Influence of dietary protein on postprandial blood glucose levels in individuals with Type 1 diabetes mellitus using intensive insulin therapy

A randomised controlled trial of additional bolus insulin using an insulin-to-protein ratio compared with insulin-to-carbohdrate ratio alone in people with type 1 diabetes following a carbohydrate-restricted diet

AIMS

Postprandial hyperglycemia can be problematic for people with type 1 diabetes (T1DM) following carbohydrate-restricted diets. Bolus insulin calculated for meal protein plus carbohydrate may help. This study evaluated the effect of additional bolus insulin using an insulin-to-protein ratio (IPR) on glycaemic control.

MATERIALS AND METHODS

Participants with T1DM aged ≥18-years were randomly allocated (1:1) to either carbohydrate and protein-based, or carbohydrate-based insulin dosing alone for 12 weeks while following a carbohydrate-restricted diet (50-100 g/day). Measurement of HbA1c and continuous glucose monitoring occurred at baseline and 12 weeks, with assessment of participant experience at 12 weeks.

RESULTS

Thirty-four participants were randomised, 22 female, mean(SD): age 39.2 years (12.6) years; diabetes duration 20.6 years (12.9); HbA1c 7.3 % (0.8), 56.7 mmol/mol (9.2). Seven in each group used insulin pump therapy. HbA1c reduced at 12 weeks with no difference between treatments: mean (SD) control 7.2 % (1.0), 55.7 mmol/mol (10.6); intervention 6.9 % (0.7), 52.3 mmol/mol (7.2) (p = 0.65). Using additional protein-based insulin dosing compared with carbohydrate alone, there was no difference in glycaemic variability, time spent in euglycemic range (TIR), or below range. Participants using IPR reported more control of their diabetes, but varying levels of distress.

CONCLUSIONS

Additional bolus insulin using an IPR did not improve glycaemic control or TIR in patients with well controlled T1DM following a carbohydrate-restricted diet. Importantly, the use of the IPR does not increase the risk of hypoglycemia and may be preferred.

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.

Pre-exercise protein intake is associated with reduced time in hypoglycaemia among adolescents with type 1 diabetes

AIM

Secondary analyses were conducted from a randomized trial of an adaptive behavioural intervention to assess the relationship between protein intake (g and g/kg) consumed within 4 h before moderate-to-vigorous physical activity (MVPA) bouts and glycaemia during and following MVPA bouts among adolescents with type 1 diabetes (T1D).

MATERIALS AND METHODS

Adolescents (n = 112) with T1D, 14.5 (13.8, 15.7) years of age and 36.6% overweight/obese, provided measures of glycaemia using continuous glucose monitoring [percentage of time above range (>180 mg/dl), time in range (70-180 mg/dl), time below range (TBR; <70 mg/dl)], self-reported physical activity (previous day physical activity recalls), and 24 h dietary recall data at baseline and 6 months post-intervention. Mixed effects regression models adjusted for design (randomization assignment, study site), demographic, clinical, anthropometric, dietary, physical activity and timing covariates estimated the association between pre-exercise protein intake on percentage of time above range, time in range and TBR during and following MVPA.

RESULTS

Pre-exercise protein intakes of 10-19.9 g and >20 g were associated with an absolute reduction of -4.41% (p = .04) and -4.83% (p = .02) TBR during physical activity compared with those who did not consume protein before MVPA. Similarly, relative protein intakes of 0.125-0.249 g/kg and ≥0.25 g/kg were associated with -5.38% (p = .01) and -4.32% (p = .03) absolute reductions in TBR during physical activity. We did not observe a significant association between protein intake and measures of glycaemia following bouts of MVPA.

CONCLUSIONS

Among adolescents with T1D, a dose of ≥10 g or ≥0.125 g/kg of protein within 4 h before MVPA may promote reduced time in hypoglycaemia during, but not following, physical activity.

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.

14. Children and Adolescents: 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.

The effect of the modified fat-protein unit algorithm compared with that of carbohydrate counting on postprandial glucose in adults with type-1 diabetes when consuming meals with differing macronutrient compositions: a randomized crossover trial

BACKGROUND

The optimization of glucose control in type-1 diabetes is challenged by postprandial glycemic variability. This study aimed to compare the postprandial glycemic effects of carbohydrate counting and the modified fat-protein unit (FPU) algorithms following meals with different protein and fat emphases in adults with type-1 diabetes.

METHODS

Thirty adults with type-1 diabetes aged 18 to 45 years participated in a randomized crossover trial. In a random order, participants consumed four test meals with equivalent energy and different macronutrient emphases on four separate mornings. The modified FPU algorithms and carbohydrate counting were used to determine the insulin dose for the test meals. A continuous glucose monitoring system was used to measured postprandial glycemia.

RESULTS

Compared with carbohydrate counting, the modified FPU algorithm significantly decreased the late postprandial mean glucose levels (p = 0.026) in high protein-fat meals. The number of hypoglycemia episodes was similar between insulin dosing algorithms for the high protein-fat meals; hypoglycemic events were considerably higher for the modified FPU in the normal protein-fat meal (p = 0.042).

CONCLUSIONS

The modified FPU algorithm may improve postprandial glycemic control after consuming high protein-fat meals in adults with type-1 diabetes but may result in increased hypoglycemia risk when used with a normal protein-fat meal.

Post-Exercise Protein Intake May Reduce Time in Hypoglycemia Following Moderate-Intensity Continuous Exercise among Adults with Type 1 Diabetes

Little is known about the role of post-exercise protein intake on post-exercise glycemia. Secondary analyses were conducted to evaluate the role of post-exercise protein intake on post-exercise glycemia using data from an exercise pilot study. Adults with T1D (n = 11), with an average age of 33.0 ± 11.4 years and BMI of 25.1 ± 3.4, participated in isoenergetic sessions of high-intensity interval training (HIIT) or moderate-intensity continuous training (MICT). Participants completed food records on the days of exercise and provided continuous glucose monitoring data throughout the study, from which time in range (TIR, 70-180 mg/dL), time above range (TAR, >180 mg/dL), and time below range (TBR, <70 mg/dL) were calculated from exercise cessation until the following morning. Mixed effects regression models, adjusted for carbohydrate intake, diabetes duration, and lean mass, assessed the relationship between post-exercise protein intake on TIR, TAR, and TBR following exercise. No association was observed between protein intake and TIR, TAR, or TBR (p-values ≥ 0.07); however, a borderline significant reduction of -1.9% (95% CI: -3.9%, 0.0%; p = 0.05) TBR per 20 g protein was observed following MICT in analyses stratified by exercise mode. Increasing post-exercise protein intake may be a promising strategy to mitigate the risk of hypoglycemia following MICT.

Carbohydrate Intake and Oral Glucose Tolerance Test Results in the Postpartum Period

CONTEXT

The American Diabetes Association (ADA) recommends a 3-day preparatory diet prior to a diagnostic oral glucose tolerance test (OGTT), a test often recommended in postpartum individuals with a history of gestational diabetes (GDM).

OBJECTIVE

Evaluate the relationship between carbohydrate intake and OGTT glucose in 2 cohorts of postpartum individuals.

METHODS

We performed analyses of postpartum individuals from 2 prospective studies with recent GDM (Balance after Baby Intervention, BABI, n = 177) or risk factors for GDM (Study of Pregnancy Regulation of INsulin and Glucose, SPRING, n = 104) .We measured carbohydrate intake using 24-hour dietary recalls (SPRING) or Food Frequency Questionnaire (BABI) and performed 2-hour 75-g OGTTs. The main outcome measure was 120-minute post-OGTT glucose.

RESULTS

There was no relationship between carbohydrate intake and 120-minute post-OGTT glucose level in either study population (SPRING: β = 0.03, [-5.5, 5.5] mg/dL, P = .99; BABI: β = -3.1, [-9.5, 3.4] mg/dL, P = .35). Adding breastfeeding status to the model did not change results (SPRING β = -0.14, [-5.7, 5.5] mg/dL, P = .95; BABI β = -3.9, [-10.4, 2.7] mg/dL, P = .25). There was, however, an inverse relationship between glycemic index and 120-minute post OGTT glucose (BABI: β = -1.1, [-2.2, -0.03] mg/dL, P = .04).

CONCLUSION

Carbohydrate intake is not associated with post-OGTT glucose levels among postpartum individuals. Dietary preparation prior to the OGTT may not be necessary in this population.

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.

Is There an Optimal Time of Day for Exercise? A Commentary on When to Exercise for People Living With Type 1 or Type 2 Diabetes

Exercise is a cornerstone of diabetes self-care because of its association with many health benefits. Several studies that have explored the best time of day to exercise to inform clinical recommendations have yielded mixed results. For example, for people with prediabetes or type 2 diabetes, there may be benefits to timing exercise to occur after meals, whereas people with type 1 diabetes may benefit from performing exercise earlier in the day. One common thread is the health benefits of consistent exercise, suggesting that the issue of exercise timing may be secondary to the goal of helping people with diabetes establish an exercise routine that best fits their life.

Protein intake and postprandial hyperglycemia in children and adolescents with type 1 diabetes mellitus, a pilot study

BACKGROUND AND AIMS

To describe the change in glucose and the resulting postprandial hyperglycemia (PPH) that occurs after dietary protein intake (PI) in children with type 1 diabetes (T1D).

METHODS

We conducted a self-controlled, non-randomized, prospective pilot study in children with T1D who were given whey protein isolate drinks (carbohydrate-free, fat-free) of increasing protein amounts (0, 12.5, 25, 37.5, 50, and 62.5 gm) on 6 sequential nights. The glucose levels were monitored with continuous glucose monitors (CGM) and glucometers for 5 h after PI. PPH was defined as glucose elevations over baseline of ≥50 mg/dL.

RESULTS

Thirty-eight subjects were recruited, and eleven subjects (6 females, 5 males) completed the intervention. Subjects had a mean (range) age of 11.6 (6-16) years, diabetes duration of 6.1 (1.4-15.5) years, HbA1c of 7.2 (5.2-8.6) % and weight of 44.5 (24.3-63.2) kg. PPH was detected in 1/11, 5/11, 6/10, 6/9, 5/9, and 8/9 subjects after receiving 0, 12.5, 25, 37.5, 50, and 62.5 gm of protein, respectively.

CONCLUSIONS

In children with T1D, the association between PPH and PI was observed at smaller protein amounts compared to studies done in adults.

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.

Dietary treatment of type 1 diabetes - once upon a time versus today

Type 1 diabetes is a disease in which nutrition is an integral part of treatment. The type of recommended diets for therapeutic purposes has changed over the years. Proper metabolic equalization of the disease is an enormous challenge and problem for patients at the same time. This review paper discusses the history of dietary treatment of type 1 diabetes and refers to current dietary recommendations and their impact on the patient's health. The important roles of glycaemic index and glycaemic load are pointed out for proper treatment and slowing of the development of complications. Attention is also paid to the role of dietary education as an integral part of therapeutic management. Continuous progress in the development of technology is of great help to the patient.

Insulin delivery patterns required to maintain postprandial euglycemia in type 1 diabetes following consumption of traditional Egyptian Ramadan Iftar meal using insulin pump therapy: A randomized crossover trial

OBJECTIVES

During Ramadan, traditional Egyptian Iftar meals have large amounts of high-glycemic index carbohydrate and fat. The efficacy of different bolus regimens on optimizing post prandial glucose (PPG) excursion following this Iftar meal was assessed.

METHODS

A randomized controlled trial evaluating 4-h PPG measured by continuous glucose-monitoring was conducted. A total of 25 youth with T1DM using insulin pumps were given the same Iftar meal (fat [45 g], protein [28 g], CHO [95 g]) on seven consecutive days. Insulin to carbohydrate ratio (ICR) was individualized, and all boluses were given upfront 20 min before Iftar. Participants were randomized to receive a standard bolus and six different split boluses delivered over 4 h in the following splits: dual wave (DW) 50/50; DW 50/50 with 20% increment (120% ICR); DW60/40; DW 60/40 with 20% increment; DW 70/30 and DW 70/30 with 20% increment.

RESULTS

Standard bolus and split 70/30 with 20% increment resulted in significantly lower early glucose excursions (120 min) with mean excursions of less than 40 mg/dL (2.2 mmol/L) compared to other conditions (p < 0.01). The split 70/30 with 20% increment significantly optimized late PPG excursion (240 min) in comparison to standard bolus (p < 0.01), as well as resulting in a significantly lower post meal glucose area under the curve compared with all other conditions (p < 0.01), with no late hypoglycemia.

CONCLUSION

To achieve physiologic PPG profile in traditional Iftar meal, a DW bolus with 20% increment given 20 min preprandial as split bolus 70/30 over 4 h, optimized both early and delayed PPG excursions.

Effects of Dietary Fat and Protein on Glucoregulatory Hormones in Adolescents and Young Adults With Type 1 Diabetes

CONTEXT

Dietary fat and protein impact postprandial hyperglycemia in people with type 1 diabetes, but the underlying mechanisms are poorly understood. Glucoregulatory hormones are also known to modulate gastric emptying and may contribute to this effect.

OBJECTIVE

Investigate the effects of fat and protein on glucagon-like peptide (GLP-1), glucagon-dependent insulinotropic polypeptide (GIP) and glucagon secretion.

METHODS

2 crossover euglycemic insulin clamp clinical trials at 2 Australian pediatric diabetes centers. Participants were 12-21 years (n = 21) with type 1 diabetes for ≥1 year. Participants consumed a low-protein (LP) or high-protein (HP) meal in Study 1, and low-protein/low-fat (LPLF) or high-protein/high-fat (HPHF) meal in Study 2, all containing 30 g of carbohydrate. An insulin clamp was used to maintain postprandial euglycemia and plasma glucoregulatory hormones were measured every 30 minutes for 5 hours. Data from both cohorts (n = 11, 10) were analyzed separately. The main outcome measure was area under the curve of GLP-1, GIP, and glucagon.

RESULTS

Meals low in fat and protein had minimal effect on GLP-1, while there was sustained elevation after HP (80.3 ± 16.8 pmol/L) vs LP (56.9 ± 18.6), P = .016, and HPHF (103.0 ± 26.9) vs LPLF (69.5 ± 31.9) meals, P = .002. The prompt rise in GIP after all meals was greater after HP (190.2 ± 35.7 pmol/L) vs LP (152.3 ± 23.3), P = .003, and HPHF (258.6 ± 31.0) vs LPLF (151.7 ± 29.4), P < .001. A rise in glucagon was also seen in response to protein, and HP (292.5 ± 88.1 pg/mL) vs LP (182.8 ± 48.5), P = .010.

CONCLUSION

The impact of fat and protein on postprandial glucose excursions may be mediated by the differential secretion of glucoregulatory hormones. Further studies to better understand these mechanisms may lead to improved personalized postprandial glucose management.

Nutritional support for a person with type 1 diabetes undertaking endurance swimming

Long distance and open water swimming have increased in popularity over recent years. Swimming a long distance in lakes, rivers and the sea present numerous challenges, including cold water exposure and maintaining adequate nutritional intake to fuel exercising muscles. Guidelines exist outlining issues to consider and potential solutions to overcome the difficulties in feeding athletes. Exercising with type 1 diabetes adds further complexity, mostly around matching insulin to the recommended high carbohydrate intake, but also because of the way in which higher circulating insulin levels affect glucose utilisation and fat oxidation. This paper describes the nutritional considerations for people with type 1 diabetes intending to undertake long distance open water events, and insulin management suggestions to trial alongside. In addition, we include personal testimony from a swimmer with type 1 diabetes describing the challenges and considerations he faced when undertaking marathon swimming.

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.

Impact of Fat Intake on Blood Glucose Control and Cardiovascular Risk Factors in Children and Adolescents with Type 1 Diabetes

Nutrition therapy is a cornerstone of type 1 diabetes (T1D) management. Glycemic control is affected by diet composition, which can contribute to the development of diabetes complications. However, the specific role of macronutrients is still debated, particularly fat intake. This review aims at assessing the relationship between fat intake and glycemic control, cardiovascular risk factors, inflammation, and microbiota, in children and adolescents with T1D. High fat meals are followed by delayed and prolonged hyperglycemia and higher glycated hemoglobin A1c levels have been frequently reported in individuals with T1D consuming high amounts of fat. High fat intake has also been associated with increased cardiovascular risk, which is higher in people with diabetes than in healthy subjects. Finally, high fat meals lead to postprandial pro-inflammatory responses through different mechanisms, including gut microbiota modifications. Different fatty acids were proposed to have a specific role in metabolic regulation, however, further investigation is still necessary. In conclusion, available evidence suggests that a high fat intake should be avoided by children and adolescents with T1D, who should be encouraged to adhere to a healthy and balanced diet, as suggested by ISPAD and ADA recommendations. This nutritional choice might be beneficial for reducing cardiovascular risk and inflammation.

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.

For a high fat, high protein breakfast, preprandial administration of 125% of the insulin dose improves postprandial glycaemic excursions in people with type 1 diabetes using multiple daily injections: A cross-over trial

AIM

To determine the glycaemic impact of an increased insulin dose, split insulin dose and regular insulin for a high fat, high protein breakfast in people with type 1 diabetes using multiple daily injections (≥4/day).

METHODS

In this cross-over trial, participants received the same high fat, high protein breakfast (carbohydrate:30 g, fat:40 g, protein:50 g) for 4 days. Four different insulin strategies were randomly allocated and tested; 100% of the insulin-to-carbohydrate ratio (ICR) given in a single dose using aspart insulin (100Asp), 125% ICR given in a single dose using aspart (125Asp) or regular insulin (125Reg) and 125% ICR given in a split dose using aspart insulin (100:25Asp). Insulin was given 0.25 hr pre-meal and for 100:25Asp, also 1 hr post-meal. Postprandial sensor glucose was measured for 5 hr.

RESULTS

In all, 24 children and adults were participated. The 5-hr incremental area under the curves for 100Asp, 125Asp, 125Reg and 100:25Asp were 620 mmol/L.min [95% CI: 451,788], 341 mmol/L.min [169,512], 675 mmol/L.min [504,847] and 434 mmol/L.min [259,608], respectively. The 5-hr incremental area under the curve for 125Asp was significantly lower than for 100Asp (p = 0.016) and for 125Reg (p = 0.002). There was one episode of hypoglycaemia in 125Reg.

CONCLUSIONS

For a high fat, high protein breakfast, giving 125% ICR preprandially, using aspart insulin significantly improved postprandial glycaemia without hypoglycaemia. There was no additional glycaemic benefit from giving insulin in a split dose (100:25%) or replacing aspart with regular insulin.

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.

Dietary Aspects to Incorporate in the Creation of a Mobile Image-Based Dietary Assessment Tool to Manage and Improve Diabetes

Diabetes is the seventh leading cause of death in United States. Dietary intake and behaviors are essential components of diabetes management. Growing evidence suggests dietary components beyond carbohydrates may critically impact glycemic control. Assessment tools on mobile platforms have the ability to capture multiple aspects of dietary behavior in real-time throughout the day to inform and improve diabetes management and insulin dosing. The objective of this narrative review was to summarize evidence related to dietary behaviors and composition to inform a mobile image-based dietary assessment tool for managing glycemic control of both diabetes types (type 1 and type 2 diabetes). This review investigated the following topics amongst those with diabetes: (1) the role of time of eating occasion on indicators of glycemic control; and (2) the role of macronutrient composition of meals on indicators of glycemic control. A search for articles published after 2000 was completed in PubMed with the following sets of keywords “diabetes/diabetes management/diabetes prevention/diabetes risk”, “dietary behavior/eating patterns/temporal/meal timing/meal frequency”, and “macronutrient composition/glycemic index”. Results showed eating behaviors and meal macronutrient composition may affect glycemic control. Specifically, breakfast skipping, late eating and frequent meal consumption might be associated with poor glycemic control while macronutrient composition and order of the meal could also affect glycemic control. These factors should be considered in designing a dietary assessment tool, which may optimize diabetes management to reduce the burden of this disease.

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.

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.

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).

Pharmacokinetic-pharmacodynamic modelling of the hypoglycaemic effect of pulsatile administration of human insulin in rats

The relationship between the plasma insulin (INS) concentration–time course and plasma glucose concentration–time course during and after pulsatile INS administration to rats was characterized using a pharmacokinetic–pharmacodynamic (PK–PD) model. A total INS dose of 0.5 IU/kg was intravenously injected in 2 to 20 pulses over a 2-h period. Compared with the single bolus administration, the area under the effect-time curve (AUE) increased depending on the number of pulses, and the AUEs for more than four pulses plateaued at a significantly larger value, which was similar to that after the infusion of a total of 0.5 IU/kg of INS over 2 h. No increase in plasma INS concentration occurred after pulsatile administration. Two indirect response models primarily reflecting the receptor-binding process (IR model) or glucose transporter 4 (GLUT4) translocation (GT model) were applied to describe the PK–PD relationship after single intravenous bolus administration of INS. These models could not explain the observed data after pulsatile administration. However, the IR-GT model, which was a combination of the IR and GT models, successfully explained the effects of pulsatile administration and intravenous infusion. These results indicate that the receptor-binding process and GLUT4 translocation are responsible for the change in AUE after pulsatile administration.

Dietary intakes of women with Type 1 diabetes before and during pregnancy: a pre-specified secondary subgroup analysis among CONCEPTT participants

AIM

To describe the dietary intakes of women with Type 1 diabetes before and during pregnancy.

METHODS

This was a pre-specified subgroup analysis of CONCEPTT involving 63 women planning pregnancy and 93 pregnant women from 14 sites in England, Scotland and Ireland. Two hundred and forty-six 3-day food diaries (104 planning pregnancy, 142 pregnant) were matched to data source and food reference codes, and analysed using dietary software. Participants were informed that food diaries would be de-identified and used only for research purposes.

RESULTS

Mean (sd) daily energy intake was 1588 (346) kcal and 1673 (384) kcal in women planning pregnancy and pregnant women respectively. Total carbohydrate intake was consistent with dietary guideline recommendations [180 (52) g planning pregnancy, 198 (54) g pregnant], but non-recommended sources (e.g. sugars, preserves, confectionery, biscuits, cakes) contributed to 46% of total daily carbohydrate intake. Fat consumption exceeded guideline recommendations [70 (21) g planning pregnancy, 72 (21) g pregnant]. Fibre [15.5 (5.3) g planning pregnancy, 15.4 (5.1) g pregnant], fruit and vegetable intakes [3.5 (2.2) and 3.1 (1.8) serves/day] were inadequate. Twelve women planning pregnancy (19%) and 24 pregnant women (26%) did not meet micronutrient requirements.

CONCLUSIONS

The diets of pregnant women from England, Scotland and Ireland are characterized by high fat, low fibre and poor-quality carbohydrate intakes. Fruit and vegetable consumption is inadequate, with one in four women at risk of micronutrient deficiencies. Further research is needed to optimize maternal nutrition for glycaemic control and for maternal and offspring health.

Nutrition habits of children and adolescents with type 1 diabetes changed in a 10 years span

BACKGROUND

Diet plays a key role in the treatment of type 1 diabetes (T1D). Dietary habits changed rapidly in the last decades and few data are available on recent dietary changes in children and adolescents with T1D.

OBJECTIVE

To test the hypothesis that diet composition changed in a 10-year period in children and adolescents with T1D.

METHODS

Two hundred and twenty-nine T1D subjects (M/F:121/108) aged 6 to 16 years were recruited: 114 (group A) enrolled in 2009, not using CGM and/or CSII, and 115 (group B) enrolled in 2019. Anthropometric biochemical (HbA1c, lipid profile), diet, and insulin therapy parameters were compared between the two groups. Multivariate logistic regression analysis was performed with HbA1c as dependent variable (HbA1c > 58 mmol/mol = 1) and nutritional variables and technology use as independent ones.

RESULTS

Energy intake of group A was not statistically different from that of group B. Group B had a significantly (P < 0.001) higher protein and lipids intake and lower total carbohydrate and fiber intake than group A. HbA1c was significantly (P < 0.01) lower in group B than in group A. Logistic regression analysis showed that MUFA (OR 0.83, 95%CI:0.693-0.998), fiber intake (OR 0.82, 95%CI:0.699-0.0969), and technology use (OR 0.15, 95%CI:0.031-0.685), adjusted for age, gender, BMI, energy intake and diabetes duration, were associated with a HbA1c higher than 58 mmol/mol) (R² = 0.27, P < 0.05).

CONCLUSIONS

In a 10-year period, diet composition of children and adolescents with T1D changed and glucometabolic control improved. Fiber and MUFA intake showed a positive effect on HbA1c, independent from technology use, supporting the importance of educating children with T1D and families to maintain healthy eating habits.

High-protein meals require 30% additional insulin to prevent delayed postprandial hyperglycaemia

AIM

To determine the amount of additional insulin required for a high-protein meal to prevent postprandial hyperglycaemia in individuals with type 1 diabetes using insulin pump therapy.

METHODS

In this randomized cross-over study, 26 participants aged 8-40 years, HbA_1c < 65 mmol/mol (8.1%), received a 50 g protein, 30 g carbohydrate, low-fat (< 1 g) breakfast drink over five consecutive days at home. A standard insulin dose (100%) was compared with additional doses of 115, 130, 145 and 160% for the protein, in randomized order. Doses were commenced 15-min pre-drink and delivered over 3 h using a combination bolus with 65% of the standard dose given up front. Postprandial glycaemia was assessed by 4 h of continuous glucose monitoring.

RESULTS

The 100% dosing resulted in postprandial hyperglycaemia. From 120 min, ≥ 130% doses resulted in significantly lower postprandial glycaemic excursions compared with 100% (P < 0.05). A 130% dose produced a mean (sd) glycaemic excursion that was 4.69 (2.42) mmol/l lower than control, returning to baseline by 4 h (P < 0.001). From 120 min, there was a significant increase in the risk of hypoglycaemia compared with control for 145% [odds ratio (OR) 25.4, 95% confidence interval (CI) 5.5-206; P < 0.001) and 160% (OR 103, 95% CI 19.2-993; P < 0.001). Some 81% (n = 21) of participants experienced hypoglycaemia following a 160% dose, whereas 58% (n = 15) experienced hypoglycaemia following a 145% dose. There were no hypoglycaemic events reported with 130%.

CONCLUSIONS

The addition of 30% more insulin to a standard dose for a high-protein meal, delivered using a combination bolus, improves postprandial glycaemia without increasing the risk of hypoglycaemia.

Factors Beyond Carbohydrate to Consider When Determining Meantime Insulin Doses: Protein, Fat, Timing, and Technology

For many years, carbohydrate counting has been a popular strategy for determining mealtime insulin doses for people with diabetes who are on a multiple daily injection regimen or continuous subcutaneous insulin infusion. This approach assumes that only carbohydrate-containing foods and beverages affect postprandial glucose levels. However, many studies have indicated that the fat and protein content of a meal can play an important role in delaying postprandial hyperglycemia and should be considered when trying to optimize postprandial glucose levels. This article reviews research on making insulin dose adjustments for high-fat and high-protein meals, as well as the timing of mealtime insulin doses.

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

OBJECTIVE

The American Diabetes Association recommends individuals with type 1 diabetes (T1D) adjust insulin for dietary fat; however, optimal adjustments are not known. This study aimed to determine 1) the relationship between the amount and type of dietary fat and glycemia and 2) the optimal insulin adjustments for dietary fat.

RESEARCH DESIGN AND METHODS

Adults with T1D using insulin pump therapy attended the research clinic on 9-12 occasions. On the first six visits, participants consumed meals containing 45 g carbohydrate with 0 g, 20 g, 40 g, or 60 g fat and either saturated, monounsaturated, or polyunsaturated fat. Insulin was dosed using individual insulin/carbohydrate ratio as a dual-wave 50/50% over 2 h. On subsequent visits, participants repeated the 20-60-g fat meals with the insulin dose estimated using a model predictive bolus, up to twice per meal, until glycemic control was achieved.

RESULTS

With the same insulin dose, increasing the amount of fat resulted in a significant dose-dependent reduction in incremental area under the curve for glucose (iAUC_glucose) in the early postprandial period (0-2 h; P = 0.008) and increase in iAUC_glucose in the late postprandial period (2-5 h; P = 0.004). The type of fat made no significant difference to the 5-h iAUC_glucose. To achieve glycemic control, on average participants required dual-wave insulin bolus: for 20 g fat, +6% insulin, 74/26% over 73 min; 40 g fat, +6% insulin, 63/37% over 75 min; and 60 g fat, +21% insulin, 49/51% over 105 min.

CONCLUSIONS

This study provides clinical guidance for mealtime insulin dosing recommendations for dietary fat in T1D.

Impact of dietary protein on postprandial glycaemic control and insulin requirements in Type 1 diabetes: a systematic review

AIM

Postprandial hyperglycaemia is a challenge for people living with Type 1 diabetes. In addition to carbohydrate, dietary protein has been shown to contribute to postprandial glycaemic excursions with recommendations to consider protein when calculating mealtime insulin doses. The aim of this review is to identify and synthesize evidence about the glycaemic impact of dietary protein and insulin requirements for individuals with Type 1 diabetes.

METHODS

A systematic literature search of relevant biomedical databases was performed to identify research on the glycaemic impact of dietary protein when consumed alone, and in combination with other macronutrients in individuals with Type 1 diabetes.

RESULTS

The review included 14 published studies dated from 1992 to 2018, and included studies that researched the impact of protein alone (n = 2) and protein in a mixed meal (n = 12). When protein was consumed alone a glycaemic effect was not seen until ≥ 75 g. In a carbohydrate-containing meal ≥ 12.5 g of protein impacted the postprandial glucose. Inclusion of fat in a high-protein meal enhanced the glycaemic response and further increased insulin requirements. The timing of the glycaemic effect from dietary protein ranged from 90 to 240 min. Studies indicate that the postprandial glycaemic response and insulin requirements for protein are different when protein is consumed alone or with carbohydrate and/or fat.

CONCLUSIONS

This systematic review provides evidence that dietary protein contributes to postprandial glycaemic excursions and insulin requirements. These insights have important implications for the education of people with Type 1 diabetes and highlights the need for more effective insulin dosing strategies for mixed macronutrient meals.

The carbohydrate-insulin model does not explain the impact of varying dietary macronutrients on the body weight and adiposity of mice

Objectives

The carbohydrate-insulin model (CIM) predicts that increases in fasting and post-prandial insulin in response to dietary carbohydrates stimulate energy intake and lower energy expenditures, leading to positive energy balance and weight gain. The objective of the present study was to directly test the CIM's predictions using C57BL/6 mice.

Methods

Diets were designed by altering dietary carbohydrates with either fixed protein or fat content and were fed to C57BL/6 mice acutely or chronically for 12 weeks. The body weight, body composition, food intake, and energy expenditures of the mice were measured. Their fasting and post-prandial glucose and insulin levels were also measured. RNA-seq was performed on RNA from the hypothalamus and subcutaneous white adipose tissue. Pathway analysis was conducted using IPA.

Results

Only the post-prandial insulin and fasting glucose levels followed the CIM's predictions. The lipolysis and leptin signaling pathways in the sWAT were inhibited in relation to the elevated fasting insulin, supporting the CIM's predicted impact of high insulin. However, because higher fasting insulin was unrelated to carbohydrate intake, the overall pattern did not support the model. Moreover, the hypothalamic hunger pathways were inhibited in relation to the increased fasting insulin, and the energy intake was not increased. The browning pathway in the sWAT was inhibited at higher insulin levels, but the daily energy expenditure was not altered.

Conclusions

Two of the predictions were partially supported (and hence also partially not supported) and the other three predictions were not supported. We conclude that the CIM does not explain the impact of dietary macronutrients on adiposity in mice.

•

Higher fasting insulin related to inhibited lipolysis and leptin pathways in sWAT, supporting CIM.

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Higher fasting insulin related to inhibited hypothalamic hunger pathway, contrasting CIM.

•

Fasting insulin decreased with higher dietary carbohydrate, overall contrasting CIM.

•

Higher dietary carbohydrate did not lead to greater EI/adiposity, or lowered EE.

Optimizing Postprandial Glucose Management in Adults With Insulin-Requiring Diabetes: Report and Recommendations

Faster-acting insulins, new noninsulin drug classes, more flexible insulin-delivery systems, and improved continuous glucose monitoring devices offer unprecedented opportunities to improve postprandial glucose (PPG) management and overall care for adults with insulin-treated diabetes. These developments led the Endocrine Society to convene a working panel of diabetes experts in December 2018 to assess the current state of PPG management, identify innovative ways to improve self-management and quality of life, and align best practices to current and emerging treatment and monitoring options. Drawing on current research and collective clinical experience, we considered the following issues for the ∼200 million adults worldwide with type 1 and insulin-requiring type 2 diabetes: (i) the role of PPG management in reducing the risk of diabetes complications; (ii) barriers preventing effective PPG management; (iii) strategies to reduce PPG excursions and improve patient quality of life; and (iv) education and clinical tools to support endocrinologists in improving PPG management. We concluded that managing PPG to minimize or prevent diabetes-related complications will require elucidating fundamental questions about optimal ways to quantify and clinically assess the metabolic dysregulation and consequences of the abnormal postprandial state in diabetes and recommend research strategies to address these questions. We also identified practical strategies and tools that are already available to reduce barriers to effective PPG management, optimize use of new and emerging clinical tools, and improve patient self-management and quality of life.

Postprandial glucose response after the consumption of three mixed meals based on the carbohydrate counting method in adults with type 1 diabetes. A randomized crossover trial

BACKGROUND & AIMS

People on intensive insulin therapy usually calculate their premeal insulin dose based on the total amount of consumed carbohydrates. However, arguments have been expressed supporting that also the protein and fat content of the meals should be considered when estimating premeal insulin dose. We examined the effectiveness of the carbohydrate counting method after consumption of mixed meals, and we further explored the effects of added extra virgin olive oil in these mixed meals, in adults with type 1 diabetes.

METHODS

Twenty adults (35.0 ± 8.9 years, BMI 27 ± 5 kg/m²) with diabetes duration 17 ± 11 years, on intensive insulin therapy with multiple injections, consumed 3 mixed meals (pasticcio, chicken with vegetables and baked giant beans), with and without the addition of 11 ml extra virgin olive oil (total of 6 meals), in random order, with the insulin dose determined by using the carbohydrate counting method. Capillary blood glucose was measured at premeal (baseline) and 30, 60, 90, 120, 150 and 180 min after meal consumption. At every visit, participants were assessed for anthropometric parameters and subjective stress.

RESULTS

Participants had mean HbA1c 7.5 ± 1.2%, mean carbohydrate to insulin ratio 9:1 IU and stable body weight, waist circumference and subjective stress throughout the study. The mean glucose concentration, for all 6 meals, 120 min postprandially was within target (<180 mg/dl) in nearly 80% of the sample. Addition of olive oil produced sustained increased postprandial glucose concentrations only to pasticcio meal, although within target, and no significant differences were noticed for the grilled chicken with vegetables or the baked giant beans (legume) meals.

CONCLUSIONS

The carbohydrate-counting method was effective for achieving postprandial glucose levels within target threshold up to 3 h postprandially. Moreover, adding small amounts of dietary fat (extra virgin olive oil) to low fat meals does not significantly alter the postprandial response within the first 3 h, whereas caused a sustained increase in postprandial blood glucose concentrations to the high energy density meal (i.e. the pasticcio meal).

Insulin in Type 1 and Type 2 Diabetes-Should the Dose of Insulin Before a Meal be Based on Glycemia or Meal Content?

The aim of this review was to investigate existing guidelines and scientific evidence on determining insulin dosage in people with type 1 and type 2 diabetes, and in particular to check whether the prandial insulin dose should be calculated based on glycemia or the meal composition, including the carbohydrates, protein and fat content in a meal. By exploring the effect of the meal composition on postprandial glycemia we demonstrated that several factors may influence the increase in glycemia after the meal, which creates significant practical difficulties in determining the appropriate prandial insulin dose. Then we reviewed effects of the existing insulin therapy regimens on glycemic control. We demonstrated that in most existing algorithms aimed at calculating prandial insulin doses in type 1 diabetes only carbohydrates are counted, whereas in type 2 diabetes the meal content is often not taken into consideration. We conclude that prandial insulin doses in treatment of people with diabetes should take into account the pre-meal glycemia as well as the size and composition of meals. However, there are still open questions regarding the optimal way to adjust a prandial insulin dose to a meal and the possible benefits for people with type 1 and type 2 diabetes if particular parameters of the meal are taken into account while calculating the prandial insulin dose. The answers to these questions may vary depending on the type of diabetes.

5. Lifestyle Management: Standards of Medical Care in Diabetes-2019

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes 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, please refer to the Standards of Care Introduction Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

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.

The effect of additional mealtime insulin bolus using an insulin-to-protein ratio compared to usual carbohydrate counting on postprandial glucose in those with type 1 diabetes who usually follow a carbohydrate-restricted diet: A randomized cross-over trial

This randomized controlled cross-over study compared postprandial glucose concentrations and incidence of hypoglycaemia for mealtime bolus insulin calculated for both meal protein and carbohydrate content, with ordinary dosing for carbohydrate content alone, in adults with type 1 diabetes who usually follow a carbohydrate-restricted diet. All 16 participants completed three test meals under each of the two conditions. The primary outcome was the time normalized Area Under the Curve (AUC) of glucose measurements. The mean (SD) AUC glucose concentration for insulin dosing for both protein and carbohydrate was 8.3 (2.1) mmol/L compared with 10.0 (2.2) mmol/L for carbohydrate alone. The difference (95% CI) was -1.76 mmol/L (-2.87 to -0.65), P = .003. The mean (SD) glucose concentration ≥ 8.0 mmol/L was 54.8 (32.4)% for dosing for protein and carbohydrate and 73.7 (26.3)% for carbohydrate alone, rate ratio (95% CI) 0.75 (0.62 to 0.89), P = .002. For glucose concentration < 4.0 mmol/L 5.5 (15.1)% and 2.8 (11.7)%; rate ratio (95% CI): 1.97 (0.90 to 4.27), P = .087. Calculating the meal insulin requirements based on the carbohydrate and protein content may have advantages over calculations based on carbohydrate alone. Further studies are required to determine how to best optimize this.