Introduction
Allergic rhinitis (AR) is a Th2/Th17-mediated inflammatory disorder that affects 15.79% of Chinese children and is increasingly accompanied by gastrointestinal comorbidities such as functional constipation (FC).1–3 Mounting evidence supports bidirectional interactions between the gut microbiota (GM) and the host immune system. In AR, GM dysbiosis has been implicated in aberrant IgE production and systemic immune activation;4 whereas in FC, compositional shifts, typically characterized by a reduction in Bacteroidetes and an expansion of Proteobacteria, are associated with impaired gut motility and compromised barrier function.5,6
Specific commensal taxa, including Bifidobacterium and Lactobacillus, are known to modulate immune homeostasis by promoting regulatory T cell (Treg) induction and suppressing pro-inflammatory Th2/Th17 responses.7,8 Conversely, enrichment of pathobionts like Klebsiella and Escherichia/Shigella can amplify systemic inflammation via Toll-like receptor signaling and NF-κB activation.9 Epidemiological data indicate that up to 20% of preschoolers with AR also develop FC, leading to aggravated systemic inflammation and diminished quality of life.10–12
Short-chain fatty acids (SCFAs, eg, butyrate) have emerged as critical mediators of gut-immune crosstalk, modulating Treg/Th17 balance and intestinal motility.13,14 As dietary fiber serves as the principal substrate for microbial SCFA production, diet exerts a pivotal influence on host-microbe homeostasis. Depletion of Faecalibacterium prausnitzii, a major butyrate producer, has been linked to Th2 hyperactivity in AR,15 while FC-driven SCFAs deficiency disrupts serotonergic (5-HT) signaling, delaying colonic transit.16 Collectively, these observations suggest that dietary patterns and metabolic outputs jointly influence disease susceptibility and progression in AR and FC. However, the manner in which SCFA-producing bacteria are perturbed in AR-FC comorbidity (ARFC), and whether such alterations contribute to immune dysregulation along the gut-nasal axis, remains to be elucidated.
Recent evidence underscores the gut-nasal axis as a critical mediator in the pathophysiology of upper airway allergic diseases.14 However, the microbial and metabolic interplay underlying ARFC comorbidity remains unexplored, particularly in preschoolers. This study hypothesized that ARFC aggravates microbial dysbiosis through the depletion of butyrate-producing taxa, thereby disrupting gut-nasal immune homeostasis. Supporting this notion, a recent multi-omics study revealed that gut microbial alterations in children with AR-FC comorbidity enhance aromatic amino acid metabolism, further implicating microbial metabolic dysfunction in disease progression.17 This study sought to address two fundamental questions: (1) Do children with comorbid allergic rhinitis and functional constipation (ARFC) exhibit distinct taxonomic and functional gut microbiota profiles? And (2) How are these microbial alterations associated with metabolic dysfunction?
To our knowledge, this is the first study to provide an integrated taxonomic and functional characterization of GM profiles in preschool children with ARFC, aimed at elucidating shared mechanisms underlying the gut-nasal axis. By systematically comparing GM across ARFC, AR, and healthy controls (HC), this work bridges a critical knowledge gap and lays the groundwork for understanding the microbial basis of this challenging comorbidity.9,18,19 Nevertheless, the lack of a constipation-only comparative group constrains definitive attribution of dysbiosis to specific disease components, highlighting an essential direction for future studies.
Materials and Methods
Materials
Participants
A total of 63 preschool children (aged 3–6 years) were consecutively recruited from the Pediatric Allergy and Gastroenterology Clinics of Longgang District Maternity & Child Healthcare Hospital (Shenzhen, China) between January and December 2023. The inclusion criteria were as follows: 1) ARFC group: Children meeting both (i) the Chinese Guidelines for Allergic Rhinitis (2022)2 and (ii) the Rome IV FC criteria;20 2) AR group: Diagnosed with AR without gastrointestinal symptoms (Rome IV questionnaire score <2); 3) Healthy controls (HC): Children with no history of allergic or chronic gastrointestinal diseases, as confirmed by pediatric examination.
Exclusion criteria (applied uniformly to all groups) were as follows: 1) Antibiotic/probiotic use within 1 month prior to sample collection; 2) Presence of organic constipation (eg, Hirschsprung disease) confirmed by a pediatric gastroenterologist; 3) Acute infections (fever >38°C or antibiotic therapy within the previous 2 weeks); 4) Chronic systemic diseases (eg, autoimmune disorders, cystic fibrosis).
Baseline and Clinical Characteristics
Demographic data (age and sex), anthropometric measurements (height, weight, and BMI z-score), dietary patterns (including breastfeeding history, and current dietary intake assessed by brief food frequency questionnaire or recall), relevant environmental exposures (eg, pet ownership, household smoking), detailed clinical symptom scores for AR (eg, Total Nasal Symptom Score – TNSS, visual analogue scale – VAS) and FC (eg, Rome IV symptom frequency/severity sub-scores, Bristol Stool Scale), medication history (beyond exclusion criteria, eg, recent antihistamine/laxative use), and family history of atopy/functional GI disorders were systematically collected for all participants at enrollment. Dietary fiber intake (g/day) and recent medication use were included as covariates in DESeq2 models for downstream analyses.
Allergic Rhinitis (AR) Severity Assessment:
Total Nasal Symptom Score (TNSS): Sum of individual symptom scores (0–3 per symptom) for four rhinorrhea, sneezing, nasal itching, and nasal congestion, yielding a total range of 0–12.2
Visual Analogue Scale (VAS): Self-reported overall nasal symptom severity marked on a 10-cm horizontal line (0 = no discomfort; 10 = severe discomfort).2
Functional Constipation (FC) Severity Assessment:
Rome IV Criteria: Diagnosis required the presence of ≥2 of the following symptoms for ≥1 month: ① ≤2 defecations per week; ② ≥1 episode of fecal incontinence per week; ③ retentive posturing; ④ painful defecation; ⑤ large fecal mass in rectum; ⑥ large-diameter stools obstructing toilet.20
Bristol Stool Scale (BSS): Stool consistency classified from Type 1 (hard lumps) to Type 7 (watery). Constipation was defined as BSS Types 1–2.20
Correlation Analysis Parameters:
Allergy severity: Composite score calculated as (TNSS + VAS)/2.
Gastrointestinal symptoms: Rome IV severity score (range 0–6) and BSS classification (Types 1–2 = 1, Types 3–7 = 0).
Respiratory manifestations: Individual symptom scores for rhinorrhea, sneezing, and nasal pruritus (each rated 0–3).
Th2-mediated inflammation: Serum total IgE levels (kU/L) extracted from clinical records.
Medication Use:
Antihistamines: Systemic H1-receptor antagonists (eg, loratadine) administered within 7 days prior to sampling.
Laxatives: Osmotic agents (eg, polyethylene glycol) administered within 72 hours before sampling.
Sample Size Calculation
Based on previously reported effect sizes from pediatric microbiome studies (eg, Zhang et al,9 which documented a Cohen’s d of 0.8 for Shannon index differences between AR and HC groups), an a priori power analysis was performed using G*Power 3.1 software (F-tests, ANOVA: Fixed effects, omnibus, one-way). The parameters were set at a significance level (α) of 0.05 and a statistical power (1−β) of 0.8. The analysis indicated that a minimum total of 66 participants (ie, 22 per group) would be required to detect significant differences. The achieved sample sizes in the present study (ARFC=32, AR=22, HC=21) met or exceeded this requirement for the primary group comparisons (ARFC vs HC, and AR vs HC), thereby providing sufficient statistical power (>80%) to detect large effect sizes (|d| > 0.8). Nonetheless, it is acknowledged that the sample remains relatively modest for detecting subtler microbial associations or for robust post hoc comparisons among all three groups, particularly after stringent multiple testing corrections.
Sequencing and Bioinformatics
Fecal genomic DNA was extracted using the PowerSoil® Kit (MoBio, USA). The V3–V4 hypervariable regions of 16S rRNA were amplified using primers 341F (5′-CCTACGGGNGGCWGCAG-3′) and 806R (5′-GGACTACHVGGGTATCTAAT-3′), followed by paired-end sequencing (2 × 250 bp) on the Illumina NovaSeq 6000 platform. After quality control (Q30 >90%), a total of 3,125,439 high-quality reads (mean 49,610 reads/sample) were retained. Amplicon sequence variants (ASVs) were inferred using the DADA2 algorithm (implemented in QIIME2 v2023.2) at 97% similarity, and taxonomic annotation was performed against the SILVA v138.1 reference database. Functional prediction was conducted using PICRUSt2 with reference to the KEGG database.
Sequence Processing Workflow:
Demultiplexing: Raw paired-end reads were demultiplexed using the demux plugin in QIIME2 (v2023.2).
Quality Trimming: Adapter sequences and low-quality bases (Q-score <20) were trimmed using cutadapt.
Denoising and Chimera removal: DADA2 was applied for denoising, chimera filtering, and ASV inference (error model: learnErrors; truncation length: forward=240 bp, reverse=200 bp).
Taxonomic Classification: ASVs were taxonomically assigned using the q2-feature-classifier plugin with SILVA v138.1 database (99% identity threshold).
Contamination Control: Sequences affiliated with non-bacterial sources (eg, chloroplast, mitochondrial) were excluded. Samples yielding fewer than 10,000 reads were removed from downstream analyses.
Functional Prediction: Functional pathways were inferred using PICRUSt2 (v2.5.0) with default parameters. KEGG Ortholog (KO) abundances were normalized to copies per million (CPM), and pathways with prevalence <10% across all samples were excluded. Functional predictions derived from 16S rRNA data were interpreted cautiously, recognizing the limited resolution relative to whole-metagenome sequencing.
Statistical Analysis
All statistical analyses were performed in R (v4.3.1) unless otherwise specified.
Diversity Analysis:
α-diversity: The Shannon diversity index was compared among groups using the Kruskal–Wallis test, followed by Dunn’s post hoc test (FDR-adjusted);
β-diversity: Bray–Curtis dissimilarity matrices were generated and visualized using principal coordinates analysis (PCoA). Statistical significance of between-group differences was assessed by PERMANOVA (999 permutations; vegan::adonis2). PCoA confidence ellipses (95%) were calculated using the stat_ellipse function in R (ggplot2 v3.5.0). Effect sizes for β-diversity differences were reported as R2 from PERMANOVA.
Differential Abundance Analyses:
Initial group-wise taxonomic differences were assessed using the Kruskal–Wallis test (FDR-adjusted P<0.05). For taxa showing significant global differences, pairwise comparisons (ARFC vs HC, ARFC vs AR, AR vs HC) were conducted using DESeq2 (negative binomial mo FDR-adjusted P < 0.05). The DESeq2 model included “group” as the primary fixed factor and adjusted for dietary fiber intake and recent medication use (antihistamines, laxatives) as covariates, where applicable. Relative abundance data (%) were used directly for non-parametric tests. For DESeq2, a variance-stabilizing transformation (VST) was applied to raw ASV counts to normalize variance prior to model fitting.
Associations between microbial genera and clinical parameters (eg, TNSS, VAS, Bristol Stool Scale, total IgE) were assessed using Spearman’s rank correlation coefficient (ρ), with P-values adjusted by Benjamini–Hochberg FDR correction.
Multiple testing and Power Considerations: All P-values from diversity, taxonomic, and functional analyses were corrected using the Benjamini–Hochberg FDR method, with statistical significance defined as FDR-adjusted P < 0.05. Post hoc power analysis confirmed >80% statistical power to detect large effect sizes (|d| > 0.8) for primary group comparisons (ARFC vs HC, AR vs HC), consistent with the a priori calculation described in Sample Size Calculation.
Results
Participant Characteristics
Baseline demographic and clinical characteristics of participants are summarized in Table 1. All children were within the narrow age range of 3–6 years and exhibited BMI z-scores within the normal range (−1 to +1 SD). There were no significant group differences in age, sex distribution, BMI z-score, breastfeeding history, or pet exposure (all P > 0.05). As expected, both the ARFC and AR groups demonstrated significantly higher allergic rhinitis symptom scores (TNSS, VAS) compared to HC (P < 0.001). The ARFC group also exhibited significantly more severe constipation symptoms than both the AR and HC groups (P < 0.001). Group-wise differences in recent medication use are detailed in Table 1.
Table 1 Demographic and Clinical Characteristics of Study Participants
GM Diversity and Composition
The α-diversity of the GM, quantified using the Shannon index, was significantly higher in the ARFC group (mean=5.2 ± 0.3) compared with the HC (mean=4.5 ± 0.4) (FDR-adjusted P =0.014) (Figure 1A). No significant difference was detected between the ARFC and AR groups (P = 0.77) or between the AR and HC (FDR-adjusted P = 0.063). β-diversity analysis based on Bray-Curtis dissimilarity showed significant overall group differences (PERMANOVA: R2=0.12, P=0.001). Pairwise comparisons indicated distinct clustering of ARFC relative to both HC (R2=0.15, P=0.001) and AR (R2=0.08, P=0.012), but no significant separation was detected between the AR and HC (R2=0.04, P=0.102) (Supplementary Table S1). Visualization indicated partial clustering of ARFC (green) with limited overlap with HC (8.3%), whereas AR (pink) and HC (blue) groups showed substantial overlap (42.1%), consistent with the non-significant PERMANOVA result (Figure 1B).
Figure 1 GM diversity and compositional differences across groups. (A) α diversity (Shannon index) comparison between the ARFC group (mean=5.2 ± 0.3) vs HC group (mean=4.5 ± 0.4) (P = 0.014). (B) Principal component analysis (PCA) based on Bray–Curtis distances (PERMANOVA, P = 0.001) revealed distinct clustering. ARFC clusters distinctly (green), while AR (pink) and healthy (blue) groups overlap partially. Ellipses denote 95% confidence intervals. AR-HC overlap: 42.1%; ARFC-HC overlap: 8.3%. Dominant genera driving separation include Prevotella, Bacteroides, and Phocaeicola.
Proteobacteria Enrichment in Disease Groups and Bacteroidetes Depletion in ARFC
At the phylum level, both ARFC and AR groups showed a marked enrichment of Proteobacteria compared to HC (ARFC: FDR-adjusted P = 0.001; AR: FDR-adjusted P = 0.005) (Figures 2 and 3, Table 2). In contrast, a significant depletion of Bacteroidetes was observed exclusively in ARFC children compared with HC (FDR-adjusted P = 0.049 vs HC) (Figure 2). No other significant differences occurred between ARFC and AR groups (Figure 4).
Table 2 Analysis of the Three Groups of Children at the Phylum Levels (Top 5)
Figure 2 Differential abundance of gut microbiota at phylum and genus levels between the ARFC and HC groups. Wilcoxon rank-sum test was applied to compare the relative abundances of bacterial phyla and genera between the ARFC group (n = 32) and the HC group (n = 21). Statistically significant differences after false discovery rate (FDR) correction are denoted by asterisks: *FDR < 0.05, **FDR < 0.01, ***FDR < 0.001. A greater number of asterisks indicates a higher level of statistical significance.
Figure 3 Differential abundance of gut microbiota at phylum and genus levels between the AR and HC groups. The Wilcoxon rank-sum test was applied to compare the relative abundances of bacterial phyla and genera between the AR group (n = 22) and the HC group (n = 21). Statistically significant differences after false discovery rate (FDR) correction are denoted by asterisks: *FDR < 0.05, **FDR < 0.01, ***FDR < 0.001. A greater number of asterisks indicates a higher level of statistical significance.
Figure 4 Differential abundance of gut microbiota at phylum and genus levels between the ARFC and AR groups. The Wilcoxon rank-sum test was applied to compare the relative abundances of bacterial phyla and genera between the ARFC group (n = 32) and the AR group (n = 22). Statistically significant differences after false discovery rate (FDR) correction are denoted by asterisks: *FDR < 0.05. A greater number of asterisks indicates a higher level of statistical significance.
Pathogen Enrichment and Depletion of Butyrate Producers in ARFC
After FDR correction, the ARFC group exhibited significantly higher abundances of Bifidobacterium, Phascolarctobacterium, Veillonella, Escherichia/Shigella, Klebsiella, and Streptococcus compared with the HC group (FDR-adjusted P <0.05), whereas Bacteroides, Faecalibacterium, Ruminococcus, Kineothrix, and Anaerostipes were markedly depleted (Figure 2 and Table 3).
Table 3 Differentially Abundant Genus Between ARFC and HC Groups
The AR group displayed similar enrichment patterns for Veillonella, Escherichia/Shigella, and Streptococcus but uniquely showed increased abundances of Enterocloster and Haemophilus (Figure 3 and Table 4).
Table 4 Differentially Abundant Genus Between AR and HC Groups
When compared with the AR group, ARFC children exhibited a significantly higher relative abundance of Bifidobacterium (4.21% vs 1.80%, FDR-adjusted P = 0.018) and a lower abundance of Veillonella (1.59% vs 2.00%, FDR-adjusted P = 0.042) (Figure 4).
AR Group as an Intermediate Phenotype
The AR group demonstrated transitional GM features between HC and ARFC. Similar to ARFC, AR children showed enrichment of Proteobacteria (5.94% vs HC 1.94%, FDR-adjusted P = 0.005). However, AR uniquely exhibited increased relative abundances of Enterocloster (1.00% vs HC 0.35%, FDR-adjusted P = 0.014) and Haemophilus (0.60% vs HC 0.10%, FDR-adjusted P = 0.024) (Table 4). The relative abundance of Bifidobacterium in ARFC (4.21%) was significantly higher than in AR (1.80%, FDR-adjusted P = 0.018) and HC (3.12%, FDR-adjusted P = 0.21), indicating comorbidity-specific enrichment in ARFC children.
Functional Pathway Analysis
Functional prediction based on KEGG annotations revealed that the ARFC group presented downregulated pathways related to carbohydrate metabolism, lipid metabolism, the immune system, and the endocrine/nervous system (FDR-adjusted P < 0.05). Conversely, pathways related to xenobiotic biodegradation and genetic information processing were upregulated (Figure 5A). The AR group showed a comparable reduction in endocrine-and nervous system-associated pathways but demonstrated increased activity in signal transduction pathways (Figure 5B). However, no significant differences in predicted functional pathways were observed between the ARFC and AR groups (Supplementary Table S2).
Figure 5 Comparison of GM function among the HC, AR, and ARFC groups. (A) 16S rRNA sequencing data were utilized to assess variations in GM functions between the ARFC and HC groups of children through functional analysis conducted via the KEGG database. (B) 16S rRNA sequencing data were utilized to assess variations in GM functions between the AR and HC groups of children through functional analysis conducted via the KEGG database.
GM-Clinical Phenotype Correlations
Within the ARFC cohort (n=32), Spearman correlation analysis was performed to explore associations between bacterial genera (≥0.1% relative abundance) and clinical parameters (see Methods’ Baseline and Clinical Characteristics):
Gastrointestinal Symptoms: ① Haemophilus abundance correlated positively with constipation severity (Rome IV score: ρ = 0.52, P = 0.008) and stool consistency (BSS: ρ= 0.48, P = 0.012); ② Dysosmobacter (ρ = −0.45, P = 0.021) and Flintibacter (ρ= −0.43, P = 0.028) were negatively correlated with constipation severity (Rome IV score).
Respiratory Symptoms: ① Haemophilus also correlated positively with rhinorrhea severity (ρ= 0.56, P= 0.003); ② Streptococcus (ρ= 0.49, P = 0.010) and Anaerotignum (ρ= 0.46, P = 0.018) correlated positively with sneezing and rhinorrhea, respectively. ③ Megamonas (ρ= −0.44, P= 0.025) and Butyricimonas (ρ= −0.41, P = 0.038) inversely correlated with rhinorrhea severity.
Th2 Inflammation: Lachnospira abundance correlated negatively with serum total IgE (ρ= −0.47, P= 0.015), whereas Phocaeicola showed a positive correlation (ρ= 0.42, P = 0.032).
Recurrent Cough Frequency: Lachnospiraceae incertae sedis (ρ= −0.50, P = 0.009), Ligilactobacillus (ρ= −0.48, P = 0.011), and Erysipelatoclostridium (ρ= −0.46, P = 0.017) abundances were inversely correlated with cough frequency.
Collectively, these findings reveal that specific GM taxa correlate with distinct clinical manifestations in ARFC. Haemophilus exhibited dual correlations, linking its enrichment to both gastrointestinal (constipation severity) and respiratory (rhinorrhea severity) symptoms (Figure 6), suggesting its potential as a comorbidity biomarker. Conversely, beneficial taxa, such as Megamonas and Butyricimonas, showed inverse associations with rhinorrhea severity, indicating protective effects against allergic inflammation.
Figure 6 Spearman correlation analysis between GM genera (relative abundance ≥ 0.1%) and clinical symptom severity within the ARFC group (n = 32). A correlation analysis was performed with eleven clinical phenotypes and genera with a relative abundance of ≥ 0.1%. The results are shown above, where significance is expressed denoted as *FDR <0.05 and **FDR <0.01.
Discussion
GM Dysbiosis in AR and FC
Our findings corroborate previous evidence linking GM dysbiosis with both allergic and gastrointestinal disorders. The elevated Proteobacteria and reduced Bacteroidetes in ARFC children (Table 1) mirror alterations reported in constipation models, where such shifts impair intestinal motility and barrier integrity.18 The increased Veillonella and Streptococcus abundances in AR and ARFC groups (Table 2 and Table 3) further support their reported roles in promoting Th2-mediated inflammation and IgE production.13,21
The enrichment of Klebsiella in children with ARFC may exacerbate inflammation through lipopolysaccharide (LPS)-mediated activation of the NF-κB pathway, leading to the release of pro-inflammatory cytokines such as TNF-α and IL-6.9 Conversely, the depletion of key butyrate-producing genera, including Faecalibacterium and Ruminococcus, likely contributes to systemic immune dysregulation and delayed colonic transit. Short-chain fatty acids (SCFAs), particularly butyrate, are crucial for promoting regulatory T-cell (Treg) differentiation and maintaining gut motility.16,22 Consequently, impaired SCFA signaling via receptors like GPR43 may disrupt the Treg/Th17 balance in both the gut and nasal mucosa.23 These observations collectively reinforce the concept of gut-airway crosstalk in comorbid conditions. Our data are consistent with the work of Kaczynska et al,18 who demonstrated that FC-induced GM dysbiosis can amplify airway inflammation via a bidirectional mechanism, a process that may be intensified in ARFC comorbidity. Similarly, the Proteobacteria overgrowth observed here aligns with findings by Acevedo-Román et al15 linking this phylum to systemic inflammation in allergic and functional comorbidities.
A notable and paradoxical finding was the significant elevation of Bifidobacterium in ARFC children, which contrasts with its typical depletion in isolated functional constipation.24 This suggests a distinct, comorbidity-specific microbial adaptation. While FC alone is often characterized by Bifidobacterium loss, its enrichment in ARFC may represent a compensatory immunomodulatory response to the combined Th2/Th17 inflammatory milieu—an observation previously unreported in this context. Specific species such as B. longum and B. infantis are known to promote Treg differentiation and IL-10 production, thereby mitigating Th2-driven responses.7,8 Thus, the systemic immune activation in ARFC may trigger a homeostatic increase in Bifidobacterium, potentially mediated by IgA upregulation or alterations in bile acid metabolism.25 The emerging literature further underscores the strain-dependent role of Bifidobacterium in regulating the gut-lung axis and its therapeutic potential for comorbid allergic-gastrointestinal disorders.26 However, the absence of an FC-only control group limits direct comparison of Bifidobacterium abundance between isolated FC and ARFC. Therefore, while the compensatory enrichment hypothesis is plausible, it requires validation in future cohorts that include an FC control group to clarify whether this effect is driven by the AR component or the comorbidity interaction itself.
Functional Implications of GM Changes
Functional predictions revealed marked alterations in KEGG pathways between ARFC and HC groups (Figure 5A). Downregulation of carbohydrate metabolism (ko00010) and lipid biosynthesis (ko00061) may reflect impaired energy extraction from dietary substrates, consistent with the depletion of SCFA-producing taxa, notably Faecalibacterium. In contrast, upregulation of xenobiotic biodegradation pathways (ko00980) likely represents microbial adaptation to altered luminal environments, although the precise mechanistic drivers remain to be elucidated.
These predictions are based on PICRUSt2, which infers function from phylogenetic marker genes and lacks resolution for strain-level activities or direct metabolite quantification.27 Consequently, the observed functional shifts should be interpreted as hypothesis-generating and warrant validation via shotgun metagenomics and targeted metabolomics, including SCFA quantification. Such integrative approaches are essential to confirm27 the functional consequences of GM dysbiosis in ARFC. Future studies integrating metabolomic profiling (eg, SCFAs quantification) are critical to validate these pathway predictions.28 Supporting this perspective, recent multi-omics analyses of children with AR–FC comorbidity demonstrated gut microbial contributions to altered aromatic amino acid metabolism, highlighting the importance of integrated functional profiling for deciphering microbial metabolic influences on disease pathophysiology.17
Clinical Relevance and Clinical Translation
The observed correlation between gut-enriched Haemophilus and rhinorrhea severity extends beyond the conventional paradigm of localized nasopharyngeal dysbiosis.29,30 Its association with both gastrointestinal (constipation severity) and respiratory (rhinorrhea severity) manifestations provides novel evidence for a functional gut–nasal axis in ARFC comorbidity, suggesting that gut-resident Haemophilus may exert systemic effects influencing distal mucosal sites. The inverse association of Megamonas/Butyricimonas with allergy severity highlights the therapeutic relevance of butyrate-producing taxa in ARFC comorbidity. Although this study primarily addresses the gut-nasal axis, the gut-lung axis in lower airway diseases (eg, asthma) remains insufficiently explored and warrants independent investigation. Notably, the negative correlations of Megamonas and Butyricimonas with rhinorrhea severity (ρ= −0.44, P=0.025; ρ= −0.41, P = 0.038) along with the depletion of key butyrate producers like Faecalibacterium (10.5% vs HC 14.84%, FDR-adjusted P = 0.039) underscore butyrate metabolism as a promising therapeutic target. Restorating Faecalibacterium abundance and enhancing butyrate signaling offer a dual benefit by attenuating Th2/Th17-driven inflammation through Treg differentiation induction16,23 and by enhancing gut motility.16,19 Such an integrated approach could simultaneously alleviate allergic and gastrointestinal symptoms, addressing the shared pathophysiology mechanisms underlying ARFC comorbidity.
Mechanistically, these effects are likely mediated via the SCFAs–GPR43 axis, which promotes Treg differentiation,16,23 suppresses Th2/Th17 responses,23 and supports intestinal transit.16,19 Dietary strategies aimed at augmenting butyrate production represent a rational translational approach; clinical evidence indicates that such interventions can improve gut motility and reduce IgE levels in allergic pediatric populations.30,31 Furthermore, supplementation with Faecalibacterium has been shown to restore mucosal integrity and Treg/Th17 balance in children with allergic disorders.32 These findings converge with recent literature advocating personalized probiotic and prebiotic interventions to modulate the gut–airway axis, reinforcing their potential applicability in comorbid allergic and gastrointestinal conditions.33
Limitations and Future Directions
This study has several important limitations. First, the absence of a constipation-only group precludes definitive attribution of observed gut microbiota (GM) alterations to the AR component, the FC component, or their interactive effect in comorbidity. For example, Proteobacteria enrichment in ARFC aligns with prior observations in isolated constipation,5,19 suggesting it may be primarily driven by the FC component. Conversely, the paradoxical elevation of Bifidobacterium, which contrasts with its typical depletion in isolated constipation,24 may represent a compensatory immunomodulatory response specific to AR or to the comorbid state. Inclusion of an FC-only group in future studies is essential to disentangle these effects.
Second, functional insights are derived from 16S rRNA gene sequencing and predictive metagenomics (PICRUSt2). While these approaches generate valuable functional hypotheses, they infer potential rather than directly measuring gene content, transcription, or metabolite production, and they lack species- or strain-level resolution.27 Accordingly, predicted pathway alterations—such as impaired carbohydrate metabolism and upregulated xenobiotic degradation—require validation through shotgun metagenomics, metatranscriptomics, and targeted metabolomic analyses (eg, SCFA quantification).28
Third, although the sample size was sufficient to detect large effect sizes for primary comparisons (ARFC vs HC), it remains modest for identifying more subtle microbial differences between ARFC and AR, particularly after stringent multiple testing corrections. Correlation analyses within the ARFC cohort (n=32) may also have limited power to detect modest associations. Larger, multi-center cohorts are warranted to validate the ARFC-specific microbial signature and to robustly examine GM-clinical relationships.
Fourth, despite comprehensive baseline data collection and statistical adjustment for recent medication use (antihistamines, laxatives) and dietary fiber intake, residual confounding cannot be fully excluded. Medications can significantly influence GM composition, and although this study adjusted for recent use, detailed pharmacokinetic data or controlled washout periods were not feasible in this clinical cohort. Future studies should incorporate granular tracking of medication history, dosage, and duration, alongside detailed dietary and environmental assessments, to minimize confounding.
To address these limitations, future research should: (i) Include well-characterized FC-only cohorts alongside AR, ARFC, and HC groups in larger, preferably multi-center studies; (ii) employ multi-omics approaches (shotgun metagenomics, metabolomics) to validate functional predictions, identify strain-level variations, and quantify key metabolites like SCFAs,27,28 as demonstrated in a recent multi-omics study of AR-FC comorbidity;17 (iii) conduct longitudinal studies to establish temporal relationships between GM shifts and symptom onset/fluctuations; and (iv) integrate detailed, prospective monitoring of medication, diet, and environmental exposures to better control for potential confounders.
Conclusion
This study provides the first characterization of GM dysbiosis in preschool children with comorbid AR and FC. ARFC patients exhibited a distinct GM profile, characterized by Proteobacteria enrichment, Bacteroidetes depletion, and reduced abundance of butyrate-producing taxa, including Faecalibacterium. The observed increase in α-diversity in ARFC may reflect pathogenic expansion, such as Klebsiella, whereas the paradoxical elevation of Bifidobacterium, contrasting with typical findings in isolated FC, suggests a compensatory immunomodulatory response within the context of concurrent Th2/Th17 inflammation. This response may enhance Treg differentiation and IL-10 production, potentially modulating systemic Th2/Th17 immune activity.7,8 Predictive functional profiling indicated impaired carbohydrate and lipid metabolism alongside upregulated xenobiotic degradation pathways, possibly reflecting constipation-associated luminal alterations. Clinically, gut Haemophilus abundance correlated with both gastrointestinal and respiratory symptom severity, supporting its potential utility as a biomarker along the gut–nasal axis in ARFC comorbidity. The absence of a constipation-only group limits definitive attribution of observed GM alterations to AR, FC, or their interaction. Consequently, the compensatory enrichment of Bifidobacterium and the functional consequences of butyrate deficiency remain speculative and require validation in future studies incorporating FC controls and multi-omics approaches, including metagenomics and metabolomics. Collectively, our findings delineate a distinct GM signature in ARFC comorbidity and identify microbial and metabolic targets with potential therapeutic relevance. Future research should validate these observations using direct functional assays and larger, well-controlled cohorts, with particular emphasis on elucidating the precise immunomodulatory roles of key taxa, such as Bifidobacterium, within the gut–nasal axis.
Data Sharing Statement
The dataset generated in this study can be accessed from the NCBI Sequence Archive (SRA) database via the following direct link: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1103935?reviewer=8ia81sf9papv0kd3vt99i69ir6, its number is PRJNA1103935. The corresponding author should be contacted if someone wants to request the data from this study.
Ethical Approval
This study was approved by the Ethics Committee of Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Approval No. LGFYKYXMMLL-2023-1).
Informed Consent
The parents, as legal guardians, voluntarily accepted scientific research on their children’s care, and the parents of the child signed a written sample submission and informed consent form. The procedures used in this study adhered to the tenets of the Declaration of Helsinki.
Acknowledgments
We thank all the participants for their support. We thank the doctors and nurses of Longgang District Maternity & Child Healthcare Hospital (Shenzhen, China) for assisting the research team in clinical examination and fecal sample collection. We also thank the authors who made their data publicly available. The authors would like to thank all the reviewers who participated in the review, as well as MJEditor (www.mjeditor.com), for providing English editing services during the preparation of this manuscript.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Funding
This work has been strongly supported by the Longgang District Science and Technology Innovation Bureau (LGWJ2023-038 and LGWJ2023-072), the Key Medical Discipline in Longgang District, and the Research Initiation Fund of Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Y2024011).
Disclosure
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
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