Epidemiological and Immunological Features of Obesity and SARS-CoV-2

  1. Eric J. Nilles
  2. Sameed M. Siddiqui
  3. Stephanie Fischinger
  4. Yannic C. Bartsch
  5. Michael de St. Aubin
  6. Guohai Zhou
  7. Matthew J. Gluck
  8. Samuel Berger
  9. Justin Rhee
  10. Eric Petersen
  11. Benjamin Mormann
  12. Michael Loesche
  13. Yiyuan Hu
  14. Zhilin Chen
  15. Jingyou Yu
  16. Makda Gebre
  17. Caroline Atyeo
  18. Matthew J. Gorman
  19. Alex Lee Zhu
  20. John Burke
  21. Matthew Slein
  22. Mohammad A. Hasdianda
  23. Guruprasad Jambaulikar
  24. Edward W. Boyer
  25. Pardis C. Sabeti
  26. Dan H. Barouch
  27. Boris Julg
  28. Adam J. Kucharski
  29. Elon R. Musk
  30. Douglas A. Lauffenburger
  31. Galit Alter
  32. Anil S. Menon

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Obesity is a key correlate of severe SARS-CoV-2 outcomes while the role of obesity on risk of SARS-CoV-2 infection, symptom phenotype, and immune response remain poorly defined. We examined data from a prospective SARS-CoV-2 cohort study to address these questions. Serostatus, body mass index, demographics, comorbidities, and prior COVID-19 compatible symptoms were assessed at baseline and serostatus and symptoms monthly thereafter. SARS-CoV-2 immunoassays included an IgG ELISA targeting the spike RBD, multiarray Luminex targeting 20 viral antigens, pseudovirus neutralization, and T cell ELISPOT assays. Our results from a large prospective SARS-CoV-2 cohort study indicate symptom phenotype is strongly influenced by obesity among younger but not older age groups; we did not identify evidence to suggest obese individuals are at higher risk of SARS-CoV-2 infection; and remarkably homogenous immune activity across BMI categories suggests immune protection across these groups may be similar.

Article activity feed

  1. Version published to 10.3390/v13112235
  2. Version published to 10.1101/2020.11.11.20229724v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.11.11.20229724: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethical Disclosures: The study protocol was approved by the Western Institutional Review Board.
    Consent: All participants provided written informed consent.
    Randomizationnot detected.
    BlindingAssay performance has been externally validated in a blinded fashion at 99·6% specific and benchmarked against commercial EUA approved assays [16]
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Luminex UMAP and Mann-Whitney U Tests were conducted using scikit-learn, a machine learning toolkit for the Python programming language.
    scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)
    Python
    suggested: (IPython, RRID:SCR_001658)

    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.

  4. Version published to 10.1101/2020.11.11.20229724v1 on medRxiv