Prognostic value of sTREM-1 in COVID-19 patients: a biomarker for disease severity and mortality

  1. Pedro V. da Silva Neto
  2. Jonatan C. S. de Carvalho
  3. Vinícius E. Pimentel
  4. Malena M. Pérez
  5. Ingryd Carmona-Garcia
  6. Nicola T. Neto
  7. Diana M. Toro
  8. Camilla N. S. Oliveira
  9. Thais F. C. Fraga-Silva
  10. Cristiane M. Milanezi
  11. Lilian C. Rodrigues
  12. Cassia F. S. L. Dias
  13. Ana C. Xavier
  14. Giovanna S. Porcel
  15. Isabelle C. Guarneri
  16. Kamila Zaparoli
  17. Caroline T. Garbato
  18. Jamille G. M. Argolo
  19. Ângelo A. F. Júnior
  20. Alessandro P. de Amorim
  21. Augusto M. Degiovani
  22. Dayane P. da Silva
  23. Debora C. Nepomuceno
  24. Rafael C. da Silva
  25. Leticia F. Constant
  26. Fátima M. Ostini
  27. Marley R. Feitosa
  28. Rogerio S. Parra
  29. Fernando C. Vilar
  30. Gilberto G. Gaspar
  31. José J. R. da Rocha
  32. Omar Feres
  33. Rita C. C. Barbieri
  34. Fabiani G. Frantz
  35. Sandra R. Maruyama
  36. Elisa M. S. Russo
  37. Angelina L. Viana
  38. Ana P. M. Fernandes
  39. Isabel K. F. M. Santos
  40. Vânia L. D. Bonato
  41. Marcelo Dias-Baruffi
  42. Adriana Malheiro
  43. Ruxana T. Sadikot
  44. Cristina R. B. Cardoso
  45. Lúcia H. Faccioli
  46. Carlos A. Sorgi

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Abstract

Background

The uncontrolled inflammatory response plays a critical role in the novel coronavirus disease (COVID-19) and triggering receptor expressed on myeloid cells-1 (TREM-1) is thought to be intricate to inflammatory signal amplification. This study aims to investigate the association between soluble TREM-1 (sTREM-1) and COVID-19 as a prognostic biomarker to predict the disease severity, lethality and clinical management.

Methods

We enrolled 91 patients with COVID-19 in domiciliary care (44 patients) or in hospital care (47 patients), who were classified after admission into mild, moderate, severe and critical groups according to their clinical scores. As non-COVID-19 control, 30 healthy volunteers were included. Data on demographic, comorbidities and baseline clinical characteristics were obtained from their medical and nurse records. Peripheral blood samples were collected at admission and after hospitalization outcome to assess cytokine profile and sTREM-1 level by specific immunoassays.

Results

Within COVID-19 patients, the highest severity was associated with the most significant elevated plasma levels sTREM-1. Using receiver operating curve analysis (ROC), sTREM-1 was found to be predictive of disease severity (AUC= 0.988) and the best cut-off value for predicting in-hospital severity was ≥ 116.5 pg/mL with the sensitivity for 93.3% and specificity for 95.8%. We also described the clinical characteristics of these patients and explored the correlation with markers of the disease aggravation. The levels of sTREM-1 were positively correlated with IL-6, IL-10, blood neutrophils counts, and critical disease scoring (r= 0.68, p <0.0001). On the other hand, sTREM-1 level was significantly negative correlated with lymphocytes counting, and mild disease (r= −0.42, p <0.0001). Higher levels of sTREM-1 were related to poor outcome and death, patients who received dexamethasone tended to have lower sTREM-1 levels.

Conclusion

Our results indicated that sTREM-1 in COVID-19 is associated with severe disease development and a prognostic marker for mortality. The use of severity biomarkers such as sTREM-1 together with patients clinical scores could improve the early recognition and monitoring of COVID-19 cases with higher risk of disease worsening.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.22.20199703: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethical approval: The procedures followed in this study were approved by the institutional ethics board of Faculdade de Ciências Farmacêuticas de Ribeirão Preto – Universidade de São Paulo and Brazil National Ethics Committee (CONEP, CAAE: 30525920.7.000.5403).
    Consent: Written informed consent was obtained from recruited participants of the study.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableThe exclusion criteria were individuals younger than 18 years of age and pregnant or lactating women.

    Table 2: Resources

    Antibodies
    SentencesResources
    In total, 30 control subjects were enrolled at the beginning of the COVID-19 pandemic with a negative test for SARS-CoV-2 and 91 patients with a positive test for SARS-CoV-2, using genomic RNA assay performed in a nasopharyngeal/oropharyngeal swab RT-PCR (Kit Biomol OneStep/COVID-19) or serology specific IgM and IgG antibodies (SARS-CoV-2 antibody test®, Guangzhou Wondfo Biotech, China).
    IgG
    suggested: None
    SARS-CoV-2
    suggested: None
    Software and Algorithms
    SentencesResources
    cells/L); lymphocytes count (x 109 cells/L); hypertension (systole); glycemia (mg/dL); male gender; clinical scores (mild, moderate, severe and critical); IL-6 (pg/mL) and IL-10 amount (pg/mL); was calculated using Pearson correlation with cor() function in R (version 4.0.2) through RStudio (version 1.1.463).
    RStudio
    suggested: (RStudio, RRID:SCR_000432)
    Comparative analysis between groups was performed using GraphPad Prism™software (version 8.4.2) (San Diego, CA, USA).
    GraphPad Prism™software
    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: We detected the following sentences addressing limitations in the study:
    There are some limitations in our study. First, chronic diseases and secondary infection in some cases might exert effects on the increased plasma sTREM-1 level besides SARS-CoV-2 infection itself. Second, this study was limited by sample size. Larger studies with continuous monitoring of sTREM-1 are necessary to further confirm the prognostic effect in patients with severe COVID-19. Exacerbation of the situation is usually a dynamic process; thus the data at a certain time point may not accurately reflect change in the patient condition. Third, some patients with critical illness were not admitted to intensive care unit, which definitely had a negative impact on the outcome of those patients. Although our findings should be evaluated with larger number of samples by multi-center research, sTREM-1 is a potential marker of diagnostic to distinguish critical COVID-19 and poor-outcome, and monitoring the sTREM-1 levels will improve treatment efficacy and reduce mortality.

    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.
    • Thank you for including a protocol registration statement.

    About SciScore

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  2. Version published to 10.1101/2020.09.22.20199703 on medRxiv