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SARS-CoV-2 Antibody responses do not predict COVID-19 disease severity (preprint)
medrxiv; 2020.
Preprint
in English
| medRxiv | ID: ppzbmed-10.1101.2020.05.15.20103580
ABSTRACT
Background:
Initial reports indicate adequate performance of some serological-based SARS-CoV-2 assays. However, additional studies are required to facilitate interpretation of results, including how antibody levels impact immunity and disease course.Methods:
In this study, a total of 968 subjects were tested for IgG antibodies reactive to SARS-CoV-2. We confirmed analytic specificity using 656 plasma samples from healthy donors, 49 sera from patients with rheumatic disease, and 90 specimens from individuals positive for PCR-based respiratory viral panel. One-hundred seventy-three cases of confirmed or suspected SARS-CoV-2 were tested for IgG. A subgroup of 37 SARS-CoV-2 PCR-positive cases was tested for nucleocapsid-specific IgM antibody using an in-house developed microarray method. Antibody levels were compared between disease severity groups.Results:
All specificity specimens were negative for SARS-CoV-2 IgG antibodies (0/656, 0%). Cross reactivity was not detected in specimens with antinuclear antibodies and rheumatoid factor, or cases with previous diagnosis of viral infection including human coronavirus. Positive agreement of IgG with PCR was 83% of samples confirmed to be more than 14 days from symptom onset, with less than 100% sensitivity attributable to a case with severe immunosuppression. Virus-specific IgM was positive in a higher proportion of cases less than 3 days from symptom onset. No association was observed between mild and severe disease course with respect to IgG and IgM levels.Conclusions:
The studied SARS-CoV-2 IgG assay had 100% specificity and no adverse cross-reactivity. Index values of IgG and IgM antibodies did not predict disease severity in our patient population.
Full text:
Available
Collection:
Preprints
Database:
medRxiv
Main subject:
Arthritis, Rheumatoid
/
Virus Diseases
/
Rheumatic Diseases
/
COVID-19
Language:
English
Year:
2020
Document Type:
Preprint
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