Severe Obstructive Sleep Apnea Is Associated with Alterations in the Nasal Microbiome and an Increase in Inflammation

Abstract

Rationale: Obstructive sleep apnea (OSA) is associated with recurrent obstruction, subepithelial edema, and airway inflammation. The resultant inflammation may influence or be influenced by the nasal microbiome.

Objectives: To evaluate whether the composition of the nasal microbiota is associated with obstructive sleep apnea and inflammatory biomarkers.

Methods: Two large cohorts were used: 1) a discovery cohort of 472 subjects from the WTCSNORE (Seated, Supine and Post-Decongestion Nasal Resistance in World Trade Center Rescue and Recovery Workers) cohort, and 2) a validation cohort of 93 subjects rom the Zaragoza Sleep cohort. Sleep apnea was diagnosed using home sleep tests. Nasal lavages were obtained from cohort subjects to measure: 1) microbiome composition (based on 16S rRNA gene sequencing), and 2) biomarkers for inflammation (inflammatory cells, IL-8, and IL-6). Longitudinal 3-month samples were obtained in the validation cohort, including after continuous positive airway pressure treatment when indicated.

Measurements and main results: In both cohorts, we identified that: 1) severity of OSA correlated with differences in microbiome diversity and composition; 2) the nasal microbiome of subjects with severe OSA were enriched with Streptococcus, Prevotella, and Veillonella; and 3) the nasal microbiome differences were associated with inflammatory biomarkers. Network analysis identified clusters of cooccurring microbes that defined communities. Several common oral commensals (e.g., Streptococcus, Rothia, Veillonella, and Fusobacterium) correlated with apnea-hypopnea index. Three months of treatment with continuous positive airway pressure did not change the composition of the nasal microbiota.

Conclusions: We demonstrate that the presence of an altered microbiome in severe OSA is associated with inflammatory markers. Further experimental approaches to explore causal links are needed.

Keywords: biomarkers; chronic rhinosinusitis; inflammation; microbiome.

Figure 1.

Similar trends in α and β diversity parameters for obstructive sleep apnea (OSA) diagnosis in the discovery and validation cohorts. (A) Shannon diversity index (SDI) differences between subjects with mild OSA, moderate OSA, severe OSA, and no OSA in the discovery cohort. Higher α diversity was noted in the group with severe OSA compared with subjects with no OSA (Mann-Whitney P = 0.005). (B) Principal coordinate analysis on the basis of weighted UniFrac distances for groups of severity of OSA and no OSA subjects (permutational multivariate ANOVA [PERMANOVA] P < 0.001) in the discovery cohort. (C) α diversity differences (based on SDI) in the validation cohort. The severe OSA group had higher α diversity than subjects with no OSA (Mann-Whitney P = 0.04). (D) Principal coordinate analysis on the basis of weighted UniFrac shows significant differences between subjects with different severity of OSA and subjects with no OSA (PERMANOVA P = 0.04) in the validation cohort. PC = principal coordinate.

Figure 2.

Taxonomic differences between severe obstructive sleep apnea (OSA) diagnosis and subjects with no OSA in the discovery and validation cohorts show oral commensal enrichment. (A) Comparison of relative abundance of top differentially enriched taxa in the discovery cohort. The nasal microbiota from subjects with severe OSA was enriched with Streptococcus, Veillonella, Granulicatella, and Fusobacterium when compared with samples from subjects with no OSA (Mann-Whitney). (B) Comparison of relative abundance of top differentially enriched taxa in the validation cohort. The nasal microbiota from subjects with severe OSA was enriched with Streptococcus, Prevotella, Pseudomonas, and Haemophilus when compared with samples from subjects with no OSA (Mann-Whitney). Samples with taxa less than 5.0 × 10⁻⁵ relative abundance of specific taxa were considered below the lower limit of detection (dotted line).

Figure 3.

Changes in nasal microbiota associated with levels of inflammatory markers in the discovery and validation cohorts. (A) β diversity differences between high and low neutrophils in the discovery cohort (permutational multivariate ANOVA [PERMANOVA] P < 0.005). (B) β diversity differences between high and low IL-8 levels in the discovery cohort (PERMANOVA P < 0.05). (C) The nasal microbiota of lavages with high neutrophils was enriched with Staphylococcus, Lactococcus, and Planococcaceae(u.g.) (Mann-Whitney). (D) The nasal microbiota of lavages with high IL-8 was enriched with Staphylococcus, Veillonella, and Planococcaceae(u.g.) (Mann-Whitney). (E) β diversity differences between high and low lymphocyte levels in the validation cohort (PERMANOVA P < 0.03). (F) Significant differences were noted in β diversity when comparing high versus low IL-8 (PERMANOVA P = 0.04). (G) The nasal microbiota of lavages with high lymphocytes was enriched with Streptococcus, Rothia, and Enterococcus (Mann-Whitney). (H) The nasal microbiota of lavages with high IL-8 was enriched with Streptococcus, Prevotella, and Rothia (Mann-Whitney). Samples with taxa less than 5.0 × 10⁻⁵ relative abundance were considered below the lower limit of detection (dotted line). lymph = lymphocytes; neut = neutrophils; PC = principal coordinate; u.g. = undefined genus.

Figure 4.

The cooccurrence network between taxa, apnea–hypopnea index (AHI4), and inflammatory biomarkers from the discovery and validation cohorts. Cooccurrence network for the top 50 genus-level taxa (>2% relative abundance in at least 10% of the samples) built using SparCC using data from both cohorts. Genera (circles) were correlated with AHI4 and inflammatory biomarkers (squares), and significantly correlated variables were kept in the network (false discovery rate < 0.20). Relative abundance of genera is represented by the color and size of the circles. Positive correlations between taxa are represented by solid blue edges and the length of edges calculated as 1 − rho. Negative correlations are dotted gray edges, and the length of the edges calculated as the absolute rho. Therefore, nodes in close proximity tend to cooccur in the community. Correlations of genera with AHI4 and inflammatory biomarkers are represented by gold edges. Cytoscape 3.6.0 was used to visualize the network with a prefuse force-directed layout. (A) The cooccurrence network using data from the discovery cohort shows differential clustering of taxa. Oral commensals, such as Streptococcus, Fusobacterium, Rothia, Veillonella, and Prevotella, tended to cooccur and correlate with AHI4. (B) The cooccurrence network using data from the validation cohort demonstrates similar cooccurrence pattern. This network showed two well-defined large clusters (note that break between the two clusters is represented by dotted black lines and was introduced to facilitate visualization). Oral commensals cooccurred with AHI4 and inflammatory markers (lymphocytes and IL-8). u.g. = undefined genus.

Figure 5.

Treatment with continuous positive airway pressure (CPAP) does not significantly alter the nasal microbiome. Longitudinal change of nasal microbiota comparing baseline samples with samples obtained after 3 months on treatment with CPAP showed no significant difference in (A) α diversity (Wilcoxon rank test P = ns), and (B) β diversity (permutational multivariate ANOVA P = ns). ns = not significant; PC = principal coordinate.