THE ROLE OF NASAL MICROBIOME IN THE PATHOGENESIS OF CHRONIC RHINOSINUSITIS: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Background: Chronic rhinosinusitis (CRS) is one of the most common diseases of the upper respiratory tract, affecting 5-15% of the population in different regions worldwide. Despite decades of research, the exact pathogenesis of CRS remains debatable, and the role of the nasal microbiome requires detailed investigation. Objective: To analyze and systematize current data (2018-2025) regarding the role of the nasal cavity and paranasal sinus microbiome in the pathogenesis of chronic rhinosinusitis and to conduct a meta-analysis. Materials and Methods: A systematic search was conducted in PubMed, Scopus, Web of Science, Cochrane Library, and Google Scholar databases. Twenty-one studies meeting the inclusion criteria were selected. The quality assessment was performed using the Newcastle-Ottawa Scale for observational studies and the Cochrane Risk of Bias 2 tool for randomized controlled trials. A meta-analysis was conducted using a random-effects model. The quality of evidence was evaluated using the GRADE system. Results: The meta-analysis revealed a statistically significant increase in the prevalence of Haemophilus influenzae in patients with CRS (OR: 2.00; 95% CI: 1.08-3.72; p=0.0276) and a decrease in the relative frequency of Corynebacterium spp. (mean difference: -5.44%; 95% CI: -8.88 to -2.00; p=0.0019). A significant reduction in bacterial diversity indices in CRS patients was established (SMD: -0.62; 95% CI: -0.90 to -0.34; p<0.0001). No statistically significant differences in the prevalence of Staphylococcus aureus were found between CRS patients and the control group; however, a strong positive correlation between Staphylococcus aureus levels and the proinflammatory cytokine IL-8 was identified (r=0.67; 95% CI: 0.55-0.76; p<0.0001). Conclusions: The meta-analysis confirms the concept of microbial dysbiosis as an important pathogenetic mechanism in chronic rhinosinusitis. Reduced bacterial diversity, increased prevalence of Haemophilus influenzae, and decreased presence of commensal Corynebacterium spp. are characteristic features of microbiome changes in CRS. Different CRS phenotypes are characterized by specific microbial profiles, emphasizing the importance of a personalized approach to diagnosis and treatment. Keywords: chronic rhinosinusitis, microbiome, dysbiosis, Staphylococcus aureus, Haemophilus influenzae, Corynebacterium, bacterial diversity, meta-analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".