IL-6 and IL-10 as predictors of disease severity in COVID-19 patients: results from meta-analysis and regression

AIMS: SARS-CoV-2, an infectious agent behind the ongoing COVID-19 pandemic, induces high levels of cytokines such as IL-1, IL-2, IL-4, IL-6, IL-10, TNF-α, IFN-γ etc in infected individuals that play a role in the underlying patho-physiology. Nonetheless, exact association and contribution of every cytokine towards COVID-19 pathology remains poorly understood. Delineation of the roles of cytokines during COVID-19 holds the key to efficient patient management in clinics. This study performed a comprehensive meta-analysis to establish association between induced cytokines and COVID-19 disease severity to help in prognosis and clinical care. MAIN METHODS: Scientific literature was searched to identify 13 cytokines (IL-1β, IL-2, IL-2R, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12, IL-17, TNF-α and IFN-γ) from 18 clinical studies. Standardized mean difference (SMD) for selected 6 cytokines IL-2, IL-4, IL-6, IL-10, TNF-α and IFN-γ between severe and non-severe COVID-19 patient groups were summarized using random effects model. A classifier was built using logistic regression model with cytokines having significant SMD as covariates. KEY FINDINGS: Out of the 13 cytokines, IL-6 and IL-10 showed statistically significant SMD across studies synthesized. Classifier with mean values of both IL-6 and IL-10 as covariates performed well with accuracy of ~92% that was significantly higher than accuracy reported in literature with IL-6 and IL-10 as individual covariates. SIGNIFICANCE: Simple panel proposed by us with only two cytokine markers can be used as predictors for fast diagnosis of patients with higher risk of COVID-19 disease deterioration and thus can be managed well for a favourable prognosis.