Literature DB >> 23003329

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.

Paolo Rossetti1, F Javier Ampudia-Blasco, Alejandro Laguna, Ana Revert, Josep Vehì, Juan F Ascaso, Jorge Bondia.   

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.

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Year:  2012        PMID: 23003329      PMCID: PMC3482847          DOI: 10.1089/dia.2012.0145

Source DB:  PubMed          Journal:  Diabetes Technol Ther        ISSN: 1520-9156            Impact factor:   6.118


  21 in total

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Review 9.  Bolus calculator: a review of four "smart" insulin pumps.

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10.  Optimizing postprandial glycemia in pediatric patients with type 1 diabetes using insulin pump therapy: impact of glycemic index and prandial bolus type.

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4.  Suspension of basal insulin to avoid hypoglycemia in type 1 diabetes treated with insulin pump.

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Review 5.  Artificial Intelligence in Decision Support Systems for Type 1 Diabetes.

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

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