Simplifying prediction of disease progression in pre-symptomatic type 1 diabetes using a single blood sample.

AIMS/HYPOTHESIS: Accurate prediction of disease progression in individuals with pre-symptomatic type 1 diabetes has potential to prevent ketoacidosis and accelerate development of disease-modifying therapies. Current tools for predicting risk require multiple blood samples taken during an OGTT. Our aim was to develop and validate a simpler tool based on a single blood draw. METHODS: Models to predict disease progression using a single OGTT time point (0, 30, 60, 90 or 120 min) were developed using TrialNet data collected from relatives with type 1 diabetes and validated in independent populations at high genetic risk of type 1 diabetes (TrialNet, Diabetes Prevention Trial-Type 1, The Environmental Determinants of Diabetes in the Young [1]) and in a general population of Bavarian children who participated in Fr1da. RESULTS: Cox proportional hazards models combining plasma glucose, C-peptide, sex, age, BMI, HbA1c and insulinoma antigen-2 autoantibody status predicted disease progression in all populations. In TrialNet, the AUC for receiver operating characteristic curves for models named M60, M90 and M120, based on sampling at 60, 90 and 120 min, was 0.760, 0.761 and 0.745, respectively. These were not significantly different from the AUC of 0.760 for the gold standard Diabetes Prevention Trial Risk Score, which requires five OGTT blood samples. In TEDDY, where only 120 min blood sampling had been performed, the M120 AUC was 0.865. In Fr1da, the M120 AUC of 0.742 was significantly greater than the M60 AUC of 0.615. CONCLUSIONS/INTERPRETATION: Prediction models based on a single OGTT blood draw accurately predict disease progression from stage 1 or 2 to stage 3 type 1 diabetes. The operational simplicity of M120, its validity across different at-risk populations and the requirement for 120 min sampling to stage type 1 diabetes suggest M120 could be readily applied to decrease the cost and complexity of risk stratification

Investigation of nomophobia and smartphone addiction predictors among adolescents in Turkey: Demographic variables and academic performance

Most individuals spend a great amount of time on their smartphones. The intense usage of smartphones leads to some physical symptoms, good and bad feelings, pathological addiction, depression, symptoms such as fear-anxiety, productivity and low academic achievement. For this reason, prevention activities must be prioritized when dealing with the intense and uncontrolled usage of smartphones. The aim of this study is to determine nomophobia levels and smartphone addiction among 12-18 age group secondary and high school students and to investigate the demographic and academic variables predicting these levels. Designed with a relational model, the population of this research consists of 612 students studying at all levels of secondary school and high school. Personal information form and two different scales were used in the research. Descriptive analyses and hierarchical linear multiple regression analysis were used in the analysis of the data obtained by means of data collection in the research. As a result of the research, there is a significant relationship between smartphone addiction and nomophobia. In this study, Model 4 has been identified to be the most important predictor of smartphone addiction and nomophobia. In Model 4, variables related to smartphone usage are included in the analysis. Recommendations have been made according to the results of the study. (C) 2018 Western Social Science Association. Published by Elsevier Inc. All rights reserved