Skip to content

Time-resolved autoantibody profiling facilitates stratification of preclinical type 1 diabetes in children

Research output: Contribution to journalArticle

  • David Endesfelder
  • Wolfgang zu Castell
  • Ezio Bonifacio
  • Marian Rewers
  • William Hagopian
  • Jin-Xiong She
  • Ake Lernmark
  • Jorma Toppari
  • Kendra Vehik
  • Alistair Williams
  • Liping Yu
  • Beena Akolkar
  • Jeffrey Krischer
  • Anette-Gabriele Ziegler
  • Peter Achenbach
  • The TEDDY Study Group
Original languageEnglish
Pages (from-to)119-130
Number of pages12
JournalDiabetes
Volume68
Issue number1
Early online date20 Dec 2018
DOIs
DateAccepted/In press - 3 Oct 2018
DateE-pub ahead of print - 20 Dec 2018
DatePublished (current) - Jan 2019

Abstract

Progression to clinical type 1 diabetes varies among children who develop b-cell autoantibodies. Differences in autoantibody patterns could relate to disease progression and etiology. Here we modeled complex longitudinal autoantibody profiles by using a novel wavelet-based algorithm. We identified clusters of similar profiles associated with various types of progression among 600 children from The Environmental Determinants of Diabetes in the Young (TEDDY) birth cohort study; these children developed persistent insulin autoantibodies (IAA), GAD autoantibodies (GADA), insulinoma-associated antigen 2 autoantibodies (IA-2A), or a combination of these, and they were followed up prospectively at 3- to 6-month intervals (median follow-up 6.5 years). Children who developed multiple autoantibody types (n = 370) were clustered, and progression from seroconversion to clinical diabetes within 5 years ranged between clusters from 6% (95% CI 0, 17.4) to 84% (59.2, 93.6). Children who seroconverted early in life (median age <2 years) and developed IAA and IA-2A that were stable-positive on follow-up had the highest risk of diabetes, and this risk was unaffected by GADA status. Clusters of children who lacked stable-positive GADA responses contained more boys and lower frequencies of the HLA-DR3 allele. Our novel algorithm allows refined grouping of b-cell autoantibody–positive children who distinctly progressed to clinical type 1 diabetes, and it provides new opportunities in searching for etiological factors and elucidating complex disease mechanisms.

Documents

Documents

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via ADA at https://doi.org/10.2337/db18-0594 . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 3 MB, PDF document

DOI

View research connections

Related faculties, schools or groups