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.

Postprandial glucose metabolism in children and adolescents with type 1 diabetes mellitus: potential targets for improvement.

The main goal of therapeutic management of type 1 Diabetes Mellitus (T1DM) is to maintain optimal glycemic control to prevent acute and long-term diabetes complications and to enable a good quality of life. Postprandial glycemia makes a substantial contribution to overall glycemic control and variability in diabetes and, despite technological advancements in insulin treatments, optimal postprandial glycemia is difficult to achieve. Several factors influence postprandial blood glucose levels in children and adolescents with T1DM, including nutritional habits and adjustment of insulin doses according to meal composition. Additionally, hormone secretion, enteroendocrine axis dysfunction, altered gastrointestinal digestion and absorption, and physical activity play important roles. Meal-time routines, intake of appropriate ratios of macronutrients, and correct adjustment of the insulin dose for the meal composition have positive impacts on postprandial glycemic variability and long-term cardiometabolic health of the individual with T1DM. Further knowledge in the field is necessary for management of all these factors to be part of routine pediatric diabetes education and clinical practice. Thus, the aim of this report is to review the main factors that influence postprandial blood glucose levels and metabolism, focusing on macronutrients and other nutritional and lifestyle factors, to suggest potential targets for improving postprandial glycemia in the management of children and adolescents with T1DM.

Carbohydrate Counting, Empowerment and Glycemic Outcomes in Adolescents and Young Adults with Long Duration of Type 1 Diabetes.

The complex treatment for diabetes type 1 (T1D) includes insulin dosing for every meal, which requires education and experience to achieve optimal outcomes. Advanced carbohydrate counting (ACC) is the recommended method. We studied ACC as part of a standard treatment with the aim to explore its associations with glycemic control and empowerment in adolescents and young adults. We used national registry data on glycemic outcomes, a study-specific questionnaire regarding the use of ACC and the Gothenburg Young Persons Empowerment Scale (GYPES) to measure empowerment. A total of 111 participants (10-28 years of age, diabetes duration >9 years, mean HbA1c of 55.4 mmol/mol) answered the questionnaire. We found that most participants (79.3%) who learn ACC, at onset or later, continue to use the method. A higher level of empowerment was associated with lower HbA1c (p = 0.021), making patient empowerment an important factor in achieving optimal glycemic outcomes. No associations were found between ACC and empowerment or glycemic outcomes. A mixed strategy, only using ACC sometimes when insulin dosing for meals, was associated with the lowest empowerment score and highest HbA1c and should warrant extra education and support from the diabetes team to reinforce a dosing strategy.

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.

The OPTIMISE study protocol: a multicentre optimisation trial comparing continuous glucose monitoring, snacking habits, sleep extension and values-guided self-care interventions to improve glucose time-in-range in young people (13-20 years) with type 1 diabetes.

Purpose: The OPTIMISE study uses a Multiphase Optimisation Strategy (MOST) to identify the best combination of four interventions targeting key diabetes self-care behaviours for use in clinical practice to improve short-term glycaemic outcomes. Methods: This 4-week intervention trial will recruit 80 young people (aged 13-20 years) with type 1 diabetes ≥ 6 months duration), and pre-enrolment HbA1c ≥ 58 mmol/mol (7.5%) in the prior 6 months. Both main intervention and interaction effects will be estimated using a linear regression model with change in glucose time-in-range (TIR; 3.9-10.0 mmol/L) as the primary outcome. Participants will be randomised to one of 16 conditions in a factorial design using four intervention components: (1) real-time continuous glucose monitoring (CGM), (2) targeted snacking education, (3) individualised sleep extension, and (4) values-guided self-care goal setting. Baseline and post-intervention glucose TIR will be assessed with blinded CGM. Changes in self-care (snacking behaviours, sleep habits and duration, and psychosocial outcomes) will be assessed at baseline and post-intervention to determine if these interventions impacted behaviour change. Discussion: The study outcomes will enable the selection of effective and efficient intervention components that increase glucose TIR in young people who struggle to achieve targets for glycaemic control. The optimised intervention will be evaluated in a future randomised controlled trial and guide the planning of effective clinical interventions in adolescents and young adults living with type 1 diabetes. Trial registration: This trial was prospectively registered with the Australian New Zealand Clinical Trials Registry on 7 October 2020 (ACTRN12620001017910) and the World Health Organisation International Clinical Trails Registry Platform on 26 July 2020 (Universal Trial Number WHO U1111-1256-1248).

Insulin delivery patterns required to maintain postprandial euglycemia in type 1 diabetes following consumption of traditional Egyptian Ramadan Iftar meal using insulin pump therapy: A randomized crossover trial.

OBJECTIVES: During Ramadan, traditional Egyptian Iftar meals have large amounts of high-glycemic index carbohydrate and fat. The efficacy of different bolus regimens on optimizing post prandial glucose (PPG) excursion following this Iftar meal was assessed. METHODS: A randomized controlled trial evaluating 4-h PPG measured by continuous glucose-monitoring was conducted. A total of 25 youth with T1DM using insulin pumps were given the same Iftar meal (fat [45 g], protein [28 g], CHO [95 g]) on seven consecutive days. Insulin to carbohydrate ratio (ICR) was individualized, and all boluses were given upfront 20 min before Iftar. Participants were randomized to receive a standard bolus and six different split boluses delivered over 4 h in the following splits: dual wave (DW) 50/50; DW 50/50 with 20% increment (120% ICR); DW60/40; DW 60/40 with 20% increment; DW 70/30 and DW 70/30 with 20% increment. RESULTS: Standard bolus and split 70/30 with 20% increment resulted in significantly lower early glucose excursions (120 min) with mean excursions of less than 40 mg/dL (2.2 mmol/L) compared to other conditions (p < 0.01). The split 70/30 with 20% increment significantly optimized late PPG excursion (240 min) in comparison to standard bolus (p < 0.01), as well as resulting in a significantly lower post meal glucose area under the curve compared with all other conditions (p < 0.01), with no late hypoglycemia. CONCLUSION: To achieve physiologic PPG profile in traditional Iftar meal, a DW bolus with 20% increment given 20 min preprandial as split bolus 70/30 over 4 h, optimized both early and delayed PPG excursions.

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.

Medical nutrition therapy for gestational diabetes mellitus in Australia: What has changed in 10 years and how does current practice compare with best practice?

BACKGROUND: The present study aimed to report Australian dietetic practice regarding management of gestational diabetes mellitus (GDM) and to make comparisons with the findings from a 2009 survey of dietitians and with the Academy of Nutrition and Dietetics Evidence-Based Nutrition Practice Guidelines (NPG). METHODS: Cross-sectional surveys were conducted in 2019 and 2009 of dietitians providing medical nutrition therapy (MNT) to women with GDM in Australia. The present study compares responses on demographics, dietetic assessment and interventions, and guideline use in 2019 vs. 2009. RESULTS: In total, 149 dietitians (2019) and 220 (2009) met survey inclusion criteria. In both surveys >60% of respondents reported dietary interventions aiming for >45% energy from carbohydrate, 15%-25% energy from protein and 15%-30% energy from fat. Many variations in MNT found in 2009 continued to be evident in 2019, including the percentage of energy from carbohydrate aimed for (30%-65% in 2019 vs. 20%-75% in 2009) and the wide range in the recommended minimum daily carbohydrate intake (40-220 and 60-300 g). Few dietitians reported aiming for the NPG minimum of 175 g of carbohydrate daily in both surveys (32% in 2019 vs. 26% in 2009). There were, however, some significant increases in MNT consistent with NPG recommendations in 2019 vs. 2009, including the minimum frequency of visits provided (49%, n = 61 vs. 33%, n = 69; p < 0.001) and provision of gestational weight gain advice (59%, n = 95 vs. 40%, n = 195; p < 0.05). CONCLUSIONS: Although many dietitians continue to provide MNT consistent with existing NPG, there is a need to support greater uptake, especially for recommendations regarding carbohydrate intake.

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.

Does weight management after gestational diabetes mellitus diagnosis improve pregnancy outcomes? A multi-ethnic cohort study.

AIMS: To assess the impact of achieving an Institute of Medicine based personalised weight target in addition to conventional glycaemic management after gestational diabetes mellitus diagnosis on maternal and neonatal outcomes. METHODS: A retrospective audit of clinical data (2016-2019) for singleton gestational diabetes pregnancies was conducted in a multi-ethnic cohort. Logistic regression analyses assessed relationships between achieving, exceeding and gaining less than a personalised weight target provided after gestational diabetes diagnosis and rates of large for gestational age, small for gestational age infants, insulin therapy initiation and neonatal outcomes. Adjusted odds ratios (aOR) were adjusted for glucose 2-h post-glucose load value, family history of type 2 diabetes, previous gestational diabetes, macrosomia in a previous pregnancy, and East and South-East Asian ethnicity. RESULTS: Of 1034 women, 44% (n = 449) achieved their personalised weight target. Women who exceeded their personalised weight target had significantly and higher mean insulin doses (28.8 ± 21.5 units vs. 22.7 ± 18.7, p = 0.006) and higher rates of large for gestational age infants (19% vs. 9.8%, p < 0.001), with aOR of 1.99 [95% CI 1.25-3.15] p = 0.004, but no difference in rates of small for gestational age infants (5.3% vs. 8.0%) (aOR 0.77 [0.41-1.44] p = 0.41). Lower rates of large for gestational age infants occurred in those who gained below their personalised weight target (aOR 0.48 [0.25-0.95] p = 0.034), but rates of small for gestational age infants concurrently increased (aOR 1.9 [1.19-3.12] p = 0.008). CONCLUSIONS: Weight management after gestational diabetes diagnosis does not appear to be too late to confer additional benefits to glucose-lowering treatment, resulting in lower mean insulin doses, and lower rates of large for gestational age infants without increasing the risk of small for gestational age infants.

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.

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.

Screening Practices for Disordered Eating in Paediatric Type 1 Diabetes Clinics.

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

Association of the use of diabetes technology with HbA1c and BMI-SDS in an international cohort of children and adolescents with type 1 diabetes: The SWEET project experience.

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

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.

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.