Conjoint Professor Bruce King

Problem: Children with Type 1 Diabetes (T1D) spend a large amount of time at school. Subsequently, school staff often need education around T1D management to optimise related outcomes. Education is particularly essential in primary school settings, due to lack of independence. Aim: With no published review summarising the provision of diabetes education to schools, this integrative literature review aimed to investigate and present available literature regarding T1D education to school staff. Eligibility criteria: This included studies published after 2013 in English, examining education provided to primary schools who support students with T1D. Systematic reviews, other non-empirical articles and records only containing abstracts were excluded. Sample: Overall, 15 relevant studies were identified. Analysis: Quality appraisal was undertaken using Joanna Briggs Critical Appraisal tools. Results: There was wide variation in the number of school staff that had received training in the management of T1D. Studies reported that even schools with glucagon available had no trained staff to administer; while many studies did not differentiate results based on geographical location such as metropolitan or rural settings. Conclusions: There is wide variation in the provision of education around T1D management in school settings. There is need for further research into differences in the provision of diabetes education across different geographical and rural areas. Implications: For children living with T1D to receive optimal management at school there needs to be improved access to ongoing support, training and awareness for school staff and a more standardised approach to the provision of diabetes education from health professionals.

Aim: To determine low-density lipoprotein cholesterol (LDL-C) screening frequency and levels, and factors associated with elevated LDL-C, in Australasian youth with type 1 diabetes (T1D). Methods: Data were extracted from the Australasian Diabetes Data Network (ADDN), a prospective clinical quality registry, on all T1D healthcare visits attended by young people aged 16¿25 years (with T1D duration of >1 year) between January 2011 and December 2020. The primary outcomes were elevated LDL-C > 2.6 mmol/L (100 mg/dL) and threshold for treatment: >3.4 mmol/L (130 mg/dL), according to consensus guidelines. Multivariable Generalised Estimated Equations (GEE) were used to examine factors associated with elevated LDL-C across all visits. Results: A cohort of 6338 young people (52.6% men) were identified, of whom 1603 (25.3%) had =1 LDL-C measurement documented. At last measurement, mean age, age at T1D diagnosis and T1D duration were 18.3 ± 2.4, 8.8 ± 4.5 and 8.9 ± 4.8 years, respectively. LDL-C was elevated in 737 (46.0%) and at the treatment threshold in 250 (15.6%). In multivariable GEE elevated LDL-C continuously was associated with older age (OR = 0.07; 0.01¿0.13, p = 0.02), female sex (OR = 0.31; 0.18¿0.43; p < 0.001), higher HbA1c (OR = 0.04; 0.01¿0.08; p = 0.01) and having an elevated BMI (OR = 0.17, 0.06¿0.39, p < 0.001). Conclusions: LDL-C screening and levels are suboptimal in this cohort, increasing future cardiovascular complication risk. There is an urgent need to understand how healthcare services can support improved screening and management of dyslipidaemia in this population.

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.

Context: The pattern and quantity of insulin required for high-protein high-fat (HPHF) meals is not well understood. Objective: This study aimed to determine the amount and delivery pattern of insulin required to maintain euglycemia for 5 hours after consuming a HPHF meal compared with a low-protein low-fat (LPLF) meal. Methods: This randomized crossover clinical trial, conducted at 2 Australian pediatric diabetes centers, included 10 patients (12-21 years of age) with type 1 diabetes for = 1 year. Participants were randomized to HPHF meal (60 g protein, 40 g fat) or LPLF meal (5 g protein, 5 g fat) with identical carbohydrate content (30 g). A modified insulin clamp technique was used to determine insulin requirements to maintain postprandial euglycemia for 5 hours. Total mean insulin requirements over 5 hours were measured. Results: The total mean insulin requirements for the HPHF meal were significantly greater than for the LPLF meal (11.0 [CI 9.2, 12.8] units vs 5.7 [CI 3.8, 7.5] units; P = 0.001). Extra intravenous insulin was required for HPHF: 0 to 2 hours (extra 1.2 [CI 0.6, 1.6] units/h), 2 to 4 hours (extra 1.1 [CI 0.6, 1.6] units/h), and 4 to 5 hours (extra 0.6 [CI 0.1, 1.1] units/h) after the meal. There were marked inter-individual differences in the quantity of additional insulin (0.3 to 5 times more for HPHF) and the pattern of insulin delivery (0%-85% of additional insulin required in the first 2 hours). Conclusion: The addition of protein and fat to a standardized carbohydrate meal almost doubled the mean insulin requirement, with most participants requiring half of the additional insulin in the first 2 hours.

Context: Cardiovascular disease occurs prematurely in type 1 diabetes. The additional risk of overweight is not well characterized. Objective: The primary aim was to measure the impact of body mass index (BMI) in youth with type 1 diabetes on cardiovascular risk factors. The secondary aim was to identify other determinants of cardiovascular risk. Design: Observational longitudinal study of 7061 youth with type 1 diabetes followed for median 7.3 (interquartile range [IQR] 4-11) years over 41 (IQR 29-56) visits until March 2019. Setting: 15 tertiary care diabetes centers in the Australasian Diabetes Data Network. Participants were aged 2 to 25 years at baseline, with at least 2 measurements of BMI and blood pressure. Main Outcome Measure: Standardized systolic and diastolic blood pressure scores and non-high-density lipoprotein (HDL) cholesterol were co-primary outcomes. Urinary albumin/creatinine ratio was the secondary outcome. Results: BMI z-score related independently to standardized blood pressure z- scores and non-HDL cholesterol. An increase in 1 BMI z-score related to an average increase in systolic/diastolic blood pressure of 3.8/1.4 mmHg and an increase in non-HDL cholesterol (coefficient + 0.16 mmol/L, 95% confidence interval [CI], 0.13-0.18; P < 0.001) and in low-density lipoprotein (LDL) cholesterol. Females had higher blood pressure z-scores, higher non-HDL and LDL cholesterol, and higher urinary albumin/creatinine than males. Indigenous youth had markedly higher urinary albumin/creatinine (coefficient + 2.15 mg/mmol, 95% CI, 1.27-3.03; P < 0.001) and higher non-HDL cholesterol than non-Indigenous youth. Continuous subcutaneous insulin infusion was associated independently with lower non-HDL cholesterol and lower urinary albumin/creatinine. Conclusions: BMI had a modest independent effect on cardiovascular risk. Females and Indigenous Australians in particular had a more adverse risk profile.

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.

Aims/hypothesis: The aim of this work was to evaluate geographical variability and trends in the prevalence of diabetic ketoacidosis (DKA), between 2006 and 2016, at the diagnosis of childhood-onset type 1 diabetes in 13 countries over three continents. Methods: An international retrospective study on DKA at diagnosis of diabetes was conducted. Data on age, sex, date of diabetes diagnosis, ethnic minority status and presence of DKA at diabetes onset were obtained from Australia, Austria, Czechia, Denmark, Germany, Italy, Luxembourg, New Zealand, Norway, Slovenia, Sweden, USA and the UK (Wales). Mean prevalence was estimated for the entire period, both overall and by country, adjusted for sex and age group. Temporal trends in annual prevalence of DKA were estimated using logistic regression analysis for each country, before and after adjustment for sex, age group and ethnic minority status. Results: During the study period, new-onset type 1 diabetes was diagnosed in 59,000 children (median age [interquartile range], 9.0¿years [5.5¿11.7]; male sex, 52.9%). The overall adjusted DKA prevalence was 29.9%, with the lowest prevalence in Sweden and Denmark and the highest in Luxembourg and Italy. The adjusted DKA prevalence significantly increased over time in Australia, Germany and the USA while it decreased in Italy. Preschool children, adolescents and children from ethnic minority groups were at highest risk of DKA at diabetes diagnosis in most countries. A significantly higher risk was also found for females in Denmark, Germany and Slovenia. Conclusions/interpretation: DKA prevalence at type 1 diabetes diagnosis varied considerably across countries, albeit it was generally high and showed a slight increase between 2006 and 2016. Increased awareness of symptoms to prevent delay in diagnosis is warranted, especially in preschool children, adolescents and children from ethnic minority groups.

Objectives: To identify biomarkers of renal disease in adolescents with type 1 diabetes (T1D) and to compare findings in adults with T1D. Methods: Twenty-five serum biomarkers were measured, using a Luminex platform, in 553 adolescents (median [interquartile range] age: 13.9 [12.6, 15.2] years), recruited to the Adolescent Type 1 Diabetes Cardio-Renal Intervention Trial. Associations with baseline and final estimated glomerular filtration rate (eGFR), rapid decliner and rapid increaser phenotypes (eGFR slopes <-3 and > 3 mL/min/1.73m2/year, respectively), and albumin-creatinine ratio (ACR) were assessed. Results were also compared with those obtained in 859 adults (age: 55.5 [46.1, 64.4) years) from the Scottish Diabetes Research Network Type 1 Bioresource. Results: In the adolescent cohort, baseline eGFR was negatively associated with trefoil factor-3, cystatin C, and beta-2 microglobulin (B2M) (B coefficient[95%CI]: -0.19 [-0.27, -0.12], P = 7.0 × 10-7; -0.18 [-0.26, -0.11], P = 5.1 × 10-6; -0.12 [-0.20, -0.05], P = 1.6 × 10-3), in addition to clinical covariates. Final eGFR was negatively associated with osteopontin (-0.21 [-0.28, -0.14], P = 2.3 × 10-8) and cystatin C (-0.16 [-0.22, -0.09], P = 1.6 × 10-6). Rapid decliner phenotype was associated with osteopontin (OR: 1.83 [1.42, 2.41], P = 7.3 × 10-6), whereas rapid increaser phenotype was associated with fibroblast growth factor-23 (FGF-23) (1.59 [1.23, 2.04], P = 2.6 × 10-4). ACR was not associated with any of the biomarkers. In the adult cohort similar associations with eGFR were found; however, several additional biomarkers were associated with eGFR and ACR. Conclusions: In this young population with T1D and high rates of hyperfiltration, osteopontin was the most consistent biomarker associated with prospective changes in eGFR. FGF-23 was associated with eGFR increases, whereas trefoil factor-3, cystatin C, and B2M were associated with baseline eGFR.

Background: Adrenal crises (AC) are acute episodes of adrenal insufficiency (AI). Manifestations include hypotension and electrolyte disturbances. Glucocorticoid stress dosing (SD) can prevent AC progression, but its effect on physiological parameters has not been assessed in a ¿real world setting¿. Aims: To assess the effect of prior self-managed glucocorticoid dose escalation on physiological markers in children with congenital adrenal hyperplasia (CAH) presenting to hospital for an acute illness. Methods: An audit of records of all children with CAH presenting to paediatric referral hospital between 2000 and 2015. Potassium, sodium and glucose levels, and hypotension were compared between children who had and had not used SD. Results: There were 321 attendances by patients with CAH and an acute illness during the study period. Any form of SD was used by 64.2% (n¿=¿206); intramuscular (IM) hydrocortisone was used by 22.1% (n¿=¿71) and oral only by 41.7% (n¿=¿134). Use of SD (oral and/or IM) was associated with a significantly lower mean potassium level (4.02¿±¿0.71 vs. 4.27¿±¿0.79¿mmol/l, P¿<.05). Linear regression analysis showed that age (beta: -0.04¿years (95% CI -0.06, -0.02)), diarrhoea (beta: -0.41 (95% CI -0.06, -0.02)) and any form of stress dosing (oral, IM or both) (beta: -0.29 (95% CI -0.55, -0.04)) were each independently and significantly associated with potassium levels. SD was not significantly associated with sodium or glucose concentrations or with estimates of hypotension. Conclusion: Patient-initiated SD resulted in a significant reduction in hyperkalaemia and lowered mean potassium levels in paediatric patients with CAH but did not alter significantly sodium and glucose concentrations or incidences of hypotension.

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.

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.

Background: Insulin pump therapy (IPT) is increasingly used in children and young people with type 1 diabetes. There are limited studies evaluating the optimal time to start IPT. Objective: The aim of this study was to determine if early initiation of IPT in children with type 1 diabetes leads to improved glycaemic control and quality of life (QOL) compared with the later introduction of IPT. Subjects: There were 38 subjects in the early pump group (EPG) (age 12.6 + 4.9 yr, 23 male) and 37 in the later pump group (LPG) (age 13.1 + 4.1 yr, 19 male). Methods: Hemoglobin A1c (HbA1c), rate of severe hypoglycemia, and diabetic ketoacidosis (DKA) were collected retrospectively over a 48-month period. Eligible subjects and/or their parents completed both a Paediatric and Paediatric Diabetes-specific Quality of Life Inventory. Results: HbA1c measurements were lower in the EPG (6.8%; 51 mmol/mol) compared to the LPG (7.9%; 63 mmol/mol), across the 48 months of the study (p < 0.0001). There was no significant difference in the rate (per patient years) of severe hypoglycaemia (0.02; 0.07) p = 0.075 between the two groups. There were no episodes of DKA in either group. There was no significant difference in QOL between the groups with both having high satisfaction rates. Conclusions: Initiation of IPT at diagnosis of type 1 diabetes in children resulted in consistently lower HbA1c with no apparent change in hypoglycemia, DKA, or QOL.

OBJECTIVE Celiac disease (CD) has a recognized association with type 1 diabetes.We examined international differences in CD prevalence and clinical characteristics of youth with coexisting type 1 diabetes and CD versus type 1 diabetes only. RESEARCH DESIGN AND METHODS Data sources were as follows: the Prospective Diabetes Follow-up Registry (DPV) (Germany/Austria); the T1D Exchange Clinic Network (T1DX) (U.S.); the National Paediatric Diabetes Audit (NPDA) (U.K. [England/Wales]); and the Australasian Diabetes Data Network (ADDN) (Australia). The analysis included 52,721 youths <18 years of age with a clinic visit between April 2013 and March 2014. Multivariable linear and logistic regression models were constructed to analyze the relationship between outcomes (HbA1c, height SD score [SDS], overweight/obesity) and type 1 diabetes/CD versus type 1 diabetes, adjusting for sex, age, and diabetes duration. RESULTS Biopsy-confirmed CD was present in 1,835 youths (3.5%) and was diagnosed at a median age of 8.1 years (interquartile range 5.3-11.2 years). Diabetes duration at CD diagnosis was <1 year in 37% of youths, 1-2 years in 18% of youths, 3-5 years in 23%of youths, and 5 years in 17%of youths. CD prevalence ranged from1.9%in the T1DX to 7.7%in the ADDNand was higher in girls than boys (4.3%vs. 2.7%, P < 0.001). Children with coexisting CDwere younger at diabetes diagnosis compared with those with type 1 diabetes only (5.4 vs. 7.0 years of age, P < 0.001) and fewer were nonwhite (15 vs. 18%, P < 0.001). Height SDS was lower in those with CD (0.36 vs. 0.48, adjusted P < 0.001) and fewer were overweight/obese (34 vs. 37%, adjusted P < 0.001),whereasmeanHbA1c valueswere comparable: 8.361.5%(67617mmol/mol) versus 8.4 6 1.6% (68 6 17 mmol/mol). CONCLUSIONS CD is a common comorbidity in youth with type 1 diabetes. Differences in CD prevalence may reflect international variation in screening and diagnostic practices, and/ or CD risk. Although glycemic control was not different, the lower height SDS supports close monitoring of growth and nutrition in this population.

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.

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.

Background: Sensor-augmented pump therapy (SAPT) with a predictive algorithm to suspend insulin delivery has the potential to reduce hypoglycemia, a known obstacle in improving physical activity in patients with type 1 diabetes. The predictive low glucose management (PLGM) system employs a predictive algorithm that suspends basal insulin when hypoglycemia is predicted. The aim of this study was to determine the efficacy of this algorithm in the prevention of exercise-induced hypoglycemia under in-clinic conditions. Methods: This was a randomized, controlled cross-over study in which 25 participants performed 2 consecutive sessions of 30 min of moderate-intensity exercise while on basal continuous subcutaneous insulin infusion on 2 study days: a control day with SAPT alone and an intervention day with SAPT and PLGM. The predictive algorithm suspended basal insulin when sensor glucose was predicted to be below the preset hypoglycemic threshold in 30 min. We tested preset hypoglycemic thresholds of 70 and 80 mg/dL. The primary outcome was the requirement for hypoglycemia treatment (symptomatic hypoglycemia with plasma glucose <63 mg/dL or plasma glucose <50 mg/dL) and was compared in both control and intervention arms. Results: Results were analyzed in 19 participants. In the intervention arm with both thresholds, only 6 participants (32%) required treatment for hypoglycemia compared with 17 participants (89%) in the control arm (P = 0.003). In participants with a 2-h pump suspension on intervention days, the plasma glucose was 84 ± 12 and 99 ± 24 mg/dL at thresholds of 70 and 80 mg/dL, respectively. Conclusions: SAPT with PLGM reduced the need for hypoglycemia treatment after moderate-intensity exercise in an in-clinic setting.

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.

This paper describes the application of Model Predictive Control incorporating Stochastic Programming to the treatment of Type 1 diabetes. The use of stochastic programming in this context is believed to be important since it addresses one of the key difficulties associated with diabetes treatment namely the inherent uncertainty associated with future food and exercise patterns. Preliminary results are presented, based on real patients, illustrating the advantages of using stochastic model predictive control for this application.

This paper develops a nonlinear insulin to carbohydrate rule for use by type 1-diabetes patients. The goal is to refine the commonly used Insulin to Carbohydrate Ratio (ICR) formula. The latter is a strictly linear rule relating carbohydrates consumed to insulin infusion. The new relationship presented in this paper is nonlinear and depends on the availability of a nonlinear dynamic model describing a patient's blood glucose response to food and insulin. Such a model can be obtained by use of nonlinear system identification tools applied to patient test data. The suggested procedure is of similar complexity to the existing standard ICR rule. Hence it has the potential to be of clinical importance especially in developing countries where sophisticated solutions such as an artificial pancreas are unlikely to be used due to excessive cost. Simulations are presented which show that there exists a significant difference between the suggested insulin provided by the rule developed here and that given by the standard ICR rule.

This paper presents a preliminary study of performance limitations that arise in the closed-loop control of blood glucose, using an autonomous artificial pancreas. It is shown that a major source of limitations is due to model uncertainty, specifically due to the combined effect of the insulin infusion system (IIS), the continuous glucose monitor (CGM) and the human glucose regulatory system. It is argued that the uncertainty associated with each of these elements compromises the achievable closed-loop bandwidth, and, in the presence of disturbances, e.g. meal intake and exercise, the closed-loop response will necessarily be poor. A proposition to overcome this problem is given based on feedforward action.