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Published in final edited form as: Environ Res. 2020 Oct 22;192:110365. doi: 10.1016/j.envres.2020.110365

Prenatal and early life exposure to particulate matter, environmental tobacco smoke and respiratory symptoms in Mexican children

Nadya Y Rivera Rivera 1, Marcela Tamayo-Ortiz 2, Adriana Mercado Garcia 3, Allan C Just 1,5, Itai Kloog 1,4,5, Martha Maria Téllez-Rojo 3, Robert O Wright 1,5, Rosalind J Wright 1,5,6, Maria José Rosa 1,5
PMCID: PMC7736115  NIHMSID: NIHMS1639986  PMID: 33223137

Abstract

Background:

Exposure to particulate matter <2.5 microns in diameter (PM2.5) and environmental tobacco smoke (ETS) are associated with respiratory morbidity starting in utero. However, their potential synergistic effects have not been completely elucidated. Here, we examined the joint effects of prenatal and early life PM2.5 and prenatal ETS exposure on respiratory outcomes in children.

Material and Methods:

We studied 536 mother-child dyads in the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) study in Mexico City. Exposure to PM2.5 was estimated using residence in pregnancy and child’s first year of life with a satellite-based spatio-temporal model. ETS exposure was assessed by caregiver’s report of any smoker in the household during the second or third trimester. Outcomes included report of ever wheeze and wheeze in the past 12 months (current wheeze) assessed when children were 6–8 years old considered in separate models. Associations were modeled using distributed lag models (DLM) with daily PM2.5 averages for pregnancy and the first year of life, adjusting for child’s sex, birth weight z-score, mother’s age and education at enrollment, maternal asthma, season of conception and stratified by prenatal ETS exposure (yes/no).

Results:

We identified a sensitive window from gestational week 14 through postnatal week 18 during which PM2.5 was associated with higher risk of ever wheeze at age 6–8 years. We also observed a critical window of PM2.5 exposure between postnatal weeks 6–39 and higher risk of current wheeze. We found significant associations between higher prenatal and early life PM2.5 exposure and higher cumulative risk ratios of ever wheeze (RR:3.76, 95%CI [1.41, 10.0] per 5 μg/m3) and current wheeze in the past year (RR:7.91, 95%CI [1.5, 41.6] per 5 μg/m3) only among children born to mothers exposed to ETS in pregnancy when compared to mothers who were not exposed.

Conclusions:

Exposure to prenatal ETS modified the association between prenatal and early life PM2.5 exposure and respiratory outcomes at age 6–8 years. It is important to consider concurrent chemical exposures to more comprehensively characterize children’s environmental risk. Interventions aimed at decreasing passive smoking might mitigate the effects of ambient air pollution.

Keywords: particulate matter, distributive lag models; prenatal exposure; wheeze; environmental tobacco smoke; children’s respiratory health

Introduction

Exposure to ambient air pollution such as particulate matter <2.5 microns in diameter (PM2.5) produces pediatric respiratory morbidity (Clark et al., 2010; Liu et al., 2016; Urman et al., 2014; Wright and Brunst, 2013). Lung growth and structural development begins prenatally through a series of orchestrated stages. In utero exposure to ambient air pollution can lead to subtle alterations in lung development, affecting the structure and function of the respiratory system in later life (Pinkerton and Joad, 2006; Wright and Brunst, 2013). Infants are also more susceptible to ambient air pollution due to their immature lungs (Heinrich and Slama, 2007). Therefore, associations between prenatal and early life ambient air pollution exposure and postnatal respiratory disorders may depend on timing of exposure as well as dose.

Recent epidemiological evidence demonstrates that exposure to ambient air pollution at different periods in pregnancy and early life is associated with later respiratory outcomes in childhood (Hsu et al., 2015; Jung et al., 2019; Pennington et al., 2018; Rosa et al., 2017a; Yang et al., 2019). In a birth cohort in Boston, a critical window of exposure in pregnancy was identified, in which PM2.5 at 16–25 weeks gestation was significantly associated with physician diagnosed asthma by age 6 years (Hsu et al., 2015). In another study conducted in Taiwan, two critical windows were detected in which residential PM2.5 during pregnancy and the first year after birth were associated with higher incidence of asthma (Jung, Chen, Tang, & Hwang, 2019). Another study in Korea found that prenatal particulate matter <10 microns in diameter (PM10) in the second trimester was associated with an increased risk of new diagnosis of asthma in school-aged children with airway hyperresponsiveness at 7 years of age (Yang et al., 2019).

Similarly to air pollution, environmental tobacco smoke (ETS) exposure starting in utero has been independently associated with greater risk of respiratory symptoms in childhood (Vardavas et al., 2016)., and might increase the susceptibility for the adverse effects of air pollution (Duijts et al., 2012; Rabinovitch et al., 2011). In the Generation R study in the Netherlands, exposure to higher levels of PM10 and NO2 during the first three years of life was associated with increased risk of wheezing during the same time period only among children who were exposed to tobacco smoke during fetal and infant life (Sonnenschein-van der Voort et al., 2012). A study conducted in New York City found a significant interaction between prenatal polycyclic aromatic hydrocarbons (PAH) levels and prenatal ETS on report of asthma at ages 5–6 years (Rosa et al., 2011). The Cohort for Childhood Origin of Asthma and Allergic Diseases (COCOA) study found stronger associations with combined exposures to indoor PM2.5/ETS during prenatal period than the postnatal period, on increased susceptibility to lower respiratory tract infections (Yang et al., 2015). Nevertheless, data simultaneously evaluating susceptibility windows and the potential synergistic effects of ambient particulate air pollution and ETS exposure starting in utero on children’s respiratory health remain sparse.

Therefore, we leveraged existing data from an established population-based prenatally enrolled longitudinal cohort in Mexico City to identify windows of susceptibility to PM2.5 starting in pregnancy and wheeze outcomes in children at age 6–8 years. We further examined whether these associations were modified by prenatal ETS exposure. We hypothesized that higher prenatal and early life exposure to PM2.5 would be associated with higher risk of childhood wheeze and these associations would be modified by prenatal ETS exposure.

Methods

Study population

Pregnant women were recruited into the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) study between July 2007 and February 2011. Women receiving prenatal care through the Mexican Social Security System (Instituto Mexicano del Seguro Social –IMSS) were eligible to participate if they met the following criteria:< 20 weeks gestation, singleton pregnancy, at least 18 years of age, had completed primary education, planned to stay in Mexico City for the next 3 years, had access to a telephone, had no medical history of heart or kidney disease, did not consume alcohol daily; no drug addiction, and did not use any steroid or anti-epilepsy medications (Burris et al., 2013). Procedures were approved by institutional review boards at the Harvard School of Public Health, Icahn School of Medicine at Mount Sinai, and the Mexican National Institute of Public Health. Women provided written informed consent. In this study, 602 PROGRESS participants had completed the follow-up visit at 6–8 years.

Assessment of PM2.5 levels and environmental tobacco smoke

Daily residential exposure to PM2.5 was estimated during pregnancy and first year of life using a previously described spatio-temporal model that incorporates Moderate Resolution Imaging Spectroradiometer (MODIS) satellite-derived Aerosol Optical Depth (AOD) measurements at a 1×1 km spatial resolution (Just et al., 2015). In brief, remote sensing data were calibrated with municipal ground level monitors of PM2.5, meteorological data and land use regression (LUR) variables (roadway density, temperature, relative humidity, planetary boundary layer and daily precipitation) to generate estimates of daily residential PM2.5 levels for each participant. Mixed effect models with spatial and temporal predictors and day-specific random effects were used to account for temporal variations in the PM2.5−AOD relationship. The model was fit with a seasonal smooth function of latitude and longitude and time-varying average incorporating local monitoring for days without AOD data. Model performance was assessed using monitor-level leave one-out cross-validation; the model performed well with an R2 of 0.724. For more in depth details on model methods and performance please see Just 2015.We also compared our DLM approach to PM2.5 levels averaged over clinically defined trimesters (in gestational weeks). In these trimester-specific analyses, we calculated the average PM2.5 over the 1st trimester defined as weeks 1–13 gestation, the 2nd trimester defined as weeks 14–27 gestation, and the 3rd trimester defined as 28 weeks’ gestation to delivery.

Smoking data was obtained via a self-reported questionnaire. We asked the participating women if anyone with whom they spend time daily smoked inside the house. Exposure to environmental tobacco smoke was defined as report of anyone smoking inside the home during the second or third trimester of pregnancy. In our sample, only 3 mothers reported smoking in pregnancy, therefore only prenatal exposure to environmental tobacco smoke was considered in the models.

Outcome measures

The International Study of Asthma and Allergies in Childhood questionnaire (Asher et al., 1995) which has been validated in Spanish (Mata Fernandez et al., 2005), was administered at the 6–8 -year visits. Ever wheeze was defined as the caregiver’s affirmative response to the question “Has your child ever had wheezing or whistling in the chest at any time in the past?” While current wheeze was defined as an affirmative response to “Has your child had wheezing or whistling of the chest in the past 12 months?”

Covariates

Covariates were selected a priori and included child sex, birth weight for gestational age z-score, maternal asthma (ever yes/no), age (continuous in years) and educational attainment at enrollment (<high school, some high school or high school graduate, >high school) and seasonality. Ultrasounds are not routinely performed as standard of care in Mexico, therefore gestational age was based on last menstrual period (LMP) and by a standardized physical examination to determine gestational age at birth via delivery sheet (Capurro et al., 1978). Physical exam gestational age was used instead of the gestational age determined by LMP if the physical examination assessment differed by more than 3 weeks from the LMP gestational age. Birth weight data were extracted from labor and delivery records for all participants (Rosa et al., 2017b). We derived birth weight for gestational age z-scores which allow for adjustment for gestational age and birth weight more precisely, reducing both bias and residual cofounding (Oken et al., 2003; Rosa et al., 2017b). Reference curves were derived from a growth curve modeling meta-analysis of preterm birth and validated with the World Health Organization (WHO) growth curves for postnatal growth (Fenton and Kim, 2013). Exposure to ETS was included as a covariate in overall models. In DLM models sine and cosine date of conception were included to adjust for seasonality (Stolwijk et al., 1999). In linear models, seasonality was adjusted for using season of conception defined according to weather patterns in Mexico City that are meaningfully related to PM (Just et al., 2015): dry-cold (January-February; November-December), dry-warm (March-April) and rainy (May-October).

Statistical Analyses

Of the 602 participants that had outcome data, two had missing PM2.5 data. Children born preterm (<37 weeks’ gestation) (n=64) were excluded from these analyses (final n=536). A modified Poisson regression approach that allows the estimation of risk ratios was used to compare DLM results to clinically defined trimesters, average PM2.5 over pregnancy and average PM2.5 over the 1st postnatal year (Zou, 2004). In order to avoid bias in estimates (Wilson et al., 2017), average trimester exposures were included in a single model. We fitted distributive lag models (DLMs) to estimate the time-varying association between estimated daily PM2.5 level from 60 days prior to conception to 1 year post birth (705 days). This method incorporates data from all time points simultaneously and assumes that the association between the outcome and exposure at a given time point, controlling for exposure at all other time points, varies smoothly as a function of time. We tried basis functions with 1–10 degrees of freedom and obtained the AIC for each of them. We then selected the model with the smallest AIC. For ever wheeze a DLM that modeled a smooth function using B-splines with 1 degrees of freedom was fit (Gasparrini, 2011; Gasparrini et al., 2010), current wheeze at 6 years was modeled using a B-spline with 3 degree of freedom, both were chosen due to their parsimony and best AIC value; additional smoothing did not significantly improve the model. A sensitive window was identified when the pointwise 95% confidence bands did not contain one. DLMs were implemented using the dlnm package version 2.3.2 (Gasparrini, 2011) in R Version 3.5.1 (Boston, MA) and other analyses were performed in SPSS version 24 (Chicago, IL). The data were also stratified by prenatal ETS exposure (yes/no). The cumulative RRs reported represent the overall cumulative effects associated with an exposure to 5 μg/m3 of PM2.5 and can be interpreted using two perspectives: either as the overall increase in the risk after a constant exposure to 5 μg/m3 of PM2.5 sustained throughout the lag period of 705 days (backward perspective), or as the sum of the contributions of an exposure to 5 μg/m3 of PM2.5 in the next 705 days (forward perspective).In sensitivity analyses we included report of concurrent ETS at the 6–8 year visit as a covariate.

Results

Participants’ demographic characteristics for the overall sample and stratified by prenatal ETS exposure status are shown in Table 1. Exposure to prenatal ETS was reported in 190/536 participants (35.4%). There was an even distribution between female and male participants. The majority of mothers included in this study had 12 or fewer years of schooling (76.1%) and only 5 mothers reported ever asthma at enrollment (0.9%). We did not see significant differences in demographic characteristics by prenatal ETS exposure. Ever wheeze was reported in 14% of participants and current wheeze was reported in 4.9% of participants. We did not see significant differences in PM2.5 levels by report of prenatal ETS but we do see a higher proportion of ever wheeze (17.9%) and current wheeze (7.9%) in the ETS exposed group compared to the unexposed group (11.8 and 3.2% respectively).

Table 1.

Characteristics of mother–child dyads in the PROGRESS cohort

Characteristic N=536 ETS exposure (n=190) No ETS exposure (n=346)
Child’s sex, n (%)
 Male 275 (51.3) 95 (50.0) 180 (52.0)
 Female 261 (48.7) 95 (50.0) 166 (48.0)
Birth weight for gestational age z-score, median (IQR) −0.47 (−1.03, 0.15) −0.55 (−1.05–0.05) −0.41 (−0.98–0.17)
Maternal education at enrollment, n (%)
 Less than high school 213 (39.7) 75 (39.5) 138 (39.9)
 Some high school or high school graduate 193 (36.0) 72 (37.9) 121 (35.0)
 More than high school 130 (24.3) 43 (22.6) 87 (25.1)
Maternal age at enrollment years, median (IQR) 27.3 (23.5–31.2) 27.1 (23.0–30.4) 27.4 (23.6–32.2)
Maternal ever asthma at enrollment, n (%) 5 (0.9) 2 (1.1) 3 (0.9)
Birth season, n (%)
 Dry cold 207 (38.6) 85 (44.7) 122 (35.3)
 Dry warm 112 (20.9) 39 (20.5) 73 (21.1)
 Rainy 217 (40.5) 66 (34.7) 151 (43.6)
PM2.5 Exposure (μg/m3) (median, IQR)
 1st trimester 21.9 (19.0–25.8) 22.4 (19.5–25.7) 21.6 (18.7–26.0)
 2nd trimester 21.3 (19.0–26.2) 21.4 (18.8–26.1) 21.3 (19.3–26.2)
 3rd trimester 22.7 (19.2–27.6) 23.6 (19.3–28.0) 22.2 (18.9–27.3)
 Average prenatal 22.9 (20.8–24.3) 23.1 (21.4–24.4) 22.8 (20.6–24.3)
 Average postnatal year 1 22.7 (20.5–23.7) 22.8 (20.6–24.0) 22.5 (20.4–23.6)
Outcomes (n, %)
 Ever wheeze 6–7 years, yes 75 (14.0) 34 (17.9) 41 (11.8)
 Current wheeze 6–7 years, yes* 26 (4.9) 15 (7.9) 11 (3.2)
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*

Caretaker report of wheeze or whistling of the chest in the past 12 months

In modified Poisson models, we did not find significant associations between any trimester average PM2.5 or average PM2.5 over pregnancy and either of our main outcomes (Table 2). Average postnatal year 1 PM2.5 was associated with higher risk of both ever wheeze and current wheeze at age 6–8 years in models adjusting for trimester averages and average PM2.5 over pregnancy but was only statistically significant for current wheeze. A 5 ug/m3 increase in PM2.5 averaged over the child’s first year of life was associated with a RR for current wheeze of 5.00 95%CI (2.01, 12.45) in models adjusting for all three trimester measures of PM2.5. The results were similar in the model adjusting for average PM2.5 over pregnancy. We also found that prenatal ETS exposure was independently associated with higher risk of current wheeze in both models.

Table 2.

Association between prenatal and early life PM2.5 levels and respiratory outcomes at ages 6–8 years using modified Poisson models

Ever wheeze RR (95% CI) p-value Current wheeze RR (95% CI) p-value
Model 1
1st trimester PM2.5 1.25 (0.88, 1.77) 0.21 0.76 (0.42, 1.66) 0.39
2nd trimester PM2.5 0.85 (0.56, 1.26) 0.42 0.84 (0.47, 2.06) 0.96
3rd trimester PM2.5 1.28 (0.94, 1.75) 0.12 0.77 (0.46, 1.31) 0.34
Postnatal year 1 1.52 (0.92, 1.77) 0.10 5.00 (2.01, 12.45) <0.001
Prenatal ETS 1.44 (0.96, 2.17) 0.08 2.52 (1.23, 5.17) 0.01
Model 2
Average pregnancy PM2.5 1.32 (0.86, 2.02) 0.20 0.59 (0.29, 1.21) 0.14
Postnatal year 1 1.58 (0.96, 2.59) 0.07 4.55 (1.93, 10.88) <0.001
Prenatal ETS 1.38 (0.97, 2.21) 0.07 2.54 (1.25, 5.19) 0.01
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Estimates shown for a 5 μg/m3 increase in PM2.5. Models adjusted for child sex, birth weight for gestational age z-score, maternal education, age and asthma ever at enrollment and season of conception

Figure 1a shows the association between a 5 μg/m3 increase in prenatal/early life PM2.5 and risk of ever wheeze in the sample as a whole, adjusted for child sex, maternal education, age and asthma ever at enrollment and season of conception. The DLM identified a significant association between increased PM2.5 during the prenatal and postnatal period from 97 days (approximately 14 weeks prenatal) through the postnatal period at 123 days (approximately 18 weeks postnatal). We also found a significant cumulative effect in the overall population (cumulative RR: 2.17, 95%CI [1.22, 3.84] per 5 μg/m3). Figure 1b shows the same model with current wheeze at age 6–8 years as the outcome. We identified a significant association between PM2.5 in the postnatal period from postnatal day 41(approximately 6 weeks) until postnatal day 271 (approximately 39 weeks) and increased risk of current wheeze at 6–8 years. We did not find a significant cumulative effect in the overall population (cumulative RR: 2.45, 95%CI [0.86, 7.00] per 5 μg/m3).

Figure 1. Associations between daily PM2.5 and risk of a) ever wheeze and b) current wheeze at age 6–8 years.

Figure 1.

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Models adjusted for child sex, birth weight for gestational age z-score, prenatal ETS, maternal education, age and asthma ever at enrollment and season of conception. Solid lines show the predicted RR of the outcome. Gray areas indicate 95% CIs. A sensitive window is identified for the weeks where the estimated pointwise 95% CI (shaded area) does not include RR=1.00.

Figures 2 and 3 show associations for PM2.5 and wheeze outcomes stratified by report of ETS exposure in pregnancy. We found a significant cumulative association between PM2.5 and risk of ever wheeze only in children whose mothers reported exposure to ETS in pregnancy (ETS RR: 3.76, 95%CI [1.41, 10.0], No ETS RR: 1.62, 95%CI [0.75, 3.52] per 5 μg/m3 respectively). We saw similar results for PM2.5 and current wheeze after stratifying by report of ETS in pregnancy (ETS RR: 7.91, 95%CI [1.5, 41.6], No RR: 0.53, 95%CI [0.1, 3.26] per 5 μg/m3 respectively. We also saw similar associations when we stratified the Poisson models (Supplemental Tables 1 and 2).

Figure 2. ETS-stratified associations between daily PM2.5 and risk of ever wheeze at age 6–8 years.

Figure 2.

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Adjusted for child sex, birth weight for gestational age z-score, maternal education, age and asthma ever at enrollment and season of conception. Solid lines show the predicted RR of the outcome. Gray areas indicate 95% CIs. A sensitive window is identified for the weeks where the estimated pointwise 95% CI (shaded area) does not include RR=1.00.

Figure 3. ETS-stratified associations between daily PM2.5 and risk of current wheeze at age 6–8 years.

Figure 3.

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Adjusted for child sex, birth weight for gestational age z-score, maternal education, age and asthma ever at enrollment and season of conception. Solid lines show the predicted RR of the outcome. Gray areas indicate 95% CIs. A sensitive window is identified for the weeks where the estimated pointwise 95% CI (shaded area) does not include RR=1.00.

In sensitivity analyses we included additional adjustment for concurrent ETS. Concurrent ETS was not independently associated with either of our outcomes and did not modify the associations with prenatal ETS or PM2.5 (supplemental figures S1–S3).

Discussion

Our analyses leveraged highly temporally resolved PM2.5 exposure data and data driven models to examine the associations between prenatal and early life exposure to PM2.5, prenatal ETS and respiratory outcomes in childhood. Our findings suggest that exposure to PM2.5 during a specific window starting in mid-pregnancy (gestational week 14) through postnatal week 18 is associated with higher risk of ever wheeze at age 6–8 years. We also observed a critical window of PM2.5 exposure between postnatal weeks 6–39 and higher risk of current wheeze. The observed associations remained significant after adjustment for a number of important potential confounders and covariates. We also found that prenatal ETS exposure modified the association between prenatal and early life PM2.5 exposure and respiratory outcomes; exposure to PM2.5 was associated with higher risk of both ever and current wheeze only in children whose mothers reported ETS exposure in pregnancy.

Our results are in line with previous human and animal studies reporting pregnancy and early life as particular vulnerable periods for ambient air pollution exposure impacting the developing respiratory system. (Garcia et al., 2019; Hansel et al., 2019; Jung et al., 2019; Norback et al., 2019; Pennington et al., 2018). Employing DLMs, investigators in Taiwan reported that increased exposure to PM2.5 during gestational weeks 6 to 22 and postnatal weeks 9 to 46 were significantly associated with increased incidence of asthma (Jung et al., 2019). In China, exposure to PM2.5 during the first 2 years of life was associated with higher prevalence of wheeze (Norback et al., 2019). Similarly, a study of 184 children in Canada reported that an average IQR (4.1 μg/m3) increase in birth year PM2.5 was associated with a significantly increased risk of asthma (Carlsten et al., 2011). In mice, pre- and early postnatal exposure to urban, vehicle-derived PM2.5 lead to a decrease in alveolar number, resulting in enlarged alveolar spaces and increased lung elastance in early life (de Barros Mendes Lopes et al., 2018). A study on rats showed that maternally PM2.5-exposed offspring pups displayed significant decreases in lung volume parameters, compliance, and airflow during expiration on postnatal day 28 (Tang et al., 2017).

Both air pollution and ETS are oxidative toxicants (Ciencewicki et al., 2008) and promotion of reactive oxygen species (ROS) is a proposed mechanism linking these exposures to respiratory outcomes (Yang et al., 2015). Starting in utero, increased systemic oxidative stress and production of pro-inflammatory cytokines may result in placental and endothelial dysfunction, and increased fetal oxidative stress with consequent effects on fetal lung development. Furthermore, newborns and infants undergo a dramatic increase in alveolar formation (Voynow and Auten, 2015) and their still developing immune, neuroendocrine and antioxidant defenses remain vulnerable to pro-oxidant exposures (Kajekar, 2007). Data from animal studies is supportive of this shared mechanism. Daily exposure to PM2.5 at a concentration that mimics 24-h exposure of the metropolitan area of São Paulo was associated with increased levels of DNA lesions in 4 week old mice and these related to the occurrence of oxidative stress in the lungs (de Oliveira et al., 2018). Mouse pups exposed to PM2.5 perinatally had decreased alveolarization and reduced lung volumes when compared to unexposed controls.(Mauad et al., 2008) A rodent study found that prenatal exposure to ETS significantly exacerbated inhaled house dust mite (HDM)-induced airway eosinophilic inflammation hyperreactivity; mucus secretion, cysteinyl leukotriene biosynthesis and type 2 cytokine production in mice offspring (Ferrini et al., 2017). A non-human primate study found exposure to low levels of ETS from gestation (day 40) to early childhood (1 year), resulted in significantly increased oxidative stress, mitochondrial dysfunction and damage, decreased mitochondrial antioxidant capacity and mitochondrial copy number in vascular tissue in offspring (Westbrook et al., 2010). Further work is needed that examines these exposures concurrently starting in utero in order to better understand the complex etiology of in their synergistic effects on the developing respiratory system.

Our study had numerous strengths including a large sample size of mother-child dyads with rigorously collected outcome and covariate data obtained longitudinally over several years. We leveraged highly spatial and temporally resolved ambient air pollution data to estimate each individual participant’s residential exposure during the entire pregnancy and first year of life. We excluded preterm born children which is a known clinical risk factor for wheeze and other respiratory symptoms (Priante et al., 2016). We used data-driven statistical methods to identify sensitive windows to air pollution exposure and the observed associations remained significant after adjustment for a number of important potential confounders and covariates. Our sample had very low smoking rates in pregnant women, only 3 mothers reported smoking in pregnancy. Therefore, our definition of prenatal exposure to environmental tobacco smoke was less likely to be confounded by smoking during pregnancy.

We also acknowledge some limitations. Air pollution and ETS exposure in microenvironments inside and outside of the home might lead to personal exposure levels that differed from our residential ambient exposure estimates, although such exposure misclassification is likely to be nondifferential and should drive effect estimates towards the null. We did not account for exposure to thirdhand smoke which is an important potential contributor to outcomes in addition to second hand smoke exposure (Protano and Vitali, 2011). We were also unable to explore how different doses (ie number of cigarettes smoked)(Palazzi et al., 2019; Protano et al., 2012) and other sources of ETS (such as electronic devices) might impact our associations (Protano et al., 2017). However, doses due to second-hand smoke from electronic devices were significantly lower than those due to combustion devices (Protano et al., 2017).We did not adjust for indoor air pollution, which is a significant exposure (Manigrasso et al., 2017), and some studies demonstrate that indoor air pollution, largely from cooking and tobacco smoke exposure, can be two to three times higher than ambient levels (McCormack et al., 2009; Wallace et al., 2003). Studies report that variations in indoor source particles are largely uncorrelated with variations in outdoor source particles (Wilson et al., 2000). Thus, although particles of indoor origin are an important potential predictor of respiratory outcomes in and of themselves, they are unlikely to confound associations between ambient particulate matter and wheeze. Notably, we did adjust for and explored tobacco smoke exposure as an effect modifier, a major contributor to indoor pollution, in these analyses and associations remained significant. As with any observational study, we cannot rule out residual confounding due to unmeasured factors that may influence respiratory symptoms in childhood. Finally, the generalizability of our findings may be limited due the composition of our cohort which consisted of low-income families living in a mega-city ranked as the 30th highest (worst) capital city globally for estimated average PM2.5 levels.

In conclusion we found associations between prenatal and early life exposure to PM2.5 and higher risk of wheeze outcomes in childhood. Perhaps most importantly, exposure to prenatal ETS modified the association between PM2.5 and these outcomes. These results underscore the importance of considering concurrent chemical exposures to more comprehensively characterize children’s environmental risk. Understanding temporal relationships and synergism between environmental toxins that affect the biological response to air pollution may provide unique insights into mechanisms affecting lung growth and assist in the development of behavioral or policy measures that will reduce exposure.

Supplementary Material

Supplementary material

Highlights.

  • Examined sensitive windows of perinatal PM2.5 exposure on wheeze in childhood.

  • Sensitive windows identified for ever and current wheeze.

  • PM2.5 exposure during windows was associated with higher risk of wheeze.

  • Exposure to prenatal ETS modified this association.

  • PM2.5 more strongly associated with ever and current wheeze in ETS exposed children.

Acknowledgements:

This work was supported by NIEHS grants R00ES027496 (Rosa MJ, PI). The PROGRESS project has been supported by NIEHS grants R01ES014930, R01ES013744, R24ES028522 P30 ES023515 (Wright RO, PI), R01ES021357 (Baccarelli A and Wright RO, MPI) and R00ES023450 (Just AC, PI). Rivera Rivera NY was partially supported by R25 HL 108857 (Claudio L, PI). This study was supported by the National Institute of Public Health/Ministry of Health of Mexico, and the National Institute of Perinatology. We thank the ABC (American British Cowdray Medical Center) in Mexico for providing some of the needed research facilities.

Abbreviations:

CI

confidence interval

DLM

Distributed lag model

ETS

Environmental tobacco smoke

PM2.5

Particulate matter less than or equal 2.5 microns in diameter

Footnotes

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Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. Asher MI, et al. , 1995. International Study of Asthma and Allergies in Childhood (Isaac) - Rationale and Methods. European Respiratory Journal. 8, 483–491. [DOI] [PubMed] [Google Scholar]
  2. Burris HH, et al. , 2013. Association between birth weight and DNA methylation of IGF2, glucocorticoid receptor and repetitive elements LINE-1 and Alu. Epigenomics. 5, 271–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Capurro H, et al. , 1978. A simplified method for diagnosis of gestational age in the newborn infant. J Pediatr. 93, 120–2. [DOI] [PubMed] [Google Scholar]
  4. Carlsten C, et al. , 2011. Traffic-related air pollution and incident asthma in a high-risk birth cohort. Occup Environ Med. 68, 291–5. [DOI] [PubMed] [Google Scholar]
  5. Ciencewicki J, et al. , 2008. Oxidants and the pathogenesis of lung diseases. Journal of Allergy and Clinical Immunology. 122, 456–468. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Clark NA, et al. , 2010. Effect of early life exposure to air pollution on development of childhood asthma. Environ Health Perspect. 118, 284–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. de Barros Mendes Lopes T, et al. , 2018. Pre- and postnatal exposure of mice to concentrated urban PM2.5 decreases the number of alveoli and leads to altered lung function at an early stage of life. Environ Pollut. 241, 511–520. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. de Oliveira AAF, et al. , 2018. Genotoxic and epigenotoxic effects in mice exposed to concentrated ambient fine particulate matter (PM2.5) from Sao Paulo city, Brazil. Part Fibre Toxicol. 15, 40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Duijts L, et al. , 2012. Fetal exposure to maternal and paternal smoking and the risks of wheezing in preschool children: the Generation R Study. Chest. 141, 876–885. [DOI] [PubMed] [Google Scholar]
  10. Fenton TR, Kim JH, 2013. A systematic review and meta-analysis to revise the Fenton growth chart for preterm infants. BMC Pediatr. 13, 59. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Ferrini M, et al. , 2017. Prenatal tobacco smoke exposure predisposes offspring mice to exacerbated allergic airway inflammation associated with altered innate effector function. Part Fibre Toxicol. 14, 30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Garcia E, et al. , 2019. Association of Changes in Air Quality With Incident Asthma in Children in California, 1993–2014. JAMA. 321, 1906–1915. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Gasparrini A, 2011. Distributed Lag Linear and Non-Linear Models in R: The Package dlnm. Journal of Statistical Software. 43, 1–20. [PMC free article] [PubMed] [Google Scholar]
  14. Gasparrini A, et al. , 2010. Distributed lag non-linear models. Statistics in Medicine. 29, 2224–2234. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hansel NN, et al. , 2019. Ambient Air Pollution and Variation in Multiple Domains of Asthma Morbidity among Peruvian Children. Ann Am Thorac Soc. 16, 348–355. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Heinrich J, Slama R. Fine particles, a major threat to children. Int J Hyg Environ Health. 2007. October;210(5):617–22. doi: 10.1016/j.ijheh.2007.07.012. Epub 2007 Sep 4. [DOI] [PubMed] [Google Scholar]
  17. Hsu HH, et al. , 2015. Prenatal Particulate Air Pollution and Asthma Onset in Urban Children. Identifying Sensitive Windows and Sex Differences. Am J Respir Crit Care Med. 192, 1052–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Jung CR, et al. , 2019. Fine particulate matter exposure during pregnancy and infancy and incident asthma. J Allergy Clin Immunol. 143, 2254–2262.e5. [DOI] [PubMed] [Google Scholar]
  19. Just AC, et al. , 2015. Using High-Resolution Satellite Aerosol Optical Depth To Estimate Daily PM2.5 Geographical Distribution in Mexico City. Environ Sci Technol. 49, 8576–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Kajekar R, 2007. Environmental factors and developmental outcomes in the lung. Pharmacol Ther. 114, 129–45. [DOI] [PubMed] [Google Scholar]
  21. Liu W, et al. , 2016. Associations of gestational and early life exposures to ambient air pollution with childhood respiratory diseases in Shanghai, China: A retrospective cohort study. Environ Int. 92-93, 284–93. [DOI] [PubMed] [Google Scholar]
  22. Manigrasso M, et al. , 2017. Temporal evolution of ultrafine particles and of alveolar deposited surface area from main indoor combustion and non-combustion sources in a model room. Sci Total Environ. 598, 1015–1026. [DOI] [PubMed] [Google Scholar]
  23. Mata Fernandez C, et al. , 2005. Validation of the Spanish version of the Phase III ISAAC questionnaire on asthma. J Investig Allergol Clin Immunol. 15, 201–10. [PubMed] [Google Scholar]
  24. Mauad T, et al. , 2008. Chronic exposure to ambient levels of urban particles affects mouse lung development. Am J Respir Crit Care Med. 178, 721–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. McCormack MC, et al. , 2009. In-home particle concentrations and childhood asthma morbidity. Environ Health Perspect. 117, 294–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Norback D, et al. , 2019. Onset and remission of childhood wheeze and rhinitis across China - Associations with early life indoor and outdoor air pollution. Environ Int. 123, 61–69. [DOI] [PubMed] [Google Scholar]
  27. Oken E, et al. , 2003. A nearly continuous measure of birth weight for gestational age using a United States national reference. BMC Pediatr. 3, 6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Palazzi P, et al. , 2019. Biomonitoring of children exposure to urban pollution and environmental tobacco smoke with hair analysis - A pilot study on children living in Paris and Yeu Island, France. Science of the Total Environment. 665, 864–872. [DOI] [PubMed] [Google Scholar]
  29. Pennington AF, et al. , 2018. Exposure to Mobile Source Air Pollution in Early-life and Childhood Asthma Incidence: The Kaiser Air Pollution and Pediatric Asthma Study. Epidemiology. 29, 22–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Pinkerton KE, Joad JP, 2006. Influence of air pollution on respiratory health during perinatal development. Clin Exp Pharmacol Physiol. 33, 269–72. [DOI] [PubMed] [Google Scholar]
  31. Priante E, et al. , 2016. Respiratory Outcome after Preterm Birth: A Long and Difficult Journey. Am J Perinatol. 33, 1040–2. [DOI] [PubMed] [Google Scholar]
  32. Protano C, et al. , 2012. How home-smoking habits affect children: a cross-sectional study using urinary cotinine measurement in Italy. International Journal of Public Health. 57, 885–892. [DOI] [PubMed] [Google Scholar]
  33. Protano C, et al. , 2017. Second-hand smoke generated by combustion and electronic smoking devices used in real scenarios: Ultrafine particle pollution and age-related dose assessment. Environ Int. 107, 190–195. [DOI] [PubMed] [Google Scholar]
  34. Protano C, Vitali M, 2011. The new danger of thirdhand smoke: why passive smoking does not stop at secondhand smoke. Environ Health Perspect. 119, A422. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Rabinovitch N, et al. , 2011. The Response of Children with Asthma to Ambient Particulate Is Modified by Tobacco Smoke Exposure. American Journal of Respiratory and Critical Care Medicine. 184, 1350–1357. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Rosa MJ, et al. , 2011. Prenatal exposure to polycyclic aromatic hydrocarbons, environmental tobacco smoke and asthma. Respir Med. 105, 869–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Rosa MJ, et al. , 2017a. Prenatal particulate matter exposure and wheeze in Mexican children: Effect modification by prenatal psychosocial stress. Ann Allergy Asthma Immunol. 119, 232–237.e1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Rosa MJ, et al. , 2017b. Prenatal exposure to PM2.5 and birth weight: A pooled analysis from three North American longitudinal pregnancy cohort studies. Environ Int. 107, 173–180. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Sonnenschein-van der Voort AM, et al. , 2012. Air pollution, fetal and infant tobacco smoke exposure, and wheezing in preschool children: a population-based prospective birth cohort. Environ Health. 11, 91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Stolwijk AM, et al. , 1999. Studying seasonality by using sine and cosine functions in regression analysis. J Epidemiol Community Health. 53, 235–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Tang W, et al. , 2017. Maternal exposure to fine particulate air pollution induces epithelial-to mesenchymal transition resulting in postnatal pulmonary dysfunction mediated by transforming growth factor-beta/Smad3 signaling. Toxicol Lett. 267, 11–20. [DOI] [PubMed] [Google Scholar]
  42. Urman R, et al. , 2014. Associations of children’s lung function with ambient air pollution: joint effects of regional and near-roadway pollutants. Thorax. 69, 540–547. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Vardavas CI, et al. , 2016. The independent role of prenatal and postnatal exposure to active and passive smoking on the development of early wheeze in children. Eur Respir J. 48, 115–24. [DOI] [PubMed] [Google Scholar]
  44. Voynow JA, Auten R, 2015. Environmental Pollution and the Developing Lung. Clinical Pulmonary Medicine. 22, 177–184. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Wallace LA, et al. , 2003. Particle concentrations in inner-city homes of children with asthma: the effect of smoking, cooking, and outdoor pollution. Environ Health Perspect. 111, 1265–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Westbrook DG, et al. , 2010. Perinatal tobacco smoke exposure increases vascular oxidative stress and mitochondrial damage in non-human primates. Cardiovasc Toxicol. 10, 216–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Wilson A, et al. , 2017. Potential for Bias When Estimating Critical Windows for Air Pollution in Children’s Health. American Journal of Epidemiology. 186, 1281–1289. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Wilson WE, et al. , 2000. Estimating separately personal exposure to ambient and nonambient particulate matter for epidemiology and risk assessment: why and how. J Air Waste Manag Assoc. 50, 1167–83. [DOI] [PubMed] [Google Scholar]
  49. Wright RJ, Brunst KJ, 2013. Programming of respiratory health in childhood: influence of outdoor air pollution. Curr Opin Pediatr. 25, 232–9. [DOI] [PubMed] [Google Scholar]
  50. Yang SI, et al. , 2015. Prenatal Particulate Matter/Tobacco Smoke Increases Infants’ Respiratory Infections: COCOA Study. Allergy Asthma Immunol Res. 7, 573–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Yang SI, et al. , 2019. Prenatal particulate matter affects new asthma via airway hyperresponsiveness in schoolchildren. Allergy. 74, 675–684. [DOI] [PubMed] [Google Scholar]
  52. Zou GY, 2004. A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology. 159, 702–706. [DOI] [PubMed] [Google Scholar]

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