Discovery and systematic assessment of early biomarkers that predict progression to severe COVID-19 disease
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Discovery and systematic assessment of early biomarkers that predict progression to severe COVID-19 disease. / Hufnagel, Katrin; Fathi, Anahita; Stroh, Nadine; Klein, Marco; Skwirblies, Florian; Girgis, Ramy; Dahlke, Christine; Hoheisel, Jörg D; Lowy, Camille; Schmidt, Ronny; Griesbeck, Anne; Merle, Uta; Addo, Marylyn M; Schröder, Christoph.
In: Communications medicine, Vol. 3, No. 1, 12.04.2023, p. 51.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Discovery and systematic assessment of early biomarkers that predict progression to severe COVID-19 disease
AU - Hufnagel, Katrin
AU - Fathi, Anahita
AU - Stroh, Nadine
AU - Klein, Marco
AU - Skwirblies, Florian
AU - Girgis, Ramy
AU - Dahlke, Christine
AU - Hoheisel, Jörg D
AU - Lowy, Camille
AU - Schmidt, Ronny
AU - Griesbeck, Anne
AU - Merle, Uta
AU - Addo, Marylyn M
AU - Schröder, Christoph
N1 - © 2023. The Author(s).
PY - 2023/4/12
Y1 - 2023/4/12
N2 - BACKGROUND: The clinical course of COVID-19 patients ranges from asymptomatic infection, via mild and moderate illness, to severe disease and even fatal outcome. Biomarkers which enable an early prediction of the severity of COVID-19 progression, would be enormously beneficial to guide patient care and early intervention prior to hospitalization.METHODS: Here we describe the identification of plasma protein biomarkers using an antibody microarray-based approach in order to predict a severe cause of a COVID-19 disease already in an early phase of SARS-CoV-2 infection. To this end, plasma samples from two independent cohorts were analyzed by antibody microarrays targeting up to 998 different proteins.RESULTS: In total, we identified 11 promising protein biomarker candidates to predict disease severity during an early phase of COVID-19 infection coherently in both analyzed cohorts. A set of four (S100A8/A9, TSP1, FINC, IFNL1), and two sets of three proteins (S100A8/A9, TSP1, ERBB2 and S100A8/A9, TSP1, IFNL1) were selected using machine learning as multimarker panels with sufficient accuracy for the implementation in a prognostic test.CONCLUSIONS: Using these biomarkers, patients at high risk of developing a severe or critical disease may be selected for treatment with specialized therapeutic options such as neutralizing antibodies or antivirals. Early therapy through early stratification may not only have a positive impact on the outcome of individual COVID-19 patients but could additionally prevent hospitals from being overwhelmed in potential future pandemic situations.
AB - BACKGROUND: The clinical course of COVID-19 patients ranges from asymptomatic infection, via mild and moderate illness, to severe disease and even fatal outcome. Biomarkers which enable an early prediction of the severity of COVID-19 progression, would be enormously beneficial to guide patient care and early intervention prior to hospitalization.METHODS: Here we describe the identification of plasma protein biomarkers using an antibody microarray-based approach in order to predict a severe cause of a COVID-19 disease already in an early phase of SARS-CoV-2 infection. To this end, plasma samples from two independent cohorts were analyzed by antibody microarrays targeting up to 998 different proteins.RESULTS: In total, we identified 11 promising protein biomarker candidates to predict disease severity during an early phase of COVID-19 infection coherently in both analyzed cohorts. A set of four (S100A8/A9, TSP1, FINC, IFNL1), and two sets of three proteins (S100A8/A9, TSP1, ERBB2 and S100A8/A9, TSP1, IFNL1) were selected using machine learning as multimarker panels with sufficient accuracy for the implementation in a prognostic test.CONCLUSIONS: Using these biomarkers, patients at high risk of developing a severe or critical disease may be selected for treatment with specialized therapeutic options such as neutralizing antibodies or antivirals. Early therapy through early stratification may not only have a positive impact on the outcome of individual COVID-19 patients but could additionally prevent hospitals from being overwhelmed in potential future pandemic situations.
U2 - 10.1038/s43856-023-00283-z
DO - 10.1038/s43856-023-00283-z
M3 - SCORING: Journal article
C2 - 37041310
VL - 3
SP - 51
JO - Communications medicine
JF - Communications medicine
SN - 2730-664X
IS - 1
ER -