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

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

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

13 Citations (Scopus)
123 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)119-130
Number of pages12
Issue number1
Early online date20 Dec 2018
Publication statusPublished - Jan 2019

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