using subcutaneous route will continue to be a challenging situation for closed-loop systems. Diverse studies have included estimations of insulin concentration in the body to avoid excessive insulin stacking [16][17][18][19]; however, hypo-and hyperglycemia are still a hazard for AP systems, and novel approaches are still required.…”