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Progression of type 1 diabetes from latency to symptomatic disease is predicted by distinct autoimmune trajectories |
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| Author: | Kwon, Bum Chul1; Anand, Vibha1; Achenbach, Peter2; |
| Organizations: |
1Center for Computational Health, IBM Research, Cambridge, MA, USA 2Institute of Diabetes Research, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich-Neuherberg, Germany 3JDRF, New York, NY, USA
4Pacific Northwest Research Institute, Seattle, WA, USA
5Center for Computational Health, IBM Research, Yorktown Heights, NY, USA 6Department of Clinical Sciences Malmö, Lund University CRC, Skåne University Hospital, Malmö, Sweden 7Institute of Biomedicine and Centre for Population Health Research, University of Turku, and Department of Pediatrics, Turku University Hospital, Turku, Finland 8University of Oulu and Oulu University Hospital, Department of Pediatrics, PEDEGO Research Unit, Oulu, Finland 9University of Colorado, Denver, CO, USA |
| Format: | article |
| Version: | published version |
| Access: | open |
| Online Access: | PDF Full Text (PDF, 2.8 MB) |
| Persistent link: | http://urn.fi/urn:nbn:fi-fe2022102162736 |
| Language: | English |
| Published: |
Springer Nature,
2022
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| Publish Date: | 2022-10-21 |
| Description: |
AbstractDevelopment 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. see all
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| Series: |
Nature communications |
| ISSN: | 2041-1723 |
| ISSN-E: | 2041-1723 |
| ISSN-L: | 2041-1723 |
| Volume: | 13 |
| Issue: | 1 |
| Article number: | 1514 |
| DOI: | 10.1038/s41467-022-28909-1 |
| OADOI: | https://oadoi.org/10.1038/s41467-022-28909-1 |
| Type of Publication: |
A1 Journal article – refereed |
| Field of Science: |
3121 General medicine, internal medicine and other clinical medicine |
| Subjects: | |
| Funding: |
We wish to thank the T1DI Study Group for their help in this work. The T1DI Study Group consists of following members: (1) JDRF—J.L.D., Olivia Lou, and Frank Martin; (2) IBM—V.A., Mohamed Ghalwash, E.K., B.C.K., Ying Li, Zhiguo Li, Bin Liu, Ashwani Malhotra, Shelley Moore, and K.N.; (3) DiPiS—Helena Elding Larsson, Josefine Jönsson, Å.L., M.L., Marlena Maziarz, and Lampros Spiliopoulos; (4) BABYDIAB—P.A., Christiane Winkler, and Anette Ziegler; (5) DIPP—Heikki Hyöty, Jorma Ilonen, Mikael Knip, J.T., and R.V.; (6) DEW-IT—Bill Hagopian, Michael Killian, and Darius Schneider; (7) DAISY—B.I.F., Jill Norris, Marian Rewers, Andrea Steck, Kathleen Waugh, and Liping Yu. We wish to thank the members of the T1DI Study Group Scientific Advisory Board (Richard Oram, University of Exeter, UK, Eoin Mckinney, University of Cambridge, UK, Bobbie-Jo Webb-Robertson, PNNL, USA, Nitesh Chawla, University of Notre Dame, USA, Soren Brunak, University of Copenhagen, DE, Len Harrison, WEHI, AU) for their invaluable input over the course of this study. 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), (DEW-IT: 1-SRA-2019-719-I-X, 1-RSC-2017-516-I-X) as well as NIH (DAISY: DK032493, DK032083, DK104351, and DK116073); 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, Lion Club International, district 101-S, The royal Physiographic society, Skåne County Council Foundation for Research and Development as well as LUDC-IRC/EXODIAB funding from the Swedish Foundation for Strategic Research (Dnr IRC15-0067) and Swedish research council (Dnr 2009-1039). Additional funding for DEW-IT was provided by the Hussman Foundation and by the Washington State Life Science Discovery Fund. |
| Dataset Reference: |
The raw data in this study have been separated and deposited in each of the five study groups: DiPiS, BABYDIAB, DIPP, DEW-IT, and DAISY. The raw data are protected and are not publicly available due to data privacy laws. The source data for figures generated in this study are provided in the Source Data file. All other data that support the findings of this study are included in Supplementary Information or can be made available upon reasonable request. Source data are provided with this paper. The code to generate the waterfall diagram is deposited in the following repository (https://github.com/bckwon/dpvis-waterfall). All other figures can be generated using any standard charting library. |
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https://github.com/bckwon/dpvis-waterfall |
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| Copyright information: |
© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
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https://creativecommons.org/licenses/by/4.0/ |