Mealtime Insulin Doses Research Articles

Splitting Mealtime Insulin Doses for Mixed Fat and Protein Meals in Children and Adolescents with Type 1 Diabetes Using Multiple Daily Injection Regimen: A Randomized Cross-Over Trial

Aims. Assessment of the glycemic outcomes of increasing and splitting mealtime insulin doses for mixed fat and protein meals in pediatric patients with type 1 diabetes mellitus (T1DM) using multiple daily injection regimen and comparing the effects of regular insulin and fast-acting insulin on glycemic outcomes following those meals. Methods. This single-center, randomized, cross-over trial included 43 children and adolescents with T1DM randomly assigned to receive three interventional insulin doses for lunch meals over 3 consecutive days; Intervention A (100% insulin-to-carbohydrate ratio (ICR) dose given as premeal insulin lispro with an additional insulin sensitivity factor-calculated correction dose after 3 hr), Intervention B (130% ICR dose split into 60% premeal insulin lispro and 40% postmeal insulin lispro after 30 min), and Intervention C (130% ICR dose split into 60% premeal insulin lispro and 40% postmeal regular insulin after 30 min). The test meal consisted of two slices of pizza (weight: 150 g, carbohydrates: 40 g, fat: 15 g, protein: 20 g, and calories: 380 kcal). Postprandial blood glucose levels were monitored for 6 hr. Results. There were no significant differences in postprandial blood glucose excursions following the three interventions. However, Intervention C had a significantly lower late (3–6 hr) blood glucose area under the curve ( p = 0.01 ). Postprandial hypoglycemia developed in 12 participants (27.9%) following Interventions A and B and in 17 participants (39.5%) following Intervention C ( p = 0.32 ). Conclusions. Using regular insulin as a postmeal portion of increased and split insulin doses provided better late postprandial glycemic outcomes following mixed fat and protein meals. However, the amount of additional insulin used needs optimization to reduce the frequency of postprandial hypoglycemia. This trial is registered with NCT04783376.

A Personalized and Adaptive Insulin Bolus Calculator Based on Double Deep Q- Learning to Improve Type 1 Diabetes Management.

Mealtime insulin dosing is a major challenge for people living with type 1 diabetes (T1D). This task is typically performed using a standard formula that, despite containing some patient-specific parameters, often leads to sub-optimal glucose control due to lack of personalization and adaptation. To overcome the previous limitations here we propose an individualized and adaptive mealtime insulin bolus calculator based on double deep Q-learning (DDQ), which is tailored to the patient thanks to a personalization procedure relying on a two-step learning framework. The DDQ-learning bolus calculator was developed and tested using the UVA/Padova T1D simulator modified to reliably mimic real-world scenarios by introducing multiple variability sources impacting glucose metabolism and technology. The learning phase included a long-term training of eight sub-population models, one for each representative subject, selected thanks to a clustering procedure applied to the training set. Then, for each subject of the testing set, a personalization procedure was performed, by initializing the models based on the cluster to which the patient belongs. We evaluated the effectiveness of the proposed bolus calculator on a 60-day simulation, using several metrics representing the goodness of glycemic control, and comparing the results with the standard guidelines for mealtime insulin dosing. The proposed method improved the time in target range from 68.35% to 70.08% and significantly reduced the time in hypoglycemia (from 8.78% to 4.17%). The overall glycemic risk index decreased from 8.2 to 7.3, indicating the benefit of our method when applied for insulin dosing compared to standard guidelines.

A Head-to-Head Comparison of Two Algorithms for Adjusting Mealtime Insulin Doses Based on CGM Trend Arrows in Adult Patients with Type 1 Diabetes: Results from an Exploratory Study.

Continuous glucose monitoring (CGM) users are encouraged to consider trend arrows before injecting a meal bolus. We evaluated the efficacy and safety of two different algorithms for trend-informed bolus adjustments, the Diabetes Research in Children Network/Juvenile Diabetes Research Foundation (DirectNet/JDRF) and the Ziegler algorithm, in type 1 diabetes. We conducted a cross-over study of type 1 diabetes patients using Dexcom G6. Participants were randomly assigned to either the DirectNet/JDRF or the Ziegler algorithm for two weeks. After a 7-day wash-out period with no trend-informed bolus adjustments, they crossed to the alternative algorithm. Twenty patients, with an average age of 36 ± 10 years, completed this study. Compared to the baseline and the DirectNet/JDRF algorithm, the Ziegler algorithm was associated with a significantly higher time in range (TIR) and lower time above range and mean glucose. A separate analysis of patients on CSII and MDI revealed that the Ziegler algorithm provides better glucose control and variability than DirectNet/JDRF in CSII-treated patients. The two algorithms were equally effective in increasing TIR in MDI-treated patients. No severe hypoglycemic or hyperglycemic episode occurred during the study. The Ziegler algorithm is safe and may provide better glucose control and variability than the DirectNet/JDRF over a two-week period, especially in patients treated with CSII.

Making sense of weekly insulins

The current issue of The Lancet Diabetes & Endocrinology sees publication of a phase 2 study of the weekly insulin under development from Eli Lilly. 1 Frias J Chien J Zhang Q et al. Safety and efficacy of once-weekly basal insulin Fc in people with type 2 diabetes previously treated with basal insulin: a multicentre, open-label, randomised, phase 2 study. Lancet Diabetes Endocrinol. 2023; (published online Feb 6.)https://doi.org/10.1016/S2213-8587(22)00388-6 Google Scholar Phase 2 studies have also been published from Novo Nordisk, who have further announced top-line results of a series of phase 3 studies to investors. 2 Rosenstock J Bajaj HS Janež A et al. Once-weekly insulin for type 2 diabetes without previous insulin treatment. N Engl J Med. 2020; 383: 2107-2116 Crossref PubMed Scopus (77) Google Scholar Once-daily basal insulin is the standard starter regimen in people with type 2 diabetes, 3 Davies MJ Aroda VR Collins BS et al. Management of hyperglycaemia in type 2 diabetes, 2022. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia. 2022; 65: 1925-1966 Crossref PubMed Scopus (20) Google Scholar and experience with other once-weekly injectables in people with type 2 diabetes, notably of GLP-1 receptor agonists, 4 Holleman C Hopcroft A Close K 3Q22 GLP-1 industry roundup. www.closeconcerns.com/knowledgebase/r/db4d2bb8Date: Nov 15, 2022 Date accessed: December 2, 2022 Google Scholar is of market preference over once-daily preparations; it is probable that clinical practice will adopt licensed weekly insulins very quickly. Weekly basal insulin might be of less relevance to people with type 1 diabetes, for whom multiple meal-time insulin doses will still be needed. Safety and efficacy of once-weekly basal insulin Fc in people with type 2 diabetes previously treated with basal insulin: a multicentre, open-label, randomised, phase 2 studyWeekly BIF achieved a similar efficacy compared with degludec despite higher fasting glucose targets in the BIF groups. Higher fasting glucose targets and lower glucose variability might have contributed to lower hypoglycaemia rates for BIF compared with degludec. These findings support continued development of BIF as a once-weekly insulin treatment for people with diabetes. Full-Text PDF

Using Reinforcement Learning to Simplify Mealtime Insulin Dosing for People with Type 1 Diabetes: In-Silico Experiments

People with type 1 diabetes (T1D) struggle to calculate the optimal insulin dose at mealtime, especially when under multiple daily injections (MDI) therapy. Effectively, they will not always perform rigorous and precise calculations; but occasionally, they might rely on intuition and previous experience. Reinforcement learning (RL) has shown outstanding results in outperforming humans on tasks requiring intuition and learning from experience. In this work, we propose an RL agent that recommends the optimal meal-accompanying insulin dose corresponding to a qualitative meal (QM) strategy that does not require precise carbohydrate counting (CC) (e.g., a usual meal at noon.). The agent is trained using the soft actor-critic approach and comprises long short-term memory (LSTM) neurons. For training, eighty virtual subjects (VS) of the FDA-accepted UVA/Padova T1D adult population were simulated using MDI therapy and QM strategy. For validation, the remaining twenty VS were examined in 26-week scenarios, including intra- and inter-day variabilities in glucose. In-silico results showed that the proposed RL approach outperforms a baseline run-to-run approach and can replace the standard CC approach. Specifically, after 26 weeks, the time-in-range (70 - 180mg/dL) and time-in-hypoglycemia (< 70mg/dL) were 73.1 ± 11.6% and 2.0 ± 1.8% using the RL-optimized QM strategy compared to 70.6± 14.8% and 1.5± 1.5% using CC. Such an approach can simplify diabetes treatment resulting in improved quality of life and glycemic outcomes.

Abstract 21: The burden of mealtime insulin dosing in adults and children with type 1 diabetes

Background and Aims: Timely and accurate mealtime insulin dosing is an ongoing challenge for people with type 1 diabetes (T1D). This multinational study aimed to assess attitudes, behaviours and the overall impact of pre-meal (15-20 minutes) bolus insulin dosing from the perspective of adults with T1D, parents of children with T1D and physicians.Materials and Methods: Online surveys were conducted in 2711 participants (recruited from an online panel) who chose to take part across USA, Canada, UK, Japan, Spain and France between 25 Nov 2019 and 06 Feb 2020. Participants were 1401 adults aged ≥18 with T1D, 350 parents who live with children with T1D aged ≤15 years and 960 physicians who have practised for 4-40 years. Adults/children had T1D for ≥6 months and were taking a bolus insulin (excluding fast-acting insulinaspart). Physicians were responsible for starting or managing treatment for T1D and the prescription of mealtime insulin. This analysis provides a country average assuming equal sizes for each country, to give an overall indication of results across the six countries in the study.Results: Of the surveyed adults with T1D (46% male; mean age was 43 years; mean of 19 years with T1D), 72% administered their bolus insulin by injection and 28% used an insulin pump. Continuous glucose monitoring (CGM) was used by 43% of adults. Of the parents surveyed (64% male; mean age was 10 years; mean of 4 years with T1D), 74% administered their bolus insulin by injection and 26% used an insulin pump, with 58% using CGM. The findings indicate that a majority (96%) of both adults with T1D and parents of children with T1D understood the importance of administering bolus insulin accurately. However, few adults (35%) and parents (47%) felt very confident in estimating the amount of insulin required accurately and 91% of adults and 96% of parents experienced challenge(s) with pre-meal insulin dosing. Almost all physicians (99.6%) reported that they believed their patients with T1D faced challenge(s) with pre-meal bolus insulin dosing. Of those surveyed, 25% of adults and 38% of parents forgot to administer pre-meal insulin ≥1 a week, and 70%of adults and 81%of parents reported that they/their child ate more or less than anticipated following their mealtime insulin ≥1 aweek. Accordingly, 68% of adults and 79% of parents indicated that they/their child needed to take corrective action ≥1 a week by either eating more or taking more insulin. A majority of participants (82% of adults, 93% of parents) felt that administering insulin 15-20 minutes before a meal had a negative impact on their/their child's day-to-day life; 91% of physicians concurred that this creates an extra burden in the day-to-day lives of people with T1D. When asked their preference, 73%of adults and 67% of parents preferred bolus insulin administration either immediately before (42% adults, 44% parents) or after (31% adults, 23% parents) a meal. A high proportion of adults with T1D (67%) and parents (72%) claimed that taking bolus insulin immediately before or after a meal would have a positive impact on their overall quality of life.Conclusion: Based on the study findings, although the importance of accurate mealtime dosing was well recognized, bolus insulin dosing still poses clear challenges to most people with T1D. Given the choice, the majority of participants would prefer to administer insulin immediately prior to or following a meal, as this was perceived to improve quality of life.

Exploring the Burden of Mealtime Insulin Dosing in Adults and Children With Type 1 Diabetes.

Timely and accurate mealtime insulin dosing can be an ongoing challenge for people with type 1 diabetes. This multinational, online study aimed to explore attitudes and behaviors around mealtime insulin dosing and the impact of mealtime dose timing, particularly with regard to premeal dosing (15–20 minutes before a meal). Although the majority of surveyed participants (96%) recognized the importance of accurate mealtime bolus insulin dosing, only a small proportion (35%) reported being “very confident” in accurate bolus insulin estimation. Given the choice, the majority of participants would prefer to administer insulin immediately before or after a meal, as this timing would improve their quality of life.

708-P: Complexity of Mealtime Insulin Dosing in Free-Living People with T1D: Novel Study with Varied Meal Macronutrient Content

Controlled in-patient studies indicate that high fat/protein meals require more insulin coverage than low fat meals with similar carbohydrate (CHO). However, it is unclear how dietary macronutrient content impacts on glycemic responses and insulin requirements in free-living T1D individuals. Adults with T1D (n=25) (mean±SD age 52±15 yrs, A1C 7.1±0.7%, 39% female) on insulin pump and continuous glucose monitoring (CGM) were enrolled in this 18-week duration study. Participants ate meals with specific macronutrient content (high glycemic index [GI], high fat [HGI-HF]; high GI, low fat [HGI-LF]; low GI, high fat [LGI-HF]; low GI, low fat [LGI-LF]). Meal, CGM, and insulin dosing data were reviewed by the study investigator, who provided dose optimization recommendations. Participants provided dosing and CGM data from 463 meals. Time-in-range (TIR) was similar for all meal types (table, p=0.72). LGI meals required significantly more insulin than HGI meals (p=0.01). Significantly more insulin per gram of CHO was used for LGI-HF vs. all other meal types (mean CHO-to-insulin ratio = 4.5 vs. 8.7 for HGI-HF, 7.6 for HGI-LF, and 5.5 for LGI-LF; p= 0.02). This exploratory study in free-living individuals with T1D highlights the complexity of mealtime insulin dosing, pointing to the need for bolus advisors that incorporate other dietary inputs in addition to CHO.View largeDownload slideView largeDownload slide DisclosureS. S. Edwards: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company. W. Wang: Employee; Self; Eli Lilly and Company. E. Toschi: Consultant; Self; Medtronic. A. Atakov-castillo: None. R. Dewar: None. G. M. Steil: Consultant; Self; Abbott Diabetes, Eli Lilly and Company. H. Wolpert: Employee; Self; Eli Lilly and Company.FundingEli Lilly and Company

ATTD 2021 Invited Speaker Abstracts.

ATTD 2021 Invited Speaker Abstracts.

Addressing Hospital-Acquired Hypoglycemia.

Hospitalized patients who have diabetes often experience hospital-acquired hypoglycemia, a potentially serious adverse event; as a result, management of this condition has become an important quality of care indicator in the inpatient environment. A growing body of research and evidence-based clinical guidelines support proper timing of point of care (POC) blood glucose (BG) measurements, mealtime insulin administration, and meal delivery to reduce the incidence of both hypoglycemic and hyperglycemic events. Monitoring and improving the timing of these three patient care interventions are recognized as a crucial step in the safe and effective care of patients with diabetes. The objective of the QI project was to improve the timing of mealtime insulin administration related to bedside BG monitoring and meal delivery for patients with diabetes who receive mealtime insulin; a secondary goal was to decrease the number of episodes of recurrent hypoglycemia. The overall strategy was to change staff members' approach to mealtime insulin management from a series of individual tasks to a process-oriented collaborative approach. Nurses on the medical-surgical unit at one hospital within a large health system formed a QI team with staff members in information technology and food and nutrition services. The team implemented an eight-week QI pilot project (July 3 to August 26, 2017) using a multidisciplinary approach to coordinate between POC BG measurement, mealtime insulin administration, and meal delivery. More than two years after the hospital-wide rollout of the practice change, follow-up analysis has shown that, on both noncritical and critical care units, recurrent hypoglycemia has decreased. For example, comparing data obtained in a six-month period before the pilot project (November 2016 through April 2017) with the same six-month period in 2018 and 2019, more than a year after the pilot project, the percentage of patient stays (admissions) on noncritical care units in which there was a recurrence of hypoglycemia fell from 41.8% (of 1,162 total hospital admissions) to 35.1% (of 792 total hospital admissions); similarly, the percentage of patient stays on critical care units in which recurrent hypoglycemia occurred decreased from 36.8% to 22.8%. Findings suggest that ensuring a consistent 30-minute window between POC BG measurement and meal delivery enabled nursing staff to perform timely POC BG measurements and administer a more optimal mealtime insulin dose. Increasing interdisciplinary communication, collaboration, and awareness of best practice guidelines relating to proper mealtime insulin administration resulted in a sustained improvement in timing between POC BG measurements and mealtime insulin administration and between mealtime insulin administration and meal delivery.

Inpatient and Outpatient Technologies to Assist in the Management of Insulin Dosing.

Several new technologies use computer algorithms to analyze a person’s blood glucose response to insulin treatment, calculate the person’s next recommended insulin dose, advise the person regarding when to check blood glucose next, and provide alerts regarding glucose control for the individual patient or across a hospital system. This article reviews U.S. Food and Drug Administration (FDA)-approved products designed to help manage insulin dosing for inpatients, as well as those available to provide people with insulin-requiring diabetes support in making adjustments to their basal and/or mealtime insulin doses. Many of these products have a provider interface that allows for remote monitoring of patients’ glucose readings and insulin doses. By alleviating some of the burdens of insulin initiation and dose adjustment, these products may facilitate improved glycemic management and patient outcomes.

An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning.

(1) Background: People living with type 1 diabetes (T1D) require self-management to maintain blood glucose (BG) levels in a therapeutic range through the delivery of exogenous insulin. However, due to the various variability, uncertainty and complex glucose dynamics, optimizing the doses of insulin delivery to minimize the risk of hyperglycemia and hypoglycemia is still an open problem. (2) Methods: In this work, we propose a novel insulin bolus advisor which uses deep reinforcement learning (DRL) and continuous glucose monitoring to optimize insulin dosing at mealtime. In particular, an actor-critic model based on deep deterministic policy gradient is designed to compute mealtime insulin doses. The proposed system architecture uses a two-step learning framework, in which a population model is first obtained and then personalized by subject-specific data. Prioritized memory replay is adopted to accelerate the training process in clinical practice. To validate the algorithm, we employ a customized version of the FDA-accepted UVA/Padova T1D simulator to perform in silico trials on 10 adult subjects and 10 adolescent subjects. (3) Results: Compared to a standard bolus calculator as the baseline, the DRL insulin bolus advisor significantly improved the average percentage time in target range (70–180 mg/dL) from to () and to () in the the adult and adolescent cohorts, respectively, while reducing hypoglycemia. (4) Conclusions: The proposed algorithm has the potential to improve mealtime bolus insulin delivery in people with T1D and is a feasible candidate for future clinical validation.

Mealtime insulin BOLUS score increases prior to clinic visits in youth with type 1 diabetes

IntroductionMultiple studies confirm the occurrence of ‘white coat adherence’ (WCA), a term describing an increase in engagement with self-care tasks just prior to a scheduled clinic appointment, across cohorts with...

Satisfaction of Healthcare Professionals and People With Diabetes With an Insulin Bolus Calculator Mobile Application.

People with diabetes (PWD) who need to take mealtime insulin to help control their blood sugar often have difficulty correctly calculating their dose due to consideration of many factors such as current blood glucose, carbohydrate consumption, active insulin duration, insulin-to-carb ratio, and insulin sensitivity. The Insulin Mentor, a bolus calculator tool in the OneTouch Reveal diabetes management app, uses an algorithm to automate many of these calculations and contains a link to a food diary to help estimate carbohydrate intake. In the current study, healthcare professionals and PWD from United States and Germany responded favorably to simulations of this calculator tool and compared it positively with other apps on the market. The Insulin Mentor may simplify the difficult process of correctly calculating mealtime insulin doses for PWD.

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.

Insulin Glargine 300U/mL and Insulin Glulisine Treatment in Patients with Type2 Diabetes: A Non-Interventional Study of Effectiveness in Routine Clinical Practice.

IntroductionThe EDITION development program confirmed that insulin glargine 300 U/mL (Gla-300) provides comparable glycemic control to insulin glargine 100 U/mL (Gla-100) but with lower hypoglycemia risk. Our study aimed to evaluate the effectiveness of Gla-300 in everyday practice.MethodsThis one-arm, non-interventional study included patients with type 2 diabetes who were switched to Gla-300-based basal-bolus therapy (BBT) and followed for 6 months. Indications for switching included inadequate glycemic control and/or hypoglycemic events with the previous regimen.ResultsOverall 229 patients were included, with mean age of 60.9 years. All glycemic variables improved between baseline and 6 months significantly (mean ± standard deviation [SD] hemoglobin A1c [HbA1c] from 8.9 ± 1.5% to 7.5 ± 1.1%, fasting blood glucose from 9.5 ± 3.1 mmol/L to 7.0 ± 2.1 mmol/L, postprandial blood glucose from 12.0 ± 3.8 mmol/L to 8.9 ± 2.5 mmol/L). Gla-300 doses were increased and mealtime insulin doses were unchanged. Rates of both non-severe and severe hypoglycemic events decreased significantly compared to pre-study and 6-month follow-up periods. Patients switched because of elevated HbA1c had higher baseline HbA1c and greater decrease in HbA1c paralleled with increase in insulin doses compared to those switched because of hypoglycemia.ConclusionsIn day-to-day practice, switching from human insulin to Gla-300-based BBT resulted in significant improvement in glycemic control and decrease in hypoglycemia risk.

What is the current state of the artificial pancreas in diabetes care?

The artificial pancreas (also referred to as closed-loop system) brings us one step closer to the decade-long dream of automated insulin delivery. The closed-loop system directs subcutaneous insulin delivery corresponding to the glucose concentration using acontrol algorithm. Evidence shows that closed-loop systems substantially improve glucose control and quality of life; however, fully automated closed-loop systems have not yet been accomplished. Active input from patients is required for mealtime insulin dosing and corrections. This article provides an overview on the current state of development of the artificial pancreas in the treatment of diabetes.

Insulin Dosing for Fat and Protein: Is it Time?

The impact of dietary fat and protein on postprandial glycemia in type 1 diabetes (T1D) and the need to adjust for them in the mealtime insulin dose have been controversial (1,2). Recently, carefully designed randomized trials in individuals living with T1D have shown protein and fat consumed in meals with carbohydrate reduce the early postprandial rise (1–2 h) and contribute to postprandial hyperglycemia in the late (3–6 h) postprandial period (3–5). In clinical practice, continuous glucose monitoring highlights the glycemic effects of different meal types demonstrating that mealtime insulin dosing strategies based on carbohydrate counting alone have limitations. There is a need for an evidence-based, safe, and practical method to guide insulin adjustments for high-fat, high-protein meals. In this issue of Diabetes Care , Bell et al. (6) address the pressing clinical question of optimal insulin adjustments for meals containing differing amounts of dietary fat. This is important because postprandial hyperglycemia has been identified as a risk factor for the development of long-term complications of diabetes (7), and higher fat diets have increased in popularity in recent years. The mechanisms by which all three macronutrients impact blood glucose levels in people with T1D is shown in Fig. 1. Dietary carbohydrate is absorbed and rapidly increases the blood glucose concentration (8). Dietary protein results in a delayed and more prolonged increase in blood glucose levels by conversion of amino acids to glucose through gluconeogenesis, as well as an influence on multiple hormones including glucagon, cortisol, growth hormone, insulin-like growth factor 1, and ghrelin, thus increasing insulin resistance (9). Dietary fat also results in a delayed glycemic response by a number of mechanisms. Free fatty acids act via peroxisome proliferator–activated receptors and free fatty acid receptors to impact …

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

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. 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. 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 ≥75g. In a carbohydrate-containing meal ≥12.5g 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 240min. 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. 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.

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

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. 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. 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 (iAUCglucose) in the early postprandial period (0-2 h; P = 0.008) and increase in iAUCglucose in the late postprandial period (2-5 h; P = 0.004). The type of fat made no significant difference to the 5-h iAUCglucose. 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. This study provides clinical guidance for mealtime insulin dosing recommendations for dietary fat in T1D.