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Prenatal polycyclic aromatic hydrocarbon exposure and asthma at age 8–9 years in a multi-site longitudinal study

Environmental Health volume 23, Article number: 26 (2024) Cite this article

Abstract

Background and aim

Studies suggest prenatal exposure to polycyclic aromatic hydrocarbons (PAHs) may influence wheezing or asthma in preschool-aged children. However, the impact of prenatal PAH exposure on asthma and wheeze in middle childhood remain unclear. We investigated these associations in socio-demographically diverse participants from the ECHO PATHWAYS multi-cohort consortium.

Methods

We included 1,081 birth parent–child dyads across five U.S. cities. Maternal urinary mono-hydroxylated PAH metabolite concentrations (OH-PAH) were measured during mid-pregnancy. Asthma at age 8–9 years and wheezing trajectory across childhood were characterized by caregiver reported asthma diagnosis and asthma/wheeze symptoms. We used logistic and multinomial regression to estimate odds ratios of asthma and childhood wheezing trajectories associated with five individual OH-PAHs, adjusting for urine specific gravity, various maternal and child characteristics, study site, prenatal and postnatal smoke exposure, and birth year and season in single metabolite and mutually adjusted models. We used multiplicative interaction terms to evaluate effect modification by child sex and explored OH-PAH mixture effects through Weighted Quantile Sum regression.

Results

The prevalence of asthma in the study population was 10%. We found limited evidence of adverse associations between pregnancy OH-PAH concentrations and asthma or wheezing trajectories. We observed adverse associations between 1/9-hydroxyphenanthrene and asthma and persistent wheeze among girls, and evidence of inverse associations with asthma for 1-hydroxynathpthalene, which was stronger among boys, though tests for effect modification by child sex were not statistically significant.

Conclusions

In a large, multi-site cohort, we did not find strong evidence of an association between prenatal exposure to PAHs and child asthma at age 8–9 years, though some adverse associations were observed among girls.

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Introduction

Asthma is a leading chronic illness among children in the U.S., with prevalence and morbidity disproportionately affecting low-income children and those from racial-ethnic minoritized groups [3, 13, 24]. Asthma presentation in children is heterogeneous, with phenotypes characterized by differences in wheezing symptoms throughout childhood, allergic sensitization, and asthma exacerbation and severity [20, 65]. Maternal environmental exposures during pregnancy are likely relevant to the etiology of asthma, though risk factors for asthma development and exacerbation differ among asthma phenotypes [27, 60, 62, 64, 65]. Prenatal exposure to ambient air pollution, including particulate matter (PM), has been implicated as a risk factor for asthma in early life [26]. However, the specific components of air pollution that may drive adverse outcomes and their relationship with different asthma phenotypes are not well-understood.

Polycyclic aromatic hydrocarbons (PAHs) are components of ambient air pollution with established adverse impacts on child airway health [39]. PAHs originate from the burning of organic materials including fossil fuels, biomass, tobacco products, and food [8]. Some low molecular weight (LMW) PAHs, including naphthalene, also have industrial and commercial household sources including moth balls and deodorizers [34]. PAHs may be volatilized in the gas phase or bind to ambient fine particulate matter (PM2.5), resulting in exposure through the inhalation pathway. Exposure to PAHs may also occur through ingestion of grilled or charred food. Many low-molecular weight PAHs are metabolized to monohydroxylated metabolites (OH-PAHs), which are commonly detected in urine in the general population and among pregnant individuals [68]. Prior research on the health effects of PAHs has largely focused on high molecular weight (HMW) PAHs and their carcinogenic properties [5, 10, 29, 40]. However, recent studies suggest that exposure to PAHs may also affect airway development and function through disruption of endocrine and immune function and/or inflammation and oxidative stress [39, 66, 69].

Prior research on the airway impacts of PAH exposure has predominantly focused on postnatal and occupational exposures [37, 41, 46]. Studies on prenatal exposure to PAHs and child airway outcomes have yielded mixed findings, with suggested associations between exposure to HMW PAHs and child asthma [35, 52] and wheeze [31, 33]. The largest and most geographically diverse prior study, conducted among participants in the multi-site ECHO-PATHWAYS consortium, linked maternal urinary metabolites of pyrene and LMW PAHs (naphthalene, phenanthrene, and fluorene) to asthma among children aged 4–6 years, but associations were sex-dependent and observed only among females [47]. Existing research is characterized by differences in method of exposure assessment, prenatal exposure windows, outcome classification, and age of outcome assessment [41]. In addition, most prior studies have focused on respiratory outcomes such as wheeze in early childhood, an age when asthma may be difficult to diagnose and objective measurements of airway obstruction and hyperreactivity are often not feasible [53].

This study expands on this literature to evaluate associations between prenatal PAH exposure and asthma among children in middle childhood (age 8–9 years), an age when persistent asthma can be identified and is more readily diagnosed compared to earlier years. We combine two cohorts of the ECHO-PATHWAYS Consortium across five U.S. cities, utilizing prenatal urinary OH-PAH concentrations in a diverse population to explore the role of individual OH-PAHs and metabolite mixtures. To interrogate whether maternal OH-PAH levels influence the development of particular asthma phenotypes, we leverage comprehensive longitudinal survey data in early and middle childhood to explore associations with wheezing trajectories (early, late, and persistent wheezing), atopic asthma, and asthma with recent exacerbation. Finally, we evaluate whether associations are modified by child sex.

Methods

Study population

This study population included pregnant individuals and their children followed from delivery (birth parent–child dyads) from two cohorts of the ECHO-PATHWAYS consortium: the Conditions Affecting Neurocognitive Development and Learning in Early Childhood (CANDLE) study and The Infant Development and the Environment Study (TIDES) [43]. CANDLE is a socio-demographically diverse pregnancy cohort in in Shelby County (Memphis), Tennessee, originally established to identify early-life risk factors affecting child neurodevelopment. Participants were recruited during the second trimester of pregnancy from prenatal care clinics and the community in 2006–2011 [63]. Inclusion criteria were: age 16–40 years, residence and planned delivery at an affiliated clinic in in Shelby County, low-risk singleton pregnancy, and English language proficiency at enrollment.

TIDES is a pregnancy cohort with four sites across the United States (San Francisco, CA; Minneapolis, MN; Rochester, NY; and Seattle, WA) and was originally established to evaluate the role of chemical exposures in early life reproductive development [9]. Inclusion criteria were: age of at least 18 years old, planning to deliver at an affiliated clinic, and having a low-risk pregnancy. In both CANDLE and TIDES, dyads were followed at regular intervals, including clinic visits and respiratory health questionnaires administered at age 4–6 years and age 8–9 years.

Inclusion criteria for the present analysis were: urinary OH-PAH and specific gravity (SG) measurements in the second trimester of pregnancy and airway outcome data at the age 8–9 year visit, including whether children had ever been diagnosed with asthma by a physician. We excluded participants with very preterm birth (gestational age less than 32 weeks) or evidence of prenatal smoking (based on maternal self-report and/or maternal urinary cotinine concentration above 200 ng/mL during pregnancy) due to competing risk factors and potentially distinct etiology of airway compromise in these populations. All ECHO-PATHWAYS research activities were approved by the University of Washington IRB and cohort study procedures were approved at each local institution.

Exposures

Urinary OH-PAH concentrations were measured in spot urine samples collected in mid-pregnancy, at a median gestational age of 21.6 weeks. Analytical procedures were performed at the Wadsworth Laboratory, New York State Department of Health and have been described in detail previously [25]. In brief, liquid–liquid extraction was performed followed by identification and quantification of OH-PAHs with a ABSCIEX 5500 triple quadrupole mass spectrometer (Applied Biosystems; Foster City, CA, USA). Recoveries of analytes in Standard Reference Materials (SRM 3672, SRM 3673) ranged from 79 to 109%. Twelve metabolites of seven parent PAHs were quantified in urine samples: two metabolites of naphthalene (1-hydroxynaphthalene [1-NAP], 2-hydroxynaphthalene [2-NAP]), four metabolites of phenanthrene (2-hydroxyphenanthrene [2-PHEN], 3-hydroxyphenanthrene [3-PHEN], 4-hydroxyphenanthrene [4-PHEN], combined 1/9-hydroxyphenanthrene [1/9-PHEN]), 1-hydroxychrysene (1-CHRY), 6-hydroxychrysene (6-CHRY), 2/3/9-hydroxyfluorene (2/3/9-FLUO), 1-hydroxypyrene (1-PYR), 3-hydroxybenzo[c]phenanthrene (3-BCP), and 1-hydroxybenzo[a]anthracene (1-BAA). Limits of detection ranged from 0.003 ng/mL to 0.04 ng/mL and were generally higher in CANDLE relative to TIDES, with the exception of 1-NAP and 2/3/9-FLUO (Table S1). Samples below the limit of detection (LOD) were imputed as LOD/√2 for the primary analyses and for descriptive statistics. As a sensitivity analysis, we used an alternate multiple imputation approach described below. Urinary SG was determined using a handheld refractometer (Atago PAL-3 pocket refractometer) at the time of sample collection. For descriptive statistics and mixtures analysis, we accounted for urinary dilution by adjusting OH-PAH concentrations for SG using the Levine-Fahey equation (Boeniger et al., 1993). In regression models, we used raw, unadjusted OH-PAH concentrations with model adjustment for urinary SG.

This analysis included five metabolites detected in > 60% of the analytic population of both cohorts: 1-NAP, 2-NAP, 2-PHEN, 3-PHEN, and 1/9-PHEN (Table S2). Two metabolites, 2/3/9-FLUO and 1-PYR, were detected in > 60% of CANDLE participants (but not TIDES participants) and were additionally included in CANDLE-only models. One metabolite, 4-PHEN, was detected > 60% of TIDES participants (but not CANDLE participants) and was included in TIDES-only models.

Outcomes

The primary outcomes for this analysis were 1) current asthma at age 8–9 years and 2) wheezing trajectories in childhood, defined based on caregiver responses to standard International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire [6] at the age 4–6, and 8–9 visit. Current asthma at age 8–9 was defined as caregiver-reported physician diagnosis of asthma and either current asthma medication use or recent (in the 12 months prior to the clinic visit) wheezing or whistling in the chest. Wheezing trajectories were derived from ISAAC questionnaire responses from the age 4–6 and 8–9 year visits: never wheezing (no history of wheeze at age 4–6 or age 8–9 years), early wheezing (affirmative history of wheeze at age 4–6 but no recent wheeze at age 8–9 years), late wheezing (no history of wheeze at age 4–6 but affirmative history of wheeze at age 8–9 years); and persistent wheezing (history of wheeze at age 4–6 and recent wheeze at age 8–9 years). These categories reflect clinical differences in wheezing and asthma phenotypes observed throughout childhood [20, 50, 65].

Two secondary outcomes were evaluated: Current asthma with recent exacerbation required current asthma at age 8–9 (as defined above) and either oral/injected steroid use in the past 12 months or hospitalization due to asthma or wheezing in the past 12 months. Combined asthma with atopic disease was defined as current asthma with history of allergic rhinitis / hay fever or history of eczema.

Statistical analysis

We used logistic regression to estimate the odds ratios and 95% confidence intervals for asthma associated with prenatal exposure to individual OH-PAHs. To evaluate associations between individual OH-PAHs and wheezing trajectories, we used multinomial regression with “never wheezing” as the reference category. Unadjusted OH-PAH concentrations were log-transformed with base-2, and we report outcomes associated with a doubling in OH-PAH levels.

We performed separate logistic regression models for each individual urinary OH-PAH concentration. Covariates were selected a priori and were identified as confounders directly or indirectly associated with prenatal PAH exposure and child airway outcomes, or as precision variables that were associated with airway outcomes alone and may improve model estimates. Models were adjusted for potential confounders in a staged approach. Minimally-adjusted models included urinary SG and child age as continuous variables and child sex and study site as categorical variables. Fully-adjusted models were additionally adjusted for SG, cohort, maternal age (continuous) at enrollment, education at enrollment (less than high school, high school completion, graduated college/technical school, or any graduate school), self-identified race (collapsed into categories of Black/African American, White, or Other due to small sample size in some subgroups), and ethnicity (Hispanic/not Hispanic), season of birth (warm or cold), inflation-adjusted household income interacted with household size, neighborhood deprivation index at the residential address during pregnancy (continuous), prenatal environmental tobacco smoke (ETS) exposure (continuous maternal urinary cotinine), reported postnatal ETS exposure (binary), maternal history of asthma (binary), firstborn status (binary), and the interaction between SG and cohort.

We evaluated associations between individual OH-PAHs and secondary asthma phenotypes (current asthma with recent exacerbation and combined asthma plus atopic disease) using logistic regression adjusting for covariates in the staged approach described above. We explored effect modification by child sex using multiplicative interaction terms between OH-PAH concentrations and child sex.

As a secondary aim exploring associations between OH-PAH mixtures and child asthma and wheeze, we used logistic weighted quantile sum (WQS) regression [16]. In brief, WQS regression is a two-stage process,first, a weighted index of quantized mixture components is estimated to reflect components most strongly associated with outcome in either a positive or negative direction. Next, the association between the weighted index and the outcome is estimated to reflect the “overall mixture effect”. We implemented WQS models using decile transformation and N = 1000 bootstraps while using the same data for model training and validation to identify PAH index weights and mixture effects in both the positive and negative direction of association with the primary outcomes (asthma and wheezing trajectories). Models were adjusted for the covariates described in the full-adjusted model for Aim 1. We elected a priori to use a novel permutation test to evaluate to control Type I error in the event of statistically significant associations between the WQS index and airway outcomes [19, 47],

As a sensitivity analysis, we evaluated associations between mutually-adjusted OH-PAHs and primary and secondary airway outcomes (rather than separate models for each metabolite). Additional sensitivity analyses were performed for the primary outcome of asthma at age 8–9, including: 1) cohort-specific models and models excluding one TIDES site sequentially to account for potential heterogeneity in the population and site-specific differences confounding; 2) models using SG-adjusted urinary OH-PAH values in addition to adjustment for SG; 4) imputation of OH-PAH measurements below the detection limit with central likelihood multiple imputation (CLMI) [11], a method for imputation in settings where limits of detection vary by analytical batch, instead of LOD/√2; 5) additional adjustment for birth weight and gestational age as potential mediators and birth year to account for secular trends; 7) multiple imputation with chained equations (MICE) for missing covariate data; and 8) inverse-probability-of-selection weighting to correct potential selection bias from differential loss-to-follow-up.. All analyses were performed using R version 4.2.2 [57] and the packages mice [15], lodi [12], and gWQS [58].

Results

Study population characteristics

There were N = 2,403 birth parent–child dyads in the CANDLE and TIDES cohorts, including N = 1,662 with OH-PAH and SG measurements during mid-pregnancy. Of these, N = 1,222 attended follow-up visits at age 8–9 years and provided answers to ISAAC questionnaires. We excluded N = 123 participants with evidence of maternal smoking during pregnancy and N = 18 children with gestational age < 32 weeks. After exclusion, the study population comprised 1,081 participants representing 698 CANDLE dyads and 383 TIDES dyads (Seattle, WA: n = 93; San Francisco, CA: n = 79; Minneapolis, MN: n = 118; Rochester, NY: n = 93). The demographic composition of CANDLE and TIDES participants excluded from the study population was similar to those included, though the study population had higher income on average than those excluded (Table S3).

Children in the study population were 52% male and ranged in age from 8–9 years, with over half of outcome assessments performed at 8 years of age (Table 1). The study population was demographically diverse, with 44% of birth parents identifying as Black or African American and 47% as White. There were notable differences in the sociodemographic characteristics of participants from the CANDLE and TIDES cohorts. Most CANDLE birth parents were Black or African American (62%) and 46% had a college/technical degree or higher. In contrast, the majority of TIDES birth parents were White (74%) and 83% graduated college. The median adjusted household income in TIDES was greater than three times that of CANDLE households.

Table 1 Characteristics of the study population by cohort and overall
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Pregnancy OH-PAH exposure

Rates of OH-PAH detection (Table S2) and concentration distributions (Table S1) differed among metabolites and by cohort. Detection rates ranged from 85% (1/9-PHEN) to 100% (2-NAP) in the overall study population. In general, median SG-adjusted OH-PAH concentrations were higher in CANDLE than TIDES. Median concentrations of naphthalene metabolites (1-NAP and 2-NAP) were higher than those of phenanthrene metabolites (2-PHEN, 3-PHEN, and 1/9-PHEN). The strongest pairwise correlations were observed between phenanthrene metabolites, with Pearson correlation coefficients ranging from 0.55–0.75 (Table S4). Correlation between other metabolites was moderate, with correlation coefficients ranging from 0.34–0.45.

We observed differences in OH-PAH exposure in the study population by cohort and sociodemographic characteristics. CANDLE participants were overrepresented among those in the highest tertile of urinary OH-PAH concentrations, relative to those in the lowest tertile (Table S5). Participants in the highest tertile of OH-PAH exposure were more likely to be Black or African American, were less likely to have graduated college at enrollment, and had lower mean household income relative to those in the lowest tertile.

Child airway outcomes

The prevalence of current asthma at age 8–9 years in the study population was 10%, representing 11.6% of CANDLE children and 7% of TIDES children (Table 2). About half of study participants were categorized as “never wheezing”; among children with a history of wheeze, most were in the “early wheezing” category, followed by “late” and “persistent wheezing”. Secondary asthma outcomes were less common in the study population, including asthma with recent exacerbation (5.0%) and asthma with atopic disease (6.8%).

Table 2 Prevalence of airway outcomes in the study population by cohort and overall
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Analytic results

The association between individual OH-PAHs and all primary and secondary outcomes were null in minimally-adjusted models (Fig. 1). In fully-adjusted models, there was a statistically significant inverse association between 1-NAP concentrations and asthma, with an odds ratio [OR] of 0.86 associated with a doubling in 1-NAP concentrations (95% confidence interval [CI]: 0.76, 0.98). We also observed inverse associations between 1-NAP and the secondary outcomes of asthma with recent exacerbation (OR = 0.76, 95% CI: 0.61, 0.95) and asthma with atopy (OR = 0.80, 95% CI: 0.69, 0.94).

Fig. 1
figure 1

Associations between prenatal OH-PAH metabolites and asthma phenotypes at age 8–9 and wheezing phenotypes in childhood. Adjusted odds ratios (OR) and 95% confidence intervals (CI) associated with a twofold increase in individual OH-PAH metabolite were determined by logistic regression (asthma outcomes) and multinomial regression (wheezing trajectories). Minimally adjusted models included child age at assessment, child sex, study site, and urinary specific gravity. Fully adjusted models additionally included the interaction between SG and cohort, maternal age, education at enrollment, race, and ethnicity; season of birth; inflation-adjusted household income interacted with household size, neighborhood deprivation index, prenatal smoke exposure, postnatal smoke exposure, maternal history of asthma, and firstborn status. Abbreviations: 1-NAP – 1-hydroxynaphthalene; 2-NAP – 2-hydroxynaphthalene; 2-PHEN – 2-hydroxyphenanthrene; 3-PHEN – 3-hydroxyphenanthrene; 1/9-PHEN – combined 1- and 9-hydroxyphenanthrene

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In effect modification analyses, sex-specific differences were minor and not in a consistent direction for the outcomes of early wheezing and late wheezing (Fig. 2). For asthma, a suggestive inverse association with 1-NAP among boys was attenuated among girls, though the p-value for the interaction term was not statistically significant (pinteraction = 0.35). We observed some adverse associations among girls only, including between 1/9-PHEN and asthma (ORfemale = 1.20, 95% CI: 1.01, 1.42; pinteraction = 0.09) and between 1/9-PHEN and persistent wheeze (ORfemale = 1.36, 95% CI: 1.03, 1.78; pinteraction = 0.06), with borderline evidence for effect modification.

Fig. 2
figure 2

Effect modification by child sex in the association between airway outcomes and OH-PAHs, represented by adjusted odds ratios (OR) and 95% confidence intervals (CI) associated with twofold increase in individual OH-PAH. Strata-specific results were derived from interaction models; p-values are for the interaction term between child sex and OH-PAH concentrations. Models were adjusted for child age and sex; study site; the interaction between urinary SG and cohort; maternal age, education at enrollment, race, and ethnicity; season of birth; inflation-adjusted household income interacted with household size, neighborhood deprivation index, prenatal smoke exposure, postnatal smoke exposure, maternal history of asthma, and firstborn status. Abbreviations: 1-NAP – 1-hydroxynaphthalene; 2-NAP – 2-hydroxynaphthalene; 2-PHEN – 2-hydroxyphenanthrene; 3-PHEN – 3-hydroxyphenanthrene; 1/9-PHEN – combined 1- and 9-hydroxyphenanthrene

Full size image

In WQS models, associations between the overall OH-PAH mixture (WQS index) and asthma were null in both the positive and negative direction (Table 3). There was a non-significant positive (adverse) association between a decile increase in the WQS index and child asthma in minimally-adjusted models (OR = 1.06, 95% CI: 0.96, 1.18) that was attenuated in fully-adjusted models (OR = 1.02, 95% CI: 0.91, 1.13), for which 1/9-PHEN dominated the WQS index mixture (weight = 0.56) (Table 3, Table S6). In WQS models describing negative (inverse) associations between the WQS index and asthma, there was a borderline inverse association in minimally-adjusted models that was attenuated in fully adjusted models, for which 1-NAP dominated the WQS index mixture (weight = 0.57).

Table 3 Associations between OH-PAH mixtures and asthma at age 8-9 years determined by logistic weighted quantile sum (WQS) regression models in which the direction of association is constrained to be positive (suggestive of adverse effects) or negative (suggestive of protective effects). The OH-PAH weights describe the proportional contribution of each metabolite to the WQS Index
Full size table

In models mutually-adjusted for all OH-PAHs, associations between 1/9-PHEN and all primary and secondary outcomes were elevated relative to separate metabolite-specific models (Table S7). For example, we observed statistically significant associations between mutually-adjusted 1/9-PHEN and asthma (OR = 1.20, 95% CI: 1.01, 1.44) and persistent wheezing (OR = 1.32, 95% CI: 1.04, 1.67). Associations between 1/9-PHEN and asthma with recent exacerbation (OR = 1.21, 95% CI: 0.97, 1.51) and asthma with atopy (OR = 1.19, 95% CI: 0.98, 1.46) were also elevated in mutually-adjusted models.

We observed differences in cohort-specific sensitivity analyses of OH-PAHs and child asthma, with adverse associations (OR > 1) for all OH-PAHs except 1-NAP in TIDES and protective associations (OR < 1) for all metabolites except 1/9-PHEN in CANDLE (Table S8). The association between 1/9-PHEN and asthma was statistically significant in TIDES (OR = 1.49, 95% CI: 1.05, 2.21) but not CANDLE (OR = 1.01, 95% CI: 0.88, 1.15). The association between 4-PHEN and asthma—evaluated only among TIDES participants due to the low detection rate of 4-PHEN in CANDLE—was also found to be elevated (OR = 1.75, 95% CI: 1.07, 2.86). The results were robust to exclusion of individual TIDES sites (Table S9).

When values below the detection limit were imputed with CLMI, the negative association between 1-NAP was attenuated and no longer statistically significant; all other estimates were largely unchanged (Table S10). Other sensitivity analyses yielded results similar to those of primary models, including use of SG-adjusted OH-PAH concentrations instead of unadjusted metabolite concentrations; additional adjustment by birth weight, gestational age, and birth year; imputation of missing covariate data with MICE; and inverse probability of censoring weighting (Table S10).

Discussion

We evaluated associations between prenatal exposure to select PAH metabolites and childhood airway outcomes in a socio-demographically diverse, multi-site cohort with comprehensive covariate characterization. We did not observe strong evidence for associations between concentrations of urinary OH-PAH metabolites—individually or in mixtures—and child asthma at age 8–9 or wheezing trajectories in childhood. Counter to our hypothesis that PAH exposure has adverse impacts on airway health, urinary concentrations of 1-NAP were inversely associated with asthma in fully-adjusted models and most sensitivity analyses. This seemingly protective association was attenuated among girls, though evidence for effect modification was lacking. Concentrations of 1/9-PHEN were positively associated with asthma and persistent wheezing in several sensitivity analyses and among girls, with borderline evidence for effect modification.

Several prior studies have evaluated the association between maternal PAHs and child airway outcomes, though most have focused on early childhood outcomes based on reported respiratory signs and symptoms. Using a similar study design, Loftus et al. [47] explored links between maternal OH-PAHs and asthma and wheeze in early childhood (age 4–6 years) in the CANDLE and TIDES cohorts. The authors identified null associations for most exposure-outcome associations, though with borderline evidence of an adverse effect of 1-NAP and 1/9-PHEN on current asthma at age 4–6 years. Notably, evidence of effect modification by child sex was strong, with elevated associations between OH-PAHs (except for 1-NAP) and current asthma among girls only,interaction p-values were statistically significant for metabolites including 1/9-PHEN, 3-PHEN, 2-PHEN, and 2-NAP.

Other studies of prenatal exposure to PAHs and child airway outcomes have largely focused on HMW parent PAHs, yielding mixed results. Differences in specific analytes measured, study design, timing of exposure assessment, age of outcome assessment, and/or case definition among prior studies complicate comparison with the present analysis. Furthermore, these studies have generally relied on small sample sizes in relatively homogenous populations, derived from three pediatric cohorts. In a birth cohort of 339 mother-infant pairs in Krakow, Poland, researchers measured personal exposure to HMW PAHs including benzo(a)anthracene, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(g,h,i)perylene, benzo(a)pyrene, chrysene/iso-chrysene, dibenzo(a,h)anthracene, indeno(1,2,3-c,d)pyrene, and pyrene over 48 h during the second trimester of pregnancy [30,31,32]. Increased maternal PAH exposure was associated with increased number and duration of respiratory symptoms (including ear infection, cough, barking cough, wheezing without cold, and sore throat) during the child’s first year of life [30] and with increased incidence risk ratio of wheeze during the first four years of life [31]. Prenatal exposure to PAHs was also assessed by DNA adducts of benzo(a)pyrene in umbilical cord blood in the same cohort; elevated benzo(a)pyrene DNA adducts were associated with increased risk of wheezing during the first two years of life, but not with wheezing events during the third and fourth year of life. [33].

In the Columbia Center for Children’s Environmental Health cohort, personal exposure to pyrene and 8 HMW PAHs (benz(a)anthracence, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(g,h,i)perylene, benzo(a)pyrene, chrysene/isochrysene, dibenz(a,h)anthracene, and indeno(1,2,3)pyrene) was measured over 48-h during the third trimester of pregnancy among a sample of 303 Dominican and African American women in New York City; child airway outcomes were measured throughout infancy and early childhood [37, 52, 59]. The researchers found prenatal PAH exposure in combination with environmental tobacco smoke exposure was associated with increased risk of cough and wheeze at 12 months and probable asthma at age 24 months, with significant interaction between PAH and ETS exposure [52]. This interaction persisted at age 5–6, with associations between prenatal exposure and asthma observed only among children with exposure to ETS [59]. Higher levels of prenatal and postnatal pyrene exposure were also associated with increased asthma and wheeze among non-atopic children (but not children with allergic sensitization) at age 5–6 years [37].

Most prior studies have evaluated associations between prenatal PAH exposure and airway outcomes in early childhood. Our analysis additionally considers asthma and wheezing trajectories in middle childhood that may represent distinct etiologies or clinical presentations. We evaluated three wheezing phenotypes identified by The Tucson Children’s Respiratory Study (TCRS) based on clinical observations throughout childhood: early, late, and persistent wheeze [50]. Similar phenotypes have also been identified through data-driven analyses of wheezing and asthma symptoms in other cohorts [27, 61]. Children with these phenotypes have been found to have differences in allergic sensitization, lung function, genetic factors, and risk factors for asthma development [65]. For example, early wheeze that resolved by middle childhood is generally associated with only mild lung function impairments that do not usually require medication use [20]. Late-onset wheezing has been linked to high likelihood of allergic sensitization [20, 27]. Children with both late-onset and persistent wheezing have been found to have decrements in lung function and airway responsiveness, while persistent wheezing is generally associated with the highest rate of asthma diagnosis [20, 27, 61].

Our primary analysis did not identify associations between maternal OH-PAHs and any wheezing phenotype, though 1/9-PHEN was significantly associated with persistent wheezing (but not other wheezing trajectories) in models mutually adjusted for other OH-PAHs and among girls only in effect modification analyses. Thus, we observed similar trends in effect estimates and modification by child sex for the outcomes of persistent wheezing and diagnosed child asthma at age 8–9 years, potentially suggesting a similar underlying mechanism and/or overlap in affected populations due to high rates of asthma diagnosis among children with persistent wheeze. While no prior studies have evaluated associations between prenatal PAH exposure and wheezing trajectories, a recent meta-analysis identified associations between postnatal traffic-related air pollution and transient and persistent asthma/wheezing, but not late-onset asthma/wheezing [42]. Studies have also linked prenatal exposure to CO, NOx, and PM2.5 to persistent asthma/wheeze specifically [55].

Our analysis of maternal OH-PAHs and secondary asthma phenotypes in children (asthma with recent exacerbation and asthma with atopy) did not yield strong associations. Findings from the Columbia Center for Children's Environmental Health birth cohort suggested that maternal personal pyrene measurements during pregnancy were associated with child asthma and wheeze at 5–6 years only among non-atopic children [35,36,37,38], and that prenatal PAH exposure was not associated with the development of seroatopy at age 5–6 years [59]. While few studies have evaluated prenatal PAH exposure in association with asthma exacerbation, Jung et al. [35] identified associations between high prenatal pyrene levels and emergency room visits for asthma before age 5–6. Further research is needed to characterize the role of prenatal PAH exposure in the development of seroatopy, atopic vs nonatopic asthma, and asthma severity in childhood.

The predominantly null results in our study may indicate that maternal OH-PAH levels in our study population are not associated with child asthma and wheezing trajectories at age 8–9. The OH-PAH concentrations measured in our study were similar to those identified by Cathey et al. [17] among pregnant individuals in Boston (N = 200) and Puerto Rico (N = 50) with the exception of 2-NAP, for which geometric mean concentrations were higher in participants from Puerto Rico relative to those in Boston and our study population. Associations between OH-PAH levels and airway outcomes may be more modest than our study was powered to detect, particularly for asthma phenotypes with low prevalence in the study population, or may be limited to OH-PAH concentrations higher than those measured in the study population. We did observe an inverse (protective) association between 1-NAP and asthma, which is not easily explained. There may be plausible mechanisms by which prenatal PAH exposure could prevent or reduce airway symptoms [59], for example by inhibiting B cell growth [49] or inducing pre-B cell apoptosis [4], which could be hypothesized to downregulate IgE production and thereby lead to a decrease in respiratory symptoms related to atopic asthma. However, most prior research has focused on the mechanisms by which prenatal PAH exposure may adversely affect childhood airway outcomes. While these mechanisms are not fully understood, adverse associations have been hypothesized to derive from oxidative stress and inflammation induced by PAH exposure. For example, PAH exposure from diesel exhaust was shown to generate reactive oxygen species in macrophages and epithelial cells that activated inflammatory signaling pathways, leading to upregulation of genes involved in regulating immune response [44]. Both LMW and HMW PAHs are also lipid-soluble and transferred across the placenta, and inflammatory responses and oxidative stress induced by PAH exposure is also hypothesized to adversely affect fetal development [7, 56]. Gestational exposure to PAHs may also trigger transcriptomic and epigenetic changes [28]. For example, a recent study found that maternal urinary OH-PAH metabolites were associated with changes in the placental transcriptome [54]. In particular, phenanthrene metabolites showed strong associations with expression of TRIP13 and genes related to vitamin absorption/digestion, consistent with our finding of stronger adverse associations for phenanthrene metabolite 1/9-PHEN relative to other OH-PAHs.

We found some evidence for effect modification by child sex, with statistically significant associations between 1/9-PHEN and the outcomes of age 8–9 asthma and persistent wheeze among girls only. However, the interaction p-values for these associations reached only borderline statistical significance; in contrast, Loftus et al. [47], identified strong evidence for effect modification by sex in the association between maternal OH-PAH levels and asthma and wheeze at age 4–6 in a similar study population. Few other studies have evaluated effect modification by child sex in the relationship between PAH exposure and airway outcomes. Liu et al. [46] identified some elevated associations between urinary 2-PHEN and asthma at age 13–19 among boys and between 4-PHEN and asthma at age 13–19 among girls, though most exposure-outcome associations were not significantly different between sexes and no formal tests of interaction were conducted. Majewska et al. [48] found that lung function growth trajectories were lower among girls exposed prenatally to HMW PAHs, but did not differ by exposure among boys, though the interaction between child sex and exposure was not statistically significant. A recent study of maternal OH-PAHs and placental gene expression may offer a potential mechanism to explain a stronger adverse effect on airway development and function in females,relative to males, placentas from female births had over 60% more genes for which expression was associated with OH-PAH concentrations, including genes related to vitamin absorption [54]. More research is needed to clarify potential effect modification by child sex and the role of specific parent compounds and metabolites.

Our findings were somewhat sensitive to model specification and certain stratified analyses. The associations between 1/9-PHEN and multiple outcomes (asthma, persistent wheeze, asthma with recent exacerbation, and asthma with atopy) were elevated in models mutually adjusted for all OH-PAHs, relative to individual metabolite models. Mutually-adjusted models have been used to control for confounding by co-occurring OH-PAHs, which showed moderate to high correlation between metabolite concentrations (rho = 0.34–0.75), but can also amplify bias an lead to inflation of effect estimates and variance [18, 67]. In cohort-specific analyses, the associations between all OH-PAHs except 1-NAP and current asthma were elevated in TIDES relative to CANDLE. However, cohort-specific estimates of associations were also imprecise with overlapping confidence intervals. Thus, the differences in estimates by cohort may be driven by random noise, particularly given the small sample size of TIDES participants relative to CANDLE.

Our study has several notable strengths. As described above, the use of airway data from multiple time points also enabled us to evaluate wheezing trajectories that may provide insight into asthma phenotypes with distinct environmental risk factors and etiologies. In addition to longitudinal outcome assessment, we also leverage data on reported asthma diagnosis in middle childhood, when asthma persistence can be more easily identified and diagnosed relative to early childhood. Our analysis used prospective data from two cohorts across five cities, representing participants with diverse demographic and socioeconomic backgrounds. These cohorts also collected comprehensive data on covariates and precision variables, enabling a high degree of statistical adjustment for potential confounders.

Our study also has various limitations. First, our estimates of PAH exposure are derived from only one spot urine sample collected during mid-pregnancy. Urinary OH-PAHs represent short-term exposure, with half-lives in the range of 2.5 – 12 h [14, 45]. Prior studies of urinary OH-PAH concentrations across pregnancy have identified intraclass correlation coefficients (ICCs) that vary widely between OH-PAH analytes and among sub-populations, ranging from 0.04 to 0.73 [17, 21, 23, 69]. Measurement from a single time point may not represent pregnancy average exposures, and noise introduced by short-term variations in exposure may contribute to null findings and obscure associations with adverse airway outcomes. Our exposures of interest also included only metabolites of LMW PAHs, namely naphthalene and phenanthrene, thus we did not capture the full range of PAHs potentially influencing asthma outcomes. HMW PAHs are predominantly excreted in feces, rather than urine [2]. Urinary metabolites of three HMW PAHs, including benz[a]anthracene, benzo[c]phenanthrene, and chrysene, were measured in our study but only detected in a small percentage of samples and were not included in the analysis. In addition, 1-NAP is a metabolite of both naphthalene and the carbamate pesticide carbaryl [51] and so we cannot distinguish associations with either parent compound for this metabolite. We further cannot rule out the potential for residual confounding due to imprecisely measured or unmeasured covariates, including postnatal PAH exposure. Finally, we do not account for potential co-exposure to correlated contaminants including heavy metals, which may have impacts on child airway health that are separate from or synergistic with OH-PAH exposure [22].

Both asthma and wheezing outcomes relied on maternal report of recent symptoms of airway obstruction and diagnoses and may be affected by imprecise recall. Misclassification of both outcomes would most likely be nondifferential with respect to exposure, potentially contributing to null findings. Longitudinal follow-up of children at more time points (e.g., annually) would result in more consistent and accurate measures of wheezing phenotypes. Nonetheless, the use of data from two time points in middle- and late-childhood to classify wheezing phenotypes has precedent in the literature [1, 36, 38, 50] in situations where more frequent outcome assessment were not possible. Furthermore, access to reported asthma diagnosis in middle childhood is a strength of the study,prior research on PAH exposure and pediatric airway outcomes has largely focused on outcomes in early childhood, when asthma diagnosis is difficult and objective measurements of airway obstruction and hyperreactivity are often not feasible. Finally, clinical histories derived from ISAAC questionnaire data are widely used to classify asthma outcomes in epidemiological studies and clinical settings [53].

In conclusion, in this large, multi-city study of maternal urinary OH-PAHs and airway outcomes in middle childhood, we did not identify strong evidence for associations between OH-PAHs and child airway outcomes. We identified borderline evidence for effect modification by child sex, with adverse associations between 1/9-PHEN and asthma and persistent wheezing among girls. Future research into the potential sex-specific impacts of prenatal PAH exposure on airway health throughout childhood are merited. Replication studies, particularly those with repeated exposure assessment throughout pregnancy, would improve scientific understanding of these linkages and the role of specific PAH parent compounds and metabolites.

Availability of data and materials

The data used for this study are not publicly available, but deidentified data may be available on request, subject to approval by the internal review board and under a formal data use agreement. Contact the corresponding author for more information. The computing code in R can be obtained from the corresponding author via email request.

References

  1. Abellan, A., Mensink-Bout, S. M., Garcia-Esteban, R., Beneito, A., Chatzi, L., Duarte-Salles, T., Fernandez, M. F., Garcia-Aymerich, J., Granum, B., Iñiguez, C., Jaddoe, V. W. V., Kannan, K., Lertxundi, A., Lopez-Espinosa, M.-J., Philippat, C., Sakhi, A. K., Santos, S., Siroux, V., Sunyer, J., … Casas, M. (2022). In utero exposure to bisphenols and asthma, wheeze, and lung function in school-age children: A prospective meta-analysis of 8 European birth cohorts. Environment International, 162, 107178. https://doi.org/10.1016/j.envint.2022.107178

  2. Agency for Toxic Substances and Disease Registry. Toxicological profile for polycyclic aromatic hydrocarbons. U.S: Department of Health and Human Services, Public Health Service; 1995.

    Google Scholar 

  3. Akinbami LJ, Moorman JE, Simon AE, Schoendorf KC. Trends in racial disparities for asthma outcomes among children 0 to 17 years, 2001–2010. Journal of Allergy and Clinical Immunology. 2014;134(3):547-553.e5. https://doi.org/10.1016/j.jaci.2014.05.037.

    Article  PubMed  Google Scholar 

  4. Allan LL, Schlezinger JJ, Shansab M, Sherr DH. CYP1A1 in polycyclic aromatic hydrocarbon-induced B lymphocyte growth suppression. Biochem Biophys Res Commun. 2006;342(1):227–35. https://doi.org/10.1016/j.bbrc.2006.01.131.

    Article  CAS  PubMed  Google Scholar 

  5. Armstrong B, Hutchinson E, Unwin J, Fletcher T. Lung Cancer Risk after Exposure to Polycyclic Aromatic Hydrocarbons: A Review and Meta-Analysis. Environ Health Perspect. 2004;112(9):970–8. https://doi.org/10.1289/ehp.6895.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Asher M, Keil U, Anderson H, Beasley R, Crane J, Martinez F, Mitchell E, Pearce N, Sibbald B, Stewart A, et, al. International Study of Asthma and Allergies in Childhood (ISAAC): Rationale and methods. Eur Respir J. 1995;8(3):483–91. https://doi.org/10.1183/09031936.95.08030483.

    Article  CAS  PubMed  Google Scholar 

  7. Autrup H, Vestergaard AB, Okkels H. Transplacental transfer of environmental genotoxins: Polycyclic aromatic hydrocarbon-albumin in non-smoking women, and the effect of maternal GSTM1 genotype. Carcinogenesis. 1995;16(6):1305–9. https://doi.org/10.1093/carcin/16.6.1305.

    Article  CAS  PubMed  Google Scholar 

  8. Baek SO, Field RA, Goldstone ME, Kirk PW, Lester JN, Perry R. A review of atmospheric polycyclic aromatic hydrocarbons: Sources, fate and behavior. Water Air Soil Pollut. 1991;60(3):279–300. https://doi.org/10.1007/BF00282628.

    Article  ADS  CAS  Google Scholar 

  9. Barrett ES, Sathyanarayana S, Janssen S, Redmon JB, Nguyen RHN, Kobrosly R, Swan SH. Environmental health attitudes and behaviors: Findings from a large pregnancy cohort study. Eur J Obstet Gynecol Reprod Biol. 2014;176:119–25. https://doi.org/10.1016/j.ejogrb.2014.02.029.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Boffetta P, Jourenkova N, Gustavsson P. Cancer risk from occupational and environmental exposure to polycyclic aromatic hydrocarbons. Cancer Causes Control. 1997;8(3):444–72. https://doi.org/10.1023/A:1018465507029.

    Article  CAS  PubMed  Google Scholar 

  11. Boss, J., Mukherjee, B., Ferguson, K. K., Aker, A., Alshawabkeh, A. N., Cordero, J. F., Meeker, J. D., & Kim, S. (2019). Estimating outcome-exposure associations when exposure biomarker detection limits vary across batches. Epidemiology (Cambridge, Mass.), https://doi.org/10.1097/EDE.0000000000001052. https://doi.org/10.1097/EDE.0000000000001052

  12. Boss, J., & Rix, A. (2020). lodi: Limit of Detection Imputation for Single-Pollutant Models (0.9.2) [Computer software]. https://cran.r-project.org/web/packages/lodi/index.html

  13. Bryant-Stephens T. Asthma disparities in urban environments. Journal of Allergy and Clinical Immunology. 2009;123(6):1199–206. https://doi.org/10.1016/j.jaci.2009.04.030.

    Article  PubMed  Google Scholar 

  14. Brzeźnicki S, Jakubowski M, Czerski B. Elimination of 1-hydroxypyrene after human volunteer exposure to polycyclic aromatic hydrocarbons. Int Arch Occup Environ Health. 1997;70(4):257–60. https://doi.org/10.1007/s004200050216.

    Article  PubMed  Google Scholar 

  15. Buuren, S. van, Groothuis-Oudshoorn, K., Vink, G., Schouten, R., Robitzsch, A., Rockenschaub, P., Doove, L., Jolani, S., Moreno-Betancur, M., White, I., Gaffert, P., Meinfelder, F., Gray, B., Arel-Bundock, V., Cai, M., Volker, T., Costantini, E., Lissa, C. van, & Oberman, H. (2023). mice: Multivariate Imputation by Chained Equations (3.16.0) [Computer software]. https://cran.r-project.org/web/packages/mice/index.html

  16. Carrico C, Gennings C, Wheeler DC, Litvak PF. Characterization of Weighted Quantile Sum Regression for Highly Correlated Data in a Risk Analysis Setting. 2014;20(1):100–20. https://doi.org/10.1007/s13253-014-0180-3.

    Article  Google Scholar 

  17. Cathey A, Ferguson KK, McElrath TF, Cantonwine DE, Pace G, Alshawabkeh A, Cordero JF, Meeker JD. Distribution and predictors of urinary polycyclic aromatic hydrocarbon metabolites in two pregnancy cohort studies. Environ Pollut. 2018;232:556–62. https://doi.org/10.1016/j.envpol.2017.09.087.

    Article  CAS  PubMed  Google Scholar 

  18. Chiu YH, Bellavia A, James-Todd T, Correia KF, Valeri L, Messerlian C, Ford JB, Mínguez-Alarcón L, Calafat AM, Hauser R, Williams PL. Evaluating effects of prenatal exposure to phthalate mixtures on birth weight: A comparison of three statistical approaches. Environment International. 2018;113:231–9. https://doi.org/10.1016/j.envint.2018.02.005.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Day DB, Sathyanarayana S, LeWinn KZ, Karr CJ, Mason WA, Szpiro AA. A Permutation Test-Based Approach to Strengthening Inference on the Effects of Environmental Mixtures: Comparison between Single-Index Analytic Methods. Environ Health Perspect. 2022;130(8): 087010. https://doi.org/10.1289/EHP10570.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Depner M, Fuchs O, Genuneit J, Karvonen AM, Hyvärinen A, Kaulek V, Roduit C, Weber J, Schaub B, Lauener R, Kabesch M, Pfefferle PI, Frey U, Pekkanen J, Dalphin J-C, Riedler J, Braun-Fahrländer C, von Mutius E, Ege MJ. Clinical and Epidemiologic Phenotypes of Childhood Asthma. Am J Respir Crit Care Med. 2014;189(2):129–38. https://doi.org/10.1164/rccm.201307-1198OC.

    Article  PubMed  Google Scholar 

  21. Dobraca D, Lum R, Sjödin A, Calafat AM, Laurent CA, Kushi LH, Windham GC. Urinary biomarkers of polycyclic aromatic hydrocarbons in pre- and peri-pubertal girls in Northern California: Predictors of exposure and temporal variability. Environ Res. 2018;165:46–54. https://doi.org/10.1016/j.envres.2017.11.011.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Feng X, Zeng G, Zhang Q, Song B, Wu K-H. Joint association of polycyclic aromatic hydrocarbons and heavy metal exposure with pulmonary function in children and adolescents aged 6–19 years. Int J Hyg Environ Health. 2022;244: 114007. https://doi.org/10.1016/j.ijheh.2022.114007.

    Article  CAS  PubMed  Google Scholar 

  23. Gaylord A, Kannan K, Lakuleswaran M, Zhu H, Ghassabian A, Jacobson MH, Long S, Liu H, Afanasyeva Y, Kahn LG, Gu B, Liu M, Mehta-Lee SS, Brubaker SG, Trasande L. Variability and correlations of synthetic chemicals in urine from a New York City-based cohort of pregnant women. Environ Pollut. 2022;309: 119774. https://doi.org/10.1016/j.envpol.2022.119774.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Gold DR, Wright R. Population Disparities in Asthma. Annu Rev Public Health. 2005;26(1):89–113. https://doi.org/10.1146/annurev.publhealth.26.021304.144528.

    Article  PubMed  Google Scholar 

  25. Guo Y, Senthilkumar K, Alomirah H, Moon H-B, Minh TB, Mohd MA, Nakata H, Kannan K. Concentrations and profiles of urinary polycyclic aromatic hydrocarbon metabolites (OH-PAHs) in several Asian countries. Environ Sci Technol. 2013;47(6):2932–8. https://doi.org/10.1021/es3052262.

    Article  ADS  CAS  PubMed  Google Scholar 

  26. Hehua Z, Qing C, Shanyan G, Qijun W, Yuhong Z. The impact of prenatal exposure to air pollution on childhood wheezing and asthma: A systematic review. Environ Res. 2017;159:519–30. https://doi.org/10.1016/j.envres.2017.08.038.

    Article  CAS  PubMed  Google Scholar 

  27. Henderson J, Granell R, Heron J, Sherriff A, Simpson A, Woodcock A, Strachan DP, Shaheen SO, Sterne JAC. Associations of wheezing phenotypes in the first 6 years of life with atopy, lung function and airway responsiveness in mid-childhood. Thorax. 2008;63(11):974–80. https://doi.org/10.1136/thx.2007.093187.

    Article  CAS  PubMed  Google Scholar 

  28. Herbstman JB, Tang D, Zhu D, Qu L, Sj, ödin A., Li, Z., Camann, D., & Perera, F. P. Prenatal Exposure to Polycyclic Aromatic Hydrocarbons, Benzo[a]pyrene–DNA Adducts, and Genomic DNA Methylation in Cord Blood. Environ Health Perspect. 2012;120(5):733–8. https://doi.org/10.1289/ehp.1104056.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. Some non-heterocyclic polycyclic aromatic hydrocarbons and some related exposures. IARC Monogr Eval Carcinog Risks Hum. 2010;92:1–853.

    PubMed Central  Google Scholar 

  30. Jedrychowski WA, Galas A, Pac A, Flak E, Camman D, Rauh V, Perera F. Prenatal Ambient Air Exposure to Polycyclic Aromatic Hydrocarbons and the Occurrence of Respiratory Symptoms over the First Year of Life. Eur J Epidemiol. 2005;20(9):775–82. https://doi.org/10.1007/s10654-005-1048-1.

    Article  CAS  PubMed  Google Scholar 

  31. Jedrychowski WA, Perera FP, Majewska R, Camman D, Spengler JD, Mroz E, Stigter L, Flak E, Jacek R. Separate and joint effects of tranplacental and postnatal inhalatory exposure to polycyclic aromatic hydrocarbons: Prospective birth cohort study on wheezing events. Pediatr Pulmonol. 2014;49(2):162–72. https://doi.org/10.1002/ppul.22923.

    Article  PubMed  Google Scholar 

  32. Jedrychowski WA, Perera FP, Maugeri U, Majewska R, Mroz E, Flak E, Camann D, Sowa A, Jacek R. Long term effects of prenatal and postnatal airborne PAH exposures on ventilatory lung function of non-asthmatic preadolescent children. Prospective birth cohort study in Krakow. The Science of the Total Environment. 2015;502:502–9. https://doi.org/10.1016/j.scitotenv.2014.09.051.

    Article  ADS  CAS  PubMed  Google Scholar 

  33. Jedrychowski WA, Perera FP, Maugeri U, Mrozek-Budzyn D, Mroz E, Klimaszewska-Rembiasz M, Flak E, Edwards S, Spengler J, Jacek R, Sowa A. Intrauterine exposure to polycyclic aromatic hydrocarbons, fine particulate matter and early wheeze. Prospective birth cohort study in 4-year olds. Pediatric Allergy and Immunology. 2010;21(4p2):e723–32. https://doi.org/10.1111/j.1399-3038.2010.01034.x.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Jia, C., & Batterman, S. (2010). A Critical Review of Naphthalene Sources and Exposures Relevant to Indoor and Outdoor Air. International Journal of Environmental Research and Public Health, 7(7), Article 7. https://doi.org/10.3390/ijerph7072903

  35. Jung, K. H., Hsu, S., Moors, K., Bernabe, K., Yan, B., Chillrud, S. N., Whyatt, R., Perzanowski, M., Hoepner, L., Goldstein, I., Zhang, B., Camann, D., Kinney, P. L., Perera, F. P., & Miller, R. L. (2011). Prenatal and Postnatal Exposure to Polycyclic Aromatic Hydrocarbons and Asthma: Effect of Atopy at Ages 5–6 yrs in an Urban Birth Cohort. Journal of Allergy and Clinical Immunology, 127(2), AB3. https://doi.org/10.1016/j.jaci.2011.01.010

  36. Jung KH, Hsu S-I, Yan B, Moors K, Chillrud SN, Ross J, Wang S, Perzanowski MS, Kinney PL, Whyatt RM, Perera FP, Miller RL. Childhood exposure to fine particulate matter and black carbon and the development of new wheeze between ages 5 and 7 in an urban prospective cohort. Environ Int. 2012;45:44–50. https://doi.org/10.1016/j.envint.2012.03.012.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Jung KH, Perzanowski M, Rundle A, Moors K, Yan B, Chillrud SN, Whyatt R, Camann D, Perera FP, Miller RL. Polycyclic aromatic hydrocarbon exposure, obesity and childhood asthma in an urban cohort. Environ Res. 2014;128:35–41. https://doi.org/10.1016/j.envres.2013.12.002.

    Article  CAS  PubMed  Google Scholar 

  38. Jung KH, Yan B, Moors K, Chillrud SN, Perzanowski MS, Whyatt RM, Hoepner L, Goldstein I, Zhang B, Camann D, Kinney PL, Perera FP, Miller RL. Repeated exposure to polycyclic aromatic hydrocarbons and asthma: Effect of seroatopy. Ann Allergy Asthma Immunol. 2012;109(4):249–54. https://doi.org/10.1016/j.anai.2012.07.019.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Kim KH, Jahan SA, Kabir E, Brown RJC. A review of airborne polycyclic aromatic hydrocarbons (PAHs) and their human health effects. Environ Int. 2013;60:71–80. https://doi.org/10.1016/j.envint.2013.07.019.

    Article  CAS  PubMed  Google Scholar 

  40. Korsh J, Shen A, Aliano K, Davenport T. Polycyclic Aromatic Hydrocarbons and Breast Cancer: A Review of the Literature. Breast Care. 2015;10(5):316–8. https://doi.org/10.1159/000436956.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Låg M, Øvrevik J, Refsnes M, Holme JA. Potential role of polycyclic aromatic hydrocarbons in air pollution-induced non-malignant respiratory diseases. Respir Res. 2020;21(1):299. https://doi.org/10.1186/s12931-020-01563-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Lau N, Norman A, Smith MJ, Sarkar A, Gao Z. Association between Traffic Related Air Pollution and the Development of Asthma Phenotypes in Children: A Systematic Review. International Journal of Chronic Diseases. 2018;2018:1–12. https://doi.org/10.1155/2018/4047386.

    Article  Google Scholar 

  43. LeWinn KZ, Karr CJ, Hazlehurst M, Carroll K, Loftus C, Nguyen R, Barrett E, Swan SH, Szpiro AA, Paquette A, Moore P, Spalt E, Younglove L, Sullivan A, Colburn T, Byington N, Sims Taylor L, Moe S, Wang S, Sathyanarayana S. Cohort profile: The ECHO prenatal and early childhood pathways to health consortium (ECHO-PATHWAYS). BMJ Open. 2022;12(10):e064288. https://doi.org/10.1136/BMJOPEN-2022-064288.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Li N, Sioutas C, Cho A, Schmitz D, Misra C, Sempf J, Wang M, Oberley T, Froines J, Nel A. Ultrafine particulate pollutants induce oxidative stress and mitochondrial damage. Environ Health Perspect. 2003;111(4):455–60. https://doi.org/10.1289/ehp.6000.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Li Z, Romanoff L, Bartell S, Pittman EN, Trinidad DA, McClean M, Webster TF, Sjödin A. Excretion Profiles and Half-Lives of Ten Urinary Polycyclic Aromatic Hydrocarbon Metabolites after Dietary Exposure. Chem Res Toxicol. 2012;25(7):1452–61. https://doi.org/10.1021/tx300108e.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Liu H, Xu C, Jiang Z-Y, Gu A. Association of polycyclic aromatic hydrocarbons and asthma among children 6–19 years: NHANES 2001–2008 and NHANES 2011–2012. Respir Med. 2016;110:20–7. https://doi.org/10.1016/j.rmed.2015.11.003.

    Article  PubMed  Google Scholar 

  47. Loftus CT, Szpiro AA, Workman T, Wallace ER, Hazlehurst MF, Day DB, Ni Y, Carroll KN, Adgent MA, Moore PE, Barrett ES, Nguyen RHN, Kannan K, Robinson M, Masterson EE, Tylavsky FA, Bush NR, LeWinn KZ, Sathyanarayana S, Karr CJ. Maternal exposure to urinary polycyclic aromatic hydrocarbons (PAH) in pregnancy and childhood asthma in a pooled multi-cohort study. Environ Int. 2022;170: 107494. https://doi.org/10.1016/J.ENVINT.2022.107494.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Majewska R, Pac A, Mróz E, Spengler J, Camann D, Mrozek-Budzyn D, Sowa A, Jacek R, Wheelock K, Perera FP. Lung function growth trajectories in non-asthmatic children aged 4–9 in relation to prenatal exposure to airborne particulate matter and polycyclic aromatic hydrocarbons – Krakow birth cohort study. Environ Res. 2018;166:150–7. https://doi.org/10.1016/j.envres.2018.05.037.

    Article  CAS  PubMed  Google Scholar 

  49. Mann KK, Doerre S, Schlezinger JJ, Sherr DH, Quadri S. The role of NF-kappaB as a survival factor in environmental chemical-induced pre-B cell apoptosis. Mol Pharmacol. 2001;59(2):302–9. https://doi.org/10.1124/mol.59.2.302.

    Article  CAS  PubMed  Google Scholar 

  50. Martinez FD, Wright AL, Taussig LM, Holberg CJ, Halonen M, Morgan WJ. Asthma and Wheezing in the First Six Years of Life. N Engl J Med. 1995;332(3):133–8. https://doi.org/10.1056/NEJM199501193320301.

    Article  CAS  PubMed  Google Scholar 

  51. Meeker, J. D., Barr, D. B., Serdar, B., Rappaport, S. M., & Hauser, R. (2007). Utility of urinary 1-naphthol and 2-naphthol levels to assess environmental carbaryl and naphthalene exposure in an epidemiology study. Journal of Exposure Science & Environmental Epidemiology, 17(4), Article 4. https://doi.org/10.1038/sj.jes.7500502

  52. Miller RL, Garfinkel R, Horton M, Camann D, Perera FP, Whyatt RM, Kinney PL. Polycyclic Aromatic Hydrocarbons, Environmental Tobacco Smoke, and Respiratory Symptoms in an Inner-city Birth Cohort. Chest. 2004;126(4):1071–8. https://doi.org/10.1378/chest.126.4.1071.

    Article  CAS  PubMed  Google Scholar 

  53. NHLBI. (2007). Expert Panel Report 3: Guidelines for the Diagnosis and Management of Asthma. https://www.nhlbi.nih.gov/health-topics/guidelines-for-diagnosis-management-of-asthma

  54. Paquette AG, Lapehn S, Freije S, MacDonald J, Bammler T, Day DB, Loftus CT, Kannan K, Alex Mason W, Bush NR, LeWinn KZ, Enquobahrie DA, Marsit C, Sathyanarayana S. Placental transcriptomic signatures of prenatal exposure to Hydroxy-Polycyclic aromatic hydrocarbons. Environ Int. 2023;172: 107763. https://doi.org/10.1016/j.envint.2023.107763.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Pennington AF, Strickland MJ, Klein M, Zhai X, Bates JT, Drews-Botsch C, Hansen C, Russell AG, Tolbert PE, Darrow LA. Exposure to Mobile Source Air Pollution in Early-life and Childhood Asthma Incidence: The Kaiser Air Pollution and Pediatric Asthma Study. Epidemiology (Cambridge, Mass). 2018;29(1):22–30. https://doi.org/10.1097/EDE.0000000000000754.

    Article  PubMed  Google Scholar 

  56. Perera FP, Jedrychowski W, Rauh V, Whyatt RM. Molecular epidemiologic research on the effects of environmental pollutants on the fetus. Environ Health Perspect. 1999;107(suppl 3):451–60. https://doi.org/10.1289/ehp.99107s3451.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. R Core Team. (2022). R: A language and environment for statistical computing (4.2.2) [Computer software]. https://www.R-project.org/

  58. Renzetti, S., Curtin, P., Just, A. C., Bello, G., & Gennings, C. (2021). gWQS: Generalized Weighted Quantile Sum Regression (3.0.4) [Computer software]. https://cran.r-project.org/web/packages/gWQS/index.html

  59. Rosa MJ, Jung KH, Perzanowski MS, Kelvin EA, Darling KW, Camann DE, Chillrud SN, Whyatt RM, Kinney PL, Perera FP, Miller RL. Prenatal exposure to polycyclic aromatic hydrocarbons, environmental tobacco smoke and asthma. Respir Med. 2011;105(6):869–76. https://doi.org/10.1016/j.rmed.2010.11.022.

    Article  PubMed  Google Scholar 

  60. Salam, M. T., Li, Y.-F., Langholz, B., Gilliland, F. D., & Children’s Health Study. Early-life environmental risk factors for asthma: Findings from the Children’s Health Study. Environ Health Perspect. 2004;112(6):760–5. https://doi.org/10.1289/ehp.6662.

    Article  Google Scholar 

  61. Savenije OE, Granell R, Caudri D, Koppelman GH, Smit HA, Wijga A, de Jongste JC, Brunekreef B, Sterne JA, Postma DS, Henderson J, Kerkhof M. Comparison of childhood wheezing phenotypes in 2 birth cohorts: ALSPAC and PIAMA. J Allergy Clin Immunol. 2011;127(6):1505-1512.e14. https://doi.org/10.1016/j.jaci.2011.02.002.

    Article  PubMed  Google Scholar 

  62. Sherriff, A., Peters, T. J., Henderson, J., Strachan, D., & and The Alspac Study Team. Risk factor associations with wheezing patterns in children followed longitudinally from birth to 3 ½ years. Int J Epidemiol. 2001;30(6):1473–84. https://doi.org/10.1093/ije/30.6.1473.

    Article  Google Scholar 

  63. Sontag-Padilla, L., Burns, R. M., Shih, R. A., Griffin, B. A., Martin, L. T., Chandra, A., & Tylavsky, F. (2015). The Urban Child Institute CANDLE Study: Methodological Overview and Baseline Sample Description. RAND Corporation. https://www.rand.org/pubs/research_reports/RR1336.html

  64. Taussig LM, Wright AL, Holberg CJ, Halonen M, Morgan WJ, Martinez FD. Tucson children’s respiratory study: 1980 to present. Journal of Allergy and Clinical Immunology. 2003;111(4):661–75. https://doi.org/10.1067/mai.2003.162.

    Article  PubMed  Google Scholar 

  65. Trivedi, M., & Denton, E. (2019). Asthma in Children and Adults—What Are the Differences and What Can They Tell us About Asthma? Frontiers in Pediatrics, 7. https://www.frontiersin.org/articles/https://doi.org/10.3389/fped.2019.00256

  66. Wang I-J, Karmaus WJJ, Yang C-C. Polycyclic aromatic hydrocarbons exposure, oxidative stress, and asthma in children. Int Arch Occup Environ Health. 2017;90(3):297–303. https://doi.org/10.1007/s00420-017-1198-y.

    Article  CAS  PubMed  Google Scholar 

  67. Weisskopf, M. G., Seals, R. M., & Webster, T. F. (2018). Bias amplification in epidemiologic analysis of exposure to mixtures. Environmental Health Perspectives, 126(4), 047003–1–047003–047008. https://doi.org/10.1289/EHP2450

  68. Woodruff TJ, Zota AR, Schwartz JM. Environmental Chemicals in Pregnant Women in the United States: NHANES 2003–2004. Environ Health Perspect. 2011;119(6):878–85. https://doi.org/10.1289/ehp.1002727.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Zhu H, Martinez-Moral M-P, Kannan K. Variability in urinary biomarkers of human exposure to polycyclic aromatic hydrocarbons and its association with oxidative stress. Environ Int. 2021;156: 106720. https://doi.org/10.1016/j.envint.2021.106720.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We are grateful for the participation of families enrolled in the CANDLE and TIDES cohorts, as well as the dedication of research staff and investigators. We acknowledge the contribution of the former TIDES Center Director Dr. Shannah Swan. This manuscript has been reviewed by PATHWAYS for scientific content and consistency of data interpretation with previous PATHWAYS publications. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Funding

This study was conducted by the ECHO PATHWAYS Consortium, funded by NIH UG3/UH3OD023271. The CANDLE study was funded by the Urban Child Institute, NIH R01HL109977, and NIH HL132338. The TIDES study was funded by NIH UG3/UH3OD023305, R01ES016863 and R01ES25169. Select surveys were administered in REDCap, which was developed with NIH funds (NCATS:UL1 TR002319). Dr. Kannan analyzed OH-PAH metabolites in TIDES with support from the New York University ECHO Cohort Center (NIH UG3/ UH3OD023305 [PI: Leonardo Trasande]). Dr. Catherine Karr was supported in part by NIH P30ES007033. Dr. Marnie Hazlehurst was supported in part by the UW NIEHS sponsored Biostatistics, Epidemiologic and Bioinformatic Training in Environmental Health (BEBTEH) Training Grant: NIEHS T32ES015459. Dr. Emily Barrett was supported in part by P30ES005022.

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Authors and Affiliations

  1. Department of Environmental and Occupational Health Sciences, University of Washington4225, Roosevelt Way NE, Suite 300, Seattle, WA, 98105, US

    Allison R. Sherris, Christine T. Loftus, Logan C. Dearborn, Marnie F. Hazlehurst, Anne M. Riederer & Catherine J. Karr

  2. Department of Biostatistics, University of Washington, Seattle, WA, US

    Adam A. Szpiro

  3. Icahn School of Medicine at Mount Sinai, New York, NY, US

    Kecia N. Carroll

  4. Vanderbilt University Medical Center, Nashville, TN, US

    Paul E. Moore & Margaret A. Adgent

  5. Rutgers University School of Public Health, Piscataway, NJ, US

    Emily S. Barrett

  6. University of California, San Francisco, CA, US

    Nicole R. Bush & Kaja Z. LeWinn

  7. Seattle Children’s Research Institute, Seattle, WA, US

    Drew B. Day & Sheela Sathyanarayana

  8. New York State Department of Health, Albany, NY, US

    Kurunthachalam Kannan

  9. University of Minnesota, Minneapolis, MN, US

    Ruby H. N. Nguyen

  10. San Diego State University, San Diego, CA, US

    Yu Ni

  11. New York University, New York, NY, US

    Morgan Robinson

  12. University of Tennessee Health Science Center, Memphis, TN, US

    Qi Zhao

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  1. Allison R. Sherris
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Contributions

A.R.S., C.J.K., C.T.L., K.N.C., and P.E.M designed the study. A.R.S. and L.D. analyzed the data. A.R.S. drafted the manuscript and figures and revised drafts. A.A.S., C.T.L., M.A., D.B.D., P.E.M. K.N.C., E.S.B., M.F.H., L.D., Y.N. and A.A.R. provided input and guidance on data analysis plan. K.K. and M.R. conducted analysis of PAH concentrations in maternal urine samples. K.N.C., C.J.K., K.Z.L, R.H.N.N., S.S., and Q.Z. oversaw cohort activities and data collection. All authors reviewed and revised the original manuscript drafts and read and approved the final manuscript.

Corresponding author

Correspondence to Allison R. Sherris.

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Ethical approval and consent to participate

The CANDLE study was approved by the University of Tennessee Health Science Center Institutional Review Board (IRB), and TIDES study procedures were approved by IRBs at each study site as well as the TIDES coordinating center. Pregnant women provided informed consent at enrollment. The current analysis was conducted by the ECHO PATHWAYS Consortium and was approved by the University of Washington IRB.

Competing interests

The authors declare no competing interests.

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Sherris, A.R., Loftus, C.T., Szpiro, A.A. et al. Prenatal polycyclic aromatic hydrocarbon exposure and asthma at age 8–9 years in a multi-site longitudinal study. Environ Health 23, 26 (2024). https://doi.org/10.1186/s12940-024-01066-2

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Keywords

Environmental Health

ISSN: 1476-069X