Estimating insulin demand for protein-containing foods using the food insulin index

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).

The Application of the Food Insulin Index in the Prevention and Management of Insulin Resistance and Diabetes: A Scoping Review

The food insulin index (FII) is a novel algorithm used to determine insulin responses of carbohydrates, proteins, and fats. This scoping review aimed to provide an overview of all scientifically relevant information presented on the application of the FII in the prevention and management of insulin resistance and diabetes. The Arksey and O'Malley framework and the PRISMA Extension for Scoping Reviews 22-item checklist were used to ensure that all areas were covered in the scoping review. Our search identified 394 articles, of which 25 articles were included. Three main themes emerged from the included articles: 1. the association of FII with the development of metabolic syndrome, insulin resistance, and diabetes, 2. the comparison of FII with carbohydrate counting (CC) for the prediction of postprandial insulin response, and 3. the effect of metabolic status on the FII. Studies indicated that the FII can predict postprandial insulin response more accurately than CC, and that a high DII and DIL diet is associated with the development of metabolic syndrome, insulin resistance, and diabetes. The FII could be a valuable tool to use in the prevention and management of T1DM, insulin resistance, and T2DM, but more research is needed in this field.

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.

Efficacy of insulin dosing algorithms for high-fat high-protein mixed meals to control postprandial glycemic excursions in people living with type 1 diabetes: A systematic review and meta-analysis

Optimizing postprandial blood glucose (PPG) levels after mixed meals that contain high fat and protein remains a challenge in the treatment of type 1 diabetes. This study evaluated the efficacy of different algorithms used for dosing insulin based on counting units of high fat and high protein (HFHP) meals with the current conventional method of counting carbohydrates alone to control PPG excursions. The MEDLINE, EMBASE, and Cochrane electronic databases were searched, with the analysis restricted to randomized control trials (RCTs). The primary outcome was the PPG (mean and standard deviation) at 240 min. The pooled final estimate was the mean difference (MD) of the PPGs at 240 min using random effect models to account for heterogeneity. In total, 15 studies were identified and included in the systemic review, of which 12 were RCTs, and three studies were non-randomized trials. The pooled MD of the PPG at 240 min was in favor of additional insulin doses in HFHP meals compared to the carbohydrate counting alone. The statistically significant results favored the combined bolus (30:70) that split over 2 h in insulin pump therapy with pooled MD of the PPG, 240 min of -24.65; 95% CI: -36.59, -8.41; and heterogeneity, 0%. Other statistically significant results favored the additional insulin added to insulin to carb ratio (ICR) of meal bolus (25-60% ICR) in multiple daily injections therapy with the pooled MD of PPG at 240 min, -21.71; 95% CI: -38.45, -4.73; and heterogeneity, 18%. Insulin treatment based on fat and protein content, in addition to carbohydrate counting, is more effective than the carbohydrate counting method alone; however, further research is warranted to determine the best equation for fat and protein counting, particularly in people with multiple daily injections.

The association between dietary insulin index and load with mental health

Background

Depression, anxiety, and stress are common mental problems. The aim of this cross-sectional study was to investigate the association between two indexes that measure postprandial insulin response to different food, dietary insulin index (DII) and insulin load (DIL), with psychological disorders.

Method

Participants (n = 10,000) aged 20–69 were randomly selected from 200 clusters in Yazd from the recruitment phase of the Yazd Health Study. The dietary intake of participants was collected by a reliable and validated food frequency questionnaire (FFQ) consisting of 178 food items. DII and DIL were calculated from the FFQ data using previously published reference values. To assess psychological disorders an Iranian validated short version of a self-reported questionnaire (Depression Anxiety Stress Scales 21 [DASS21]) was used.

Results

No significant association was observed between DIL and DII with odds of depression or anxiety using crude or adjusted models. However, individuals in the highest quartiles of DIL had the lowest odds of stress (OR: 0.69; 95% CI 0.48–1.01, P-trend = 0.047). This association remained significant after adjustment for potential confounders in model II including marital status, smoking, education, job status, salt intake, and multi-vitamin supplement use (OR: 0.38; 95% CI 0.16–0.91, P-trend = 0.039) and the third and final model which is further adjusted for BMI (OR: 0.39; 95% CI 0.16–0.91, P-trend = 0.041).

Conclusion

Overall, consumption of foods with higher DII as well as DIL were associated with lower stress scores; however, no significant relationship was observed between DII or DIL with respective depression or anxiety scores.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40359-022-00925-2.

A Personalized Diet Approach Study: Interaction between PPAR-γ Pro12Ala and Dietary Insulin Indices on Metabolic Markers in Diabetic Patients

BACKGROUND

The objectives were to investigate the effect of the interaction between peroxisome proliferator-activated receptor gamma (PPAR-γ) Pro12Ala polymorphisms and dietary insulin load and insulin index (DIL and DII) on Cardio-metabolic Markers among diabetic patients.

METHODS

This cross-sectional study was conducted on 393 diabetic patients. Food-frequency questionnaire (FFQ) was used for DIL and DII calculation. PPAR-γ Pro12Ala was genotyped by the PCR-RFLP method. Biochemical markers including TC, LDL, HDL, TG, SOD, CRP, TAC, PTX3, PGF2α. IL18, leptin and ghrelin were measured by standard protocol.

RESULT

Risk-allele carriers (CG, GG) had higher obesity indices WC (P _interaction =0.04), BMI (P _interaction =0.006) and, WC (P _interaction =0.04) compared with individuals with the CC genotype when they consumed a diet with higher DIL and DII respectively. Besides, carriers of the G allele who were in the highest tertile of DIL, had lower HDL (P _interaction =0.04) and higher PGF2α (P _interaction =0.03) and PTX3 (P _interaction =0.03). Moreover, the highest tertile of the DII, showed an increase in IL18 (P _interaction =0.01) and lower SOD (P _interaction =0.03) for risk allele carriers compared to those with CC homozygotes.

CONCLUSION

We revealed PPAR-γ Pro12Ala polymorphism was able to intensify the effect of DIL and DII on CVD risk factors; risk-allele carriers who consumed a diet with high DIL and DII score have more likely to be obese and have higher inflammatory markers. Also, protective factor against CVD risk factors were reduced significantly in this group compared to CC homozygotes. This article is protected by copyright. All rights reserved.

Association of Dietary Glycemic Index, Glycemic Load, Insulin Index, and Insulin Load with Bacterial Vaginosis in Iranian Women: A Case-Control Study

Background

Inconsistent findings have been reported for associations between dietary indices and bacterial vaginosis (BV). The aim of this study was to examine the association of dietary glycemic index (DGI), glycemic load (DGL), insulin index (DII), and insulin load (DIL) with BV among Iranian women.

Methods

The current case-control study consisted of 144 new cases of BV and 151 controls. The diagnosis of BV was made based on the Amsel criterion in hospital clinics in Tehran, Iran, from November 2020 until June 2021. DGI, DGL, DII, and DIL were calculated from a validated semiquantitative food frequency questionnaire. The association between dietary carbohydrate indices and odds of BV were assessed adjusting for potential confounders through an estimation of two multivariate regression models.

Results

The multivariate adjusted odds ratio (OR) comparing the highest tertile of dietary DGI and DGL with the lower tertile was 2.99 (95% confidence interval (CI): 1.47–6.81; P_trend = 0.003) and 4.01 (95% CI: 1.22–5.91; P_trend = 0.029), respectively. In a fully adjusted model, the top tertile of dietary fiber compared to the bottom was associated with 88% (95% CI: 0.14-0.33) lower odds of BV (P_trend < 0.001). DII and DIL were not significantly associated with odds of BV in both crude and adjusted regression models.

Conclusion

The findings support the hypothesis of moderate, direct associations between DGI or DGL and BV. Also, a diet high in fiber decreases odds of BV.

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.

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.

Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches

Prediabetes affects one in three people and has a 10% annual conversion rate to type 2 diabetes without lifestyle or medical interventions. Management of glycemic health is essential to prevent progression to type 2 diabetes. However, there is currently no commercially-available and noninvasive method for monitoring glycemic health to aid in self-management of prediabetes. There is a critical need for innovative, practical strategies to improve monitoring and management of glycemic health. In this study, using a dataset of 25,000 simultaneous interstitial glucose and noninvasive wearable smartwatch measurements, we demonstrated the feasibility of using noninvasive and widely accessible methods, including smartwatches and food logs recorded over 10 days, to continuously detect personalized glucose deviations and to predict the exact interstitial glucose value in real time with up to 84% and 87% accuracy, respectively. We also establish methods for designing variables using data-driven and domain-driven methods from noninvasive wearables toward interstitial glucose prediction.

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.

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Capillary blood reference intervals for platelet parameters in healthy full-term neonates in China

Background

No consensus has been reached on capillary blood reference intervals for platelet parameters in full-term neonates. We aimed to establish neonatal capillary blood reference intervals for platelet parameters and evaluate influences of sex, gestational age and postnatal age on platelet parameters.

Methods

This study was a prospective investigation and implemented in 594 healthy full-term neonates from 12 to 84 h of age, using SYSMEX XN-9000 haematology automatic analyser by means of capillary blood. Reference intervals for platelet parameters were defined by an interval of 2.5th − 97.5th percentiles.

Results

Capillary reference interval for platelet count was (152–464) × 10⁹/L. No significance was found between sex-divided reference intervals for platelet parameters. The values of platelet count changed minimally across gestational age (37–41 weeks) and postnatal age (12–84 h). Reference intervals for other platelet parameters were affected by these factors to a different extent.

Conclusions

We established capillary blood reference intervals for platelet parameters in the first days after birth of full-term neonates in China.

New Insight into Diabetes Management: From Glycemic Index to Dietary Insulin Index

BACKGROUND

Despite efforts to control hyperglycemia, diabetes management is still challenging. This may be due to focusing on reducing hyperglycemia and neglecting the importance of hyperinsulinemia; while insulin resistance and resultant hyperinsulinemia preceded diabetes onset and may contribute to disease pathogenesis.

OBJECTIVE

The present narrative review attempts to provide a new insight into the management of diabetes by exploring different aspects of glycemic index and dietary insulin index.

RESULTS

The current data available on this topic is limited and heterogeneous. Conventional diet therapy for diabetes management is based on reducing postprandial glycemia through carbohydrate counting, choosing foods with low-glycemic index and low-glycemic load. Since these indicators are only reliant on the carbohydrate content of foods and do not consider the effects of protein and fat on the stimulation of insulin secretion, they cannot provide a comprehensive approach to determine the insulin requirements.

CONCLUSION

Selecting foods based on carbohydrate counting, glycemic index or glycemic load are common guides to control glycemia in diabetic patients, but neglect the insulin response, thus leading to failure in diabetes management. Therefore, paying attention to insulinemic response along with glycemic response seems to be more effective in managing diabetes.

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.

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).

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.

An additional bolus of rapid-acting insulin to normalise postprandial cardiovascular risk factors following a high-carbohydrate high-fat meal in patients with type 1 diabetes: A randomised controlled trial

AIM

To evaluate an additional rapid-acting insulin bolus on postprandial lipaemia, inflammation and pro-coagulation following high-carbohydrate high-fat feeding in people with type 1 diabetes.

METHODS

A total of 10 males with type 1 diabetes [HbA_1c 52.5 ± 5.9 mmol/mol (7.0% ± 0.5%)] underwent three conditions: (1) a low-fat (LF) meal with normal bolus insulin, (2), a high-fat (HF) meal with normal bolus insulin and (3) a high-fat meal with normal bolus insulin with an additional 30% insulin bolus administered 3-h post-meal (HFA). Meals had identical carbohydrate and protein content and bolus insulin dose determined by carbohydrate-counting. Blood was sampled periodically for 6-h post-meal and analysed for triglyceride, non-esterified-fatty acids, apolipoprotein B48, glucagon, tumour necrosis factor alpha, fibrinogen, human tissue factor activity and plasminogen activator inhibitor-1. Continuous glucose monitoring captured interstitial glucose responses.

RESULTS

Triglyceride concentrations following LF remained similar to baseline, whereas triglyceride levels following HF were significantly greater throughout the 6-h observation period. The additional insulin bolus (HFA) normalised triglyceride similarly to low fat 3-6 h following the meal. HF was associated with late postprandial elevations in tumour necrosis factor alpha, whereas LF and HFA was not. Fibrinogen, plasminogen activator inhibitor-1 and tissue factor pathway levels were similar between conditions.

CONCLUSION

Additional bolus insulin 3 h following a high-carbohydrate high-fat meal prevents late rises in postprandial triglycerides and tumour necrosis factor alpha, thus improving cardiovascular risk profile.

The β-cell burden index of food: A proposal

The quantity and quality of dietary fat and/or carbohydrate may alter one or more of the basic components of the insulin-glucose system, which in turn affect the pathways leading to alterations in glucose homeostasis and, possibly, to cardiovascular disease. This viewpoint article, reviewing some of the currently available tools aiming at quantifying the impact of dietary carbohydrates on the glucose-insulin homeostatic loop, highlights the unmet need of a more thorough assessment of the complex interaction between dietary factors and the glucose-insulin system. A novel index, the "β-cell burden index", may turn out to be a valuable tool to quantify the role played by the diet in shaping the risk of type 2 diabetes, cardiovascular disease and other metabolic and degenerative disorders, ideally orienting their prevention with strategies based on dietary modifications.

Optimized Mealtime Insulin Dosing for Fat and Protein in Type 1 Diabetes: Application of a Model-Based Approach to Derive Insulin Doses for Open-Loop Diabetes Management

OBJECTIVE

To determine insulin dose adjustments required for coverage of high-fat, high-protein (HFHP) meals in type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

Ten adults with T1D received low-fat, low-protein (LFLP) and HFHP meals with identical carbohydrate content, covered with identical insulin doses. On subsequent occasions, subjects repeated the HFHP meal with an adaptive model-predictive insulin bolus until target postprandial glycemic control was achieved.

RESULTS

With the same insulin dose, the HFHP increased the glucose incremental area under the curve over twofold (13,320 ± 2,960 vs. 27,092 ± 1,709 mg/dL ⋅ min; P = 0.0013). To achieve target glucose control following the HFHP, 65% more insulin was required (range 17%-124%) with a 30%/70% split over 2.4 h.

CONCLUSIONS

This study demonstrates that insulin dose calculations need to consider meal composition in addition to carbohydrate content and provides the foundation for new insulin-dosing algorithms to cover meals of varying macronutrient composition.

Algorithms to Improve the Prediction of Postprandial Insulinaemia in Response to Common Foods

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

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

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

Impact of fat, protein, and glycemic index on postprandial glucose control in type 1 diabetes: implications for intensive diabetes management in the continuous glucose monitoring era

BACKGROUND

Continuous glucose monitoring highlights the complexity of postprandial glucose patterns present in type 1 diabetes and points to the limitations of current approaches to mealtime insulin dosing based primarily on carbohydrate counting.

METHODS

A systematic review of all relevant biomedical databases, including MEDLINE, Embase, CINAHL, and the Cochrane Central Register of Controlled Trials, was conducted to identify research on the effects of dietary fat, protein, and glycemic index (GI) on acute postprandial glucose control in type 1 diabetes and prandial insulin dosing strategies for these dietary factors.

RESULTS

All studies examining the effect of fat (n = 7), protein (n = 7), and GI (n = 7) indicated that these dietary factors modify postprandial glycemia. Late postprandial hyperglycemia was the predominant effect of dietary fat; however, in some studies, glucose concentrations were reduced in the first 2-3 h, possibly due to delayed gastric emptying. Ten studies examining insulin bolus dose and delivery patterns required for high-fat and/or high-protein meals were identified. Because of methodological differences and limitations in experimental design, study findings were inconsistent regarding optimal bolus delivery pattern; however, the studies indicated that high-fat/protein meals require more insulin than lower-fat/protein meals with identical carbohydrate content.

CONCLUSIONS

These studies have important implications for clinical practice and patient education and point to the need for research focused on the development of new insulin dosing algorithms based on meal composition rather than on carbohydrate content alone.

Physical activity and type 1 diabetes: time for a rewire?

While being physically active bestows many health benefits on individuals with type 1 diabetes, their overall blood glucose control is not enhanced without an effective balance of insulin dosing and food intake to maintain euglycemia before, during, and after exercise of all types. At present, a number of technological advances are already available to insulin users who desire to be physically active with optimal blood glucose control, although a number of limitations to those devices remain. In addition to continued improvements to existing technologies and introduction of new ones, finding ways to integrate all of the available data to optimize blood glucose control and performance during and following exercise will likely involve development of "smart" calculators, enhanced closed-loop systems that are able to use additional inputs and learn, and social aspects that allow devices to meet the needs of the users.

The impact of a pure protein load on the glucose levels in type 1 diabetes patients treated with insulin pumps

We aimed to estimate the impact of ingestion of a pure protein load on the glucose levels in T1DM patients treated with insulin pumps. We examined 10 T1DM patients (6 females, mean age-32.3 years, mean HbA1c-6.8%) treated with insulin pumps equipped with a continuous glucose monitoring system (CGMS). In Phase I, baseline insulin infusion was optimized to minimize the differences in fasting glucose levels to less than 30 mg/dL between any two time points between 9 a.m. and 3 p.m. In Phase II, the patients were exposed to single pure protein load. CGMS record was performed and the glucose pattern was defined for 6 hours of each phase. The maximal glucose level increment was similar for the entire duration of the fasting and the protein load test (26.6 versus 27.6 mg/dL, resp., P < 0.78). There was only a borderline difference in change between baseline versus 6th hour glucose (12.5 and 19.0 mg/dL, P = 0.04). Glucose variability, assessed by standard deviation of mean glucose levels, was 36.4 and 37.9 mg/dL, respectively (P = 0.01). The administration of a pure protein load does not seem to have a clinically significant impact on glucose levels in T1DM patients treated with insulin pumps.