The relationship between carbohydrate and the mealtime insulin dose in type 1 diabetes

A primary focus of the nutritional management of type 1 diabetes has been on matching prandial insulin therapy with carbohydrate amount consumed. Different methods exist to quantify carbohydrate including counting in one gram increments, 10 g portions or 15 g exchanges. Clinicians have assumed that counting in one gram increments is necessary to precisely dose insulin and optimize postprandial control. Carbohydrate estimations in portions or exchanges have been thought of as inadequate because they may result in less precise matching of insulin dose to carbohydrate amount. However, studies examining the impact of errors in carbohydrate quantification on postprandial glycemia challenge this commonly held view. In addition it has been found that a single mealtime bolus of insulin can cover a range of carbohydrate intake without deterioration in postprandial control. Furthermore, limitations exist in the accuracy of the nutrition information panel on a food label. This article reviews the relationship between carbohydrate quantity and insulin dose, highlighting limitations in the evidence for a linear association. These insights have significant implications for patient education and mealtime insulin dose calculations

Algorithms to improve the prediction of postprandial insulinaemia in response to common foods

Dietary patterns that induce excessive insulin secretion may contribute to worsening insulin resistance and beta-cell dysfunction. Our aim was to generate mathematical algorithms to improve the prediction of postprandial glycaemia and insulinaemia for foods of known nutrient composition, glycemic index (GI) and glycemic load (GL). We used an expanded database of food insulin index (FII) values generated by testing 1000 kJ portions of 147 common foods relative to a reference food in lean, young, healthy volunteers. Simple and multiple linear regression analyses were applied to validate previously generated equations for predicting insulinaemia, and develop improved predictive models. Large differences in insulinaemic responses within and between food groups were evident. GL, GI and available carbohydrate content were the strongest predictors of the FII, explaining 55%, 51% and 47% of variation respectively. Fat, protein and sugar were significant but relatively weak predictors, accounting for only 31%, 7% and 13% of the variation respectively. Nutritional composition alone explained only 50% of variability. The best algorithm included a measure of glycemic response, sugar and protein content and explained 78% of variation. Knowledge of the GI or glycaemic response to 1000 kJ portions together with nutrient composition therefore provides a good approximation for ranking of foods according to their “insulin demand”.11 page(s

Validation of the food insulin index in lean, young, healthy individuals, and type 2 diabetes in the context of mixed meals : an acute randomized crossover trial

Background: The Food Insulin Index (FII) is a novel classification of single foods based on insulin responses in healthy subjects relative to an isoenergetic reference food. Objective: Our aim was to compare day-long responses to 2 nutrient-matched diets predicted to have either high or low insulin demand in healthy controls and individuals with type 2 diabetes (T2DM). Design: Twenty adults (10 healthy adults and 10 adults with T2DM) were recruited. On separate mornings, subjects consumed either a high- or low-FII diet in random order. Diets consisted of 3 consecutive meals (breakfast, morning tea, and lunch), matched for macronutrients, fiber, and glycemic index (GI), but with 2-fold difference in insulin demand as predicted by the FII of the component foods. Postprandial glycemia and insulinemia were measured in capillary plasma at regular intervals over 8 h. Results: As predicted by their GI, there were no differences in glycemic responses between the 2 diets in either group (mean ± SEM; healthy: 6.2 ± 0.2 compared with 6.1 ± 0.1 mmol/L · min, P = 0.429; T2DM: 9.9 ± 1.3 compared with 10.3 ± 1.6 mmol/L · min, P = 0.485). Compared with the high-FII diet, mean postprandial insulin response over 8 h was 53% lower with the low-FII diet in healthy subjects (mean ± SEM; incremental AUCinsulin 31,900 ± 4100 pmol/L · min compared with 68,100 ± 11,400 pmol/L · min, P = 0.003) and 41% lower in subjects with T2DM (mean ± SEM; incremental AUCinsulin 11,000 ± 1800 pmol/L · min compared with 18,700 ± 3100 pmol/L · min, P = 0.018). Incremental AUCinsulin was statistically significantly different between diets when groups were combined (P = 0.001). Conclusions: The FII algorithm may be a useful tool for reducing postprandial hyperinsulinemia in T2DM, thereby potentially improving insulin resistance and β-cell function. This trial was registered at the Australian New Zealand Clinical Trials Registry as ACTRN12611000654954.6 page(s

Efficacy of carbohydrate counting in type 1 diabetes : a systematic review and meta-analysis

Background: Although carbohydrate counting is the recommended dietary strategy for achieving glycaemic control in people with type 1 diabetes, the advice is based on narrative review and grading of the available evidence. We aimed to assess by systematic review and meta-analysis the efficacy of carbohydrate counting on glycaemic control in adults and children with type 1 diabetes. Methods: We screened and assessed randomised controlled trials of interventions longer than 3 months that compared carbohydrate counting with general or alternate dietary advice in adults and children with type 1 diabetes. Change in glycated haemoglobin (HbA1c) concentration was the primary outcome. The results of clinically and statistically homogenous studies were pooled and meta-analysed using the random-effects model to provide estimates of the efficacy of carbohydrate counting. Findings: We identified seven eligible trials, of 311 potentially relevant studies, comprising 599 adults and 104 children with type 1 diabetes. Study quality score averaged 7·6 out of 13. Overall there was no significant improvement in HbA₁c concentration with carbohydrate counting versus the control or usual care (-0·35% [-3·9 mmol/mol], 95% CI -0·75 to 0·06; p=0·096). We identified significant heterogeneity between studies, which was potentially related to differences in study design. In the five studies in adults with a parallel design, there was a 0·64% point (7·0 mmol/mol) reduction in HbA₁c with carbohydrate counting versus control (95% CI -0·91 to -0·37; p<0·0001). Interpretation: There is some evidence to support the recommendation of carbohydrate counting over alternate advice or usual care in adults with type 1 diabetes. Additional studies are needed to support promotion of carbohydrate counting over other methods of matching insulin dose to food intake.8 page(s

The role of dietary protein and fat in glycaemic control in type 1 diabetes: implications for intensive diabetes management

A primary focus of the management of type 1 diabetes has been on matching prandial insulin therapy with carbohydrate amount consumed. However, even with the introduction of more flexible intensive insulin regimes, people with type 1 diabetes still struggle to achieve optimal glycaemic control. More recently, dietary fat and protein have been recognised as having a significant impact on postprandial blood glucose levels. Fat and protein independently increase the postprandial glucose excursions and together their effect is additive. This article reviews how the fat and protein in a meal impact the postprandial glycaemic response and discusses practical approaches to managing this in clinical practice. These insights have significant implications for patient education, mealtime insulin dose calculations and dosing strategies

Metabolic profiling of plasma amino acids shows that histidine increases following the consumption of pork

Amino acid (AA) status is determined by factors including nutrition, metabolic rate, and interactions between the metabolism of AA, carbohydrates, and lipids. Analysis of the plasma AA profile, together with markers of glucose and lipid metabolism, will shed light on metabolic regulation. The objectives of this study were to investigate the acute responses to the consumption of meals containing either pork (PM) or chicken (CM), and to identify relationships between plasma AA and markers of glycemic and lipemic control. A secondary aim was to explore AA predictors of plasma zinc concentrations. Ten healthy adults participated in a postprandial study on two separate occasions. In a randomized cross-over design, participants consumed PM or CM. The concentrations of 21 AA, glucose, insulin, triglycerides, nonesterified fatty acids, and zinc were determined over 5 hours postprandially. The meal composition did not influence glucose, insulin, triglyceride, nonesterified fatty acid, or zinc concentrations. Plasma histidine was higher following the consumption of PM (P=0.014), with consistently higher changes observed after 60 minutes (P,0.001). Greater percentage increases were noted at limited time points for valine and leucine + isoleucine in those who consumed CM compared to PM. In linear regression, some AAs emerged as predictors of the metabolic responses, irrespective of the meal that was consumed. The present study demonstrates that a single meal of PM or CM produces a differential profile of AA in the postprandial state. The sustained increase in histidine following the consumption of a PM is consistent with the reported effects of lean pork on cardiometabolic risk factors.8 page(s

Clinical application of the Food Insulin Index for mealtime insulin dosing in adults with type 1 diabetes : a randomized controlled trial

Background: The Food Insulin Index (FII) is a novel algorithm for ranking foods based on their insulin demand relative to an isoenergetic reference food. We compared the effect of carbohydrate counting (CC) versus the FII algorithm for estimating insulin dosage on glycemic control in type 1 diabetes. Materials and Methods: In a randomized, controlled trial, adults (n = 26) using insulin pump therapy were assigned to using either traditional CC or the novel Food Insulin Demand (FID) counting for 12 weeks. Subjects participated in group education and individual sessions. At baseline and on completion of the trial, glycated hemoglobin A1c (HbA1c), day-long glycemia (6-day continuous glucose monitoring), fasting lipids, and C-reactive protein were determined. Results: Changes in HbA1c from baseline to 12 weeks were small and not significant in both groups (mean ± SEM; FII vs. CC, −0.1 ± 0.1% vs. −0.3 ± 0.2%; P = 0.855). The incremental area under the curve following breakfast declined significantly among the FID counters with no change in the CC group (FID vs. CC, −93 ± 41 mmol/L/min [P = 0.043] vs. 4 ± 50 mmol/L/min [P = 0.938]; between groups, P = 0.143). The mean amplitude of the glycemic excursion (MAGE) was significantly reduced among the FID counters (FID vs. CC, −6.1 ± 1.0 vs. −1.3 ± 1.0 mmol/L; P = 0.003), and only the FID counters experienced a trend (−44% vs. +11%; P = 0.057) to reduced hypoglycemia. Conclusions: In a 12-week pilot study, MAGE and postprandial glycemia following breakfast were significantly improved with FII counting versus CC, despite no significant differences in HbA1c.8 page(s