SARS-CoV-2 Antibody Responses Do Not Predict COVID-19 Disease Severity

  1. William S Phipps
  2. Jeffrey A SoRelle
  3. Quan-Zhen Li
  4. Lenin Mahimainathan
  5. Ellen Araj
  6. John Markantonis
  7. Chantale Lacelle
  8. Jyoti Balani
  9. Hiren Parikh
  10. E Blair Solow
  11. David R Karp
  12. Ravi Sarode
  13. Alagarraju Muthukumar

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Abstract

Objectives

Initial reports indicate adequate performance of some serology-based severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) assays. However, additional studies are required to facilitate interpretation of results, including how antibody levels impact immunity and disease course.

Methods

A total of 967 subjects were tested for IgG antibodies reactive to SARS-CoV-2, including 172 suspected cases of SARS-CoV-2, 656 plasma samples from healthy donors, 49 sera from patients with rheumatic disease, and 90 specimens from individuals positive for polymerase chain reaction (PCR)–based respiratory viral panel. A subgroup of SARS-CoV-2 PCR-positive cases was tested for IgM antibodies by proteome array method.

Results

All specificity and cross-reactivity specimens were negative for SARS-CoV-2 IgG antibodies (0/795, 0%). 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. Measures of IgG and IgM antibodies did not predict disease severity in our patient population.

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  1. Version published to 10.1093/ajcp/aqaa123
  2. ScreenIT

    SciScore for 10.1101/2020.05.15.20103580: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: Patient samples: This study was approved by the UT Southwestern Institutional Review Board.A total of 968 individuals (996 total specimens) were included in this study, including 656 healthy controls, 29 patients with systemic lupus erythematosus, 20 with rheumatoid arthritis, 90 with previous positive respiratory viral PCR panel, and 173 confirmed or suspected cases of COVID-19 (Fig. 1).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    The test is a chemiluminescent microparticle immunoassay (CMIA) for qualitative detection of IgG antibodies against SARS-CoV-2 nucleocapsid protein (NCP) in human serum and plasma.
    SARS-CoV-2 nucleocapsid protein (NCP
    suggested: None
    Patient serum samples were diluted 1:100 and incubated with the antigens on the array and the IgM antibody specificities detected with cy5-conjugated anti-human IgM (1:1000, Jackson ImmunoResearch).
    IgM
    suggested: None
    anti-human IgM
    suggested: None
    Software and Algorithms
    SentencesResources
    SARS-CoV-2 IgG Testing: SARS-CoV-2 IgG (Abbott 06R86) testing was performed on the Abbott ARCHITECT i2000SR in accordance with manufacturer’s specifications.
    Abbott
    suggested: (Abbott, RRID:SCR_010477)
    The NSI of NCP IgM was used to generate heat maps using Cluster and Treeview software (http://bonsai.hgc.jp/~mdehoon/software/cluster/index.html).
    Cluster
    suggested: (Cluster, RRID:SCR_013505)
    Treeview
    suggested: (TreeView, RRID:SCR_013503)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.05.15.20103580v1 on medRxiv