A1 Refereed original research article in a scientific journal
Time-Resolved Autoantibody Profiling Facilitates Stratification of Preclinical Type 1 Diabetes in Children
Authors: Endesfelder D, Castell WZ, Bonifacio E, Rewers M, Hagopian WA, She JX, Lernmark Å, Toppari J, Vehik K, Williams AJK, Yu L, Akolkar B, Krischer JP, Ziegler AG, Achenbach P, Achenbach P; for the TEDDY Study Group
Publication year: 2019
Journal: Diabetes
Journal name in source: Diabetes
Journal acronym: Diabetes
Volume: 68
Issue: 1
First page : 119
Last page: 130
Number of pages: 12
ISSN: 0012-1797
eISSN: 1939-327X
DOI: https://doi.org/10.2337/db18-0594
Abstract
Progression to clinical type 1 diabetes varies between children developing beta-cell autoantibodies. Differences in autoantibody patterns could relate to disease progression and etiology. Here we modeled complex longitudinal autoantibody profiles using a novel wavelet-based algorithm. We identified clusters of similar profiles, associated with different types of progression, among 600 children from The Environmental Determinants of Diabetes in the Young birth cohort study who developed persistent autoantibodies against insulin (IAA), GAD (GADA) and/or insulinoma-associated antigen-2 (IA-2A), and were followed prospectively in 3 to 6 months intervals (median follow-up 6.5 years). Among multiple autoantibody-positive children (n=370), progression from seroconversion to clinical diabetes ranged between clusters from 6% (95%CI [0, 17.4]) to 84% (59.2, 93.6) within 5 years. Highest diabetes risks had children who seroconverted early in life (median age <2 years) and developed IAA and IA-2A that were stable-positive on follow-up, and these risks were unaffected by GADA status. Clusters lacking stable-positive GADA responses showed higher proportions of boys and lower frequencies of the HLA-DR3 allele. Our novel algorithm allows refined grouping of beta-cell autoantibody-positive children with distinct progression to clinical type 1 diabetes and provides new opportunities in searching for etiological factors and elucidating complex disease mechanisms.
Progression to clinical type 1 diabetes varies between children developing beta-cell autoantibodies. Differences in autoantibody patterns could relate to disease progression and etiology. Here we modeled complex longitudinal autoantibody profiles using a novel wavelet-based algorithm. We identified clusters of similar profiles, associated with different types of progression, among 600 children from The Environmental Determinants of Diabetes in the Young birth cohort study who developed persistent autoantibodies against insulin (IAA), GAD (GADA) and/or insulinoma-associated antigen-2 (IA-2A), and were followed prospectively in 3 to 6 months intervals (median follow-up 6.5 years). Among multiple autoantibody-positive children (n=370), progression from seroconversion to clinical diabetes ranged between clusters from 6% (95%CI [0, 17.4]) to 84% (59.2, 93.6) within 5 years. Highest diabetes risks had children who seroconverted early in life (median age <2 years) and developed IAA and IA-2A that were stable-positive on follow-up, and these risks were unaffected by GADA status. Clusters lacking stable-positive GADA responses showed higher proportions of boys and lower frequencies of the HLA-DR3 allele. Our novel algorithm allows refined grouping of beta-cell autoantibody-positive children with distinct progression to clinical type 1 diabetes and provides new opportunities in searching for etiological factors and elucidating complex disease mechanisms.
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