What can we learn about chronic rhinosinusitis through mucus?

Abstract

Purpose of review: Sinonasal mucus biomarkers have emerged as a powerful, noninvasive tool to better understand the immunopathology of chronic rhinosinusitis (CRS). This review highlights the evolving role of mucus biomarkers as they move from discovery-phase research toward potential clinical implementation and identifies critical gaps that must be addressed before their integration into routine decision-making.

Recent findings: Mucus biomarkers provide valuable insights into CRS endotypes, disease severity, olfactory dysfunction, and potentially biologic choice. Recent studies have shown associations between mucus cytokine and protein profiles with postoperative olfactory recovery, polyp recurrence, and responsiveness to biologic therapy. Advances in collection devices hold potential to standardize sampling methods, while machine learning approaches are increasingly being applied to high-dimensional biomarker datasets, improving the ability to predict outcomes and guide therapy selection. Despite these advances, unresolved challenges include optimal sampling site, assay standardization, and prospective validation in clinical trials.

Summary: Mucus biomarkers are transforming our understanding of CRS by linking local immune dysfunction with clinical manifestations, such as quality-of-life impairment, olfactory loss, and disease recurrence. With continued refinement, integration of mucus biomarker profiling with machine learning and clinical datasets may enable precision diagnostics and personalized treatment strategies for CRS.

Keywords: biomarker; chronic sinusitis; machine learning; mucus; olfaction.