Dietary fat acutely increases glucose concentrations and insulin requirements in patients with type 1 diabetes: implications for carbohydrate-based bolus dose calculation and intensive diabetes management

Personalized insulin dosing using reinforcement learning for high-fat meals and aerobic exercises in type 1 diabetes: a proof-of-concept trial

In type 1 diabetes, high-fat meals require more insulin to prevent hyperglycemia while meals followed by aerobic exercises require less insulin to prevent hypoglycemia, but the adjustments needed vary between individuals. We propose a decision support system with reinforcement learning to personalize insulin doses for high-fat meals and postprandial aerobic exercises. We test this system in a single-arm 16-week study in 15 adults on multiple daily injections therapy (NCT05041621). The primary objective of this study is to assess the feasibility of the novel learning algorithm. This study looks at glucose outcomes and patient reported outcomes. The postprandial incremental area under the glucose curve is improved from the baseline to the evaluation period for high-fat meals (378 ± 222 vs 38 ± 223 mmol/L/min, p = 0.03) and meals followed by exercises (-395 ± 192 vs 132 ± 181 mmol/L/min, p = 0.007). The postprandial time spent below 3.9 mmol/L is reduced after high-fat meals (5.3 ± 1.6 vs 1.8 ± 1.5%, p = 0.003) and meals followed by exercises (5.3 ± 1.2 vs 1.4 ± 1.1%, p = 0.003). Our study shows the feasibility of automatically personalizing insulin doses for high-fat meals and postprandial exercises. Randomized controlled trials are warranted.

Prediction of personalised postprandial glycaemic response in type 1 diabetes mellitus

Objectives

Patients with type 1 diabetes (T1D) face unique challenges in glycaemic control due to the complexity and uniqueness of the dietary structure in China, especially in terms of postprandial glycaemic response (PPGR). This study aimed to establish a personalized model for predicting PPGR in patients with T1D.

Materials and methods

Data provided by the First People's Hospital of Yunnan Province, 13 patients with T1D, were recruited and provided with an intervention for at least two weeks. All patients were asked to wear a continuous glucose monitoring (CGM) device under free-living conditions during the study period. To tackle the challenge of incomplete data from wearable devices for CGM measurements, the GAIN method was used in this paper to achieve a more rational interpolation process. In this study, patients' PPGRs were calculated, and a LightGBM prediction model was constructed based on a Bayesian hyperparameter optimisation algorithm and a random search algorithm, which integrated glucose measurement, insulin dose, dietary nutrient content, blood measurement and anthropometry as inputs.

Results

The experimental outcomes revealed that the PPGR prediction model presented in this paper demonstrated superior accuracy (R=0.63) compared to both the carbohydrate content only model (R=0.14) and the baseline model emulating the standard of care for insulin administration (R=0.43). In addition, the interpretation of the model using the SHAP method showed that blood glucose levels at meals and blood glucose trends 30 minutes before meals were the most important features of the model.

Conclusion

The proposed model offers a heightened precision in predicting PPGR in patients with T1D, so it can better guide the diet plan and insulin intake dose of patients with T1D.

Safety of User-Initiated Intensification of Insulin Delivery Using Cambridge Hybrid Closed-Loop Algorithm

OBJECTIVE

Many hybrid closed-loop (HCL) systems struggle to manage unusually high glucose levels as experienced with intercurrent illness or pre-menstrually. Manual correction boluses may be needed, increasing hypoglycemia risk with overcorrection. The Cambridge HCL system includes a user-initiated algorithm intensification mode ("Boost"), activation of which increases automated insulin delivery by approximately 35%, while remaining glucose-responsive. In this analysis, we assessed the safety of "Boost" mode.

METHODS

We retrospectively analyzed data from closed-loop studies involving young children (1-7 years, n = 24), children and adolescents (10-17 years, n = 19), adults (≥24 years, n = 13), and older adults (≥60 years, n = 20) with type 1 diabetes. Outcomes were calculated per participant for days with ≥30 minutes of "Boost" use versus days with no "Boost" use. Participants with <10 "Boost" days were excluded. The main outcome was time spent in hypoglycemia <70 and <54 mg/dL.

RESULTS

Eight weeks of data for 76 participants were analyzed. There was no difference in time spent <70 and <54 mg/dL between "Boost" days and "non-Boost" days; mean difference: -0.10% (95% confidence interval [CI] -0.28 to 0.07; P = .249) time <70 mg/dL, and 0.03 (-0.04 to 0.09; P = .416) time < 54 mg/dL. Time in significant hyperglycemia >300 mg/dL was 1.39 percentage points (1.01 to 1.77; P < .001) higher on "Boost" days, with higher mean glucose and lower time in target range (P < .001).

CONCLUSIONS

Use of an algorithm intensification mode in HCL therapy is safe across all age groups with type 1 diabetes. The higher time in hyperglycemia observed on "Boost" days suggests that users are more likely to use algorithm intensification on days with extreme hyperglycemic excursions.

Understanding the Impact of Different Doses of Reducose® Mulberry Leaf Extract on Blood Glucose and Insulin Responses after Eating a Complex Meal: Results from a Double-Blind, Randomised, Crossover Trial

Non-communicable diseases (NCDs) are becoming an increasingly important health concern due to a rapidly ageing global population. The fastest growing NCD, type 2 diabetes mellitus (T2DM), is responsible for over 2 million deaths annually. Lifestyle changes, including dietary changes to low glycemic response (GR) foods, have been shown to reduce the risk of developing T2DM. The aim of this study was to investigate whether three different doses of Reducose®, a mulberry leaf extract, could lower the GR and insulinemic responses (IR) to a full meal challenge in healthy individuals. A double-blind, randomised, placebo-controlled, repeat-measure, crossover design trial was conducted by the Oxford Brookes Centre for Nutrition and Health; 37 healthy individuals completed the study. Participants consumed capsules containing either 200 mg, 225 mg, 250 mg Reducose® or placebo before a test meal consisting of 150 g white bread and egg mayo filler. Capillary blood samples were collected at 15-min intervals in the first hour and at 30-min intervals over the second and third hours to determine glucose and plasma insulin levels. The consumption of all three doses of Reducose® resulted in significantly lower blood glucose and plasma insulin levels compared to placebo. All three doses of Reducose® (200 mg, 225 mg, 250 mg) significantly lowered glucose iAUC 120 by 30% (p = 0.003), 33% (p = 0.001) and 32% (p = 0.002), respectively, compared with placebo. All three doses of Reducose® (200 mg, 225 mg, 250 mg) significantly lowered the plasma insulin iAUC 120 by 31% (p = 0.024), 34% (p = 0.004) and 38% (p < 0.001), respectively. The study demonstrates that the recommended dose (250 mg) and two lower doses (200 mg, 225 mg) of Reducose® can be used to help lower the GR and IR of a full meal containing carbohydrates, fats and proteins.

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).

Effect of a Dietary Intervention on Insulin Requirements and Glycemic Control in Type 1 Diabetes: A 12-Week Randomized Clinical Trial

This study compared the effects of a low-fat vegan diet to those of a portion-controlled diet in people with type 1 diabetes. Over 12 weeks, the average total daily dose of insulin decreased significantly and insulin sensitivity increased significantly in the vegan group, while no significant changes were observed in the group receiving the portion-controlled diet. Total and LDL cholesterol decreased in the vegan group, as did the ratio of blood urea nitrogen to creatinine. A1C decreased in both groups. These findings suggest that a low-fat vegan diet may yield improvements in insulin sensitivity, insulin requirements, glycemic control, and markers of cardiovascular and renal health compared with a portion-controlled diet in people with type 1 diabetes.

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.

Postprandial Glucose Variability Following Typical Meals in Youth Living with Type 1 Diabetes

We explored the association between macronutrient intake and postprandial glucose variability in a large sample of youth living with T1D and consuming free-living meals. In the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study, youth took photographs before and after their meals on 3 days during a 10 day observation period. We used the remote food photograph method to obtain the macronutrient content of youth's meals. We also collected physical activity, continuous glucose monitoring, and insulin use data. We measured glycemic variability using standard deviation (SD) and coefficient of variation (CV) of glucose for up to 3 h after meals. Our sample included 208 youth with T1D (mean age: 14 ± 2 years, mean HbA1c: 54 ± 14.2 mmol/mol [7.1 ± 1.3%]; 40% female). We observed greater postprandial glycemic variability (SD and CV) following meals with more carbohydrates. In contrast, we observed less postprandial variability following meals with more fat (SD and CV) and protein (SD only) after adjusting for carbohydrates. Insulin modality, exercise after meals, and exercise intensity did not influence associations between macronutrients and postprandial glycemic variability. To reduce postprandial glycemic variability in youth with T1D, clinicians should encourage diversified macronutrient meal content, with a goal to approximate dietary guidelines for suggested carbohydrate intake.

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.

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.

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.

Interactions between Vitamin D Genetic Risk and Dietary Factors on Metabolic Disease-Related Outcomes in Ghanaian Adults

The Ghanaian population is experiencing an upsurge in obesity and type 2 diabetes (T2D) due to rapid urbanization. Besides dietary factors, vitamin D-related genetic determinants have also been shown to contribute to the development of obesity and T2D. Hence, we aimed to examine the interactions between dietary factors and vitamin D-related genetic variants on obesity and T2D related outcomes in a Ghanaian population. Three hundred and two healthy Ghanaian adults (25-60 years old) from Oforikrom, Municipality in Kumasi, Ghana were randomly recruited and had genetic tests, dietary consumption analysis, and anthropometric and biochemical measurements of glucose, HbA1c, insulin, cholesterol, and triglycerides taken. A significant interaction was identified between vitamin D-GRS and fiber intake (g/day) on BMI (pinteraction = 0.020) where those who were consuming low fiber (≤16.19 g/d) and carrying more than two risk alleles for vitamin D deficiency (p = 0.01) had a significantly higher BMI. In addition, an interaction between vitamin D-GRS and fat intake (g/day) on HbA1c (total fat, pinteraction = 0.029) was found, where participants who had a lower total fat intake (≤36.5 g/d), despite carrying more than two risk alleles, had significantly lower HbA1c (p = 0.049). In summary, our study has identified novel gene-diet interactions of vitamin D-GRS with dietary fiber and fat intakes on metabolic traits in Ghanaian adults.

Decreased Need for Correction Boluses with Universal Utilisation of Dual-Wave Boluses in Children with Type 1 Diabetes

Insulin pumps offer standard (SB), square and dual-wave boluses (DWB). Few recommendations exist on how to use these dosing options. Several studies suggest that the DWB is more effective for high-fat or high-carbohydrate meals. Our objective was to test whether time in range (TIR) improves in children with type 1 diabetes (T1D) using the universal utilization of the dual-wave boluses for all evening meals regardless of the composition of the meal. This was a 28-day long prospective randomized open-label single-center crossover study. Twenty-eight children with T1DM using a Medtronic 640G pump and continuous glucose monitoring system were randomly assigned to receive either DWB or SB for all meals starting from 6:00 p.m. based solely on the food carbohydrate count. DWB was set for 50/50% with the second part extended over 2 h. After two weeks patients switched into the alternative treatment arm. TIR (3.9−10 mmol/L), time below range (TBR) (<3.9 mmol/L) and time above range (TAR) (>10 mmol/L) and sensor glucose values were measured and compared between the groups. Twenty-four children aged 7−14 years completed the study according to the study protocol. There were no statistically significant differences in mean TIR (60.9% vs. 58.8%; p = 0.3), TBR (1.6% vs. 1.7%; p = 0.7) or TAR (37.5 vs. 39%; p = 0.5) between DWB and SB groups, respectively. Subjects in the DWB treatment arm administered significantly less correction boluses between 6 p.m. and 6 a.m. compared to those in the SB group (1.2 ± 0.8 vs. 1.7 ± 0.8, respectively; p < 0.01). DWB for evening meals in which insulin is calculated solely on the food carbohydrate content did not improve TIR compared to standard bolus in children with T1D. However, DWB enabled to use significantly less correction boluses to achieve euglycemia by the morning compared to the SB.

Significance of Postprandial Insulin and Triglycerides to Evaluate the Metabolic Response of Composite Meals Differing in Nutrient Composition – A Randomized Cross-Over Trial

Background and aims

GlucoTRIG, based on postprandial plasma insulin and triglyceride concentrations, has been recently developed as a novel index to determine the postprandial metabolic response to the meals. This study aimed to test GlucoTRIG as a measure for ranking composite meals for their metabolic effects.

Methods

In a randomized cross-over trial, healthy adult volunteers (both males and females; n = 10 for each meal) consumed three is caloric (2000 kj) test meals (meal 1, meal 2, meal 3) of varying macronutrient composition. Postmeal consumption, venous blood samples were collected to determine plasma insulin and plasma triglycerides for estimating the GlucoTRIG value using (Triglycerides_180min × Insulin_180min) - (Triglycerides_0min × Insulin_0min).

Results

The GlucoTRIG values differed significantly (p = 0.0085) across meals. The statistical significance remains even after adjusting for confounding variables such as baseline diet, insulin, and triglycerides. The meal (M3) with a high fiber, low total fat content and containing less refined foods (fruits, beans, vegetables, plain yogurt) exhibited a significantly (p = 0.007) lower GlucoTRIG value (10 ± 7.7) compared to the other two meals, M1 (77 ± 19.8) and M2 (38 ± 12.1) which contained low processed foods, and were relatively high in fat and low in fiber meals. No statistically significant differences were observed between M1 and M2 meal.

Conclusions

GlucoTRIG is a physiologically based index that may be useful to rank composite meals for reducing the risk of metabolic diseases. Further research focusing on the application of GlucoTRIG to foods, meals, and diets is warranted.

ACTRN12619000973112 (Australian New Zealand Clinical Trials Registry, ANZCTR).

Prediction of Personal Glycemic Responses to Food for Individuals With Type 1 Diabetes Through Integration of Clinical and Microbial Data

OBJECTIVE

Despite technological advances, results from various clinical trials have repeatedly shown that many individuals with type 1 diabetes (T1D) do not achieve their glycemic goals. One of the major challenges in disease management is the administration of an accurate amount of insulin for each meal that will match the expected postprandial glycemic response (PPGR). The objective of this study was to develop a prediction model for PPGR in individuals with T1D.

RESEARCH DESIGN AND METHODS

We recruited individuals with T1D who were using continuous glucose monitoring and continuous subcutaneous insulin infusion devices simultaneously to a prospective cohort and profiled them for 2 weeks. Participants were asked to report real-time dietary intake using a designated mobile app. We measured their PPGRs and devised machine learning algorithms for PPGR prediction, which integrate glucose measurements, insulin dosages, dietary habits, blood parameters, anthropometrics, exercise, and gut microbiota. Data of the PPGR of 900 healthy individuals to 41,371 meals were also integrated into the model. The performance of the models was evaluated with 10-fold cross validation.

RESULTS

A total of 121 individuals with T1D, 75 adults and 46 children, were included in the study. PPGR to 6,377 meals was measured. Our PPGR prediction model substantially outperforms a baseline model with emulation of standard of care (correlation of R = 0.59 compared with R = 0.40 for predicted and observed PPGR respectively; P < 10-10). The model was robust across different subpopulations. Feature attribution analysis revealed that glucose levels at meal initiation, glucose trend 30 min prior to meal, meal carbohydrate content, and meal's carbohydrate-to-fat ratio were the most influential features for the model.

CONCLUSIONS

Our model enables a more accurate prediction of PPGR and therefore may allow a better adjustment of the required insulin dosage for meals. It can be further implemented in closed loop systems and may lead to rationally designed nutritional interventions personally tailored for individuals with T1D on the basis of meals with expected low glycemic response.

The effect of a high fat meal on heart rate variability and arterial stiffness in adolescents with or without type 1 diabetes

AIM

Type 1 diabetes (T1D) is associated with increased arterial stiffness and cardiac autonomic neuropathy. We tested whether those variables are acutely affected by a high fat meal (HFM).

METHODS

Responses to a HFM were measured in adolescents with T1D (N = 14) or without T1D (Control, N = 21). Heart rate variability (HRV), arterial stiffness, blood pressure (BP), and energy expenditure (EE) were measured before (baseline) and four times over 180 min postprandially.

RESULTS

T1D had higher blood glucose and insulin, but the suppression of fatty acids (~40%) and rise in triglycerides (~60%) were similar between groups. T1D had 9% higher EE, but postprandial increase in EE was similar to Controls. T1D had ~7 to 24% lower baseline HRV but a similar postprandial decline of ~8 to 25% as Controls. Both groups had a similar 2 to 5% increase in BP after the meal. Rate pressure product increased postprandially in both groups and was higher in T1D. Pulsewave velocity and augmentation index did not differ between groups or change postprandially.

CONCLUSION

Adolescents with T1D have evidence of cardiac autonomic dysfunction and increased EE, but those variables, along with arterial stiffness, are not acutely made worse by a HFM.

Dietary determinants of postprandial blood glucose control in adults with type 1 diabetes on a hybrid closed-loop system

AIMS/HYPOTHESIS

The aim of this work was to assess the relationship between meal nutrients and postprandial blood glucose response (PGR) in individuals with type 1 diabetes on a hybrid closed-loop system (HCLS).

METHODS

The dietary composition of 1264 meals (398 breakfasts, 441 lunches and 425 dinners) was assessed by 7-day food records completed by 25 individuals with type 1 diabetes on HCLSs (12 men/13 women, mean ± SD age 40 ± 12 years, mean ± SD HbA_1c 51 ± 10 mmol/mol [6.9 ± 0.2%]). For each meal, PGR (continuous glucose monitoring metrics, glucose incremental AUCs) and insulin doses (pre-meal boluses, post-meal microboluses automatically delivered by the pump and adjustment boluses) over 6 h were evaluated.

RESULTS

Breakfast, lunch and dinner significantly differed with respect to energy and nutrient intake and insulin doses. The blood glucose postprandial profile showed an earlier peak after breakfast and a slow increase until 4 h after lunch and dinner (p < 0.001). Mean ± SD postprandial time in range (TIR) was better at breakfast (79.3 ± 22.2%) than at lunch (71.3 ± 23.9%) or dinner (70.0 ± 25.9%) (p < 0.001). Significant negative predictors of TIR at breakfast were total energy intake, per cent intake of total protein and monounsaturated fatty acids, glycaemic load and absolute amounts of cholesterol, carbohydrates and simple sugars consumed (p < 0.05 for all). No significant predictors were detected for TIR at lunch. For TIR at dinner, a significant positive predictor was the per cent intake of plant proteins, while negative predictors were glycaemic load and intake amounts of simple sugars and carbohydrate (p < 0.05 for all).

CONCLUSIONS/INTERPRETATION

This study shows that nutritional factors other than the amount of carbohydrate significantly influence postprandial blood glucose control. These nutritional determinants vary between breakfast, lunch and dinner, with differing effects on postprandial blood glucose profile and insulin requirements, thus remaining a challenge to HCLSs.

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.

Substantial Intra-Individual Variability in Post-Prandial Time to Peak in Controlled and Free-Living Conditions in Children with Type 1 Diabetes

The optimal time to bolus insulin for meals is challenging for children and adolescents with type 1 diabetes (T1D). Current guidelines to control glucose excursions do not account for individual differences in glycaemic responses to meals. This study aimed to examine the within- and between-person variability in time to peak (TTP) glycaemic responses after consuming meals under controlled and free-living conditions. Participants aged 8–15 years with T1D ≥ 1 year and using a continuous glucose monitor (CGM) were recruited. Participants consumed a standardised breakfast for six controlled days and maintained their usual daily routine for 14 free-living days. CGM traces were collected after eating. Linear mixed models were used to identify within- and between-person variability in the TTP after each of the controlled breakfasts, free-living breakfasts (FLB), and free-living dinners (FLD) conditions. Thirty participants completed the study (16 females; mean age and standard deviation (SD) 10.5 (1.9)). The TTP variability was greater within a person than the variability between people for all three meal types (between-person vs. within-person SD; controlled breakfast 18.5 vs. 38.9 min; FLB 14.1 vs. 49.6 min; FLD 5.7 vs. 64.5 min). For the first time, the study showed that within-person variability in TTP glycaemic responses is even greater than between-person variability.

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

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

INTRODUCTION

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

RESEARCH DESIGN AND METHODS

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

RESULTS

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

CONCLUSIONS

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

TRIAL REGISTRATION NUMBER

NCT03320993.

Carbohydrate restriction for diabetes: rediscovering centuries-old wisdom

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

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

CONTEXT

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

Perspective: Plant-Based Eating Pattern for Type 2 Diabetes Prevention and Treatment: Efficacy, Mechanisms, and Practical Considerations

A plant-based eating pattern is associated with a reduced risk of developing type 2 diabetes and is highly effective in its treatment. Diets that emphasize whole grains, vegetables, fruits, and legumes and exclude animal products improve blood glucose concentrations, body weight, plasma lipid concentrations, and blood pressure and play an important role in reducing the risk of cardiovascular and microvascular complications. This article reviews scientific evidence on the effects of plant-based diets for the prevention and treatment of type 2 diabetes. The mechanisms by which plant-based diets improve body weight, insulin sensitivity, and β-cell function are described. Practical considerations including education, nutrition adequacy, and adjusting medications will enhance the success of patients who have diabetes.

The Ambulatory Glucose Profile: Opportunities for Enhancement

The ambulatory glucose profile (AGP) and the frequency distribution for glucose by ranges are well established as standard methods for display, analysis, and interpretation of glucose data arising from self-monitoring, continuous glucose monitoring, and automated insulin delivery systems. In this review, we consider several refinements that may further improve the utility of the AGP. These include (1) display of the AGP together with information regarding dietary intake, medication administration (e.g., insulin), glucose lowering (pharmacodynamic) activity of medications, and physical activity measured by accelerometers or heart rate; (2) display of average time below range (%TBR), time above range (%TAR), and time in range (%TIR) by time of day to indicate timing of hypoglycemic and hyperglycemic episodes; (3) detailed analysis of postprandial excursions for each of the major meals after synchronizing by onset of meals and adjusting for the premeal glucose levels, enabling comparisons of magnitude, shape, and patterns; (4) methods to characterize distinct patterns on different days of the week; (5) display of glucose on a nonlinear scale to improve the balance between deviations associated with hypoglycemia versus hyperglycemia; (6) use of time scales other than midnight-to-midnight to facilitate analysis of time segments of particular interest (e.g., overnight); (7) options to display individual glucose values to assist in the identification of dates and times of outliers and episodes of hypoglycemia and hyperglycemia; and (8) methods to compare AGPs obtained from different individuals or groups receiving alternative interventions in terms of therapy or technology. These refinements, individually or collectively, can potentially further enhance the effectiveness of the AGP for assessment of glucose levels, patterns, and variability. We discuss several questions regarding implementation and optimization of these methods.

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.

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.

Beyond protein intake: does dietary fat intake in the year preceding pregnancy and during pregnancy have an impact on gestational diabetes mellitus?

Purpose

Studies regarding the association between dietary fat intake and gestational diabetes mellitus (GDM) are limited and provide conflicting findings. Thus, the study aims to examine the association of dietary fat intake in the year preceding pregnancy and during pregnancy with the risk of GDM, taking the relevance of dietary protein intake on GDM into consideration.

Methods

A prospective study was conducted in 6299 singleton pregnancies, using the data from the Nutrition in Pregnancy and Growth in Southwest China (NPGSC). A validated food frequency questionnaire was used to assess dietary fat intake in the year preceding pregnancy and during the first and second trimesters of pregnancy. Logistic regression analysis was used to assess the prospective associations of dietary fat intake and the type and source of dietary fats in different time windows with GDM risk.

Results

Higher intake of total fat [OR (95% CI): 2.21 (1.19–4.20), P = 0.02] during 12–22 weeks of gestation was associated with higher GDM risk. However, adjustment for animal protein intake greatly attenuated this association [OR (95% CI): 1.81 (0.93, 3.64), P = 0.11]. Total fat intake neither in the year preceding pregnancy nor during the early pregnancy was associated with GDM risk. Moreover, insignificant associations were observed between intakes of vegetable fat, animal fat, cholesterol, saturated fatty acid, monounsaturated fatty acid and polyunsaturated fatty acid one year before pregnancy and during the first and second trimesters and GDM risk.

Conclusion

Our study indicated that dietary fat intake one year before pregnancy and across the two pregnancy trimesters preceding the diagnosis of GDM has no relevance on GDM risk among Chinese women, particularly those with normal BMI, low, or normal calorie intake.

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.

The Association Between Glycemic Variability and Macronutrients in Young Children with T1D

OBJECTIVE

There is limited information regarding the potential effect macronutrients have on postprandial glycemic variability in young children with type 1 diabetes (T1D). To date, studies examining nutrition and glycemic outcomes either assess these factors at a single timepoint, or aggregate large datasets for group level analyses. This study examined how inter- and intraindividual fluctuations in carbohydrate, fat, and protein intake impact glycemic variability in the postprandial period for young children with T1D.

METHODS

Thirty-nine young children, aged 2-6 years, wore a continuous glucose monitor for 72 hr, while their parents completed detailed diet records of all food intake. The analyses tested three multilevel models to examine intra- and interindividual differences between food intake and postprandial glycemic variability.

RESULTS

The results suggest carbohydrate intake, relates to greater postprandial glycemic variability. In contrast, the results reveal the inverse effect for protein, suggesting a tendency for young children who ate more protein at some meals to have lower postprandial glycemic variability, with the exception of lunch. There was no effect for fat on postprandial glycemic variability.

CONCLUSION

These results suggest protein consumption may be an important consideration when aiming for optimal glycemic levels for some meals. When counseling parents of young children with T1D on common behaviors underlying glycemic excursion, pediatric psychologists may consider discussing the nutritional make up of children's meals. Further, the results demonstrate retaining longitudinal data at the person level, versus aggregating individual data for group level analyses, may offer new information regarding macronutrient intake and glycemic outcomes.

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.

Effective management of type 1 diabetes in children and young people

Type 1 diabetes is the most common type of diabetes among children and young people, and requires careful management to ensure that blood glucose levels stay as close as possible to the target range. Suboptimal management can lead to serious health consequences, including damage to various organs and body systems. Many children with type 1 diabetes are not diagnosed until they develop diabetic ketoacidosis, which is distressing and potentially life-threatening. This article provides an overview of the management of type 1 diabetes in children and young people, including the insulin replacement therapy and dietary management required. It also emphasises the importance of regular and ongoing monitoring of blood glucose levels, quarterly measurement of glycated haemoglobin, and the management of hyperglycaemia and hypoglycaemia.

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).

Nutrition and Exercise Performance in Adults With Type 1 Diabetes

The best nutritional practices for exercise and sports performance are largely activity specific. The presence of type 1 diabetes undeniably bestows additional factors to consider to manage exercise and ensure adequate nutrients and fuels are available for optimal performance. Whether participating in sports or physical activity on a recreational basis or striving to achieve a high level of athletic performance, individuals with type 1 diabetes must pay attention to their nutritional and dietary patterns, including intake of macronutrients, micronutrients, fluids and supplements, such as caffeine to maintain metabolic and glycemic balance. Performance aside, nutritional recommendations may also differ on an individual basis relative to exercise, glycemic management and body weight goals. Balancing all these dietary factors can be challenging for individuals with type 1 diabetes, and many related aspects have yet to be fully researched in this population.

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.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

GlucoTRIG: a novel tool to determine the nutritional quality of foods and meals in general population

BACKGROUND

This study aimed to develop a novel criterion, GlucoTRIG, to rank meals for healthiness, that considers both glycaemic (serum insulin) and lipaemic (serum triglycerides) responses.

METHODS

Healthy volunteers (n = 10) were recruited with the aim of deriving a standard GlucoTRIG value for a reference meal. Volunteers consumed the reference meal (2 regular slices of wholemeal bread; 250 mL chocolate flavoured milk; 7 g butter and 11 g peanut butter) comprising of carbohydrate, fat and protein (41, 40 and 16% energy respectively) on three different occasions with a minimum washout period of 3 days. The GlucoTRIG value was determined as the difference between the product of insulin and triglyceride obtained from venous blood samples at baseline and the product of insulin and triglyceride at 180 min.

RESULTS

There were no significant differences in the participants' dietary intakes and their metabolic parameters between three visits (P > 0.005). The GlucoTRIG value obtained from three mean values of the reference meal was found to be 19 ± 3.5. There were no significant (P = 0.2303) differences observed between the GlucoTRIG values for the three visits.

CONCLUSION

GlucoTRIG, consisting of both glycaemic and lipaemic responses, may be a physiologically relevant tool to rank foods and meals for reducing the risk of metabolic diseases.

TRIAL REGISTRATION

ACTRN12619000973112.

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.