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Over the last years significant advances have been achieved in the development of technologies for diabetes management. Continuous subcutaneous insulin infusion (CSII), continuous glucose monitoring (CGM), predictive low glucose management (PLGM), hybrid closed loop (HCL) and advanced hybrid closed loop (AHCL) systems allow better diabetes management, thus reducing the burden of the disease and the risk of chronic complications. This review summarizes the main characteristics of the currently available HCL and AHCL systems and their primary effects in children and adolescents with type 1 diabetes (T1D). The findings of trials assessing the glucose control (time in range, HbA1c values, hypoglycemic events), the health-related quality of life and the existing limits of the use of these technologies are reported. The most recent data clearly confirm the ability of the HCL and AHCL insulin delivery systems to safely achieve a significant improvement of glucose control and quality of life in the pediatric population with T1D. Further studies are underway to overcame current barriers and future improvements in the usability of these technologies are awaited to facilitate their use in the routine clinical practice. The HCL and AHCL algorithms are the key features of today's insulin delivery systems that mark a crucial step towards fully automated closed loop systems.

Objective Transient neonatal diabetes mellitus (TNDM) is caused by activating mutations in ABCC8 and KCNJ11 genes (KATP/TNDM) or by chromosome 6q24 abnormalities (6q24/TNDM). We wanted to assess whether these different genetic aetiologies result in distinct clinical features. Design Retrospective analysis of the Italian data set of patients with TNDM. Methods Clinical features and treatment of 22 KATP/TNDM patients and 12 6q24/TNDM patients were compared. Results Fourteen KATP/TNDM probands had a carrier parent with abnormal glucose values, four patients with 6q24 showed macroglossia and/or umbilical hernia. Median age at diabetes onset and birth weight were lower in patients with 6q24 (1 week; −2.27 SD) than those with KATP mutations (4.0 weeks; −1.04 SD) (P = 0.009 and P = 0.007, respectively). Median time to remission was longer in KATP/TNDM than 6q24/TNDM (21.5 weeks vs 12 weeks) (P = 0.002). Two KATP/TNDM patients entered diabetes remission without pharmacological therapy. A proband with the ABCC8/L225P variant previously associated with permanent neonatal diabetes entered 7-year long remission after 1 year of sulfonylurea therapy. Seven diabetic individuals with KATP mutations were successfully treated with sulfonylurea monotherapy; four cases with relapsing 6q24/TNDM were treated with insulin, metformin or combination therapy. Conclusions If TNDM is suspected, KATP genes should be analyzed first with the exception of patients with macroglossia and/or umbilical hernia. Remission of diabetes without pharmacological therapy should not preclude genetic analysis. Early treatment with sulfonylurea may induce long-lasting remission of diabetes in patients with KATP mutations associated with PNDM. Adult patients carrying KATP/TNDM mutations respond favourably to sulfonylurea monotherapy.

Background. Autoimmune polyglandular syndrome type 1 (APS-1) with or without reversible metaphyseal dysplasia is a rare genetic disorder due to inactivating variants of the autoimmune regulator, AIRE, gene. Clinical variability of APS-1 relates to pleiotropy, and the general dysfunction of self-tolerance to organ-specific antigens and autoimmune reactions towards peripheral tissues caused by the underlying molecular defect. Thus, early recognition of the syndrome is often delayed, mostly in cases with atypical presentation, and the molecular confirm through the genetic analysis of the AIRE gene might be of great benefit. Methods. Our methods were to investigate, with a multigene panel next generation sequencing approach, two clinical cases, both presenting with idiopathic hypoparathyroidism, also comprising the AIRE gene, as well as to comment our findings as part of a more extensive review of literature data. Results. In the first clinical case, two compound heterozygote pathogenic variants of the AIRE gene were identified, thus indicating an autosomal recessive inheritance of the disease. In the second case, only one AIRE gene variant was found and an atypical dominant negative form of APS-1 suggested, later confirmed by further medical ascertainments. Conclusions. APS-1 might present with variable and sometimes monosymptomatic presentations and, if not recognized, might associate with severe complications. In this context, next generation diagnostics focused on a set of genes causative of partially overlapping disorders may allow early diagnosis.

Objectives To assess the optimal setting of the predictive low glucose management (PLGM) algorithm for preventing exercise-induced hypoglycemia in adolescents with type 1 diabetes. Methods Thirty-four adolescents, 15 to 20 years, wearing PLGM system, were followed during 3 days exercise during a diabetes camp. PLGM threshold was set at 70 mg/dL between 8 am and 10 pm and 90 mg/dL during 10 pm and 8 am Adolescents were divided into group A and B, with PLGM threshold at 90 and 70 mg/dL, respectively, during exercise. Time spent in hypoglycemia and AUC for time slots 8 am to 1 pm, 1 to 4 pm, 4 to 11 pm, 11 pm to 3 am, 3 to 8 am, in 3 days were compared between groups by Wilcoxon rank sum test. Results We analyzed 31 patients (median age 15.0 years, 58.1% males, median diabetes duration 7.0 years, hemoglobin A1c [HbA1c] 7.1%). No significant difference has been observed in time spent in hypoglycemia between groups using threshold 70 or 90. Time spent in target was similar in both groups, as well as time spent in hypo or hyperglycemia. The trends of blood glucose over the 3 days in the 2 groups over-lapped without significant differences. Conclusions A PLGM threshold of 90 mg/dL during the night was associated with reduced time in hypoglycemia in adolescents doing frequent physical exercise, while maintaining 65.1% time in range during the day. However, a threshold of 70 mg/dL seems to be safe in the duration of the physical exercise. PLGM system in adolescents with type 1 diabetes was effective to prevent hypoglycemia during and after exercise, irrespective of the PLGM thresholds used.

To analyse clinical and socioeconomic factors associated with diabetic ketoacidosis (DKA) at diagnosis of type 1 diabetes (T1D) in Italy. Incident cases less than 15 years of age, were recruited during 2014-2016 from 31 paediatric centres. Age, gender, venous pH, presence of coeliac or thyroid disease, HbA1c, minority status, residence, family history of T1D and parents’ level of education were collected. DKA was defined according to ISPAD criteria. Point and 95% CI of DKA frequency were estimated. Two logistic regressions were performed to evaluate the role of clinical and socioeconomic characteristics on the risk of overall and severe DKA, considered as present vs. absent. A total of 1430 cases were evaluable. Severe and mild DKA frequencies were of 12.4% (95% CI: 10.8-14.3%) and 26.4% (95% CI: 24.1-28.7%), respectively. Females, minority status and younger children were at significant higher risk of DKA; family history of T1D and high maternal education were significantly associated with a lower risk. Despite prevention campaigns intended to reduce the DKA recently implemented, the frequency of DKA at T1D diagnosis remains high in Italy. Multiple factors affect the risk of DKA at the onset of type 1 diabetes, suggesting potential opportunity to intervene between symptom onset and development of diabetic ketoacidosis for both parents and clinicians. Factors associated with the risk of DKA at diagnosis of type 1 diabetes in Italyoverall DKAsevere DKAOR95%CIpOR95%pAge class5-9 vs. 0-4 y0.660-45-0.970.0330.480.27-0.830.00910-14 vs. 0-4 y0.680.47-1.000.0490.620.36-1.050.073GenderMales vs. females0.730.54-0.980.0370.680.44-1.050.086Mother’s level of educationHigh vs. Low0.760.60-0.950.0170.590.42-0.830.003Family history of T1DNo vs. Yes0.430.22-0.790.0090.360.11-0.920.006Minority statusNo vs. Yes2.051.29-3.270.0021.290.60-2.600.499 Disclosure V. Cherubini: Advisory Panel; Self; Eli Lilly and Company. Research Support; Self; Novo Nordisk A/S, Menarini Group. R. Bonfanti: Advisory Panel; Self; Roche Diabetes Care Health and Digital Solutions, LifeScan, Inc., Abbott, A. Menarini Diagnostics, Eli Lilly and Company, Medtronic, Sanofi-Aventis, Novo Nordisk A/S. E. Skrami: None. L. Ferrito: None. R. Gesuita: None.