Does the fat-protein meal increase postprandial glucose level in type 1 diabetes patients on insulin pump: the conclusion of a randomized study

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.

Using Continuous Glucose Monitoring to Passively Classify Naturalistic Binge Eating and Vomiting Among Adults With Binge-Spectrum Eating Disorders: A Preliminary Investigation

OBJECTIVE

Binge eating and self-induced vomiting are common, transdiagnostic eating disorder (ED) symptoms. Efforts to understand these behaviors in research and clinical settings have historically relied on self-report measures, which may be biased and have limited ecological validity. It may be possible to passively detect binge eating and vomiting using data collected by continuous glucose monitors (CGMs; minimally invasive sensors that measure blood glucose levels), as these behaviors yield characteristic glucose responses.

METHOD

This study developed machine learning classification algorithms to classify binge eating and vomiting among 22 adults with binge-spectrum EDs using CGM data. Participants wore Dexcom G6 CGMs and reported eating episodes and disordered eating symptoms using ecological momentary assessment for 2 weeks. Group-level random forest models were generated to distinguish binge eating from typical eating episodes and to classify instances of vomiting.

RESULTS

The binge eating model had accuracy of 0.88 (95% CI: 0.83, 0.92), sensitivity of 0.56, and specificity of 0.90. The vomiting model demonstrated accuracy of 0.79 (95% CI: 0.62, 0.91), sensitivity of 0.88, and specificity of 0.71.

DISCUSSION

Results suggest that CGM may be a promising avenue for passively classifying binge eating and vomiting, with implications for innovative research and clinical applications.

Continuous glucose monitoring as an objective measure of meal consumption in individuals with binge-spectrum eating disorders: A proof-of-concept study

OBJECTIVE

Going extended periods of time without eating increases risk for binge eating and is a primary target of leading interventions for binge-spectrum eating disorders (B-EDs). However, existing treatments for B-EDs yield insufficient improvements in regular eating and subsequently, binge eating. These unsatisfactory clinical outcomes may result from limitations in assessment and promotion of regular eating in therapy. Detecting the absence of eating using passive sensing may improve clinical outcomes by facilitating more accurate monitoring of eating behaviours and powering just-in-time adaptive interventions. We developed an algorithm for detecting meal consumption (and extended periods without eating) using continuous glucose monitor (CGM) data and machine learning.

METHOD

Adults with B-EDs (N = 22) wore CGMs and reported eating episodes on self-monitoring surveys for 2 weeks. Random forest models were run on CGM data to distinguish between eating and non-eating episodes.

RESULTS

The optimal model distinguished eating and non-eating episodes with high accuracy (0.82), sensitivity (0.71), and specificity (0.94).

CONCLUSIONS

These findings suggest that meal consumption and extended periods without eating can be detected from CGM data with high accuracy among individuals with B-EDs, which may improve clinical efforts to target dietary restriction and improve the field's understanding of its antecedents and consequences.

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

AIMS

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

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).

Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges

The discourse amongst diabetes specialists and academics regarding technology and artificial intelligence (AI) typically centres around the 10% of people with diabetes who have type 1 diabetes, focusing on glucose sensors, insulin pumps and, increasingly, closed-loop systems. This focus is reflected in conference topics, strategy documents, technology appraisals and funding streams. What is often overlooked is the wider application of data and AI, as demonstrated through published literature and emerging marketplace products, that offers promising avenues for enhanced clinical care, health-service efficiency and cost-effectiveness. This review provides an overview of AI techniques and explores the use and potential of AI and data-driven systems in a broad context, covering all diabetes types, encompassing: (1) patient education and self-management; (2) clinical decision support systems and predictive analytics, including diagnostic support, treatment and screening advice, complications prediction; and (3) the use of multimodal data, such as imaging or genetic data. The review provides a perspective on how data- and AI-driven systems could transform diabetes care in the coming years and how they could be integrated into daily clinical practice. We discuss evidence for benefits and potential harms, and consider existing barriers to scalable adoption, including challenges related to data availability and exchange, health inequality, clinician hesitancy and regulation. Stakeholders, including clinicians, academics, commissioners, policymakers and those with lived experience, must proactively collaborate to realise the potential benefits that AI-supported diabetes care could bring, whilst mitigating risk and navigating the challenges along the way.

Mitigating iftar-related glycemic excursions in adolescents and young adults with type 1 diabetes on MiniMed™ 780G advanced hybrid closed loop system: a randomized clinical trial for adjunctive oral vildagliptin therapy during Ramadan fasting

BACKGROUND

Ramadan Iftar meal typically causes glucose excursions. Dipeptidyl peptidase-4 inhibitors increase glucagon-like peptide-1 and thus, decrease blood glucose levels with low risk of hypoglycemia.

AIM

To investigate the efficacy and safety of vildagliptin as an add-on therapy on glucose excursions of Iftar Ramadan meals among adolescents and young adults with type 1 diabetes mellitus (T1DM) using advanced hybrid closed-loop (AHCL) treatment.

METHODS

Fifty T1DM patients on MiniMed™ 780G AHCL were randomly assigned either to receive vildagliptin (50 mg tablet) with iftar meal during Ramadan month or not. All participants received pre-meal insulin bolus based on insulin-to-carbohydrate ratio (ICR) for each meal constitution.

RESULTS

Vildagliptin offered blunting of post-meal glucose surges (mean difference - 30.3 mg/dL [- 1.7 mmol/L] versus - 2.9 mg/dL [- 0.2 mmol/L] in control group; p < 0.001) together with concomitant exceptional euglycemia with time in range (TIR) significantly increased at end of Ramadan in intervention group from 77.8 ± 9.6% to 84.7 ± 8.3% (p = 0.016) and time above range (180-250 mg/dL) decreased from 13.6 ± 5.1% to 9.7 ± 3.6% (p = 0.003) without increasing hypoglycemia. A significant reduction was observed in automated daily correction boluses and total bolus dose by 23.9% and 16.3% (p = 0.015 and p < 0.023, respectively) with less aggressive ICR settings within intervention group at end of Ramadan. Coefficient of variation was improved from 37.0 ± 9.4% to 31.8 ± 7.1%; p = 0.035). No severe hypoglycemia or diabetic ketoacidosis were reported.

CONCLUSION

Adjunctive vildagliptin treatment mitigated postprandial hyperglycemia compared with pre-meal bolus alone. Vildagliptin significantly increased TIR while reducing glycemic variability without compromising safety. Trial registration This trial was registered under ClinicalTrials.gov Identifier no. NCT06021119.

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.

Using Continuous Glucose Monitoring to Detect and Intervene on Dietary Restriction in Individuals With Binge Eating: The SenseSupport Withdrawal Design Study

BACKGROUND

Dietary restraint is a key factor for maintaining engagement in binge eating among individuals with binge eating disorder (BED) and bulimia nervosa (BN). Reducing dietary restraint is a mechanism of change in cognitive behavioral therapy (CBT) for individuals with BN and BED. However, many individuals who undergo CBT fail to adequately reduce dietary restraint during treatment, perhaps owing to difficulty in using treatment skills (eg, regular eating) to reduce dietary restraint during their daily lives. The SenseSupport system, a novel just-in-time, adaptive intervention (JITAI) system that uses continuous glucose monitoring to detect periods of dietary restraint, may improve CBT to reduce dietary restraint during treatment by providing real-time interventions.

OBJECTIVE

This study aimed to describe the feasibility, acceptability, and initial evaluation of SenseSupport. We presented feasibility, acceptability, target engagement, and initial treatment outcome data from a small trial using an ABAB (A=continuous glucose monitoring data sharing and JITAIs-Off, B=continuous glucose monitoring data sharing and JITAIs-On) design (in which JITAIs were turned on for 2 weeks and then turned off for 2 weeks throughout the treatment).

METHODS

Participants (N=30) were individuals with BED or BN engaging in ≥3 episodes of ≥5 hours without eating per week at baseline. Participants received 12 sessions of CBT and wore continuous glucose monitors to detect eating behaviors and inform the delivery of JITAIs. Participants completed 4 assessments and reported eating disorder behaviors, dietary restraint, and barriers to app use weekly throughout treatment.

RESULTS

Retention was high (25/30, 83% after treatment). However, the rates of continuous glucose monitoring data collection were low (67.4% of expected glucose data were collected), and therapists and participants reported frequent app-related issues. Participants reported that the SenseSupport system was comfortable, minimally disruptive, and easy to use. The only form of dietary restraint that decreased significantly more rapidly during JITAIs-On periods relative to JITAIs-Off periods was the desire for an empty stomach (t₄₃=1.69; P=.049; Cohen d=0.25). There was also a trend toward greater decrease in overall restraint during JITAs-On periods compared with JITAIs-Off periods, but these results were not statistically significant (t₄₃=1.60; P=.06; Cohen d=0.24). There was no significant difference in change in the frequency of binge eating during JITAIs-On periods compared with JITAIs-Off periods (P=.23). Participants demonstrated clinically significant, large decreases in binge eating (t₂₄=10.36; P<.001; Cohen d=2.07), compensatory behaviors (t₂₄=3.40; P=.001; Cohen d=0.68), and global eating pathology (t₂₄=6.25; P<.001; Cohen d=1.25) from pre- to posttreatment.

CONCLUSIONS

This study describes the successful development and implementation of the first intervention system combining passive continuous glucose monitors and JITAIs to augment CBT for binge-spectrum eating disorders. Despite the lower-than-anticipated collection of glucose data, the high acceptability and promising treatment outcomes suggest that the SenseSupport system warrants additional investigation via future, fully powered clinical trials.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04126694; https://clinicaltrials.gov/ct2/show/NCT04126694.

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.

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

OBJECTIVE

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

AIM

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

RESEARCH DESIGN AND METHODS

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

RESULTS

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

CONCLUSION

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

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.

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

OBJECTIVE

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

RESEARCH DESIGN AND METHODS

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

RESULTS

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

CONCLUSIONS

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

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Hybrid Closed-loop to Manage Gastroparesis in People With Type 1 Diabetes: a Case Series

BACKGROUND

Gastroparesis is associated with unpredictable gastric emptying and can lead to erratic glucose profiles and negative impacts on quality-of-life. Many people with gastroparesis are unable to meet glycemic targets and there is a need for new approaches for this population. Hybrid closed-loop systems improve glucose control and quality-of-life but evidence for their use in people with diabetic gastroparesis is limited.

METHODS

We present a narrative review of the challenges associated with type 1 diabetes management for people with gastroparesis and present a case series of 7 people with type 1 diabetes and gastroparesis. We compare glycemic control before and during the first 12 months of hybrid closed-loop therapy. Data were analyzed using electronic patient records and glucose management platforms. We also discuss future advancements for closed-loop systems that may benefit this population.

RESULTS

Five of 7 patients had data available for time in range before and during hybrid closed-loop therapy, and all had an improvement in percentage time in target glucose range, with the overall mean time in range increasing from 26.0% ± 15.7% to 58.4% ± 8.6% during HCL use, (P = .004). There were significant reductions in HbA1c (83 ± 9 mmol/mol to 71 ± 14 mmol/mol) and mean glucose from 13.0 ± 1.7 mmol/L (234 ± 31 mg/dL) to 10.0 ± 0.7 mmol/L (180 ± 13 mg/dL) with use of a hybrid closed-loop system. Importantly, this was achieved without an increase in time in hypoglycemia (P = .50).

CONCLUSION

Hybrid closed-loop systems may represent a valuable approach to improve glycemic control for people with type 1 diabetes and gastroparesis. Prospective studies are required to confirm these findings.

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.

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

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

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

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.

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.

Application of Artificial Intelligence in Diabetes Education and Management: Present Status and Promising Prospect

Despite the rapid development of science and technology in healthcare, diabetes remains an incurable lifelong illness. Diabetes education aiming to improve the self-management skills is an essential way to help patients enhance their metabolic control and quality of life. Artificial intelligence (AI) technologies have made significant progress in transforming available genetic data and clinical information into valuable knowledge. The application of AI tech in disease education would be extremely beneficial considering their advantages in promoting individualization and full-course education intervention according to the unique pictures of different individuals. This paper reviews and discusses the most recent applications of AI techniques to various aspects of diabetes education. With the information and evidence collected, this review attempts to provide insight and guidance for the development of prospective, data-driven decision support platforms for diabetes management, with a focus on individualized patient management and lifelong educational interventions.

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.

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

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

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

OBJECTIVE

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

RESEARCH DESIGN AND METHODS

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

RESULTS

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

CONCLUSIONS

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

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

BACKGROUND

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSION

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

Demystifying the Pizza Bolus: The Effect of Dough Fermentation on Glycemic Response-A Sensor-Augmented Pump Intervention Trial in Children with Type 1 Diabetes Mellitus

Background: Glycemia following pizza consumption is typically managed with a dual-wave insulin bolus. This study evaluated the effect of a simple bolus on glycemia following consumption of traditionally prepared pizzas with long (24 h) or short (8 h) dough fermentation periods. Research Design and Methods: On two separate evenings, children with type 1 diabetes (n = 38) receiving sensor-integrated pump therapy consumed traditionally prepared pizza with either short (pizza A) or long (pizza B) dough fermentation, and blood glucose was monitored over 11 h. A simple insulin bolus was administered 15 min preprandially. The carbohydrate and amino acid contents of the two types of pizza were analyzed by liquid chromatography and high-resolution mass spectrometry (LC-HRMS). Results: The mean (±standard deviation) time in range 3.9-10.0 mmol/L was 73.2% ± 23.2%, and 50.8% ± 26.7% of glucose measurements were within the range 3.9-7.8 mmol/L. However, during the 2 h after bolus administration, the mean time in range 3.9-7.8 mmol/L was significantly greater with pizza B than with pizza A (73.3% ± 31.5% vs. 51.8% ± 37.4%, respectively, P = 0.009), and the time in hyperglycemia (>10 mmol/L) was significantly shorter (mean percentage 6.1% ± 19.0% vs. 17.7% ± 29.8%, respectively, P = 0.019). LC-HRMS analysis showed that long fermentation was associated with a lower carbohydrate content in the pizza, and a higher amino acid content. Conclusions: Glycemia following consumption of traditionally prepared pizza can be managed using a simple bolus 15 min before eating. Glycemic control can be further improved by increasing the dough fermentation time. Study registration: NCT03748251, Clinicaltrials.gov.

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.

App-Based Insulin Calculators: Current and Future State

PURPOSE OF REVIEW

To perform a comprehensive literature review and critical assessment of peer-reviewed manuscripts addressing the efficacy, safety, or usability of insulin calculator apps.

RECENT FINDINGS

Managing diabetes with insulin can be complex, and literacy and numeracy skills pose barriers to manual insulin dose calculations. App-based insulin calculators are promising tools to help people with diabetes administer insulin safely and have potential to improve glycemic control. While a large number of apps which assist with insulin dosing are available, there is limited data evaluating their efficacy, safety, and usability. Recently, a need for regulatory oversight has been recognized, but few apps meet federal standards. Thus, choosing an appropriate app is challenging for both patients and providers. An electronic literature review was performed to identify insulin calculator apps with either evidence for efficacy, safety or usability published in peer-reviewed literature or with FDA/CE approval. Twenty apps were identified intended for use by patients with diabetes on insulin. Of these, nine included insulin calculators. Summaries of each app, including pros and cons, are provided. Insulin-calculator apps have the potential to improve self-management of diabetes. While current literature demonstrates improvements in quality of life and glycemic control after use of these programs, larger trials are needed to collect outcome and safety data. Also, further human factor analysis is needed to assure these apps will be adopted appropriately by people with diabetes. App features including efficacy and safety data need to be easily available for consumer review and decision making. Higher standards need to be set for app developers to ensure safety and efficacy.

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

Accuracy of Automatic Carbohydrate, Protein, Fat and Calorie Counting Based on Voice Descriptions of Meals in People with Type 1 Diabetes

The aim of this work was to assess the accuracy of automatic macronutrient and calorie counting based on voice descriptions of meals provided by people with unstable type 1 diabetes using the developed expert system (VoiceDiab) in comparison with reference counting made by a dietitian, and to evaluate the impact of insulin doses recommended by a physician on glycemic control in the study&rsquo;s participants. We also compared insulin doses calculated using the algorithm implemented in the VoiceDiab system. Meal descriptions were provided by 30 hospitalized patients (mean hemoglobin A1c of 8.4%, i.e., 68 mmol/mol). In 16 subjects, the physician determined insulin boluses based on the data provided by the system, and in 14 subjects, by data provided by the dietitian. On one hand, differences introduced by patients who subjectively described their meals compared to those introduced by the system that used the average characteristics of food products, although statistically significant, were low enough not to have a significant impact on insulin doses automatically calculated by the system. On the other hand, the glycemic control of patients was comparable regardless of whether the physician was using the system-estimated or the reference content of meals to determine insulin doses.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Dietary intake and risk of non-severe hypoglycemia in adolescents with type 1 diabetes

AIMS

To determine the association between dietary intake and risk of non-severe hypoglycemia in adolescents with type 1 diabetes.

METHODS

Type 1 adolescents from a randomized trial wore a blinded continuous glucose monitoring (CGM) system at baseline for one week in free-living conditions. Dietary intake was calculated as the average from two 24-h dietary recalls. Non-severe hypoglycemia was defined as having blood glucose <70mg/dL for ≥10min but not requiring external assistance, categorized as daytime and nocturnal (11PM-7AM). Data were analyzed using logistic regression models.

RESULTS

Among 98 participants with 14,277h of CGM data, 70 had daytime hypoglycemia, 66 had nocturnal hypoglycemia, 55 had both, and 17 had neither. Soluble fiber and protein intake were positively associated with both daytime and nocturnal hypoglycemia. Glycemic index, monounsaturated fat, and polyunsaturated fat were negatively associated with daytime hypoglycemia only. Adjusting for total daily insulin dose per kilogram eliminated all associations.

CONCLUSIONS

Dietary intake was differentially associated with daytime and nocturnal hypoglycemia. Over 80% of type 1 adolescents had hypoglycemia in a week, which may be attributed to the mismatch between optimal insulin dose needed for each meal and actually delivered insulin dose without considering quality of carbohydrate and nutrients beyond carbohydrate.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT01286350.

Bolus Calculator Settings in Well-Controlled Prepubertal Children Using Insulin Pumps Are Characterized by Low Insulin to Carbohydrate Ratios and Short Duration of Insulin Action Time

BACKGROUND

The "500 rule" has been used extensively to find the insulin to carbohydrate ratio (ICR) for carbohydrate counting (CC). Duration of insulin action (DIA) is often recommended to be set to 4 hours. Data are lacking on validating these routines in young children.

METHODS

ICR was calculated by dividing carbohydrate grams by insulin units. Insulin sensitivity factor (ISF) was defined by the 100 rule (100 divided by total daily insulin dose [TDD]). DIA was set to 3 hours. ICR, ISF, and DIA were adjusted continuously. Data for this retrospective analysis were taken from pump downloads at a routine visit. ICR and ISF were recalculated to rules (ICR/ISF multiplied by TDD).

RESULTS

A total of 21 prepubertal children aged 7.0 ± 2.3 (mean ± SD), range 2-10 years, with diabetes duration 3.0 ± 1.9, range 0.5-7.7 years, used the pump bolus calculator for CC. HbA1c IFCC (NGSP) was 53 ± 6 mmol/mol (7.0 ± 0.5%). None had experienced severe hypoglycemia (unconsciousness/seizures) since diabetes diagnosis. TDD was 0.7 ± 0.1 U/kg/24 h (range 0.5-1.0), and the percentage basal insulin 38 ± 11%. Median breakfast rule was 211 (Q, quartiles 162;310), and for other meals 434 (Q 301;496). Median ISF rule was 113 (Q 100;128) in the morning, and 120 (Q 104;134) during the rest of the day. DIA was 2.6 ± 0.5 h (range 2-3) and target BG 5.3 ± 0.4 mmol/l (range 5.0-6.0).

CONCLUSIONS

Prepubertal children seem to need more bolus insulin for meals than calculated from the 500 rule, especially at breakfast, but less insulin for corrections than calculated from the 100 rule. Two to 3 hours seems to be the appropriate range for DIA in this age group.