The Role of Dietary Protein and Fat in Glycaemic Control in Type 1 Diabetes: Implications for Intensive Diabetes Management

Factor Associated with Adherence to the Protein and Fat Counting Strategy by Adults with Type 1 Diabetes Mellitus

Carbohydrate counting is one of the dietary strategies used for the management of type 1 diabetes (T1DM), and counting proteins and fats allows individuals to achieve better glycemic and metabolic control, reducing glycemic variability and long-term complications. The aim of this paper is to analyze the factors associated with adherence to the protein- and fat-counting strategy in adults with T1DM. This cross-sectional study was conducted from November 2021 to June 2022 through an online questionnaire. We applied Pearson's Chi-square test with adjusted residual analysis and a binomial logistic regression test using SPSS software, version 24.0, considering p < 0.05 as indicative of statistical significance. There was an association between performing protein and lipid counting and having a higher education level, income exceeding three minimum wages, and having adequate glycated hemoglobin. Performing protein and lipid counting increased the chances of having adequate HbA1c by 4.3 times. Protein and lipid counting was a predictor of having adequate HbA1c. The results suggest that considering the practice of counting proteins and fats is important as a strategy to optimize glycemic control.

Menus prescribed for diabetes management by Malawian referral hospitals are loaded with calories from carbohydrates and may worsen diabetes condition

Background: Management of diabetes demands reduction of carbohydrates and moderation of total calories, fats and protein to promote weight loss while controlling postprandial blood glucose. Hospitals prescribe menus to diabetic patients to achieve these reasons. Whether these menus provide meals that help improve diabetes outcome remains unknown. Aim: Therefore, this study assessed six menus from six different referral hospitals in Malawi. Method: We formulated three separate diets (n = 3) encompassing breakfast, 10 am snack, lunch, 3 pm snack and super that a diabetic person would consume in a day as prescribed by each menu. We developed nutritionally adequate meals based on foods listed on the menu. We calculated, using the Malawi Food Composition Table (MFCT), total available carbohydrates, energy, protein, total fats, SAFA, MUFA and PUFA that each diet provides. We averaged the nutrient content from the three diets. Results: Menus from QECH and ST Joseph Mission Hospital provide significantly higher total carbohydrates (p = 0.001), total energy (p = 0.000) and fats (p = 0.01) but contain similar proteins (p = 0.761). The proportion of energy from carbohydrates for all the menus is very high and ranges from 68-81% while protein and fat proportions are lower and range from 7-16% and 7-20%, respectively. Conclusion: These menus have high propensity to increase postprandial blood glucose and promote weight gain that could be harmful to a diabetic person. All the menus deviate from a somehow recommended energy contribution of approximately 50:25:25 from carbohydrates, fat and protein, respectively.

Postprandial glucose-management strategies in type 1 diabetes: Current approaches and prospects with precision medicine and artificial intelligence

Postprandial glucose control can be challenging for individuals with type 1 diabetes, and this can be attributed to many factors, including suboptimal therapy parameters (carbohydrate ratios, correction factors, basal doses) because of physiological changes, meal macronutrients and engagement in postprandial physical activity. This narrative review aims to examine the current postprandial glucose-management strategies tested in clinical trials, including adjusting therapy settings, bolusing for meal macronutrients, adjusting pre-exercise and postexercise meal boluses for postprandial physical activity, and other therapeutic options, for individuals on open-loop and closed-loop therapies. Then we discuss their challenges and future avenues. Despite advancements in insulin delivery devices such as closed-loop systems and decision-support systems, many individuals with type 1 diabetes still struggle to manage their glucose levels. The main challenge is the lack of personalized recommendations, causing suboptimal postprandial glucose control. We suggest that postprandial glucose control can be improved by (i) providing personalized recommendations for meal macronutrients and postprandial activity; (ii) including behavioural recommendations; (iii) using other personalized therapeutic approaches (e.g. glucagon-like peptide-1 receptor agonists, sodium-glucose co-transporter inhibitors, amylin analogues, inhaled insulin) in addition to insulin therapy; and (iv) integrating an interpretability report to explain to individuals about changes in treatment therapy and behavioural recommendations. In addition, we suggest a future avenue to implement precision recommendations for individuals with type 1 diabetes utilizing the potential of deep reinforcement learning and foundation models (such as GPT and BERT), employing different modalities of data including diabetes-related and external background factors (i.e. behavioural, environmental, biological and abnormal events).

Postprandial glucose metabolism in children and adolescents with type 1 diabetes mellitus: potential targets for improvement

The main goal of therapeutic management of type 1 Diabetes Mellitus (T1DM) is to maintain optimal glycemic control to prevent acute and long-term diabetes complications and to enable a good quality of life. Postprandial glycemia makes a substantial contribution to overall glycemic control and variability in diabetes and, despite technological advancements in insulin treatments, optimal postprandial glycemia is difficult to achieve. Several factors influence postprandial blood glucose levels in children and adolescents with T1DM, including nutritional habits and adjustment of insulin doses according to meal composition. Additionally, hormone secretion, enteroendocrine axis dysfunction, altered gastrointestinal digestion and absorption, and physical activity play important roles. Meal-time routines, intake of appropriate ratios of macronutrients, and correct adjustment of the insulin dose for the meal composition have positive impacts on postprandial glycemic variability and long-term cardiometabolic health of the individual with T1DM. Further knowledge in the field is necessary for management of all these factors to be part of routine pediatric diabetes education and clinical practice. Thus, the aim of this report is to review the main factors that influence postprandial blood glucose levels and metabolism, focusing on macronutrients and other nutritional and lifestyle factors, to suggest potential targets for improving postprandial glycemia in the management of children and adolescents with T1DM.

A model of subcutaneous pramlintide pharmacokinetics and its effect on gastric emptying: Proof-of-concept based on populational data

Pramlintide, an amylin analog, has been coming up as an agent in type 1 diabetes dual-hormone therapies (insulin/pramlintide). Since pramlintide slows down gastric emptying, it allows for easing glucose control and reducing the burden of meal announcements. Pre-clinical in silico evaluations are a key step in the development of any closed-loop strategy. However, mathematical models are needed, and pramlintide models in the literature are scarce. This work proposes a proof-of-concept pramlintide model, describing its subcutaneous pharmacokinetics (PK) and its effect on gastric emptying (PD). The model is validated with published populational (clinical) data. The model development is divided into three stages: intravenous PK, subcutaneous PK, and PD modeling. In each stage, a set of model structures are proposed, and their performance is assessed using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). In order to evaluate the modulation of the rate of gastric emptying, a literature meal model was used. The final pramlintide model comprises four compartments and a function that modulates gastric emptying depending on plasma pramlintide. Results show an appropriate fit for the data. Some aspects are left as open questions due to the lack of specific data (e.g., the influence of meal composition on the pramlintide effect). Moreover, further validation with individual data is necessary to propose a virtual cohort of patients.

Sensor-Based Glucose Metrics during Different Diet Compositions in Type 1 Diabetes-A Randomized One-Week Crossover Trial

By reducing carbohydrate intake, people with type 1 diabetes may reduce fluctuations in blood glucose, but the evidence in this area is sparse. The aim of this study was to investigate glucose metrics during a one-week low-carbohydrate-high-fat (HF) and a low-carbohydrate-high-protein (HP) diet compared with an isocaloric high-carbohydrate (HC) diet. In a randomized, three-period cross-over study, twelve adults with insulin-pump-treated type 1 diabetes followed an HC (energy provided by carbohydrate: 48%, fat: 33%, protein: 19%), HF (19%, 62%, 19%), and an HP (19%, 57%, 24%) diet for one week. Glucose values were obtained during intervention periods using a Dexcom G6 continuous glucose monitoring system. Participant characteristics were: 33% females, median (range) age 50 (22-70) years, diabetes duration 25 (11-52) years, HbA1c 7.3 (5.5-8.3)% (57 (37-67) mmol/mol), and BMI 27.3 (21.3-35.9) kg/m2. Glycemic variability was lower with HF (30.5 ± 6.2%) and HP (30.0 ± 5.5%) compared with HC (34.5 ± 4.1%) (PHF-HC = 0.009, PHP-HC = 0.003). There was no difference between groups in mean glucose (HF: 8.7 ± 1.1, HP: 8.2 ± 1.0, HC: 8.7 ± 1.0 mmol/L, POverall = 0.08). Time > 10.0 mmol/L was lower with HP (22.3 ± 11.8%) compared with HF (29.4 ± 12.1%) and HC (29.5 ± 13.4%) (PHF-HP = 0.037, PHC-HP = 0.037). In conclusion, a one-week HF and, specifically, an HP diet improved glucose metrics compared with an isocaloric HC diet.

Effects of a Low-Carbohydrate-High-Protein Pre-Exercise Meal in Type 1 Diabetes-a Randomized Crossover Trial

CONTEXT

Current guidelines for exercise-related glucose management focus on reducing bolus and/or basal insulin doses and considering carbohydrate intake. Yet far less attention has been paid to the potential role of other macronutrients alongside carbohydrates on glucose dynamics around exercise.

OBJECTIVE

To investigate the effects of a low-carbohydrate-high-protein (LCHP) compared with a high-carbohydrate-low-protein (HCLP) pre-exercise meal on the metabolic, hormonal, and physiological responses to exercise in adults with insulin pump-treated type 1 diabetes.

METHODS

Fourteen adults (11 women, 3 men) with insulin pump-treated type 1 diabetes (median [range] HbA1c of 50 [43-59] mmol/mol (6.7% [6.1%-7.5%]), age of 49 [25-65] years, and body mass index of 24.0 [19.3-27.1] kg/m2) completed an unblinded, 2-arm, randomized, crossover study. Participants ingested isocaloric meals that were either LCHP (carbohydrate 21%, protein 52%, fat 27%) or HCLP (carbohydrate 52%, protein 21%, fat 27%) 90 minutes prior to undertaking 45 minutes of cycling at moderate intensity. Meal insulin bolus was dosed according to meal carbohydrate content but reduced by 25%. Basal insulin rates were reduced by 35% from meal ingestion to end of exercise.

RESULTS

Around exercise the coefficient of variability was lower during LCHP (LCHP: 14.5 ± 5.3 vs HCLP: 24.9 ± 7.7%, P = .001). Over exercise, LCHP was associated with a lesser drop (LCHP: Δ-1.49 ± 1.89 vs HCLP: Δ-3.78 ± 1.95 mmol/L, P = .001). Mean insulin concentration was 30% lower during exercise for LCHP compared with HCLP (LCHP: 25.5 ± 11.0 vs HCLP: 36.5 ± 15.9 mU/L, P < .001).

CONCLUSION

Ingesting a LCHP pre-exercise meal lowered plasma glucose variability around exercise and diminished the drop in plasma glucose over exercise.

A Nutritional Approach to Optimizing Pump Therapy in Type 1 Diabetes Mellitus

Achieving optimal glucose control in individuals with type 1 diabetes (T1DM) continues to pose a significant challenge. While continuous insulin infusion systems have shown promise as an alternative to conventional insulin therapy, there remains a crucial need for greater awareness regarding the necessary adaptations for various special circumstances. Nutritional choices play an essential role in the efficacy of diabetes management and overall health status for patients with T1DM. Factors such as effective carbohydrate counting, assessment of the macronutrient composition of meals, and comprehending the concept of the glycemic index of foods are paramount in making informed pre-meal adjustments when utilizing insulin pumps. Furthermore, the ability to handle such situations as physical exercise, illness, pregnancy, and lactation by making appropriate adjustments in nutrition and pump settings should be cultivated within the patient-practitioner relationship. This review aims to provide healthcare practitioners with practical guidance on optimizing care for individuals living with T1DM. It includes recommendations on carbohydrate counting, managing mixed meals and the glycemic index, addressing exercise-related challenges, coping with illness, and managing nutritional needs during pregnancy and lactation. Additionally, considerations relating to closed-loop systems with regard to nutrition are addressed. By implementing these strategies, healthcare providers can better equip themselves to support individuals with T1DM in achieving improved diabetes management and enhanced quality of life.

Recovery Phase Nutrition and Insulin Strategies for a Collegiate Distance Runner with Type 1 Diabetes Mellitus: A Case Study

PURPOSE

There is scant published research regarding nutrition and insulin strategies for athletic performance in collegiate distance runners with type 1 diabetes mellitus (CDRT1). Acute carbohydrate supplementation (CHOsup) and insulin reduction used to minimize hypoglycemia during exercise may result in deteriorated glycemic control post exercise in CDRT1. The present case study of a CDRT1 investigated outcomes associated with a moderate-carbohydrate (ModCHO) diet and 24 h insulin adjustment during recovery phases for improved glycemic control and reduced use of acute strategies.

METHODS

During an 8-day period, a female CDRT1 followed a ModCHO (~4 g/kg/day) nutrition program. Recovery phase adjustments to insulin doses were made using an equation developed to estimate reduced insulin needs post exercise, as a function of exercise intensity and duration. Daily training was performed in the fasted state at 6:00 a.m. and included additional exercise strategies to reduce glycemic variability when needed. Daily blood glucose time-in-range (TIR) and use of CHOsup were assessed. Athlete well-being was determined using the Student-Athlete Well-Being Scale (SAWS)TM at baseline, and days 1, 3, and 7.

RESULTS

Throughout the 8-day period, mean TIR increased (77% versus < 50%) and the magnitude of glycemic excursions decreased (~3.8-15 versus ~3.0-26 mmol/L) relative to a prior comparison period. Minimal pre-exercise CHOsup was employed and CHOsup during exercise was not required. Additionally, the athlete achieved a new lifetime best in the 5000 m run and maintained positive well-being.

CONCLUSION

The present case study provides examples of recovery phase strategies (i.e., ModCHO diet and 24 h insulin adjustments) that may support glycemic control and athletic performance in CDRT1 and provides potential considerations for nutrition and insulin strategies for use by athletes and coaches.

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.

Pure-protein load for children with type 1 diabetes: is any additional insulin needed? A randomized controlled study

AIMS

Study in adults with T1D showed that delivery of insulin for pure-protein meals may not be obligatory. The aim of this study was to assess the effects of whey isolate protein drink consisting of 50 g/200 kcal from pure protein on postprandial glycemia (PPG) following with square-wave insulin bolus in comparison with no insulin strategy in T1D children on insulin pumps.

METHODS

This was a randomized, double-blind, cross-over study including 58 children with mean: age 14.62 ± 3.64 years. Participants were randomly assigned into two treatment orders: NB-SQ (no bolus on the first day) and SQ-NB (square-bolus on the first day). The primary outcome was PPG during a 5-h follow-up. The secondary outcome was the frequency of hypoglycemia and glycemic variability parameters.

RESULTS

PPG [mg/dl] since 150 min of the follow-up was significantly lower when square-wave bolus was delivered (group SQ vs NB); at 150, 180, 210, 240, 270, 300 min: 130.6 versus 154.5 (p = 0.009), 153.4 versus 124.9 (p = 0.004), 151.0 versus 118.7 (p = 0.003), 146.4 versus 114.2 (p = 0.002), 141.2 versus 107.7 (p = 0.001), 131.0 versus 105.1 (p = 0.005). We observed statistically significant difference in overall rate of hypoglycemia < 70 mg/dl between groups SQ versus NB: 6.8% versus 2.5% (p = 0.001). The overall rate of hypoglycemia below 54 mg/dl was < 1% (p = 0.452).

CONCLUSIONS

A meal containing 50 g of pure protein may be consumed without additional insulin dose. An additional square-wave bolus may be beneficial in reducing PPG. To avoid hypoglycemia, lower insulin dose should be calculated for 100 kcal from protein than for individual insulin-to-carb ratio.

The Effect of Ginger (Zingiber officinale Roscoe) Aqueous Extract on Postprandial Glycemia in Nondiabetic Adults: A Randomized Controlled Trial

Ginger has shown beneficial effects on blood glucose control due to its antioxidant and anti-inflammatory properties. The present study investigated the effect of ginger aqueous extract on postprandial glucose levels in nondiabetic adults and characterized its antioxidant activity. Twenty-four nondiabetic participants were randomly assigned into two groups (NCT05152745), the intervention group (n = 12) and the control group (n = 12). Both groups were administered 200 mL of an oral glucose tolerance test (OGTT), after which participants in the intervention group ingested 100 mL of ginger extract (0.2 g/100 mL). Postprandial blood glucose was measured while fasting and after 30, 60, 90, and 120 min. The total phenolic content, flavonoid content, and antioxidant activity of ginger extract were quantified. In the intervention group, the incremental area under the curve for glucose levels decreased significantly (p < 0.001) and the maximum glucose concentration significantly reduced (p < 0.001). The extract possessed a polyphenolic content of 13.85 mg gallic acid equivalent/L, a flavonoid content of 3.35 mg quercetin equivalent/L, and a high superoxide radical inhibitory capacity (45.73%). This study showed that ginger has a beneficial effect on glucose homeostasis under acute conditions and encourages the use of ginger extract as a promising source of natural antioxidants.

Protein Ingestion in Reducing the Risk of Late-Onset Post-Exercise Hypoglycemia: A Pilot Study in Adolescents and Youth with Type 1 Diabetes

Dietary protein causes dose-dependent hyperglycemia in individuals with type 1 diabetes (T1D). This study investigated the effect of consuming 50 g of protein on overnight blood glucose levels (BGLs) following late-afternoon moderate-intensity exercise. Six participants (3M:3F) with T1D, HbA1c 7.5 ± 0.8% (58.0 ± 8.7 mmol/mol) and aged 20.2 ± 3.1 years exercised for 45 min at 1600 h and consumed a protein drink or water alone at 2000 h, on two separate days. A basal insulin euglycemic clamp was employed to measure the mean glucose infusion rates (m-GIR) required to maintain euglycemia on both nights. The m-GIR on the protein and water nights during the hypoglycemia risk period and overnight were 0.27 ± 043 vs. 1.60 ± 0.66 mg/kg/min (p = 0.028, r = 0.63) and 0.51 ± 0.16 vs. 1.34 ± 0.71 mg/kg/min (p = 0.028, r = 0.63), respectively. Despite ceasing intravenous glucose infusion on the protein night, the BGLs peaked at 9.6 ± 1.6 mmol/L, with a hypoglycemia risk period mean of 7.8 ± 1.5 mmol/L compared to 5.9 ± 0.4 mmol/L (p = 0.028) on the water night. The mean plasma glucagon levels were 51.5 ± 14.1 and 27.2 ± 10.1 ng/L (p = 0.028) on the protein and water night, respectively. This suggests that an intake of protein is effective at reducing the post-exercise hypoglycemia risk, potentially via a glucagon-mediated stimulation of glucose production. However, 50 g of protein may be excessive for maintaining euglycemia.

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.

A New Meal Absorption Model for Artificial Pancreas Systems

BACKGROUND

Artificial pancreas (AP) systems reduce the treatment burden of Type 1 Diabetes by automatically regulating blood glucose (BG) levels. While many disturbances stand in the way of fully closed-loop (automated) control, unannounced meals remain the greatest challenge. Furthermore, different types of meals can have significantly different glucose responses, further increasing the uncertainty surrounding the meal.

METHODS

Effective attenuation of a meal requires quick and accurate insulin delivery because of slow insulin action relative to meal effects on BG. The proposed Variable Hump (VH) model adapts to meals of varying compositions by inferring both meal size and shape. To appropriately address the uncertainty of meal size, the model divides meal absorption into two disjoint regions: a region with coarse meal size predictions followed by a fine-grain region where predictions are fine-tuned by adapting to the meal shape.

RESULTS

Using gold-standard triple tracer meal data, the proposed VH model is compared to three simpler second-order response models. The proposed VH model increased model fit capacity by 22% and prediction accuracy by 12% relative to the next best models. A 47% increase in the accuracy of uncertainty predictions was also found. In a simple control scenario, the controller governed by the proposed VH model provided insulin just as fast or faster than the controller governed by the other models in four out of the six meals. While the controllers governed by the other models all delivered at least a 25% excess of insulin at their worst, the VH model controller only delivered 9% excess at its worst.

CONCLUSIONS

The VH Model performed best in accuracy metrics and succeeded over the other models in providing insulin quickly and accurately in a simple implementation. Use in an AP system may improve prediction accuracy and lead to better control around mealtimes.

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.

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.

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.

The Effect of Prandial Insulin Applied for Fat Protein Units on Postprandial Glucose Excursions in Type 1 Diabetes Patients with Insulin Pump Therapy: Results of a Randomized, Controlled, Cross-Over Study

OBJECTIVE

This randomized cross-over study aimed to compare different algorithms for calculating prandial insulin considering the fat and protein content of a standardized meal in type 1 diabetes patients using insulin pump therapy (CSII).

METHODS

Twenty-six patients received a standardized evening meal for three consecutive days using different algorithms for insulin dose adjustment: A) exclusive consideration of carbohydrate content without considering fat-protein content, B) high-dose algorithm considering additional insulin for fat protein units (FPUs) with the same factor as for carbohydrates, and C) low-dose algorithm considering additional insulin for FPUs with half the factor as for carbohydrates. The primary outcome was the proportion of interstitial glucose values in the target range (≥ 70 to ≤ 180 mg/dl) during the post-prandial 12-hour follow-up period. Secondary outcomes were the occurrence of hypo- and hyperglycemic episodes and the coverage with carbohydrates for treatment of hypoglycemia.

RESULTS

The percentage of glucose values in the target range was significantly higher when fat-protein content was not considered, whereas, in the hyperglycemic range, it did not differ significantly among the three groups. The percentage of hypoglycemic glucose values were the highest in the groups considering fat-protein content and lowest in the group not considering FPUs with no significant difference between the two groups in terms of FPUs.

CONCLUSIONS

In adult type 1 diabetes patients using CSII, it is not recommended to consider a high fat and protein content in the diet when calculating prandial insulin dosage with the selected algorithms, as this increases the risk of hypoglycemia disproportionately.

AGP und Ernährung – Mit CGM postprandiale Glukoseverläufe analysieren

Ernährungstherapien zählen zu den Grundlagen eines effektiven Diabetesmanagements bei Menschen sowohl mit Typ-1-, als auch mit Typ-2-Diabetes. Auch für Menschen mit Prädiabetes oder Adipositas sind Lebensstilinterventionen, einschließlich Ernährungsempfehlungen, Bestandteil der grundlegenden Therapie. Es wird empfohlen, die Ernährung individuell an die persönlichen Umstände, Präferenzen und metabolischen Ziele anzupassen. Im Zeitalter der Digitalisierung finden mHealth-Interventionen, beispielsweise in Form von kontinuierlich Glukose messenden Systemen (CGM), vermehrt Einzug in die Ernährungstherapie. Das ambulante Glukoseprofil (AGP) zeigt eine strukturierte und grafische Zusammenstellung der durch CGM gewonnenen Daten. Nach einer Bewertung der glykämischen Situation (Hypoglykämien, Variabilität und Stabilität der Glukosewerte) kann das AGP auch als Unterstützung bezüglich einer Ernährungsanpassung dienen. Ziel dieser Publikation ist es, eine allgemeine Übersicht über die Ernährungsempfehlungen, speziell in Deutschland, zu ermöglichen und den Nutzen kontinuierlicher Glukosemessungen in Bezug auf Ernährung zu beschreiben.

AGP and Nutrition - Analysing postprandial glucose courses with CGM

Nutritional therapies are one of the fundamentals of effective management of diabetes type 1 and type 2. Lifestyle interventions, including nutritional recommendations, are also part of the basic therapy for people with prediabetes or obesity. It is recommended that the diet should be individually adapted to personal circumstances, preferences and metabolic goals. In the age of digitalisation, mHealth interventions, like continuous glucose monitoring systems (CGM), are increasingly finding their way into nutrition therapy. The ambulatory glucose profile (AGP), a structured and graphical compilation of the obtained CGM data, can also be used as a support for dietary adjustment. After assessment of the glycaemic situation (hypoglycaemia, variability and stability of glucose levels). This publication aims to provide a general overview of nutritional recommendations, especially in Germany, and to describe the benefits of CGM measurements with regard to nutrition.

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.

Automated meal detection from continuous glucose monitor data through simulation and explanation

Background

Artificial pancreas systems aim to reduce the burden of type 1 diabetes by automating insulin dosing. These systems link a continuous glucose monitor (CGM) and insulin pump with a control algorithm, but require users to announce meals, without which the system can only react to the rise in blood glucose.

Objective

We investigate whether CGM data can be used to automatically infer meals in daily life even in the presence of physical activity, which can raise or lower blood glucose.

Materials and Methods

We propose a novel meal detection algorithm that combines simulations with CGM, insulin pump, and heart rate monitor data. When observed and predicted glucose differ, our algorithm uses simulations to test whether a meal may explain this difference. We evaluated our method on simulated data and real-world data from individuals with type 1 diabetes.

Results

In simulated data, we detected meals earlier and with higher accuracy than was found in prior work (25.7 minutes, 1.2 g error; compared with 48.3 minutes, 17.2 g error). In real-world data, we discovered a larger number of plausible meals than was found in prior work (30 meals, 76.7% accepted; compared with 33 meals, 39.4% accepted).

Discussion

Prior research attempted meal detection from CGM, but had delays and lower accuracy in real data or did not allow for physical activity. Our approach can be used to improve insulin dosing in an artificial pancreas and trigger reminders for missed meal boluses.

Conclusions

We demonstrate that meal information can be robustly inferred from CGM and body-worn sensor data, even in challenging environments of daily life.

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.

Effect of high protein and fat diet on postprandial blood glucose levels in children and adolescents with type 1 diabetes in Cairo, Egypt

BACKGROUND AND AIMS

To determine the effect of high protein and high fat meals on post prandial glycemia in patients with type 1 diabetes.

METHODS

This study included 51 children and adolescents with type 1 diabetes who were following up at Diabetes, Endocrine and Metabolism Pediatric Unit (DEMPU), Abo Elrish Children's hospital, Cairo University. Post prandial blood glucose levels were recorded and compared following three breakfast meals with varying protein and fat content (standard carbohydrate meal, high fat meal, and high protein meal) over a period of 5 hours on 3 consecutive days.

RESULTS

High protein meal resulted in hyperglycemia with the peak level at 3.5 hours and continued for 5 hours post prandial while high fat meal caused early hyperglycemia reached the peak at 2 hours then declined towards 5 hours. Comparison of the three different breakfast meals revealed statistically significant difference regarding the postprandial glycemia at 30, 60, 90,120, 180, 210, 240, 270, 300 min.

CONCLUSION

Meals high in protein caused sustained increase in postprandial glucose levels over a period of 5 h. However, high fat meals caused early postprandial hyperglycemia. Protein and fat content of meals affect the timing and values of the peak blood glucose as well as the duration of postprandial hyperglycemia. Therefore, fat/protein unit should be taken in consideration while calculating the bolus insulin dose and anticipating the postprandial glucose response.

Type 1 Diabetes and Physical Exercise: Moving (forward) as an Adjuvant Therapy

Type 1 diabetes is characterized by an autoimmune β-cell destruction resulting in endogenous insulin deficiency, potentially leading to micro- and macrovascular complications. Besides an exogenous insulin therapy and continuous glucose monitoring, physical exercise is recommended in adults with type 1 diabetes to improve overall health. The close relationship between physical exercise, inflammation, muscle contraction, and macronutrient intake has never been discussed in detail about type 1 diabetes. The aim of this narrative review was to detail the role of physical exercise in improving clinical outcomes, physiological responses to exercise and different nutrition and therapy strategies around exercise. Physical exercise has several positive effects on glucose uptake and systemic inflammation in adults with type 1 diabetes. A new approach via personalized therapy adaptations must be applied to target beneficial effects on complications as well as on body weight management. In combination with pre-defined macronutrient intake around exercise, adults with type 1 diabetes can expect similar physiological responses to physical exercise, as seen in their healthy counterparts. This review highlights interesting findings from recent studies related to exercise and type 1 diabetes. However, there is limited research available accompanied by a proper number of participants in the cohort of type 1 diabetes. Especially for this group of patients, an increased understanding of the impact of physical exercise can improve its effectiveness as an adjuvant therapy to move (forward).

Insulin Management for Type 1 Diabetic Patients During Social Alcohol Consumption: The SPRITZ Study

BACKGROUND

There is no data available on the best insulin treatment to counteract the effects of glucose excursions due to a moderate alcohol intake associated with portions of slight fat and protein-containing food, as often the case during social happenings or "happy hours".

INTRODUCTION

This study analyzes the glycemic control and quality of life in 8 adult type 1 diabetic (T1D) patients on insulin-pump therapy which were invited to consume a traditional Italian aperitif ("Spritz" and chips).

METHODS

Patients consumed Spritz aperitif twice: using their habitual bolus, based on carbohydrates (CHO) counting (V1), or with a personalized, advanced bolus (V2) calculated from insulin/Kcal derived from Fats and Proteins (FPU). Post-prandial glucose was continuously monitored; glucose incremental areas (iAUC), glucose peak and time to peak, and estimated change from V1 to V2 from repeated- measures models were computed. Each patient fulfilled validated questionnaires on quality of life, knowledge about diabetes and CHO counting.

RESULTS

After the educational program, a reduced iAUC (0-80 min: -306, p=ns; 40-80 min: -400, p=0.07) due to greater (p=0.03) and prolonged double-wave insulin boluses was observed. Blood glucose peak and time to peak were also reduced. Moreover, improvements in the psycho-affective dimension, as well as in the alimentary knowledge were detected.

CONCLUSION

Therefore, a personalized educational program on CHO + FPU counting together with insulin bolus management can improve glycemic control during social consumption of alcohol, with positive reflections on the psycho-affective dimension. Further studies are mandatory to confirm such preliminary results.

The Beneficial Effect of the Mobile Application Euglyca in Children and Adolescents with Type 1 Diabetes Mellitus: A Randomized Controlled Trial

Background: Euglyca^® is a mobile application which we developed for children and adolescents suffering type 1 diabetes mellitus (T1DM) for calculation of the appropriate insulin bolus dose by importing in the equation carbohydrates, lipids, glucose levels, and personalized parameters. Aim of this study is to evaluate the efficacy of this application on patients' glycemic control and satisfaction. Subjects and Methods: Eighty children and adolescents (aged 13.5 ± 2.8 years old, mean ± standard deviation) with T1DM were included in the study and were randomly and equally assigned in two groups. Patients were asked to use Euglyca for the calculation of the bolus insulin dose in the E group and to pursue their routine calculations in the C group (controls). At baseline and at 3, 6, and 12 months following the initial visit, glycated hemoglobin (HbA1c) values, percentages of hypoglycemias, hyperglycemias, and normoglycemias were determined for each patient, while Diabetes Treatment Satisfaction Questionnaire (DTSQ) was used to assess patients' treatment satisfaction at baseline and at 6 and 12 months. Results: Children and adolescents in the E group had a statistically significant decrease in HbA1c values and increase in percentages of normoglycemias and DTSQ scores, in comparison to children and adolescents in the C group. In the E group, a statistically significant positive linear correlation was found between DTSQ score and percentages of normoglycemias and a statistically significant negative correlation between changes in percentages of normoglycemias (Δnormoglycemias) and changes in HbA1c (ΔHbA1c). Conclusions: The use of the mobile application Euglyca contributes to the improvement of glycemic control and treatment satisfaction of children and adolescents with T1DM.

The Metabolic Concept of Meal Sequence vs. Satiety: Glycemic and Oxidative Responses with Reference to Inflammation Risk, Protective Principles and Mediterranean Diet

With increasing exposure to eating opportunities and postprandial conditions becoming dominant states, acute effects of meals are garnering interest. In this narrative review, meal components, combinations and course sequence were questioned vis-à-vis resultant postprandial responses, including satiety, glycemic, oxidative and inflammatory risks/outcomes vs. protective principles, with reference to the Mediterranean diet. Representative scientific literature was reviewed and explained, and corresponding recommendations discussed and illustrated. Starting meals with foods, courses and/or preloads high in innate/added/incorporated water and/or fibre, followed by protein-based courses, delaying carbohydrates and fatty foods and minimizing highly-processed/sweetened hedonic foods, would increase satiety-per-calorie vs. obesogenic passive overconsumption. Similarly, starting with high-water/fibre dishes, followed by high-protein foods, oils/fats, and delayed/reduced slowly-digested whole/complex carbohydrate sources, optionally closing with simpler carbohydrates/sugars, would reduce glycaemic response. Likewise, starting with foods high in innate/added/incorporated water/fibre/antioxidants, high monounsaturated fatty acid foods/oils, light proteins and whole/complex carbohydrate foods, with foods/oils low in n-6 polyunsaturated fatty acids (PUFA) and n-6:n-3 PUFA ratios, and minimal-to-no red meat and highly/ultra-processed foods/lipids, would reduce oxidative/inflammatory response. Pyramids illustrating representative meal sequences, from most-to-least protective foods, visually communicate similarities between axes, suggesting potential unification for optimal meal sequence, consistent with anti-inflammatory nutrition and Mediterranean diet/meal principles, warranting application and outcome evaluation.

Enzymatic Hydrolysis of a Collagen Hydrolysate Enhances Postprandial Absorption Rate-A Randomized Controlled Trial

Collagen is characterized by its high content of glycine, proline and hydroxyproline, and is found to exert beneficial effects on joint pain related to activity and osteoarthritis. However, to exert any beneficial effects it is essential that collagen is optimally absorbed. This study aimed to investigate the postprandial absorption of collagen and elucidate the impact of an exogenous enzymatic hydrolysis on absorption rate and bioavailability. A randomized, blinded, cross-over study was conducted where ten healthy male subjects received either 35 g enzymatically hydrolyzed collagen protein (EHC), 35 g non-enzymatically hydrolyzed collagen protein (NC) or placebo (250 mL water) on three nonconsecutive days. Blood samples were drawn before, and up to 240 min following, ingestion and the blood metabolome was characterized by nuclear magnetic resonance (NMR)-based metabolomics. A significant increase in the plasma concentration of nearly all amino acids (AAs) was observed over a 240 min period for both EHC and NC. In addition, the absorption rate and bioavailability of glycine, proline and hydroxyproline were significantly higher for EHC (p < 0.05). In conclusion, ingestion of collagen hydrolysates increases postprandial plasma concentrations of AAs over a period of 240 min, and an enzymatic hydrolysis increases the absorption rate and bioavailability of the collagen-rich AAs glycine, proline and hydroxyproline.

Impact of macronutrient content of meals on postprandial glucose control in the context of closed-loop insulin delivery: A randomized cross-over study

The aim of this randomized four-way cross-over study was to examine the effect of added protein and/or fat in standard meals with a fixed carbohydrate content on postprandial glucose control with closed-loop insulin delivery in adults with type 1 diabetes. Participants (n = 15) consumed breakfast meals with a fixed carbohydrate content (75 ± 1 g) and added protein and/or fat (35 ± 2 g): (1) carbohydrate-only (standard), (2) high protein (HP), (3) high fat (HF) and (4) high fat + protein (HFHP). The closed-loop insulin delivery algorithm generated insulin bolus and infusion rates. The addition of fat, protein or both did not impact 5-hour post-meal sensor glucose area under the curve (AUC) (main outcome), mean sensor glucose or glycaemic peak as compared with a standard meal (P > 0.05). However, time to glycaemic peak was delayed by 40 minutes (P = 0.03) and 5-hour post-meal basal insulin requirements were 39% higher (P = 0.04) with an HFHP meal compared with a standard meal. In conclusion, in the context of closed-loop insulin delivery, protein and/or fat meal content affects the timing of postprandial glycaemic peak, insulin requirements and late glycaemic excursion, without impacting overall 5-hour AUC.

A randomized comparison of three prandial insulin dosing algorithms for children and adolescents with Type 1 diabetes

AIM

To compare systematically the impact of two novel insulin-dosing algorithms (the Pankowska Equation and the Food Insulin Index) with carbohydrate counting on postprandial glucose excursions following a high fat and a high protein meal.

METHODS

A randomized, crossover trial at two Paediatric Diabetes centres was conducted. On each day, participants consumed a high protein or high fat meal with similar carbohydrate amounts. Insulin was delivered according to carbohydrate counting, the Pankowska Equation or the Food Insulin Index. Subjects fasted for 5 h following the test meal and physical activity was standardized. Postprandial glycaemia was measured for 300 min using continuous glucose monitoring.

RESULTS

33 children participated in the study. When compared to carbohydrate counting, the Pankowska Equation resulted in lower glycaemic excursion for 90-240 min after the high protein meal (p < 0.05) and lower peak glycaemic excursion (p < 0.05). The risk of hypoglycaemia was significantly lower for carbohydrate counting and the Food Insulin Index compared to the Pankowska Equation (OR 0.76 carbohydrate counting vs. the Pankowska Equation and 0.81 the Food Insulin Index vs. the Pankowska Equation). There was no significant difference in glycaemic excursions when carbohydrate counting was compared to the Food Insulin Index.

CONCLUSION

The Pankowska Equation resulted in reduced postprandial hyperglycaemia at the expense of an increase in hypoglycaemia. There were no significant differences when carbohydrate counting was compared to the Food Insulin Index. Further research is required to optimize prandial insulin dosing.

Low carbohydrate diet and improved glycaemic control in a patient with type one diabetes

We present the case of an adult female with type 1 diabetes, whose HbA1c was trending at 58 mmol/mol (7.5%) for the past 3 years. In August 2016, she reduced her total daily carbohydrate intake to 30–50 g and adjusted her other macronutrients to compensate for the calorific deficit. Her HbA1c fell to 34 mmol/mol (5.3%) by January 2017 and average daily blood glucose readings decreased significantly from 10.4 to 6.1 mmol/L. Moreover, she observed a marked reduction of average daily glucose variability. Notably, there were no significant episodes of hypo- or hyperglycaemia and her lipid profile remained static. Subjectively, she described an improvement in her quality of life and the dietary transition was extremely well tolerated. We discuss these findings in detail and the potential clinical benefits for patients with type 1 diabetes that can be gained by following a low carbohydrate diet.

Rapid-acting and Regular Insulin are Equal for High Fat-Protein Meal in Individuals with Type 1 Diabetes Treated with Multiple Daily Injections

INTRODUCTION

The fat and protein content can impact late postprandial glycemia; therefore, prolonged insulin boluses for high-fat/-protein meals are recommended for patients with type 1 diabetes on insulin pump therapy. It is not clear how to translate these findings to multiple daily injection (MDI) therapy. We hypothesized that regular insulin with a slower onset and a longer duration of action might be advantageous for such meals.

METHODS

Twenty-five patients with well-controlled type 1 diabetes (mean HbA1c 6.8%, 51 mmol/mol, no episodes of hypoglycemia) on MDI therapy, aged 27.9 ± 4.3 years and well trained in flexible intensive insulin therapy, were given three test breakfasts with the same carbohydrate (CHO) content. The amount of fat and protein was low (LFP) or high (HFP). For LFP meals, patients received a rapid-acting insulin; for HFP meals, a rapid-acting or regular insulin was given in individual doses according to the CHO content and individual insulin-CHO ratios. Postprandial glycemia was determined by 6-h continuous glucose monitoring.

RESULTS

Acute postprandial glucose levels measured for 2 h were similar after LFP and two HFP meals (7.8 ± 2.0, 8.1 ± 2.1, 8.0 ± 1.9 mmol/l). Late postprandial glycemia measured from 2 to 6 h was significantly lower after the LFP meal (6.7 ± 1.8 mmol/l, p < 0.05) than after the HFP meals, but there was no difference between the rapid-acting or regular insulin on HFP days (8.6 ± 2.6 and 8.9 ± 2.8 mmol/l, NS).

CONCLUSION

The preliminary results of this study indicate no benefit to cover fat-protein meals with regular insulin in individuals with type 1 diabetes treated with MDI.

The additional dose of insulin for high-protein mixed meal provides better glycemic control in children with type 1 diabetes on insulin pumps: randomized cross-over study

BACKGROUND

Delivery of insulin for high-protein low-fat meals with carbohydrates on the basis of carbohydrates leads to higher late postprandial glycemia. Studies with mixed meals demonstrated lower blood glucose level after dual wave bolus. The objective of our study was to assess the impact of additional dose of insulin in dual wave bolus for high-protein mixed meal on the postprandial glycemia.

MATERIALS AND METHODS

We performed a randomized, double-blind, two-way cross-over study, including 58 children with type 1 diabetes, aged 14.7 ± 2.2 years. Participants were randomly assigned into two treatment orders: NORMAL-DUAL or DUAL-NORMAL BOLUS. They consumed standardized high-protein, low-fat meals with carbohydrates. The primary outcome was postprandial glycemia (PPG) based on capillary blood glucose measurements (CBGM). The secondary outcomes were the frequency of hypoglycemia, area under glucose curve, mean amplitude of glycemic excursion (MAGE) and glycemic rise.

RESULTS

PPG assessed at 180 min was significantly lower when dual wave bolus was delivered (NORMAL 162 mg/dL [9 mmol/L] vs DUAL 130.0 mg/dL [7.22 mmol/L]; P = .004). There were no differences in CBGM between both groups at 60 and 120 min. We found differences between the groups in MAGE at 120 min (NORMAL 82.86 mg/dL [4.6 mmol/L] versus DUAL 54.76 mg/dL [3.04 mmol/L]; P = .0008). We observed no differences in the number of hypoglycemic episodes in both groups.

CONCLUSION

Applying an additional dose of insulin in dual wave bolus for high-protein mixed meal improved PPG. We observed no statistically significant increase in the number of hypoglycemic episodes associated with this intervention.

The potential effect of ultra-long insulin degludec on glycemic variability

Despite the therapeutic advances in the treatment of diabetes, metabolic control instability due to glycemic variability (GV) is frequently observed in patients with diabetes on intensive insulin therapy and is associated with hyperglycemic peaks and hypoglycemic episodes. Hyperglycemia associated with GV has been implicated in the development of chronic complications due to its pro-oxidative consequences. On the other hand, hypoglycemia can be associated with increased cardiovascular risk secondarily to adrenergic activation. The ultra-long-acting insulin analogue, insulin degludec (IDeg), presents a flat and stable glucose-lowering effect both in Type 1 and Type 2 diabetes patients. In pharmacodynamic studies, IDeg has been associated with a lower variability in its insulin action than other alternatives for basal insulin, which might have clinical advantages for the stability of the glycemic control. The main objective of this review is to present pharmacological and clinical data regarding the efficacy and safety of IDeg for the treatment of diabetes focusing on its effects on GV and on hypoglycemia frequency.

Optimizing the combination insulin bolus split for a high-fat, high-protein meal in children and adolescents using insulin pump therapy

AIMS

To determine the optimum combination bolus split to maintain postprandial glycaemia with a high-fat and high-protein meal in young people with Type 1 diabetes.

METHODS

A total of 19 young people (mean age 12.9 ± 6.7 years) participated in a randomized, repeated-measures trial comparing postprandial glycaemic control across six study conditions after a high-fat and high-protein meal. A standard bolus and five different combination boluses were delivered over 2 h in the following splits: 70/30 = 70% standard /30% extended bolus; 60/40=60% standard/40% extended bolus; 50/50=50% standard/50% extended bolus; 40/60=40% standard/60% extended bolus; and 30/70=30% standard/70% extended bolus. Insulin dose was determined using the participant's optimized insulin:carbohydrate ratio. Continuous glucose monitoring was used to assess glucose excursions for 6 h after the test meal.

RESULTS

Standard bolus and combination boluses 70/30 and 60/40 controlled the glucose excursion up to 120 min. From 240 to 300 min after the meal, the glucose area under the curve was significantly lower for combination bolus 30/70 compared with standard bolus (P=0.004).

CONCLUSIONS

High-fat and high-protein meals require a ≥60% insulin:carbohydrate ratio as a standard bolus to control the initial postprandial rise. Additional insulin at an insulin:carbohydrate ratio of up to 70% is needed in the extended bolus for a high fat and protein meal to prevent delayed hyperglycaemia.

Late to the Party: Importance of Dietary Fat and Protein in the Intensive Management of Type 1 Diabetes. A Case Report

Insulin dosing in type 1 diabetes (T1D) has been focused primarily on carbohydrate intake, but recent evidence highlights the importance of dietary fat and protein in glycemic excursions. Several methods have been developed to incorporate dietary fat and protein into insulin dose calculations, including fat–protein units (FPUs) that estimate insulin requirements based on ingested fat and protein, as well as extended combination insulin boluses. However, insulin dosing based on meal fat and protein content is challenging to incorporate into clinical practice.

We present the case of a 40-year-old man with T1D using continuous subcutaneous insulin infusions and continuous glucose monitoring. He followed a diet that restricted carbohydrate intake, with compensatory increases in dietary protein and fat. He had poor glycemic control with frequent postprandial hyperglycemia. He began incorporating FPUs into his insulin dosing calculations and used extended dual wave boluses to administer prandial insulin. Over the next 6 months he experienced a significant improvement in glycemic control.

Fat and protein have both been shown to cause delayed postprandial hyperglycemia, leading to poor glycemic control with carbohydrate-focused insulin dosing in our patient on a high-fat, high-protein diet. It is difficult to incorporate dietary fat and protein into insulin dosing in the clinical setting. However, our patient experienced an improvement in glycemic control with the application of FPUs and dual wave boluses in prandial insulin dosing, showing that methods such as these can be used successfully in T1D management.