The Impact of Hypo- and Hyperglycemia on Cognition and Brain Development in Young Children with Type 1 Diabetes.

Human and experimental animal data suggest both hyperglycemia and hypoglycemia can lead to altered brain structure and neurocognitive function in type 1 diabetes (T1D). Young children with T1D are prone to extreme fluctuations in glucose levels. The overlap of these potential dysglycemic insults to the brain during the time of most active brain and cognitive development may cause cellular and structural injuries that appear to persist into adult life. Brain structure and cognition in persons with T1D are influenced by age of onset, exposure to glycemic extremes such as severe hypoglycemic episodes, history of diabetic ketoacidosis, persistent hyperglycemia, and glucose variability. Studies using brain imaging techniques have shown brain changes that appear to be influenced by metabolic abnormalities characteristic of diabetes, changes apparent at diagnosis and persistent throughout adulthood. Some evidence suggests that brain injury might also directly contribute to psychological and mental health outcomes. Neurocognitive deficits manifest across multiple cognitive domains. Moreover, impaired executive function and mental health can affect patients' adherence to treatment. This review summarizes the current data on the impact of glycemic extremes on brain structure and cognitive function in youth with T1D and the use of new diabetes technologies that may reduce these complications.

Type 1 diabetes mellitus management in young children: implementation of current technologies.

The use of advanced technologies for diabetes management is on the rise among pediatric patients with type 1 diabetes (T1D). Continuous subcutaneous insulin infusion (CSII), continuous glucose monitoring, predictive low glucose suspend, hybrid closed-loop insulin delivery systems-all enable better diabetes management and glycemic control. However, when used by children, and especially very young children, specific aspects must be taken into consideration, including technical parameters, ease of use, parental stress, and satisfaction. The unique characteristics of T1D in children aged <6 years are reviewed and studies of the pros and cons of different technologies in this specific age group are presented. Addressing such issues when implementing advanced technologies among very young children with T1D will enable better diabetes management and will hopefully ease a tremendous burden of both children and families.

Adjusting insulin doses in patients with type 1 diabetes who use insulin pump and continuous glucose monitoring: Variations among countries and physicians.

AIMS: To evaluate physicians' adjustments of insulin pump settings based on continuous glucose monitoring (CGM) for patients with type 1 diabetes and to compare these to automated insulin dose adjustments. METHODS: A total of 26 physicians from 16 centres in Europe, Israel and South America participated in the study. All were asked to adjust insulin dosing based on insulin pump, CGM and glucometer downloads of 15 patients (mean age 16.2 ± 4.3 years, six female, mean glycated haemoglobin 8.3 ± 0.9% [66.8 ± 7.3 mmol/mol]) gathered over a 3-week period. Recommendations were compared for the relative changes in the basal, carbohydrate to insulin ratio (CR) and correction factor (CF) plans among physicians and among centres and also between the physicians and an automated algorithm, the Advisor Pro (DreaMed Diabetes Ltd, Petah Tikva, Israel). Study endpoints were the percentage of comparison points for which there was full agreement on the trend of insulin dose adjustments (same trend), partial agreement (increase/decrease vs no change) and full disagreement (opposite trend). RESULTS: The percentages for full agreement between physicians on the trend of insulin adjustments of the basal, CR and CF plans were 41 ± 9%, 45 ± 11% and 45.5 ± 13%, and for complete disagreement they were 12 ± 7%, 9.5 ± 7% and 10 ± 8%, respectively. Significantly similar results were found between the physicians and the automated algorithm. The algorithm magnitude of insulin dose change was at least equal to or less than that proposed by the physicians. CONCLUSIONS: Physicians provide different insulin dose recommendations based on the same datasets. The automated advice of the Advisor Pro did not differ significantly from the advice given by the physicians in the direction or magnitude of the insulin dosing.