Circulating proteins to predict COVID-19 severity.

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Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_CABA50A49D9F
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Circulating proteins to predict COVID-19 severity.
Journal
Scientific reports
Author(s)
Su C.Y., Zhou S., Gonzalez-Kozlova E., Butler-Laporte G., Brunet-Ratnasingham E., Nakanishi T., Jeon W., Morrison D.R., Laurent L., Afilalo J., Afilalo M., Henry D., Chen Y., Carrasco-Zanini J., Farjoun Y., Pietzner M., Kimchi N., Afrasiabi Z., Rezk N., Bouab M., Petitjean L., Guzman C., Xue X., Tselios C., Vulesevic B., Adeleye O., Abdullah T., Almamlouk N., Moussa Y., DeLuca C., Duggan N., Schurr E., Brassard N., Durand M., Del Valle D.M., Thompson R., Cedillo M.A., Schadt E., Nie K., Simons N.W., Mouskas K., Zaki N., Patel M., Xie H., Harris J., Marvin R., Cheng E., Tuballes K., Argueta K., Scott I., Greenwood CMT, Paterson C., Hinterberg M.A., Langenberg C., Forgetta V., Pineau J., Mooser V., Marron T., Beckmann N.D., Kim-Schulze S., Charney A.W., Gnjatic S., Kaufmann D.E., Merad M., Richards J.B.
Working group(s)
Mount Sinai COVID-19 Biobank Team
ISSN
2045-2322 (Electronic)
ISSN-L
2045-2322
Publication state
Published
Issued date
17/04/2023
Peer-reviewed
Oui
Volume
13
Number
1
Pages
6236
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Abstract
Predicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measured 4701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical risk factors to predict COVID-19 severity in 417 subjects and tested these models in a separate cohort of 569 individuals. For severe COVID-19, a baseline model including age and sex provided an area under the receiver operator curve (AUC) of 65% in the test cohort. Selecting 92 proteins from the 4701 unique protein abundances improved the AUC to 88% in the training cohort, which remained relatively stable in the testing cohort at 86%, suggesting good generalizability. Proteins selected from different COVID-19 severity were enriched for cytokine and cytokine receptors, but more than half of the enriched pathways were not immune-related. Taken together, these findings suggest that circulating proteins measured at early stages of disease progression are reasonably accurate predictors of COVID-19 severity. Further research is needed to understand how to incorporate protein measurement into clinical care.
Keywords
Humans, COVID-19/diagnosis, Proteins, Risk Factors, Disease Progression, Retrospective Studies
Pubmed
Web of science
Open Access
Yes
Create date
25/04/2023 14:15
Last modification date
23/01/2024 8:34
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