Circulating proteins to predict COVID-19 severity

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dc.contributor.author

Su, Chen-Yang

Zhou, Sirui

Gonzalez-Kozlova, Edgar

Butler-Laporte, Guillaume

Brunet-Ratnasingham, Elsa

Nakanishi, Tomoko

Jeon, Wonseok

Morrison, David R.

Laurent, Laetitia

Afilalo, Jonathan

Afilalo, Marc

Henry, Danielle

Chen, Yiheng

Carrasco-Zanini, Julia

Farjoun, Yossi

Pietzner, Maik

Kimchi, Nofar

Afrasiabi, Zaman

Rezk, Nardin

Bouab, Meriem

Petitjean, Louis

Guzman, Charlotte

Xue, Xiaoqing

Tselios, Chris

Vulesevic, Branka

Adeleye, Olumide

Abdullah, Tala

Almamlouk, Noor

Moussa, Yara

DeLuca, Chantal

Duggan, Naomi

Schurr, Erwin

Brassard, Nathalie

Durand, Madeleine

Del Valle, Diane Marie

Thompson, Ryan

Cedillo, Mario A.

Schadt, Eric

Nie, Kai

Simons, Nicole W.

Mouskas, Konstantinos

Zaki, Nicolas

Patel, Manishkumar

Xie, Hui

Harris, Jocelyn

Marvin, Robert

Cheng, Esther

Tuballes, Kevin

Argueta, Kimberly

Scott, Ieisha

The Mount Sinai COVID-19 Biobank Team

Greenwood, Celia M. T.

Paterson, Clare

Hinterberg, Michael A.

Langenberg, Claudia

Forgetta, Vincenzo

Pineau, Joelle

Mooser, Vincent

Marron, Thomas

Beckmann, Noam D.

Kim-schulze, Seunghee

Charney, Alexander W.

Gnjatic, Sacha

Kaufmann, Daniel E.

Merad, Miriam

Richards, J. Brent

dc.date.accepted

2023-03-17

dc.date.accessioned

2024-01-04T20:53:47Z

dc.date.available

2024-01-04T20:53:47Z

dc.date.issued

2023-04-17

dc.date.submitted

2022-06-02

dc.description.abstract - en

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.

dc.identifier.doi

https://doi.org/10.1038/s41598-023-31850-y

dc.identifier.issn

2045-2322

dc.identifier.pubmedID

37069249

dc.identifier.uri

https://open-science.canada.ca/handle/123456789/1455

dc.language.iso

en

dc.publisher

Springer Nature

dc.rights - en

Creative Commons Attribution 4.0 International (CC BY 4.0)

dc.rights - fr

Creative Commons Attribution 4.0 International (CC BY 4.0)

dc.rights.openaccesslevel - en

Gold

dc.rights.openaccesslevel - fr

Or

dc.rights.uri - en

https://creativecommons.org/licenses/by/4.0/

dc.rights.uri - fr

https://creativecommons.org/licenses/by/4.0/deed.fr

dc.subject - en

Health

dc.subject - fr

Santé

dc.subject.en - en

Health

dc.subject.fr - fr

Santé

dc.title - en

Circulating proteins to predict COVID-19 severity

dc.type - en

Article

dc.type - fr

Article

local.acceptedmanuscript.articlenum

6236

local.article.journaltitle

Scientific Reports

local.article.journalvolume

13

local.pagination

1-15

local.peerreview - en

Yes

local.peerreview - fr

Oui

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