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BACKGROUND/OBJECTIVE: The Food Insulin Index (FII) is a novel algorithm for ranking foods on the basis of insulin responses in healthy subjects relative to an isoenergetic reference food. Our aim was to compare postprandial glycemic responses in adults with type 1 diabetes who used both carbohydrate counting and the FII algorithm to estimate the insulin dosage for a variety of protein-containing foods. SUBJECTS/METHODS: A total of 11 adults on insulin pump therapy consumed six individual foods (steak, battered fish, poached eggs, low-fat yoghurt, baked beans and peanuts) on two occasions in random order, with the insulin dose determined once by the FII algorithm and once with carbohydrate counting. Postprandial glycemia was measured in capillary blood glucose samples at 15-30 min intervals over 3 h. Researchers and participants were blinded to treatment. RESULTS: Compared with carbohydrate counting, the FII algorithm significantly reduced the mean blood glucose level (5.7 ± 0.2 vs 6.5 ± 0.2 mmol/l, P = 0.003) and the mean change in blood glucose level (-0.7 ± 0.2 vs 0.1 ± 0.2 mmol/l, P = 0.001). Peak blood glucose was reached earlier using the FII algorithm than using carbohydrate counting (34 ± 5 vs 56 ± 7 min, P = 0.007). The risk of hypoglycemia was similar in both treatments (48% vs 33% for FII vs carbohydrate counting, respectively, P = 0.155). CONCLUSIONS: In adults with type 1 diabetes, compared with carbohydrate counting, the novel FII algorithm improved postprandial hyperglycemia after consumption of protein-containing foods. European Journal of Clinical Nutrition (2014) 68, 1055-1059; doi: 10.1038/ejcn.2014.126; published online 9 July 2014, INTRODUCTION Type 1 diabetes is an autoimmune disorder resulting in an absolute endogenous insulin deficiency that is characterized by chronic hyperglycemia. Effective management of type 1 diabetes requires exogenous insulin [...]

Objective: Although carbohydrate counting is routine practice in type 1 diabetes, hyperglycemic episodes are common. A food insulin index (FII) has been developed and validated for predicting the normal insulin demand generated by mixed meals in healthy adults. We sought to compare a novel algorithm on the basis of the FII for estimating mealtime insulin dose with carbohydrate counting in adults with type 1 diabetes.Research Design and Methods: A total of 28 patients using insulin pump therapy consumed two different breakfast meals of equal energy, glycemic index, fiber, and calculated insulin demand (both FII = 60) but approximately twofold difference in carbohydrate content, in random order on three consecutive mornings. On one occasion, a carbohydrate-counting algorithm was applied to meal A (75 g carbohydrate) for determining bolus insulin dose. On the other two occasions, carbohydrate counting (about half the insulin dose as meal A) and the FII algorithm (same dose as meal A) were applied to meal B (41 g carbohydrate). A real-time continuous glucose monitor was used to assess 3-h postprandial glycemia.Results: Compared with carbohydrate counting, the FII algorithm significantly decreased glucose incremental area under the curve over 3 h (-52%, P = 0.013) and peak glucose excursion (-41%, P = 0.01) and improved the percentage of time within the normal blood glucose range (4-10 mmol/L) (31%, P = 0.001). There was no significant difference in the occurrence of hypoglycemia.Conclusions: An insulin algorithm based on physiological insulin demand evoked by foods in healthy subjects may be a useful tool for estimating mealtime insulin dose in patients with type 1 diabetes. [ABSTRACT FROM AUTHOR]