Identification of a 5-Plex Cytokine Signature that Differentiates Patients with Multiple Systemic Inflammatory Diseases

Abstract

Patients with non-infectious systemic inflammation may suffer from one of many diseases, including hyperinflammation (HI), autoinflammatory disorders (AID), and systemic autoimmune disease (AI). Despite their clinical overlap, the pathophysiology and patient management differ between these disorders. We aimed to investigate blood biomarkers able to discriminate between patient groups. We included 44 patients with active clinical and/or genetic systemic inflammatory disease (9 HI, 27 AID, 8 systemic AI) and 16 healthy controls. We quantified 55 serum proteins and combined multiple machine learning algorithms to identify five proteins (CCL26, CXCL10, ICAM-1, IL-27, and SAA) that maximally separated patient groups. High ICAM-1 was associated with HI. AID was characterized by an increase in SAA and decrease in CXCL10 levels. A trend for higher CXCL10 and statistically lower SAA was observed in patients with systemic AI. Principal component analysis and unsupervised hierarchical clustering confirmed separation of disease groups. Logistic regression modelling revealed a high statistical significance for HI (P = 0.001), AID, and systemic AI (P < 0.0001). Predictive accuracy was excellent for systemic AI (AUC 0.94) and AID (0.91) and good for HI (0.81). Further research is needed to validate findings in a larger prospective cohort. Results will contribute to a better understanding of the pathophysiology of systemic inflammatory disorders and can improve diagnosis and patient management.