Amount and Type of Dietary Fat, Postprandial Glycemia, and Insulin Requirements in Type 1 Diabetes: A Randomized Within-Subject Trial

5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes-2024

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Effect of Isocaloric Meals on Postprandial Glycemic and Metabolic Markers in Type 1 Diabetes-A Randomized Crossover Trial

The aim of this study was to assess the effect of four isocaloric meals with different macronutrient compositions on postprandial blood glucose, lipids, and glucagon in adults with type 1 diabetes (T1D). Seventeen subjects tested four isocaloric meals in a randomized crossover design. The meal compositions were as follows: high-carbohydrate (HC); high-carbohydrate with extra fiber (HC-fiber); low-carbohydrate high-protein (HP); and low-carbohydrate high-fat (HF). Blood glucose and lipid measurements were collected up to 4 h and glucagon up to 3 h postprandially. Mean postprandial glucose excursions were lower after the HP compared to the HC (p = 0.036) and HC-fiber meals (p = 0.002). There were no differences in mean glucose excursions after the HF meal compared to the HC and HP meals. The HF meal resulted in higher triglyceride excursions compared to the HP meal (p < 0.001) but not compared to the HC or HC-fiber meals. Glucagon excursions were higher at 180 min after the HP meal compared to the HC and HF meals. In conclusion, the low-carbohydrate HP meal showed the most favorable glycemic and metabolic effects during a 4 h postprandial period in subjects with T1D.

A new proposal for a second insulin bolus to optimize postprandial glucose profile in adolescents with type 1 diabetes

AIMS

To evaluate whether a second insulin bolus, calculated with a new approach, could improve postprandial glucose (PPG) after the intake of real-life high-fat (HF) and high-protein (HP) mixed meals.

METHODS

Fifteen adolescents with T1D treated with non-automated insulin pumps and CGM were enrolled. Patients received standard, HF and HP mixed meals treated with one pre-meal insulin bolus; based on differences in PPG between standard, HF and HP meals, correction boluses were calculated (30% and 60% of pre-meal bolus for HF and HP meals, respectively). Then patients received the same HF or HP meal treated with pre-meal bolus plus second insulin bolus after 3 h. Differences between postprandial variables after HF and HP meals treated with one or two insulin boluses were assessed by paired Student's t-test.

RESULTS

Treating HF and HP meals with two insulin boluses significantly reduced the postprandial BG-AUC (21% and 26% respectively, p < 0.05), increased %TIR (from 52.5 to 78.3% for HF meal; from 32.7 to 57.1% for HP meal; p < 0.01), and reduced mean BG and %TAR (p < 0.01), with no differences in %TBR.

CONCLUSIONS

The new way to calculate and administer correction boluses 3 h after HF and HP meals is effective and safe in reducing PPG and the hypoglycemia risk.

Insulin-to-carbohydrate ratios: An overview for nurses

ABSTRACT

Carbohydrate counting is an efficacious technique to safely achieve glycemic targets and improve outcomes for patients using meal-time insulin. This article provides nurses with the knowledge and skills to assist and support their patients. It also reviews how to evaluate glycemic control and provides guidelines for referral to diabetes education programs.

5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes-2023

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Reduced Carbohydrate Diet Influence on Postprandial Glycemia—Results of a Short, CGM-Based, Interventional Study in Adolescents with Type 1 Diabetes

Therapy for type 1 diabetes (T1DM) focuses on maintaining optimal blood glucose levels, achieved with intensive insulin treatment, proper nutrition, and physical activity. The aim of this study was to investigate postprandial glycemic changes under low (30%) and standard (50%) carbohydrate diets in adolescents with T1DM. A single-center cross-over nutritional study was conducted, during which 26 adolescent patients provided 220 continuous glucose-monitored (CGM) meals data from the two consecutive 3-day nutritional plans. Overall, the 50% carbohydrate diet was associated with higher postprandial glucose variability in the small meals (afternoon snacks, second breakfast) and greater postprandial peaks for other meals (breakfast, dinner, supper). Nevertheless, after the adjustment of a patient’s individual clinical variables (age, Tanner classification, glucose disposal rate), we observed that mean postprandial glucose was higher for afternoon snacks and lower for suppers in the 30% carbohydrate diet. Although a 30% carbohydrate diet seems to offer better postprandial glycemia, it requires additional attention from the physician and patient when it comes to modifying daily carbohydrate intake. Increased fat/protein content and size of the main meal lead to a prolonged postprandial glycemic response, which may affect the insulin treatment and result in suboptimal glycemic control.

Additional insulin dosing for fat and protein in children with type 1 diabetes using multiple daily injections

OBJECTIVE

High-fat high-protein (HFHP) meals are associated with post-prandial hyperglycemia in type 1 diabetes (T1D), administration of additional insulin for such meals is recommended in order to optimize glucose levels. Optimal timing of additional insulin for HFHP meals in children and young people receiving multiple daily injections (MDI) remains unclear.

AIM

To investigate the glycemic impact of additional insulin doses given before or after eating a HFHP meal in children with T1D using MDI.

RESEARCH DESIGN AND METHODS

A randomized, controlled three period crossover trial of 27 participants aged 13 years (6.1-17.7) at two Pediatric Diabetes centers was conducted. Additional rapid-acting insulin for the fat-protein content of a standardized HFHP meal was given at three time points + 0 + 1 + 2 h of usual pre - prandial carbohydrate insulin ; calculated using an algorithm extrapolated from current evidence base and clinical recommendations. Post-prandial glucose (PPG) parameters were calculated for 420 minutes using continuous glucose monitoring. The primary outcome was mean PPG excursion. Secondary outcomes included peak glucose, time to peak and hypoglycemia incidence.

RESULTS

There was no difference in post-prandial glucose parameters when additional HFHP insulin was administered at + 0 , + 1 , or + 2 h : mean glucose excursion (mmol/L) (SE): 1.9(0.7), 1.2(0.7), 2.5(0.7); p = 0.5); mean peak glucose (mmol/L)(SE): 10.9(0.9), 11.5(0.8), 11.5(0.9); p = 0.9; time to peak glucose (mins)(SE): 82.3(35.4), 113.6(30.9), 95.1(32.1); p = 0.8. Mild hypoglycemia was common (55%) in all groups (p = 0.97).

CONCLUSION

We found no benefit in giving additional insulin as a split dose for HFHP meals in children using MDI, mild hypoglycemia was common. Future studies would benefit from refinement of the insulin dose algorithm.

Nutritional Strategies of an Athlete with Type 1 Diabetes Mellitus During a 217-km Ultramarathon

Considering the challenges in meeting the high nutritional demand during ultramarathons, the aim of this study was to analyze the nutritional strategies and glycemic response of an athlete with type 1 diabetes (DM1) during participation in a 217-km ultramarathon. A 36-y-old male athlete who was diagnosed with DM1 15 y earlier was studied during participation in the Brazil 135 ultramarathon. Food consumption and blood glucose were recorded during the race, and nutritional intake was calculated after the race. The athlete completed the race in 51 h 18 min. He consumed a total of 3592 kcal, 532 g carbohydrate, 166 g protein, 92 g lipid, and 14 L of water during the race. Glycemic values ranged from 3.6 to 18.2 mmol·L^-1. Most glycemic values (47%) ranged from 3.9 to 10 mmol·L^-1, whereas 5% were <3.9 mmol·L^-1, 16% were >10 to 13.9 mmol·L^-1, and 32% were >13.9 mmol·L^-1. This case report describes the dietary profile of an athlete with DM1 during a 217-km ultramarathon. Although the athlete implemented strategies that differed from those recommended in the literature, food and nutrient intake and the glycemic management strategy adopted allowed him to successfully finish the race. These results suggest that past personal experiences can be considered and that nutritional recommendations for athletes with DM1 should be individualized.

5. Facilitating Behavior Change and Well-being to Improve Health Outcomes: Standards of Medical Care in Diabetes-2022

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

'Old Is Gold': How Traditional Indian Dietary Practices Can Support Pediatric Diabetes Management

Nutrition is crucial for maintaining normal growth, development, and glycemic control in young people with diabetes (PwD). Undue restrictions cause nutrient deficiencies as well as poor adherence to meal plans. Widespread availability of low-cost, ultra-processed, and hyperpalatable food is further damaging. Most families struggle to find ways to provide nutritious, yet attractive, food with a low glycemic index (GI). India is one of the oldest continuous civilizations with a rich and diverse cultural and culinary heritage. Traditional dietary practices, including the centuries-old 'Thali' (meaning plate) concept, emphasize combinations (grains, lentils, vegetables, dairy, spices, prebiotics and probiotics, and fats) of local, seasonal, and predominantly plant-based ingredients. These practices ensure that all of the necessary food groups are provided and fit well with current evidence-based recommendations, including the International Society for Pediatric and Adolescent Diabetes (ISPAD) 2018 Guidelines. Techniques for the preparation, cooking, and preservation of food further impact the GI and nutrient availability. These practices benefit nutrient density, diet diversity, and palatability and thus improve adherence to meal plans and glycemic control. This narrative review describes the ancient wisdom, food composition, and culinary practices from across India which are still valuable today. These may be of benefit worldwide to improve glycemic control as well as quality of life, especially in PwD.

Does dietary fat cause a dose dependent glycemic response in youth with type 1 diabetes?

OBJECTIVE

To determine the glycemic impact of dietary fat alone consumed without prandial insulin in individuals with T1D.

RESEARCH DESIGN AND METHODS

Thirty participants with T1D (aged 8-18 years) consumed a test drink with either 20 g glucose or 1, 13, 26, 39, 51 g of fat with negligible carbohydrate/protein on 6 consecutive evenings, in a randomized order without insulin. Continuous glucose monitoring was used to measure glucose levels for 8 h postprandially. Primary outcome was mean glycemic excursion at each 30 min interval for each test condition. Generalized linear mixed models with a random effect for people with diabetes were used to test for an increase in blood glucose excursion with increasing quantity of fat.

RESULTS

Glycemic excursions after 20 g glucose were higher than after fat drinks over the first 2 h (p < 0.05). Glycemic excursion for the fat drinks demonstrated a dose response, statistically significant from 4 h (p = 0.026), such that increasing loads of fat caused a proportionally larger increase in glycemic excursion, remaining statistically significant until 8 h (p < 0.05). Overall, for every 10 g fat added to the drink, glucose concentrations rose by a mean of 0.28 mmol L^-1 from 330 min (95% CI 0.15 to 0.39, p < 0.001).

CONCLUSIONS

Fat ingested without other macronutrients increases glucose excursions from 4 to 8 h after ingestion, in a dose dependent manner. These observations may impact on insulin dosing for high-fat foods in individuals with T1D.

Insulin strategies for dietary fat and protein in type 1 diabetes: A systematic review

AIM

To identify and report the efficacy of insulin strategies used to manage glycaemia following fat and/or fat and protein meals in type 1 diabetes.

METHODS

A systematic literature search of medical databases from 1995 to 2021 was undertaken. Inclusion criteria were randomised controlled trials that reported at least one of the following glycaemic outcomes: mean glucose, area under the curve, time in range or hypoglycaemic episodes.

RESULTS

Eighteen studies were included. Thirteen studies gave additional insulin. Five studies gave an additional 30%-43% of the insulin-to-carbohydrate ratio (ICR) for 32-50 g of fat and 31%-51% ICR for 7-35 g of fat with 12-27 g of protein added to control meals. A further eight studies gave -28% to +75% ICR using algorithms based on fat and protein for meals with 19-50 g of carbohydrate, 2-79 g of fat and 10-60 g of protein, only one study reported a glycaemic benefit of giving less than an additional 24% ICR. Eight studies evaluated insulin delivery patterns. Four of six studies in pump therapy, and one of two studies in multiple daily injections showed the combination of bolus and split dose, respectively, were superior. Five studies examined the insulin dose split, four demonstrated 60%-125% ICR upfront was necessary. Two studies investigated the timing of insulin delivery, both reported administration 15 min before the meal lowered postprandial glycaemia.

CONCLUSIONS

Findings highlight the glycaemic benefit of an additional 24%-75% ICR for fat and fat and protein meals. For these meals, there is supportive evidence for insulin delivery in a combination bolus with a minimum upfront dose of 60% ICR, 15 min before the meal.

The Role of Protein and Fat Intake on Insulin Therapy in Glycaemic Control of Paediatric Type 1 Diabetes: A Systematic Review and Research Gaps

Carbohydrate counting (CHC) is the established form of calculating bolus insulin for meals in children with type 1 diabetes (T1DM). With the widespread use of continuous glucose monitoring (CGM) observation time has become gapless. Recently, the impact of fat, protein and not only carbohydrates on prolonged postprandial hyperglycaemia have become more evident to patients and health-care professionals alike. However, there is no unified recommendation on how to calculate and best administer additional bolus insulin for these two macronutrients. The aim of this review is to investigate: the scientific evidence of how dietary fat and protein influence postprandial glucose levels; current recommendations on the adjustment of bolus insulin; and algorithms for insulin application in children with T1DM. A PubMed search for all articles addressing the role of fat and protein in paediatric (sub-)populations (<18 years old) and a mixed age population (paediatric and adult) with T1DM published in the last 10 years was performed. Conclusion: Only a small number of studies with a very low number of participants and high degree of heterogeneity was identified. While all studies concluded that additional bolus insulin for (high) fat and (high) protein is necessary, no consensus on when dietary fat and/or protein should be taken into calculation and no unified algorithm for insulin therapy in this context exists. A prolonged postprandial observation time is necessary to improve individual metabolic control. Further studies focusing on a stratified paediatric population to create a safe and effective algorithm, taking fat and protein into account, are necessary.

Effect of meal composition and alcohol consumption on postprandial glucose concentration in subjects with type 1 diabetes: a randomized crossover trial

INTRODUCTION

Meal composition is known to affect glycemic variability and glucose control in type 1 diabetes. The objective of this work was to evaluate the effect of high carbohydrate meals of different nutritional composition and alcohol on the postprandial glucose response in patients with type 1 diabetes.

RESEARCH DESIGN AND METHODS

Twelve participants were recruited to this randomized crossover trial. Following a 4-week run-in period, participants received a mixed meal on three occasions with the same carbohydrate content but different macronutrient composition: high protein-high fat with alcohol (0.7g/kg body weight, beer), high protein-high fat without alcohol, and low protein-low fat without alcohol at 2-week intervals. Plasma and interstitial glucose, insulin, glucagon, growth hormone, cortisol, alcohol, free fatty acids, lactate, and pH concentrations were measured during 6 hours. A statistical analysis was then carried out to determine significant differences between studies.

RESULTS

Significantly higher late postprandial glucose was observed in studies with higher content of fats and proteins (p=0.0088). This was associated with lower time in hypoglycemia as compared with the low protein and fat study (p=0.0179), at least partially due to greater glucagon concentration in the same period (p=0.04). Alcohol significantly increased lactate, decreased pH and growth hormone, and maintained free fatty acids suppressed during the late postprandial phase (p<0.001), without significant changes in plasma glucose.

CONCLUSIONS

Our data suggest that the addition of proteins and fats to carbohydrates increases late postprandial blood glucose. Moreover, alcohol consumption together with a mixed meal has relevant metabolic effects without any increase in the risk of hypoglycemia, at least 6 hours postprandially.

TRIAL REGISTRATION NUMBER

NCT03320993.

Carbohydrate restriction for diabetes: rediscovering centuries-old wisdom

Carbohydrate restriction, used since the 1700s to prolong survival in people with diabetes, fell out of favor after the discovery of insulin. Despite costly pharmacological and technological developments in the last few decades, current therapies do not achieve optimal outcomes, and most people with diabetes remain at high risk for micro- and macrovascular complications. Recently, low-carbohydrate diets have regained popularity, with preliminary evidence of benefit for body weight, postprandial hyperglycemia, hyperinsulinemia, and other cardiometabolic risk factors in type 2 diabetes and, with more limited data, in type 1 diabetes. High-quality, long-term trials are needed to assess safety concerns and determine whether this old dietary approach might help people with diabetes attain clinical targets more effectively, and at a lower cost, than conventional treatment.

Physical Activity, Dietary Patterns, and Glycemic Management in Active Individuals with Type 1 Diabetes: An Online Survey

Individuals with type 1 diabetes (T1D) are able to balance their blood glucose levels while engaging in a wide variety of physical activities and sports. However, insulin use forces them to contend with many daily training and performance challenges involved with fine-tuning medication dosing, physical activity levels, and dietary patterns to optimize their participation and performance. The aim of this study was to ascertain which variables related to the diabetes management of physically active individuals with T1D have the greatest impact on overall blood glucose levels (reported as A1C) in a real-world setting. A total of 220 individuals with T1D completed an online survey to self-report information about their glycemic management, physical activity patterns, carbohydrate and dietary intake, use of diabetes technologies, and other variables that impact diabetes management and health. In analyzing many variables affecting glycemic management, the primary significant finding was that A1C values in lower, recommended ranges (<7%) were significantly predicted by a very-low carbohydrate intake dietary pattern, whereas the use of continuous glucose monitoring (CGM) devices had the greatest predictive ability when A1C was above recommended (≥7%). Various aspects of physical activity participation (including type, weekly time, frequency, and intensity) were not significantly associated with A1C for participants in this survey. In conclusion, when individuals with T1D are already physically active, dietary changes and more frequent monitoring of glucose may be most capable of further enhancing glycemic management.

The Association of Fried Meat Consumption With the Gut Microbiota and Fecal Metabolites and Its Impact on Glucose Homoeostasis, Intestinal Endotoxin Levels, and Systemic Inflammation: A Randomized Controlled-Feeding Trial

OBJECTIVE

This randomized controlled-feeding trial aimed to determine the impact of fried meat intake on the gut microbiota and fecal cometabolites and whether such impacts influenced host glucose homoeostasis, intestinal endotoxin levels, and systemic inflammation.

RESEARCH DESIGN AND METHODS

A total of 117 overweight adults were randomized into two groups. Fifty-nine participants were provided fried meat four times per week, and 58 participants were restricted from fried meat intake, while holding food group and nutrient compositions constant, for 4 weeks. The gut microbiota was analyzed by 16S rRNA sequencing. Glucose and insulin concentrations at 0, 30, 60, and 120 min of an oral glucose tolerance test, fecal microbiota-host cometabolite levels, and intestinal endotoxin and inflammation serum biomarker levels were measured. The area under the curve (AUC) for insulin, insulinogenic index (IGI), and muscle insulin resistance index (MIRI) were calculated.

RESULTS

The participants who consumed fried meat had lower IGI values than the control subjects, but they had higher MIRI and AUC values of insulin and lipopolysaccharide (LPS), TNF-α, IL-10, and IL-1β levels (P < 0.05). Fried meat intake lowered microbial community richness and decreased Lachnospiraceae and Flavonifractor abundances while increasing Dialister, Dorea, and Veillonella abundances (P FDR <0.05), provoking a significant shift in the fecal cometabolite profile, with lower 3-indolepropionic acid, valeric acid, and butyric acid concentrations and higher carnitine and methylglutaric acid concentrations (P FDR <0.05). Changes in these cometabolite levels were significantly associated with changes in IGI and MIRI values and LPS, FGF21, TNF-α, IL-1β, and IL-10 levels (P < 0.05).

CONCLUSIONS

Fried meat intake impaired glucose homoeostasis and increased intestinal endotoxin and systemic inflammation levels by influencing the gut microbiota and microbial-host cometabolites.

Additional Insulin Is Required in Both the Early and Late Postprandial Periods for Meals High in Protein and Fat: A Randomized Trial

CONTEXT

The pattern and quantity of insulin required for high-protein high-fat (HPHF) meals is not well understood.

OBJECTIVE

This study aimed to determine the amount and delivery pattern of insulin required to maintain euglycemia for 5 hours after consuming a HPHF meal compared with a low-protein low-fat (LPLF) meal.

METHODS

This randomized crossover clinical trial, conducted at 2 Australian pediatric diabetes centers, included 10 patients (12-21 years of age) with type 1 diabetes for ≥ 1 year. Participants were randomized to HPHF meal (60 g protein, 40 g fat) or LPLF meal (5 g protein, 5 g fat) with identical carbohydrate content (30 g). A modified insulin clamp technique was used to determine insulin requirements to maintain postprandial euglycemia for 5 hours. Total mean insulin requirements over 5 hours were measured.

RESULTS

The total mean insulin requirements for the HPHF meal were significantly greater than for the LPLF meal (11.0 [CI 9.2, 12.8] units vs 5.7 [CI 3.8, 7.5] units; P = 0.001). Extra intravenous insulin was required for HPHF: 0 to 2 hours (extra 1.2 [CI 0.6, 1.6] units/h), 2 to 4 hours (extra 1.1 [CI 0.6, 1.6] units/h), and 4 to 5 hours (extra 0.6 [CI 0.1, 1.1] units/h) after the meal. There were marked inter-individual differences in the quantity of additional insulin (0.3 to 5 times more for HPHF) and the pattern of insulin delivery (0%-85% of additional insulin required in the first 2 hours).

CONCLUSION

The addition of protein and fat to a standardized carbohydrate meal almost doubled the mean insulin requirement, with most participants requiring half of the additional insulin in the first 2 hours.

In children and young people with type 1 diabetes using Pump therapy, an additional 40% of the insulin dose for a high-fat, high-protein breakfast improves postprandial glycaemic excursions: A cross-over trial

AIM

To determine the insulin requirement for a high-fat, high-protein breakfast to optimise postprandial glycaemic excursions in children and young people with type 1 diabetes using insulin pumps.

METHODS

In all, 27 participants aged 10-23 years, BMI <95^th percentile (2-18 years) or BMI <30 kg/m² (19-25 years) and HbA_1c ≤64 mmol/mol (≤8.0%) consumed a high-fat, high-protein breakfast (carbohydrate: 30 g, fat: 40 g and protein: 50 g) for 4 days. In this cross-over trial, insulin was administered, based on the insulin-to-carbohydrate ratio (ICR) of 100% (control), 120%, 140% and 160%, in an order defined by a randomisation sequence and delivered in a combination bolus, 60% ¼ hr pre-meal and 40% over 3 hr. Postprandial sensor glucose was assessed for 6 hr.

RESULTS

Comparing 100% ICR, 140% ICR and 160% ICR resulted in significantly lower 6-hr areas under the glucose curves: mean (95%CI) (822 mmol/L.min [605,1039] and 567 [350,784] vs 1249 [1042,1457], p ≤ 0.001) and peak glucose excursions (4.0 mmol/L [3.0,4.9] and 2.7 [1.7,3.6] vs 6.0 [5.0,6.9],p < 0.001). Rates of hypoglycaemia for 100%-160% ICR were 7.7%, 7.7%, 12% and 19% respectively (p ≥ 0.139). With increasing insulin dose, a step-wise reduction in mean glucose excursion was observed from 1 to 6 hr (p = 0.008).

CONCLUSIONS

Incrementally increasing the insulin dose for a high-fat, high-protein breakfast resulted in a predictable, dose-dependent reduction in postprandial glycaemia: 140% ICR improved postprandial glycaemic excursions without a statistically significant increase in hypoglycaemia. These findings support a safe, practical method for insulin adjustment for high-fat, high-protein meals that can be readily implemented in practice to improve postprandial glycaemia.

Prospective Independent Evaluation of the Carbohydrate Counting Accuracy of Two Smartphone Applications

INTRODUCTION

Smartphone applications (apps) have been designed that help patients to accurately count their carbohydrate intake in order to optimize prandial insulin dose matching. Our aim was to evaluate the accuracy of two carbohydrate (carb) counting apps.

METHODS

Medical students, in the role of mock patients, evaluated meals using two smartphone apps: Foodvisor^® (which uses automatic food photo recognition technology) and Glucicheck^® (which requires the manual entry of carbohydrates with the help of a photo gallery). The macronutrient quantifications obtained with these two apps were compared to a reference quantification.

RESULTS

The carbohydrate content of the entire meal was underestimated with Foodvisor^® (Foodvisor^® quantification minus gold standard quantification = - 7.2 ± 17.3 g; p < 0.05) but reasonably accurately estimated with Glucicheck^® (Glucicheck^® quantification minus gold standard quantification = 1.4 ± 13.4 g; ns). The percentage of meals with an absolute error in carbohydrate quantification above 20 g was greater for Foodvisor^® compared to Glucicheck^® (30% vs 14%; p < 0.01).

CONCLUSION

The carb counting accuracy was slightly better when using Glucicheck^® compared to Foodvisor^®. However, both apps provided a lower mean absolute carb counting error than that usually made by T1D patients in everyday life, suggesting that such apps may be a useful adjunct for estimating carbohydrate content.

The current schemes of insulin therapy: Pro and contra

Insulin regimens have been evolving for a century. The schemes used for type 1 (T1D) and type 2 (T2D) diabetes differ due to differences in pathophysiology but share important features. Insulin is required for both types of diabetes when other means of controlling glucose are insufficient. For T1D this requires multiple daily injections or continuous subcutaneous infusion assisted by CGM, whereas in early T2D basal insulin together with oral agents or GLP-1RA is usually effective. In both cases current schemes typically maintain HbA1c levels between 7 and 8%, a range that limits but does not eliminate the long-term complications of diabetes, but do not restore glycemic control to a fully protective level. Inability to control postprandial hyperglycemia without problematic weight gain and hypoglycemia is a leading obstacle in both T1D and long-duration T2D. A greater share of prandial dosing decisions will have to be provided by smart electronic systems. Further changes in the structure or formulation of insulin are of uncertain potential, but schemes including delivery of amylin, GLP-1, and glucagon show promise. More reliable access to insulins, delivery devices, and capable medical advisors will be needed to optimize replacement of this essential hormone.

Post-exercise recovery for the endurance athlete with type 1 diabetes: a consensus statement

There has been substantial progress in the knowledge of exercise and type 1 diabetes, with the development of guidelines for optimal glucose management. In addition, an increasing number of people living with type 1 diabetes are pushing their physical limits to compete at the highest level of sport. However, the post-exercise recovery routine, particularly with a focus on sporting performance, has received little attention within the scientific literature, with most of the focus being placed on insulin or nutritional adaptations to manage glycaemia before and during the exercise bout. The post-exercise recovery period presents an opportunity for maximising training adaption and recovery, and the clinical management of glycaemia through the rest of the day and overnight. The absence of clear guidance for the post-exercise period means that people with type 1 diabetes should either develop their own recovery strategies on the basis of individual trial and error, or adhere to guidelines that have been developed for people without diabetes. This Review provides an up-to-date consensus on post-exercise recovery and glucose management for individuals living with type 1 diabetes. We aim to: (1) outline the principles and time course of post-exercise recovery, highlighting the implications and challenges for endurance athletes living with type 1 diabetes; (2) provide an overview of potential strategies for post-exercise recovery that could be used by athletes with type 1 diabetes to optimise recovery and adaptation, alongside improved glycaemic monitoring and management; and (3) highlight the potential for technology to ease the burden of managing glycaemia in the post-exercise recovery period.

The comparison of two mealtime insulin dosing algorithms for high and low glycaemic index meals in adolescents with type 1 diabetes

AIMS

Postprandial glycaemic variability carries on being a clinical challenge in optimizing glucose control in type 1 diabetes. The aim of this study was to compare the postprandial glycaemic effects of carbohydrate counting and food insulin index algorithms following the consumption of protein-rich, high-fat meals with different glycaemic index (GI) in adolescents with type 1 diabetes.

METHODS

A randomized, single-blind and crossover trial included 15 adolescents aged 14-18 years with type 1 diabetes. Participants consumed two different test meals with similar energy, macronutrients and food insulin index but the approximately twofold difference in GI, in random order on four consecutive mornings at their home. Insulin dose for high- and low-GI test meals was determined by using the carbohydrate counting and food insulin index algorithms. Four-hour postprandial glycaemia was assessed by the continuous glucose monitoring system.

RESULTS

Compared with carbohydrate counting, the food insulin index algorithm significantly decreased peak glucose excursion (-57%, p = 0.02), incremental area under the curve (-65%, p = 0.02) and coefficient variation of blood glucose (-37%, p = 0.03) in the high-GI meal, though there was no difference between the two algorithms in the low-GI meal. The occurrence of hypoglycaemia did not significantly differ between insulin dosing algorithms for the high-GI (p = 0.58) and low-GI (p = 0.20) meals.

CONCLUSIONS

The food insulin index algorithm may be beneficial for postprandial glycaemic control after the consumption of high-GI meals in adolescents with type 1 diabetes.

Insulin pump dosing strategies for meals varying in fat, protein or glycaemic index or grazing-style meals in type 1 diabetes: A systematic review

BACKGROUND

Nutritional composition and food patterns influence postprandial glycaemia in type 1 diabetes (T1D). For optimal glycaemic control, insulin dose and delivery pattern must be matched accordingly. This systematic review aimed to compare insulin dosing strategies for meals varying in fat, protein and glycaemic index (GI), and prolonged meals in T1D.

METHODS

Studies in adults and/or children with T1D on insulin pump therapy comparing the glycaemic effects of different insulin pump bolus types for these meal types were identified from biomedical databases (MEDLINE, PREMEDLINE, Embase, CINAHL and Cochrane Central Register of Controlled Trials; March 1995-April 2020) and systematically reviewed.

RESULTS

All eleven publications investigating high-fat meals (234 participants) and all seven studies investigating high-protein meals (129 participants) showed a dual-wave bolus was superior. Additional insulin further improved postprandial glycaemia, although increasing risk of hypoglycaemia in 5 of 14 studies. One study investigating GI found a dual-wave bolus reduced postprandial glycaemia and risk of hypoglycaemia. No studies were identified for grazing/degustation-style meals. Due to heterogeneity, meta-analysis was not possible.

CONCLUSION

Dual-wave boluses improve postprandial glycaemia in high-fat, high-protein and low-GI meals. Further research is needed to identify optimal bolus delivery split, duration and optimal total dose adjustment.

Assessing Mealtime Macronutrient Content: Patient Perceptions Versus Expert Analyses via a Novel Phone App

Background: People with type 1 diabetes estimate meal carbohydrate content to accurately dose insulin, yet, protein and fat content of meals also influences postprandial glycemia. We examined accuracy of macronutrient content estimation via a novel phone app. Participant estimates were compared with expert nutrition analyses performed via the Remote Food Photography Method© (RFPM©). Methods: Data were collected through a novel phone app. Participants were asked to take photos of meals/snacks on the day of and day after scheduled exercise, enter carbohydrate estimates, and categorize meals as low, typical, or high protein and fat. Glycemia was measured via continuous glucose monitoring. Results: Participants (n = 48) were 15-68 years (34 ± 14 years); 40% were female. The phone app plus RFPM© analysis captured 88% ± 29% of participants' estimated total energy expenditure. The majority (70%) of both low-protein and low-fat meals were accurately classified. Only 22% of high-protein meals and 17% of high-fat meals were accurately classified. Forty-nine percent of meals with <30 g of carbohydrates were overestimated by an average of 25.7 ± 17.2 g. The majority (64%) of large carbohydrate meals (≥60 g) were underestimated by an average of 53.6 ± 33.8 g. Glycemic response to large carbohydrate meals was similar between participants who underestimated or overestimated carbohydrate content, suggesting that factors beyond carbohydrate counting may impact postprandial glycemic response. Conclusions: Accurate estimation of total macronutrients in meals could be leveraged to improve insulin decision support tools and closed loop insulin delivery systems; development of tools to improve macronutrient estimation skills should be considered.

Real-world data on the use of insulin glargine 300 U/mL in Japanese patients with type 1 diabetes: twelve-month results from a post-marketing surveillance study (X-STAR study)

BACKGROUND

With limited real-world insulin glargine 300 U/mL (Gla-300) data among Japanese patients with type 1 diabetes mellitus (T1DM) available, the authors describe its effectiveness and safety in Japanese T1DM patients switching to Gla-300.

RESEARCH DESIGN AND METHODS

X-STAR was a 12-month prospective, observational, post-marketing study in Japanese patients with diabetes mellitus from 2015 to 2018: insulin-experienced T1DM patients initiating Gla-300 were analyzed.

RESULTS

Of 774 patients, mean (±standard deviation) HbA1c (%) and fasting plasma glucose (mg/dL) decreased from 8.27 ± 1.55 to 8.15 ± 1.35 (by -0.12 ± 1.30 [p = 0.013]) and 167.9 ± 92.6 to 153.9 ± 70.9 (by -13.9 ± 103.8 [p = 0.067]) from baseline to month 12, respectively. A total of 16.3% achieved HbA1c <7.0% at month 12. Gla-300 dose increased by 1.13 ± 3.18 U/day (0.02 ± 0.05 U/kg/day) (p < 0.001), with a + 0.22 ± 2.70 (p = 0.037) body-weight change (kg) from baseline 60.83 ± 12.81 to 12-month 61.06 ± 12.89. Adverse drug reactions (ADRs) and serious ADRs occurred in 9.82% and 0.78% of the patients, respectively. Hypoglycemia was the most common ADR (9.30%). In total, 88.9% adhered to Gla-300 administration schedules, whereas <40% adhered to exercise and dietary instructions, respectively.

CONCLUSIONS

Gla-300 showed no unprecedented safety concerns for insulin-experienced T1DM patients in Japanese clinical settings. Our results provide insights into strategies for blunted Gla-300 up-titration dose, despite insufficient HbA1c control and lifestyle modification.

Data-Driven Insights on Behavioral Factors that Affect Diabetes Management

The prevalence of personal health data from wearable devices enables new opportunities to understand the impact of behavioral factors on health. Unlike consumer devices that are often auxiliary, such as Fitbit and Garmin, wearable medical devices like continuous glucose monitoring (CGM) devices and insulin pumps are becoming critical in diabetes care to minimize the occurrence of adverse glycemic events. Joint analysis of CGM and insulin pump data can provide unparalleled insights on how to modify treatment regimen to improve diabetes management outcomes. In this paper, we employ a data-driven approach to study the relationship between key behavioral factors and proximal diabetic management indicators. Our dataset includes an average of 161 days of time-matched CGM and insulin pump data from 34 subjects with Type 1 Diabetes (T1D). By employing hypothesis testing and association mining, we observe that smaller meals and insulin doses are associated with better glycemic outcomes compared to larger meals and insulin doses. Meanwhile, the occurrence of interrupted sleep is associated with poorer glycemic outcomes. This paper introduces a method for inferring disrupted sleep from wearable diabetes-device data and provides a baseline for future research on sleep quality and diabetes. This work also provides insights for development of decision-support tools for improving short- and long-term outcomes in diabetes care.

Additional Insulin for Coping with Fat- and Protein-Rich Meals in Adolescents with Type 1 Diabetes: The Protein Unit

OBJECTIVE

Dietary proteins raise blood glucose levels; dietary fats delay this rise. We sought to assess the insulin amount required to normalize glucose levels after a fat- and protein-rich meal (FPRM).

METHODS

Sixteen adolescents (5 female) with type 1 diabetes (median age: 18.2 years; range: 15.2-24.0; duration: 7.1 years; 2.3-14.3; HbA1c: 7.2%; 6.2-8.3%) were included. FPRM (carbohydrates 57 g; protein 92 g; fat 39 g; fibers 7 g; calories 975 Kcal) was served in the evening, with 20 or 40% extra insulin compared to a standard meal (SM) (carbohydrates 70 g; protein 28 g; fat 19 g; fibers 10 g; calories 579 Kcal) or carbohydrates only. Insulin was administered for patients on intensified insulin therapy or as a 4-hour-delayed bolus for those on pump therapy. The 12-hour post-meal glucose levels were compared between FPRM and SM, with the extra insulin amount calculated based on 100 g proteins as a multiple of the carbohydrate unit.

RESULTS

Glucose levels (median, mg/dL) 12-hour post-meal with 20% extra insulin vs. 40% vs. insulin dose for SM were 116 vs. 113 vs. 91. Glucose-AUC over 12-hour post-meal with 20% extra insulin vs. 40% vs. insulin dose for SM was 1603 mg/dL/12 h vs. 1527 vs. 1400 (no significance). Glucose levels in the target range with 20% extra insulin vs. 40% were 60% vs. 69% (p=0.1). Glucose levels <60 mg/dL did not increase with 40% extra insulin. This corresponds to the 2.15-fold carbohydrate unit for 100 g protein.

CONCLUSIONS

We recommend administering the same insulin dose given for 1 carbohydrate unit (10 g carbs) to cover 50 g protein.

What Makes Nutrition Research So Difficult to Conduct and Interpret?

Conducting and interpreting nutrition research involves consideration of the research question, study design, wide variability of nutrients in foods and dietary patterns, food environment, approaches used to collect and analyze dietary data, and manner in which results are reported. This article reviews all of these considerations with regard to diabetes-related nutrition research.

Acute Effect of Pistachio Intake on Postprandial Glycemic and Gut Hormone Responses in Women With Gestational Diabetes or Gestational Impaired Glucose Tolerance: A Randomized, Controlled, Crossover Study

Long-term consumption of pistachios could potentially improves glucose homeostasis. Impaired postprandial glucose, insulin, and glucagon-like peptide-1 (GLP-1) responses have been reported in gestational diabetes mellitus (GDM) patients. The objective of this study was to evaluate the acute effects of two isocaloric test meals, 42 g pistachios and 100 g whole-wheat bread (WWB) on postprandial glucose, insulin, and gut derived incretin levels in Chinese women with gestational impaired glucose tolerance (GIGT) or GDM. Expected glucose and insulin responses were observed after WWB consumption. Isocaloric pistachio intake had minimal effect on blood glucose or insulin. In both GIGT and GDM patients, significant higher GLP-1 levels were observed at 90 and 120 min after pistachio compared to WWB intake. Significant lower gastric inhibitory polypeptide (GIP) levels were observed at 30 and 60 min in GDM patients or 120 min in GIGT patients after pistachio compared to WWB intake. In summary, isocaloric pistachio intake induced significantly lower postprandial glucose, insulin and GIP but higher GLP-1 levels compared to WWB. Our data suggest pistachios are effective alternative to a low-fat, high-carbohydrate food to improve postprandial glucose, insulin, and GLP-1 response in women with GDM and GIGT.

Carbohydrate Intake in the Context of Exercise in People with Type 1 Diabetes

Although the benefits of regular exercise on cardiovascular risk factors are well established for people with type 1 diabetes (T1D), glycemic control remains a challenge during exercise. Carbohydrate consumption to fuel the exercise bout and/or for hypoglycemia prevention is an important cornerstone to maintain performance and avoid hypoglycemia. The main strategies pertinent to carbohydrate supplementation in the context of exercise cover three aspects: the amount of carbohydrates ingested (i.e., quantity in relation to demands to fuel exercise and avoid hypoglycemia), the timing of the intake (before, during and after the exercise, as well as circadian factors), and the quality of the carbohydrates (encompassing differing carbohydrate types, as well as the context within a meal and the associated macronutrients). The aim of this review is to comprehensively summarize the literature on carbohydrate intake in the context of exercise in people with T1D.