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Longitudinal profiles of plasma gelsolin, cytokines and antibody expression predict COVID-19 severity and hospitalization outcomes
Preprint
in En
| PREPRINT-MEDRXIV
| ID: ppmedrxiv-22275882
ABSTRACT
BackgroundPrognostic markers for COVID-19 disease outcome are currently lacking. Plasma gelsolin (pGSN) is an actin-binding protein and an innate immune marker involved in disease pathogenesis and viral infections. Here, we demonstrate the utility of pGSN as a prognostic marker for COVID-19 disease outcome; a test performance that is significantly improved when combined with cytokines and antibodies compared to other conventional markers such as CRP and ferritin. MethodsBlood samples were longitudinally collected from hospitalized COVID-19 patients as well as COVID-19 negative controls and the levels of pGSN in g/mL, cytokines and anti-SARS-CoV-2 spike protein antibodies assayed. Mean{+/-}SEM values were correlated with clinical parameters to develop a prognostic platform. ResultspGSN levels were significantly reduced in COVID-19 patients compared to healthy individuals. Additionally, pGSN levels combined with plasma IL-6, IP-10 and M-CSF significantly distinguished COVID-19 patients from healthy individuals. While pGSN and anti-spike IgG titers together strongly predict COVID-19 severity and death, the combination of pGSN and IL-6 was a significant predictor of milder disease and favorable outcomes. ConclusionTaken together, these findings suggest that multi-parameter analysis of pGSN, cytokines and antibodies could predict COVID-19 hospitalization outcomes with greater certainty compared with conventional clinical laboratory markers such as CRP and ferritin. This research will inform and improve clinical management and health system interventions in response to SARS-CoV-2 infection. Trial RegistrationN/A FundingThe Ottawa Hospital Department of Medicine - Special Pandemic Agile Research Competition
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Full text:
1
Collection:
09-preprints
Database:
PREPRINT-MEDRXIV
Type of study:
Prognostic_studies
/
Rct
Language:
En
Year:
2022
Document type:
Preprint