Bayesian distributed lag interaction models to identify perinatal windows of vulnerability in children's health

Distributed lag models for retrospective cohort data with application to a study of built environment and body weight

Distributed lag models (DLMs) estimate the health effects of exposure over multiple time lags prior to the outcome and are widely used in time series studies. Applying DLMs to retrospective cohort studies is challenging due to inconsistent lengths of exposure history across participants, which is common when using electronic health record databases. A standard approach is to define subcohorts of individuals with some minimum exposure history, but this limits power and may amplify selection bias. We propose alternative full-cohort methods that use all available data while simultaneously enabling examination of the longest time lag estimable in the cohort. Through simulation studies, we find that restricting to a subcohort can lead to biased estimates of exposure effects due to confounding by correlated exposures at more distant lags. By contrast, full-cohort methods that incorporate multiple imputation of complete exposure histories can avoid this bias to efficiently estimate lagged and cumulative effects. Applying full-cohort DLMs to a study examining the association between residential density (a proxy for walkability) over 12 years and body weight, we find evidence of an immediate effect in the prior 1-2 years. We also observed an association at the maximal lag considered (12 years prior), which we posit reflects an earlier ($\ge$12 years) or incrementally increasing prior effect over time. DLMs can be efficiently incorporated within retrospective cohort studies to identify critical windows of exposure.

Approaches in landscape fire smoke pregnancy research and the impact on offspring: A review of knowledge gaps and recommendations

The increase in wildfires and bushfires due to climate change means that more people, including pregnant women and their fetuses will be exposed to landscape fire smoke. Although there is evidence to suggest that pregnancy landscape fire exposure is associated with lower birth weight, preterm birth and pregnancy loss, there is a lack of information on many other perinatal outcomes, as well as information on subsequent respiratory outcomes in children. Furthermore, due to the generally short term (hours/days) and intermittent nature of landscape fire smoke exposure, the knowledge to date has largely relied on natural experiments and ecological studies which can be subject to misclassification of exposure and a lack of precision. On the other hand, general urban outdoor air pollution exposure during pregnancy and subsequent perinatal and respiratory effects has been well studied. In particular, as air exposure modelling has improved so have the adaptations of methods to analyze the effects of air pollution exposure during pregnancy enabling critical windows of exposure to be identified. In this narrative review we summarize the current state of knowledge about the perinatal and respiratory effects of pregnancy landscape fire and particulate matter <2.5 μm in diameter (PM2.5) air pollution exposure, including a comment on analysis methods to date, and an assessment of how methodologies used in general air pollution research in relation to pregnancy exposure can be further harnessed for landscape fire smoke exposure pregnancy research.

Prenatal exposure to ambient fine particulate matter and child lung function in the CANDLE cohort

BACKGROUND

Ambient fine particulate matter (PM2.5) exposure adversely impacts child airway health; however, research on prenatal PM2.5 exposure, and child lung function is limited. We investigated these associations in the ECHO-PATHWAYS Consortium, focusing on the role of exposure timing during different phases of fetal lung development.

METHODS

We included 675 children in the CANDLE cohort born between 2007 and 2011 in Memphis, TN, USA. Prenatal exposure to ambient PM2.5 was estimated using a spatiotemporal model based on maternal residential history and averaged over established prenatal periods of lung development. Forced expiratory volume in the first second (FEV1) and forced vital capacity (FVC) were measured by spirometry at age 8-9 years. We used linear regression and Bayesian Distributed Lag Interaction Models (BDLIM) to estimate associations between exposure and lung function z-scores, adjusting for maternal/child characteristics, prenatal/postnatal tobacco exposure, and birth year/season, and evaluating effect modification by child sex and allergic sensitization.

RESULTS

The average ambient concentration of PM2.5 during pregnancy was 11.1 µg/m3 (standard deviation:1.0 µg/m3). In the adjusted linear regression and BDLIM models, adverse, but not statistically significant, associations were observed between exposure during the pseudoglandular (5-16 weeks of gestation) and saccular (24-36 weeks) phases of lung development and FEV1 and FVC. The strongest association was between a 2 μg/m3 higher concentration of PM2.5 during the saccular phase and FEV1 z-score (-0.176, 95% Confidence Interval [CI]: -0.361, 0.010). The FEV1/FVC ratio was not associated with PM2.5 in any exposure window. No effect modification by child sex or allergic sensitization was observed.

CONCLUSIONS

We did not find strong evidence of associations between prenatal ambient PM2.5 exposure and child lung function in a large, well-characterized study sample. However, there was a suggested adverse association between FEV1 and exposure during late pregnancy. The saccular phase of lung development might be an important window for exposure to PM2.5.

Increased Risk of Gestational Hypertension by Periconceptional Exposure to Ambient Air Pollution and Effect Modification by Prenatal Depression

BACKGROUND

Air pollution has been associated with gestational hypertension (GH) and preeclampsia, but susceptible windows of exposure and potential vulnerability by comorbidities, such as prenatal depression, remain unclear.

METHODS

We ascertained GH and preeclampsia cases in a prospective pregnancy cohort in Los Angeles, CA. Daily levels of ambient particulate matters (with a diameter of ≤10 μm [PM10] or ≤2.5 μm [PM2.5]), nitrogen dioxide, and ozone were averaged for each week from 12 weeks preconception to 20 gestational weeks. We used distributed lag models to identify susceptible exposure windows, adjusting for potential confounders. Analyses were additionally stratified by probable prenatal depression to explore population vulnerability.

RESULTS

Among 619 participants, 60 developed preeclampsia and 42 developed GH. We identified a susceptible window for exposure to PM2.5 from 1 week preconception to 11 weeks postconception: higher exposure (5 µg/m3) within this window was associated with an average of 8% (95% CI, 1%-15%) higher risk of GH. Among participants with probable prenatal depression (n=179; 32%), overlapping sensitive windows were observed for all pollutants from 8 weeks before to 10 weeks postconception with increased risk of GH (PM2.5, 16% [95% CI, 3%-31%]; PM10, 39% [95% CI, 13%-72%]; nitrogen dioxide, 65% [95% CI, 17%-134%]; and ozone, 45% [95% CI, 9%-93%]), while the associations were close to null among those without prenatal depression. Air pollutants were not associated with preeclampsia in any analyses.

CONCLUSIONS

We identified periconception through early pregnancy as a susceptible window of air pollution exposure with an increased risk of GH. Prenatal depression increases vulnerability to air pollution exposure and GH.

Sensitive development windows of prenatal air pollution and cognitive functioning in preschool age Mexican children

Introduction

Neurotoxicity resulting from air pollution is of increasing concern. Considering exposure timing effects on neurodevelopmental impairments may be as important as the exposure dose. We used distributed lag regression to determine the sensitive windows of prenatal exposure to fine particulate matter (PM2.5) on children's cognition in a birth cohort in Mexico.

Methods

Analysis included 553 full-term (≥37 weeks gestation) children. Prenatal daily PM2.5 exposure was estimated using a validated satellite-based spatiotemporal model. McCarthy Scales of Children's Abilities (MSCA) were used to assess children's cognitive function at 4-5 years old (lower scores indicate poorer performance). To identify susceptibility windows, we used Bayesian distributed lag interaction models to examine associations between prenatal PM2.5 levels and MSCA. This allowed us to estimate vulnerable windows while testing for effect modification.

Results

After adjusting for maternal age, socioeconomic status, child age, and sex, Bayesian distributed lag interaction models showed significant associations between increased PM2.5 levels and decreased general cognitive index scores at 31-35 gestation weeks, decreased quantitative scale scores at 30-36 weeks, decreased motor scale scores at 30-36 weeks, and decreased verbal scale scores at 37-38 weeks. Estimated cumulative effects (CE) of PM2.5 across pregnancy showed significant associations with general cognitive index (C E ^ = -0.35, 95% confidence interval [CI] = -0.68, -0.01), quantitative scale (C E ^ = -0.27, 95% CI = -0.74, -0.02), motor scale (C E ^ = -0.25, 95% CI = -0.44, -0.05), and verbal scale (C E ^ = -0.2, 95% CI = -0.43, -0.02). No significant sex interactions were observed.

Conclusions

Prenatal exposure to PM2.5, particularly late pregnancy, was inversely associated with subscales of MSCA. Using data-driven methods to identify sensitive window may provide insight into the mechanisms of neurodevelopmental impairment due to pollution.

Multiple exposure distributed lag models with variable selection

Distributed lag models are useful in environmental epidemiology as they allow the user to investigate critical windows of exposure, defined as the time periods during which exposure to a pollutant adversely affects health outcomes. Recent studies have focused on estimating the health effects of a large number of environmental exposures, or an environmental mixture, on health outcomes. In such settings, it is important to understand which environmental exposures affect a particular outcome, while acknowledging the possibility that different exposures have different critical windows. Further, in studies of environmental mixtures, it is important to identify interactions among exposures and to account for the fact that this interaction may occur between two exposures having different critical windows. Exposure to one exposure early in time could cause an individual to be more or less susceptible to another exposure later in time. We propose a Bayesian model to estimate the temporal effects of a large number of exposures on an outcome. We use spike-and-slab priors and semiparametric distributed lag curves to identify important exposures and exposure interactions and discuss extensions with improved power to detect harmful exposures. We then apply these methods to estimate the effects of exposure to multiple air pollutants during pregnancy on birthweight from vital records in Colorado.

Heterogeneous Distributed Lag Models to Estimate Personalized Effects of Maternal Exposures to Air Pollution

Children's health studies support an association between maternal environmental exposures and children's birth outcomes. A common goal is to identify critical windows of susceptibility-periods during gestation with increased association between maternal exposures and a future outcome. The timing of the critical windows and magnitude of the associations are likely heterogeneous across different levels of individual, family, and neighborhood characteristics. Using an administrative Colorado birth cohort we estimate the individualized relationship between weekly exposures to fine particulate matter (PM 2.5) during gestation and birth weight. To achieve this goal, we propose a statistical learning method combining distributed lag models and Bayesian additive regression trees to estimate critical windows at the individual level and identify characteristics that induce heterogeneity from a high-dimensional set of potential modifying factors. We find evidence of heterogeneity in the PM 2.5 -birth weight relationship, with some mother-child dyads showing a 3 times larger decrease in birth weight for an IQR increase in exposure (5.9 to 8.5 PM 2.5 μg/m3) compared to the population average. Specifically, we find increased vulnerabilitity for non-Hispanic mothers who are either younger, have higher body mass index or lower educational attainment. Our case study is the first precision health study of critical windows.

Prenatal ambient air pollutant mixture exposure and neurodevelopment in urban children in the Northeastern United States

BACKGROUND

Studies of prenatal air pollution (AP) exposure on child neurodevelopment have mostly focused on a single pollutant. We leveraged daily exposure data and implemented novel data-driven statistical approaches to assess effects of prenatal exposure to a mixture of seven air pollutants on cognitive functioning in school-age children from an urban pregnancy cohort.

METHODS

Analyses included 236 children born at ≥37 weeks gestation. Maternal prenatal daily exposure levels for nitrogen dioxide (NO2), ozone (O3), and constituents of fine particles [elemental carbon (EC), organic carbon (OC), nitrate (NO3-), sulfate (SO42-), ammonium (NH4+)] were estimated based on residential addresses using validated satellite-based hybrid models or global 3-D chemical-transport models. Children completed Wide Range Assessment of Memory and Learning (WRAML-2) and Conners' Continuous Performance Test (CPT-II) at 6.5 ± 0.9 years of age. Time-weighted levels for mixture pollutants were estimated using Bayesian Kernel Machine Regression Distributed Lag Models (BKMR-DLMs), with which we also explored the interactions in the exposure-response functions among pollutants. Resulting time-weighted exposure levels were used in Weighted Quantile Sum (WQS) regressions to examine AP mixture effects on outcomes, adjusted for maternal age, education, child sex, and prenatal temperature.

RESULTS

Mothers were primarily ethnic minorities (81% Hispanic and/or black) reporting ≤12 years of education (68%). Prenatal AP mixture (per unit increase in WQS estimated AP index) was associated with decreased WRAML-2 general memory (GM; β = -0.64, 95%CI = -1.40, 0.00) and memory-related attention/concentration (AC; β = -1.03, 95%CI = -1.78, -0.27) indices, indicating poorer memory functioning, as well as increased CPT-II omission errors (OE; β = 1.55, 95%CI = 0.34, 2.77), indicating increased attention problems. When stratified by sex, association with AC index was significant among girls, while association with OE was significant among boys. Traffic-related pollutants (NO2, OC, EC) and SO42- were major contributors to these associations. There was no significant evidence of interactions among mixture components.

CONCLUSIONS

Prenatal exposure to an AP mixture was associated with child neurocognitive outcomes in a sex- and domain-specific manner.

Preconceptional and prenatal exposure to air pollutants and risk of gestational diabetes in the MADRES prospective pregnancy cohort study

Background

Air pollution has been associated with gestational diabetes mellitus (GDM). We aim to investigate susceptible windows of air pollution exposure and factors determining population vulnerability.

Methods

We ascertained GDM status in the prospective Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) pregnancy cohort from Los Angeles, California, USA. We calculated the relative risk of GDM by exposure to ambient particulate matter (PM10; PM2.5), nitrogen dioxide (NO2), and ozone (O3) in each week from 12 weeks before to 24 weeks after conception, adjusting for potential confounders, with distributed lag models to identify susceptible exposure windows. We examined effect modification by prenatal depression, median-split pre-pregnancy BMI (ppBMI) and age.

Findings

Sixty (9.7%) participants were diagnosed with GDM among 617 participants (mean age: 28.2 years, SD: 5.9; 78.6% Hispanic, 11.8% non-Hispanic Black). GDM risk increased with exposure to PM2.5, PM10, and NO2 in a periconceptional window ranging from 5 weeks before to 5 weeks after conception: interquartile-range increases in PM2.5, PM10, and NO2 during this window were associated with increased GDM risk by 5.7% (95% CI: 4.6-6.8), 8.9% (8.1-9.6), and 15.0% (13.9-16.2), respectively. These sensitive windows generally widened, with greater effects, among those with prenatal depression, with age ≥28 years, or with ppBMI ≥27.5 kg/m2, than their counterparts.

Interpretation

Preconception and early-pregnancy are susceptible windows of air pollutants exposure that increased GDM risk. Prenatal depression, higher age, or higher ppBMI may increase one's vulnerability to air pollution-associated GDM risk.

Funding

National Institutes of Health, Environmental Protection Agency.

Semiparametric distributed lag quantile regression for modeling time-dependent exposure mixtures

Studying time-dependent exposure mixtures has gained increasing attentions in environmental health research. When a scalar outcome is of interest, distributed lag (DL) models have been employed to characterize the exposures effects distributed over time on the mean of final outcome. However, there is a methodological gap on investigating time-dependent exposure mixtures with different quantiles of outcome. In this paper, we introduce semiparametric partial-linear single-index (PLSI) DL quantile regression, which can describe the DL effects of time-dependent exposure mixtures on different quantiles of outcome and identify susceptible periods of exposures. We consider two time-dependent exposure settings: discrete and functional, when exposures are measured in a small number of time points and at dense time grids, respectively. Spline techniques are used to approximate the nonparametric DL function and single-index link function, and a profile estimation algorithm is proposed. Through extensive simulations, we demonstrate the performance and value of our proposed models and inference procedures. We further apply the proposed methods to study the effects of maternal exposures to ambient air pollutants of fine particulate and nitrogen dioxide on birth weight in New York University Children's Health and Environment Study (NYU CHES).

Prenatal Ambient Air Pollutant Mixture Exposure and Early School-age Lung Function

Research linking prenatal ambient air pollution with childhood lung function has largely considered one pollutant at a time. Real-life exposure is to mixtures of pollutants and their chemical components; not considering joint effects/effect modification by co-exposures contributes to misleading results.

Methods

Analyses included 198 mother-child dyads recruited from two hospitals and affiliated community health centers in Boston, Massachusetts, USA. Daily prenatal pollutant exposures were estimated using satellite-based hybrid chemical-transport models, including nitrogen dioxide(NO2), ozone(O3), and fine particle constituents (elemental carbon [EC], organic carbon [OC], nitrate [NO3 -], sulfate [SO4 2-], and ammonium [NH4 +]). Spirometry was performed at age 6.99 ± 0.89 years; forced expiratory volume in 1s (FEV1), forced vital capacity (FVC), and forced mid-expiratory flow (FEF25-75) z-scores accounted for age, sex, height, and race/ethnicity. We examined associations between weekly-averaged prenatal pollution mixture levels and outcomes using Bayesian Kernel Machine Regression-Distributed Lag Models (BKMR-DLMs) to identify susceptibility windows for each component and estimate a potentially complex mixture exposure-response relationship including nonlinear effects and interactions among exposures. We also performed linear regression models using time-weighted-mixture component levels derived by BKMR-DLMs adjusting for maternal age, education, perinatal smoking, and temperature.

Results

Most mothers were Hispanic (63%) or Black (21%) with ≤12 years of education (67%). BKMR-DLMs identified a significant effect for O3 exposure at 18-22 weeks gestation predicting lower FEV1/FVC. Linear regression identified significant associations for O3, NH4 +, and OC with decreased FEV1/FVC, FEV1, and FEF25-75, respectively. There was no evidence of interactions among pollutants.

Conclusions

In this multi-pollutant model, prenatal O3, OC, and NH4 + were most strongly associated with reduced early childhood lung function.

Estimating perinatal critical windows of susceptibility to environmental mixtures via structured Bayesian regression tree pairs

Maternal exposure to environmental chemicals during pregnancy can alter birth and children's health outcomes. Research seeks to identify critical windows, time periods when exposures can change future health outcomes, and estimate the exposure-response relationship. Existing statistical approaches focus on estimation of the association between maternal exposure to a single environmental chemical observed at high temporal resolution (e.g., weekly throughout pregnancy) and children's health outcomes. Extending to multiple chemicals observed at high temporal resolution poses a dimensionality problem and statistical methods are lacking. We propose a regression tree-based model for mixtures of exposures observed at high temporal resolution. The proposed approach uses an additive ensemble of tree pairs that defines structured main effects and interactions between time-resolved predictors and performs variable selection to select out of the model predictors not correlated with the outcome. In simulation, we show that the tree-based approach performs better than existing methods for a single exposure and can accurately estimate critical windows in the exposure-response relation for mixtures. We apply our method to estimate the relationship between five exposures measured weekly throughout pregnancy and birth weight in a Denver, Colorado, birth cohort. We identified critical windows during which fine particulate matter, sulfur dioxide, and temperature are negatively associated with birth weight and an interaction between fine particulate matter and temperature. Software is made available in the R package dlmtree.

Advancing Exposomic Research in Prenatal Respiratory Disease Programming

Disease programming reflects interactions between genes and the environment. Unlike the genome, environmental exposures and our response to exposures change over time. Starting in utero, the respiratory system and related processes develop sequentially in a carefully timed cascade, thus effects depend on both exposure dose and timing. A multitude of environmental and microbial exposures influence respiratory disease programming. Effects result from toxin-induced shifts in a host of molecular, cellular, and physiologic states and their interacting systems. Moreover, pregnant women and the developing child are not exposed to a single toxin, but to complex mixtures.

Association Between Ambient Air Pollution and Birth Weight by Maternal Individual- and Neighborhood-Level Stressors

IMPORTANCE

Fetal growth is precisely programmed and could be interrupted by environmental exposures during specific times during pregnancy. Insights on potential sensitive windows of air pollution exposure in association with birth weight are needed.

OBJECTIVE

To examine the association of sensitive windows of ambient air pollution exposure with birth weight and heterogeneity by individual- and neighborhood-level stressors.

DESIGN, SETTING, AND PARTICIPANTS

Data on a cohort of low-income Hispanic women with singleton term pregnancy were collected from 2015 to 2021 in the ongoing Maternal and Developmental Risks from Environmental and Social Stressors cohort in Los Angeles, California.

EXPOSURES

Daily ambient particulate matter with aerodynamic diameter less than 10 μm (PM10) and aerodynamic diameter less than 2.5 μm (PM2.5), nitrogen dioxide (NO2), and 8-hour maximum ozone were assigned to residential locations. Weekly averages from 12 weeks before conception to 36 gestational weeks were calculated. Individual-level psychological stressor was measured by the Perceived Stress Scale. Neighborhood-level stressor was measured by the CalEnviroScreen 4.0.

MAIN OUTCOMES AND MEASURES

Sex-specific birth weight for gestational age z score (BWZ). The associations between air pollutant and BWZ were estimated using distributed lag models to identify sensitive windows of exposure, adjusting for maternal and meteorologic factors. We stratified the analyses by Perceived Stress Scale and CalEnviroScreen 4.0. We converted the effect size estimation in BWZ to grams to facilitate interpretation.

RESULTS

The study included 628 pregnant women (mean [SD] age, 22.18 [5.92] years) and their newborns (mean [SD] BWZ, -0.08 [1.03]). On average, an interquartile range (IQR) increase in PM2.5 exposure during 4 to 22 gestational weeks was associated with a -9.5 g (95% CI, -10.4 to -8.6 g) change in birth weight. In stratified models, PM2.5 from 4 to 24 gestational weeks was associated with a -34.0 g (95% CI, -35.7 to -32.4 g) change in birth weight and PM10 from 9 to 14 gestational weeks was associated with a -39.4 g (95% CI, -45.4 to -33.4) change in birth weight in the subgroup with high Perceived Stress Scale and high CalEnviroScreen 4.0 scores. In this same group, NO2 from 9 to 14 gestational weeks was associated with a -40.4 g (95% CI, -47.4 to -33.3 g) change in birth weight and, from 33 to 36 gestational weeks, a -117.6 g (95% CI, -125.3 to -83.7 g) change in birth weight. Generally, there were no significant preconception windows for any air pollutants or ozone exposure with birth weight.

CONCLUSIONS AND RELEVANCE

In this cohort study, early pregnancy to midpregnancy exposures to PM2.5, PM10, and NO2 were associated with lower birth weight, particularly for mothers experiencing higher perceived stress and living in a neighborhood with a high level of stressors from environmental pollution.

In utero particulate matter exposure in association with newborn mitochondrial ND4L10550A>G heteroplasmy and its role in overweight during early childhood

BACKGROUND

Mitochondria play an important role in the energy metabolism and are susceptible to environmental pollution. Prenatal air pollution exposure has been linked with childhood obesity. Placental mtDNA mutations have been associated with prenatal particulate matter exposure and MT-ND4L_10550A>G heteroplasmy has been associated with BMI in adults. Therefore, we hypothesized that in utero PM_2.5 exposure is associated with cord blood MT-ND4L_10550A>G heteroplasmy and early life growth. In addition, the role of cord blood MT-ND4L_10550A>G heteroplasmy in overweight during early childhood is investigated.

METHODS

This study included 386 mother-newborn pairs. Outdoor PM_2.5 concentrations were determined at the maternal residential address. Cord blood MT-ND4L_10550A>G heteroplasmy was determined using Droplet Digital PCR. Associations were explored using logistic regression models and distributed lag linear models. Mediation analysis was performed to quantify the effects of prenatal PM_2.5 exposure on childhood overweight mediated by cord blood MT-ND4L_10550A>G heteroplasmy.

RESULTS

Prenatal PM_2.5 exposure was positively associated with childhood overweight during the whole pregnancy (OR = 2.33; 95% CI: 1.20 to 4.51; p = 0.01), which was mainly driven by the second trimester. In addition, prenatal PM_2.5 exposure was associated with cord blood MT-ND4L_10550A>G heteroplasmy from gestational week 9 - 13. The largest effect was observed in week 10, where a 5 µg/m³ increment in PM_2.5 was linked with cord blood MT-ND4L_10550A>G heteroplasmy (OR = 0.93; 95% CI: 0.87 to 0.99). Cord blood MT-ND4L_10550A>G heteroplasmy was also linked with childhood overweight (OR = 3.04; 95% CI: 1.15 to 7.50; p = 0.02). The effect of prenatal PM_2.5 exposure on childhood overweight was mainly direct (total effect OR = 1.18; 95% CI: 0.99 to 1.36; natural direct effect OR = 1.20; 95% CI: 1.01 to 1.36)) and was not mediated by cord blood MT-ND4L_10550A>G heteroplasmy.

CONCLUSIONS

Cord blood MT-ND4L_10550A>G heteroplasmy was linked with childhood overweight. In addition, in utero exposure to PM_2.5 during the first trimester of pregnancy was associated with cord blood MT-ND4L_10550A>G heteroplasmy in newborns. Our analysis did not reveal any mediation of cord blood MT-ND4L_10550A>G heteroplasmy in the association between PM_2.5 exposure and childhood overweight.

CRITICAL WINDOW VARIABLE SELECTION FOR MIXTURES: ESTIMATING THE IMPACT OF MULTIPLE AIR POLLUTANTS ON STILLBIRTH

Understanding the role of time-varying pollution mixtures on human health is critical as people are simultaneously exposed to multiple pollutants during their lives. For vulnerable subpopulations who have well-defined exposure periods (e.g., pregnant women), questions regarding critical windows of exposure to these mixtures are important for mitigating harm. We extend critical window variable selection (CWVS) to the multipollutant setting by introducing CWVS for mixtures (CWVSmix), a hierarchical Bayesian method that combines smoothed variable selection and temporally correlated weight parameters to: (i) identify critical windows of exposure to mixtures of time-varying pollutants, (ii) estimate the time-varying relative importance of each individual pollutant and their first order interactions within the mixture, and (iii) quantify the impact of the mixtures on health. Through simulation we show that CWVSmix offers the best balance of performance in each of these categories in comparison to competing methods. Using these approaches, we investigate the impact of exposure to multiple ambient air pollutants on the risk of stillbirth in New Jersey, 2005-2014. We find consistent elevated risk in gestational weeks 2, 16-17, and 20 for non-Hispanic Black mothers, with pollution mixtures dominated by ammonium (weeks 2, 17, 20), nitrate (weeks 2, 17), nitrogen oxides (weeks 2, 16), PM2.5 (week 2), and sulfate (week 20). The method is available in the R package CWVSmix.

KERNEL MACHINE AND DISTRIBUTED LAG MODELS FOR ASSESSING WINDOWS OF SUSCEPTIBILITY TO ENVIRONMENTAL MIXTURES IN CHILDREN'S HEALTH STUDIES

Exposures to environmental chemicals during gestation can alter health status later in life. Most studies of maternal exposure to chemicals during pregnancy have focused on a single chemical exposure observed at high temporal resolution. Recent research has turned to focus on exposure to mixtures of multiple chemicals, generally observed at a single time point. We consider statistical methods for analyzing data on chemical mixtures that are observed at a high temporal resolution. As motivation, we analyze the association between exposure to four ambient air pollutants observed weekly throughout gestation and birth weight in a Boston-area prospective birth cohort. To explore patterns in the data, we first apply methods for analyzing data on (1) a single chemical observed at high temporal resolution, and (2) a mixture measured at a single point in time. We highlight the shortcomings of these approaches for temporally-resolved data on exposure to chemical mixtures. Second, we propose a novel method, a Bayesian kernel machine regression distributed lag model (BKMR-DLM), that simultaneously accounts for nonlinear associations and interactions among time-varying measures of exposure to mixtures. BKMR-DLM uses a functional weight for each exposure that parameterizes the window of susceptibility corresponding to that exposure within a kernel machine framework that captures non-linear and interaction effects of the multivariate exposure on the outcome. In a simulation study, we show that the proposed method can better estimate the exposure-response function and, in high signal settings, can identify critical windows in time during which exposure has an increased association with the outcome. Applying the proposed method to the Boston birth cohort data, we find evidence of a negative association between organic carbon and birth weight and that nitrate modifies the organic carbon, elemental carbon, and sulfate exposure-response functions.

Prenatal PM2.5 exposure and infant temperament at age 6 months: Sensitive windows and sex-specific associations

BACKGROUND

Prenatal exposure to fine particulate matter with a diameter of ≤2.5 μm (PM2.5) has been linked to adverse neurodevelopmental outcomes in later childhood, while research on early infant behavior remains sparse.

OBJECTIVES

We examined associations between prenatal PM2.5 exposure and infant negative affectivity, a stable temperamental trait associated with longer-term behavioral and mental health outcomes. We also examined sex-specific effects.

METHODS

Analyses included 559 mother-infant pairs enrolled in the PRogramming of Intergenerational Stress Mechanisms (PRISM) cohort. Daily PM2.5 exposure based on geocoded residential address during pregnancy was estimated using a satellite-based spatiotemporal model. Domains of negative affectivity (Sadness, Distress to Limitations, Fear, Falling Reactivity) were assessed using the Infant Behavior Questionnaire-Revised (IBQ-R) when infants were 6 months old. Subscale scores were calculated as the mean of item-specific responses; the global Negative Affectivity (NA) score was derived by averaging the mean of the four subscale scores. Bayesian distributed lag interaction models (BDLIMs) were used to identify sensitive windows for prenatal PM2.5 exposure on global NA and its subscales, and to examine effect modification by sex.

RESULTS

Mothers were primarily racial/ethnic minorities (38% Black, 37% Hispanic), 40% had ≤12 years of education; most did not smoke during pregnancy (87%). In the overall sample, BDLIMs revealed that increased PM2.5 at mid-pregnancy was associated with higher global NA, Sadness, and Fear scores, after adjusting for covariates (maternal age, education, race/ethnicity, sex). Among boys, increased PM2.5 at early pregnancy was associated with decreased Fear scores, while exposure during late pregnancy was associated with increased Fear scores (cumulative effect estimate = 0.57, 95% CI: 0.03-1.41). Among girls, increased PM2.5 during mid-pregnancy was associated with higher Fear scores (cumulative effect estimate = 0.82, 95% CI: 0.05-1.91).

CONCLUSIONS

Prenatal PM2.5 exposure was associated with negative affectivity at age 6 months, and the sensitive windows may vary by subdomains and infant sex.

Assessing the health estimation capacity of air pollution exposure prediction models

BACKGROUND

The era of big data has enabled sophisticated models to predict air pollution concentrations over space and time. Historically these models have been evaluated using overall metrics that measure how close predictions are to monitoring data. However, overall methods are not designed to distinguish error at timescales most relevant for epidemiologic studies, such as day-to-day errors that impact studies of short-term health associations.

METHODS

We introduce frequency band model performance, which quantifies health estimation capacity of air quality prediction models for time series studies of air pollution and health. Frequency band model performance uses a discrete Fourier transform to evaluate prediction models at timescales of interest. We simulated fine particulate matter (PM_2.5), with errors at timescales varying from acute to seasonal, and health time series data. To compare evaluation approaches, we use correlations and root mean squared error (RMSE). Additionally, we assess health estimation capacity through bias and RMSE in estimated health associations. We apply frequency band model performance to PM_2.5 predictions at 17 monitors in 8 US cities.

RESULTS

In simulations, frequency band model performance rates predictions better (lower RMSE, higher correlation) when there is no error at a particular timescale (e.g., acute) and worse when error is added to that timescale, compared to overall approaches. Further, frequency band model performance is more strongly associated (R² = 0.95) with health association bias compared to overall approaches (R² = 0.57). For PM_2.5 predictions in Salt Lake City, UT, frequency band model performance better identifies acute error that may impact estimated short-term health associations.

CONCLUSIONS

For epidemiologic studies, frequency band model performance provides an improvement over existing approaches because it evaluates models at the timescale of interest and is more strongly associated with bias in estimated health associations. Evaluating prediction models at timescales relevant for health studies is critical to determining whether model error will impact estimated health associations.

Critical windows of perinatal particulate matter (PM2.5) exposure and preadolescent kidney function

Air pollution exposure, especially particulate matter ≤2.5 μm in diameter (PM2.5), is associated with poorer kidney function in adults and children. Perinatal exposure may occur during susceptible periods of nephron development. We used distributed lag nonlinear models (DLNMs) to examine time-varying associations between early life daily PM2.5 exposure (periconceptional through age 8 years) and kidney parameters in preadolescent children aged 8-10 years. Participants included 427 mother-child dyads enrolled in the PROGRESS birth cohort study based in Mexico City. Daily PM2.5 exposure was estimated at each participant's residence using a validated satellite-based spatio-temporal model. Kidney function parameters included estimated glomerular filtration rate (eGFR), serum cystatin C, and blood urea nitrogen (BUN). Models were adjusted for child's age, sex and body mass index (BMI) z-score, as well as maternal education, indoor smoking report and seasonality (prenatal models were additionally adjusted for average first year of life PM2.5 exposure). We also tested for sex-specific effects. Average perinatal PM2.5 was 22.7 μg/m3 and ranged 16.4-29.3 μg/m3. Early pregnancy PM2.5 exposures were associated with higher eGFR in preadolescence. Specifically, we found that PM2.5 exposure between weeks 1-18 of gestation was associated with increased preadolescent eGFR, whereas exposure in the first 14 months of life after birth were associated with decreased eGFR. Specifically, a 5 μg/m3 increase in PM2.5 during the detected prenatal window was associated with a cumulative increase in eGFR of 4.44 mL/min/1.732 (95%CI: 1.37, 7.52), and during the postnatal window we report a cumulative eGFR decrease of -10.36 mL/min/1.732 (95%CI: -17.68, -3.04). We identified perinatal windows of susceptibility to PM2.5 exposure with preadolescent kidney function parameters. Follow-up investigating PM2.5 exposure with peripubertal kidney function trajectories and risk of kidney disease in adulthood will be critical.

Prenatal Fine Particulate Matter, Maternal Micronutrient Antioxidant Intake, and Early Childhood Repeated Wheeze: Effect Modification by Race/Ethnicity and Sex

Fine particulate matter (PM2.5) potentiates in utero oxidative stress influencing fetal development while antioxidants have potential protective effects. We examined associations among prenatal PM2.5, maternal antioxidant intake, and childhood wheeze in an urban pregnancy cohort (n = 530). Daily PM2.5 exposure over gestation was estimated using a satellite-based spatiotemporally resolved model. Mothers completed the modified Block98 food frequency questionnaire. Average energy-adjusted percentile intake of β-carotene, vitamins (A, C, E), and trace minerals (zinc, magnesium, selenium) constituted an antioxidant index (AI). Maternal-reported child wheeze was ascertained up to 4.1 ± 2.8 years. Bayesian distributed lag interaction models (BDLIMs) were used to examine time-varying associations between prenatal PM2.5 and repeated wheeze (≥2 episodes) and effect modification by AI, race/ethnicity, and child sex. Covariates included maternal age, education, asthma, and temperature. Women were 39% Black and 33% Hispanic, 36% with ≤high school education; 21% of children had repeated wheeze. Higher AI was associated with decreased wheeze in Blacks (OR = 0.37 (0.19-0.73), per IQR increase). BDLIMs identified a sensitive window for PM2.5 effects on wheeze among boys born to Black mothers with low AI (at 33-40 weeks gestation; OR = 1.74 (1.19-2.54), per µg/m3 increase in PM2.5). Relationships among prenatal PM2.5, antioxidant intake, and child wheeze were modified by race/ethnicity and sex.

Prenatal particulate matter exposure and mitochondrial mutational load at the maternal-fetal interface: Effect modification by genetic ancestry

Prenatal ambient particulate matter (PM2.5) exposure impacts infant development and alters placental mitochondrial DNA abundance. We investigated whether the timing of PM2.5 exposure predicts placental mitochondrial mutational load using NextGen sequencing in 283 multi-ethnic mother-infant dyads. We observed increased PM2.5exposure, particularly during mid- to late-pregnancy and among genes coding for NADH dehydrogenase and subunits of ATP synthase, was associated with a greater amount of nonsynonymous mutations. The strongest associations were observed for participants of African ancestry. Further work is needed to tease out the role of mitochondrial genetics and its impact on offspring development and emerging disease disparities.

Spatially and Temporally Resolved Ambient PM2.5 in Relation to Preterm Birth

Growing evidence suggests that maternal exposure to ambient fine particulate matter (PM_2.5) during pregnancy is associated with preterm birth; however, few studies have examined critical windows of exposure, which can help elucidate underlying biologic mechanisms and inform public health messaging for limiting exposure. Participants included 891 mother-newborn pairs enrolled in a U.S.-based pregnancy cohort study. Daily residential PM_2.5 concentrations at a 1 × 1 km² resolution were estimated using a satellite-based hybrid model. Gestational age at birth was abstracted from electronic medical records and preterm birth (PTB) was defined as <37 completed weeks of gestation. We used Critical Window Variable Selection to examine weekly PM_2.5 exposure in relation to the odds of PTB and examined sex-specific associations using stratified models. The mean ± standard deviation PM_2.5 level averaged across pregnancy was 8.13 ± 1.10 µg/m³. PM_2.5 exposure was not associated with an increased odds of PTB during any gestational week. In sex-stratified models, we observed a marginal increase in the odds of PTB with exposure occurring during gestational week 16 among female infants only. This study does not provide strong evidence supporting an association between weekly exposure to PM_2.5 and preterm birth.

Prenatal PM2.5 affects atopic dermatitis depending on maternal anxiety and gender: COCOA study

BACKGROUND

The prevalence of atopic dermatitis (AD) is increasing worldwide. Prenatal particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) and maternal anxiety during pregnancy has been suggested as a potential causes of AD. This study investigated the effects of prenatal PM2.5 and maternal anxiety on AD and identified the critical period of PM2.5 exposure for AD in infants.

METHODS

This study included 802 children from the COCOA birth cohort study with follow-up data at 1 year of age. PM2.5 was estimated by land-use regression models and prenatal anxiety was measured with a questionnaire. AD was diagnosed by doctor at 1 year of age. Logistic regression analysis and Bayesian distributed lag interaction models were applied.

RESULTS

Higher PM2.5 during the first trimester of pregnancy, higher prenatal maternal anxiety, and male gender were associated with AD at 1 year of age (adjusted odds ratio [aOR] and 95% confidence interval [CI]: 1.86 [1.08-3.19], 1.58 [1.01-2.47], and 1.54 [1.01-2.36], respectively). Higher PM2.5 during the first trimester and higher maternal anxiety during pregnancy showed an additive effect on the risk of AD (aOR: 3.13; 95% CI: 1.56-6.28). Among boys exposed to higher maternal anxiety during pregnancy, gestational weeks 5-8 were the critical period of PM2.5 exposure for the development of AD.

CONCLUSIONS

Higher PM2.5 exposure during gestational weeks 5-8 increased the probability of AD in infancy, especially in boys with higher maternal anxiety. Avoiding PM2.5 exposure and maternal anxiety from the first trimester may prevent infant AD.

Prenatal Ambient Ultrafine Particle Exposure and Childhood Asthma in the Northeastern United States

Rationale: Ambient ultrafine particles (UFPs; with an aerodynamic diameter < 0.1 μm) may exert greater toxicity than other pollution components because of their enhanced oxidative capacity and ability to translocate systemically. Studies examining associations between prenatal UFP exposure and childhood asthma remain sparse. Objectives: We used daily UFP exposure estimates to identify windows of susceptibility of prenatal UFP exposure related to asthma in children, accounting for sex-specific effects. Methods: Analyses included 376 mother-child dyads followed since pregnancy. Daily UFP exposure during pregnancy was estimated by using a spatiotemporally resolved particle number concentration prediction model. Bayesian distributed lag interaction models were used to identify windows of susceptibility for UFP exposure and examine whether effect estimates varied by sex. Incident asthma was determined at the first report of asthma (3.6 ± 3.2 yr). Covariates included maternal age, education, race, and obesity; child sex; nitrogen dioxide (NO2) and temperature averaged over gestation; and postnatal UFP exposure. Measurements and Main Results: Women were 37.8% Black and 43.9% Hispanic, with 52.9% reporting having an education at the high school level or lower; 18.4% of children developed asthma. The cumulative odds ratio (95% confidence interval) for incident asthma per doubling of the UFP exposure concentration across pregnancy was 4.28 (1.41-15.7), impacting males and females similarly. Bayesian distributed lag interaction models indicated sex differences in the windows of susceptibility, with the highest risk of asthma seen in females exposed to higher UFP concentrations during late pregnancy. Conclusions: Prenatal UFP exposure was associated with asthma development in children, independent of correlated ambient NO2 and temperature. Findings will benefit future research and policy-makers who are considering appropriate regulations to reduce the adverse effects of UFPs on child respiratory health.

Mid-pregnancy PM2.5 exposure affects sex-specific growth trajectories via ARRDC3 methylation

Prenatal particulate matter <2.5 μm (PM_2.5) is associated with adverse birth growth. However, the longitudinal growth impacts have been little studied, and no mechanistic relationships have been described. We investigated the association between prenatal PM_2.5 exposure and growth trajectories, and the possible role of epigenetics. We enrolled 1313 neonates with PM_2.5 data measured by ordinary kriging from the COhort for Childhood Origin of Asthma and allergic diseases, followed up at 1, 3, and 5 years to evaluate growth. Differential DNA methylation and pyrosequencing of cord blood leukocytes was evaluated according to the prenatal PM_2.5 levels and birth weight (BW). PM_2.5 exposure during the second trimester (T2) caused the lowest BW in both sexes, further adjusted for indoor PM_2.5 levels [female, aOR 1.39 (95% CI 1.05-1.83); male, aOR 1.36 (95% CI 1.04-1.79)]. Bayesian distributed lag models with indoor PM_2.5 adjustments revealed a sensitive window for BW effects at 10-26 weeks gestation, but only in females. Latent class mixture models indicated that a persistently low weight-for-height percentile trajectory was more prevalent in the highest PM_2.5 exposure quartile at T2 in females, compared to a persistently high trajectory (36.5% vs. 20.3%, P = 0.022). Also, in the females only, the high PM_2.5 and low BW neonates showed significantly greater ARRDC3 methylation changes. ARRDC3 methylation was also higher only in females with low weight at 5 years of age. Higher fetal PM_2.5 exposure during T2 may cause a decreased growth trajectory, especially in females, mediated by ARRDC3 hyper-methylation-associated energy metabolism.

Association Between Gestational Exposure to Toxicants and Autistic Behaviors Using Bayesian Quantile Regression

Autism spectrum disorder, which is characterized by impaired social communication and stereotypic behaviors, affects 1%-2% of children. Although prenatal exposure to toxicants has been associated with autistic behaviors, most studies have been focused on shifts in mean behavior scores. We used Bayesian quantile regression to assess the associations between log2-transformed toxicant concentrations and autistic behaviors across the distribution of behaviors. We used data from the Maternal-Infant Research on Environmental Chemicals study, a pan-Canadian cohort (2008-2011). We measured metal, pesticide, polychlorinated biphenyl, phthalate, bisphenol-A, and triclosan concentrations in blood or urine samples collected during the first trimester of pregnancy. Using the Social Responsiveness Scale (SRS), in which higher scores denote more autistic-like behaviors, autistic behaviors were assessed in 478 children aged 3-4 years old. Lead, cadmium, and most phthalate metabolites were associated with mild increases in SRS scores at the 90th percentile of the SRS distribution. Manganese and some pesticides were associated with mild decreases in SRS scores at the 90th percentile of the SRS distribution. We identified several monotonic trends in which associations increased in magnitude from the bottom to the top of the SRS distribution. These results suggest that quantile regression can reveal nuanced relationships and, thus, should be more widely used by epidemiologists.

Prenatal air pollution exposure and neurodevelopment: A review and blueprint for a harmonized approach within ECHO

BACKGROUND

Air pollution exposure is ubiquitous with demonstrated effects on morbidity and mortality. A growing literature suggests that prenatal air pollution exposure impacts neurodevelopment. We posit that the Environmental influences on Child Health Outcomes (ECHO) program will provide unique opportunities to fill critical knowledge gaps given the wide spatial and temporal variability of ECHO participants.

OBJECTIVES

We briefly describe current methods for air pollution exposure assessment, summarize existing studies of air pollution and neurodevelopment, and synthesize this information as a basis for recommendations, or a blueprint, for evaluating air pollution effects on neurodevelopmental outcomes in ECHO.

METHODS

We review peer-reviewed literature on prenatal air pollution exposure and neurodevelopmental outcomes, including autism spectrum disorder, attention deficit hyperactivity disorder, intelligence, general cognition, mood, and imaging measures. ECHO meta-data were compiled and evaluated to assess frequency of neurodevelopmental assessments and prenatal and infancy residential address locations. Cohort recruitment locations and enrollment years were summarized to examine potential spatial and temporal variation present in ECHO.

DISCUSSION

While the literature provides compelling evidence that prenatal air pollution affects neurodevelopment, limitations in spatial and temporal exposure variation exist for current published studies. As >90% of the ECHO cohorts have collected a prenatal or infancy address, application of advanced geographic information systems-based models for common air pollutant exposures may be ideal to address limitations of published research.

CONCLUSIONS

In ECHO we have the opportunity to pioneer unifying exposure assessment and evaluate effects across multiple periods of development and neurodevelopmental outcomes, setting the standard for evaluation of prenatal air pollution exposures with the goal of improving children's health.

A Spatiotemporal Prediction Model for Black Carbon in the Denver Metropolitan Area, 2009-2020

Studies on health effects of air pollution from local sources require exposure assessments that capture spatial and temporal trends. To facilitate intraurban studies in Denver, Colorado, we developed a spatiotemporal prediction model for black carbon (BC). To inform our model, we collected more than 700 weekly BC samples using personal air samplers from 2018 to 2020. The model incorporated spatial and spatiotemporal predictors and smoothed time trends to generate point-level weekly predictions of BC concentrations for the years 2009-2020. Our results indicate that our model reliably predicted weekly BC concentrations across the region during the year in which we collected data. We achieved a 10-fold cross-validation R² of 0.83 and a root-mean-square error of 0.15 μg/m³ for weekly BC concentrations predicted at our sampling locations. Predicted concentrations displayed expected temporal trends, with the highest concentrations predicted during winter months. Thus, our prediction model improves on typical land use regression models that generally only capture spatial gradients. However, our model is limited by a lack of long-term BC monitoring data for full validation of historical predictions. BC predictions from the weekly spatiotemporal model will be used in traffic-related air pollution exposure-disease associations more precisely than previous models for the region have allowed.

Treed distributed lag nonlinear models

In studies of maternal exposure to air pollution, a children's health outcome is regressed on exposures observed during pregnancy. The distributed lag nonlinear model (DLNM) is a statistical method commonly implemented to estimate an exposure-time-response function when it is postulated the exposure effect is nonlinear. Previous implementations of the DLNM estimate an exposure-time-response surface parameterized with a bivariate basis expansion. However, basis functions such as splines assume smoothness across the entire exposure-time-response surface, which may be unrealistic in settings where the exposure is associated with the outcome only in a specific time window. We propose a framework for estimating the DLNM based on Bayesian additive regression trees. Our method operates using a set of regression trees that each assume piecewise constant relationships across the exposure-time space. In a simulation, we show that our model outperforms spline-based models when the exposure-time surface is not smooth, while both methods perform similarly in settings where the true surface is smooth. Importantly, the proposed approach is lower variance and more precisely identifies critical windows during which exposure is associated with a future health outcome. We apply our method to estimate the association between maternal exposures to PM$_{2.5}$ and birth weight in a Colorado, USA birth cohort.

DNA methylation of insulin-like growth factor 2 and H19 cluster in cord blood and prenatal air pollution exposure to fine particulate matter

BACKGROUND

The IGF2 (insulin-like growth factor 2) and H19 gene cluster plays an important role during pregnancy as it promotes both foetal and placental growth. We investigated the association between cord blood DNA methylation status of the IGF2/H19 gene cluster and maternal fine particulate matter exposure during fetal life. To the best of our knowledge, this is the first study investigating the association between prenatal PM2.5 exposure and newborn DNA methylation of the IGF2/H19.

METHODS

Cord blood DNA methylation status of IGF2/H19 cluster was measured in 189 mother-newborn pairs from the ENVIRONAGE birth cohort (Flanders, Belgium). We assessed the sex-specific association between residential PM2.5 exposure during pregnancy and the methylation level of CpG loci mapping to the IGF2/H19 cluster, and identified prenatal vulnerability by investigating susceptible time windows of exposure. We also addressed the biological functionality of DNA methylation level in the gene cluster.

RESULTS

Prenatal PM2.5 exposure was found to have genetic region-specific significant association with IGF2 and H19 during specific gestational weeks. The association was found to be sex-specific in both gene regions. Functionality of the DNA methylation was annotated by the association to fetal growth and cellular pathways.

CONCLUSIONS

The results of our study provided evidence that prenatal PM2.5 exposure is associated with DNA methylation in newborns' IGF2/H19. The consequences within the context of fetal development of future phenotyping should be addressed.

Prenatal PM2.5 exposure and vitamin D-associated early persistent atopic dermatitis via placental methylation

BACKGROUND

The effects of prenatal particulate matter with an aerodynamic diameter ranging from 0.1 μm to 2.5 μm (PM_2.5) and vitamin D on atopic dermatitis (AD) phenotypes have not been evaluated. DNA methylation and cord blood (CB) vitamin D could represent a plausible link between prenatal PM_2.5 exposure and AD in an offspring.

OBJECTIVE

To determine the critical windows of prenatal PM_2.5 exposure on the AD phenotypes, if vitamin D modulated these effects, and if placental DNA methylation mediated these effects on AD in offspring.

METHODS

Mother-child pairs were enrolled from the birth cohort of the Cohort for Childhood Origin of Asthma and allergic diseases (COCOA) study. PM_2.5 was estimated by land-use regression models, and CB vitamin D was measured by chemiluminescence immunoassay. AD was identified by the parental report of a physician's diagnosis. We defined the following 4 AD phenotypes according to onset age (by the age of 2 years) and persistence (by the age of 3 years): early-onset transient and persistent, late onset, and never. Logistic regression analysis and Bayesian distributed lag interaction model were used. DNA methylation microarray was analyzed using an Infinium Human Methylation EPIC BeadChip (Illumina, San Diego, California) in placenta.

RESULTS

PM_2.5 exposure during the first trimester of pregnancy, especially during 6 to 7 weeks of gestation, was associated with early-onset persistent AD. This effect increased in children with low CB vitamin D, especially in those with PM_2.5 exposure during 3 to 7 weeks of gestation. AHRR (cg16371648), DPP10 (cg19211931), and HLADRB1 (cg10632894) were hypomethylated in children with AD with high PM_2.5 and low CB vitamin D.

CONCLUSION

Higher PM_2.5 during the first trimester of pregnancy and low CB vitamin D affected early-onset persistent AD, and the most sensitive window was 6 to 7 weeks of gestation. Placental DNA methylation mediated this effect.

Term birthweight and critical windows of prenatal exposure to average meteorological conditions and meteorological variability

BACKGROUND

Heat stress during pregnancy may limit fetal growth, with ramifications throughout the life course. However, critical exposure windows are unknown, and effects of meteorological variability have not been investigated.

OBJECTIVES

We aimed to identify sensitive windows for the associations of mean and variability of temperature and humidity with term birthweight.

METHODS

We analyzed data from two French mother-child cohorts, EDEN and PELAGIE (n = 4771), recruited in 2002-2006. Temperature exposure was assessed using a satellite-based model with daily 1-km² resolution, and relative humidity exposure data were obtained from Météo France monitors. Distributed lag models were constructed using weekly means and standard deviation (SD, to quantify variability) from the first 37 gestational weeks. Analyses were then stratified by sex. Results for each exposure were adjusted for the other exposures, gestational age at birth, season and year of conception, cohort and recruitment center, and individual confounders.

RESULTS

There was no evidence of association between term birthweight and mean temperature. We identified a critical window in weeks 6-20 for temperature variability (cumulative change in term birthweight of -54.2 g [95% CI: -102, -6] for a 1 °C increase in SD of temperature for each week in that window). Upon stratification by sex of the infant, the relationship remained for boys (weeks 1-21, cumulative change: -125 g [95% CI: -228, -21]). For mean humidity, there was a critical window in weeks 26-37, with a cumulative change of -28 g (95% CI: -49, -7) associated with a 5% increase in humidity for each week. The critical window was longer and had a stronger association in boys (weeks 29-37; -37 g, 95% CI: -63, -11) than girls (week 14; -1.8 g, 95% CI: -3.6, -0.1).

DISCUSSION

Weekly temperature variability and mean humidity during critical exposure windows were associated with decreased term birthweight, especially in boys.

Prenatal particulate air pollution and newborn telomere length: Effect modification by maternal antioxidant intakes and infant sex

BACKGROUND

Evidence links gestational exposure to particulate matter with an aerodynamic diameter of less than 2.5 μm (PM_2.5) with changes in leukocyte telomere length in cord blood with some studies showing sex-specific effects. PM_2.5 exposure in utero increases oxidative stress, which can impact telomere biology. Thus, maternal antioxidant intakes may also modify the particulate air pollution effects.

METHODS

We examined associations among prenatal PM_2.5 exposure and newborn relative leukocyte telomere length (rLTL), and the modifying effects of maternal antioxidant intake and infant sex. We estimated daily PM_2.5 exposures over gestation using a validated spatiotemporally resolved satellite-based model. Maternal dietary and supplemental antioxidant intakes over the prior three months were ascertained during the second trimester using the modified Block98 food frequency questionnaire; high and low antioxidant intakes were categorized based on a median split. We employed Bayesian distributed lag interaction models (BDLIMs) to identify both sensitive windows of exposure and cumulative effect estimates for prenatal PM_2.5 exposure on newborn rLTL, and to examine effect modification by maternal antioxidant intakes. A 3-way interaction between PM_2.5, maternal antioxidant intake and infant sex was also explored.

RESULTS

For the main effect of PM_2.5, BDLIMs identified a sensitive window at 12-20 weeks gestation for the association between increased prenatal PM_2.5 exposure and shorter newborn rLTL and a cumulative effect of PM_2.5 over gestation on newborn telomere length [cumulative effect estimate (CEE) = -0.29 (95% CI -0.49 to -0.10) per 1μg/m³ increase in PM_2.5]. In models examining maternal antioxidant intake effects, BDLIMs found that children born to mothers reporting low antioxidant intakes were most vulnerable [CEE of low maternal antioxidant intake = -0.31 (95% CI -0.55 to -0.06) vs high maternal antioxidant intake = -0.07 (95% CI -0.34 to 0.17) per 1μg/m³ increase in PM_2.5]. In exploratory models examining effect modification by both maternal antioxidant intakes and infant sex, the cumulative effect remained significant only in boys whose mothers reported low antioxidant intakes [CEE = -0.38 (95% CI -0.80 to -0.004)]; no sensitive windows were identified in any group.

CONCLUSIONS

Prenatal PM_2.5 exposure in mid-gestation was associated with reduced infant telomere length. Higher maternal antioxidant intakes mitigated these effects.

A spatially varying distributed lag model with application to an air pollution and term low birth weight study

Distributed lag models have been used to identify critical pregnancy periods of exposure (i.e. critical exposure windows) to air pollution in studies of pregnancy outcomes. However, much of the previous work in this area has ignored the possibility of spatial variability in the lagged health effect parameters that may result from exposure characteristics and/or residual confounding. We develop a spatially varying Gaussian process model for critical windows called 'SpGPCW' and use it to investigate geographic variability in the association between term low birth weight and average weekly concentrations of ozone and PM_2:5 during pregnancy by using birth records from North Carolina. SpGPCW is designed to accommodate areal level spatial correlation between lagged health effect parameters and temporal smoothness in risk estimation across pregnancy. Through simulation and a real data application, we show that the consequences of ignoring spatial variability in the lagged health effect parameters include less reliable inference for the parameters and diminished ability to identify true critical window sets, and we investigate the use of existing Bayesian model comparison techniques as tools for determining the presence of spatial variability. We find that exposure to PM_2:5 is associated with elevated term low birth weight risk in selected weeks and counties and that ignoring spatial variability results in null associations during these periods. An R package (SpGPCW) has been developed to implement the new method.

Identifying critical windows of prenatal particulate matter (PM2.5) exposure and early childhood blood pressure

BACKGROUND

Exposure to air pollution is associated with increased blood pressure (BP) in adults and children. Some evidence suggests that air pollution exposure during the prenatal period may contribute to adverse cardiorenal health later in life. Here we apply a distributed lag model (DLM) approach to identify critical windows that may underlie the association between prenatal particulate matter ≤ 2.5 μm in diameter (PM_2.5) exposure and children's BP at ages 4-6 years.

METHODS

Participants included 537 mother-child dyads enrolled in the Programming Research in Obesity, GRowth Environment, and Social Stress (PROGRESS) longitudinal birth cohort study based in Mexico City. Prenatal daily PM_2.5 exposure was estimated using a validated satellite-based spatio-temporal model and BP was measured using the automated Spacelabs system with a sized cuff. We used distributed lag models (DLMs) to examine associations between daily PM_2.5 exposure and systolic and diastolic BP (SBP and DBP), adjusting for child's age, sex and BMI, as well as maternal education, preeclampsia and indoor smoking report during the second and third trimester, seasonality and average postnatal year 1 PM_2.5 exposure.

RESULTS

We found that PM_2.5 exposure between weeks 11-32 of gestation (days 80-226) was significantly associated with children's increased SBP. Similarly, PM_2.5 exposure between weeks 9-25 of gestation (days 63-176) was significantly associated with increased DBP. To place this into context, a constant 10 μg/m³ increase in PM_2.5 sustained throughout this critical window would predict a cumulative increase of 2.6 mmHg (CI: 0.5, 4.6) in SBP and 0.88 mmHg (CI: 0.1, 1.6) in DBP at ages 4-6 years. In a stratified analysis by sex, this association persisted in boys but not in girls.

CONCLUSIONS

Second and third trimester PM_2.5 exposure may increase children's BP in early life. Further work investigating PM_2.5 exposure with BP trajectories later in childhood will be important to understanding cardiorenal trajectories that may predict adult disease. Our results underscore the importance of reducing air pollution exposure among susceptible populations, including pregnant women.

Neonatal blood pressure in association with prenatal air pollution exposure, traffic, and land use indicators: An ENVIRONAGE birth cohort study

Elevated blood pressure (BP) in early life may lead to cardiovascular morbidity and mortality in later life. Air pollution exposure has been associated with increased BP in adults and children, but the contribution of prenatal air pollution exposure has rarely been assessed. In addition, we are not aware of any study on neonatal BP and maternal residential traffic and land use indicators during pregnancy. We investigated the association between newborn BP and prenatal air pollution, traffic and land use indicators, using data from 427 term (gestational age > 36 weeks) births from the ENVIRONAGE birth cohort. Newborn BP was measured using an automated device within 4 days after birth. Daily maternal residential air pollutants during pregnancy, including particulate matter with an aerodynamic diameter ≤ 2.5 μm (PM_2.5) and ≤10 μm (PM₁₀), black carbon (BC), and nitrogen dioxide (NO₂), were modelled using a high-resolution spatial-temporal model. The association between newborn BP and air pollution during the last 15 weeks of pregnancy was assessed using distributed lag models. Each 5 μg/m³ increment in prenatal PM_2.5 exposure was associated with a 2.4 mm Hg (95% CI, 0.5 to 4.2) higher systolic and a 1.8 mm Hg (95% CI, 0.2 to 3.5) higher diastolic BP at birth. Overall estimates for PM₁₀ were similar but those for NO₂ and BC did not reach significance. Associations between newborn BP and exposures during the last 4 to 5 weeks of pregnancy were significant for all pollutants. An IQR (20.3%) increment in percentage residential greenness in a 5 km radius was associated with a 1.2 mm Hg (95% CI, -2.5 to 0.1; p = 0.07) lower systolic and a 1.2 mm Hg (95% CI, -2.4 to -0.0; p = 0.05) lower diastolic BP. An IQR (4.1%) increment in percentage industrial area in a 5 km radius was associated with a 1.0 mm Hg (95% CI, 0.1 to 1.9; p = 0.03) higher diastolic BP. Residential traffic indicators did not significantly associate with newborn BP. Prenatal air pollution exposure, greenness, and industrial area at maternal residence may affect offspring BP from birth onwards.

Statistical Approaches for Investigating Periods of Susceptibility in Children's Environmental Health Research

PURPOSE OF REVIEW

Children's environmental health researchers are increasingly interested in identifying time intervals during which individuals are most susceptible to adverse impacts of environmental exposures. We review recent advances in methods for assessing susceptible periods.

RECENT FINDINGS

We identified three general classes of modeling approaches aimed at identifying susceptible periods in children's environmental health research: multiple informant models, distributed lag models, and Bayesian approaches. Benefits over traditional regression modeling include the ability to formally test period effect differences, to incorporate highly time-resolved exposure data, or to address correlation among exposure periods or exposure mixtures. Several statistical approaches exist for investigating periods of susceptibility. Assessment of susceptible periods would be advanced by additional basic biological research, further development of statistical methods to assess susceptibility to complex exposure mixtures, validation studies evaluating model assumptions, replication studies in different populations, and consideration of susceptible periods from before conception to disease onset.

Association between prenatal particulate air pollution exposure and telomere length in cord blood: Effect modification by fetal sex

INTRODUCTION

In utero particulate matter exposure produces oxidative stress that impacts cellular processes that include telomere biology. Newborn telomere length is likely critical to an individual's telomere biology; reduction in this initial telomere setting may signal increased susceptibility to adverse outcomes later in life. We examined associations between prenatal particulate matter with diameter ≤2.5 µm (PM_2.5) and relative leukocyte telomere length (LTL) measured in cord blood using a data-driven approach to characterize sensitive windows of prenatal PM_2.5 effects and explore sex differences.

METHODS

Women who were residents of Mexico City and affiliated with the Mexican Social Security System were recruited during pregnancy (n = 423 for analyses). Mothers' prenatal exposure to PM_2.5 was estimated based on residence during pregnancy using a validated satellite-based spatio-temporally resolved prediction model. Leukocyte DNA was extracted from cord blood obtained at delivery. Duplex quantitative polymerase chain reaction was used to compare the relative amplification of the telomere repeat copy number to single gene (albumin) copy number. A distributed lag model incorporating weekly averages for PM_2.5 over gestation was used in order to explore sensitive windows. Sex-specific associations were examined using Bayesian distributed lag interaction models.

RESULTS

In models that included child's sex, mother's age at delivery, prenatal environmental tobacco smoke exposure, pre-pregnancy BMI, gestational age, birth season and assay batch, we found significant associations between higher PM_2.5 exposure during early pregnancy (4-9 weeks) and shorter LTL in cord blood. We also identified two more windows at 14-19 and 34-36 weeks in which increased PM_2.5 exposure was associated with longer LTL. In stratified analyses, the mean and cumulative associations between PM_2.5 and shortened LTL were stronger in girls when compared to boys.

CONCLUSIONS

Increased PM_2.5 during specific prenatal windows was associated with shorter LTL and longer LTL. PM_2.5 was more strongly associated with shortened LTL in girls when compared to boys. Understanding sex and temporal differences in response to air pollution may provide unique insight into mechanisms.

Critical window variable selection: estimating the impact of air pollution on very preterm birth

Understanding the impact that environmental exposure during different stages of pregnancy has on the risk of adverse birth outcomes is vital for protection of the fetus and for the development of mechanistic explanations of exposure-disease relationships. As a result, statistical models to estimate critical windows of susceptibility have been developed for several different reproductive outcomes and pollutants. However, these current methods fail to adequately address the primary objective of this line of research; how to statistically identify a critical window of susceptibility. In this article, we introduce critical window variable selection (CWVS), a hierarchical Bayesian framework that directly addresses this question while simultaneously providing improved estimation of the risk parameters. Through simulation, we show that CWVS outperforms existing competing techniques in the setting of highly temporally correlated exposures in terms of (i) correctly identifying critical windows and (ii) accurately estimating risk parameters. We apply all competing methods to a case/control analysis of pregnant women in North Carolina, 2005-2008, with respect to the development of very preterm birth and exposure to ambient ozone and particulate matter $<$ 2.5 $\mu$m in aerodynamic diameter, and identify/estimate the critical windows of susceptibility. The newly developed method is implemented in the R package CWVS.

Prenatal particulate air pollution exposure and sleep disruption in preschoolers: Windows of susceptibility

BACKGROUND

The programming of sleep architecture begins in pregnancy and depends upon optimal in utero formation and maturation of the neural connectivity of the brain. Particulate air pollution exposure can disrupt fetal brain development but associations between fine particulate matter (PM_2.5) exposure during pregnancy and child sleep outcomes have not been previously explored.

METHODS

Analyses included 397 mother-child pairs enrolled in a pregnancy cohort in Mexico City. Daily ambient prenatal PM_2.5 exposure was estimated using a validated satellite-based spatio-temporally resolved prediction model. Child sleep periods were estimated objectively using wrist-worn, continuous actigraphy over a 1-week period at age 4-5 years. Data-driven advanced statistical methods (distributed lag models (DLMs)) were employed to identify sensitive windows whereby PM_2.5 exposure during gestation was significantly associated with changes in sleep duration or efficiency. Models were adjusted for maternal education, season, child's age, sex, and BMI z-score.

RESULTS

Mother's average age was 27.7 years, with 59% having at least a high school education. Children slept an average of 7.7 h at night, with mean 80.1% efficiency. The adjusted DLM identified windows of PM_2.5 exposure between 31 and 35 weeks gestation that were significantly associated with decreased sleep duration in children. In addition, increased PM_2.5 during weeks 1-8 was associated with decreased sleep efficiency. In other exposure windows (weeks 39-40), PM_2.5 was associated with increased sleep duration.

CONCLUSION

Prenatal PM_2.5 exposure is associated with altered sleep in preschool-aged children in Mexico City. Pollutant exposure during sensitive windows of pregnancy may have critical influence upon sleep programming.

Prenatal nitrate air pollution exposure and reduced child lung function: Timing and fetal sex effects

BACKGROUND

Prenatal particulate air pollution exposure may alter lung growth and development in utero in a time-sensitive and sex-specific manner, resulting in reduced lung function in childhood. Such relationships have not been examined for nitrate (NO3-).

METHODS

We implemented Bayesian distributed lag interaction models (BDLIMs) to identify sensitive prenatal windows for the influence of NO3- on lung function at age 7 years, assessing effect modification by fetal sex. Analyses included 191 mother-child dyads. Daily ambient NO3- exposure over pregnancy was estimated using a hybrid chemical transport (Geos-Chem)/land-use regression model. Spirometry was performed at mean (SD) age of 6.99 (0.89) years, with forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) z-scores accounting for child age, sex, height and race/ethnicity.

RESULTS

Most mothers were Hispanic (65%) or Black (22%), had ≤ high school education (67%), and never smoked (71%); 17% children had asthma. BDILMs adjusted for maternal age and education and child's asthma identified an early sensitive window of 6-12 weeks gestation, during which increased NO3- was significantly associated with reduced FEV1 z-scores specifically among boys. BDLIM analyses demonstrated similar sex-specific patterns for FVC.

CONCLUSION

Early gestational NO3- exposure is associated with reduced child lung function, especially in boys.

Investigating the Impact of Maternal Residential Mobility on Identifying Critical Windows of Susceptibility to Ambient Air Pollution During Pregnancy

Identifying periods of increased vulnerability to air pollution during pregnancy with respect to the development of adverse birth outcomes can improve understanding of possible mechanisms of disease development and provide guidelines for protection of the child. Exposure to air pollution during pregnancy is typically based on the mother's residence at delivery, potentially resulting in exposure misclassification and biasing the estimation of critical windows of pregnancy. In this study, we determined the impact of maternal residential mobility during pregnancy on defining weekly exposure to particulate matter less than or equal to 10 μm in aerodynamic diameter (PM10) and estimating windows of susceptibility to term low birth weight. We utilized data sets from 4 Connecticut birth cohorts (1988-2008) that included information on all residential addresses between conception and delivery for each woman. We designed a simulation study to investigate the impact of increasing levels of mobility on identification of critical windows. Increased PM10 exposure during pregnancy weeks 16-18 was associated with an increased probability of term low birth weight. Ignoring residential mobility when defining weekly exposure had only a minor impact on the identification of critical windows for PM10 and term low birth weight in the data application and simulation study. Identification of critical pregnancy windows was robust to exposure misclassification caused by ignoring residential mobility in these Connecticut birth cohorts.

Prenatal fine particulate exposure and early childhood asthma: Effect of maternal stress and fetal sex

BACKGROUND

The impact of prenatal ambient air pollution on child asthma may be modified by maternal stress, child sex, and exposure dose and timing.

OBJECTIVE

We prospectively examined associations between coexposure to prenatal particulate matter with an aerodynamic diameter of less than 2.5 microns (PM2.5) and maternal stress and childhood asthma (n = 736).

METHODS

Daily PM2.5 exposure during pregnancy was estimated using a validated satellite-based spatiotemporally resolved prediction model. Prenatal maternal negative life events (NLEs) were dichotomized around the median (high: NLE ≥ 3; low: NLE < 3). We used Bayesian distributed lag interaction models to identify sensitive windows for prenatal PM2.5 exposure on children's asthma by age 6 years, and determine effect modification by maternal stress and child sex.

RESULTS

Bayesian distributed lag interaction models identified a critical window of exposure (19-23 weeks' gestation, cumulative odds ratio, 1.15; 95% CI, 1.03-1.26; per interquartile range [1.7 μg/m3] increase in prenatal PM2.5 level) during which children concomitantly exposed to prenatal PM2.5 and maternal stress had increased risk of asthma. No significant association was seen in children born to women reporting low prenatal stress. When examining modifying effects of prenatal stress and fetal sex, we found that boys born to mothers with higher prenatal stress were most vulnerable (19-21 weeks' gestation; cumulative odds ratio, 1.28; 95% CI, 1.15-1.41; per interquartile range increase in PM2.5).

CONCLUSIONS

Prenatal PM2.5 exposure during sensitive windows is associated with increased risk of child asthma, especially in boys concurrently exposed to elevated maternal stress.

Prenatal particulate matter exposure and mitochondrial dysfunction at the maternal-fetal interface: Effect modification by maternal lifetime trauma and child sex

BACKGROUND

Prenatal ambient fine particulate matter (PM_2.5) and maternal chronic psychosocial stress have independently been linked to changes in mithochondrial DNA copy number (mtDNAcn), a marker of mitochondrial response and dysfunction. Further, overlapping research shows sex-specific effects of PM_2.5 and stress on developmental outcomes. Interactions among PM_2.5, maternal stress, and child sex have not been examined in this context.

METHODS

We examined associations among exposure to prenatal PM_2.5, maternal lifetime traumatic stressors, and mtDNAcn at birth in a sociodemographically diverse pregnancy cohort (N=167). Mothers' daily exposure to PM_2.5 over gestation was estimated using a satellite-based spatio-temporally resolved prediction model. Lifetime exposure to traumatic stressors was ascertained using the Life Stressor Checklist-Revised; exposure was categorized as high vs. low based on a median split. Quantitative real-time polymerase chain reaction (qPCR) was used to determine mtDNAcn in placenta and cord blood leukocytes. Bayesian Distributed Lag Interaction regression models (BDLIMs) were used to statistically model and visualize the PM_2.5 timing-dependent pattern of associations with mtDNAcn and explore effect modification by maternal lifetime trauma and child sex.

RESULTS

Increased PM_2.5 exposure across pregnancy was associated with decreased mtDNAcn in cord blood (cumulative effect estimate=-0.78; 95%CI -1.41, -0.16). Higher maternal lifetime trauma was associated with reduced mtDNAcn in placenta (β=-0.33; 95%CI -0.63, -0.02). Among women reporting low trauma, increased PM_2.5 exposure late in pregnancy (30-38weeks gestation) was significantly associated with decreased mtDNAcn in placenta; no significant association was found in the high trauma group. BDLIMs identified a significant 3-way interaction between PM_2.5, maternal trauma, and child sex. Specifically, PM_2.5 exposure between 25 and 40weeks gestation was significantly associated with increased placental mtDNAcn among boys of mothers reporting high trauma. In contrast, PM_2.5 exposure in this same window was significantly associated with decreased placental mtDNAcn among girls of mothers reporting low trauma. Similar 3-way interactive effects were observed in cord blood.

CONCLUSIONS

These results indicate that joint exposure to PM_2.5 in late pregnancy and maternal lifetime trauma influence mtDNAcn at the maternal-fetal interface in a sex-specific manner. Additional studies will assist in understanding if the sex-specific patterns reflect distinct pathophysiological processes in addition to mitochondrial dysfunction.

Prenatal Nitrate Exposure and Childhood Asthma. Influence of Maternal Prenatal Stress and Fetal Sex

RATIONALE

Impact of ambient pollution upon children's asthma may differ by sex, and exposure dose and timing. Psychosocial stress can also modify pollutant effects. These associations have not been examined for in utero ambient nitrate exposure.

OBJECTIVES

We implemented Bayesian-distributed lag interaction models to identify sensitive prenatal windows for the influence of nitrate (NO3-) on child asthma, accounting for effect modification by sex and stress.

METHODS

Analyses included 752 mother-child dyads. Daily ambient NO3- exposure during pregnancy was derived using a hybrid chemical transport (Geos-Chem)/land-use regression model and natural log transformed. Prenatal maternal stress was indexed by a negative life events score (high [>2] vs. low [≤2]). The outcome was clinician-diagnosed asthma by age 6 years.

MEASUREMENTS AND MAIN RESULTS

Most mothers were Hispanic (54%) or black (29%), had a high school education or less (66%), never smoked (80%), and reported low prenatal stress (58%); 15% of children developed asthma. BDILMs adjusted for maternal age, race, education, prepregnancy obesity, atopy, and smoking status identified two sensitive windows (7-19 and 33-40 wk gestation), during which increased NO3- was associated with greater odds of asthma, specifically among boys born to mothers reporting high prenatal stress. Cumulative effects of NO3- across pregnancy were also significant in this subgroup (odds ratio = 2.64, 95% confidence interval = 1.27-5.39; per interquartile range increase in ln NO3-).

CONCLUSIONS

Prenatal NO3- exposure during distinct sensitive windows was associated with incident asthma in boys concurrently exposed to high prenatal stress.