V Gingras1, R Rabasa-Lhoret2, V Messier3, M Ladouceur4, L Legault5, A Haidar6. 1. Institut de recherches cliniques de Montréal, Montreal, Quebec, Canada; Department of nutrition, Université de Montréal, Montreal, Quebec, Canada. 2. Institut de recherches cliniques de Montréal, Montreal, Quebec, Canada; Department of nutrition, Université de Montréal, Montreal, Quebec, Canada; Montreal Diabetes Research Center (MDRC), Montreal, Quebec, Canada; Research Center of the Université de Montréal Hospital Center (CRCHUM), Montreal, Quebec, Canada; Division of Experimental Medicine, McGill University, Montreal, Quebec, Canada. Electronic address: remi.rabasa-lhoret@ircm.qc.ca. 3. Institut de recherches cliniques de Montréal, Montreal, Quebec, Canada. 4. Research Center of the Université de Montréal Hospital Center (CRCHUM), Montreal, Quebec, Canada. 5. Montreal Children's Hospital, McGill University Health Center, Montreal, Quebec, Canada. 6. Institut de recherches cliniques de Montréal, Montreal, Quebec, Canada; Division of Experimental Medicine, McGill University, Montreal, Quebec, Canada.
Abstract
AIM: Carbohydrate-counting is a complex task for many patients with type 1 diabetes. This study examined whether an artificial pancreas, delivering insulin and glucagon based on glucose sensor readings, could alleviate the burden of carbohydrate-counting without degrading glucose control. METHODS:Twelve adults were recruited into a randomized, three-way, crossover trial (ClinicalTrials.gov identifier No. NCT01930097). Participants were admitted on three occasions from 7AM to 9PM and consumed a low-carbohydrate breakfast (women: 30g; men: 50g), amedium-carbohydrate dinner (women: 50g; men: 70g) and ahigh-carbohydrate lunch (women: 90g; men: 120g). At each visit, glucose levels were randomly regulated by: (1) conventional pump therapy; (2) an artificial pancreas (AP) accompanied by prandial boluses, matching the meal's carbohydrate content based on insulin-to-carbohydrate ratios (AP with carbohydrate-counting); or (3) an AP accompanied by prandial boluses based on qualitative categorization (regular or large) of meal size (AP without carbohydrate-counting). RESULTS: The AP without carbohydrate-counting achieved similar incremental AUC values compared with carbohydrate-counting after the low- (P=0.54) and medium- (P=0.38) carbohydrate meals, but yielded higher post-meal excursions after the high-carbohydrate meal (P=0.004). The AP with and without carbohydrate-counting yielded similar mean glucose levels (8.2±2.1mmol/L vs. 8.4±1.7mmol/L; P=0.52), and both strategies resulted in lower mean glucose compared with conventional pump therapy (9.6±2.0mmol/L; P=0.02 and P=0.03, respectively). CONCLUSION: The AP with qualitative categorization of meal size could alleviate the burden of carbohydrate-counting without compromising glucose control, although more categories of meal sizes are probably needed to effectively control higher-carbohydrate meals.
RCT Entities:
AIM: Carbohydrate-counting is a complex task for many patients with type 1 diabetes. This study examined whether an artificial pancreas, delivering insulin and glucagon based on glucose sensor readings, could alleviate the burden of carbohydrate-counting without degrading glucose control. METHODS: Twelve adults were recruited into a randomized, three-way, crossover trial (ClinicalTrials.gov identifier No. NCT01930097). Participants were admitted on three occasions from 7AM to 9PM and consumed a low-carbohydrate breakfast (women: 30g; men: 50g), a medium-carbohydrate dinner (women: 50g; men: 70g) and a high-carbohydrate lunch (women: 90g; men: 120g). At each visit, glucose levels were randomly regulated by: (1) conventional pump therapy; (2) an artificial pancreas (AP) accompanied by prandial boluses, matching the meal's carbohydrate content based on insulin-to-carbohydrate ratios (AP with carbohydrate-counting); or (3) an AP accompanied by prandial boluses based on qualitative categorization (regular or large) of meal size (AP without carbohydrate-counting). RESULTS: The AP without carbohydrate-counting achieved similar incremental AUC values compared with carbohydrate-counting after the low- (P=0.54) and medium- (P=0.38) carbohydrate meals, but yielded higher post-meal excursions after the high-carbohydrate meal (P=0.004). The AP with and without carbohydrate-counting yielded similar mean glucose levels (8.2±2.1mmol/L vs. 8.4±1.7mmol/L; P=0.52), and both strategies resulted in lower mean glucose compared with conventional pump therapy (9.6±2.0mmol/L; P=0.02 and P=0.03, respectively). CONCLUSION: The AP with qualitative categorization of meal size could alleviate the burden of carbohydrate-counting without compromising glucose control, although more categories of meal sizes are probably needed to effectively control higher-carbohydrate meals.
Authors: Gregory P Forlenza; Faye M Cameron; Trang T Ly; David Lam; Daniel P Howsmon; Nihat Baysal; Georgia Kulina; Laurel Messer; Paula Clinton; Camilla Levister; Stephen D Patek; Carol J Levy; R Paul Wadwa; David M Maahs; B Wayne Bequette; Bruce A Buckingham Journal: Diabetes Technol Ther Date: 2018-04-16 Impact factor: 6.118