Cytokine biomarkers of COVID-19
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Abstract
We used a new strategy to screen cytokines associated with SARS-CoV-2 infection. Cytokines that can classify populations in different states of SARS-CoV-2 infection were first screened in cross-sectional serum samples from 184 subjects by 2 statistical analyses. The resultant cytokines were then analyzed for their interrelationships and fluctuating features in sequential samples from 38 COVID-19 patients. Three cytokines, M-CSF, IL-8 and SCF, which were clustered into 3 different correlation groups and had relatively small fluctuations during SARS-CoV-2 infection, were selected for the construction of a multiclass classification model. This model discriminated healthy individuals and asymptomatic and nonsevere patients with accuracy of 77.4% but was not successful in classifying severe patients. Further searching led to a single cytokine, hepatocyte growth factor (HGF), which classified severe from nonsevere COVID-19 patients with a sensitivity of 84.6% and a specificity of 97.9% under a cutoff value of 1128 pg/ml. The level of this cytokine did not increase in nonsevere patients but was significantly elevated in severe patients. Considering its potent antiinflammatory function, we suggest that HGF might be a new candidate therapy for critical COVID-19. In addition, our new strategy provides not only a rational and effective way to focus on certain cytokine biomarkers for infectious diseases but also a new opportunity to probe the modulation of cytokines in the immune response.
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SciScore for 10.1101/2020.05.31.20118315: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: The study was approved by the Ethics Commission of Chongqing Medical University (reference number: 2020006–1).
Consent: Written informed consent was waived by the Ethics Commission of the designated hospital for emerging infectious diseases.Randomization Briefly, all the subjects or healthy controls (the cross-sectional samples) were randomly divided into 5 sets using the ‘caret’ package of R software; 3 of the 5 sets were used for training, and the remaining 2 were used for testing. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open …
SciScore for 10.1101/2020.05.31.20118315: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: The study was approved by the Ethics Commission of Chongqing Medical University (reference number: 2020006–1).
Consent: Written informed consent was waived by the Ethics Commission of the designated hospital for emerging infectious diseases.Randomization Briefly, all the subjects or healthy controls (the cross-sectional samples) were randomly divided into 5 sets using the ‘caret’ package of R software; 3 of the 5 sets were used for training, and the remaining 2 were used for testing. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
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:A limitation of this study is that the sample size, especially that of severe patients, was small, and no critical and fatal cases were included. The accuracy of the cytokine biomarkers to classify populations with different states of SARS-CoV-2 infection needs to be tested further in a large and totally independent population. In addition, since all the cytokines were tested only when the patients had already been in corresponding states, it is not known how these cytokine biomarkers would predict which patients could develop severe illness.
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: Please consider improving the rainbow (“jet”) colormap(s) used on page 23. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
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.
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