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Objective <p>The concept of disease control is increasingly gaining importance in the long-term management of chronic rhinosinusitis (CRS). Eosinophilic inflammation has been identified as a high-risk factor for uncontrolled CRS. Although evidence suggests that dysbacteriosis is involved in the pathogenesis of eosinophilic CRS, its association with disease control has not been explored. We attempt to explore the inflammatory patterns and nasal bacterial dysbiosis among patients with uncontrolled CRS.</p> Methods <p>We performed a cross-sectional study of 48 patients with uncontrolled CRS, 44 patients with controlled CRS, and 58 healthy controls. Uncontrolled CRS was defined according to European Position Paper on Rhinosinusitis and Nasal Polyps 2020. The nasal mucus and peripheral venous blood were collected for inflammatory endotype analysis. The bacterial microbiota of the swab from the middle meatus was profiled by sequencing the V3–V4 region of the 16S rRNA gene.</p> Results <p>Uncontrolled CRS showed significantly higher levels of mucus eosinophil-derived neurotoxin (EDN) (P &lt; 0.001), blood eosinophil counts (P = 0.002), blood basophil counts (P = 0.020), and blood lymphocyte counts (P = 0.033) than patients with controlled CRS. The nasal mucus EDN level was the best predictor of uncontrolled CRS, with the highest area under the receiver operating characteristic curve (AUC) of 0.798 (95% confidence interval [CI] = 0.692–0.904) compared to other inflammatory parameters. Patients with uncontrolled CRS exhibited a significant increase in the abundance of seven genera. Except for <i>Ralstonia</i> and <i>Acinetobacter</i>, the other five genera had a mean relative abundance &lt; 1%, including <i>Klebsiella</i> and <i>Pseudomonas</i>. By random forest analysis, we established a model for the nasal microbiome with an AUC of 0.949 (95% CI = 0.903–0.996). Upon incorporating peripheral eosinophil and basophil counts into the model, we found an enhancement in diagnostic capability, with an AUC of 0.974 (95% CI = 0.944–1.000).</p> Conclusions <p>Patients with uncontrolled CRS have distinct local and systematic inflammatory patterns and bacterial dysbiosis compared to both controlled CRS and healthy controls, which sheds light on the pathogenesis of uncontrolled status in CRS.</p>

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Distinct inflammatory patterns and nasal bacterial dysbiosis in uncontrolled chronic rhinosinusitis

  • 期刊论文

  • 发表日期：

    2025年4月16日

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  • Yunfan Zhang,
  • Zheng Liu,
  • Fan Yuan,
  • Xiaoxi Huang,
  • Dawei Wu

摘要

Objective

The concept of disease control is increasingly gaining importance in the long-term management of chronic rhinosinusitis (CRS). Eosinophilic inflammation has been identified as a high-risk factor for uncontrolled CRS. Although evidence suggests that dysbacteriosis is involved in the pathogenesis of eosinophilic CRS, its association with disease control has not been explored. We attempt to explore the inflammatory patterns and nasal bacterial dysbiosis among patients with uncontrolled CRS.

Methods

We performed a cross-sectional study of 48 patients with uncontrolled CRS, 44 patients with controlled CRS, and 58 healthy controls. Uncontrolled CRS was defined according to European Position Paper on Rhinosinusitis and Nasal Polyps 2020. The nasal mucus and peripheral venous blood were collected for inflammatory endotype analysis. The bacterial microbiota of the swab from the middle meatus was profiled by sequencing the V3–V4 region of the 16S rRNA gene.

Results

Uncontrolled CRS showed significantly higher levels of mucus eosinophil-derived neurotoxin (EDN) (P < 0.001), blood eosinophil counts (P = 0.002), blood basophil counts (P = 0.020), and blood lymphocyte counts (P = 0.033) than patients with controlled CRS. The nasal mucus EDN level was the best predictor of uncontrolled CRS, with the highest area under the receiver operating characteristic curve (AUC) of 0.798 (95% confidence interval [CI] = 0.692–0.904) compared to other inflammatory parameters. Patients with uncontrolled CRS exhibited a significant increase in the abundance of seven genera. Except for Ralstonia and Acinetobacter, the other five genera had a mean relative abundance < 1%, including Klebsiella and Pseudomonas. By random forest analysis, we established a model for the nasal microbiome with an AUC of 0.949 (95% CI = 0.903–0.996). Upon incorporating peripheral eosinophil and basophil counts into the model, we found an enhancement in diagnostic capability, with an AUC of 0.974 (95% CI = 0.944–1.000).

Conclusions

Patients with uncontrolled CRS have distinct local and systematic inflammatory patterns and bacterial dysbiosis compared to both controlled CRS and healthy controls, which sheds light on the pathogenesis of uncontrolled status in CRS.