Distinct Patterns of Blood Cytokines Beyond a Cytokine Storm Predict Mortality in COVID-19

  1. Christian Herr
  2. Sebastian Mang
  3. Bahareh Mozafari
  4. Katharina Guenther
  5. Thimoteus Speer
  6. Martina Seibert
  7. Sanjay Kumar Srikakulam
  8. Christoph Beisswenger
  9. Felix Ritzmann
  10. Andreas Keller
  11. Rolf Mueller
  12. Sigrun Smola
  13. Dominic Eisinger
  14. Michael Zemlin
  15. Guy Danziger
  16. Thomas Volk
  17. Sabrina Hoersch
  18. Marcin Krawczyk
  19. Frank Lammert
  20. Thomas Adams
  21. Gudrun Wagenpfeil
  22. Michael Kindermann
  23. Constantin Marcu
  24. Zuhair Wolf Dietrich Ataya
  25. Marc Mittag
  26. Konrad Schwarzkopf
  27. Florian Custodis
  28. Daniel Grandt
  29. Harald Schaefer
  30. Kai Eltges
  31. Philipp M Lepper
  32. Robert Bals

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Abstract

No abstract available

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  1. Version published to 10.2147/jir.s320685
  2. ScreenIT

    SciScore for 10.1101/2021.05.04.21256497: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The studies have been approved by the ethics committee of the Ärztekammer des Saarlandes, and all patients or their legal representatives gave their informed consent.
    Consent: The studies have been approved by the ethics committee of the Ärztekammer des Saarlandes, and all patients or their legal representatives gave their informed consent.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    The following assays were used: adiponectin, alpha-1-antitrypsin (AAT), alpha-2-macroglobulin (A2Macro), apolipoprotein(a) (Lp(a)), beta-2-microglobulin (B2M), brain-derived neurotrophic factor (BDNF), c-reactive protein (CRP), complement C3 (C3), EN-RAGE, eotaxin-1, factor VII, ferritin (FRTN), fibrinogen, granulocyte-macrophage colony-stimulating factor (GM-CSF), haptoglobin, immunoglobulin a (IgA), immunoglobulin m (IgM), intercellular adhesion molecule 1 (ICAM-1), interferon gamma (IFN-gamma), interleukin-1 alpha (IL-1 alpha), interleukin-1 beta (IL-1 beta), interleukin-1 receptor antagonist (IL-1ra), interleukin-2 (IL-2), interleukin-3 (IL-3), interleukin-4 (IL-4), interleukin-5 (IL-5), interleukin-6 (IL-6), interleukin-7 (IL-7), interleukin-8 (IL-8), interleukin-10 (IL-10), interleukin-12 Subunit p40 (IL-12p40), interleukin-12 subunit p70 (IL-12p70), interleukin-17 (IL-17), interleukin-18 (IL-18), macrophage inflammatory protein-1 alpha (MIP-1 alpha), macrophage inflammatory protein-1 beta (MIP-1 beta), matrix metalloproteinase-3 (MMP-3), matrix metalloproteinase-9 (MMP-9), monocyte chemotactic protein 1 (MCP-1), myoglobin, plasminogen activator inhibitor 1 (PAI-1), pulmonary and activation-regulated chemokine (PARC), serum amyloid p-component (SAP), stem cell factor (SCF), t-cell-specific protein RANTES (RANTES), thyroxine-binding globulin (TBG), tissue inhibitor of metalloproteinases 1 (TIMP-1), tumor necrosis factor alpha (TNF-alpha), tumor necrosis factor beta (TNF-beta), tumor necrosis factor receptor 2 (TNFR2), vascular cell adhesion molecule-1 (VCAM-1), vascular endothelial growth factor (VEGF), vitamin d-binding protein (VDBP), von Willebrand factor (vWF)), AXL receptor tyrosine kinase (AXL), chemokine CC-4 (HCC-4), FASLG receptor (FAS), hepatocyte growth factor (HGF), TNF-related apoptosis-inducing ligand receptor 3 (TRAIL-R3)), alpha-fetoprotein (AFP), cancer antigen 125 (CA-125), cancer antigen 19-9 (CA-19-9), carcinoembryonic antigen (CEA), human chorionic gonadotropin beta (hCG), neuron-specific enolase (NSE)), matrix metalloproteinase-1 (MMP-1), matrix metalloproteinase-7 (MMP-7), matrix metalloproteinase-9, total (MMP-9, total), angiopoietin-1 (ANG-1), carbonic anhydrase 9 (CA-9), decorin, interleukin-18-binding protein (IL-18bp), platelet endothelial cell adhesion molecule (PECAM-1), pulmonary surfactant-associated protein D (SP-D).
    HCC-4
    suggested: None
    Software and Algorithms
    SentencesResources
    A correlation matrix was generated by using R language (version 3.6.1) and plotted using ggplot2 library.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    Statistical analyses were performed using SPSS version 27.0.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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:
    The present study has limitations and strengths: The use of established, rapid multiplex assays allows to translate these findings to validation studies and clinical practice. One important aspect is the direct comparison to non-COVID-19 pneumonia and a control group. The present study has included relatively few COVID-patients and does not cover very mild or asymptomatic individuals nor sequential sampling. Overfitting might be a result of low patient numbers and validation in other cohorts is mandatory. In conclusion, COVID-19 shows a distinct inflammatory blood marker profile separate from conventional pneumonia and extrapulmonary disease. Several markers were associated with disease severity and lethal outcome. We recommend a thorough investigation which of these biomarkers might be causative for immune dysregulation in severe COVID-19 or just a by-product of immune activation. Ideally, proteins strongly connected to COVID-19 pathophysiology could reveal potential targets for immunomodulating therapies for patients at risk.

    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.

    Results from scite Reference Check: We found no unreliable references.

    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.

  3. Version published to 10.1101/2021.05.04.21256497 on medRxiv