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Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome
Following acute infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) a significant proportion of individuals develop prolonged symptoms, a serious condition termed post-acute coronavirus disease 2019 (COVID-19) syndrome (PACS) or long COVID. Predictors of PACS are needed. In a...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8789854/ https://www.ncbi.nlm.nih.gov/pubmed/35078982 http://dx.doi.org/10.1038/s41467-021-27797-1 |
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author | Cervia, Carlo Zurbuchen, Yves Taeschler, Patrick Ballouz, Tala Menges, Dominik Hasler, Sara Adamo, Sarah Raeber, Miro E. Bächli, Esther Rudiger, Alain Stüssi-Helbling, Melina Huber, Lars C. Nilsson, Jakob Held, Ulrike Puhan, Milo A. Boyman, Onur |
author_facet | Cervia, Carlo Zurbuchen, Yves Taeschler, Patrick Ballouz, Tala Menges, Dominik Hasler, Sara Adamo, Sarah Raeber, Miro E. Bächli, Esther Rudiger, Alain Stüssi-Helbling, Melina Huber, Lars C. Nilsson, Jakob Held, Ulrike Puhan, Milo A. Boyman, Onur |
author_sort | Cervia, Carlo |
collection | PubMed |
description | Following acute infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) a significant proportion of individuals develop prolonged symptoms, a serious condition termed post-acute coronavirus disease 2019 (COVID-19) syndrome (PACS) or long COVID. Predictors of PACS are needed. In a prospective multicentric cohort study of 215 individuals, we study COVID-19 patients during primary infection and up to one year later, compared to healthy subjects. We discover an immunoglobulin (Ig) signature, based on total IgM and IgG3 levels, which – combined with age, history of asthma bronchiale, and five symptoms during primary infection – is able to predict the risk of PACS independently of timepoint of blood sampling. We validate the score in an independent cohort of 395 individuals with COVID-19. Our results highlight the benefit of measuring Igs for the early identification of patients at high risk for PACS, which facilitates the study of targeted treatment and pathomechanisms of PACS. |
format | Online Article Text |
id | pubmed-8789854 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-87898542022-02-07 Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome Cervia, Carlo Zurbuchen, Yves Taeschler, Patrick Ballouz, Tala Menges, Dominik Hasler, Sara Adamo, Sarah Raeber, Miro E. Bächli, Esther Rudiger, Alain Stüssi-Helbling, Melina Huber, Lars C. Nilsson, Jakob Held, Ulrike Puhan, Milo A. Boyman, Onur Nat Commun Article Following acute infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) a significant proportion of individuals develop prolonged symptoms, a serious condition termed post-acute coronavirus disease 2019 (COVID-19) syndrome (PACS) or long COVID. Predictors of PACS are needed. In a prospective multicentric cohort study of 215 individuals, we study COVID-19 patients during primary infection and up to one year later, compared to healthy subjects. We discover an immunoglobulin (Ig) signature, based on total IgM and IgG3 levels, which – combined with age, history of asthma bronchiale, and five symptoms during primary infection – is able to predict the risk of PACS independently of timepoint of blood sampling. We validate the score in an independent cohort of 395 individuals with COVID-19. Our results highlight the benefit of measuring Igs for the early identification of patients at high risk for PACS, which facilitates the study of targeted treatment and pathomechanisms of PACS. Nature Publishing Group UK 2022-01-25 /pmc/articles/PMC8789854/ /pubmed/35078982 http://dx.doi.org/10.1038/s41467-021-27797-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access 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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Cervia, Carlo Zurbuchen, Yves Taeschler, Patrick Ballouz, Tala Menges, Dominik Hasler, Sara Adamo, Sarah Raeber, Miro E. Bächli, Esther Rudiger, Alain Stüssi-Helbling, Melina Huber, Lars C. Nilsson, Jakob Held, Ulrike Puhan, Milo A. Boyman, Onur Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome |
title | Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome |
title_full | Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome |
title_fullStr | Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome |
title_full_unstemmed | Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome |
title_short | Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome |
title_sort | immunoglobulin signature predicts risk of post-acute covid-19 syndrome |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8789854/ https://www.ncbi.nlm.nih.gov/pubmed/35078982 http://dx.doi.org/10.1038/s41467-021-27797-1 |
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