Abstract
BACKGROUND: Olfactory dysfunction is a common symptom of chronic rhinosinusitis (CRS). We previously identified several cytokines potentially linked to smell loss, potentially supporting an inflammatory etiology for CRS-associated olfactory dysfunction. In the current study we sought to validate patterns of olfactory dysfunction in CRS using hierarchical cluster analysis, machine learning algorithms, and multivariate regression. METHODS: CRS patients undergoing functional endoscopic sinus surgery were administered the Smell Identification Test (SIT) preoperatively. Mucus was collected from the middle meatus using an absorbent polyurethane sponge and 17 inflammatory mediators were assessed using a multiplexed flow-cytometric bead assay. Hierarchical cluster analysis was performed to characterize inflammatory patterns and their association with SIT scores. The random forest approach was used to identify cytokines predictive of olfactory function. RESULTS: One hundred ten patients were enrolled in the study. Hierarchical cluster analysis identified 5 distinct CRS clusters with statistically significant differences in SIT scores observed between individual clusters (p < 0.001). A majority of anosmic patients were found in a single cluster, which was additionally characterized by nasal polyposis (100%) and a high incidence of allergic fungal rhinosinusitis (50%) and aspirin-exacerbated respiratory disease (AERD) (33%). A random forest approach identified a strong association between olfaction and the cytokines interleukin (IL)-5 and IL-13. Multivariate modeling identified AERD, computed tomography (CT) score, and IL-2 as the variables most predictive of olfactory function. CONCLUSION: Olfactory dysfunction is associated with specific CRS endotypes characterized by severe nasal polyposis, tissue eosinophilia, and AERD. Mucus IL-2 levels, CT score, and AERD were independently associated with smell loss.