Discovery and systematic assessment of early biomarkers that predict progression to severe COVID-19 disease

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 -