Use of Machine Learning and 71-Plex Immune Mediator Analysis to Identify Nasal Mucus Biomarkers Associated With...

BackgroundThe mechanisms driving chronic rhinosinusitis with nasal polyps (CRSwNP)-related olfactory loss remain largely unknown. Here we sought to identify novel modulators of olfactory function via the examination of nasal mucus biomarkers using an expansive 71-cytokine plex analyzed via machine learning models.MethodsOlfactory testing was performed via 40-question smell identify test (UPSIT). During endoscopic sinus surgery, sponges were placed in the middle meatus of individuals with CRSwNP (n = 15). Nasal mucus samples were screened by multiplex analysis for 71-cytokine/chemokines. Results underwent analysis with statistical and machine learning model approaches to assess whether protein concentrations were predictive of olfactory dysfunction.ResultsIn CRSwNP, multiple machine learning models revealed novel cytokines IL-21 and MIP-1δ as positive predictors of greater olfactory dysfunction. Other cytokines detected by more than one model as predictive of olfactory dysfunction were IL-18, MCP-1, IL-22, and BCA-1. Other cytokines identified to be predictive by at least one model were FLT-3L, LIF, IL-20, SCF, IL-23, and TPO.ConclusionUsing a 71-cytokine/chemokine plex analyzed via machine learning, we identified potentially novel roles for MIP-1δ and IL-21 as modulators of olfactory function in CRSwNP. Use of machine learning for the analysis of nasal mucus cytokines, may serve as powerful tool to analyze complex multiplex immune mediator data.