Cytokine ranking via mutual information algorithm correlates cytokine profiles with presenting disease severity in patients infected with SARS-CoV-2.

Although the range of immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is variable, cytokine storm is observed in a subset of symptomatic individuals. To further understand the disease pathogenesis and, consequently, to develop an additional tool for clinicians to evaluate patients for presumptive intervention, we sought to compare plasma cytokine levels between a range of donor and patient samples grouped by a COVID-19 Severity Score (CSS) based on the need for hospitalization and oxygen requirement. Here we utilize a mutual information algorithm that classifies the information gain for CSS prediction provided by cytokine expression levels and clinical variables. Using this methodology, we found that a small number of clinical and cytokine expression variables are predictive of presenting COVID-19 disease severity, raising questions about the mechanism by which COVID-19 creates severe illness. The variables that were the most predictive of CSS included clinical variables such as age and abnormal chest x-ray as well as cytokines such as macrophage colony-stimulating factor, interferon-inducible protein 10, and interleukin-1 receptor antagonist. Our results suggest that SARS-CoV-2 infection causes a plethora of changes in cytokine profiles and that particularly in severely ill patients, these changes are consistent with the presence of macrophage activation syndrome and could furthermore be used as a biomarker to predict disease severity.

Interleukin 6, soluble interleukin 2 receptor alpha (CD25), monocyte colony-stimulating factor, and hepatocyte growth factor linked with systemic hyperinflammation, innate immunity hyperactivation, and organ damage in COVID-19 pneumonia.

BACKGROUND: Patients infected by SARS-CoV-2 can develop interstitial pneumonia, requiring hospitalisation or mechanical ventilation. Increased levels of inflammatory biomarkers are associated with development of acute respiratory distress syndrome (ARDS). The aim of the present study was to determine which cytokines are associated with respiratory insufficiency in patients hospitalised for COVID-19. PATIENTS AND METHODS: Data on 67 consecutive patients were collected between March 8 and March 30, 2020. PaO2/FiO2 ratio (P/F) was calculated at hospital admission. The following cytokines were analysed: interleukin (IL)-6, IL-1α, IL-18, tumour necrosis factor (TNF)-ß, macrophage colony-stimulating factor (M-CSF), macrophage migration inhibitory factor (MIF), soluble IL-2 receptor alpha (sIL-2Rα; CD25), IL-12ß, IL-3, interferon (IFN) α2a, monokine induced by gamma interferon (MIG), monocyte-chemotactic protein 3 (MCP3) and hepatocyte growth factor (HGF). RESULTS: P/F lower than 300 was recorded in 22 out of 67 patients (32.8%). P/F strongly correlated with IL-6 (r = -0.62, P < 0.0001), M-CSF (r = -0.63, P < 0.0001), sIL-2Rα (r = -0.54, P < 0.0001), and HGF (r = -0.53, P < 0.0001). ROC curve analyses for IL-6 (AUC 0.83, 95% CI 0.73-0.93, P < 0.0001), M-CSF (AUC 0.87, 95% CI 0.79-0.96, P < 0.0001), HGF (AUC 0.81, 95% CI 0.70-0.93, P < 0.0001), and sIL-2Rα (AUC 0.80, 95% CI, 0.69-0.90, P < 0.0001) showed that these four soluble factors were highly significant. All four soluble factors correlated with LDH, white blood cell count, neutrophil count, lymphocyte count, and CRP. CONCLUSION: IL-6, M-CSF, sIL-2Rα, and HGF are possibly involved in the main biological processes of severe COVID-19, mirroring the level of systemic hyperinflammatory state, the level of lung inflammation, and the severity of organ damage.