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Ghalwash, M.* ; Anand, V.* ; Ng, K.* ; Dunne, J.L.* ; Lou, O.* ; Lundgren, M.* ; Hagopian, W.A.* ; Rewers, M.* ; Ziegler, A.-G. ; Veijola, R.*

Data-driven phenotyping of presymptomatic type 1 diabetes using longitudinal autoantibody profiles.

Diabetes Care 47, 1424-1431 (2024)
DOI PMC
Free by publisher: Publ. Version/Full Text online available 12/2024
OBJECTIVE: To characterize distinct islet autoantibody profiles preceding stage 3 type 1 diabetes. RESEARCH DESIGN AND METHODS: The T1DI (Type 1 Diabetes Intelligence) study combined data from 1,845 genetically susceptible prospectively observed children who were positive for at least one islet autoantibody: insulin autoantibody (IAA), GAD antibody (GADA), or islet antigen 2 antibody (IA-2A). Using a novel similarity algorithm that considers an individual's temporal autoantibody profile, age at autoantibody appearance, and variation in the positivity of autoantibody types, we performed an 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 developed IAA in early life (median age 1.6 years) followed by GADA (1.9 years) and then IA-2A (2.1 years). Their 10-year risk was 89.9% (95% CI 81.9-95.4). A high 5-year risk was also found in children with persistent IAA and GADA (39.1%) and children with persistent GADA and IA-2A (30.9%). A lower 5-year risk (10.5%) was observed in children with a late appearance of persistent GADA (6.1 years). The lowest 5-year diabetes risk (1.6%) was associated with positivity for a 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.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Islet Autoantibodies; Risk; Progression; Children; Autoimmunity; Appearance; Patterns
ISSN (print) / ISBN 0149-5992
e-ISSN 1935-5548
Journal Diabetes Care
Quellenangaben Volume: 47, Issue: 8, Pages: 1424-1431 Article Number: , Supplement: ,
Publisher American Diabetes Association
Publishing Place Alexandria, Va.
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Diabetes Research Type 1 (IDF)
Grants Washington StateLife Science Discovery Fund
National Institutes of Health
European Union
Novo Nordisk Foundation
Academy of Finland
Centre of Excellence in Molecular Systems Immunology and Physiology Research
Royal Physiographic Society
Skane County Council Foundation for Research and Development
Swedish Foundation for Strategic Research
Swedish Research Council
Centers for Disease Control and Prevention
University of Washington Diabetes Research Center
Hussman Foundation
Lions Club International
SUS funds
University Hospitals in Finland
Diabetes Research Foundation (Finland)
Sigrid Juselius Foundation(Finland)
German Federal Ministry of Education and Research
Swedish Childhood Diabetes Foundation
Swedish Diabetes Association
NordiskInsulin Fund
JDRF