Saliva viral load is a dynamic unifying correlate of COVID-19 severity and mortality

  1. Julio Silva
  2. Carolina Lucas
  3. Maria Sundaram
  4. Benjamin Israelow
  5. Patrick Wong
  6. Jon Klein
  7. Maria Tokuyama
  8. Peiwen Lu
  9. Arvind Venkataraman
  10. Feimei Liu
  11. Tianyang Mao
  12. Ji Eun Oh
  13. Annsea Park
  14. Arnau Casanovas-Massana
  15. Chantal B. F. Vogels
  16. M. Catherine Muenker
  17. Joseph Zell
  18. John B. Fournier
  19. Melissa Campbell
  20. Michael Chiorazzi
  21. Edwin Ruiz Fuentes
  22. Mary E Petrone
  23. Chaney C. Kalinich
  24. Isabel M. Ott
  25. Annie Watkins
  26. Adam J. Moore
  27. Maura Nakahata
  28. Yale IMPACT Team
  29. Nathan D. Grubaugh
  30. Shelli Farhadian
  31. Charles Dela Cruz
  32. Albert I. Ko
  33. Wade L. Schulz
  34. Aaron Ring
  35. Shuangge Ma
  36. Saad Omer
  37. Anne L Wyllie
  38. Akiko Iwasaki

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Abstract

While several clinical and immunological parameters correlate with disease severity and mortality in SARS-CoV-2 infection, work remains in identifying unifying correlates of coronavirus disease 2019 (COVID-19) that can be used to guide clinical practice. Here, we examine saliva and nasopharyngeal (NP) viral load over time and correlate them with patient demographics, and cellular and immune profiling. We found that saliva viral load was significantly higher in those with COVID-19 risk factors; that it correlated with increasing levels of disease severity and showed a superior ability over nasopharyngeal viral load as a predictor of mortality over time (AUC=0.90). A comprehensive analysis of immune factors and cell subsets revealed strong predictors of high and low saliva viral load, which were associated with increased disease severity or better overall outcomes, respectively. Saliva viral load was positively associated with many known COVID-19 inflammatory markers such as IL-6, IL-18, IL-10, and CXCL10, as well as type 1 immune response cytokines. Higher saliva viral loads strongly correlated with the progressive depletion of platelets, lymphocytes, and effector T cell subsets including circulating follicular CD4 T cells (cTfh). Anti-spike (S) and anti-receptor binding domain (RBD) IgG levels were negatively correlated with saliva viral load showing a strong temporal association that could help distinguish severity and mortality in COVID-19. Finally, patients with fatal COVID-19 exhibited higher viral loads, which correlated with the depletion of cTfh cells, and lower production of anti-RBD and anti-S IgG levels. Together these results demonstrated that viral load – as measured by saliva but not nasopharyngeal β€” is a dynamic unifying correlate of disease presentation, severity, and mortality over time.

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  3. ScreenIT

    SciScore for 10.1101/2021.01.04.21249236: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Study Cohort and data collection: Statistical and data analyses: Statistical and data analyses were performed using Jmp Pro 15.0.0 (SAS Institute), and GraphPad Prism 8.4.3.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    Results from OddPub: Thank you for sharing your data.

    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 10.1101/2021.01.04.21249236v2 on medRxiv
  5. ScreenIT

    SciScore for 10.1101/2021.01.04.21249236: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.Randomizationnot detected.BlindingInvestigators were blinded to generation patient information of data raw from and blood health and status plasma at the samples.Power Analysisnot detected.Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Antibody clones and vendors used anti-hHLA-DR (G46-6) (1:400
    anti-hHLA-DR ( G46-6 )
    suggested: None
    After three washes with PBS-T (PBS with 0.1% Tween-20) and 50 ΞΌl of HRP anti-Human IgG Antibody (GenScript #A00166, 1:5,000) or anti-Human IgM-Peroxidase
    anti-Human IgG
    suggested: None
    anti-Human IgM-Peroxidase
    suggested: None
    Software and Algorithms
    SentencesResources
    All clinical data were obtained using EPIC EHR and REDCap 9.3.6 software.
    REDCap
    suggested: (REDCap, RRID:SCR_003445)
    FlowJo software version 10.6 (tree Star) was used for data analysis.
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    Statistical and data analyses Statistical and data analyses were performed using Jmp Pro 15.0.0 (SAS Institute), and GraphPad Prism 8.4.3.
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    Results from OddPub: Thank you for sharing your data.

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

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

    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.

  6. Version published to 10.1101/2021.01.04.21249236v1 on medRxiv