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Aims: We aimed to analyze the relationship between epilepsy and glutamic acid decarboxylase autoantibodies (GADA) in patients with type 1 diabetes mellitus (T1DM) and the impact of GADA on demographic, clinical, and metabolic data in T1DM patients with epilepsy., Methods: We searched for patients with T1DM ≤20 years and GADA measurements, and within this group for patients with epilepsy. We formed groups: T1DM + Epilepsy + GADA positive; T1DM + Epilepsy + GADA negative; T1DM + GADA positive; T1DM + GADA negative. We used logistic regression to analyze the relationship between epilepsy and GADA with odds ratio adjusted for sex, duration of diabetes (DOD), and age at diabetes onset (ADO). We used logistic regression with odds ratio adjusted for DOD and ADO onset using epilepsy as a dependent variable and GADA, HbA1c, ketoacidosis, severe hypoglycemia (SH), sex, celiac disease, and autoimmune thyroiditis as independent variables. We conducted regression analyses adjusted for sex, DOD, and ADO to analyze differences in clinical/metabolic parameters between the groups., Results: Epilepsy was not more frequent in GADA-positive patients (GPP). Logistic regression including all patients with GADA measurements showed that hypoglycemia with coma (HC) correlated with epilepsy when compared to no SH. We found no differences in clinical and metabolic data between GPP and GADA-negative patients (GNP) with epilepsy. SH occurred more often in GPP with epilepsy in comparison to GPP without epilepsy. GNP with epilepsy had a higher rate of HC than GPP without epilepsy., Conclusion: We found no relationship between epilepsy and GADA. A relationship between T1DM and epilepsy might be explainable by SH., (© 2020 The Authors. Pediatric Diabetes published by John Wiley & Sons Ltd.)

Aims: To analyse current therapeutic strategies for prandial insulin substitution in a large number of children and adolescents with Type 1 diabetes in Germany and Austria, along with changes in therapeutic habits and outcome., Methods: We classified the data of 26 687 patients, treated from 1995 to 2005 in 152 paediatric clinics, using a database established for quality control and scientific surveys in paediatric diabetology (DPV)., Results: Seventy-three per cent of all patients (mean age 13.6 years., mean duration of diabetes 5.4 years.) were treated with > or = 4 daily injections (intensified conventional treatment; ICT), 14% with continuous subcutaneous insulin infusion (CSII), 13% with 1-3 injections per day (conventional treatment). Frequency of daily injections increased with age, duration of diabetes and insulin dose. The insulin dose at breakfast was higher than for the evening meal or lunch, from diagnosis onwards. Individuals using insulin analogues received up to 11% higher insulin doses per day compared with patients treated with human insulin. The time of day, age, duration of diabetes, female gender, insulin analogues and ICT all had a significant influence on prandial insulin doses. Although the number of patients treated with ICT or CSII increased over the period of observation, mean glycated haemoglobin (HbA(1c)) was approximately 8.0% each year, and decreased by only 0.01%., Conclusions: Eighty-seven per cent of patients were treated with ICT or CSII. However, while this percentage increased over the observation period, mean HbA(1c) was almost constant. Longer duration of diabetes, increasing age, female gender, insulin analogues and ICT were associated with higher prandial insulin doses.

Objective: We hypothesized systematic differences in the patterns of programmed basal insulin infusion rates in children and adolescents with type 1 diabetes on continuous subcutaneous insulin infusion (CSII). We aimed at classification of basal insulin infusion rate regimens and comparing patients' underlying clinical characteristics., Research Design and Methods: The German/Austrian diabetes data acquisition system for prospective surveillance database for quality control and scientific surveys in pediatric diabetology served as the primary data source. Latest (September 2004) basal insulin infusion rates of all 1,248 patients with type 1 diabetes on CSII (0.38-18 years) were analyzed (dataset 1). Basal insulin infusion rates per hour were expressed relative to mean basal insulin infusion rates per 24 h. Unsupervised clustering was used to classify basal insulin infusion rate patterns. Clinical characteristics of patients falling into distinct basal insulin infusion rate clusters were compared by Kruskal-Wallis test. Changes of basal insulin infusion rates in 64 patients were followed from initial settings before CSII to latest programming in an independent dataset 2., Results: Seven different basal insulin infusion rate patterns occurred in dataset 1. A dawn-dusk pattern was used in 708 patients (14.9 +/- 2.4 years) with the peak basal insulin infusion rate at 5 a.m. Additional patterns showed only one basal insulin infusion rate oscillation per 24 h with a backshift of peak basal insulin infusion rates in younger children (P < 0.000001) (1 A.M.: n = 152, 12.4 years and 9 P.M.: n = 117, 8.9 years). All but two patients in dataset 2 were initially set on dawn-dusk patterns but showed a comparable diversification of basal insulin infusion rates during follow-up with backshift of peak basal insulin infusion rates in younger children (P < 0.01)., Conclusions: Pediatric diabetologists shape distinct basal insulin infusion rate profiles during treatment of CSII patients, mainly reflecting differences in age. Our data strongly suggest that age-dependent endocrine changes during childhood (e.g., puberty) affect circadian distribution of insulin needs in CSII, which should be kept in mind when considering basal insulin infusion rate strategies in children and adolescents.