Multisystem Inflammatory Syndrome in Children (MIS-C) is a potentially life-threatening complication of COVID-19. The pathophysiological mechanisms leading to severe disease are poorly understood. This study leveraged clinical samples from a well-characterized cohort of children hospitalized with COVID-19 or MIS-C to compare immune-mediated biomarkers. Our objective was to identify selected immune molecules that could explain, in part, why certain SARS-CoV-2-infected children developed MIS-C. We hypothesized that type-2 helper T cell-mediated inflammation can elicit autoantibodies, which may account for some of the differences observed between the moderate–severe COVID-19 (COVID⁺) and MIS-C cohort. We enumerated blood leukocytes and measured levels of selected serum cytokines, chemokines, antibodies to COVID-19 antigens, and autoantibodies in children presenting to an academic medical center in Connecticut, United States. The neutrophil/lymphocyte and eosinophil/lymphocyte ratios were significantly higher in those in the MIS-C versus COVID⁺ cohort. IgM and IgA, but not IgG antibodies to SARS-CoV-2 receptor binding domain were significantly higher in the MIS-C cohort than the COVID⁺ cohort. The serum levels of certain type-2 cytokines (interleukin (IL)-4, IL-5, IL-6, IL-8, IL-10, IL-13, and IL-33) were significantly higher in children with MIS-C compared to the COVID⁺ and SARS-CoV-2-negative cohorts. IgG autoantibodies to brain antigens and pentraxin were higher in children with MIS-C compared to SARS-CoV-19-negative controls, and children with MIS-C had higher levels of IgG anti-contactin-associated protein-like 2 (caspr2) compared to the COVID⁺ and SARS-CoV-19-negative controls. We speculate that autoimmune responses in certain COVID-19 patients may induce pathophysiological changes that lead to MIS-C. The triggers of autoimmunity and factors accounting for type-2 inflammation require further investigation.

The development of new point-of-care diagnostic testing tools for the detection of infectious diseases such as COVID-19 are a key aspect of clinical care and research. Accurate predictive classification methods are required to correctly identify and treat patients. Here, the onset of multisystem inflammatory syndrome in children (MIS-C), a more serious form of COVID-19, was predicted in a pediatric population using a set of multivariate immunological biomarker expression values. A first-stage bivariate detection of statistically significant biomarkers was obtained from a chosen set of standard cytokines and chemokine biomarkers considered relevant to COVID-19-related infection and disease. To incorporate the observed correlation structure among the resulting set of significant biomarkers, dimension reduction was then applied in the form of principal components. A second-stage logistic regression model using a small number of the principal component variables provided a highly predictive classification model for MIS-C. The resulting model was shown to compare favorably with an artificial neural network-based predictive model.