Progression of type 1 diabetes from latency to symptomatic disease is predicted by distinct autoimmune trajectories.


Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
21 03 2022
Historique:
received: 23 04 2021
accepted: 16 02 2022
entrez: 22 3 2022
pubmed: 23 3 2022
medline: 13 4 2022
Statut: epublish

Résumé

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.

Identifiants

pubmed: 35314671
doi: 10.1038/s41467-022-28909-1
pii: 10.1038/s41467-022-28909-1
pmc: PMC8938551
doi:

Substances chimiques

Autoantibodies 0
HLA-DR Antigens 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, N.I.H., Extramural Research Support, U.S. Gov't, P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

1514

Subventions

Organisme : NIDDK NIH HHS
ID : R37 DK032493
Pays : United States
Organisme : NIDDK NIH HHS
ID : P30 DK116073
Pays : United States
Organisme : NIDDK NIH HHS
ID : R37 DK032083
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK032493
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK032083
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK026190
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK104351
Pays : United States

Informations de copyright

© 2022. The Author(s).

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Auteurs

Bum Chul Kwon (BC)

Center for Computational Health, IBM Research, Cambridge, MA, USA. bumchul.kwon@us.ibm.com.

Vibha Anand (V)

Center for Computational Health, IBM Research, Cambridge, MA, USA.

Peter Achenbach (P)

Institute of Diabetes Research, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich-Neuherberg, Germany.

William Hagopian (W)

Pacific Northwest Research Institute, Seattle, WA, USA.

Jianying Hu (J)

Center for Computational Health, IBM Research, Yorktown Heights, NY, USA.

Eileen Koski (E)

Center for Computational Health, IBM Research, Yorktown Heights, NY, USA.

Åke Lernmark (Å)

Department of Clinical Sciences Malmö, Lund University CRC, Skåne University Hospital, Malmö, Sweden.

Markus Lundgren (M)

Department of Clinical Sciences Malmö, Lund University CRC, Skåne University Hospital, Malmö, Sweden.

Kenney Ng (K)

Center for Computational Health, IBM Research, Cambridge, MA, USA.

Jorma Toppari (J)

Institute of Biomedicine and Centre for Population Health Research, University of Turku, and Department of Pediatrics, Turku University Hospital, Turku, Finland.

Riitta Veijola (R)

University of Oulu and Oulu University Hospital, Department of Pediatrics, PEDEGO Research Unit, Oulu, Finland.

Brigitte I Frohnert (BI)

University of Colorado, Denver, CO, USA.

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Classifications MeSH