Symptoms of COVID-19 infection and magnitude of antibody response in a large community-based study

  1. Thomas W. McDade
  2. Joshua M. Schrock
  3. Richard D’Aquila
  4. Brian Mustanski
  5. Nanette Benbow
  6. Lauren A. Vaught
  7. Nina L. Reiser
  8. Matt E. Velez
  9. Ryan R. Hsieh
  10. Daniel T. Ryan
  11. Rana Saber
  12. Elizabeth M. McNally
  13. Alexis R. Demonbreun

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Abstract

Background

The majority of COVID-19 cases are asymptomatic, or minimally symptomatic with management in the home. Little is known about the frequency of specific symptoms in the general population, and how symptoms predict the magnitude of antibody response to SARS-CoV-2 infection.

Methods

We quantified IgG antibodies against the SARS-CoV-2 receptor binding domain (RBD) in home-collected dried blood spot samples from 3,365 adults participating in a community-based seroprevalence study in the city of Chicago, USA, collected between June 24 and November 11, 2020.

Results

17.8% of the sample was seropositive for SARS-CoV-2. A cluster of symptoms (loss of sense of smell or taste, fever, shortness of breath, muscle or body aches, cough, fatigue, diarrhea, headache) was associated with stronger anti-RBD IgG responses among the seropositives. 39.2% of infections were asymptomatic, and 2 or fewer symptoms were reported for 66.7% of infections. Total number of symptoms was positively but weakly associated with IgG response: Median anti-RBD IgG was 0.95 ug/mL for individuals with 3 or more symptoms, in comparison with 0.61 ug/mL for asymptomatic infections.

Conclusion

We document high rates of asymptomatic and mild infection in a large community-based cohort, and relatively low levels of anti-SARS-CoV-2 IgG antibody in the general population of previously exposed individuals.

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  1. ScreenIT

    SciScore for 10.1101/2021.02.04.21251170: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: A web-based, “no contact” research platform asked all participants to fill out a short questionnaire to determine study eligibility, followed by electronic informed consent and a longer survey.
    IRB: All samples were de-identified and all research activities were implemented under protocols approved by the institutional review board at Northwestern University (#STU00212457 and #STU00212472).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Measurement of SARS-CoV-2 IgG antibodies: Samples were analyzed for anti-RBD SARS-CoV-2 IgG antibodies with a quantitative enzyme-linked immunosorbent assay (ELISA) protocol that was previously validated for use with DBS.(4) The assay is based on a widely used protocol that has received Emergency Use Authorization from the US FDA,(5) and analysis of matched DBS and serum samples indicates near perfect agreement in results across the assay range.
    SARS-CoV-2 IgG
    suggested: None
    anti-RBD SARS-CoV-2 IgG
    suggested: None
    Software and Algorithms
    SentencesResources
    Polychoric correlation—which is appropriate for dichotomous ordinal variables—was used to analyze strength of association between individual symptoms and log IgG response, and to construct a correlation matrix for factor analysis of patterns of association among symptoms.(7) All analyses were implemented using Stata/SE, version 15.1 (StataCorp, College Station, TX).
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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.

  2. Version published to 10.1101/2021.02.04.21251170v1 on medRxiv