Clinical, Immunologic, and Genetic Characteristics in Patients With Syndrome of Undifferentiated Recurrent Fevers

未分化复发性发热综合征患者的临床、免疫学和遗传学特征

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Abstract

OBJECTIVE: Syndrome of undifferentiated recurrent fevers (SURF) is characterized by recurrent fevers and autoinflammation without a confirmed molecular diagnosis of a hereditary recurrent fever syndrome, and not fulfilling criteria for periodic fever, adenitis, pharyngitis, aphthous stomatitis syndrome (PFAPA). The goal of this study was to characterize clinical features of patients with SURF compared to patients with PFAPA and to analyze their cytokine signature, genetic variations, and responses to treatment. METHODS: We enrolled 46 patients observed at Cincinnati Children's Hospital Medical Center. Baseline data and inflammatory cytokines were collected at enrollment, and their clinical course was followed. Cytokine analysis was performed using a cytokine multiplex assay. Many patients had specific or whole exome genetic testing. RESULTS: The prevalence of rash and arthralgias were higher in patients with SURF compared to patients with PFAPA. Pharyngitis and adenopathy were less frequent. A subset of patients with SURF clustered together with elevated proinflammatory cytokines and more frequently required biologic therapy. Focused analysis of whole exome sequencing  data revealed that variants of unknown clinical significance (VUCS) were frequently identified in genes implicated in B cell development, immunodeficiencies, and inflammatory bowel disease risk. Treatments for patients with SURF commonly included on-demand steroids, colchicine, and anti-interleukin-1 therapy. CONCLUSION: Our findings suggest SURF is a heterogeneous group but has distinct clinical and immunologic features from disorders such as PFAPA. Patients have frequent VUCS, which may have relevance to disease pathogenesis. A subset of patients showed more inflammation and an increased need for biologic therapy. Further research is necessary to define whether there exist distinct SURF endotypes and to better predict treatment outcomes.

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