Dr Carmel Smart

Abstract: Dietary management has been a mainstay of care in Type 1 diabetes since before the discovery of insulin when severe carbohydrate restriction was advocated. The use of insulin facilitated re-introduction of carbohydrate into the diet. Current management guidelines focus on a healthy and varied diet with consideration of glycaemic load, protein and fat. As a result of frustration with glycaemic outcomes, low-carbohydrate diets have seen a resurgence in popularity. To date, low-carbohydrate diets have not been well studied in the management of Type 1 diabetes. Studies looking at glycaemic outcomes from low-carbohydrate diets have largely been cross-sectional, without validated dietary data and with a lack of control groups. The participants have been highly motivated self-selected individuals who follow intensive insulin management practices, including frequent blood glucose monitoring and additional insulin corrections with tight glycaemic targets. These confounders limit the ability to determine the extent of the impact of dietary carbohydrate restriction on glycaemic outcomes. Carbohydrate-containing foods including grains, fruit and milk are important sources of nutrients. Hence, low-carbohydrate diets require attention to vitamin and energy intake to avoid micronutrient deficiencies and growth issues. Adherence to restricted diets is challenging and can have an impact on social normalcy. In individuals with Type 1 diabetes, adverse health risks such as diabetic ketoacidosis, hypoglycaemia, dyslipidaemia and glycogen depletion remain clinical concerns. In the present paper, we review studies published to date and provide clinical recommendations for ongoing monitoring and support for individuals who choose to adopt a low-carbohydrate diet. Strategies to optimize postprandial glycaemia without carbohydrate restriction are presented.

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.

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.

Low carbohydrate diets for the management of type 1 diabetes have been popularised by social media. The promotion of a low carbohydrate diet in lay media is in contrast to published pediatric diabetes guidelines that endorse a balanced diet from a variety of foods for optimal growth and development in children with type 1 diabetes. This can be a source of conflict in clinical practice. We describe a series of 6 cases where adoption of a low carbohydrate diet in children impacted growth and cardiovascular risk factors with potential long-term sequelae. These cases support current clinical guidelines for children with diabetes that promote a diet where total energy intake is derived from balanced macronutrient sources.

Background: Young children with type 1 diabetes (T1D) present unique challenges for intensive diabetes management. We describe an intensive diabetes program adapted for young children and compare glycemic control, anthropometry, dietary practices and insulin regimens before and after implementation. Methods: Cross sectional data from children with T1D aged =0.5 to <7.0 years attending the John Hunter Children's Hospital (JHCH), Australia in 2004, 2010 and 2016 were compared. Outcome measures were glycemic control assessed by hemoglobin A (HbA ); severe hypoglycemia episodes; body mass index standard deviation scores (BMI-SDS); diabetes ketoacidosis (DKA) episodes; and insulin regimen¿twice daily injections, multiple daily injections, or continuous subcutaneous insulin infusion. Results: Mean HbA declined by 12 mmol/mol over the study period (P <.01). The proportion of children achieving a mean HbA < 58 mmol/mol increased significantly from 31% in 2004 to 64% in 2010 (P <.01), and from 64% in 2010 to 83% in 2016 (P =.04). The mean BMI-SDS was significantly lower in 2010 when compared with 2004 (P<.01); however, this trend plateaued between 2010 and 2016 (P =.97). Severe hypoglycemia and DKA occurred infrequently. The prevalence of overweight or obesity increased from 2010 to 2016 (P =.03). Conclusions: The JHCH intensive diabetes management program has resulted in 83% of young children in 2016 achieving target glycemia without an increase in severe hypoglycemia or DKA. Overweight remains a challenge in this population warranting action to reduce weight and protect these children from future obesity-related health risks. 1c 1c 1c 1c

Aim: To determine the glycaemic impact of increasing protein quantities when consumed with consistent amounts of carbohydrate in individuals with Type 1 diabetes on intensive insulin therapy. Methods: Participants with Type 1 diabetes [aged 10¿40 years, HbA = 64 mmol/mol (8%), BMI = 91st percentile] received a 30-g carbohydrate (negligible fat) test drink daily over 5 days in randomized order. Protein (whey isolate 0 g/kg carbohydrate, 0 g/kg lipid) was added in amounts of 0 (control), 12.5, 25, 50 and 75 g. A standardized dose of insulin was given for the carbohydrate. Postprandial glycaemia was assessed by 5 h of continuous glucose monitoring. Results: Data were collected from 27 participants (15 male). A dose¿response relationship was found with increasing amount of protein. A significant negative relationship between protein dose and mean excursion was seen at the 30- and 60-min time points (P = 0.007 and P = 0.002, respectively). No significant relationship was seen at the 90- and 120-min time points. Thereafter, the dose¿response relationship inverted, such that there was a significant positive relationship for each of the 150¿300-min time points (P < 0.004). Mean glycaemic excursions were significantly greater for all protein-added test drinks from 150 to 300 min (P < 0.005) with the 75-g protein load, resulting in a mean excursion that was 5 mmol/l higher when compared with the control test drink (P < 0.001). Conclusions: Increasing protein quantity in a low-fat meal containing consistent amounts of carbohydrate decreases glucose excursions in the early (0¿60-min) postprandial period and then increases in the later postprandial period in a dose-dependent manner. 1c

Type 1 diabetes is a challenging condition to manage for various physiological and behavioural reasons. Regular exercise is important, but management of different forms of physical activity is particularly difficult for both the individual with type 1 diabetes and the health-care provider. People with type 1 diabetes tend to be at least as inactive as the general population, with a large percentage of individuals not maintaining a healthy body mass nor achieving the minimum amount of moderate to vigorous aerobic activity per week. Regular exercise can improve health and wellbeing, and can help individuals to achieve their target lipid profile, body composition, and fitness and glycaemic goals. However, several additional barriers to exercise can exist for a person with diabetes, including fear of hypoglycaemia, loss of glycaemic control, and inadequate knowledge around exercise management. This Review provides an up-to-date consensus on exercise management for individuals with type 1 diabetes who exercise regularly, including glucose targets for safe and effective exercise, and nutritional and insulin dose adjustments to protect against exercise-related glucose excursions.

Aims: To determine the optimum combination bolus split to maintain postprandial glycaemia with a high-fat and high-protein meal in young people with Type 1 diabetes. Methods: A total of 19 young people (mean age 12.9 ± 6.7 years) participated in a randomized, repeated-measures trial comparing postprandial glycaemic control across six study conditions after a high-fat and high-protein meal. A standard bolus and five different combination boluses were delivered over 2 h in the following splits: 70/30 = 70% standard /30% extended bolus; 60/40=60% standard/40% extended bolus; 50/50=50% standard/50% extended bolus; 40/60=40% standard/60% extended bolus; and 30/70=30% standard/70% extended bolus. Insulin dose was determined using the participant's optimized insulin:carbohydrate ratio. Continuous glucose monitoring was used to assess glucose excursions for 6 h after the test meal. Results: Standard bolus and combination boluses 70/30 and 60/40 controlled the glucose excursion up to 120 min. From 240 to 300 min after the meal, the glucose area under the curve was significantly lower for combination bolus 30/70 compared with standard bolus (P=0.004). Conclusions: High-fat and high-protein meals require a =60% insulin:carbohydrate ratio as a standard bolus to control the initial postprandial rise. Additional insulin at an insulin:carbohydrate ratio of up to 70% is needed in the extended bolus for a high fat and protein meal to prevent delayed hyperglycaemia.

Aims/hypothesis: Identifying women with gestational diabetes mellitus who are more likely to require insulin therapy vs medical nutrition therapy (MNT) alone would allow risk stratification and early triage to be incorporated into risk-based models of care. The aim of this study was to develop and validate a model to predict therapy type (MNT or MNT plus insulin [MNT+I]) for women with gestational diabetes mellitus (GDM). Methods: Analysis was performed of de-identified prospectively collected data (1992¿2015) from women diagnosed with GDM by criteria in place since 1991 and formally adopted and promulgated as part of the more detailed 1998 Australasian Diabetes in Pregnancy Society management guidelines. Clinically relevant variables predictive of insulin therapy by univariate analysis were dichotomised and included in a multivariable regression model. The model was tested in a separate clinic population. Results: In 3317 women, seven dichotomised significant independent predictors of insulin therapy were maternal age >30¿years, family history of diabetes, pre-pregnancy obesity (BMI =30¿kg/m ), prior GDM, early diagnosis of GDM (<24¿weeks gestation), fasting venous blood glucose level (=5.3¿mmol/l) and HbA at GDM diagnosis =5.5% (=37¿mmol/mol). The requirement for MNT+I could be estimated according to the number of predictors present: 85.7¿93.1% of women with 6¿7 predictors required MNT+I compared with 9.3¿14.7% of women with 0¿1 predictors. This model predicted the likelihood of several adverse outcomes, including Caesarean delivery, early delivery, large for gestational age and an abnormal postpartum OGTT. The model was validated in a separate clinic population. Conclusions/interpretation: This validated model has been shown to predict therapy type and the likelihood of several adverse perinatal outcomes in women with GDM. 2 1c

Objective: Retrospective continuous glucose monitoring (CGM) can guide insulin pump adjustments, however, interpretation of data and recommending new pump settings is complex and subjective. We aimed to compare the safety and glycaemic profiles of children after their diabetologist or a novel algorithm (PumpTune) adjusted their insulin pump settings. Research design and methods: In a randomized cross-over trial of 22 patients aged 6¿14 yr with type 1 diabetes with mean Hba1c 7.4% (57 mmol/mol) using CSII, CGM was used over two periods each of 6.5 d to assess percentage time glucose remained within, above and below 3.9¿10.0 mmol/L. Before the start of one period pump settings were adjusted by the patient's diabetologist, and before the other insulin pump settings were adjusted by PumpTune. Results: A total of 63.4% of the sensor glucose levels were within target range with PumpTune settings and 57.4% were within range with the clinician settings (p = 0.016). The time spent above target range with PumpTune was 26.9% and with clinician settings was 33.5% (p = 0.021). The time spent below target range with PumpTune was 9.7% and with clinician settings was 9.2% (p = 0.77). The mean number of times when a sensor glucose level <2.75 mmol/L was recorded with PumpTune settings was 2.9 compared with 3.7 with clinician settings (p = 0.39). There were no serious adverse outcomes and no difference in parent-assessed satisfaction. Conclusions: Automated insulin pump adjustment with PumpTune is feasible and warrants testing in a larger more varied population over a longer time. In this well-controlled group of children, PumpTune achieved a more favorable glucose profile.

Aim: To determine the effects of protein alone (independent of fat and carbohydrate) on postprandial glycaemia in individuals with Type¿1 diabetes mellitus using intensive insulin therapy. Methods: Participants with Type¿1 diabetes mellitus aged 7-40¿years consumed six 150¿ml whey isolate protein drinks [0¿g (control), 12.5, 25, 50, 75 and 100] and two 150¿ml glucose drinks (10 and 20¿g) without insulin, in randomized order over 8¿days, 4¿h after the evening meal. Continuous glucose monitoring was used to assess postprandial glycaemia. Results: Data were collected from 27 participants. Protein loads of 12.5 and 50¿g did not result in significant postprandial glycaemic excursions compared with control (water) throughout the 300¿min study period (P¿>¿0.05). Protein loads of 75 and 100¿g resulted in lower glycaemic excursions than control in the 60-120¿min postprandial interval, but higher excursions in the 180-300¿min interval. In comparison with 20¿g glucose, the large protein loads resulted in significantly delayed and sustained glucose excursions, commencing at 180¿min and continuing to 5¿h. Conclusions: Seventy-five grams or more of protein alone significantly increases postprandial glycaemia from 3 to 5¿h in people with Type¿1 diabetes mellitus using intensive insulin therapy. The glycaemic profiles resulting from high protein loads differ significantly from the excursion from glucose in terms of time to peak glucose and duration of the glycaemic excursion. This research supports recommendations for insulin dosing for large amounts of protein.

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, 10 g portions or 15 g 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.

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.

The usual recommendation made by clinicians to type 1 diabetics is that they should inject insulin when consuming a meal or, preferably, slightly earlier. However, in practice, insulin injection maybe delayed. In this paper we develop a fundamental limit on performance when insulin is delivered at some time other than the preferred time. The paper develops an optimal injection policy which minimizes the maximum blood glucose response whilst ensuring that the minimum response does not fall below a pre-specified level. The result provides a 'gold standard' against which other insulin injection policies can be compared. Implementation issues are also briefly described.