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OBJECTIVE To determine the glycemic impact of dietary fat alone consumed without prandial insulin in individuals with T1D. RESEARCH DESIGN AND METHODS Thirty participants with T1D (aged 8-18 years) consumed a test drink with either 20 g glucose or 1, 13, 26, 39, 51 g of fat with negligible carbohydrate/protein on 6 consecutive evenings, in a randomized order without insulin. Continuous glucose monitoring was used to measure glucose levels for 8 h postprandially. Primary outcome was mean glycemic excursion at each 30 min interval for each test condition. Generalized linear mixed models with a random effect for people with diabetes were used to test for an increase in blood glucose excursion with increasing quantity of fat. RESULTS Glycemic excursions after 20 g glucose were higher than after fat drinks over the first 2 h (p

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.

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 (RA) 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. This article is protected by copyright. All rights reserved.

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 Conclusions BMI had a modest independent effect on cardiovascular risk. Females and Indigenous Australians in particular had a more adverse risk profile.

Aims To identify foods that cause problematic postprandial blood glucose levels (BGLs) in children and young people with type 1 diabetes, the strategies families use to manage these foods and the impact of continuous glucose monitoring (CGM) on nutritional management. Methods This was a cross-sectional survey of 100 families attending a paediatric diabetes centre in Australia. Results Participants (n = 100) had a mean age of 13.0 ± 3.6 years; diabetes duration 5.2 ± 4.0 years; HbA1c 53 ± 0.9 mmol/mol (7.0 ± 0.8%); 52% used multiple daily injections (MDI, ≥4 injections/day); 48% used insulin pump therapy; and overall, 60% used CGM. Ninety-one participants (91%) identified problematic foods, including pizza (60%), pasta (55%) and rice (31%). Of these, 96% used one or more strategies to manage BGLs, including correcting BGLs more often (51%), use of a combination bolus (39%) and increasing the meal insulin dose (32%). Participants who gave additional meal insulin (n = 28) increased the dose by 10% to 25%. All MDI users (n = 15) gave additional insulin pre-prandially. Of those using CGM, 88% (n = 53) reported an increased awareness of the glycaemic impact of foods, and 27% (n = 16) had subsequently made changes to their management including avoiding and/or restricting new foods (n = 7). Conclusions Families with type 1 diabetes reported foods such as pizza, pasta and rice as problematic and used strategies such as increasing the insulin dose to minimise their glycaemic impact. CGM contributed to the awareness of problematic foods. Clinicians should discuss these foods and, if challenging, provide targeted strategies including adjusting the insulin dose and delivery pattern to improve postprandial glycaemia.

AIM To determine the amount of additional insulin required for a high-protein meal to prevent postprandial hyperglycaemia in individuals with type 1 diabetes using insulin pump therapy. METHODS In this randomized cross-over study, 26 participants aged 8-40 years, HbA1c

IMPORTANCE: Hybrid closed-loop (HCL) therapy has improved glycemic control in children and adolescents with type 1 diabetes; however, the efficacy of HCL on glycemic and psychosocial outcomes has not yet been established in a long-term randomized clinical trial. OBJECTIVE: To determine the percentage of time spent in the target glucose range using HCL vs current conventional therapies of continuous subcutaneous insulin infusion or multiple daily insulin injections with or without continuous glucose monitoring (CGM). DESIGN, SETTING, AND PARTICIPANTS: This 6-month, multicenter, randomized clinical trial included 172 children and adolescents with type 1 diabetes; patients were recruited between April 18, 2017, and October 4, 2019, in Australia. Data were analyzed from July 25, 2020, to February 26, 2021. INTERVENTIONS: Eligible participants were randomly assigned to either the control group for conventional therapy (continuous subcutaneous insulin infusion or multiple daily insulin injections with or without CGM) or the intervention group for HCL therapy. MAIN OUTCOMES AND MEASURES: The primary outcome was the percentage of time in range (TIR) within a glucose range of 70 to 180 mg/dL, measured by 3-week masked CGM collected at the end of the study in both groups. Secondary outcomes included CGM metrics for hypoglycemia, hyperglycemia, and glycemic variability and psychosocial measures collected by validated questionnaires. RESULTS: A total of 135 patients (mean [SD] age, 15.3 [3.1] years; 76 girls [56%]) were included, with 68 randomized to the control group and 67 to the HCL group. Patients had a mean (SD) diabetes duration of 7.7 (4.3) years and mean hemoglobin A(1c) of 64 (11) mmol/mol, with 110 participants (81%) receiving continuous subcutaneous insulin infusion and 72 (53%) receiving CGM. In the intention-to-treat analyses, TIR increased from a mean (SD) of 53.1% (13.0%) at baseline to 62.5% (12.0%) at the end of the study in the HCL group and from 54.6% (12.5%) to 56.1% (12.2%) in the control group, with a mean adjusted difference between the 2 groups of 6.7% (95% CI, 2.7%-10.8%; P = .002). Hybrid closed-loop therapy also reduced the time that patients spent in a hypoglycemic (

ContextP450 oxidoreductase deficiency (PORD) is a rare genetic disorder that is associated with significant morbidity. However there has been limited analysis of reported PORD cases.ObjectiveTo determine, based on the cohort of reported PORD cases, genotype-phenotype relationships for skeletal malformations, maternal virilisation in pregnancy, adrenal insufficiency, and disorders of sexual development (DSD).Data SourcesPubMed and Web of Science from January 2004 to February 2018.Study SelectionPublished case reports/series of patients with PORD. Eligible patients were unique, had biallelic mutations, and their clinical features were reported.Data ExtractionPatient data were manually extracted from the text of case reports/series. A malformation score, representing the severity of skeletal malformations, was calculated for each patient.Data SynthesisOf the 211 patients published in the literature, 90 were eligible for inclusion. More than 60 unique mutations were identified in this cohort. Four groups of mutations were identified, through regression modeling, as having significantly different skeletal malformation scores. Maternal virilization in pregnancy, reported for 21% of patients, was most common for R457H mutations. Adrenal insufficiency occurred for the majority of patients (78%) and was typically mild, with homozygous R457H mutations being the least deficient. DSD affected most patients (72%), but were less common for males (46XY) with homozygous R457H mutations.ConclusionsPORD is a complex disorder with many possible mutations affecting a large number of enzymes. By analyzing the cohort of reported PORD cases, this study identified clear relationships between genotype and several important phenotypic features.

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 Overweight and obesity are frequently reported in young persons with type 1 diabetes, however its relative magnitude in comparison to the general population is not well understood. This study compared the prevalence of overweight and obesity in young persons with type 1 diabetes to a reference population and explored possible associated factors, including gender, age, HbA1c , insulin regimen, age at diagnosis, diabetes duration, socio-economic status and cardiovascular disease risk factors. METHODS A cross-sectional review was undertaken of data collected from youth (3-17 years) in 2016 and young adults (18-30 years) in 2015 with a diagnosis of type 1 diabetes for > 3 months attending diabetes centres in Newcastle, Australia. Rates of overweight and obesity were compared with matched population survey results. RESULTS Data from 308 youth and 283 young adults were included. In girls, significantly higher prevalence of overweight and obesity were seen in the 5-8 (43% vs. 18%), 13-16 (41% vs. 27%), 18-24 (46% vs. 34%) and 25-30 (60% vs. 43%) years age groups; whereas in boys increased prevalence was observed in the 5-8 years age group only (41% vs. 18%). Rates of overweight and obesity increased with age across sexes. In youth, BMI standard deviation score was correlated with socio-economic status, insulin regimen, blood pressure and blood lipids (P

OBJECTIVE To determine if the relationship between meal carbohydrate quantity and the insulin to carbohydrate ratio (ICR) required to maintain glycaemia is linear in people with type 1 diabetes. METHODS We used an open labelled randomized four-arm cross-over study design. Participants (N = 31) aged 12-27 years, HbA1c ≤ 64 mmol/mol (8.0%) received insulin doses based on the individual's ICR and the study breakfast carbohydrate quantity and then consumed four breakfasts containing 20, 50, 100 and 150 g of carbohydrate over four consecutive days in randomized order. The breakfast fat and protein percentages were standardized. Postprandial glycaemia was assessed by 5 h continuous glucose monitoring. The primary outcome was percent time in range (TIR) and secondary outcomes included hypoglycaemia, glucose excursion and incremental area under the curve. Statistical analysis included linear mixed modelling and Wilcoxon signed rank tests. RESULTS The 20 g carbohydrate breakfast had the largest proportion of TIR (0.74 ± 0.29 p

OBJECTIVE Continuous glucose monitoring (CGM) is increasingly used in type 1 diabetes management; however, funding models vary. This study determined the uptake rate and glycemic outcomes following a change in national health policy to introduce universal subsidized CGM funding for people with type 1 diabetes aged RESEARCH DESIGN AND METHODS Longitudinal data from 12 months before the subsidy until 24 months after were analyzed. Measures and outcomes included age, diabetes duration, HbA1c, episodes of diabetic ketoacidosis and severe hypoglycemia, insulin regimen, CGM uptake, and percentage CGM use. Two data sources were used: the Australasian Diabetes Database Network (ADDN) registry (a prospective diabetes database) and the National Diabetes Service Scheme (NDSS) registry that includes almost all individuals with type 1 diabetes nationally. RESULTS CGM uptake increased from 5% presubsidy to 79% after 2 years. After CGM introduction, the odds ratio (OR) of achieving the HbA1c target of 75% of time, and had a lower HbA1c at 24 months compared with those with usage CONCLUSIONS Following the national subsidy, CGM use was high and associated with sustained improvement in glycemic control. This information will inform economic analyses and future policy and serve as a model of evaluation diabetes technologies.

OBJECTIVE To compare glucose control with hybrid closed-loop (HCL) when challenged by high intensity exercise (HIE), moderate intensity exercise (MIE), and resistance exercise (RE) while profiling counterregulatory hormones, lactate, ketones, and kinetic data in adults with type 1 diabetes. RESEARCH DESIGN AND METHODS This study was an open-label multisite randomized crossover trial. Adults with type 1 diabetes undertook 40 min of HIE, MIE, and RE in random order while using HCL (Medtronic MiniMed 670G) with a temporary target set 2 h prior to and during exercise and 15 g carbohydrates if pre-exercise glucose was RESULTS Median TIR was 81% (67, 93%), 91% (80, 94%), and 80% (73, 89%) for 0–14 h post–exercise commencement for HIE, MIE, and RE, respectively (n = 30), with no difference between exercise types (MIE vs. HIE; P = 0.11, MIE vs. RE, P = 0.11; and HIE vs. RE, P = 0.90). Time-below-range was 0% for all exercise bouts. For HIE and RE compared with MIE, there were greater increases, respectively, in noradrenaline (P = 0.01 and P = 0.004), cortisol (P < 0.001 and P = 0.001), lactate (P ≤ 0.001 and P ≤ 0.001), and heart rate (P = 0.007 and P = 0.015). During HIE compared with MIE, there were greater increases in growth hormone (P = 0.024). CONCLUSIONS Under controlled conditions, HCL provided satisfactory glucose control with no difference between exercise type. Lactate, counterregulatory hormones, and kinetic data differentiate type and intensity of exercise, and their measurement may help inform insulin needs during exercise. However, their potential utility as modulators of insulin dosing will be limited by the pharmacokinetics of subcutaneous insulin delivery.

Aim To identify and report the efficacy of insulin strategies used to manage glycaemia following fat and/or fat and protein meals in type 1 diabetes. Methods A systematic literature search of medical databases from 1995 to 2021 was undertaken. Inclusion criteria were randomised controlled trials that reported at least one of the following glycaemic outcomes: mean glucose, area under the curve, time in range or hypoglycaemic episodes. Results Eighteen studies were included. Thirteen studies gave additional insulin. Five studies gave an additional 30%-43% of the insulin-to-carbohydrate ratio (ICR) for 32-50 g of fat and 31%-51% ICR for 7-35 g of fat with 12-27 g of protein added to control meals. A further eight studies gave -28% to +75% ICR using algorithms based on fat and protein for meals with 19-50 g of carbohydrate, 2-79 g of fat and 10-60 g of protein, only one study reported a glycaemic benefit of giving less than an additional 24% ICR. Eight studies evaluated insulin delivery patterns. Four of six studies in pump therapy, and one of two studies in multiple daily injections showed the combination of bolus and split dose, respectively, were superior. Five studies examined the insulin dose split, four demonstrated 60%-125% ICR upfront was necessary. Two studies investigated the timing of insulin delivery, both reported administration 15 min before the meal lowered postprandial glycaemia. Conclusions Findings highlight the glycaemic benefit of an additional 24%-75% ICR for fat and fat and protein meals. For these meals, there is supportive evidence for insulin delivery in a combination bolus with a minimum upfront dose of 60% ICR, 15 min before the meal.

Context Dietary fat and protein impact postprandial hyperglycemia in people with type 1 diabetes, but the underlying mechanisms are poorly understood. Glucoregulatory hormones are also known to modulate gastric emptying and may contribute to this effect. Objective Investigate the effects of fat and protein on glucagon-like peptide (GLP-1), glucagon-dependent insulinotropic polypeptide (GIP) and glucagon secretion. Methods 2 crossover euglycemic insulin clamp clinical trials at 2 Australian pediatric diabetes centers. Participants were 12-21 years (n = 21) with type 1 diabetes for ≥1 year. Participants consumed a low-protein (LP) or high-protein (HP) meal in Study 1, and low-protein/low-fat (LPLF) or high-protein/high-fat (HPHF) meal in Study 2, all containing 30 g of carbohydrate. An insulin clamp was used to maintain postprandial euglycemia and plasma glucoregulatory hormones were measured every 30 minutes for 5 hours. Data from both cohorts (n = 11, 10) were analyzed separately. The main outcome measure was area under the curve of GLP-1, GIP, and glucagon. Results Meals low in fat and protein had minimal effect on GLP-1, while there was sustained elevation after HP (80.3 ± 16.8 pmol/L) vs LP (56.9 ± 18.6), P = .016, and HPHF (103.0 ± 26.9) vs LPLF (69.5 ± 31.9) meals, P = .002. The prompt rise in GIP after all meals was greater after HP (190.2 ± 35.7 pmol/L) vs LP (152.3 ± 23.3), P = .003, and HPHF (258.6 ± 31.0) vs LPLF (151.7 ± 29.4), P < .001. A rise in glucagon was also seen in response to protein, and HP (292.5 ± 88.1 pg/mL) vs LP (182.8 ± 48.5), P = .010. Conclusion The impact of fat and protein on postprandial glucose excursions may be mediated by the differential secretion of glucoregulatory hormones. Further studies to better understand these mechanisms may lead to improved personalized postprandial glucose management.

AIM To determine the glycaemic impact of an increased insulin dose, split insulin dose and regular insulin for a high fat, high protein breakfast in people with type 1 diabetes using multiple daily injections (≥4/day). METHODS In this cross-over trial, participants received the same high fat, high protein breakfast (carbohydrate:30 g, fat:40 g, protein:50 g) for 4 days. Four different insulin strategies were randomly allocated and tested; 100% of the insulin-to-carbohydrate ratio (ICR) given in a single dose using aspart insulin (100Asp), 125% ICR given in a single dose using aspart (125Asp) or regular insulin (125Reg) and 125% ICR given in a split dose using aspart insulin (100:25Asp). Insulin was given 0.25 hr pre-meal and for 100:25Asp, also 1 hr post-meal. Postprandial sensor glucose was measured for 5 hr. RESULTS In all, 24 children and adults were participated. The 5-hr incremental area under the curves for 100Asp, 125Asp, 125Reg and 100:25Asp were 620 mmol/L.min [95% CI: 451,788], 341 mmol/L.min [169,512], 675 mmol/L.min [504,847] and 434 mmol/L.min [259,608], respectively. The 5-hr incremental area under the curve for 125Asp was significantly lower than for 100Asp (p = 0.016) and for 125Reg (p = 0.002). There was one episode of hypoglycaemia in 125Reg. CONCLUSIONS For a high fat, high protein breakfast, giving 125% ICR preprandially, using aspart insulin significantly improved postprandial glycaemia without hypoglycaemia. There was no additional glycaemic benefit from giving insulin in a split dose (100:25%) or replacing aspart with regular insulin.

AIM To determine the insulin requirement for a high-fat, high-protein breakfast to optimise postprandial glycaemic excursions in children and young people with type 1 diabetes using insulin pumps. METHODS In all, 27 participants aged 10-23 years, BMI

Aims To determine if the frequency of severe diabetic ketoacidosis at presentation of new‐onset type 1 diabetes to an Australian tertiary centre increased during the initial period of restrictions resulting from the COVID‐19 pandemic (March to May 2020). Methods Data were collected on presentations of newly diagnosed type 1 diabetes as well as on all paediatric presentations to the emergency department of a tertiary centre between 2015 and 2020. Data from the period of initial COVID restrictions in Australia (March to May 2020) were compared to the period March to May of the previous 5 years (pre‐pandemic periods). Results The number of new diagnoses of type 1 diabetes was comparable in the pandemic period and pre‐pandemic periods (11 in 2020 vs range 6–10 in 2015–2019). The frequency of severe diabetic ketoacidosis was significantly higher in the pandemic period compared to the pre‐pandemic periods (45% vs 5%; P

Background: In insulin therapy, the blood glucose level is constrained from below by the hypoglycemic threshold, that is, the blood glucose level must remain above this threshold. It has been shown that this constraint fundamentally limits the ability to lower the maxima of the blood glucose level predicted by many mathematical models of glucose metabolism. However, it is desirable to minimize hyperglycemia as well. Hence, a desirable insulin input is one that minimizes the maximum glucose concentration while causing it to remain above the hypoglycemic, or higher, threshold. It has been shown that this input, which we call optimal, is characterized by glucose profiles for which either each maximum of the glucose concentration is followed by a minimum or each minimum is followed by a maximum. Methods: We discuss the implication of this inherent control limitation for clinical practice and test, through simulation, the robustness of the optimal input to a number of different model and parameter uncertainties. We further develop guidelines on how to design an optimal insulin input that is robust to such uncertainties. Results: The optimal input is in general not robust to uncertainties. However, a number of strategies may be used to ensure the blood glucose level remains above the hypoglycemic threshold and the maximum blood glucose level achieved is less than that achieved by standard therapy. Conclusions: An understanding of the limitations on the controllability of the blood glucose level is important for future treatment improvements and the development of artificial pancreas systems.

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

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 fat and protein to prevent delayed hyperglycaemia. This article is protected by copyright. All rights reserved.

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., publishedVersion

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

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

Improved frequency of sensor use improves glycaemic control. Furthermore, there is no deterioration of glycaemic control with increased sensor use in individuals on Predictive Low Glucose Management (PLGM) system. Younger children are more likely to have better sensor uptake than older children.

Background: There has been a change in the management of type 1 diabetes (T1D) with the availability of continuous glucose monitoring (CGM) systems. In Australia, subsidized CGM is available to patients below the age of 21. A randomized controlled trial (RCT) evaluating hybrid closed loop therapy is currently underway and the baseline data provided an opportunity to analyze the glycemic patterns in patients on contemporary management. Methods: Adolescents and young adults with T1D and HbA1c Results: Data were analyzed from 100 participants (47M, 53F; pump 80, MDI 20) with (mean±SD) age 14.9±2.7 years, diabetes duration 7.6±0.9 years, HbA1c 7.9±0.9% of which 55% participants were on real-time CGM. The (mean±SD) SG was 9.6±1.6mmol/l with fasting SG of 8.8±1.9mmol/l. The SG coefficient of variation was 41.3±6.04%. The median [IQR] %time in range (3.9 to 10mmol/l) was 55.9 [44.1,62.3]. The %time in hypoglycemia (10 and >13.9mmol/l) were 35.5[24.7,45.7] and 14.1[8.1,18.8] respectively. Conclusion: Majority of adolescents in the trial were on insulin pump therapy with half of them on CGM. The baseline CGM data of these participants provide a snapshot of the glycemic management in patients with T1D on current therapy; especially highlighting the reduced time in hypoglycemia compared to earlier trials. The data can further inform the conduct of future trials. Disclosure M.B. Abraham: None. G.J. Smith: None. M. de Bock: None. B.R. King: None. J. Fairchild: None. G. Ambler: None. F. Cameron: None. E.A. Davis: None. D.N. ONeal: None. T. Jones: Research Support; Self; Dexcom, Inc., Medtronic MiniMed, Inc. Speaker's Bureau; Self; Eli Lilly and Company, Roche Diabetes Care.

Dietary protein increases the postprandial glycemic response and insulin requirements in people with type 1 diabetes (T1D). The aim of this study was to determine the appropriate amount of additional insulin required for the protein content of a meal to prevent postprandial hyperglycemia. Twenty-nine subjects (12 male) with T1D using CSII (HbA1c Disclosure M.A. Paterson: None. C.E. Smart: None. J. Rafferty: None. B. Fenton: None. D. Price: None. D.C. Foskett: None. A. Maher: None. P. Howley: None. B.R. King: None. Funding John Hunter Children's Hospital; Dexcom External Research Program

Introduction: Type 1 diabetes guidelines recommend that mealtime insulin dosing accounts for fat and protein. However, implementation remains a challenge. Aim: To evaluate an individual’s glycemic response to a high fat, high protein meal (HFHP) and develop a personalized ‘metabolic digital twin’ (MDTwin) capable of predicting an optimal insulin dose and delivery pattern which accounts for meal fat and protein. Method: Participants (n=9) using pump therapy; mean age 13.9 ±3.2 years (5 male), T1D duration 4.8 ±3.8 years, and HbA1c 49 mmol/mol (6.7 ±0.6%) wore Dexcom G5 CGMS and were given an insulin dual wave bolus (insulin to carbohydrate ratio [ICR] x1, ICRx1.2, ICRx1.4 or ICRx1.6) and ate a HFHP (C:30g, F:40g, P:50g) on 4 mornings. Individual MDTwins were fitted by System Identification methodology combined with a statistical quantification using the ICRx1 and ICRx1.6 CGMS traces. MDTwins were validated using the ICRx1.2 and ICRx1.4 traces. Results: The MDTwins identified individual insulin and distribution requirements to achieve optimal postprandial glycemic control (Figure). MDTwins identified marked inter-individual variability in insulin dose and distribution requirements. Conclusion: MDTwins can potentially be used to inform and individualize insulin dosing strategies for meal fat and protein to produce optimal glycemic outcomes. Further research is required. Disclosure T.A. Smith: None. M.M. Seron: None. G.C. Goodwin: None. A.M. Medioli: None. B.R. King: None. C.E. Smart: None. M. Harris: None. D.N. ONeal: None. J. Rafferty: None. P. Howley: None. Funding Diabetes Australia

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.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 glucose63 mg/dL or plasma glucose50 mg/dL) and was compared in both control and intervention arms.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.SAPT with PLGM reduced the need for hypoglycemia treatment after moderate-intensity exercise in an in-clinic setting.

Sensor-augmented pump therapy (SAPT) with algorithms to predict impending low blood glucose and suspend insulin delivery has the potential to reduce hypoglycemia exposure. The aim of this study was to determine whether predictive low glucose management (PLGM) system is effective in preventing insulin-induced hypoglycemia in controlled experiments.Two protocols were used to induce hypoglycemia in an in-clinic environment. (A) Insulin bolus: Insulin was administered as a manual bolus through the pump. (B) Increased basal insulin: Hypoglycemia was induced by increasing basal rates overnight to 180%. For both protocols, participants were randomized and studied on 2 separate days; a control day with SAPT alone and an intervention day with SAPT and PLGM activated. The predictive algorithm was programmed to suspend basal insulin infusion when sensor glucose was predicted to be80 mg/dL in 30 min. The primary outcome was the requirement for hypoglycemia treatment (symptomatic hypoglycemia or plasma glucose50 mg/dL) and was compared in both control and intervention arms.With insulin bolus, 24/28 participants required hypoglycemia treatment with SAPT alone compared to 5/28 participants when PLGM was activated (P ≤ 0.001). With increased basal rates, all the eight SAPT-alone participants required treatment for hypoglycemia compared to only one with SAPT and PLGM. There was no post pump-suspend hyperglycemia with insulin bolus (P = 0.4) or increased basal rates (P = 0.69) in participants with 2-h pump suspension on intervention days.SAPT with PLGM reduced the requirement for hypoglycemia treatment following insulin-induced hypoglycemia in an in-clinic setting.

The Predictive Low-Glucose Management (PLGM) system suspends basal insulin when hypoglycemia is predicted and reduces hypoglycemia. The aim of this analysis was to explore the characteristics of automated insulin suspension and sensor glucose (SG) responses following PLGM-initiated pump suspension.Children and adolescents with type 1 diabetes used the Medtronic MiniMed™ 640G pump as part of a randomized controlled trial. Data collected on a subgroup of participants on PLGM (suspend before low enabled) from CareLinkThere were 20,183 suspend before low events in 8523 days (2.37 events/day). The mean suspend duration was 55.0 ± 32.7 min (day 50.0 ± 30.1, night 71.7 ± 35.1; P 0.001). Although a 2-h pump suspension was more frequent at night (day 5%, night 18%), a patient-initiated resumption occurred more during day (day 34%, night 12%). SG values did not reach3.5 and3 mmol/L in 79% and 91% of the events, respectively. The 2-h SG following pump resumption was higher following autoresumption during the day (day vs. night 9.3 mmol/L vs. 8.4 mmol/L; P 0.001).Longer suspends and fewer glycemic excursions occur at night compared with day. The higher glycemic daytime excursions could be due to carbohydrate consumption to increase glucose levels and highlights the need for health care professionals to educate patients about carbohydrate intake around pump suspension.

Aim To quantify the insulin requirement for a high-protein meal compared with a low-protein meal, controlling for carbohydrate and fat content. Methods In this crossover study, young people with Type 1 diabetes were randomized to consume a high- (60 g) or low-protein meal (5 g), each containing 30 g carbohydrate and 8 g fat. A variation of the insulin clamp technique was used to determine the insulin requirements to maintain euglycaemia for the following 5 h. Results A total of 11 participants (mean ± sd age 16.5 ± 2.7 years, HbA1c 52 ± 8.7 mmol/mol [6.9 ± 0.8%], diabetes duration 6.9±5.1 years) completed the study. The mean insulin requirements for the high-protein meal were higher than for the low-protein meal [10.3 (CI 8.2, 12.57) vs 6.7 units (CI 4.7, 8.8); P=0.001], with inter-individual requirements ranging from 0.9 to six times the low-protein meal requirement. Approximately half the additional insulin [1.1 units/h (CI 0.5, 1.8; P=0.001)] was given in the first 2 h, compared with an additional 0.5 units/h (CI -0.2, 1.2; P=0.148) in the second 2 h and 0.1 units (CI -0.6, 0.8; P=0.769) in the final hour. Conclusions A high-protein meal requires ~50% more insulin to maintain euglycaemia than a low-protein meal that contains the same quantity of carbohydrate. The majority is required within the first 2 h. Inter-individual differences exist in insulin requirements for dietary protein.

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.

Glycemic control in the young child with type 1 diabetes (T1D) has traditionally been challenging. Unpredictability in appetite and dietary intake has made the achievement of glycemic targets difficult. This study aimed to assess the nutritional quality and dietary behaviors of young children with T1D that attend a clinic that meets glycemic targets in this age group. Carers of children aged Twenty one children participated (mean age: 4.7 ±1.3y, 57% male, mean BMI z score: 0.9 ± 0.9, duration since diagnosis 1.6 ±1.1y, 38% insulin pump therapy, mean HbA1c: 6.3 ± 0.7% (45±7mmol/mol)). Offering food ’whenever they are hungry’ was associated with higher HbA1c than routine meals (7.2% vs. 6.1%, p=0.02). HbA1c was not correlated with daily total CHO intake, which ranged from 126g to 236g/day. Almost all carers (95%) gave mealtime insulin before food. Most carers (55%) substituted when 6-10 grams (range 1g to >15g) of CHO less than the predicted quantity was consumed. Children consumed saturated fat in excess of recommendations (15 ± 3% E) with no children meeting recommended serves of vegetables. Young children with type 1 diabetes can achieve target glycemic control and can safely dose insulin before meals. Continuous eating episodes were positively associated with HbA1c. Dietary quality remains a concern in this age group. Disclosure R.L. Seckold: None. P. Howley: None. B.R. King: None. K. Bell: Other Relationship; Self; Novo Nordisk Inc.. C.E. Smart: None.

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.

IntroductionManual determination of insulin dosing largely fails to optimise glucose control in type 1 diabetes. Automated insulin delivery via closed-loop systems has improved glucose control in short-term studies. The objective of the present study is to determine the effectiveness of 6 months’ closed-loop compared with manually determined insulin dosing on time-in-target glucose range in adults with type 1 diabetes.Methods and analysisThis open-label, seven-centre, randomised controlled parallel group clinical trial will compare home-based hybrid closed-loop versus standard diabetes therapy in Australia. Adults aged ≥25 years with type 1 diabetes using intensive insulin therapy (via multiple daily injections or insulin pump, total enrolment target n=120) will undertake a run-in period including diabetes and carbohydrate-counting education, clinical optimisation and baseline data collection. Participants will then be randomised 1:1 either to 26 weeks of MiniMed 670G hybrid closed-loop system therapy (Medtronic, Northridge, CA, USA) or continuation of their current diabetes therapy. The hybrid closed-loop system delivers insulin automatically to address basal requirements and correct to target glucose level, while bolus doses for meals require user initiation and carbohydrate estimation. Analysis will be intention to treat, with the primary outcome time in continuous glucose monitoring (CGM) target range (3.9–10.0 mmol/L) during the final 3 weeks of intervention. Secondary outcomes include: other CGM parameters, HbA1c, severe hypoglycaemia, psychosocial well-being, sleep, cognition, electrocardiography, costs, quality of life, biomarkers of vascular health and hybrid closed-loop system performance. Semistructured interviews will assess the expectations and experiences of a subgroup of hybrid closed-loop users.Ethics and disseminationThe study has Human Research Ethics Committee approval. The study will be conducted in accordance with the principles of the Declaration of Helsinki and Good Clinical Practice. Results will be disseminated at scientific conferences and via peer-reviewed publications.Trial registration numberNCT12617000520336.