American Diabetes Association
Browse
1/1
2 files

Data-Driven Phenotyping of Presymptomatic Type 1 Diabetes Using Longitudinal Autoantibody Profiles

Download all (1.1 MB)
figure
posted on 2024-06-11, 17:29 authored by Mohamed Ghalwash, Vibha Anand, Kenney Ng, Jessica L. Dunne, Olivia Lou, Markus Lundgren, William A. Hagopian, Marian Rewers, Anette-G. Ziegler, Riitta Veijola

Objective. To characterize distinct islet autoantibody profiles preceding stage 3 type 1 diabetes

Research Design and Methods: Type 1 Diabetes Intelligence (T1DI) study combined data from 1845 genetically susceptible, prospectively followed children who were positive for at least one islet autoantibody against insulin, glutamic acid decarboxylase or insulinoma antigen-2 (IAA, GADA, IA-2A). Using a novel similarity algorithm that considers individual’s temporal autoantibody profile, age at autoantibody appearance, and variation in positivity of autoantibody types, we performed unsupervised hierarchical clustering analysis. Progression rates to diabetes were analyzed via survival analysis.

Results: We identified five main clusters of individuals with distinct autoantibody profiles characterized by seroconversion age and sequence of appearance of the three autoantibodies. The highest 5-year risk from first positive autoantibody to type 1 diabetes (69.9%; 95% CI 60.0-79.2) was observed in children who first develop IAA in early life (median age 1.6yr) followed by GADA (1.9yr) and then IA-2A (2.1yr). Their 10-year risk was 89.9% (95% CI 81.9-95.4). High 5-year risk was also found in children with persistent IAA and GADA (39.1%), or children with persistent GADA and IA-2A (30.9%). Lower 5-year risk (10.5%) was observed in children with late appearance of persistent GADA (6.1yr). The lowest 5-year diabetes risk (1.6%) was associated with positivity for single, often reverting autoantibody.

Conclusions: The novel clustering algorithm identified children with distinct islet autoantibody profiles and progression rates to diabetes. These results are useful for prediction, selection of individuals for prevention trials, and studies investigating various pathways to type 1 diabetes.

Funding

This work was supported by funding from JDRF (IBM: 1-RSC-2017-368-I-X, 1-IND-2019-717-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) as well as NIH (DAISY: DK032493, DK032083, DK104351; 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, Lions Club International, district 101-S, The Royal Physiographic Society, Skåne County Council Foundation for Research and Development, the LUDC-IRC/EXODIAB funding from the Swedish foundation for strategic research (Dnr IRC15-0067) and the Swedish research council (Dnr 2009-1039). DEW-IT was funded by the Centers for Disease Control and Prevention UR6/CCU017247, with additional support from the University of Washington Diabetes Research Center P30 DK017047, the Hussman Foundation and the Washington State Life Science Discovery Fund.

History

Usage metrics

    Diabetes Care

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC