ISSN 1662-4009 (online)

ey0019.10-6 | New paradigms | ESPEYB19

10.6. Progression of type 1 diabetes from latency to symptomatic disease is predicted by distinct autoimmune trajectories

BC Kwon , V Anand , P Achenbach , JL Dunne , W Hagopian , J Hu , E Koski , AE Lernmark , M Lundgren , K Ng , J Toppari , R Veijola , BI Frohnert

T1DI Study Group. Nat Commun. 2022 Mar 21;13(1):1514. https://pubmed.ncbi.nlm.nih.gov/35314671/Brief Summary: This study of 5 birth cohorts of individuals at high risk for type 1 diabetes (T1D) used machine learning methods to explore trajectories from autoantibodies appearance to T1D progression. They identified 11 distinct latent health states and individuals progressed according to one o...

ey0020.8-4 | Important for Clinical Practice | ESPEYB20

8.4. Two-age islet-autoantibody screening for childhood type 1 diabetes: a prospective cohort study

M Ghalwash , JL Dunne , M Lundgren , M Rewers , AG Ziegler , V Anand , J Toppari , R Veijola , W Hagopian , Type 1 Diabetes Intelligence Study Group

Brief summary: Using data from the Type 1 Diabetes Intelligence (T1DI) cohort (n=24 662), this prospective study aimed to identify optimal ages for initial islet autoantibody (IAb) screening to predict the development of clinical type 1 diabetes (T1D). The identified optimal screening ages were 2 years and 6 years, with sensitivity of 82% and positive predictive value of 79% for T1D by age 15 years.Screening for T1D is a growing research topic a...