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Publication - Professor Polly Bingley

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

    Citation

    Endesfelder, D, Castell, Wz, Bonifacio, E, Rewers, M, Hagopian, W, She, J-X, Lernmark, A, Toppari, J, Vehik, K, Williams, A, Yu, L, Akolkar, B, Krischer, J, Ziegler, A-G, Achenbach, P, & Bingley, P, 2019, ‘Time-resolved autoantibody profiling facilitates stratification of preclinical type 1 diabetes in children’. Diabetes, vol 68., pp. 119-130

    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.

    Full details in the University publications repository