Gut microbiota diversity and C-Reactive Protein are predictors of disease severity in COVID-19 patients

  1. André Moreira-Rosário
  2. Cláudia Marques
  3. Hélder Pinheiro
  4. João Ricardo Araújo
  5. Pedro Ribeiro
  6. Rita Rocha
  7. Inês Mota
  8. Diogo Pestana
  9. Rita Ribeiro
  10. Ana Pereira
  11. Maria José de Sousa
  12. José Pereira-Leal
  13. José de Sousa
  14. Juliana Morais
  15. Diana Teixeira
  16. Júlio César Rocha
  17. Marta Silvestre
  18. Nuno Príncipe
  19. Nuno Gatta
  20. José Amado
  21. Lurdes Santos
  22. Fernando Maltez
  23. Ana Boquinhas
  24. Germano de Sousa
  25. Nuno Germano
  26. Gonçalo Sarmento
  27. Cristina Granja
  28. Pedro Póvoa
  29. Ana Faria
  30. Conceição Calhau

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Abstract

Risk factors for COVID-19 disease severity are still poorly understood. Considering the pivotal role of gut microbiota on host immune and inflammatory functions, we investigated the association between changes in gut microbiota composition and the clinical severity of COVID-19. We conducted a multicentre cross-sectional study prospectively enrolling 115 COVID-19 patients categorized according to: 1) WHO Clinical Progression Scale - mild 19 (16.5%), moderate 37 (32.2%) or severe 59 (51.3%); and 2) location of recovery from COVID-19 - ambulatory 14 (household isolation; 12.2%), hospitalized in ward 40 (34.8%) or intensive care unit 61 (53.0%). Gut microbiota analysis was performed through 16S rRNA gene sequencing and data obtained was further related with clinical parameters of COVID-19 patients. Risk factors for COVID-19 severity were identified by univariate and multivariable logistic regression models.

In comparison with mild COVID-19 patients, the gut microbiota of moderate and severe patients has: a) lower Firmicutes/Bacteroidetes ratio, b) higher abundance of Proteobacteria; and c) lower abundance of beneficial butyrate-producing bacteria such as Roseburia and Lachnospira genera. Multivariable regression analysis showed that Shannon index diversity (odds ratio [OR] 2.85 [95% CI 1.09-7.41]; p=0.032) and C-Reactive Protein (OR 3.45 [95% CI 1.33-8.91]; p=0.011) were risk factors for COVID-19 severe disease (a score of 6 or higher in WHO clinical progression scale).

In conclusion, our results demonstrated that hospitalised moderate and severe COVID-19 patients have microbial signatures of gut dysbiosis and for the first time, the gut microbiota diversity is pointed out as a prognostic biomarker for COVID-19 disease severity.

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  1. ScreenIT

    SciScore for 10.1101/2021.04.20.440658: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Ethic committees and institutional review boards from participating centres approved the study protocol considering it a minimal-risk research using data collected for routine clinical practice and waived the requirement to obtain informed consent.
    Consent: Ethic committees and institutional review boards from participating centres approved the study protocol considering it a minimal-risk research using data collected for routine clinical practice and waived the requirement to obtain informed consent.
    Field Sample Permit: Faecal samples were collected with a stool collection kit (EasySampler, ALPCO) containing RNAlater (Sigma-Aldrich).
    Sex as a biological variablenot detected.
    RandomizationMissing data management: Considering that multiple imputation can give rise to biased results when missing data are not random (22), regression analyses were based on complete data.
    Blindingnot detected.
    Power AnalysisIn order to achieve a statistical power of 80% and a two-sided significance level of 0.05, and considering the total sample size of 115 individuals, the study was powered to detect a mean difference of 0.15 in the Shannon’s Diversity Index between mild-to-moderate and severe COVID-19 patients.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Sequencing data was filtered for length (cutadapt -m 80) and for quality (fastx_trimmer -l 280) after which the V3 and V4 regions were extracted (Mothur align.seqs and screen.seqs).
    Mothur
    suggested: (mothur, RRID:SCR_011947)
    The taxonomy of each sample was determined using Kraken2 (https://ccb.jhu.edu/software/kraken2/) and Bracken (https://ccb.jhu.edu/software/bracken) softwares, using our custom 16S database (GutHealth_DB).
    Kraken2
    suggested: None
    This database was manually curated by enriching GreenGenes (versions 13_5 and 13_8) with clinically relevant taxa from NCBI RefSeq 16s rRNA sequences (04/2019).
    GreenGenes
    suggested: (Greengenes, RRID:SCR_002830)
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    Statistical analysis: Statistical analysis was performed using the SPSS version 27 software (SPSS Inc.) and R statistical software package, version V.3.5.1.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)
    Univariate and multivariate weighted logistic regression models were used in order to evaluate risk factors associated with the severity of COVID-19 (a score of 6 or more in WHO Clinical Progression Scale).
    WHO Clinical Progression Scale
    suggested: None

    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: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04355741CompletedGut Microbiota, "Spark and Flame" of COVID-19 Disease

    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 10.1101/2021.04.20.440658v1 on bioRxiv