OBJECTIVE We evaluated the safety and efficacy of day-and-night fully closed-loop insulin therapy using faster (Faster-CL) compared with standard insulin aspart (Standard-CL) in young adults with type 1 diabetes. RESEARCH DESIGN AND METHODS In a double-blind, randomized, crossover trial, 20 participants with type 1 diabetes on insulin pump therapy (11 females, aged 21.3 ± 2.3 years, HbA_1c 7.5 ± 0.5% [58.5 ± 5.5 mmol/mol]) underwent two 27-h inpatient periods with unannounced afternoon moderate-vigorous exercise and unannounced/uncovered meals. We compared Faster-CL and Standard-CL in random order. During both interventions, the fuzzy-logic control algorithm DreaMed GlucoSitter was used. Glucose sensor data were analyzed by intention-to-treat principle with the difference (between Faster-CL and Standard-CL) in proportion of time in range 70–180 mg/dL (TIR) over 27 h as the primary end point. RESULTS The proportion of TIR was similar for both arms: 53.3% (83% overnight) in Faster-CL and 57.9% (88% overnight) in Standard-CL (P = 0.170). The proportion of time in hypoglycemia <70 mg/dL was 0.0% for both groups. Baseline-adjusted interstitial prandial glucose increments 1 h after meals were greater in Faster-CL compared with Standard-CL (P = 0.017). The gaps between measured plasma insulin and estimated insulin-on-board levels at the beginning, at the end, and 2 h after the exercise were smaller in the Standard-CL group (P = 0.029, P = 0.003, and P = 0.004, respectively). No severe adverse events occurred. CONCLUSIONS Fully closed-loop insulin delivery using either faster or standard insulin aspart was safe and efficient in achieving near-normal glucose concentrations outside postprandial periods. The closed-loop algorithm was better adjusted to the standard insulin aspart.

Introduction: To evaluate time in tight range (TITR) 70–140 mg/dL (3.9–7.8 mmol/L), its correlation with standard continuous glucose monitoring (CGM) metrics and the clinical variables that possibly have a substantial impact on its value, in a large cohort of pediatric subjects using different treatment strategies. Materials and Methods: A total of 854 children and adolescents with type 1 diabetes were consecutively recruited in this real world, dual center, cross-sectional study. Participants were categorized into four treatment groups (multiple daily injections [MDI] + real-time CGM, MDI + intermittently scanned CGM, sensor augmented pump, and hybrid closed loop [HCL]). Demographical and clinical data, including CGM data, were collected and analyzed. Results: The overall study population exhibited an average TITR of 36.4% ± 12.8%. HCL users showed higher TITR levels compared to the other treatment groups ( P < 0.001). A time in range (TIR) cut-off value of 71.9% identified subjects achieving a TITR ≥50% (area under curve [AUC] 0.98; 95% confidence interval 0.97–0.99, P < 0.001), and a strong positive correlation between these two metrics was observed ( r = 0.95, P < 0.001). An increase in TIR of 1% was associated with 1.84 ( R ² Nagelkerke = 0.35, P < 0.001) increased likelihood of achieving TITR ≥50%. Use of HCL systems ( B = 7.78; P < 0.001), disease duration ( B = −0.26, P = 0.006), coefficient of variation ( B = −0.30, P = 0.004), and glycated hemoglobin ( B = −8.82; P < 0.001) emerged as significant predictors of TITR levels. Conclusions: Our study highlights that most children and adolescents with type 1 diabetes present TITR levels below 50%, except those using HCL. Tailored interventions and strategies should be implemented to increase TITR.

Background: The aim of this multicenter observational real-world study was to investigate glycemic outcomes in children and adolescents with type 1 diabetes over the first 6-month use of MiniMed™ 780G. The secondary objective was to evaluate demographic and clinical factors that may be significantly associated with the achievement of therapeutic goals. Methods: Demographic, anamnestic, and clinical data of study participants were collected at the time of enrollment. Data on ambulatory glucose profile were acquired at 3 and 6 months after activating automatic mode. Aggregated glucose metrics and device settings of the entire study period were analyzed to identify predictors of optimal glycemic control, assessed by the concomitant achievement of time in range (TIR) >70%, coefficient of variation (CV) <36%, glucose management indicator (GMI) <7%, and time below range (TBR) <4%. Results: Our study cohort consisted of 111 children and adolescents (54.1% female) aged 7–18 years. All the most relevant clinical targets were achieved according to recommendations from the International Consensus both at 3 and 6 months. When considering aggregated data, primary goals in terms of TIR, CV, GMI, and TBR were achieved, respectively, by 72.1%, 74.8%, 68.5%, and 74.8% of participants. In addition, 44 individuals (39.6%) concomitantly addressed all the above clinical targets. Regression analysis revealed that older age, briefer duration of disease, and shorter active insulin time were significant predictors of optimal glucose control. Comparing two groups of individuals stratified according to the glycated hemoglobin (HbA1c) mean value in the year preceding MiniMed 780G use, achieving glycemic targets was observed in the subgroup with lower HbA1c. Conclusions: Our study highlights the effectiveness and safety of MiniMed 780G in the pediatric population. More extensive and personalized training on advanced hybrid closed-loop use should be considered for younger people and those with long disease duration.

Glycemia risk index (GRI) is a novel composite metric for the evaluation of the safety of glycemic management and control. The aim of this study was to evaluate GRI and its correlations with continuous glucose monitoring (CGM) metrics by analyzing real-life CGM data in 1067 children/adolescents with type 1 diabetes (T1D) using four different treatment strategies (intermittently scanned CGM [isCGM]-multiple daily injections [MDIs]; real-time CGM-MDIs; rtCGM-insulin pump; hybrid closed-loop [HCL] therapy). GRI was positively correlated with high blood glucose index, low blood glucose index, mean glycemia, its standard deviation, coefficient of variation, and HbA1c. The four treatment strategy groups showed significantly different GRI with the lowest value in the HCL group (30.8) and the highest in the isCGM-MDIs group (68.4). These findings support the use of GRI for the assessment of the glycemic risk and the safety of specific treatment in pediatric subjects with T1D.

Objective: Vitamin D may potentially play a central role in glucose homeostasis and β-cell function (BCF), although studies are not consistent. Aim of our study was to test the hypotheses of a direct relationship between vitamin D, insulin sensitivity (IS) and BCF in overweight and obese non-diabetic children. Design and methods: Cross-sectional study carried out at the Childhood Obesity Outpatient Clinic, University Hospital of Verona. One hundred twenty-two Caucasian overweight and obese children (age: 12.8 ± 0.2 years) were enrolled. Exclusion criteria: genetic or endocrine causes of obesity, chronic diseases or therapies. Patients underwent oral glucose tolerance test. HOMA-IR, Matsuda index and insulinogenic index were calculated. BCF was reconstructed by mathematical modeling and described by Derivative and Proportional Control. Total 25-hydroxyvitamin D and vitamin D-binding protein (VDBP) were measured. Two SNPs (rs4588 and rs7041) in the VDBP gene were studied, and bioavailable vitamin D (BVD) was calculated. Results: Hypovitaminosis D was documented in 90% of patients. Forty-seven subjects were homozygous for both SNPs. Total vitamin D was positively correlated with Matsuda index (P = 0.002), VDBP (P = 0.045), and negatively with BMI SDS (P = 0.043), HOMA-IR (P = 0.008), HOMA-B (P = 0.001), IGI (P = 0.007), derivative control (P = 0.036) and proportional control (P = 0.018). Total vitamin D, adjusted for age, gender, BMI SDS, puberty and seasonality of vitamin D measurement, was a predictor of Matsuda index, HOMA-IR, HOMA-B, IGI, proportional control (all P < 0.05). BVD was positively correlated with total vitamin D (P < 0.001) and negatively with BMI SDS (P = 0.041). Conclusions: Hypovitaminosis D negatively influences BCF and IS, suggesting that vitamin D levels might be implicated in glucose metabolism impairment in overweight and obese individuals.