Use of Machine Learning and 71-Plex Immune Mediator Analysis to Identify Nasal Mucus Biomarkers Associated With Olfactory Loss in Patients with CRSwNP

Abstract

Background

The 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.

Methods

Olfactory 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.

Results

In 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.

Conclusion

Using 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.