Modifiable lifestyle risk factors for overweight and obesity in children and adolescents with type 1 diabetes: A systematic review.

This review aims to identify and report epidemiological associations between modifiable lifestyle risk factors for overweight or obesity in children and adolescents with type 1 diabetes (T1D). A systematic literature search of medical databases from 1990 to 2023 was undertaken. Inclusion criteria were observational studies reporting on associations between dietary factors, disordered eating, physical activity, sedentary and sleep behaviours and measures of adiposity in children and adolescents (<18 years) with T1D. Thirty-seven studies met inclusion criteria. Studies were mostly cross-sectional (89 %), and 13 studies included adolescents up to 19 years which were included in this analysis. In adolescents with T1D, higher adiposity was positively associated with disordered eating behaviours (DEB) and a higher than recommended total fat and lower carbohydrate intake. A small amount of evidence suggested a positive association with skipping meals, and negative associations with diet quality and sleep stage. There were no published associations between overweight and physical activity, sedentary behaviours and eating disorders. Overall, the findings infer relationships between DEB, fat and carbohydrate intake and adiposity outcomes in people with T1D. Prospective studies are needed to determine causal relationships and to investigate sleep stages. High quality studies objectively measuring physical activity and include body composition outcomes are needed.

Healthy weight and overweight adolescents with type 1 diabetes mellitus do not meet recommendations for daily physical activity and sleep.

AIMS: Physical activity (PA) plays an important role in the prevention of cardiovascular disease (CVD), particularly in individuals with type 1 diabetes mellitus (T1DM) who are at increased risk. Our aim was to determine levels of moderate-to-vigorous physical activity (MVPA), sedentary behaviour and sleep in adolescents with T1DM, and identify barriers to PA. METHODS: Participants aged 12-18 with T1DM wore an accelerometer and continuous glucose monitor for 24 h over 7-days. Data was processed into PA metrics and sleep. Pearson correlations were used to test associations between MVPA and metabolic measures. Barriers to PA were measured using a questionnaire. RESULTS: Thirty-seven adolescents provided valid accelerometer data. Mean daily MVPA was 44.0 min [SD 17.6] with 16.2% achieving the guideline of ≥ 60 min/day. Participants had 11 h [SD 1.2] of sedentary behaviour and 7.6 h [SD 1.5] of sleep/day. There was no difference in MVPA in overweight or obese (53.8%) vs. healthy weight (44.2%) adolescents (45.0 min [SD 16.6] vs. 43.1 min [SD 18.8]). Only 39.6% reported one or more diabetes specific barrier to PA. CONCLUSION: Adolescents with T1DM engage in insufficient MVPA and sleep, irrespective of body weight status, suggesting the need for targeted interventions.

Late Afternoon Vigorous Exercise Increases Postmeal but Not Overnight Hypoglycemia in Adults with Type 1 Diabetes Managed with Automated Insulin Delivery.

Aim: To compare evening and overnight hypoglycemia risk after late afternoon exercise with a nonexercise control day in adults with type 1 diabetes using automated insulin delivery (AID). Methods: Thirty adults with type 1 diabetes using AID (Minimed 670G) performed in random order 40 min high intensity interval aerobic exercise (HIE), resistance (RE), and moderate intensity aerobic exercise (MIE) exercise each separated by >1 week. The closed-loop set-point was temporarily increased 2 h pre-exercise and a snack eaten if plasma glucose was ≤126 mg/dL pre-exercise. Exercise commenced at ∼16:00. A standardized meal was eaten at ∼20:40. Hypoglycemic events were defined as a continuous glucose monitor (CGM) reading <70 mg/dL for ≥15 min. Four-hour postevening meal and overnight (00:00-06:00) CGM metrics for exercise were compared with the prior nonexercise day. Results: There was no severe hypoglycemia. Between 00:00 and 06:00, the proportion of nights with hypoglycemia did not differ postexercise versus control for HIE (18% vs. 11%; P = 0.688), RE (4% vs. 14%; P = 0.375), and MIE (7% vs. 14%; P = 0.625). Time in range (TIR) (70-180 mg/dL), >75% for all nights, did not differ between exercise conditions and control. Hypoglycemia episodes postmeal after exercise versus control did not differ for HIE (22% vs. 7%; P = 0.219) and MIE (10% vs. 14%; P > 0.999), but were greater post-RE (39% vs. 10%; P = 0.012). Conclusions: Overnight TIR was excellent with AID without increased hypoglycemia postexercise between 00:00 and 06:00 compared with nonexercise days. In contrast, hypoglycemia risk was increased after the first meal post-RE, suggesting the importance of greater vigilance and specific guidelines for meal-time dosing, particularly with vigorous RE. ACTRN12618000905268.

Adolescents with type 1 diabetes can achieve glycemic targets on intensive insulin therapy without excessive weight gain.

INTRODUCTION: The aim of this study was to compare glycemic control and body mass index standard deviation score (BMI-SDS) before and after implementation of intensive insulin therapy using multiple daily injection (MDI) or continuous subcutaneous insulin infusion (CSII) in adolescents with type 1 diabetes (T1D) attending a large multidisciplinary paediatric diabetes clinic in Australia. METHODS: Prospective data were collected for cross-sectional comparison of youth aged 10.0-17.9 years (n = 669) from routine follow-up visits to the diabetes clinic in 2004, 2010, and 2016. Outcome measures included HbA1c; BMI-SDS; and insulin regimen. RESULTS: BMI-SDS remained stable between 2004 to 2016 in the 10-13 and 14-17 year age group (0.7 vs. 0.5, p = .12 and 0.7 vs. 0.7, p = .93, respectively). BMI-SDS was not different across HbA1c groups; <53 mmol/mol (7.0%), 53 to <75 mmol/mol (<7.0 to <9.0%) and >75 mmol/mol (>9.0%) in 2004 (p = .873), 2010 (p = .10) or 2016 (p = .630). Mean HbA1c decreased from 2004 to 2016 in the 10-13 year (69 mmol/mol (8.4%) vs. 57 mmol/mol (7.4%), p = <.001) and 14-17 year group (72 mmol/mol (8.7%) vs. 63 mmol/mol (7.9%), p = <.001). Prior to the implementation of MDI and CSII in 2004 only 10% of 10-13 year olds and 8% of 14-17 year olds achieved the international target for glycemic control (HbA1c 53 mmol/mol [<7.0%]). In 2016, this increased to 31% of 10-13 year olds and 21% of 14-17 year olds. CONCLUSIONS: BMI-SDS did not increase with the change to intensive insulin therapy despite a doubling in the number of adolescents achieving the recommended glycemic target of <7.0% (53 mmol/mol). HbA1c was not associated with weight gain.

Effects of Dietary Fat and Protein on Glucoregulatory Hormones in Adolescents and Young Adults With Type 1 Diabetes.

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.

The relationship between meal carbohydrate quantity and the insulin to carbohydrate ratio required to maintain glycaemia is non-linear in young people with type 1 diabetes: A randomized crossover trial.

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 < 0.04). Hypoglycaemia was more frequent in the 50 g (n = 13, 42%) and 100 g (n = 15, 50%) breakfasts compared to the 20 g (n = 6, 20%) and 150 g (n = 7, 26%) breakfasts (p < 0.029). The 150 g breakfast glucose excursion pattern was different from the smaller breakfasts with the lowest glucose excursion 0-2 h and the highest excursion from 3.5 to 5 h. CONCLUSIONS: A non-linear relationship between insulin requirement and breakfast carbohydrate content was observed, suggesting that strengthened ICRs are needed for meals with ≤20 and ≥150 g of carbohydrate. Meals with ≥150 g of carbohydrate may benefit from dual wave bolusing.

A Randomized Crossover Trial Comparing Glucose Control During Moderate-Intensity, High-Intensity, and Resistance Exercise With Hybrid Closed-Loop Insulin Delivery While Profiling Potential Additional Signals in Adults With Type 1 Diabetes.

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 <126 mg/dL to prevent hypoglycemia. Primary outcome was median (interquartile range) continuous glucose monitoring time-in-range (TIR; 70-180 mg/dL) for 14 h post-exercise commencement. Accelerometer data and venous glucose, ketones, lactate, and counterregulatory hormones were measured for 280 min post-exercise commencement. 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.

Universal Subsidized Continuous Glucose Monitoring Funding for Young People With Type 1 Diabetes: Uptake and Outcomes Over 2 Years, a Population-Based Study.

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 <21 years. 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 <7.0% improved at 12 months (OR 2.5, P < 0.001) and was maintained at 24 months (OR 2.3, P < 0.001). The OR for suboptimal glycemic control (HbA1c ≥9.0%) decreased to 0.34 (P < 0.001) at 24 months. Of CGM users, 65% used CGM >75% of time, and had a lower HbA1c at 24 months compared with those with usage <25% (7.8 ± 1.3% vs. 8.6 ± 1.8%, respectively, P < 0.001). Diabetic ketoacidosis was also reduced in this group (incidence rate ratio 0.49, 95% CI 0.33-0.74, P < 0.001). 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.

Diabetic Ketoacidosis at Onset of Type 1 Diabetes and Long-term HbA1c in 7,961 Children and Young Adults in the Australasian Diabetes Data Network.

OBJECTIVE: The relationship between diabetic ketoacidosis (DKA) at diagnosis of type 1 diabetes and long-term glycemic control varies between studies. We aimed, firstly, to characterize the association of DKA and its severity with long-term HbA1c in a large contemporary cohort, and secondly, to identify other independent determinants of long-term HbA1c. RESEARCH DESIGN AND METHODS: Participants were 7,961 children and young adults diagnosed with type 1 diabetes by age 30 years from 2000 to 2019 and followed prospectively in the Australasian Diabetes Data Network (ADDN) until 31 December 2020. Linear mixed-effect models related variables to HbA1c. RESULTS: DKA at diagnosis was present in 2,647 participants (33.2%). Over a median 5.6 (interquartile range 3.2, 9.4) years of follow-up, participants with severe, but not moderate or mild, DKA at diagnosis had a higher mean HbA1c (+0.23%, 95% CI 0.11,0.28; [+2.5 mmol/mol, 95% CI 1.4,3.6]; P < 0.001) compared with those without DKA. Use of continuous subcutaneous insulin infusion (CSII) was independently associated with a lower HbA1c (-0.28%, 95% CI -0.31, -0.25; [-3.1 mmol/mol, 95% CI -3.4, -2.8]; P < 0.001) than multiple daily injections, and CSII use interacted with severe DKA to lower predicted HbA1c. Indigenous status was associated with higher HbA1c (+1.37%, 95% CI 1.15, 1.59; [+15.0 mmol/mol, 95% CI 12.6, 17.4]; P < 0.001), as was residing in postcodes of lower socioeconomic status (most vs. least disadvantaged quintile +0.43%, 95% CI 0.34, 0.52; [+4.7 mmol/mol, 95% CI 3.4, 5.6]; P < 0.001). CONCLUSIONS: Severe, but not mild or moderate, DKA at diagnosis was associated with a marginally higher HbA1c over time, an effect that was modified by use of CSII. Indigenous status and lower socioeconomic status were independently associated with higher long-term HbA1c.

Does dietary fat cause a dose dependent glycemic response in youth with type 1 diabetes?

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 < 0.05). Glycemic excursion for the fat drinks demonstrated a dose response, statistically significant from 4 h (p = 0.026), such that increasing loads of fat caused a proportionally larger increase in glycemic excursion, remaining statistically significant until 8 h (p < 0.05). Overall, for every 10 g fat added to the drink, glucose concentrations rose by a mean of 0.28 mmol L-1 from 330 min (95% CI 0.15 to 0.39, p < 0.001). CONCLUSIONS: Fat ingested without other macronutrients increases glucose excursions from 4 to 8 h after ingestion, in a dose dependent manner. These observations may impact on insulin dosing for high-fat foods in individuals with T1D.

Effect of a Hybrid Closed-Loop System on Glycemic and Psychosocial Outcomes in Children and Adolescents With Type 1 Diabetes: A Randomized Clinical Trial.

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 A1c 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 (<70 mg/dL) range (difference, -1.9%; 95% CI, -2.5% to -1.3%) and improved glycemic variability (coefficient of variation difference, -5.7%; 95% CI, -10.2% to -0.9%). Hybrid closed-loop therapy was associated with improved diabetes-specific quality of life (difference, 4.4 points; 95% CI, 0.4-8.4 points), with no change in diabetes distress. There were no episodes of severe hypoglycemia or diabetic ketoacidosis in either group. Conclusions and Relevance: In this randomized clinical trial, 6 months of HCL therapy significantly improved glycemic control and quality of life compared with conventional therapy in children and adolescents with type 1 diabetes. Trial Registration: ANZCTR identifier: ACTRN12616000753459.

Insulin strategies for dietary fat and protein in type 1 diabetes: A systematic review.

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.

Additional Insulin Is Required in Both the Early and Late Postprandial Periods for Meals High in Protein and Fat: A Randomized Trial.

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.

For a high fat, high protein breakfast, preprandial administration of 125% of the insulin dose improves postprandial glycaemic excursions in people with type 1 diabetes using multiple daily injections: A cross-over trial.

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.

In children and young people with type 1 diabetes using Pump therapy, an additional 40% of the insulin dose for a high-fat, high-protein breakfast improves postprandial glycaemic excursions: A cross-over trial.

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 <95th percentile (2-18 years) or BMI <30 kg/m2 (19-25 years) and HbA1c ≤64 mmol/mol (≤8.0%) consumed a high-fat, high-protein breakfast (carbohydrate: 30 g, fat: 40 g and protein: 50 g) for 4 days. In this cross-over trial, insulin was administered, based on the insulin-to-carbohydrate ratio (ICR) of 100% (control), 120%, 140% and 160%, in an order defined by a randomisation sequence and delivered in a combination bolus, 60% » hr pre-meal and 40% over 3 hr. Postprandial sensor glucose was assessed for 6 hr. RESULTS: Comparing 100% ICR, 140% ICR and 160% ICR resulted in significantly lower 6-hr areas under the glucose curves: mean (95%CI) (822 mmol/L.min [605,1039] and 567 [350,784] vs 1249 [1042,1457], p ≤ 0.001) and peak glucose excursions (4.0 mmol/L [3.0,4.9] and 2.7 [1.7,3.6] vs 6.0 [5.0,6.9],p < 0.001). Rates of hypoglycaemia for 100%-160% ICR were 7.7%, 7.7%, 12% and 19% respectively (p ≥ 0.139). With increasing insulin dose, a step-wise reduction in mean glucose excursion was observed from 1 to 6 hr (p = 0.008). CONCLUSIONS: Incrementally increasing the insulin dose for a high-fat, high-protein breakfast resulted in a predictable, dose-dependent reduction in postprandial glycaemia: 140% ICR improved postprandial glycaemic excursions without a statistically significant increase in hypoglycaemia. These findings support a safe, practical method for insulin adjustment for high-fat, high-protein meals that can be readily implemented in practice to improve postprandial glycaemia.

Families' reports of problematic foods, management strategies and continuous glucose monitoring in type 1 diabetes: A cross-sectional study.

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.

Determinants of Cardiovascular Risk in 7000 Youth With Type 1 Diabetes in the Australasian Diabetes Data Network.

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.