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Abstract
Aims/hypothesisAccurate 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.MethodsModels 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.ResultsCox 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/interpretationPrediction 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.Graphical abstract
Highlights
Interest in autoantibody screening for type 1 diabetes risk has increased following the demonstration that early diagnosis prevents ketoacidosis [1,2,3] and provides opportunities to delay disease progression with immune therapies [4, 5]
The TrialNet multipleautoantibody validation population comprised individuals who met the same glucose, HbA1c, age and BMI criteria, and who had all measures required to calculate the Diabetes Prevention Trial Risk Score (DPTRS) and the newer risk scores. These participants were not included in the training population because they underwent OGTT testing two or more visits after screening positive to multiple autoantibodies, lacked data for HLA genotype or did not have data for ZnT8A, which was only introduced into TrialNet in 2012
We describe models to predict progression to insulindependent type 1 diabetes that are simpler than the previously validated DPTRS, DPTRS60 and Index60 risk scores and yet have comparable performance in the contemporary TrialNet and Fr1da populations
Summary
Interest in autoantibody screening for type 1 diabetes risk has increased following the demonstration that early diagnosis prevents ketoacidosis [1,2,3] and provides opportunities to delay disease progression with immune therapies [4, 5]. Stage 3 satisfies current diagnostic criteria for diabetes mellitus [12] and is usually accompanied by symptoms of hyperglycaemia While this staging system is important for helping the medical and lay communities understand the progression of a largely silent autoimmune disease, staging is used to determine eligibility for prevention trials. OGTTs performed in a number of research studies have collected glucose values at the 30, 60 and 90 min time points to further define risk characteristics [13]. These additional time points greatly increase the costs and complicate the logistics of the OGTT. In order to conserve limited resources and increase participation, some screening programmes are transitioning from a multiple-time-point OGTT to the standard clinical model in which sampling is performed only at baseline and 120 min
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