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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. [ABSTRACT FROM AUTHOR]

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 lowprotein/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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]