L Bozzetto1, M Giorgini1, A Alderisio1, L Costagliola1, A Giacco1, G Riccardi1, A A Rivellese2, G Annuzzi1. 1. Department of Clinical Medicine and Surgery, Federico II University, Via Pansini, 5, 80131, Naples, Italy. 2. Department of Clinical Medicine and Surgery, Federico II University, Via Pansini, 5, 80131, Naples, Italy. rivelles@unina.it.
Abstract
AIMS: To evaluate feasibility and effectiveness on short-term blood glucose control of using glycaemic load counting (GLC) versus carbohydrate counting (CC) for prandial insulin dosing in patients with type 1 diabetes (T1D). METHODS:Nine T1D patients on insulin pump, aged 26-58 years, HbA1c 7.7 ± 0.8 % (61 ± 8.7 mmol/mol), participated in this real-life setting study. By a crossover design, patients were randomised to calculate their pre-meal insulin dose based on the insulin/glycaemic load ratio (GLC period) or the insulin/carbohydrate ratio (CC period) for 1 week, shifting to the alternate method for the next week, when participants duplicated their first week food plan. Over either week, a blind subcutaneous continuous glucose monitoring was performed, and a 7-day food record was filled in. RESULTS:Total daily insulin doses (45 ± 10 vs. 44 ± 9 I.U.; M ± SD, p = 0.386) and basal infusion (26 ± 7 vs. 26 ± 8 I.U., p = 0.516) were not different during GLC and CC periods, respectively. However, the range of insulin doses (difference between highest and lowest insulin dose) was wider during GLC, with statistical significance at dinner (8.4 ± 6.2 vs. 6.0 ± 3.9 I.U., p = 0.041). Blood glucose iAUC after lunch was lower, albeit not significantly, during GLC than CC period (0.6 ± 8.6 vs. 3.4 ± 8.2 mmol/l∙3 h, p = 0.059). Postprandial glucose variability, evaluated as the maximal amplitude after meal (highest minus lowest glucose value), was significantly lower during GLC than CC period at lunch (4.22 ± 0.28 vs. 5.47 ± 0.39 mmol/l, p = 0.002) and dinner (3.89 ± 0.33 vs. 4.89 ± 0.33, p = 0.026). CONCLUSIONS: Calculating prandial insulin bolus based on glycaemic load counting is feasible in a real-life setting and may improve postprandial glucose control in people with T1D.
RCT Entities:
AIMS: To evaluate feasibility and effectiveness on short-term blood glucose control of using glycaemic load counting (GLC) versus carbohydrate counting (CC) for prandial insulin dosing in patients with type 1 diabetes (T1D). METHODS: Nine T1D patients on insulin pump, aged 26-58 years, HbA1c 7.7 ± 0.8 % (61 ± 8.7 mmol/mol), participated in this real-life setting study. By a crossover design, patients were randomised to calculate their pre-meal insulin dose based on the insulin/glycaemic load ratio (GLC period) or the insulin/carbohydrate ratio (CC period) for 1 week, shifting to the alternate method for the next week, when participants duplicated their first week food plan. Over either week, a blind subcutaneous continuous glucose monitoring was performed, and a 7-day food record was filled in. RESULTS: Total daily insulin doses (45 ± 10 vs. 44 ± 9 I.U.; M ± SD, p = 0.386) and basal infusion (26 ± 7 vs. 26 ± 8 I.U., p = 0.516) were not different during GLC and CC periods, respectively. However, the range of insulin doses (difference between highest and lowest insulin dose) was wider during GLC, with statistical significance at dinner (8.4 ± 6.2 vs. 6.0 ± 3.9 I.U., p = 0.041). Blood glucose iAUC after lunch was lower, albeit not significantly, during GLC than CC period (0.6 ± 8.6 vs. 3.4 ± 8.2 mmol/l∙3 h, p = 0.059). Postprandial glucose variability, evaluated as the maximal amplitude after meal (highest minus lowest glucose value), was significantly lower during GLC than CC period at lunch (4.22 ± 0.28 vs. 5.47 ± 0.39 mmol/l, p = 0.002) and dinner (3.89 ± 0.33 vs. 4.89 ± 0.33, p = 0.026). CONCLUSIONS: Calculating prandial insulin bolus based on glycaemic load counting is feasible in a real-life setting and may improve postprandial glucose control in people with T1D.
Authors: Stamatina Zavitsanou; Jennifer Massa; Sunil Deshpande; Jordan E Pinsker; Mei Mei Church; Camille Andre; Francis J Doyle Iii; Alicia Michelson; Jamie Creason; Eyal Dassau; David M Eisenberg Journal: Diabetes Technol Ther Date: 2019-06-21 Impact factor: 6.118