The significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers, and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past 6 years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage.

OBJECTIVE To assess the glucagon response to hypoglycemia and identify influencing factors in patients with type 1 diabetes compared with nondiabetic control subjects. RESEARCH DESIGN AND METHODS Hyperinsulinemic hypoglycemic clamp studies were performed in all participants. The glucagon response to both hypoglycemia and arginine was measured, as well as epinephrine, cortisol, and growth hormone responses to hypoglycemia. Residual β-cell function was assessed using fasting and stimulated C-peptide. RESULTS Twenty-eight nonobese adolescents with type 1 diabetes (14 female, mean age 14.9 years [range 11.2–19.8]) and 12 healthy control subjects (6 female, 15.3 years [12.8–18.7]) participated in the study. Median duration of type 1 diabetes was 0.66 years (range 0.01–9.9). The glucagon peak to arginine stimulation was similar between groups (P = 0.27). In contrast, the glucagon peak to hypoglycemia was reduced in the group with diabetes (95% CI): 68 (62–74) vs. 96 (87–115) pg/mL (P < 0.001). This response was greater than 3 SDs from baseline for only 7% of subjects with type 1 diabetes in comparison with 83% of control subjects and was lost at a median duration of diabetes of 8 months and as early as 1 month after diagnosis (R = −0.41, P < 0.01). There was no correlation in response with height, weight, BMI, and HbA_1c. Epinephrine, cortisol, and growth hormone responses to hypoglycemia were present in both groups. CONCLUSIONS The glucagon response to hypoglycemia in adolescents with type 1 diabetes is influenced by the duration of diabetes and can be lost early in the course of the disease.

Regular physical activity and exercise (PA) are cornerstones of diabetes care for individuals with type 1 diabetes. In recent years, the availability of automated insulin delivery (AID) systems has improved the ability of people with type 1 diabetes to achieve the recommended glucose target ranges. PA provide additional health benefits but can cause glucose fluctuations, which challenges current AID systems. While an increasing number of clinical trials and reviews are being published on different AID systems and PA, it seems prudent at this time to collate this information and develop a position statement on the topic. This joint European Association for the Study of Diabetes (EASD)/International Society for Pediatric and Adolescent Diabetes (ISPAD) position statement reviews current evidence on AID systems and provides detailed clinical practice points for managing PA in children, adolescents and adults with type 1 diabetes using AID technology. It discusses each commercially available AID system individually and provides guidance on their use in PA. Additionally, it addresses different glucose responses to PA and provides stratified therapy options to maintain glucose levels within the target ranges for these age groups. Graphical Abstract

Aim To determine the incidence of and risk factors for psychiatric disorders in early adulthood in patients with childhood onset type 1 diabetes (T1D). Methods In this retrospective‐cohort study, we identified a population‐based childhood onset T1D cohort and an age and sex matched (5:1) non‐diabetic comparison cohort. Data linkage was used to access inpatient hospitalization data, mental health support service data, and mortality data to follow‐up both cohorts into early adulthood. Results The mean age of T1D diagnosis was 9.5 years (SD 4.1), with a mean age at end of follow‐up of 26.4 years (SD 5.2, max 37.7). The diagnosis of any psychiatric disorder was observed for 187 of 1302 (14.3%) in the T1D cohort and 400 of 6422 (6.2%) in the comparison cohort [adjusted hazard ratio (HR) 2.3; 95% CI 1.9, 2.7]. Anxiety, eating, mood, and personality and behaviour disorders were observed at higher rates within the T1D cohort. Comorbid psychiatric disorders were more frequent, at the cohort level, within the T1D cohort (2‐3 disorders 3.76% vs 1.56%) and service utilization was higher (15+ contacts 6.8% vs 2.8%); though these differences did not remain when restricted to only those individuals diagnosed during follow‐up. A history of poor glycaemic control was associated with an increased risk of anxiety, mood, and ‘any’ disorder (HR ranging from 1.35 to 1.42 for each 1% increase in mean paediatric HbA1c). Conclusion Our findings highlight the need for access to mental health support services as part of routine patient care for young adults with T1D, and for better predictive tools to facilitate targeting at‐risk patients with early intervention programs.