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. Author manuscript; available in PMC: 2024 Mar 1.
Published in final edited form as: Environ Res. 2022 Dec 23;220:115150. doi: 10.1016/j.envres.2022.115150

Urinary metabolites of polycyclic aromatic hydrocarbons and short-acting beta agonist or systemic corticosteroid asthma medication use within NHANES

Stephen P Uong a, Haider Hussain b, Erin Thanik b, Stephanie Lovinsky-Desir c, Jeanette A Stingone a
PMCID: PMC9969867  NIHMSID: NIHMS1872104  PMID: 36572332

Abstract

Background:

Within cross-sectional studies like the U.S. National Health and Nutritional Examination Survey (NHANES), researchers have observed positive associations between polycyclic aromatic hydrocarbon (PAH) exposure and asthma diagnosis. It is unclear whether similar relationships exist for measures of acute asthma outcomes, including short-term asthma medication use to alleviate symptoms. We examined the relationship between markers of recent PAH exposure and 30-day short-acting beta agonist (SABA) or systemic corticosteroid use, an indicator for recent asthma symptoms.

Materials and Methods:

For 16,550 children and adults across multiple waves of NHANES (2005-2016), we fit quasi-Poisson multivariable regression models to describe the association between urinary 1-hydroxypyrene (a metabolite of PAH) and SABA or systemic corticosteroid use. We assessed for effect modification by age group and asthma controller medication use. All models were adjusted for urinary creatinine, age, female/male designation, race/ethnicity, poverty, insurance coverage, and serum cotinine.

Results:

After controlling for confounding, an increase of one standard deviation of 1-hydroxypyrene was associated with greater prevalence of recent SABA or systemic corticosteroid use (PR: 1.06, 95% CI: 1.03-1.10). The results were similar among those with ever asthma diagnosis and across urine creatinine dilution methods. We did not observe effect modification by age group (p-interaction = 0.22) or asthma controller medication use (p-interaction = 0.73).

Conclusion:

Markers of recent PAH exposure was positively associated with SABA or systemic corticosteroid use, across various urine dilution adjustment methods. It is important to ensure appropriate temporality between exposures and outcomes in cross-sectional studies.

Keywords: polycyclic aromatic hydrocarbons, indoor and outdoor air pollution, asthma medications, asthma symptoms, asthma exacerbations, urine dilution adjustment

INTRODUCTION

Asthma is a chronic inflammatory respiratory disorder characterized by airway hyperresponsiveness and symptoms related to reduced airflow, including wheezing, coughing, and chest tightness. In 2018, the United States Centers for Disease Control Prevention (CDC) estimated that about 25 million Americans or 7.7% of Americans suffered from prevalent asthma (CDC National Center for Environmental Health, 2018). The prevalence of asthma has been increasing worldwide, possibly partially due to increased indoor and outdoor air pollution exposure (Ferkol & Schraufnagel, 2014; Seaton et al., 1994). A sizeable literature suggests that air pollution from various sources may both cause asthma exacerbations and potentially induce the onset of asthma (Anderson et al., 2013; Orellano et al., 2017; Schildcrout et al., 2006; Thurston et al., 2020). Continued research characterizing the relationship between air pollution and asthma is important to inform air quality standards and public health action to reduce burden from asthma.

Polycyclic aromatic hydrocarbons (PAH) are formed from the incomplete combustion of organic materials and come from various sources, such as indoor or outdoor air pollution, diet, and tobacco smoking (Baek et al., 1991; Khaiwal et al., 2008; Lu & Zhu, 2007; Phillips, 1999). Metabolites excreted in the urine from PAH exposure can serve as a biological marker of recent indoor or outdoor air pollution exposure and have been used in several previous studies (Castano-Vinyals et al., 2004; Han et al., 2018; Li et al., 2008; Liu et al., 2016). This includes studies performed using the U.S. National Health and Nutrition Examination Survey (NHANES) to evaluate the relationship between markers of recent PAH exposures and asthma-related outcomes (Han et al., 2018; Liu et al., 2016). These studies have generally observed a positive association between markers of recent PAH exposure and asthma-related outcomes, including ever asthma diagnoses and symptoms such as wheezing that are common for colds, allergies, or other conditions (Han et al., 2018; Liu et al., 2016).

As researchers continue to characterize determinants of asthma, it is informative to differentiate between conceptualizing asthma onset or asthma exacerbations as the outcome and consider the different timing of the outcomes compared to recent environmental exposures in cross-sectional studies. It can be problematic to use previous or ever asthma diagnosis as an outcome when comparing it to a recent environmental exposures, such as indicated by metabolites excreted in the urine from PAH exposure (Han et al., 2018; Liu et al., 2016; Odebeatu et al., 2019). The half-life of 1-hydroxypyrene has been estimated to be 1-2 days (Jongeneelen et al., 1988; Viau, 1999). A single measure of urinary PAH may be representative of lifetime exposures if it was measured among individuals who did not move substantial distances across their life course and lived in an area or housing with stable levels of indoor and outdoor air pollution. Conceptualizing a single urinary PAH measure as a marker of long-term exposure requires very strong assumptions about mobility and temporal trends in ambient air pollution and stability of the home or indoor environment. It is more appropriate to consider this biomarker an indicator of recent exposure to PAH, and likely is not representative of exposures prior to the receipt of an asthma diagnosis in past years.

Furthermore, ever asthma diagnosis may not be a good indicator of current asthma diagnosis due to variation over the life course and different seasons (Harju et al., 1997; Zein et al., 2019). Acute measures such as asthma exacerbations or other outcomes reflective of recent asthma symptoms, such as short-term medication use, are more likely to reflect recent environmental exposures compared to previous or ever asthma diagnoses. In a report prepared by an expert panel and coordinated by the National Institutes of Health, the asthma management guidelines describe that quick-relief medications, including short-acting beta agonists (SABAs) and systemic corticosteroids, are used to mitigate bronchoconstriction and acute asthma symptoms such as coughing, chest tightness, and wheezing (National Heart, Lung, and Blood Institute, National Asthma Education and Prevention Program., 2007). Measuring the use of such medications may be a useful proxy measure of asthma symptoms, particularly when use is assessed during a short time window concurrent with the measurement of urinary biomarkers. Previous studies have found that SABA and systemic corticosteroid use were predictive and associated with asthma-related exacerbations and symptoms (Bleecker et al., 2020; Stanford et al., 2012).

Another limitation of previous studies has been the use of suboptimal dilution adjustment methods. In using a corresponding urine metabolite concentration as a proxy measurement for indoor or outdoor PAH exposure, differences in urine dilution may introduce measurement error bias (O’Brien Katie M. et al., 2016). Creatinine is a waste product of muscle metabolism, and previous researchers have used its resulting concentration to correct for differences in urine dilution (i.e. creatinine standardization) due to its stable filtration rate within an individual (O’Brien et al., 2017; Wyss & Kaddurah-Daouk, 2000). Variation in individual characteristics, such as age and gender, may result in different urine creatinine concentrations due to differences in muscle mass and kidney function (Arndt, 2009; Cocker et al., 2011; James et al., 1988). Failure to account for some of these individual characteristics in creatinine standardization may result in confounding bias. Previous studies used various methods to correct for urine dilution, including creatinine standardization and creatinine adjustment in statistical analyses (Han et al., 2018; Liu et al., 2016). A simulation study suggested that the common practice of standardizing urinary metabolites using creatinine without correcting for individual factors related to urine dilution may introduce bias (O’Brien Katie M. et al., 2016). In comparing several dilution methods, the study demonstrated that standardizing with creatinine corrected for individual factors (i.e. covariate-adjusted standardization) may reduce overall bias most effectively from urinary dilution (O’Brien Katie M. et al., 2016).

Among children and adults from NHANES, we aimed to assess the relationship between 1-hydroxypyrene (a marker for recent PAH exposure) and 30-day SABA or systemic corticosteroid use (indicators for recent asthma symptoms) and determine if choice of urinary dilution method impacted results. Although we did not find any other studies that assessed modification of the relationship between PAH exposure and asthma outcomes by age or controller medication use, previous studies suggest that these two factors could modify the relationship between air pollution more generally and asthma (Alhanti et al., 2016; Diaz Lozano Patino & Siegel, 2018; Lewis et al., 2013; Samoli et al., 2011). Therefore, we also assessed whether there was evidence of effect modification of the relationship between 1-hydroxypyrene and SABA or systemic corticosteroid use by age group or controller asthma medication use.

MATERIALS AND METHODS

Study Population

The study population included children and adults who were sampled for laboratory examinations from NHANES. We included six waves of the cross-sectional, nationally representative surveys from 2005 to 2016. Research staff designed NHANES to assess the health and nutritional status across the U.S. population, and they collected patient demographics, health history, laboratory samples, and prescription medication use as part of the study (Johnson et al., 2014; Zipf et al., 2013). Previous studies have described the sampling design (Johnson et al., 2014; Liu et al., 2016; Zipf et al., 2013). Briefly, the CDC sampled participants representative of the non-institutionalized U.S. population through a complex, multistage, probability design that oversampled certain groups of the population by age, race/ethnicity, and income. A one-third sample of participants aged 6 years or older and all participants aged 3-5 in the 2015-2016 wave were eligible for laboratory urine PAH tests from the mobile examination centers (n = 16,803). We excluded those with missing or zero laboratory weights for the complex survey design from the study (n = 253 or 1.53%) for a final sample size of 16,550 participants. The study did not meet the criteria of human subjects research as it was an analysis of publicly-available data.

Exposure

Study participants provided urine specimens at local mobile examination centers. Study staff shipped the specimens to the National Center for Environmental Health for processing and storage under frozen conditions (−20°C) until testing. We used 1-hydroxypyrene urine concentrations as a marker of PAH exposure. In a previous NHANES study, the authors determined that only 1-hydroxypyrene, out of 10 PAH metabolites measured in urine, was associated with ever asthma diagnosis (Liu et al., 2016). Furthermore, 1-hydroxypyrene urine concentration has been frequently used in previous studies as a biomarker for PAH exposure and shown to increase with repeated exposures to PAH mixtures in animal models (Bouchard et al., 2002; Ifegwu et al., 2012). To account for urinary dilution, we incorporated several creatinine urine dilution adjustment methods as previously described (O’Brien Katie M. et al., 2016). Briefly, these methods are summarized in Supplementary Table 1 and included combinations of the following: standardizing 1-hydroxypyrene by unadjusted creatinine, standardizing 1-hydroxypyrene by creatinine adjusted by age, female/male designation, and body mass index (BMI), adjusting in final models for creatinine, or adjusting in final models for residuals from a first model that fit creatinine as a function of 1-hydroxypyrene. After our sensitivity analysis of the dilution adjustment methods, we ultimately defined the exposure used in our statistical analyses as 1-hydroxypyrene standardized with covariate-adjusted creatinine and adjusted for creatinine in our final models as recommend by a previous simulation analysis (O’Brien Katie M. et al., 2016).

Outcome

We defined our outcome as any self-reported use of SABA or systemic corticosteroid prescription medications within thirty days prior to the survey date using questionnaire data from the NHANES Dietary Supplement and Prescription Medication section. These medications are used to treat active asthma symptoms or exacerbations. Those who indicated that they had taken the medications were asked to show the interviewer the medication containers or to verbally confirm the medication name. SABAs included albuterol, levalbuterol, and pirbuterol, and systemic corticosteroids included methylprednisolone, prednisolone, prednisone, and dexamethasone.

Confounders and Effect Modifiers

We included several self-reported characteristics in the study, including age at screening, female/male designation, race/ethnicity, family poverty income ratio (PIR), and serum cotinine that we conceptualized as confounding factors from our constructed causal directed acyclic graph (Greenland et al., 1999). We determined female/male designation from a survey question that asked study participants if they were “male or female, if not obvious.” The PIR represented the ratio of total family income by respective poverty threshold. A PIR value below 1.00 indicated family income below the poverty level, and a value 1.00 or greater indicated family income above the poverty level. In Table 1, we categorized smoking status by the following ranges of serum cotinine concentration: less than 1 ng/mL as nonsmoking, 1 to 10 ng/mL as nonsmoking with heavy secondhand smoke, and more than 10 ng/mL as actively smoking (Prevention, 2017). We defined ever asthma diagnosis from a self-report by the study participant of ever having had an asthma diagnosis from a doctor or other health professional. We defined controller asthma medication use as those typically taken on a long-term basis to maintain control of persistent asthma from self-reports of prescription medications taken in the past thirty days, including inhaled corticosteroids, mast cell/leukotriene modifiers, monoclonal antibodies and long-acting beta agonists. Based on the 2007 National Asthma Education and Prevention Program asthma guidelines, we included any use of the following medications: beclomethasone dipropionate, budesonide, flunisolide, fluticasone propionate, triamcinolone acetonide, cromolyn sodium, montelukast, zafirlukast, formoterol, salmeterol, theophylline, mometasone furoate, nedocromil, and omalizumab (Falk et al., 2016; National Heart, Lung, and Blood Institute, National Asthma Education and Prevention Program., 2007).

Table 1.

Characteristics of Study Population by Quartile of PAH Exposure, NHANES 2005-20161

Characteristics PAH Exposure2 Quartile
Total
(n = 16,550)		 Q1 (Lowest)
(n = 3,885)		 Q2
(n = 3,877)		 Q3
(n = 3,939)		 Q4 (Highest)
(n = 4,207)
Survey Year
2005-2006 2,593 (15.8%) 1,025 (25.5%) 575 (12.6%) 415 (10.5%) 400 (11.7%)
2007-2008 2,674 (16.1%) 738 (18.0%) 634 (16.1%) 590 (15.4%) 618 (15.0%)
2009-2010 2,777 (16.4%) 662 (16.7%) 691 (18.1%) 656 (16.6%) 737 (15.7%)
2011-2012 2,551 (16.7%) 563 (16.0%) 648 (18.2%) 646 (16.0%) 628 (17.4%)
2013-2014 2,724 (17.0%) 393 (9.3%) 629 (17.1%) 790 (20.8%) 837 (21.3%)
2015-2016 3,231 (18.0%) 504 (14.5%) 700 (18.0%) 842 (20.7%) 987 (19.0%)
Age at Screening
3-123 2,968 (9.3%) 220 (3.1%) 539 (7.9%) 842 (11.7%) 1,187 (14.1%)
12-18 2,489 (10.6%) 629 (9.8%) 708 (13.4%) 643 (11.8%) 424 (7.6%)
19-45 5,213 (39.5%) 1,157 (35.9%) 1,248 (39.0%) 1,279 (42.1%) 1,392 (42.1%)
46-70 4,247 (31.6%) 1,188 (35.6%) 1,016 (31.5%) 905 (27.8%) 1,018 (32.2%)
>70 1,633 (9.0%) 691 (15.6%) 366 (8.2%) 270 (6.6%) 186 (4.0%)
Mean (SD) 39.80 (20.85) 46.05 (20.90) 39.37 (20.64) 36.73 (20.25) 36.61 (19.46)
Female/Male Designation
Female 8,312 (51.3%) 1,504 (41.4%) 1,969 (52.1%) 2,179 (54.8%) 2,291 (55.6%)
Male 8,238 (48.7%) 2,381 (58.6%) 1,908 (47.9%) 1,760 (45.2%) 1,916 (44.4%)
Race/Ethnicity
Non-Hispanic White 6,229 (65.2%) 1,464 (66.1%) 1,361 (63.3%) 1,451 (64.5%) 1,661 (65.8%)
Mexican American 3,153 (9.7%) 727 (8.9%) 849 (11.0%) 768 (10.4%) 709 (8.9%)
Non-Hispanic Black 3,802 (12.0%) 1,075 (14.3%) 840 (11.5%) 813 (11.0%) 933 (11.6%)
Other Hispanic 1,611 (5.6%) 277 (4.3%) 367 (5.5%) 441 (6.3%) 478 (6.6%)
Other 1,755 (7.5%) 342 (6.4%) 460 (8.6%) 466 (7.8%) 426 (7.1%)
Family Poverty Income Ratio
Mean (SD) 2.88 (1.65) 3.15 (1.59) 3.03 (1.64) 2.82 (1.64) 2.53 (1.66)
Any Health Insurance Coverage 13,437 (83.5%) 3,302 (88.2%) 3,177 (85.0%) 3,170 (82.4%) 3,219 (77.9%)
Smoking Status
Nonsmoking (serum cotinine < 1 ng/mL) 11,266 (72.9%) 3,239 (91.1%) 2,989 (83.6%) 2,679 (73.1%) 1,957 (43.9%)
Nonsmoking, heavy SHS (serum cotinine 1-10 ng/mL) 938 (5.0%) 144 (3.5%) 205 (4.8%) 260 (6.3%) 289 (5.5%)
Actively smoking (serum cotinine > 10 ng/mL) 2,889 (22.0%) 217 (5.4%) 384 (11.5%) 654 (20.6%) 1,533 (50.6%)
Ever Diagnosed with Asthma 2,517 (15.1%) 527 (14.4%) 608 (15.3%) 611 (15.7%) 680 (15.2%)
Prescription asthma medication use within the past 30 days
 SABA or Systemic Corticosteroid 740 (4.1%) 159 (3.5%) 151 (3.4%) 187 (4.3%) 208 (5.1%)
  SABA4 626 (3.4%) 127 (2.8%) 124 (2.7%) 160 (3.7%) 187 (4.4%)
  Systemic Corticosteroid5 155 (0.9%) 40 (0.9%) 35 (0.9%) 37 (0.9%) 35 (0.9%)
 Controller6 575 (3.7%) 122 (3.0%) 138 (4.0%) 138 (3.8%) 145 (3.6%)
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1

Sample sizes and percentages are shown unless otherwise specified (Mean and SD shown for age at screening and family poverty income ratio). Sample sizes are unweighted, and percentages are weighted due to the complex survey design.

2

PAH standardized by covariate-adjusted creatinine.

3

Only survey wave 2015-2016 included children aged 3 to 5.

4

Short-acting beta-agonists (SABAs) included albuterol, levalbuterol, pirbuterol.

5

Systemic corticosteroids included methylprednisolone, prednisolone, prednisone, dexamethasone.

6

Controller medications included beclomethasone dipropionate, budesonide, flunisolide, fluticasone propionate, fluticasone propionate, triamcinolone acetonide, montelukast, zafirlukast, formoterol, salmeterol, theophylline, and mometasone furoate.

Statistical Analysis

To calculate prevalence ratios (PR) and account for the complex survey design, we used quasi-Poisson multivariable regression models in the R survey package in assessing the association between a biological marker of PAH exposure and SABA or systemic corticosteroid use (Thompson et al., 1998; Zou, 2004). Creatinine-adjusted models included only 1-hydroxypyrene and urinary creatinine concentration, and adjusted models included 1-hydroxypyrene in addition to urinary creatinine concentration, age, female/male designation, race/ethnicity, poverty income ratio, and serum cotinine concentration. Furthermore, we assessed whether the association would differ by type of asthma medication (SABA or systemic corticosteroid). We assessed linearity through associations of quartiles of 1-hydroxypyrene concentrations with SABA or systemic corticosteroid use and ultimately included 1-hydroxypyrene as a single continuous term (Supplemental Table 1). In sensitivity analyses, we compared results between urine dilution adjustment methods and evaluated the association in a subset of study participants with ever asthma diagnosis. Additionally, we assessed whether adjustment by survey year or BMI would substantially change the results due to potential confounding from period effects or body size respectively. We evaluated effect measure modification by age group (younger than 18, 19-45, 46-70, and older than 70) and controller asthma medication use with Rao-Scott Likelihood Ratio Chi-Square tests using a significance level of 0.05. We completed all analyses in R 4.0.3 primarily using the survey package.

RESULTS

For our analysis, we included 16,550 participants across the six waves of NHANES who were sampled for laboratory urine PAH tests and did not lack laboratory test results or have missing survey weights (n = 253 or 1.53% among those sampled for a laboratory urine PAH test). We described characteristics of the study population in total and by quartiles of 1-hydroxypyrene concentration in Table 1. Of the study population, about half (51.3%) of study participants were female, most (83.5%) were covered by health insurance, and 15.1% ever had an asthma diagnosis. The mean age was 39 (SD = 20.9), and the mean PIR was 2.88 (SD = 1.65). Most (65.2%) participants were non-Hispanic White, 9.7% were Mexican American, 12.0% were Non-Hispanic Black, 5.6% were other Hispanic, and 7.5% were of another race/ethnicity. In the whole study population, 4.1% of participants used SABAs or systemic corticosteroids in the last thirty days (3.4% SABA and 0.9% systemic corticosteroid use). Among participants diagnosed with asthma, 19.6% of participants used SABAs or systemic corticosteroids in the last thirty days (18.5% SABA and 2.2% systemic corticosteroid use). Of those who reported SABA or systemic corticosteroid use, 28.1% did not report an asthma diagnosis. Finally, 3.2% of participants used controller medications.

In the overall study population, an increase of one standard deviation of covariate-adjusted 1-hydroxypyrene was associated with greater prevalence of SABA or systemic corticosteroid use (PR: 1.08, 95% CI: 1.04-1.11) (Table 2). After controlling for confounding by age, female/male designation, race/ethnicity, poverty income ratio, insurance coverage, smoking, and urine creatinine, the prevalence ratio was slightly attenuated but remained similar (PR: 1.06, 95% CI: 1.03-1.10). Results were similar in a subset of those who ever had an asthma diagnosis (creatinine-adjusted PR: 1.05, 95% CI: 1.01-1.09; fully adjusted PR: 1.04, 95% CI: 1.01-1.08). Furthermore, the results were similar with SABA use (fully adjusted PR: 1.07, 95% CI: 1.03-1.11), but there was no association between 1-hydroxypyrene and systemic corticosteroid use (fully adjusted PR: 0.95, 95% CI: 0.77-1.19) (Table 3).

Table 2.

Association Between PAH and SABA or Systemic Corticosteroid Use, Overall and Among those with Asthma Diagnosis, NHANES 2005-20161

Subset Creatinine-Adjusted
 Fully Adjusted2
95 % CI
 95 % CI
PR Low High PR Low High
Overall
 PAH3 1.07 1.04 1.10 1.06 1.03 1.10
Ever Diagnosed with Asthma
 PAH3 1.05 1.01 1.09 1.04 1.01 1.07
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1

Those with missing 1-hydroxypyrene or creatinine measurements were excluded from multivariable analyses (n = 642).

2

Adjusted for urinary creatinine, age, female/male designation, race/ethnicity, poverty income ratio, insurance coverage, and serum cotinine.

3

Contrast is a 1-unit change in PAH standardized with covariate-adjusted creatinine.

Table 3.

Association of PAH with SABA and Systemic Corticosteroid Use, Overall and Among those with Asthma Diagnosis, NHANES 2005-2016

Short-Acting Beta-Agonists1
 Systemic Corticosteroids2
Minimally Adjusted3
 Fully Adjusted4
 Minimally Adjusted3
 Fully Adjusted4
95 % CI
 95 % CI
 95 % CI
 95 % CI
PR Low High PR Low High PR Low High PR Low High
Overall
 PAH5 1.08 1.05 1.12 1.07 1.03 1.11 0.88 0.60 1.27 0.95 0.77 1.19
Ever Diagnosed with Asthma
 PAH5 1.05 1.01 1.09 1.04 1.01 1.07 0.87 0.63 1.18 0.94 0.67 1.31
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1

Short-acting beta-agonists included albuterol, levalbuterol, pirbuterol.

2

Systemic corticosteroids included methylprednisolone, prednisolone, prednisone, dexamethasone.

3

Adjusted for urinary creatinine.

4

Adjusted for urinary creatinine, gender, race/ethnicity, poverty income ratio, insurance coverage, and smoking category.

5

PAH standardized with covariate-adjusted creatinine.

After comparing several urine dilution adjustment methods in our sensitivity analysis, we observed similar associations between 1-hydroxypyrene and SABA or systemic corticosteroid use. A comparison of results from the different urine dilution methods can be found in Supplemental Table 2. In our additional sensitivity analysis, adjustment by survey year and BMI did not substantially change our results and was therefore excluded from our final fully adjusted models. We did not observe effect modification by controller asthma medication use (p-interaction = 0.73) or age group (p-interaction = 0.22), but there was suggestion of a greater association between 1-hydroxypyrene and SABA or systemic corticosteroid with increasing age (Figure 1).

Figure 1. Stratified Prevalence Ratios of PAH Exposure and SABA or Systemic Corticosteroid Use by Age Group and Controller Medication Use, NHANES 2005-2016.

Figure 1.

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There was no evidence of effect modification between PAH exposure and SABA or systemic corticosteroid use by age group, but there was suggestion of differences in the magnitude of association across age groups (p-interaction = 0.22). There was no effect modification between PAH exposure and SABA or systemic corticosteroid use by controller asthma medication use (p-interaction = 0.73).

DISCUSSION

In a nationally representative, cross-sectional study, we observed a positive relationship between a marker of recent PAH exposure and SABA or systemic corticosteroid use, indicators for recent asthma symptoms. Subsequent analyses suggest this association is only observed with SABA use. The associations were robust across multiple urine dilution adjustment methods. Furthermore, the results were similar in a subgroup of those ever diagnosed with asthma, indicating that potentially missed asthma symptoms from restricting to only study participants diagnosed with asthma and confounding from differential medication access by asthma diagnosis was minimal. We did not observe effect modification by controller asthma medication use or age group.

Continuing to characterize the relationship between PAH exposure and asthma is important to inform public health action that would mitigate sources of PAH exposure, such as setting air quality standards and providing support to communities with increased vulnerability to indoor and outdoor air pollution exposure. This study adds to a sizable literature that PAH exposure contributes to asthma symptoms and exacerbations. Our study provides evidence that the impacts of acute exposure on respiratory health are not limited to participant-reported symptoms but contribute more specifically to use of rescue medication. Data from national surveys such as NHANES provides ongoing opportunities to study the prevalence and burden of asthma from PAH exposure in a nationally representative study population, and robust study designs will minimize limitations on causal inference from using cross-sectional data.

We found that the association between a marker of recent PAH exposure and SABA or systemic corticosteroid use was robust regardless of urine dilution adjustment method used. We compared various dilution adjustment methods proposed in a previous study that included combinations of the following: standardizing 1-hydroxypyrene by unadjusted creatinine, standardizing 1-hydroxypyrene by creatinine adjusted by age, female/male designation, and BMI, adjusting in final models for creatinine, or adjusting in final models for residuals from a first model that fit creatinine as a function of 1-hydroxypyrene (O’Brien Katie M. et al., 2016). Previous studies of other environmental exposures and pregnancy-related outcomes have demonstrated consistent results in similar comparisons of dilution adjustment methods (Buckley et al., 2016; Etzel et al., 2017). In our final models, we standardized 1-hydroxypyrene concentrations with adjusted creatinine and adjusted for creatinine as recommended in a previous study to minimize measurement error bias (O’Brien Katie M. et al., 2016). Additionally, we observed large differences in markers of PAH exposure by smoking status (Table 1). To account for PAH exposure that may come from tobacco smoke, we adjusted for serum cotinine in our analyses (Lu & Zhu, 2007).

Differences in medication use/health access by asthma diagnosis and potential selection bias suggest that we should examine the full population, not just those previously diagnosed with asthma. A previous study showed that fewer than half of children receiving respiratory medication had a formal asthma diagnosis, suggesting that many of those who received respiratory medications may have been excluded if the study population was restricted only to those diagnosed with asthma (Zuidgeest et al., 2008). Several previous studies have shown that asthma medications were used among those without an asthma diagnosis, and healthcare and medication access may differ between those with and without an asthma diagnosis (Aaron et al., 2017; Stempel et al., 2006; Yeatts et al., 2003; Zuidgeest et al., 2008). Furthermore, restricting to those with an asthma diagnosis could induce a selection bias, particularly if individuals with an asthma diagnosis are more likely to be currently exposed to PAH (Dahabreh & Kent, 2011). Together, these suggest a need to examine the full population, not just those with a physician diagnosis of asthma.

In our study, we included SABA or systemic corticosteroid asthma medication use, indicators for recent asthma symptoms, as a more temporally appropriate outcome compared to asthma diagnosis. Previous cross-sectional studies have examined the association between metabolites from various environmental exposures (air pollution, tobacco smoke, perfluoroalkyl chemicals, and phthalates) and asthma diagnosis (Eisner Mark D, 2002; Humblet Olivier et al., 2014; Liu et al., 2016; Odebeatu et al., 2019). Many of these metabolites have short half-lives and serve as proxies for recent exposures. Compared to asthma diagnosis, asthma symptoms may be more temporally appropriate due to the short-term nature of PAH exposure assessment from the concentration of metabolites excreted in the urine following PAH exposure. Previous studies have included symptoms such as wheezing as a possible indicator for undiagnosed asthma (Ehrlich et al., 2005; Yeatts et al., 2003). However, those symptoms are also common for other conditions, such as colds, allergies, and chronic obstructive pulmonary disease, so SABA or systemic corticosteroid asthma medication use is a more specific indicator for asthma symptoms. Considering that previous studies have shown that asthma medications were used among those without an asthma diagnosis and may therefore capture more instances of asthma symptoms, we did not restrict our primary analysis to only those previously diagnosed with asthma (Aaron et al., 2017; Stempel et al., 2006; Yeatts et al., 2003; Zuidgeest et al., 2008). However, the potential impact of missed asthma symptoms when restricting the study population to only those diagnosed with asthma was minimal as our results were similar when our analysis was restricted to only those who were ever diagnosed with asthma.

Our study concluded that there was no statistical evidence of effect modification, but there were suggestive differences in the magnitudes of association in PAH exposure and SABA or systemic corticosteroid use between children and adults. A few studies suggested that children may be at greater risk of adverse health effects due to greater PAH exposure, although we were not able to find studies that tested for the effect modification of PAH exposure and asthma by age (Karimi et al., 2015; Miller et al., 2010). A study of hospital patients in China reported suggested differences in PAH metabolites between asthma cases and controls by age in stratified results but did not present any formal statistical test results (Huang et al., 2018). Many more studies have investigated the effect modification of air pollution more generally and asthma by age. Previous studies have suggested that age modified the association between ambient air pollution exposure and asthma exacerbation emergency department visits, with varied findings (Alhanti et al., 2016; Halonen et al., 2008; Samoli et al., 2011; Silverman & Ito, 2010; Son et al., 2013). Other researchers have noted that elderly populations may be more vulnerable to the onset and exacerbation of various health conditions resulting from indoor air pollution exposure (Diaz Lozano Patino & Siegel, 2018). In a previous analysis of three metropolitan areas, the positive associations between ambient air pollution exposure and emergency asthma-related visits were strongest in 5-18 year old compared to younger and older age groups (Alhanti et al., 2016). Other studies have suggested that children had a higher risk of asthma-related hospitalization compared to adult populations following air pollution exposure (Silverman & Ito, 2010; Son et al., 2013). In contrast to these previous studies, there may have been a suggestive stronger association in older age groups. It is plausible that a greater risk of asthma exacerbations from PAH exposure may be observed in elderly populations due to increased vulnerability of the respiratory system associated with aging (Sharma & Goodwin, 2006). A previous study suggested that various forms of air pollution had a more immediate effect on asthma or COPD visits among elderly populations as contrasted to delayed impacts (3- to 5-day lag) in children. Considering how rare the use of SABA or systemic corticosteroid medications was in our study population (about 4%, n = 740), we had limited statistical power to test effect modification when our study population was broken down into age groups. We recommend testing effect modification by age in larger studies that are more appropriately powered to investigate these differences.

In our study, we differentiated between types of asthma medication in analyzing SABA or systemic corticosteroid use as our outcome and controller medication use as an effect modifier. A previous cross-sectional study assessed the relationship between history of tuberculosis, smoking, and other characteristics with asthma medication use, but the study did not differentiate between type of asthma medications (Ehrlich et al., 2005). SABAs or systemic corticosteroids used to relieve symptoms represent a shorter-term outcome compared to long-term controller medications that are used to control asthmatic symptoms (Pollart & Elward, 2009). Frequent use of SABAs has been associated with asthma exacerbations and may be an indicator of poor asthma control, which could potentially modify the relationship between 1-hydroxypyrene and SABA or systemic corticosteroid use (Baron et al., 2021; Nwaru et al., 2020). We were unable to quantify medication frequency during the last 30 days; therefore, we were unable to precisely assess asthma control. Controller medications are used decrease frequency of symptoms and to prevent asthma exacerbations in those with persistent asthma (Pollart & Elward, 2009). However, we did not observe evidence of effect modification by controller medication use. Asthma controller medications are prescribed for those with more persistent asthma symptoms (as compared to intermittent asthma symptoms), and future studies designed to quantify frequency of symptoms would be needed to accurately assess the association between PAH exposure and asthma severity or control.

A systematic review concluded that good adherence to asthma medications lowered the risk of severe asthma exacerbations (Engelkes et al., 2015). It is likely that using rescue asthma medications as a proxy for asthma symptoms resulted in the capture of more severe exacerbations due to the inclusion of systemic corticosteroids. However, we only observed a positive association of 1-hydroxypyrene with use of SABAs but not with systemic corticosteroids. Most individuals with asthma or asthma-like symptoms have SABAs readily accessible as part of their disease management plan. In contrast, access to systemic corticosteroids would likely only occur if SABAs were not sufficient for treatment and a clinician provided a new prescription. This could suggest that acute PAH exposure, as measured by the short-term biomarker, does not cause symptoms severe enough to require treatment beyond use of a SABA.

Furthermore, differential medication and healthcare access may influence SABA or systemic corticosteroid use. Based on previous studies that have discussed the impacts of socioeconomic status on differential pollution exposure and healthcare/medication access, we conceptualized socioeconomic status as a confounder in the association between the biomarker for PAH exposure and SABA or systemic corticosteroid use (Amstislavski et al., 2012; Hajat Anjum et al., 2013; Phelan et al., 2010). Adjusting for family income poverty ratio and any health insurance coverage may have controlled for some confounding due to socioeconomic status. However, both of the adjusted variables only capture certain aspects of socioeconomic status, so there may still have been residual confounding due to other aspects of socioeconomic status that may have resulted in differences in both PAH exposure and healthcare or medication access. A prior study concluded that adolescents with undiagnosed wheezing were not receiving adequate health care for their illness as compared to adolescents diagnosed with asthma (Yeatts et al., 2003). Additionally, another study suggested that children diagnosed with asthma were 14 times more likely and children who had a prescription for select asthma medications were 7 times more likely to be dispensed oral corticosteroids (to treat severe asthma exacerbations) than children without asthma diagnoses or prescriptions (Stempel et al., 2006). We observed a slightly attenuated but similar association in a subgroup of those with ever asthma diagnosis, suggesting that differential healthcare utilization and access by having an asthma diagnosis had minimal impact on our findings.

Strengths in our study included using a nationally representative dataset and considering measurement issues in PAH exposure and asthma symptoms. In our study, we included recent SABA or systemic corticosteroid asthma medication use, which may be more temporally relevant to recent PAH exposure compared to ever asthma diagnosis. For the biomarker of PAH exposure, we demonstrated that our associations were robust regardless of urine dilution method used to account for differences in creatinine by age, sex, and BMI. Furthermore, we disaggregated asthma medication use in two categories: 1) SABA or systemic corticosteroid medication use for our outcome and 2) controller medication use for effect modification assessment and were able to better separate efforts to treat from efforts to prevent asthma exacerbations.

CONCLUSION

In a nationally representative cross-sectional study, we demonstrated a positive association between a biomarker of PAH exposure and recent SABA or systemic corticosteroid use, indicators of recent asthma symptoms. It is important to ensure appropriate temporality between proposed exposure and outcome assessments within cross-sectional studies. Our results were robust to the urine creatinine dilution adjustment method used, and we encourage others to perform similar sensitivity analyses to reduce potential for residual confounding.

Supplementary Material

Supplementary Material

ACKNOWLEDGEMENTS

The authors would like to thank Domonique Reed, Sasinya Scott, Nadine Alexander, Shabnaz Siddiq, Christina Mehranbod, Nadav Sprague, and Aleya Khalifa for their thoughtful comments on multiple drafts of the manuscript.

FUNDING

This research was supported by grants from the National Institute of Environmental Health Sciences, National Institutes of Health [Grants: #T32ES007322, #R00ES027022].

ABBREVIATIONS:

PAH

polycyclic aromatic hydrocarbon

SABA

short-acting beta agonist

BMI

body mass index

PIR

poverty income ratio

NHANES

United States National Health and Nutritional Examination Survey

CDC

United States Centers for Disease Control and Disease Prevention

US

United States

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