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Distinct Nasal Microbiome Profiles and Prediction Model for Allergic Rhinitis, Nonallergic Rhinitis, and Healthy
Kantima Kanchanapoomi1, Iyarit Thaipisuttikul2, Perapon Nitayanon2
1Division of Allergy and Clinical Immunology, Department of Pediatrics, Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand.
Insights
Pediatric allergic rhinitis (AR) and nonallergic rhinitis (NAR) show distinct nasal microbiome differences compared to healthy children. These findings aid in differentiating rhinitis types using microbial signatures.
Area of Science:
- Microbiology
- Pediatric Allergy
- Bioinformatics
Background:
- Limited pediatric data exists on nasal microbiota in allergic rhinitis (AR) and nonallergic rhinitis (NAR) compared to adults.
- Nasal microbiome composition is increasingly recognized as a factor in respiratory health and disease.
Purpose of the Study:
- To compare the nasal microbiomes of children with AR, NAR, and healthy controls (HC).
- To investigate factors influencing pediatric nasal microbiomes.
- To develop a predictive model for differentiating AR, NAR, and HC using microbiome data.
Main Methods:
- 16S rDNA sequencing of nasal swab samples from 60 children (AR, NAR, HC).
- Analysis of bacterial composition, alpha and beta diversity.
- Collection of demographic data and influencing factors (e.g., pets, breastfeeding).
Main Results:
- Significant differences in nasal microbiome diversity and composition were found among AR, NAR, and HC groups.
- Specific bacterial taxa (e.g., Escherichia-Shigella, Dolosigranulum) were differentially abundant in AR and NAR groups compared to HC.
- Household pets and breastfeeding duration impacted nasal microbiome diversity; a predictive model achieved 83% accuracy.
Conclusions:
- Preliminary evidence suggests distinct nasal microbiome profiles in pediatric AR, NAR, and HC.
- Differential microbial abundances may indicate specific rhinitis phenotypes.
- Findings require validation in larger cohorts but offer insights into rhinitis pathogenesis.
Objectives:
Adult studies reported differences in nasal microbiota composition among patients with allergic rhinitis (AR), nonallergic rhinitis (NAR), and healthy controls (HCs), whereas pediatric data remain limited. This study compared the nasal microbiomes of children with AR, NAR, and HC, investigated factors influencing these microbiomes, and developed a predictive model to differentiate these conditions based on microbiome data.
Methods:
Nasal swab samples were collected from children with AR, NAR, and HC. Microbial characterization was performed using 16S rDNA sequencing to analyze bacterial composition. Relevant demographic data and influencing factors were collected.
Results:
Sixty participants (median age 6.3 [4.3-8.4] years, 51.6% males) were categorized into AR (n = 24), NAR (n = 14), and HC (n = 22). Significant differences in alpha and beta diversity were observed among groups (p < 0.01 and p < 0.05, respectively). The AR and NAR groups exhibited lower Pielou's evenness than HC (FDR-adjusted p < 0.01 and p = 0.02, respectively). Compared to HC, the AR group showed a higher abundance of Escherichia-Shigella, Negativicoccus, and Campylobacter. In contrast, Dolosigranulum was enriched while the Enterobacteriaceae family was depleted in the NAR group. Household pets and breastfeeding duration significantly influenced nasal microbiome diversity regardless of the disease groups. A prediction model of nasal microbial distribution for AR, NAR, and HC identified 14 taxa critical for distinguishing these groups (accuracy of 0.83).
Conclusion:
This pilot study identified preliminary differences in nasal microbiome diversity and composition among children with AR, NAR, and HC. Differential microbial abundances may reflect distinct rhinitis phenotypes. However, these findings are hypothesis-generating and require validation in larger, independent cohorts.
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