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Esther Gómez-Sánchez
Hospital Clínico Universitario de Valladolid: Hospital Clinico Universitario de Valladolid
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9457-1394
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Pneumonia is the leading cause of hospital admission and mortality in coronavirus disease 2019 (COVID-19), attributed to a cytokine storm. The objective of our study is to characterize this profile to identify the cytokines responsible for lung damage and mortality.
Methods: Plasma samples of 108 prospectively recruited COVID-19 patients were collected between March and April 2020. Patients were divided into four groups according to the severity of respiratory symptoms: 34 mild (no oxygen support), 26 moderate (low oxygen support using nasal cannula), 16 severe (high oxygen support) and 32 critical (mechanical ventilation). A 45-plex Human XL Cytokine Luminex Performance Panel kit was used in duplicate for each plasma sample. Twenty-eight healthy volunteers were used for normalization of the results.
Results: Multiple cytokines showed statistically significant differences when comparing mild and critical patients (HGF, PDGFBB, PIGF-1, IL-1α, MCP-1, VEGFA, IL-15 and IL-2). The best multivariable model included HGF, IL-1α, IL-2 and IL-27. High HGF levels were associated with the critical group (OR = 3.51; p < 0.001; 95%CI = 1.95–6.33). Moreover, high IL-1α (OR = 1.36; p = 0.01; 95%CI = 1.07–1.73) and low IL-27 (OR = 0.58; p < 0.005; 95%CI = 0.39–0.85) greatly increased the risk of ending up in the severe group. This model was especially sensitive in order to predict critical status (AUC = 0.794; specificity = 69.74%; sensitivity = 81.25%). Furthermore, high levels of HGF and IL-1α showed significant results in the survival analysis (p = 0.033 and p = 0.011, respectively).
Conclusions: Our study showed that HGF, IL-1α and IL 27 at hospital admission were strongly associated with severe/critical COVID-19 patients and therefore are excellent predictors of bad prognosis. Indeed, HGF and IL-1α were also mortality biomarkers.
Keywords
COVID-19, cytokines, HGF, IL-1α, severity of respiratory
Figure 1
Figure 2
Figure 3
This is a list of supplementary files associated with this preprint. Click to download.
- Additionalfile1.docx
Additional file 1: format .docx Supplement table 1 Cytokine/Chemokine detection percentage (Those marked do not exceed 20% detection and are excluded from the analysis).
- Additionalfile2.docx
Additional file 2: format .docx Supplement table 2 Comparison between the value of cytokines according to their degree of severity.
- Additionalfile3.docx
Additional file 3: format .docx Supplement table 3 Likelihood-ratio test (LRT) to check the assumption of proportional odds by comparing the proportional odds model with a multinomial model.
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research
Esther Gómez-Sánchez
Hospital Clínico Universitario de Valladolid: Hospital Clinico Universitario de Valladolid
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9457-1394
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 07 Apr, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Critical Care & Emergency Medicine
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Pneumonia is the leading cause of hospital admission and mortality in coronavirus disease 2019 (COVID-19), attributed to a cytokine storm. The objective of our study is to characterize this profile to identify the cytokines responsible for lung damage and mortality.
Methods: Plasma samples of 108 prospectively recruited COVID-19 patients were collected between March and April 2020. Patients were divided into four groups according to the severity of respiratory symptoms: 34 mild (no oxygen support), 26 moderate (low oxygen support using nasal cannula), 16 severe (high oxygen support) and 32 critical (mechanical ventilation). A 45-plex Human XL Cytokine Luminex Performance Panel kit was used in duplicate for each plasma sample. Twenty-eight healthy volunteers were used for normalization of the results.
Results: Multiple cytokines showed statistically significant differences when comparing mild and critical patients (HGF, PDGFBB, PIGF-1, IL-1α, MCP-1, VEGFA, IL-15 and IL-2). The best multivariable model included HGF, IL-1α, IL-2 and IL-27. High HGF levels were associated with the critical group (OR = 3.51; p < 0.001; 95%CI = 1.95–6.33). Moreover, high IL-1α (OR = 1.36; p = 0.01; 95%CI = 1.07–1.73) and low IL-27 (OR = 0.58; p < 0.005; 95%CI = 0.39–0.85) greatly increased the risk of ending up in the severe group. This model was especially sensitive in order to predict critical status (AUC = 0.794; specificity = 69.74%; sensitivity = 81.25%). Furthermore, high levels of HGF and IL-1α showed significant results in the survival analysis (p = 0.033 and p = 0.011, respectively).
Conclusions: Our study showed that HGF, IL-1α and IL 27 at hospital admission were strongly associated with severe/critical COVID-19 patients and therefore are excellent predictors of bad prognosis. Indeed, HGF and IL-1α were also mortality biomarkers.
Figure 1
Figure 2
Figure 3
This is a list of supplementary files associated with this preprint. Click to download.
- Additionalfile1.docx
Additional file 1: format .docx Supplement table 1 Cytokine/Chemokine detection percentage (Those marked do not exceed 20% detection and are excluded from the analysis).
- Additionalfile2.docx
Additional file 2: format .docx Supplement table 2 Comparison between the value of cytokines according to their degree of severity.
- Additionalfile3.docx
Additional file 3: format .docx Supplement table 3 Likelihood-ratio test (LRT) to check the assumption of proportional odds by comparing the proportional odds model with a multinomial model.
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