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

Plain language summaryWe aimed to identify components of the blood present during the early phase of SARS-CoV-2 infection that distinguish people who are likely to develop severe symptoms of COVID-19. Blood from people who later developed a mild or moderate course of disease were compared to blood from people who later had a severe or critical course of disease. Here, we identified a combination of three proteins that were present in the blood of patients with COVID-19 who later developed a severe or critical disease. Identifying the presence of these proteins in patients at an early stage of infection could enable physicians to treat these patients early on to avoid progression of the disease. Hufnagel, Fathi et al. use an antibody microarray-based approach to identify plasma protein biomarkers present during the early phases of SARS CoV-2 infection that distinguish people who will go on to develop severe COVID-19. A multimarker panel of proteins enables people at high risk of developing a severe or critical disease course to be identified. BackgroundThe 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.MethodsHere 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.ResultsIn 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.ConclusionsUsing 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.