Dr Carmel Smart

Background: Nutrition education is central to pediatric type 1 diabetes management. Dietary management guidelines for type 1 diabetes are evidence based, but implementation may be challenging and inconsistent. We describe variation in the practice of nutrition education across pediatric diabetes centers globally and explore associations with A1c and BMI. Methods: In 2018, 77 pediatric diabetes clinics in the SWEET network received a survey about nutrition education. Using data submitted to the registry, regression analysis corrected for age, diabetes duration, BMI, and sex was used to compare survey parameters with A1c and BMI. Results: Fifty-three centers who collectively cared for 22,085 patients aged 0 to 18 with type 1 diabetes responded. Median A1c was 7.68% [IQR 7.37¿8.03], age 13.13 y [12.60¿13.54], insulin pump use 39.1%, and continuous glucose monitor use 37.3%. 34% reported screening for disordered eating, but only 15.1% used validated screening tools. Recommending insulin boluses for snacks in patients taking insulin via injection varied, with 23% of the clinics giving this recommendation to half or fewer patients. In regression analysis, instructing patients to take insulin for snacks was the only survey parameter associated with the percent of clinic percent of patients attaining A1c <7.5% (<58 mmol/mol, P = 0.018) and < 7.0% (<53 mmol/mol, P = 0.026). Conclusions: There is considerable variation in nutrition education for pediatric patients with type 1 diabetes across this international registry. Consistently recommending independent of treatment modality (insulin pump or injections) that patients take insulin for snacks and more uniformity in screening for disordered eating are improvement opportunities.

Background: Pediatric diabetes clinics around the world rapidly adapted care in response to COVID-19. We explored provider perceptions of care delivery adaptations and challenges for providers and patients across nine international pediatric diabetes clinics. Methods: Providers in a quality improvement collaborative completed a questionnaire about clinic adaptations, including roles, care delivery methods, and provider and patient concerns and challenges. We employed a rapid analysis to identify main themes. Results: Providers described adaptations within multiple domains of care delivery, including provider roles and workload, clinical encounter and team meeting format, care delivery platforms, self-management technology education, and patient-provider data sharing. Providers reported concerns about potential negative impacts on patients from COVID-19 and the clinical adaptations it required, including fears related to telemedicine efficacy, blood glucose and insulin pump/pen data sharing, and delayed care-seeking. Particular concern was expressed about already vulnerable patients. Simultaneously, providers reported 'silver linings' of adaptations that they perceived as having potential to inform care and self-management recommendations going forward, including time-saving clinic processes, telemedicine, lifestyle changes compelled by COVID-19, and improvements to family and clinic staff literacy around data sharing. Conclusions: Providers across diverse clinical settings reported care delivery adaptations in response to COVID-19¿particularly telemedicine processes¿created challenges and opportunities to improve care quality and patient health. To develop quality care during COVID-19, providers emphasized the importance of generating evidence about which in-person or telemedicine processes were most beneficial for specific care scenarios, and incorporating the unique care needs of the most vulnerable patients.

Objective: To examine the association between the use of diabetes technology (insulin pump [CSII], glucose sensor [CGM] or both) and metabolic control (HbA1c) as well as body adiposity (BMI-SDS) over-time in a cohort of children and adolescents with type 1 diabetes (T1D), that have never used these technologies before. Subjects and methods: Four thousand six hundred forty three T1D patients (2¿18 years, T1D =1 year, without celiac disease, no CSII and/or CGM before 2016) participating in the SWEET prospective multicenter diabetes registry, were enrolled. Data were collected at two points (2016; 2019). Metabolic control was assessed by glycated hemoglobin (HbA1c) and body adiposity by BMI-SDS (WHO). Patients were categorized by treatment modality (multiple daily injections [MDI] or CSII) and the use or not of CGM. Linear regression models, adjusted for age, gender, duration of diabetes and region, were applied to assess differences in HbA1c and BMI-SDS among patient groups. Results: The proportion of patients using MDI with CGM and CSII with CGM significantly increased from 2016 to 2019 (7.2%¿25.7%, 7.8%¿27.8% respectively; p < 0.001). Linear regression models showed a significantly lower HbA1c in groups that switched from MDI to CSII with or without CGM (p < 0.001), but a higher BMI-SDS (from MDI without CGM to CSII with CGM p < 0.05; from MDI without CGM to CSII without CGM p < 0.01). Conclusions: Switching from MDI to CSII is significantly associated with improvement in glycemic control but increased BMI-SDS over-time. Diabetes technology may improve glucose control in youths with T1D although further strategies to prevent excess fat accumulation are needed.

Background: Type 1 Diabetes (T1D) is associated with increased risk of eating disorders. This study aimed to (1) assess adherence of Australasian paediatric T1D clinics to international guidelines on screening for disordered eating and (2) identify barriers and enablers to the use of screening tools for the identification of disordered eating. Methods: A 24-item survey covering five content domains: clinic characteristics, identification of disordered eating, screening tool use, training and competence, and pathways for referral, was sent to Australasian clinics caring for =150 children and adolescents with T1D. Results: Of 13 eligible clinics, 10 participated. Two reported rates of disordered eating of >20%, while eight reported rates < 5%. All clinics used the routine clinical interview as the primary method of screening for disordered eating. Only one used screening tools; these were not diabetes-specific or routinely used. Barriers to use of screening tools included shortage of time and lack of staff confidence around use (n = 7, 70%). Enablers included staff training in disordered eating. Conclusions: Screening tools for disordered eating are not utilised by most Australasian paediatric T1D clinics. Overall, low reported rates of disordered eating suggest that it may be undetected, potentially missing an opportunity for early intervention.

Objectives: To assess the clinical and demographic characteristics of children and adolescents across Australia and New Zealand (NZ) with type 2 diabetes. Methods: We performed a descriptive audit of data prospectively reported to the Australasian Diabetes Data Network (ADDN) registry. Data were collected from six tertiary pediatric diabetes centers across Australia (New South Wales, Queensland, South Australia, Western Australia, and Victoria) and NZ (Auckland). Children and adolescents diagnosed with type 2 diabetes aged = 18 years with data reported to ADDN between 2012 and 2017 were included. Age, sex, ethnicity, HbA1c, blood pressure, BMI, waist circumference and lipid profile at first visit were assessed. Results: There were 269 cases of type 2 diabetes in youth reported to ADDN between 2012 and 2017. The most common ethnicities were Indigenous Australian in 56/243 (23%) and NZ Maori or Pacifica in 47 (19%). Median age at diagnosis was 13.7 years and 94% of participants were overweight or obese. Indigenous Australian and Maori/Pacifica children were younger at diagnosis compared with nonindigenous children: median 13.3 years (indigenous Australian); 13.1 years (Maori/Pacifica); 14.1 years (nonindigenous), p = 0.005. HbA1c was higher in indigenous Australian (9.4%) and Maori/Pacifica youth (7.8%) compared with nonindigenous (6.7%) p < 0.001. BMI-SDS was higher in Maori/Pacifica youth (2.3) compared with indigenous Australian (2.1) and nonindigenous (2.2) p = 0.011. Conclusions: Indigenous Australian and Maori/Pacifica youth in ADDN were younger and had worse glycaemic control at diagnosis of type 2 diabetes. Our findings underscore the need to consider targeted and earlier screening in these ¿high-risk¿ populations.

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 A1c (HbA1c); 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 HbA1c declined by 12 mmol/mol over the study period (P <.01). The proportion of children achieving a mean HbA1c < 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.

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, HbA1c = 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.

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/m2), prior GDM, early diagnosis of GDM (<24¿weeks gestation), fasting venous blood glucose level (=5.3¿mmol/l) and HbA1c 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.

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.