American Diabetes Association
Browse
Kwon_et_al._DB22-0360_Supplementary_(1) (1).pdf (1.04 MB)

Islet Autoantibody Levels Differentiate Progression Trajectories in Individuals with Presymptomatic Type 1 Diabetes

Download (1.04 MB)
figure
posted on 2022-09-01, 16:54 authored by Bum Chul Kwon, Peter Achenbach, Vibha Anand, Brigitte I. Frohnert, William Hagopian, Jianying Hu, Eileen Koski, Åke Lernmark, Olivia Lou, Frank Martin, Kenney Ng, Jorma Toppari, Riitta Veijola, T1DI Study Group

Our previous data-driven analysis of evolving patterns of islet autoantibodies (IAbs) against insulin (IAA), glutamic acid decarboxylase (GADA) and islet antigen 2 (IA-2A) discovered three trajectories characterized by either multiple IAbs (TR1), IAA (TR2), or GADA (TR3) as the first appearing autoantibodies. Here we examined the evolution of IAb levels within these trajectories in 2,145 IAb-positive participants followed from early life and compared those who progressed to type 1 diabetes (n=643) to those remaining undiagnosed (n=1,502). Using thresholds determined by 5-year diabetes risk, four levels were defined for each IAb and overlayed onto each visit. In diagnosed participants, high IAA levels were seen in TR1 and TR2 at ages <3 years, whereas IAA remained at lower levels in the undiagnosed. Proportions of dwell times (total duration of follow-up at a given level) at the four IAb levels differed between the diagnosed and undiagnosed for GADA and IA-2A in all three trajectories (p<0.001), but for IAA dwell times differed only within TR2 (p<0.05). Overall, undiagnosed participants more frequently had low IAb levels and later appearance of IAb than diagnosed participants. In conclusion, while it has been long appreciated that the number of autoantibodies is an important predictor of type 1 diabetes, consideration of autoantibody levels within the three autoimmune trajectories improved differentiation of IAb positive children who progressed to type 1 diabetes from those who did not.

Funding

This work was supported by funding from JDRF (IBM: 1-RSC-2017-368-I-X, 1-IND-2019-717-I-X, #2-RSC-2020-980-I-X), (DAISY: 1-SRA-2019-722-I-X, 1-RSC-2017-517-I-X, 5-ECR-2017-388-A-N), (DiPiS: 1-SRA-2019-720-I-X, 1-RSC-2017-526-I-X), (DIPP: 1-RSC-2018-555-I-X, 1-SRA-2019-721-I-X), (DEW-IT: 1-SRA-2019-719-I-X, 1-RSC-2017-516-I-X), (BABYDIAB: 1-SRA-2019-723-I-X) as well as NIH (DAISY: DK032493, DK032083, DK104351; and DK116073; DiPiS: DK26190 and the CDC (DEW-IT: UR6/CCU017247). The DIPP study was funded by JDRF (grants 1-SRA-2016-342-M-R, 1-SRA-2019-732-M-B); European Union (grant BMH4-CT98-3314); Novo Nordisk Foundation; Academy of Finland (Decision No 292538 and Centre of Excellence in Molecular Systems Immunology and Physiology Research 2012-2017, Decision No. 250114); Special Research Funds for University Hospitals in Finland; Diabetes Research Foundation, Finland; and Sigrid Juselius Foundation, Finland. The BABYDIAB study was funded by the German Federal Ministry of Education and Research to the German Center for Diabetes Research. The DiPiS study was funded by Swedish Research Council (grant no. 14064), Swedish Childhood Diabetes Foundation, Swedish Diabetes Association, Nordisk Insulin Fund, SUS funds, Lion Club International, district 101-S, The royal Physiographic society, Skåne County Council Foundation for Research and Development as well as LUDC-IRC/EXODIAB funding from the Swedish foundation for strategic research (Dnr IRC15-0067) and Swedish research council (Dnr 2009-1039). Additional funding for DEW-IT was provided by the Hussman Foundation and by the Washington State Life Science Discovery Fund.

History

Usage metrics

    Diabetes

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC