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

Kwon, Bum Chul ; Anand, Vibha ; Achenbach, Peter ; Dunne, Jessica L ; Hagopian, William ; Hu, Jianying ; Koski, Eileen ; Lernmark, Åke LU orcid ; Lundgren, Markus LU and Ng, Kenney , et al. (2022) In Nature Communications 13. p.1-9
Abstract

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... (More)

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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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Nature Communications
volume
13
article number
1514
pages
1 - 9
publisher
Nature Publishing Group
external identifiers
  • scopus:85126679252
  • pmid:35314671
ISSN
2041-1723
DOI
10.1038/s41467-022-28909-1
language
English
LU publication?
yes
additional info
© 2022. The Author(s).
id
b144512f-e3e6-4a65-9102-2dc4423d068c
date added to LUP
2022-03-30 09:43:20
date last changed
2024-06-08 15:25:40
@article{b144512f-e3e6-4a65-9102-2dc4423d068c,
  abstract     = {{<p>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 &lt; 0.0001). Age, sex, and HLA-DR status further refine the progression rates within trajectories, enabling clinically useful prediction of disease onset.</p>}},
  author       = {{Kwon, Bum Chul and Anand, Vibha and Achenbach, Peter and Dunne, Jessica L and Hagopian, William and Hu, Jianying and Koski, Eileen and Lernmark, Åke and Lundgren, Markus and Ng, Kenney and Toppari, Jorma and Veijola, Riitta and Frohnert, Brigitte I}},
  issn         = {{2041-1723}},
  language     = {{eng}},
  pages        = {{1--9}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Communications}},
  title        = {{Progression of type 1 diabetes from latency to symptomatic disease is predicted by distinct autoimmune trajectories}},
  url          = {{http://dx.doi.org/10.1038/s41467-022-28909-1}},
  doi          = {{10.1038/s41467-022-28909-1}},
  volume       = {{13}},
  year         = {{2022}},
}