16S rRNA Profiling Reveals Nasal-Nasopharyngeal Microbial Signatures for Precision Stratification of Pediatric OSA with Allergic Comorbidity

16S rRNA Profiling Reveals Nasal-Nasopharyngeal Microbial Signatures for Precision Stratification of Pediatric OSA with Allergic Comorbidity | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 15 September 2025 V1 Latest version Share on 16S rRNA Profiling Reveals Nasal-Nasopharyngeal Microbial Signatures for Precision Stratification of Pediatric OSA with Allergic Comorbidity Authors : Ruoqi Li 0009-0006-7244-4323 , Jie Wang , Liting Jin , Xiaoqiong Wang , Hui Zhang , Liyan Ni , Xi Lin , and Xuejun Liu [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175793645.50057375/v1 137 views 91 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: The comorbidity of obstructive sleep apnea (OSA) and allergic rhinitis (AR) in children worsens disease outcomes, yet the role of nasal-nasopharyngeal microbiota remains unclear. This study aimed to characterize microbial profiles in children with OSA, with or without AR. Methods: We enrolled 48 children (4–12 years) into three groups: OSA without AR (n=20), OSA with AR (n=18), and healthy controls (n=10). Mucosal samples from multiple nasal-nasopharyngeal sites were analyzed using 16S rRNA sequencing. Microbial diversity, composition, and biomarkers were assessed and correlated with the apnea-hypopnea index (AHI). Results: Alpha diversity was highest in the OSA with AR group. Beta diversity differed significantly across groups. Comorbidity-specific biomarkers were identified. A random forest model distinguished OSA from controls (AUC = 0.798) and OSA with AR from OSA without AR (AUC = 0.975) using five genera each. Incertae_Sedis abundance correlated with AHI in all OSA patients, whereas Romboutsia correlated with AHI only in the OSA with AR group. Conclusion: The nasal-nasopharyngeal microbiome provides reliable biomarkers for non-invasive stratification and severity assessment of pediatric OSA, especially in discriminating AR comorbidity. 16S rRNA Profiling Reveals Nasal-Nasopharyngeal Microbial Signatures for Precision Stratification of Pediatric OSA with Allergic Comorbidity Ruoqi Li 1,5* , Jie Wang 2 *, Liting Jin 1 , Xiaoqiong Wang 1 , Hui Zhang 3 , Liyan Ni 1 , Xi Lin 4* , Xuejun Liu 1* * Ruoqi Li and Jie Wang shared first author. * Xi Lin and Xuejun Liu shared corresponding author. 1 Department of Otolaryngology-Head and Neck Surgery, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang province, China. 2 Department of Otolaryngology-Head and Neck Surgery, Longquan People’s Hospital, Lishui, Zhejiang province, China. 3 Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang province, China. 4 Key Laboratory of Medical Genetics of Zhejiang Province, Key Laboratory of Laboratory Medicine, Ministry of Education, China, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang province, China. 5 The Second School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang province, China. Running Title: Precision Stratification of Pediatric OSA Correspondence: Xuejun Liu, PHD. Department of Otolaryngology-Head and Neck Surgery, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou Avenue 1111, Wenzhou,325027, Zhejiang province, China. Email: [email protected] . ORCID identifier: 0009-0005-0130-9952. Word count: 3404 words (excluding abstract, references and figure legends). Figures: 6. Material in the electronic repository: Supplementary figures and ethical approval. Supplementary figure 1:Analysis of α-diversity. Supplementary figure 2: Analysis of β-diversity using Jaccard distance. Supplementary figure 3: Analysis of taxa distribution across sampling sites. Ethical Approval: 2025-K-05-01. Conflict of interest The authors declare that there is no conflict of interest. Financial support This study was supported by Wenzhou Municipal Science and Technology Bureau, China (Grants 2022Y1563, Y20240326), Zhejiang Provincial Clinical Research Center for Pediatric Diseases, China (Grants ZJEK2310Y) and Wenzhou Medical Association Scientific Research Project, China (Grants 202303KZ1). Abstract Background: The comorbidity of obstructive sleep apnea (OSA) and allergic rhinitis (AR) in children worsens disease outcomes, yet the role of nasal-nasopharyngeal microbiota remains unclear. This study aimed to characterize microbial profiles in children with OSA, with or without AR. Methods: We enrolled 48 children (4–12 years) into three groups: OSA without AR (n=20), OSA with AR (n=18), and healthy controls (n=10). Mucosal samples from multiple nasal-nasopharyngeal sites were analyzed using 16S rRNA sequencing. Microbial diversity, composition, and biomarkers were assessed and correlated with the apnea-hypopnea index (AHI). Results: Alpha diversity was highest in the OSA with AR group. Beta diversity differed significantly across groups. Comorbidity-specific biomarkers were identified. A random forest model distinguished OSA from controls (AUC = 0.798) and OSA with AR from OSA without AR (AUC = 0.975) using five genera each. Incertae_Sedis abundance correlated with AHI in all OSA patients, whereas Romboutsia correlated with AHI only in the OSA with AR group. Conclusion: The nasal-nasopharyngeal microbiome provides reliable biomarkers for non-invasive stratification and severity assessment of pediatric OSA, especially in discriminating AR comorbidity. Keywords Pediatric obstructive Sleep Apnea, Allergic Rhinitis, Nasal Microbiome, Microbial Biomarker, 16S rRNA Sequencing Main text Introduction Pediatric obstructive sleep apnea syndrome (OSAS) affects 1.2–5.7% of children, with no significant gender disparity—unlike in adults [1,2]. Its core pathology involves recurrent partial or complete upper airway obstruction during sleep, which disrupts breathing, impairs restorative sleep, and compromises growth, cognitive function, and quality of life [3]. While adenoidal hypertrophy driven by chronic inflammation is the primary etiology, allergic rhinitis (AR) is another major contributor. Substantial evidence links AR and OSAS: AR is highly prevalent in children who snore [4], and nasal obstruction from AR is a recognized risk factor for OSAS [5]. These conditions also share common underlying risk factors [6], and their comorbidity exacerbates adverse outcomes, including growth impairment, cognitive deficits, behavioral issues, and neurodevelopmental delays [7]. Despite established clinical associations, the precise pathogenesis of OSAS—particularly the mechanisms driving its variable severity—remains unclear. Recent research implicates the human microbiome, often termed the ”second genome,” as a critical player in disease pathogenesis. Microbial communities dynamically interact with the host through metabolites and immune signaling, and dysbiosis in this ecosystem is strongly associated with obesity, inflammatory diseases, autoimmunity, and cancer [8–11]. In OSAS, intermittent hypoxia alters gut microbiota composition, while gut dysbiosis may conversely exacerbate comorbidities like obesity and cardiovascular disease by disrupting sleep via immune-metabolic pathways [12]. Specific microbial signatures in adenotonsillar tissue are linked to chronic tonsillitis or hypertrophy in pediatric OSAS [13], and enrichment of Scardovia in saliva is reported in adult OSAS patients [14]. Bacterial biofilms in adenoid tissue may promote epithelial injury and recurrent infections [15], while intermittent hypoxia likely remodels the upper airway microbiome [16]. Critically, these microbial shifts coincide with inflammatory cell infiltration and systemic inflammation [6,18], suggesting microbiome-driven inflammation is a key mechanism in OSAS pathogenesis. The respiratory microbiome—spanning the nasal cavity to the nasopharynx—sustains airway health by modulating immunity and resisting pathogens. Its balance depends on microbial interactions and environmental factors [19], and its disruption is mechanistically tied to chronic respiratory diseases like asthma and chronic rhinosinusitis [20,21]. Patients with AR—a major OSAS comorbidity—exhibit distinct nasal microbiome profiles that foster an ”allergic inflammatory microenvironment” [22]. Yet, the causal relationship between these microbial shifts and AR remains unresolved: Does AR reshape the microbiome, or do microbiome alterations drive AR progression? This ambiguity raises a pivotal question: When OSAS and AR coexist, how do they interact within the contiguous nasal and nasopharyngeal microbial niches? Moreover, how might they independently or synergistically influence disease severity and progression? Notably, while studies have sporadically characterized microbiomes in isolated OSAS or AR, the integrated microbial ecology of the upper respiratory tract (nasal cavity and nasopharynx) in children—especially those with comorbid OSAS-AR—remains unexplored. A deeper understanding of microbial signatures in this comorbidity, their association with severity, and their mechanistic roles is essential. Such insights would advance fundamental knowledge of pathogenesis and could reveal novel non-invasive biomarkers (e.g., specific bacterial consortia distinguishing OSAS with and without AR) and inform targeted therapies. To address this gap, our study provides the first systematic profiling of the upper respiratory nasal microbiome in children with OSAS, with or without AR comorbidity. We further aim to identify key microbial drivers and their mechanistic contributions to disease progression. Methods Study Design and Ethical Approval This study was designed as a prospective cohort study. The protocol received approval from the Ethics Committee of the Second Affiliated Hospital of Wenzhou Medical University (2025-K-05-01) and was conducted in accordance with the ethical principles of the Declaration of Helsinki. Written informed consent was provided by the legal guardians of all participating children once they had received a comprehensive explanation of the study’s purpose and procedures. 2. Study Participants Study participants were enrolled, diagnosed, and grouped according to relevant Chinese clinical guidelines. Obstructive sleep apnea (OSA) was diagnosed based on the 2020 Chinese pediatric OSA guideline using the obstructive apnea–hypopnea index (OAHI), defined as the number of obstructive apneas, mixed apneas, and hypopneas per hour of sleep［23］. Children were classified as non-OSA controls (OAHI < 1 event/hour) or OSA (OAHI ≥ 1 event/hour), with the latter further stratified into mild (1 ≤ OAHI < 5), moderate (5 ≤ OAHI < 10), and severe (OAHI ≥ 10) subgroups. The OSA-18 quality of life survey was used adjunctively; controls required a score below 60, while the OSA group required above 70. Allergic rhinitis (AR) was diagnosed per the 2022 Chinese AR guideline［24］, requiring at least two typical nasal symptoms (sneezing, rhinorrhea, itching, or congestion) for ≥1 hour daily, pale edematous nasal mucosa with watery secretion on endoscopy, and a positive allergen test (skin prick test, specific IgE, or nasal provocation test). Three groups of children aged 4–12 years were enrolled: an OSAS without AR group (n = 20) meeting OSA criteria (OAHI ≥ 1, OSA-18 > 70) with negative AR testing; an OSAS with AR group (n = 18) meeting both OSA and full AR criteria; and a healthy control group (n = 10) of children undergoing elective ear surgery with OAHI < 1, OSA-18 < 60, and no clinical evidence of AR, sinusitis, or other nasal-nasopharyngeal diseases. Exclusion criteria included prior adenoid/tonsillectomy, craniofacial anomalies, nasal septal deviation, sinusitis, psychiatric disorders, or missing clinical data. All groups were matched for sex, age, and body mass index (BMI). 3. Clinical Assessment Methods All participants underwent full-night (≥7 hours) sleep monitoring using a validated Morpheus Ox portable monitor (Wide Med, Israel). Key parameters included the obstructive apnea–hypopnea index (OAHI), lowest oxygen saturation (LSaO₂), mean oxygen saturation (MSaO₂), and percentage of sleep time with oxygen saturation below 90% (SIT90). Data were automatically analyzed by built-in software and then manually verified by staff blinded to group assignments. This portable device has demonstrated good agreement with laboratory polysomnography in children [25]. Upper airway anatomy was evaluated via electronic nasopharyngoscopy. Adenoid hypertrophy was graded from I (≤25% obstruction) to IV (76–100%), with the OSA group requiring Grade III or higher (>50% obstruction) and controls limited to Grade II or lower. Tonsil size was assessed using the Brodsky scale (0–4), and all OSA children exhibited Grade 2 or higher hypertrophy, as confirmed by two otolaryngologists. Controls had Grade 1 or lower tonsil enlargement. Allergic rhinitis (AR) was diagnosed as previously outlined in the Study Participants section, incorporating clinical symptoms, endoscopic signs, and objective allergen test results. 4. Sample Collection, DNA Extraction, and 16S rRNA Sequencing Prior to sampling, all participants refrained from systemic antibiotics or corticosteroids for one month and avoided nasal irrigation or topical medications for one week. Under endoscopic guidance, mucosal samples were collected from the inferior turbinate, middle turbinate, and adenoid surface using sterile swabs, immediately frozen in liquid nitrogen, and stored at –80°C. Total DNA was extracted and the V3–V4 regions of the 16S rRNA gene were amplified with primers 341F/805R. Purified amplicons were sequenced on the Illumina NovaSeq platform (2×250 bp). After adapter trimming with Cutadapt, DADA2 was used for quality control, denoising, and generation of amplicon sequence variants (ASVs), which were taxonomically classified against the SILVA and NT-16S databases. Alpha diversity was assessed using Chao1 and Observed species indices. Beta diversity was evaluated with Unweighted Unifrac and Jaccard distances, visualized via PCoA, and tested with ANOSIM/PERMANOVA. LEfSe identified differentially abundant taxa (LDA > 3), and a Random Forest model was used to select diagnostic biomarkers based on mean decrease accuracy. 5. Statistical Analysis Statistical analysis was performed using SPSS Statistics (version 26.0, IBM Corp.) and the OmicStudio platform (https://www.omicstudio.cn/tool). Continuous normally distributed data are presented as mean ± SD and compared using ANOVA; non-normal data as median (IQR) with Kruskal–Wallis test. Categorical data are expressed as counts (%) and compared with chi-square or Fisher’s exact test. Spearman’s correlation and ROC analysis (AUC) were used for microbial biomarker evaluation. A p-value < 0.05 was considered significant. Results 1. Basic Characteristics of the Study Subjects A total of 48 children aged 4–12 years were included and categorized into three groups: OSAS without AR (n = 20), OSAS with AR (n = 18), and controls (n = 10). The groups showed no significant differences in sex, age, or BMI (P > 0.05), indicating well-matched baseline demographics. Controls were children scheduled for elective ear surgery who met strict criteria including AHI < 1 event/hour, OSA-18 score < 60, and absence of nasal diseases. However, these children may not fully represent a healthy population due to potential unmeasured confounders such as subtle developmental variations or health care access bias. Although groups were carefully matched, further validation in community-based healthy children is warranted. 2. Microbial Diversity in the Nasal-Nasopharyngeal Region Sequencing depth was deemed sufficient, as both the Chao1 and observed species richness curves plateaued after approximately 40,000 sequences (Fig. 1A–B), indicating comprehensive capture of the microbial community.Notably, the OSAS with AR group exhibited the highest alpha diversity, with Chao1 and observed species indices significantly greater than those of both the control group and the OSAS without AR group (P < 0.0001; Fig. 1C–D). The OSAS without AR group showed the lowest diversity, suggesting a reduction in microbial complexity, while controls displayed intermediate alpha diversity levels. No other alpha diversity metrics differed significantly (Fig. S1). Fig. 1 | Analysis of α-diversity. Beta diversity analysis using unweighted Unifrac distances revealed significant overall differences in microbial community structure among the three groups (ANOSIM: R = 0.2656, P = 0.001), with between-group variation exceeding within-group variation. PCoA visualization supported this clustering (Fig. 2B), with the first two axes explaining 23.54% of total variance. PERMANOVA confirmed a significant group effect (R² = 0.084, P = 0.001). Although partial overlap occurred—particularly between the two OSAS groups—the overall separation indicates distinct microbiota profiles. Analysis based on Jaccard distance yielded consistent conclusions (Fig. S2). Fig. 2 | Analysis of microbial community differences based on Unweighted Unifrac distance. 3. Analysis of Species Composition The Sankey diagram (Fig. 3) effectively visualized differences in microbial composition among the OSAS without AR, OSAS with AR, and control groups across phylum, order, and genus levels, highlighting key taxonomic shifts and hierarchical relationships. While all groups shared major phyla such as Proteobacteria, Firmicutes, and Actinobacteriota, their relative abundances varied considerably. Particularly noteworthy was the pronounced enrichment of Proteobacteria-derived Haemophilus in the OSAS with AR group, in contrast to the stronger representation of Moraxella within the Pseudomonadales order in both the control and OSAS without AR groups. A complete list of microbial taxa at each taxonomic rank is provided in Fig. 3. Fig. 3 | Microbial composition analysis. 4. Group-Specific Biomarkers Identified by LEfSe Analysis LEfSe analysis revealed significant differences in microbial abundance between pediatric OSAS patients and controls, with distinctive biomarkers identified for each group (Fig. 4). The OSAS with AR group was notably enriched in Synergistota at the phylum level, and in taxa such as Peptostreptococcales-Tissierellales and Psychrobacter at the order and genus levels, respectively. The OSAS without AR group showed marked enrichment in Cyanobacteria and Acetobacterales. In contrast, the control group was characterized by higher abundance of Bacteroidota, Bacteroidales, and genera including Porphyromonas and Alloprevotella. Interestingly, no genus was specifically associated with the OSAS without AR group. Phylogenetic clustering highlighted group-specific enrichment patterns, such as the Synergistota–Synergistales branch in the OSAS with AR group and the Bacteroidota–Bacteroidales–Porphyromonas/Alloprevotella lineage in controls. Full details of differentially abundant features are presented in Fig. 4. Fig. 4 | LEfSe analysis of differentially abundant taxa in the upper respiratory microbiota across the three groups. 5. Analysis of Diagnostic Value and Clinical Relevance of Microbial Markers To identify microbial markers distinguishing pediatric OSAS patients from healthy controls, we combined OSAS children with and without AR into a unified OSAS group (Fig. 5). A random forest model identified five discriminatory genera, with a diagnostic classifier constructed from these taxa achieving an AUC of 0.798. Among these, g__Escherichia-Shigella demonstrated the highest discriminatory power (MDA = 8.66). Furthermore, the relative abundance of g__Incertae_Sedis was significantly correlated with disease severity (assessed by AHI, P < 0.05; r = 0.211, p = 0.035), an association that was consistent across all nasal-nasopharyngeal sampling sites. Fig. 5 | Diagnostic biomarkers for OSAS. We next identified five microbial taxa that effectively distinguished between the OSAS with AR group and the OSAS without AR group (Fig. 6). A classifier based on these markers showed exceptional accuracy (AUC = 0.975). Notably, within the OSAS with AR group, the abundance of g__Romboutsia was positively correlated with AHI-based severity (P < 0.01), whereas no such association was observed in the OSAS without AR group. The abundance of g__Romboutsia did not differ significantly across sampling locations within the nasal-nasopharyngeal region. Fig. 6 | AR comorbidity-specific biomarkers. Discussion Obstructive sleep apnea syndrome (OSAS) and allergic rhinitis (AR) are common pediatric disorders that impose substantial health and socioeconomic burdens, both individually and as comorbidities [26]. Although both conditions are associated with alterations in the nasal microbiota [22,27–29], the nasal-nasopharyngeal microbiome in children with OSAS remains poorly characterized. Using 16S rRNA sequencing, we characterized the mucosal microbiome in pediatric OSAS patients with or without AR compared to healthy controls and correlated microbial profiles with AHI-based disease severity. We found that the nasal-nasopharyngeal microbial community structure significantly differed between children with OSAS, regardless of AR comorbidity, and healthy controls. Microbial diversity and composition not only distinguished OSAS patients from controls but also clearly separated the OSAS with AR and OSAS without AR groups. Furthermore, differential microbial abundance was associated with OSAS severity as measured by AHI, and AR comorbidity exerted a pronounced effect on microbiome structure. These findings highlight the potential role of the nasal-nasopharyngeal microbiota in the pathophysiology of OSAS and its interaction with AR. α-Diversity analysis indicated significantly higher microbial richness (Chao1 and Observed Species indices, P < 0.0001) in the OSAS with AR group, while the OSAS without AR group showed the lowest diversity—below even healthy controls—suggesting opposing roles of AR comorbidity and OSAS alone in reshaping the upper airway microbiota. This reduction in the OSAS without AR group aligns with decreased gut microbial diversity in pediatric OSAS [30], though absent in mild-to-moderate elderly OSA [31], whereas AR comorbidity counteracted diversity loss, consistent with studies of AR alone reporting elevated diversity in perennial allergic rhinitis [32] and allergic rhinoconjunctivitis [33], with higher evenness linked to more severe AR symptoms [28]. β-Diversity significantly differed between OSAS groups and controls (P < 0.05), corroborating earlier reports of separation in asthmatic cohorts [34] and children with adenoid hypertrophy plus AR [17]. Together, these findings indicate that OSAS-AR comorbidity enhances microbial diversity and compositional variation, likely driven by shared inflammation altering nasal-nasopharyngeal niches—inhibiting some colonizers while promoting dominant taxa and variants [17]—through mechanisms such as anatomical changes, chronic hypoxia, and immune dysregulation. Taxonomic characterization of the nasal-nasopharyngeal microbiota demonstrated a conserved core community dominated by Proteobacteria, Firmicutes, Actinobacteriota, Bacteroidota, and Fusobacteriota, along with nine orders and ten genera, yet with clear comorbidity-specific differentiation: the OSAS with AR group exhibited marked Haemophilus predominance, while Moraxella was more abundant in both control and OSAS without AR groups. These findings partially align with prior reports of Moraxella dominance in OSAS and AR [35,36], while the pronounced Haemophilus enrichment in OSAS with AR mirrors observations in chronic rhinosinusitis without polyps [37] but contrasts with AR-alone studies. This supports the concept of a unique microbial ecology under OSAS-AR comorbidity, distinct from either condition alone—consistent with known influences of anatomical site and age on microbial composition [32,38]. Overall, within a shared phylum-level profile, comorbidity drives genus-level divergence, suggesting a disease-specific ecological niche. Future studies should address the causality and immunomodulatory roles of these microbial changes. LEfSe analysis indicated that children with OSAS exhibited microbial marker alterations at the phylum and order levels, irrespective of AR comorbidity. The OSAS without AR group showed no genus-level biomarkers, suggesting dysbiosis primarily at higher taxonomic levels, while the OSAS with AR group displayed multi-level enrichment forming a continuous chain from phylum (e.g., Synergistota) to genus (e.g., Filifactor), likely facilitated by an AR-related hypoxic microenvironment. Healthy controls were enriched in Porphyromonas, Veillonella, Alloprevotella, and Actinobacillus, contrasting with literature linking severe OSAS to oral commensals like Streptococcus, Prevotella, and Veillonella in nasal or adenoid samples [29,31,35]. This suggests that nasal-nasopharyngeal dysbiosis in pediatric OSAS differs from previously reported oral/nasal alterations. The anaerobic enrichment in the OSAS with AR group highlights anatomical specificity and potentially distinct mechanistic pathways, warranting further study into how these taxa interact with the host and modulate immune and inflammatory processes in OSAS and AR pathogenesis. Through genus-level analysis of the nasal-nasopharyngeal mucosal microbiome, this study identifies a multi-level microbial signature system for pediatric OSAS, wherein a random forest model selected five key genera—Escherichia-Shigella, W5053, Clostridium, Akkermansia, and Incertae_Sedis—that effectively distinguished OSAS patients from healthy controls (AUC = 0.798), with Escherichia-Shigella as the top contributor (MDA = 8.66). Drawing from prior associations between this genus and pro-inflammatory mechanisms in gut-related studies [37,38], we speculate that its elevated abundance in the upper airways may promote local inflammation via TLR4 activation or epithelial barrier disruption, providing new insight into OSAS-related microbiopathological processes. Furthermore, we report the first set of highly discriminative genus-level markers capable of accurately differentiating between OSAS with and without AR (AUC = 0.975), underscoring the subtype-specific responsiveness of the nasal-nasopharyngeal microbiota. Notably, one key marker, NK4A214_group, previously unassociated with OSAS or AR, has been linked to neuropsychiatric conditions—showing negative correlations with neurotransmitter release and anti-inflammatory activity in an ADHD model [39] and abundance fluctuations tied to serotonin levels and prenatal depression risk [40]. In assessing disease severity, key microbial biomarkers exhibited distinct comorbidity-specific associations. Among all children with OSAS, the relative abundance of Incertae Sedis was positively correlated with the AHI, a relationship consistently observed across mucosal samples from the middle turbinate, inferior turbinate, and adenoid, underscoring its broad utility as a marker for OSAS severity. Of note, variations in the abundance of Incertae Sedis have been documented in several other conditions, including positive correlations with γ-aminobutyric acid (GABA) levels in the gut and brain [41], protective roles in obesity and metabolic disease models where its increase contributes to apigenin’s effects on weight reduction, inflammation attenuation, and insulin sensitivity improvement [42], associations with neurobehavioral outcomes in preterm infants, and altered distribution under estrogen deficiency [43]. These diverse links suggest that Incertae Sedis may modulate host pathways related to inflammation, metabolism, or neurotransmission, though its specific role in OSAS-related hypoxia and direct association with AHI elevation are reported here for the first time. In the OSAS with AR group, Romboutsia was the only genus showing a significant positive correlation with AHI, with abundance increasing with disease severity consistently across sampling sites. Existing reports on Romboutsia present contradictory results: one gut microbiota study found lower abundance in AR patients compared to healthy individuals [44], contrasting with our finding of elevated abundance in the nasal-nasopharyngeal microbiota of OSAS with AR patients. In obesity models, Romboutsia abundance correlated negatively with cognitive function and positively with pro-inflammatory cytokines, including TNFα, IL-6, and IL-1β [45]. In ulcerative colitis, it was positively associated with expression of pro-inflammatory hub genes and proposed as a potential pathobiont [46], while in animal models of asthma and AR, shifts in its abundance were linked to Th2 immune responses (e.g., IL-4) and to the immunomodulatory effects of curcumin or Shufeng Xingbi therapy [47,48]. Together, these data suggest that Romboutsia may act as a pro-inflammatory organism, and its positive correlation with AHI specifically in the OSAS with AR group implies a potential role in driving disease progression under comorbid conditions. In the OSAS without AR group, no genus demonstrated a significant correlation with AHI, highlighting how atopic inflammation such as that in AR may reshape microbial-host interactions, though the precise mechanisms remain to be elucidated. Collectively, our findings support a hierarchical diagnostic framework for pediatric OSAS based on nasal-nasopharyngeal microbiota profiles. A core set of genera, including Escherichia–Shigella, could serve for initial screening, while more specific markers accurately identify AR comorbidity. Notably, Incertae Sedis and Romboutsia correlated with AHI in the OSAS without AR and OSAS with AR groups, respectively, suggesting comorbidity-specific severity indicators. Both taxa showed consistent abundance across multiple sampling sites, underscoring their reliability as biomarkers. Several limitations should be acknowledged. The taxonomic uncertainty of Incertae Sedis and the mechanistic role of Romboutsia require further metagenomic validation. The cross-sectional design precludes causal inference, necessitating longitudinal studies post-intervention such as surgery or immunotherapy. Finally, potential confounders like environment or medication use highlight the need for covariate adjustment in future research. Despite these limitations, the proposed three-tier microbial marker system provides a novel foundation for precise diagnosis and stratification of pediatric OSAS. Acknowledgments Supported by Zhejiang Provincial Clinical Research Center for Pediatric Diseases, Wenzhou Municipal Science and Technology Bureau and Wenzhou Medical Association Scientific Research Project. Key Message This study provides the first detailed characterization of the nasal-nasopharyngeal microbiome in children with OSAS, revealing distinct microbial profiles between the OSAS without AR group and the OSAS with AR group. We identified a set of microbial biomarkers that effectively distinguish OSAS patients from healthy controls, achieving an AUC of 0.798, and accurately differentiate between OSAS with and without AR, with an AUC of 0.975. Furthermore, the genera Incertae_Sedis and Romboutsia were consistently correlated with disease severity across sampling sites—Incertae_Sedis in the OSAS without AR group and Romboutsia in the OSAS with AR group—suggesting their potential as comorbidity-specific biomarkers. These findings enhance our understanding of the microbial ecology underlying pediatric OSAS and its interaction with AR, supporting the future development of microbiome-based diagnostic tools and personalized treatment strategies. Ethical Approval Ethical approval was granted by the Ethics Committee of the Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University (2025-K-05-01). References [1] Marcus CL, Brooks LJ, Draper KA, et al. Diagnosis and management of childhood obstructive sleep apnea syndrome. Pediatrics. 2012;130(3):576-584. doi:10.1542/peds.2012-1671 [2] Brockmann PE, Gozal D. Neurocognitive Consequences in Children with Sleep Disordered Breathing: Who Is at Risk?. 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(C–D) Significant differences in Chao1 and Observed species indices were observed among the three groups (Kruskal–Wallis test, P < 0.0001). The OSAS with AR group showed markedly higher richness compared to both the Control and OSAS without AR group. Fig. 2 | Analysis of microbial community differences based on Unweighted Unifrac distance. (A) ANOSIM boxplot reveals significant differences in microbial community structure between groups (R = 0.2656, P = 0.001), with distinct distribution patterns observed between the “Between” group compared to the Control, OSAS without AR group, and OSAS with AR group. (B) Principal coordinate analysis (PCoA) based on Unweighted Unifrac distance shows that the first two axes (PCoA1 = 14.22%; PCoA2 = 9.32%) collectively explain 23.54% of the total variance. Samples from the three groups (Control, grey; OSAS without AR group, blue; OSAS with AR group, red) demonstrate partial separation, consistent with results from the Jaccard distance matrix, further supporting significant intergroup differences in microbial composition. Fig. 3 | Microbial composition analysis. Sankey diagram illustrating differences in microbial community composition among the Control, OSAS without AR, and OSAS with AR groups. Fig. 4 | LEfSe analysis of differentially abundant taxa in the upper respiratory microbiota across the three groups. (A) Cladogram and (B) bar plot of LDA scores (phylum, order, and genus levels). The cladogram illustrates the phylogenetic distribution of microbial taxa significantly enriched (LDA score > 3) in specific groups (color coded: grey, Control group; blue, OSAS without AR group; red, OSAS with AR group). The bar plot displays linear discriminant analysis (LDA) effect sizes, with representative enriched taxa labeled. Fig. 5 | Diagnostic biomarkers for OSAS. (A) Random forest model and (B) ROC curve (AUC = 0.798) showing the discriminative performance of five biomarker genera, including g_Escherichia-Shigella, in distinguishing all OSAS patients from healthy controls. (C) The abundance of g_Incertae_Sedis correlated with OSAS severity (Kruskal–Wallis test, P < 0.05). Fig. 6 | AR comorbidity-specific biomarkers. (A) Random forest model and (B) ROC curve (AUC = 0.975) demonstrating the discriminative capacity of five biomarker genera, such as g_Lachnospiraceae_NK4A136_group, in differentiating the OSAS with AR group from the OSAS without AR group. (C) The abundance of g_Romboutsia correlated with OSAS severity specifically within the OSAS with AR group. Information & Authors Information Version history V1 Version 1 15 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Ruoqi Li 0009-0006-7244-4323 The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University View all articles by this author Jie Wang Longquan People's Hospital View all articles by this author Liting Jin The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University View all articles by this author Xiaoqiong Wang The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University View all articles by this author Hui Zhang The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University View all articles by this author Liyan Ni The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University View all articles by this author Xi Lin Wenzhou Medical University School of Laboratory Medicine and Life Sciences View all articles by this author Xuejun Liu [email protected] The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University View all articles by this author Metrics & Citations Metrics Article Usage 137 views 91 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ruoqi Li, Jie Wang, Liting Jin, et al. 16S rRNA Profiling Reveals Nasal-Nasopharyngeal Microbial Signatures for Precision Stratification of Pediatric OSA with Allergic Comorbidity. 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