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Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies
Significant progress has been made in preventing severe COVID-19 disease through the development of vaccines. However, we still lack a validated baseline predictive biologic signature for the development of more severe disease in both outpatients and inpatients infected with SARS-CoV-2. The objectiv...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10661735/ https://www.ncbi.nlm.nih.gov/pubmed/37985892 http://dx.doi.org/10.1038/s41598-023-46343-1 |
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author | Castro-Pearson, Sandra Samorodnitsky, Sarah Yang, Kaifeng Lotfi-Emran, Sahar Ingraham, Nicholas E. Bramante, Carolyn Jones, Emma K. Greising, Sarah Yu, Meng Steffen, Brian Svensson, Julia Åhlberg, Eric Österberg, Björn Wacker, David Guan, Weihua Puskarich, Michael Smed-Sörensen, Anna Lusczek, Elizabeth Safo, Sandra E. Tignanelli, Christopher J. |
author_facet | Castro-Pearson, Sandra Samorodnitsky, Sarah Yang, Kaifeng Lotfi-Emran, Sahar Ingraham, Nicholas E. Bramante, Carolyn Jones, Emma K. Greising, Sarah Yu, Meng Steffen, Brian Svensson, Julia Åhlberg, Eric Österberg, Björn Wacker, David Guan, Weihua Puskarich, Michael Smed-Sörensen, Anna Lusczek, Elizabeth Safo, Sandra E. Tignanelli, Christopher J. |
author_sort | Castro-Pearson, Sandra |
collection | PubMed |
description | Significant progress has been made in preventing severe COVID-19 disease through the development of vaccines. However, we still lack a validated baseline predictive biologic signature for the development of more severe disease in both outpatients and inpatients infected with SARS-CoV-2. The objective of this study was to develop and externally validate, via 5 international outpatient and inpatient trials and/or prospective cohort studies, a novel baseline proteomic signature, which predicts the development of moderate or severe (vs mild) disease in patients with COVID-19 from a proteomic analysis of 7000 + proteins. The secondary objective was exploratory, to identify (1) individual baseline protein levels and/or (2) protein level changes within the first 2 weeks of acute infection that are associated with the development of moderate/severe (vs mild) disease. For model development, samples collected from 2 randomized controlled trials were used. Plasma was isolated and the SomaLogic SomaScan platform was used to characterize protein levels for 7301 proteins of interest for all studies. We dichotomized 113 patients as having mild or moderate/severe COVID-19 disease. An elastic net approach was used to develop a predictive proteomic signature. For validation, we applied our signature to data from three independent prospective biomarker studies. We found 4110 proteins measured at baseline that significantly differed between patients with mild COVID-19 and those with moderate/severe COVID-19 after adjusting for multiple hypothesis testing. Baseline protein expression was associated with predicted disease severity with an error rate of 4.7% (AUC = 0.964). We also found that five proteins (Afamin, I-309, NKG2A, PRS57, LIPK) and patient age serve as a signature that separates patients with mild COVID-19 and patients with moderate/severe COVID-19 with an error rate of 1.77% (AUC = 0.9804). This panel was validated using data from 3 external studies with AUCs of 0.764 (Harvard University), 0.696 (University of Colorado), and 0.893 (Karolinska Institutet). In this study we developed and externally validated a baseline COVID-19 proteomic signature associated with disease severity for potential use in both outpatients and inpatients with COVID-19. |
format | Online Article Text |
id | pubmed-10661735 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106617352023-11-20 Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies Castro-Pearson, Sandra Samorodnitsky, Sarah Yang, Kaifeng Lotfi-Emran, Sahar Ingraham, Nicholas E. Bramante, Carolyn Jones, Emma K. Greising, Sarah Yu, Meng Steffen, Brian Svensson, Julia Åhlberg, Eric Österberg, Björn Wacker, David Guan, Weihua Puskarich, Michael Smed-Sörensen, Anna Lusczek, Elizabeth Safo, Sandra E. Tignanelli, Christopher J. Sci Rep Article Significant progress has been made in preventing severe COVID-19 disease through the development of vaccines. However, we still lack a validated baseline predictive biologic signature for the development of more severe disease in both outpatients and inpatients infected with SARS-CoV-2. The objective of this study was to develop and externally validate, via 5 international outpatient and inpatient trials and/or prospective cohort studies, a novel baseline proteomic signature, which predicts the development of moderate or severe (vs mild) disease in patients with COVID-19 from a proteomic analysis of 7000 + proteins. The secondary objective was exploratory, to identify (1) individual baseline protein levels and/or (2) protein level changes within the first 2 weeks of acute infection that are associated with the development of moderate/severe (vs mild) disease. For model development, samples collected from 2 randomized controlled trials were used. Plasma was isolated and the SomaLogic SomaScan platform was used to characterize protein levels for 7301 proteins of interest for all studies. We dichotomized 113 patients as having mild or moderate/severe COVID-19 disease. An elastic net approach was used to develop a predictive proteomic signature. For validation, we applied our signature to data from three independent prospective biomarker studies. We found 4110 proteins measured at baseline that significantly differed between patients with mild COVID-19 and those with moderate/severe COVID-19 after adjusting for multiple hypothesis testing. Baseline protein expression was associated with predicted disease severity with an error rate of 4.7% (AUC = 0.964). We also found that five proteins (Afamin, I-309, NKG2A, PRS57, LIPK) and patient age serve as a signature that separates patients with mild COVID-19 and patients with moderate/severe COVID-19 with an error rate of 1.77% (AUC = 0.9804). This panel was validated using data from 3 external studies with AUCs of 0.764 (Harvard University), 0.696 (University of Colorado), and 0.893 (Karolinska Institutet). In this study we developed and externally validated a baseline COVID-19 proteomic signature associated with disease severity for potential use in both outpatients and inpatients with COVID-19. Nature Publishing Group UK 2023-11-20 /pmc/articles/PMC10661735/ /pubmed/37985892 http://dx.doi.org/10.1038/s41598-023-46343-1 Text en © The Author(s) 2023 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Castro-Pearson, Sandra Samorodnitsky, Sarah Yang, Kaifeng Lotfi-Emran, Sahar Ingraham, Nicholas E. Bramante, Carolyn Jones, Emma K. Greising, Sarah Yu, Meng Steffen, Brian Svensson, Julia Åhlberg, Eric Österberg, Björn Wacker, David Guan, Weihua Puskarich, Michael Smed-Sörensen, Anna Lusczek, Elizabeth Safo, Sandra E. Tignanelli, Christopher J. Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies |
title | Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies |
title_full | Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies |
title_fullStr | Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies |
title_full_unstemmed | Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies |
title_short | Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies |
title_sort | development of a proteomic signature associated with severe disease for patients with covid-19 using data from 5 multicenter, randomized, controlled, and prospective studies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10661735/ https://www.ncbi.nlm.nih.gov/pubmed/37985892 http://dx.doi.org/10.1038/s41598-023-46343-1 |
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