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Endesfelder, D. ; zu Castell-Rüdenhausen, W. ; Bonifacio, E.* ; Rewers, M.* ; Hagopian, W.A.* ; She, J.X.* ; Lernmark, A.* ; Toppari, J.* ; Vehik, K.* ; Williams, A.J.K.* ; Yu, L.* ; Akolkar, B.* ; Krischer, J.P.* ; Ziegler, A.-G. ; Achenbach, P.

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

Diabetes 68, 119-130 (2018)

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Progression to clinical type 1 diabetes varies among children who develop beta-cell autoantibodies. Differences in autoantibody patterns could relate to disease progression and etiology. Here we modeled complex longitudinal auto-antibody 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 from6%(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 beta-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.

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