期刊
NATURE COMMUNICATIONS
卷 13, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-022-28909-1
关键词
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资金
- 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]
- NIH [DAISY: DK032493, DK032083, DK104351, DK116073]
- European Union [BMH4-CT98-3314]
- Novo Nordisk Foundation
- Academy of Finland [292538]
- Academy of Finland (Centre of Excellence in Molecular Systems Immunology and Physiology Research 2012-2017) [250114]
- Special Research Funds for University Hospitals in Finland
- Diabetes Research Foundation, Finland
- Sigrid Juselius Foundation, Finland
- Swedish Research Council [14064, 2009-1039]
- Swedish Childhood Diabetes Foundation
- Swedish Diabetes Association
- Nordisk Insulin Fund
- Lion Club International [101-S]
- Skane County Council Foundation for Research and Development
- LUDC-IRC/EXODIAB funding from the Swedish Foundation for Strategic Research [IRC15-0067]
- Hussman Foundation
- Washington State Life Science Discovery Fund
- CDC [DEW-IT: UR6/CCU017247]
- German Federal Ministry of Education and Research
- SUS funds
- royal Physiographic society
- The JDRF [DIPP: 1-RSC-2018-555-I-X, DEW-IT: 1-SRA-2019-719-I-X, 1-RSC-2017-516-I-X, 1-SRA-2016-342-M-R, 1-SRA-2019-732-M-B]
- [DiPiS: DK26190]
The development of islet autoimmunity in children with type 1 diabetes can be predicted, but the presence of autoantibodies does not always result in noticeable symptoms. Through long-term sampling and analysis of clinical characteristics, it was found that disease progression follows three distinct trajectories, with the ability to further predict disease onset based on age, sex, and HLA-DR status.
Development of islet autoimmunity precedes the onset of type 1 diabetes in children, however, the presence of autoantibodies does not necessarily lead to manifest disease and the onset of clinical symptoms is hard to predict. Here we show, by longitudinal sampling of islet autoantibodies (IAb) to insulin, glutamic acid decarboxylase and islet antigen-2 that disease progression follows distinct trajectories. Of the combined Type 1 Data Intelligence cohort of 24662 participants, 2172 individuals fulfill the criteria of two or more follow-up visits and IAb positivity at least once, with 652 progressing to type 1 diabetes during the 15 years course of the study. Our Continuous-Time Hidden Markov Models, that are developed to discover and visualize latent states based on the collected data and clinical characteristics of the patients, show that the health state of participants progresses from 11 distinct latent states as per three trajectories (TR1, TR2 and TR3), with associated 5-year cumulative diabetes-free survival of 40% (95% confidence interval [CI], 35% to 47%), 62% (95% CI, 57% to 67%), and 88% (95% CI, 85% to 91%), respectively (p < 0.0001). Age, sex, and HLA-DR status further refine the progression rates within trajectories, enabling clinically useful prediction of disease onset.
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