The Nasal Microbiome and Associations With Environmental Exposures and Respiratory Health

Javier Perez‐Garcia; Anne K Bozack; Sheryl L Rifas‐Shiman; Yanjiao Zhou; Joanne Sordillo; Brent Coull; Heike Luttmann‐Gibson; Joel Schwartz; Marie‐France Hivert; Emily Oken; Diane R Gold; Andres Cardenas

doi:10.1111/all.70089

. 2025 Oct 11;81(3):750–763. doi: 10.1111/all.70089

The Nasal Microbiome and Associations With Environmental Exposures and Respiratory Health

Javier Perez‐Garcia ^1,^✉, Anne K Bozack ¹, Sheryl L Rifas‐Shiman ², Yanjiao Zhou ³, Joanne Sordillo ⁴, Brent Coull ⁵, Heike Luttmann‐Gibson ⁶, Joel Schwartz ⁶, Marie‐France Hivert ^2,⁷, Emily Oken ², Diane R Gold ^6,⁸, Andres Cardenas ^1,^✉

PMCID: PMC12954560 PMID: 41074693

ABSTRACT

Background

The nasal microbiome is directly in contact with the external environment and may play a role in respiratory health. This study aimed to evaluate the association of the nasal microbiome with air pollutants, meteorological conditions, and respiratory health in adolescents.

Methods

We analyzed the nasal microbiome in 416 adolescents from the Project Viva cohort (mean age 13 years and 52% female). We tested for the association of alpha diversity, nasotypes, and bacterial genera abundance with environmental exposures from the past 2 days to the past year (PM_2.5, NO₂, O₃, temperature, humidity, residential greenness) and respiratory outcomes (asthma, hay fever, wheezing, IgE, aeroallergen sensitization, FeNO, lung function) through regression models adjusted for confounders and corrected using a false discovery rate (FDR) < 5%.

Results

Bacterial diversity was positively associated with hay fever and short‐term exposure to NO₂, while it was negatively correlated with temperature (FDR < 0.05). Adolescents whose nasal microbiome was dominated by Moraxella were exposed in the past week to lower O₃ levels (ORs: 0.73–0.76) and higher temperature and humidity (ORs: 1.19–1.26). Staphylococcus dominance was positively associated with aeroallergen sensitization compared to Propionibacterium dominance (OR: 4.48, FDR = 0.03). Thirteen and eight bacterial genera abundance were associated with short‐to‐medium‐term exposures (PM_2.5, NO₂, temperature) and respiratory outcomes (hay fever, wheezing, IgE, FeNO, lung function) (FDR < 0.05). Staphylococcus, Corynebacterium, Pelomonas, Lactococcus, Lachnospiraceae (unclassified), and Faecalibacterium abundance were associated with both environmental exposures and respiratory traits.

Conclusions

Nasal microbiome diversity was associated with hay fever, NO₂, and temperature exposure. Multiple short‐to‐medium‐term environmental exposures and respiratory outcomes were associated with nasotypes and bacterial genera abundance in adolescents.

Keywords: air pollutants, allergy and immunology, environmental health, microbiome, pediatrics

This study evaluates the association of the nasal microbiome with air pollutants, meteorological conditions, and respiratory health in adolescents. Bacterial diversity is positively associated with hay fever and short‐term NO₂ exposure, and negatively with temperature. A Moraxella‐dominated nasotype is linked to lower O₃ exposure and higher temperature and humidity, whereas a Staphylococcus‐dominated nasotype is associated with increased risk of aeroallergen sensitization. Staphylococcus, Corynebacterium, Pelomonas, Lactococcus, Lachnospiraceae (unclassified), and Faecalibacterium abundance is associated with both environmental exposures and respiratory traits. 16S rRNA seq, 16S ribosomal RNA sequencing; CI, confidence interval; FeNO, fractional exhaled nitric oxide; FEV₁/FVC, forced expiratory volume in one second/forced vital capacity; NO₂, nitrogen dioxide; O₃, ozone; PM_2.5, particulate matter ≤ 2.5 μm; temp., temperature.

graphic file with name ALL-81-750-g004.jpg

Abbreviations

16S rRNA: 16S ribosomal RNA
BDR: bronchodilator drug response
BMI: body mass index
CI: confidence Interval
EPA: United States Environmental Protection Agency
FDR: false discovery rate
FeNO: fractional exhaled nitric oxide
FEV₁: forced expiratory volume in one second
FVC: forced vital capacity
IgE: immunoglobulin E
NDVI: Normalized Difference Vegetation Index
NO₂: dioxide nitrogen
O₃: ozone
OR: odds ratio
OTU: operational taxonomic unit
PM_2.5: particulate matter with a diameter < 2.5 μm
SD: standard deviation
SES: socioeconomic status

1. Introduction

Chronic respiratory diseases rank among the most prevalent diseases worldwide and have a substantial impact on the quality of life of patients, particularly in children [1]. Asthma represents the most prevalent pediatric chronic disease, often associated with atopy, eosinophilia, allergic comorbidities, and airway hyperresponsiveness to numerous factors such as allergens, viruses, and air pollutants [1]. The continuous increase in the prevalence of respiratory diseases supports the key role of environmental exposures in their development, which are particularly relevant in the current context of the climate emergency [2, 3]. The increments in temperatures and air pollution contribute to the exposure to aeroallergens and lung function decline, particularly in children, whose tissues and organs are more susceptible [2, 4].

The human microbiome—microorganisms inhabiting the human body and their genetic makeup—plays a fundamental role in respiratory health and allergies [5, 6, 7, 8]. The airway and gut microbiomes contribute to respiratory health through the regulation of immune and inflammatory responses and the integrity of the epithelial barrier [5, 6, 7, 8]. Microbial exposures can also modulate the risk effect of genetic variants in asthma [9]. The nasal microbiome is of high interest to identify noninvasive biomarkers of respiratory health and capture the effect of the environment, given its continuity through the lung and its position as the first line of the airways in contact with environmental exposures [5, 6, 10].

Air pollutants are major risk factors for respiratory diseases since their inhalation induces oxidative stress, airway inflammation, and disruption of the barrier epithelial defense [11, 12, 13]. Pollutants can induce bacterial dysbiosis in the respiratory microbiome by introducing environmental microorganisms to the airways, inhibiting host immune defense mechanisms, and inducing tissue injuries in the airway microenvironment that facilitate the growth of certain bacteria [11, 12, 13].

Previous studies have supported the relationship between the respiratory microbiome, air pollution, and respiratory diseases [6, 11, 12, 13]. However, only a few studies with limited sample sizes have investigated the association of the respiratory microbiome and air pollution [11]. Regarding respiratory health, most nasal microbiome studies have focused on asthma, and there is limited information about its role in other allergies or respiratory and inflammatory phenotypes [6, 14].

We hypothesized that environmental exposures to air pollution and meteorological conditions induce nasal bacterial dysbiosis, and that bacterial diversity and composition are associated with allergic and respiratory diseases. Therefore, we aimed to identify differences in the nasal microbiome associated with air pollutants related to industrial and traffic emissions and fuel combustion, meteorological conditions, and multiple phenotypes and markers of respiratory health.

2. Methods

An extensive Section 2 is reported in the Supporting Information.

2.1. Study Population

We analyzed 416 adolescents from the Massachusetts‐based Project Viva pre‐birth cohort study [15, 16]. Mothers provided written informed consent at recruitment and postpartum follow‐up visits. Participants provided consent for nasal swab collection, and study protocols were approved by the Institutional Review Board of Harvard Pilgrim Health Care. Research staff collected demographic and clinical characteristics of adolescents through interviews and self‐administered questionnaires with mothers and offspring. Participants who did not, to the best of our ascertainment, have an acute illness were not included in this study.

2.2. Exposures and Outcomes Measurements

We used machine‐learning algorithms to estimate daily ground‐level concentrations of particulate matter with a diameter of less than 2.5 μm (PM_2.5), nitrogen dioxide (NO₂), ozone (O₃), and ambient temperature and relative humidity. We estimated average ambient exposures in different periods relative to sample collection, from the past 2 days to the past year. Residential greenness was estimated based on satellite‐derived Normalized Difference Vegetation Index (NDVI).

A full description of the clinical assessment of respiratory health is described elsewhere [17]. We characterized participants for asthma, hay fever, wheezing, total IgE serum levels, aeroallergen sensitization (specific IgE levels > 0.35 IU/mL), fractional exhaled nitric oxide (FeNO), pre‐bronchodilator forced expiratory volume in one second (FEV₁), the forced vital capacity (FVC), the FEV₁/FVC ratio, and bronchodilator drug response (BDR).

2.3. Microbiome Profiling

Genomic DNA was isolated from nasal swabs to profile the nasal microbiome by targeted sequencing of the 16S ribosomal RNA (16S rRNA) gene (V1–V3 hypervariable regions). We clustered the sequencing reads into operational taxonomic units (OTUs) and filtered out contaminants, low‐abundance OTUs, and samples with < 10,000 reads. To minimize differences related to sequencing depth, we rarefied sequencing reads before estimating diversity indices.

We clustered individuals into different groups based on the nasal microbiome composition (nasotypes) using the partitioning around medoids (PAM) on the Jensen‐Shannon Divergence distance [18, 19]. The optimal number of clusters (n = 6, Figure S1) was selected based on the maximization of the silhouette and Calinski–Harabasz indices, and cluster stability was inspected using a leave‐one‐out (LOO) approach (Figure S2, Adjusted Rand Index = 0.99).

2.4. Statistical Analyses

Potential demographic and technical confounders of the microbiome were detected through PERMANOVA tests on beta diversity (Table S1). We depicted bivariate Pearson correlation heatmaps to illustrate the relationship among environmental exposures at different time points and alpha diversity indices. We applied regression models to test the association of environmental exposures and respiratory health with alpha diversity (linear models), nasotypes (multinomial models), and bacterial abundances (negative binomial models). For differentially abundant analyses, we collapsed OTUs at the genus level and retained those present in > 10% of samples.

Statistical analyses were performed using R (version 4.4.1). Multiple comparisons were adjusted using a false discovery rate (FDR) of 5%. Regression models were adjusted for age, sex, self‐reported race and ethnicity, body mass index (z‐score), sine and cosine of the season of sample collection, and sequencing run. We conducted sensitivity analyses for socioeconomic status factors.

3. Results

3.1. Study Population

We analyzed 416 adolescents with available data from the nasal microbiome. The study design and analytic workflow are described in Figure 1. The main demographic and clinical characteristics of the study sample are reported in Table 1. Briefly, adolescents had a mean (SD) age of 13.0 (0.7) years, and 51.9% were female. Most self‐identified as Non‐Hispanic White (65.1%), with a smaller proportion identifying as Non‐Hispanic Black (16.1%), Hispanic (9.6%), or > 1 race or other race (9.1%). A subset of adolescents had a history of respiratory and allergic diseases in the past year, such as asthma (26.7% ever had asthma and 12.9% currently), hay fever (20.6%), wheezing (11.3%), and aeroallergen sensitization (58.8% from the 274 adolescents with IgE sensitization testing). For respiratory biomarkers, the mean (SD) for FeNO was 22.8 (17.2) ppb, and for total serum IgE was 115.6 (135.2) IU/mL.

Study design and analytic workflow. We profiled the microbiome from nasal swabs collected from 416 adolescents by targeted sequencing of the 16S rRNA gene (V1–V3 regions). We retrospectively collected data about environmental exposures (PM_2.5, NO₂, O₃, ambient temperature, relative humidity, and greenness) from the past 2 days until the past year relative to nasal sample collection. Additionally, we also assessed respiratory health and biomarkers during sample collection (asthma, hay fever, wheezing, aeroallergen sensitization, IgE levels, FeNO, lung function, and BDR). We tested for the association of the nasal microbiome with environmental exposures and respiratory health independently at three levels. First, we tested for the association with intraindividual bacterial diversity (alpha diversity). Second, we observed that analyzed participants can be clustered into six groups based on overall nasal microbiome profile. We evaluated whether these clusters were associated with any of these environmental exposures or respiratory health. Finally, we inspected whether individual abundances of specific bacterial genera were associated with these exposures or outcome.

TABLE 1.

Clinical and demographic characteristics of teens from Project Viva.

Variable	Sample size	Project Viva (n = 416)
Age (years)	415	13 (0.7)
Sex (female)	416	216 (51.9)
Race and ethnicity	416
Non‐Hispanic White		271 (65.1)
Non‐Hispanic Black		67 (16.1)
Hispanic		40 (9.6)
> 1 race or other race		38 (9.1)
BMI (z‐score)	409	0.4 (1.1)
Season	416
Fall		96 (23.1)
Spring		96 (23.1)
Summer		165 (39.7)
Winter		59 (14.2)
Microbiome clusters	416
Corynebacterium		141 (33.9)
Staphylococcus		89 (21.4)
Propionibacterium		87 (20.9)
Staphylococcus–Streptococcus		45 (10.8)
Unclassified Neisseriaceae		36 (8.7)
Moraxella		18 (4.3)
Maternal education (college graduate)	414	287 (69.3)
Household income (> $70,000/year)	376	251 (66.8)
Current asthma	412	53 (12.9)
Asthma ever	412	110 (26.7)
Hay fever	388	80 (20.6)
Wheezing	391	44 (11.3)
FEV1 (z‐score)	397	−0.1 (1)
FVC (z‐score)	397	0.1 (0.9)
FEV₁/FVC (z‐score)	397	−0.4 (1)
BDR (%)	335	3.3 (5.3)
Total IgE (UI/L)	256	115.6 (135.2)
Aeroallergen sensitization	274	161 (58.8)
FeNO (ppb)	371	22.8 (17.2)
Environmental exposures (past year)
NO₂ (ppb)	415	20.2 (5.1)
PM_2.5 (μg/m³)	415	7.2 (0.8)
O₃ (ppb)	408	38.8 (1.1)
Temperature (°C)	407	3.7 (0.9)
Relative humidity (%)	403	66.5 (1.3)
NDVI (1230 m)	409	0.6 (0.1)

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Note: Descriptives are represented by the mean (standard deviation) for continuous variables and the count (proportion) for categorical variables.

Abbreviations: BDR, bronchodilator drug response; BMI, body mass index; FeNO, fractional exhaled nitric oxide; FEV₁, forced expiratory volume in one second; FVC, forced vital capacity; NDVI, Normalized Difference Vegetation Index.

3.2. Air Pollution and Environmental Exposures

On average, adolescents were exposed to ambient air pollutant levels below the United States Environmental Protection Agency (EPA) standard levels, including a mean (SD) exposure during the year before the early teen visit of 7.2 (0.8) μg/m³ for PM_2.5, 20.2 (5.1) ppb for NO₂, and 38.8 (1.1) ppb for O₃. During the year before the study visit, the mean (SD) temperature was 3.7 (0.9) °C and the mean relative humidity was 66.1 (4.3) %. We observed similar trends for the other exposure times relative to nasal sample collection (i.e., past 2 days, 1 week, 1 month, and 3 months) (Table S2). Additionally, mean (SD) greenness exposure was estimated at 0.6 (0.1) NDVI with a 1230‐m resolution, similar to 270‐m and 90‐m resolutions (Table S2).

Estimated environmental pollutants and meteorological conditions were moderately to highly correlated across different sampling points as well as among the pollutants and meteorological conditions themselves (Figure S3). Overall, PM_2.5 exposure was positively associated with all other environmental exposures except NDVI. NO₂ and O₃ were negatively correlated with temperature and relative humidity, respectively. Among air pollutants, NO₂ showed a consistent and marked negative correlation with NDVI greenness (Figure S3).

3.3. Nasal Bacterial Diversity Associations

3.3.1. Environmental Exposures

In unadjusted correlation analyses, alpha bacterial diversity, as measured with both observed and Shannon indexes, was significantly associated with short‐to‐mid‐term exposure (from the past 2 days to the past 3 months) to air pollutants and meteorological conditions (FDR < 0.05), including NO₂, O₃, temperature, and humidity (Figure 2A, Tables S3 and S4). NO₂ was consistently positively correlated with alpha diversity indexes, while temperature and O₃ showed negative correlations. We did not observe significant correlations between alpha diversity metrics and PM_2.5 exposure or greenness.

(A) Correlogram displaying the correlation among environmental exposures and two alpha diversity indices: Observed and Shannon. The colors indicate the strength of the correlation, with dark blue representing strong positive correlations and red representing strong negative correlations. Blank spaces correspond to nonsignificant correlations (p > 0.05). Environmental exposures showing no significant correlation are not represented. (B) Box plots of alpha diversity indices in participants with (yellow, n = 80) and without (green, n = 308) hay fever. Raw and adjusted p‐values correspond to regression models adjusted for confounders.

In adjusted regression models by child age, sex, ethnicity, and race, BMI, season of sample collection, and sequencing run, we only detected nominal associations for NO₂ and temperature with bacterial diversity. Specifically, NO₂ exposure (past month) was positively associated with bacterial richness (β = 0.02, p = 0.04), while temperature (past month) was negatively associated with bacterial diversity (β = −0.03, p = 0.03) (Figure 2A, Table S5). These associations remained significant in sensitivity analyses adjusted for maternal education and household income (Table S6).

3.3.2. Respiratory Health

In adjusted regression models, we observed that individuals with a history of hay fever showed increased bacterial diversity measured by the richness (β = 0.44, FDR = 0.015) and Shannon indices (β = 0.29, FDR = 0.048) (Figure 2B, Table S7). This finding remained significant after adjustment for SES factors (Table S6). We did not observe any other association between alpha diversity and other respiratory outcomes (Table S7).

3.4. Associations With Nasal Microbiome Clusters

We clustered adolescents from Project Viva into six different groups based on their nasal microbiome profiles, which were defined by the dominance of specific bacteria: Corynebacterium (n = 139), Staphylococcus (n = 88), Propionibacterium (n = 86), Staphylococcus–Streptococcus (n = 41), Neisseriaceae unclassified (n = 36), and Moraxella (n = 18) (Figure 3).

Taxa bar plots of bacterial genera with an average relative abundance > 1% in Project Viva participants. The y‐axis shows the median proportion of each genus for the nasal microbiome clusters (n = 6) identified in our study population. The number of individuals included in each cluster is also indicated. Microbiome clusters are designated based on the most dominant(s) bacterial genera. Bacterial genera with a relative abundance < 1% were grouped as “Other” for graphical representation.

3.4.1. Environmental Exposures

Short‐term exposures to O₃, temperature, and relative humidity in the past week were associated with microbiome cluster assignment likelihood (FDR < 0.05) and remained significant after adjusting for SES factors in sensitivity analyses (Table S8). Pairwise comparisons showed that lower O₃ exposure (odds ratio [OR]: 0.73–0.76) and greater exposure to temperature (OR: 1.19–1.24) and humidity (OR: 1.19–1.26) were associated with increased odds of having a microbiome dominated by Moraxella than other nasal microbiome profiles (FDR < 0.05) (Figure 4A, Table S9). No association was detected between greenness and microbiome clusters (Table S10).

Forest plots of pairwise associations of the nasal microbiome clusters with (A) environmental exposures and (B) aeroallergen sensitization. The contrasts (Effect cluster – Reference cluster) are plotted on the y‐axis, while odds ratios (OR) and 95% confidence intervals are plotted on the x‐axis. The OR indicates the increase in the odds of being in the effect group compared to the reference group for each increase of one unit in environmental exposure (A) or when comparing individuals with and without aeroallergen sensitization (B). The vertical dashed line represents the null (OR = 1.0). (A) Only the exposures significantly associated with overall cluster assignment were followed up in this pairwise comparison, including the exposures in the past week to O₃ (ppb), temperature (°C), and humidity (%). *False discovery rate (FDR) < 0.05.

3.4.2. Respiratory Health

Aeroallergen sensitization was the only respiratory outcome associated with cluster assignment likelihood (FDR = 0.022, Table S11). Specifically, adolescents with a microbiome cluster dominated by Staphylococcus showed a positive association with aeroallergen sensitization when compared to those with a nasal cluster dominated by Propionibacterium (OR: 4.48, 95% CI: 1.78–11.29, FDR = 0.025, Figure 4B, Table S12).

3.5. Differentially Abundant Bacterial Genera Analysis

We detected 48 bacterial genera present in at least 10% of nasal samples. The most abundant genera (mean ± SD) included Corynebacterium (33.5% ± 27.7%), Staphylococcus (23.5% ± 26.6%), Propionibacterium (16.7% ± 22.8%), unclassified Neisseriaceae (4.7% ± 12.9%), Dolosigranulum (4.4% ± 8.1%), Moraxella (3.7% ± 13.8%), and Streptococcus (3.2% ± 7.4%). We tested for the association between the abundance of the 48 common bacterial genera and environmental exposures and respiratory outcomes.

3.5.1. Environmental Exposures

We observed 19 significant associations between 13 unique bacterial genera and short‐to‐medium‐term exposures (from the past 2 days to the past 3 months) to PM_2.5, NO₂, and/or temperature (FDR < 0.05 and significant in pseudo‐count sensitivity analyses) (Table 2, Figure S4, Table S13). The highest number of associations was detected for NO₂, particularly for mid‐term exposure over the past month. These included positive associations with Ruminococcaceae (unclassified), Lachnospiraceae (unclassified), Roseburia, Dialister, Alistipes, and Faecalibacterium, and negative associations with Staphylococcus and Corynebacterium. The associations with Roseburia, Alistipes, Faecalibacterium, and Staphylococcus remained borderline significant in sensitivity analyses for SES factors (p > 0.05) (Table S13).

TABLE 2.

Summary table of differentially abundant bacterial genera associated with environmental exposures and/or respiratory outcomes.

Bacterial taxa	Exposure/Outcome	log₂(FC)	SE	FDR
Environmental exposures
Dialister	PM_2.5 (past 3 months)	0.37	0.07	4.2 × 10⁻⁴
Roseburia	PM_2.5 (past 3 months)	0.37	0.07	4.2 × 10⁻⁴
Ruminococcaceae (unclassified)	NO₂ (past month)	0.06	0.01	0.001
Faecalibacterium	PM_2.5 (past 3 months)	0.29	0.07	0.002
Lachnospiraceae (unclassified)	NO₂ (past month)	0.06	0.01	0.002
Alistipes	PM_2.5 (past 3 months)	0.29	0.07	0.008
Roseburia ^a	NO₂ (past month)	0.06	0.01	0.005
Dialister	NO₂ (past month)	0.05	0.01	0.008
Alistipes ^a	NO₂ (past month)	0.05	0.01	0.012
Pelomonas	Temperature (past 2 days)	0.05	0.01	0.006
Faecalibacterium ^a	NO₂ (past month)	0.05	0.01	0.017
Staphylococcus ^a	NO₂ (past week)	−0.05	0.02	0.019
Staphylococcus	NO₂ (past 2 days)	−0.04	0.01	0.011
Pseudomonas	Temperature (past 2 days)	0.06	0.02	0.011
Corynebacterium	NO₂ (past 2 days)	−0.05	0.01	0.012
Propionibacterium	PM_2.5 (past 2 days)	0.11	0.04	0.013
Lactococcus	Temperature (past month)	0.19	0.06	0.036
Haemophilus	PM_2.5 (past week)	−0.10	0.03	0.042
Roseburia	Temperature (past week)	0.09	0.03	0.046
Respiratory outcomes
Leptotrichia	FVC (z‐score)	−0.28	0.06	7.3 × 10⁻⁵
Kingella	FVC (z‐score)	−0.24	0.05	2.6 × 10⁻⁴
Granulicatella	FVC (z‐score)	−0.22	0.06	0.006
Corynebacterium	FEV₁/FVC (z‐score)	−0.36	0.11	0.011
Faecalibacterium	FeNO	0.01	0.004	0.012
Rothia	Hay fever	0.45	0.14	0.012
Rothia ^a	Total IgE	0.002	5.7 × 10⁻⁴	0.014
Lachnospiraceae (unclassified) ^a	FeNO	0.01	0.004	0.020
Clostridiales (unclassified)	FeNO	−0.01	0.004	0.026
Lactococcus ^a	FeNO	0.01	0.004	0.027
Kingella	FeNO	−0.01	0.004	0.033
Pasteurellaceae (unclassified)	FeNO	−0.01	0.004	0.036
Staphylococcus ^a	Wheeze	−0.84	0.31	0.036
Pelomonas ^a	FeNO	0.01	0.004	0.049

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Note: Only significant associations after multiple comparisons adjustment (FDR < 0.05) and pseudo‐count sensitivity analysis implemented in ANCOMBC2 are reported. Models were corrected for age, sex, self‐reported race/ethnicity, BMI (z‐score), sine and cosine of the season of sample collection, and sequencing run.

Abbreviations: FC, fold‐change; FDR, false discovery rate; SE, standard error.

^{^a}

Associations that did not remained significant in sensitivity analyses adjusted by socioeconomic status factors (household income and maternal education).

PM_2.5 exposure, mainly in the past 3 months, was positively associated with Dialister, Roseburia, Faecalibacterium, Alistipes, and Propionibacterium, and negatively associated with Haemophilus. Finally, the average ambient temperature from the past 2 days to the past month was positively associated with Pelomonas, Pseudomonas, Lactococcus, and Roseburia. We only observed four bacterial taxa associated with greenness (FDR < 0.05), but only the negative association of Alistipes with NDVI at 90‐m resolution remained significant after accounting for SES (Table S14).

3.5.2. Respiratory Health

We found 14 associations between 12 unique bacterial genera with wheeze, hay fever, FeNO, total IgE levels, and/or lung function (FVC and FEV₁/FVC z‐scores) (Table 2, Figure S5, Table S15). Most significant associations were detected for reduced lung function and Leptotrichia, Kingella, Granulicatella, and Corynebacterium abundance. FeNO was the respiratory trait associated with a higher number of bacterial genera, including a positive association with Faecalibacterium and negative associations with Clostridiales (unclassified), Kingella, and Pasteurellaceae (unclassified). FeNO was also associated with Lachnospiraceae (unclassified), Lactococcus, and Pelomonas, but the associations were not significant after SES adjustment (p > 0.05). Furthermore, Rothia was positively associated with hay fever. Rothia and Staphylococcus were associated with IgE levels and wheezing, respectively, but the associations remained borderline significant in sensitivity analysis for SES (p > 0.05).

A total of six bacterial genera were significantly associated with both environmental exposures and respiratory health (Figure 5). Short‐term exposure to NO₂ was negatively associated with Staphylococcus and Corynebacterium, which were negatively associated with wheezing and lung function, respectively. Furthermore, four bacterial genera positively associated with FeNO (Pelomonas, Lactococcus, Lachnospiraceae (unclassified), and Faecalibacterium) showed positive associations with short‐ to medium‐term exposures to NO₂, PM_2.5, and/or temperature.

Heatmap of regression coefficients for those bacterial genera significantly associated with any environmental exposure and respiratory health. Bacterial genera are displayed on the x‐axis, while exposures and outcomes are represented on the y‐axis. Positive coefficients are represented in blue, while negative ones are in red. The color gradient ranges from red (lowest negative coefficients) to blue (highest positive coefficients). Coefficient estimates are indicated for each variable pair. Bacterial taxa were independently tested with environmental exposures and respiratory outcomes. Only significant associations after multiple comparison adjustments (FDR < 0.05) are represented.

4. Discussion

To the best of our knowledge, this is the first study to systematically examine the association of the nasal microbiome with respiratory health and multiple short‐to‐long‐term environmental exposures, including air pollution, ambient conditions, and greenness. We analyzed the microbiome at three different layers: diversity, nasotypes, and bacterial genera abundance. We found that higher bacterial diversity was associated with hay fever and short‐term NO₂ exposure, whereas lower diversity was linked to higher short‐term temperature exposure. Adolescents whose nasal microbiome was dominated by Moraxella were exposed to lower O₃ levels and higher temperature and humidity in the past week. Additionally, Staphylococcus dominance was positively associated with aeroallergen sensitization compared to Propionibacterium dominance. The abundance of 13 and 8 individual bacterial genera was associated with short‐to‐medium‐term exposures (PM_2.5, NO₂, and/or temperature) and respiratory outcomes (hay fever, wheezing, IgE, lung function, and FeNO). Staphylococcus, Corynebacterium, Pelomonas, Lactococcus, Lachnospiraceae (unclassified), and Faecalibacterium abundance were associated both with environmental exposures and respiratory traits.

We observed that short‐term NO₂ exposure was associated with greater bacterial richness and diversity, with no associations observed for long‐term exposures until the past year for any pollutant. The impact of NO₂ on bacterial diversity had been examined in two studies, which reported no associations for 1‐day exposure and pre‐ and post‐natal exposures [20, 21]. Conversely, we observed that O₃ exposure and ambient temperature were correlated with lower bacterial diversity, an opposite effect potentially explained by the inverse correlation observed for NO₂ and O₃ levels [22] and the antibacterial properties of O₃ [23, 24]. Although the negative association between O₃ and bacterial diversity was not significant after adjusting for confounders in our study, it aligns with previous findings in smaller subsets of nasal samples from children with asthma and healthy adults [20, 25]. Higher temperature and O₃ have also been shown to reduce bacterial diversity and increase allergic symptoms in mice, which could promote pathogenic overgrowth and respiratory infections [26, 27].

Regarding respiratory health, we found that hay fever was associated with greater nasal bacterial richness and diversity. This finding may seem counterintuitive since a highly diverse microbiome is typically associated with a healthy state [28]; however, this does not hold for all body sites and diseases [29]. For hay fever, although previous studies reported divergent conclusions, several studies supported that subjects with hay fever have greater nasal bacterial diversity [14, 28, 30, 31]. We hypothesize that increased bacterial richness in the nasal nares may be related to exacerbated inflammation. However, the role of the nasal microbiome in hay fever remains inconclusive to date, due to small sample sizes (n = 16–160) [14, 28, 30, 31] and the challenge posed by the seasonal nature of rhinitis, which may relate to seasonal microbiome changes.

Although the influence of ambient conditions on the human nasal microbiome is unknown, it is known that the gut microbiome composition is sensitive to ambient and internal temperature [32]. In the upper airways, temperature gradients, along with anatomic and physiological features, lead to distinct bacterial communities with unique gene expression patterns along the nasal passage [33]. In our study, by unsupervised clustering of the nasal microbiome, we identified a cluster of subjects characterized by the dominance of Moraxella, who were more likely to be exposed to lower O₃ levels and higher ambient temperature and relative humidity. Moraxella encompasses gram‐negative pathogenic species (e.g., M. catarrhalis) extensively associated with asthma, particularly during childhood, with early life exposures often linked to future episodes of asthma and wheezing [5, 6, 7]. Moraxella colonization in early life might be influenced by alterations in the epigenome and trained innate immunity at birth [34]. Furthermore, the presence of Moraxella may modify the impact of PM_2.5 exposure on respiratory health through alterations in extracellular vesicles in plasma [35].

Unbiased microbiome‐driven clustering can reveal groups of patients at higher risk for severe asthma, exacerbations, drug unresponsiveness, or having distinct inflammatory phenotypes [6, 18, 19, 36, 37]. Here, we identified a cluster of individuals with a nasal microbiome dominated by Staphylococcus positively associated with aeroallergen sensitization than those dominated by Propionibacterium. Staphylococcus dominance has been associated with the release of inflammatory factors in nasal epithelial cells, enhanced Th2 inflammatory response, and increased IgE levels [30, 38]. Conversely, Propionibacterium are gram‐positive commensals in the nasal microbiome of healthy individuals (e.g., P. acnes) [14, 28], whose protective effect for aeroallergen sensitization might relate to their ability to induce Th1 and Treg responses, reduce IgE levels, and ameliorate atopic symptoms [14, 39].

We detected differences not only in overall microbiome profiles but also in the abundance of specific bacterial genera. We observed that short‐to‐medium‐term exposures to PM_2.5, NO₂, and temperature were associated with up to 13 bacterial genera. Roseburia and Dialister were associated with these three different environmental exposures over varying periods. Roseburia is a gram‐positive genus within the Clostridia class whose abundance in the lower airways has been associated with severe asthma in children [40]. Dialister is a gram‐negative Firmicutes bacterium mostly associated with non‐exacerbated asthma and suggestively associated with indoor PM_2.5 cooking emissions [41, 42, 43].

Moreover, increasing exposures to PM_2.5, NO₂, and/or temperature were negatively associated with common members of the nasal microbiota, such as Staphylococcus, Corynebacterium, or Haemophilus. The potential effect of this environment‐induced dysbiosis on respiratory health is uncertain since these dominant genera encompass both commensal and pathogenic bacteria with protective and risk effects for wheezing, asthma, and exacerbations [5, 6, 44]. Indeed, we observed that Staphylococcus was associated with a reduced risk of wheezing, although this finding was borderline significant in sensitivity analyses adjusted by SES factors. In contrast, Corynebacterium, the most abundant genus in the nose frequently related to healthy status and stable asthma [7, 45, 46], was associated with lower FEV₁/FVC in our population. Notably, previous studies also reported that air pollution is associated with lower nasal abundances of Corynebacterium [13, 21, 47, 48], suggesting that air pollution may disrupt sinonasal homeostasis and induce Th2 inflammation by the decrease of this genus [48].

Among the microbiome associations with environmental exposures and respiratory health, we also found that Faecalibacterium was associated with higher PM_2.5 short‐term exposure and airway inflammation measured by FeNO. Faecalibacterium is a gram‐positive Firmicutes commensal from the gut and producers of precursors of short‐chain fatty acids associated with protective effects for asthma and atopy [49, 50]. However, its presence in the airways has been associated with worse respiratory outcomes, such as severe asthma [40].

Despite existing evidence, we did not observe an association between asthma and the nasal microbiome in our study [5, 6, 7]. We hypothesize that our ability to detect microbiome differences might be related to the low number of active asthma cases in our cohort (12%) and the characteristic heterogeneity of the disease. Nonetheless, we identified bacterial genera (e.g., Rothia) associated with other allergic traits, such as hay fever and IgE. Rothia are gram‐positive Actinobacteria commonly commensals within the respiratory tract. Our associations with allergic traits are supported by previous associations of Rothia airway abundance with eosinophilic asthma signatures [51, 52]. Additionally, Rothia nasal abundance has been associated with reduced risks for asthma exacerbations in patients receiving inhaled corticosteroid therapies [5, 41], which are the most effective treatment for allergic asthma [1].

Our study has several strengths. Firstly, we analyzed nasal samples, which are surrogates of the lower airways and are exposed primarily to environmental pollution, from a large subset of 416 adolescents. It represents the largest sample size in studies of air pollution and the nasal microbiome [11, 12, 13, 20, 21], which is also approximately higher than 85% of microbiome studies of asthma and hay fever [7, 30, 31]. Secondly, we widely characterized our participants for multiple respiratory and airway inflammation biomarkers, as well as for various environmental exposures, using highly accurate and validated machine‐learning‐based spatiotemporal models [53]. Thirdly, we applied state‐of‐the‐art statistical approaches to control for microbiome confounders, multiple testing, and biases related to microbiome data to minimize false positive associations at the level of bacterial diversity, microbiome‐based clusters, and bacterial genera abundance. The study of the respiratory microbiome offers an opportunity to discover potential therapeutic interventions for allergic diseases aimed at modifying bacterial communities, such as the use of probiotics [54].

However, some limitations must be acknowledged. Firstly, the cross‐sectional design restricts our ability to determine the direction of causation between nasal microbiome alterations and respiratory outcomes. Although this limits the temporal association between respiratory outcomes and the microbiome, observational omics studies have proven supportive for biomarker discovery and represent the first step toward the identification of microbiome‐based therapies [55, 56]. Secondly, the associations identified may be confounded by the prescription of medications for allergic and respiratory conditions, as well as antibiotics, which could induce microbiome dysbiosis with significant interindividual variability [57]. Although we attempted to identify and correct our analysis for demographic, clinical, and technical covariates, moderate‐to‐weak associations might be impacted by unmeasured confounders (e.g., antibiotic prescription), a limitation inherent to observational studies. Thirdly, replication in other independent populations will be required to ensure the transferability and generalizability of our findings. Fourthly, despite the benefits of 16S‐rRNA‐based sequencing, it lacks information at the species taxonomic level and other microorganisms than bacteria. Shotgun metagenomics and functional assays will contribute to identifying bacterial species and molecular pathways underlying the associations [58, 59]. Overall, while we reported associations between bacterial taxa and both environmental exposures and respiratory outcomes, the characteristics of this study do not permit the evaluation of a causal link between these exposures and outcomes, nor the differentiation of bacterial species leading to observed associations.

In conclusion, short‐to‐medium‐term exposures to air pollution (NO₂, PM_2.5, and O₃), temperature, and humidity, as well as phenotypes and biomarkers of respiratory health (hay fever, wheezing, aeroallergen sensitization, IgE, lung function, and FeNO) were associated with the nasal microbiome in terms of differences in diversity, microbiome community structure, and bacterial genera abundances. Future research should determine whether microbiome changes may mediate the effects of environmental exposures on respiratory health.

Author Contributions

J.P.‐G. and A.C. were involved in the conceptualization and design of the study; J.P.‐G. and A.K.B. in data curation and formal analyses; S.L.R.‐S., Y.Z., J.S., B.C., H.L.‐G., J.S., M.‐F.H., E.O., and D.R.G. helped in study conceptualization, sample collection, cohort management, and data analysis and interpretation. J.P.‐G. leads the visualization and writing of the original draft. D.R.G. and A.C. were responsible for funding acquisition and project supervision. All authors were involved in the critical revision of the manuscript and have read and agreed to the published version of the manuscript. The authors agree to be accountable for all aspects of the work, ensuring its scientific accuracy and integrity.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Figure S1: Principal coordinate plot of the Jensen‐Shannon Divergence metric. The first five principal coordinates (PCos) are represented in the x and y axes. Each dot corresponds to one individual sample, which is colored based on the cluster assignment using the partitioning around medoids (PAM) method on the Jensen‐Shannon Divergence metric.

Figure S2: Frequency bar plot of cluster assignment through the leave‐one‐out (LOO) partitioning around medoids (PAM) method. Each bar corresponds to a single individual (x axis), and it represents the frequency of cluster assignment in the 416 iterations from the LOO PAM clustering method (y axis). Clusters are defined based on the PAM method using all samples.

Figure S3: Correlogram displaying the strengths of Pearson correlations among environmental exposures (PM_2.5, NO₂, O₃, temperature, relative humidity, and greenness) at different time measurements. Time points include the past 2 days (t₂), past week (t₇), past month (t₃₀), past 3 months (t₉₀), and past year (t₃₆₅). Distance points for greenness measures include 90 m (d₉₀), 270 m (d₂₇₀), and 1230 m (d₁₂₃₀). The colors indicate the strength of the Pearson correlation, with dark blue representing strong positive correlations and red representing strong negative correlations. Blank spaces correspond to nonsignificant correlations (p > 0.05).

Figure S4: Bar graph displaying log fold‐changes of bacterial genera abundance significantly associated with environmental exposures (FDR < 5%). Error bars represent the standard error of the estimate. The bacterial genera are colored based on their taxonomic classification at the phylum level. We tested 48 bacterial genera, but we only present those that showed significant associations for simplicity. Rows are grouped according to environmental exposures, and columns to exposure time relative to sample collection. We only represented exposures showing any significant association, including NO₂ (ppb), PM_2.5 (μg/m³), and temperature (°C). No significant associations were detected for O₃, humidity, and any exposure in the past year. The vertical line represents the null (log₂(fold‐change) = 0).

Figure S5: Bar graph displaying log fold‐changes of bacterial genera abundance significantly associated with respiratory outcomes (FDR < 5%). Error bars represent the standard error of the estimate. The bacterial genera are represented based on their taxonomic classification at the phylum level. We tested 48 bacterial genera with 11 respiratory traits, including asthma ever, current asthma, wheezing, hay fever, aeroallergen sensitization, total IgE (UI/L), FeNO (ppb), FEV₁ (z‐scores), FVC (z‐scores), FEV₁/FVC (z‐scores), and BDR (%). We only present those bacterial genera and respiratory traits that showed significant associations for simplicity. The vertical line represents the null (log₂(fold‐change) = 0).

Table S1: Summary table of PERMANOVA tests.

Table S2: Exposure levels for evaluated environmental exposures at different conditions.

Table S3: Correlation between environmental exposures and alpha diversity.

Table S4: Correlation between greenness and alpha diversity.

Table S5: Association between environmental exposures at different time points and alpha diversity.

Table S6: Sensitivity analyses for the associations with alpha diversity.

Table S7: Association between respiratory outcomes and alpha diversity.

Table S8: Overall association between environmental exposures and the six nasal microbiome clusters.

Table S9: Pairwise associations for nasal microbiome clusters associated with environmental exposures.

Table S10: Overall association between greenness and the six nasal microbiome clusters.

Table S11: Overall association between respiratory outcomes and the six nasal microbiome clusters.

Table S12: Pairwise associations for nasal microbiome clusters associated with aeroallergen sensitization.

Table S13: Differentially abundant bacterial genera associated with environmental exposures.

Table S14: Differentially abundant bacterial genera associated with greenness.

Table S15: Differentially abundant bacterial genera associated with respiratory outcomes.

ALL-81-750-s001.zip^{(6MB, zip)}

Acknowledgments

This work was supported by grants R01ES031259, R01HD034568, R24ES030894, and P30‐ES000002 from the United States National Institutes of Health (NIH). The study funders had no role in study design, data collection, data analysis, interpretation, or report writing. The authors thank all the participants (mothers and teens) from the birth cohort Project Viva, as well as all researchers, clinicians, and people involved in the establishment of this cohort.

Funding: This work was supported by grants R01ES031259, R01HD034568, R24ES030894, and P30‐ES000002 from the United States National Institutes of Health (NIH). The study funders had no role in study design, data collection, data analysis, interpretation, or report writing.

Contributor Information

Javier Perez‐Garcia, Email: jpegarci@stanford.edu.

Andres Cardenas, Email: andresca@stanford.edu.

Data Availability Statement

The summary statistic reports of all association analyses supporting the main conclusions of this study are reported in the main text and Supporting Information. Data included in this manuscript are not publicly available because Project Viva's historic consents did not allow for public data sharing. In accordance with Project Viva policies, datasets are available upon reasonable request via the Project Viva ROADMaP portal. Please see https://www.projectviva.org/for‐investigators for information about how to request data. All data collection instruments are also available via the ROADMaP. The full summary statistics of the differentially abundant analyses that support the findings of this study are openly available in Zenodo at https://zenodo.org, reference number 15265814.

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