Consensus guidance for monitoring individuals with islet autoantibody-positive pre-stage 3 type 1 diabetes.

Given the proven benefits of screening to reduce diabetic ketoacidosis (DKA) likelihood at the time of stage 3 type 1 diabetes diagnosis, and emerging availability of therapy to delay disease progression, type 1 diabetes screening programmes are being increasingly emphasised. Once broadly implemented, screening initiatives will identify significant numbers of islet autoantibody-positive (IAb+) children and adults who are at risk of (confirmed single IAb+) or living with (multiple IAb+) early-stage (stage 1 and stage 2) type 1 diabetes. These individuals will need monitoring for disease progression; much of this care will happen in non-specialised settings. To inform this monitoring, JDRF in conjunction with international experts and societies developed consensus guidance. Broad advice from this guidance includes the following: (1) partnerships should be fostered between endocrinologists and primary-care providers to care for people who are IAb+; (2) when people who are IAb+ are initially identified there is a need for confirmation using a second sample; (3) single IAb+ individuals are at lower risk of progression than multiple IAb+ individuals; (4) individuals with early-stage type 1 diabetes should have periodic medical monitoring, including regular assessments of glucose levels, regular education about symptoms of diabetes and DKA, and psychosocial support; (5) interested people with stage 2 type 1 diabetes should be offered trial participation or approved therapies; and (6) all health professionals involved in monitoring and care of individuals with type 1 diabetes have a responsibility to provide education. The guidance also emphasises significant unmet needs for further research on early-stage type 1 diabetes to increase the rigour of future recommendations and inform clinical care.

Consensus Guidance for Monitoring Individuals With Islet Autoantibody-Positive Pre-Stage 3 Type 1 Diabetes.

Given the proven benefits of screening to reduce diabetic ketoacidosis (DKA) likelihood at the time of stage 3 type 1 diabetes diagnosis, and emerging availability of therapy to delay disease progression, type 1 diabetes screening programs are being increasingly emphasized. Once broadly implemented, screening initiatives will identify significant numbers of islet autoantibody-positive (IAb+) children and adults who are at risk for (confirmed single IAb+) or living with (multiple IAb+) early-stage (stage 1 and stage 2) type 1 diabetes. These individuals will need monitoring for disease progression; much of this care will happen in nonspecialized settings. To inform this monitoring, JDRF, in conjunction with international experts and societies, developed consensus guidance. Broad advice from this guidance includes the following: 1) partnerships should be fostered between endocrinologists and primary care providers to care for people who are IAb+; 2) when people who are IAb+ are initially identified, there is a need for confirmation using a second sample; 3) single IAb+ individuals are at lower risk of progression than multiple IAb+ individuals; 4) individuals with early-stage type 1 diabetes should have periodic medical monitoring, including regular assessments of glucose levels, regular education about symptoms of diabetes and DKA, and psychosocial support; 5) interested people with stage 2 type 1 diabetes should be offered trial participation or approved therapies; and 6) all health professionals involved in monitoring and care of individuals with type 1 diabetes have a responsibility to provide education. The guidance also emphasizes significant unmet needs for further research on early-stage type 1 diabetes to increase the rigor of future recommendations and inform clinical care.

Metabolite-based dietary supplementation in human type 1 diabetes is associated with microbiota and immune modulation.

BACKGROUND: Short-chain fatty acids (SCFAs) produced by the gut microbiota have beneficial anti-inflammatory and gut homeostasis effects and prevent type 1 diabetes (T1D) in mice. Reduced SCFA production indicates a loss of beneficial bacteria, commonly associated with chronic autoimmune and inflammatory diseases, including T1D and type 2 diabetes. Here, we addressed whether a metabolite-based dietary supplement has an impact on humans with T1D. We conducted a single-arm pilot-and-feasibility trial with high-amylose maize-resistant starch modified with acetate and butyrate (HAMSAB) to assess safety, while monitoring changes in the gut microbiota in alignment with modulation of the immune system status. RESULTS: HAMSAB supplement was administered for 6 weeks with follow-up at 12 weeks in adults with long-standing T1D. Increased concentrations of SCFA acetate, propionate, and butyrate in stools and plasma were in concert with a shift in the composition and function of the gut microbiota. While glucose control and insulin requirements did not change, subjects with the highest SCFA concentrations exhibited the best glycemic control. Bifidobacterium longum, Bifidobacterium adolescentis, and vitamin B7 production correlated with lower HbA1c and basal insulin requirements. Circulating B and T cells developed a more regulatory phenotype post-intervention. CONCLUSION: Changes in gut microbiota composition, function, and immune profile following 6 weeks of HAMSAB supplementation were associated with increased SCFAs in stools and plasma. The persistence of these effects suggests that targeting dietary SCFAs may be a mechanism to alter immune profiles, promote immune tolerance, and improve glycemic control for the treatment of T1D. TRIAL REGISTRATION: ACTRN12618001391268. Registered 20 August 2018, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375792 Video Abstract.

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.

A position statement on screening and management of prediabetes in adults in primary care in Australia.

Prediabetes has a high prevalence, with early detection essential to facilitate optimal management to prevent the development of conditions such as type 2 diabetes and cardiovascular disease. Prediabetes can include impaired fasting glucose, impaired glucose tolerance and elevated HbA1c. This position statement outlines the approaches to screening and management of prediabetes in primary care. There is good evidence to implement intensive, structured lifestyle interventions for individuals with impaired glucose tolerance. The evidence for those with impaired fasting glucose or elevated HbA1c is less clear, but individuals should still be provided with generalised healthy lifestyle strategies. A multidisciplinary approach is recommended to implement healthy lifestyle changes through education, nutrition and physical activity. Individuals should aim to lose weight (5-10% of body mass) using realistic and sustainable dietary approaches supported by an accredited practising dietitian, where possible. Physical activity and exercise should be used to facilitate weight maintenance and reduce blood glucose. Moderate-vigorous intensity aerobic exercise and resistance training should be prescribed by an accredited exercise physiologist, where possible. When indicated, pharmacotherapy, metabolic surgery and psychosocial care should be considered, in order to enhance the outcomes associated with lifestyle change. Individuals with prediabetes should generally be evaluated annually for their diabetes status.

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

Dietary intake and eating patterns of young children with type 1 diabetes achieving glycemic targets.

Introduction: Young children with type 1 diabetes (T1D) consume more saturated fat and less fruit and vegetables than recommended. A common challenge in this age group is unpredictable appetite potentially impacting the way parents manage diabetes cares at mealtimes. This small study aimed to assess nutritional intake and mealtime routines of young children with T1D in a clinic where the majority of children were achieving glycemic targets. A secondary aim was to explore association of eating pattern with HbA1c. Methods: A retrospective, cross-sectional review of children aged less than 7.0 years with T1D attending a pediatric diabetes service in Australia was performed (n=24). Baseline characteristics, glycated hemoglobin (HbA1c), a 3-day weighed food diary and a mealtime management survey were collected. Results: Twenty-two children (55% male) were included aged 4.9±1.3 years (mean±SD), HbA1c 47±10 mmol/mol (6.4%±0.9%), body mass index Z-score 0.8±0.9 and diabetes duration 1.7±1.1 years. Preprandial insulin use was reported in 95% of children. Macronutrient distribution (% energy intake) was carbohydrate (48%±4%), protein (16%±2%) and fat (33%±5%) with saturated fat (15%±3%). The majority of children did not meet vegetable and lean meat/protein intake recommendations (0% and 28%, respectively). HbA1c was not correlated with daily total carbohydrate, protein or fat intake (p>0.05). HbA1c was significantly higher in children offered food in a grazing pattern compared with those offered regular meals (mean 61 mmol/mol vs 43 mmol/mol (7.7% vs 6.1%), p=0.01). Conclusions: Dietary quality is a concern in young children with T1D with excessive saturated fat and inadequate vegetable intake. Our results suggest that young children meeting glycemic targets give insulin before meals and follow a routine eating pattern.

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

OBJECTIVE: To determine insulin dose adjustments required for coverage of high-fat, high-protein (HFHP) meals in type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: Ten adults with T1D received low-fat, low-protein (LFLP) and HFHP meals with identical carbohydrate content, covered with identical insulin doses. On subsequent occasions, subjects repeated the HFHP meal with an adaptive model-predictive insulin bolus until target postprandial glycemic control was achieved. RESULTS: With the same insulin dose, the HFHP increased the glucose incremental area under the curve over twofold (13,320 ± 2,960 vs. 27,092 ± 1,709 mg/dL ⋅ min; P = 0.0013). To achieve target glucose control following the HFHP, 65% more insulin was required (range 17%-124%) with a 30%/70% split over 2.4 h. CONCLUSIONS: This study demonstrates that insulin dose calculations need to consider meal composition in addition to carbohydrate content and provides the foundation for new insulin-dosing algorithms to cover meals of varying macronutrient composition.

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

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

Clinical Application of the Food Insulin Index for Mealtime Insulin Dosing in Adults with Type 1 Diabetes: A Randomized Controlled Trial.

BACKGROUND: The Food Insulin Index (FII) is a novel algorithm for ranking foods based on their insulin demand relative to an isoenergetic reference food. We compared the effect of carbohydrate counting (CC) versus the FII algorithm for estimating insulin dosage on glycemic control in type 1 diabetes. MATERIALS AND METHODS: In a randomized, controlled trial, adults (n = 26) using insulin pump therapy were assigned to using either traditional CC or the novel Food Insulin Demand (FID) counting for 12 weeks. Subjects participated in group education and individual sessions. At baseline and on completion of the trial, glycated hemoglobin A1c (HbA1c), day-long glycemia (6-day continuous glucose monitoring), fasting lipids, and C-reactive protein were determined. RESULTS: Changes in HbA1c from baseline to 12 weeks were small and not significant in both groups (mean ± SEM; FII vs. CC, -0.1 ± 0.1% vs. -0.3 ± 0.2%; P = 0.855). The incremental area under the curve following breakfast declined significantly among the FID counters with no change in the CC group (FID vs. CC, -93 ± 41 mmol/L/min [P = 0.043] vs. 4 ± 50 mmol/L/min [P = 0.938]; between groups, P = 0.143). The mean amplitude of the glycemic excursion (MAGE) was significantly reduced among the FID counters (FID vs. CC, -6.1 ± 1.0 vs. -1.3 ± 1.0 mmol/L; P = 0.003), and only the FID counters experienced a trend (-44% vs. +11%; P = 0.057) to reduced hypoglycemia. CONCLUSIONS: In a 12-week pilot study, MAGE and postprandial glycemia following breakfast were significantly improved with FII counting versus CC, despite no significant differences in HbA1c.

The relationship between carbohydrate and the mealtime insulin dose in type 1 diabetes.

A primary focus of the nutritional management of type 1 diabetes has been on matching prandial insulin therapy with carbohydrate amount consumed. Different methods exist to quantify carbohydrate including counting in one gram increments, 10g portions or 15g exchanges. Clinicians have assumed that counting in one gram increments is necessary to precisely dose insulin and optimize postprandial control. Carbohydrate estimations in portions or exchanges have been thought of as inadequate because they may result in less precise matching of insulin dose to carbohydrate amount. However, studies examining the impact of errors in carbohydrate quantification on postprandial glycemia challenge this commonly held view. In addition it has been found that a single mealtime bolus of insulin can cover a range of carbohydrate intake without deterioration in postprandial control. Furthermore, limitations exist in the accuracy of the nutrition information panel on a food label. This article reviews the relationship between carbohydrate quantity and insulin dose, highlighting limitations in the evidence for a linear association. These insights have significant implications for patient education and mealtime insulin dose calculations.

Validation of the food insulin index in lean, young, healthy individuals, and type 2 diabetes in the context of mixed meals: an acute randomized crossover trial.

BACKGROUND: The Food Insulin Index (FII) is a novel classification of single foods based on insulin responses in healthy subjects relative to an isoenergetic reference food. OBJECTIVE: Our aim was to compare day-long responses to 2 nutrient-matched diets predicted to have either high or low insulin demand in healthy controls and individuals with type 2 diabetes (T2DM). DESIGN: Twenty adults (10 healthy adults and 10 adults with T2DM) were recruited. On separate mornings, subjects consumed either a high- or low-FII diet in random order. Diets consisted of 3 consecutive meals (breakfast, morning tea, and lunch), matched for macronutrients, fiber, and glycemic index (GI), but with 2-fold difference in insulin demand as predicted by the FII of the component foods. Postprandial glycemia and insulinemia were measured in capillary plasma at regular intervals over 8 h. RESULTS: As predicted by their GI, there were no differences in glycemic responses between the 2 diets in either group (mean ± SEM; healthy: 6.2 ± 0.2 compared with 6.1 ± 0.1 mmol/L · min, P = 0.429; T2DM: 9.9 ± 1.3 compared with 10.3 ± 1.6 mmol/L · min, P = 0.485). Compared with the high-FII diet, mean postprandial insulin response over 8 h was 53% lower with the low-FII diet in healthy subjects (mean ± SEM; incremental AUCinsulin 31,900 ± 4100 pmol/L · min compared with 68,100 ± 11,400 pmol/L · min, P = 0.003) and 41% lower in subjects with T2DM (mean ± SEM; incremental AUCinsulin 11,000 ± 1800 pmol/L · min compared with 18,700 ± 3100 pmol/L · min, P = 0.018). Incremental AUCinsulin was statistically significantly different between diets when groups were combined (P = 0.001). CONCLUSIONS: The FII algorithm may be a useful tool for reducing postprandial hyperinsulinemia in T2DM, thereby potentially improving insulin resistance and ß-cell function. This trial was registered at the Australian New Zealand Clinical Trials Registry as ACTRN12611000654954.

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

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

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

BACKGROUND: Continuous glucose monitoring highlights the complexity of postprandial glucose patterns present in type 1 diabetes and points to the limitations of current approaches to mealtime insulin dosing based primarily on carbohydrate counting. METHODS: A systematic review of all relevant biomedical databases, including MEDLINE, Embase, CINAHL, and the Cochrane Central Register of Controlled Trials, was conducted to identify research on the effects of dietary fat, protein, and glycemic index (GI) on acute postprandial glucose control in type 1 diabetes and prandial insulin dosing strategies for these dietary factors. RESULTS: All studies examining the effect of fat (n = 7), protein (n = 7), and GI (n = 7) indicated that these dietary factors modify postprandial glycemia. Late postprandial hyperglycemia was the predominant effect of dietary fat; however, in some studies, glucose concentrations were reduced in the first 2-3 h, possibly due to delayed gastric emptying. Ten studies examining insulin bolus dose and delivery patterns required for high-fat and/or high-protein meals were identified. Because of methodological differences and limitations in experimental design, study findings were inconsistent regarding optimal bolus delivery pattern; however, the studies indicated that high-fat/protein meals require more insulin than lower-fat/protein meals with identical carbohydrate content. CONCLUSIONS: These studies have important implications for clinical practice and patient education and point to the need for research focused on the development of new insulin dosing algorithms based on meal composition rather than on carbohydrate content alone.

Metabolic profiling of plasma amino acids shows that histidine increases following the consumption of pork.

Amino acid (AA) status is determined by factors including nutrition, metabolic rate, and interactions between the metabolism of AA, carbohydrates, and lipids. Analysis of the plasma AA profile, together with markers of glucose and lipid metabolism, will shed light on metabolic regulation. The objectives of this study were to investigate the acute responses to the consumption of meals containing either pork (PM) or chicken (CM), and to identify relationships between plasma AA and markers of glycemic and lipemic control. A secondary aim was to explore AA predictors of plasma zinc concentrations. Ten healthy adults participated in a postprandial study on two separate occasions. In a randomized cross-over design, participants consumed PM or CM. The concentrations of 21 AA, glucose, insulin, triglycerides, nonesterified fatty acids, and zinc were determined over 5 hours postprandially. The meal composition did not influence glucose, insulin, triglyceride, nonesterified fatty acid, or zinc concentrations. Plasma histidine was higher following the consumption of PM (P=0.014), with consistently higher changes observed after 60 minutes (P<0.001). Greater percentage increases were noted at limited time points for valine and leucine + isoleucine in those who consumed CM compared to PM. In linear regression, some AAs emerged as predictors of the metabolic responses, irrespective of the meal that was consumed. The present study demonstrates that a single meal of PM or CM produces a differential profile of AA in the postprandial state. The sustained increase in histidine following the consumption of a PM is consistent with the reported effects of lean pork on cardiometabolic risk factors.

Efficacy of carbohydrate counting in type 1 diabetes: a systematic review and meta-analysis.

BACKGROUND: Although carbohydrate counting is the recommended dietary strategy for achieving glycaemic control in people with type 1 diabetes, the advice is based on narrative review and grading of the available evidence. We aimed to assess by systematic review and meta-analysis the efficacy of carbohydrate counting on glycaemic control in adults and children with type 1 diabetes. METHODS: We screened and assessed randomised controlled trials of interventions longer than 3 months that compared carbohydrate counting with general or alternate dietary advice in adults and children with type 1 diabetes. Change in glycated haemoglobin (HbA1c) concentration was the primary outcome. The results of clinically and statistically homogenous studies were pooled and meta-analysed using the random-effects model to provide estimates of the efficacy of carbohydrate counting. FINDINGS: We identified seven eligible trials, of 311 potentially relevant studies, comprising 599 adults and 104 children with type 1 diabetes. Study quality score averaged 7·6 out of 13. Overall there was no significant improvement in HbA1c concentration with carbohydrate counting versus the control or usual care (-0·35% [-3·9 mmol/mol], 95% CI -0·75 to 0·06; p=0·096). We identified significant heterogeneity between studies, which was potentially related to differences in study design. In the five studies in adults with a parallel design, there was a 0·64% point (7·0 mmol/mol) reduction in HbA1c with carbohydrate counting versus control (95% CI -0·91 to -0·37; p<0·0001). INTERPRETATION: There is some evidence to support the recommendation of carbohydrate counting over alternate advice or usual care in adults with type 1 diabetes. Additional studies are needed to support promotion of carbohydrate counting over other methods of matching insulin dose to food intake. FUNDING: None.