Semiparametric latent variable regression models for spatiotemporal modelling of mobile source particles in the greater Boston area

Air Pollution, Neonatal Immune Responses, and Potential Joint Effects of Maternal Depression

Prenatal maternal exposure to air pollution may cause adverse health effects in offspring, potentially through altered immune responses. Maternal psychosocial distress can also alter immune function and may increase gestational vulnerability to air pollution exposure. We investigated whether prenatal exposure to air pollution is associated with altered immune responses in cord blood mononuclear cells (CBMCs) and potential modification by maternal depression in 463 women recruited in early pregnancy (1999-2001) into the Project Viva longitudinal cohort. We estimated black carbon (BC), fine particulate matter (PM2.5), residential proximity to major roadways, and near-residence traffic density, averaged over pregnancy. Women reported depressive symptoms in mid-pregnancy (Edinburgh Postnatal Depression Scale) and depression history by questionnaire. Immune responses were assayed by concentrations of three cytokines (IL-6, IL-10, and TNF-α), in unstimulated or stimulated (phytohemagglutinin (PHA), cockroach extract (Bla g 2), house dust mite extract (Der f 1)) CBMCs. Using multivariable linear or Tobit regression analyses, we found that CBMCs production of IL-6, TNF-a, and IL-10 were all lower in mothers exposed to higher levels of PM2.5 during pregnancy. A suggestive but not statistically significant pattern of lower cord blood cytokine concentrations from ever (versus never) depressed women exposed to PM2.5, BC, or traffic was also observed and warrants further study.

Recommendations for Assessment of Environmental Exposures in Longitudinal Life Course Studies Such as the National Children's Study

An important step toward understanding the relationship between the environment and child health and development is the comprehensive cataloging of external environmental factors that may modify health and development over the life course. Our understanding of the environmental influences on health is growing increasingly complex. Significant key questions exist as to what genes, environment, and life stage mean to defining normal variations and altered developmental trajectories throughout the life course and also across generations. With the rapid advances in genetic technology came large-scale genomic studies to search for the genetic etiology of complex diseases. While genome-wide association studies (GWAS) have revealed genetic factors and networks that advance our understanding to some extent, it is increasingly recognized that disease causation is largely non-genetic and reflects interactions between an individual's genetic susceptibility and his or her environment. Thus, the full promise of the human genome project to prevent or treat disease and promote good health arguably depends on a commitment to understanding the interactions between our environment and our genetic makeup and requires a design with prospective environmental data collection that considers critical windows of susceptibility that likely correspond to the expression of specific genes and gene pathways. Unlike the genome, which is static, relevant exposures as well as our response to exposures, change over time. This has fostered the complementary concept of the exposome ideally defined as the measure of all exposures of an individual over a lifetime and how those exposures relate to health. The exposome framework considers multiple external exposures (e.g., chemical, social) and behaviors that may modify exposures (e.g., diet), as well as consequences of environmental exposures indexed via biomarkers of physiological response or measures of behavioral response throughout the lifespan. The exposome concept can be applied in prospective developmental studies such as the National Children's Study (NCS) with the practical understanding that even a partial characterization will bring major advances to health. Lessons learned from the NCS provide an important opportunity to inform future studies that can leverage these evolving paradigms in elucidating the role of environment on health across the life course.

Cyclists' exposure to air pollution: in situ evaluation with a cargo bike platform

Cyclists' exposure to air pollutants near roadways has been associated with numerous health effects. While the adverse health effects concerning aerosols have traditionally been assessed with data of particle mass concentrations, it appears that the number concentration is also another important indicator of toxicity. Thus, to holistically evaluate one's exposure to aerosol particles, assessments should be based on mass concentrations and number concentrations. In order to assess individual cyclists' exposure as they move through space and time, spatiotemporal high-resolution approaches are needed. Therefore, a mobile, fast-response monitoring platform was developed that uses a cargo bicycle as a base. Data of particle mass concentrations (PM₁, PM_2.5, PM₁₀) and particle number concentrations (PN₁₀) were collected along two different routes, one characterized by high-intensity vehicle traffic and one by low-intensity vehicle traffic. While high spatiotemporal heterogeneity was observed for all measured quantities, the PN₁₀ concentrations fluctuated the most. High concentrations of PN₁₀ could be clearly associated with vehicle traffic. For PM_2.5, this relation was less pronounced. Mean particle concentrations of all measures were significantly higher along the high-traffic route. Comparing route exposures, the inhalation of PM_2.5 was similar between both routes, whereas along the high-traffic route, cyclists were exposed to twice the particle number. We conclude that the cargo bike, featuring high-frequency mobile measurements, was useful to characterize the spatial distribution of mass concentrations and number concentrations across an urban environment. Overall, our results suggest that the choice of route is a key factor in reducing cyclists' exposure to air pollution.

Association between ambient beta particle radioactivity and lower hemoglobin concentrations in a cohort of elderly men

Although ionizing radiation is known to have detrimental effects on red blood cells, the effect of environmental radioactivity associated with ambient particulate matter (PM) is unknown. We hypothesized that exposure to ambient PM-associated beta particle radioactivity (PR_β) would be associated with a lower hemoglobin concentration. We studied 1.704 participants from the Normative Aging Study (NAS) over 36 years (1981-2017) who lived in Eastern, MA and the surrounding area. Exposures to PR_β was assessed using USEPA's RadNet monitoring network that measures gross beta radiation associated with ambient PM. Mixed effect models with a random intercept adjusting for potential confounders was used, including ambient black carbon (BC) and particulate matter ≤2.5 μm (PM_2.5) concentrations. Greater cumulative PR_β activities at 7-, 14-, 21- and 28-days before the hemoglobin determination were associated with lower hemoglobin concentrations. The greatest effect was for a 28-day moving average. An IQR of 0.83 × 10^-4 Bq/m³ of ambient PR_β was associated with a 0.12 g/dL decrease in hemoglobin concentration (95%CI: -0.18 to -0.05). The effects of PR_β were similar when the models were adjusted for ambient BC or PM_2.5. This is the first study to demonstrate an association between environmental ionizing radiation released from particulate matter with a lower hemoglobin concentration, suggesting that ambient radiation may contribute to the development of anemia.

Synthesis of Harvard Environmental Protection Agency (EPA) Center studies on traffic-related particulate pollution and cardiovascular outcomes in the Greater Boston Area

The association between particulate pollution and cardiovascular morbidity and mortality is well established. While the cardiovascular effects of nationally regulated criteria pollutants (e.g., fine particulate matter [PM_2.5] and nitrogen dioxide) have been well documented, there are fewer studies on particulate pollutants that are more specific for traffic, such as black carbon (BC) and particle number (PN). In this paper, we synthesized studies conducted in the Greater Boston Area on cardiovascular health effects of traffic exposure, specifically defined by BC or PN exposure or proximity to major roadways. Large cohort studies demonstrate that exposure to traffic-related particles adversely affect cardiac autonomic function, increase systemic cytokine-mediated inflammation and pro-thrombotic activity, and elevate the risk of hypertension and ischemic stroke. Key patterns emerged when directly comparing studies with overlapping exposure metrics and population cohorts. Most notably, cardiovascular risk estimates of PN and BC exposures were larger in magnitude or more often statistically significant compared to those of PM_2.5 exposures. Across multiple exposure metrics (e.g., short-term vs. long-term; observed vs. modeled) and different population cohorts (e.g., elderly, individuals with co-morbidities, young healthy individuals), there is compelling evidence that BC and PN represent traffic-related particles that are especially harmful to cardiovascular health. Further research is needed to validate these findings in other geographic locations, characterize exposure errors associated with using monitored and modeled traffic pollutant levels, and elucidate pathophysiological mechanisms underlying the cardiovascular effects of traffic-related particulate pollutants. Implications: Traffic emissions are an important source of particles harmful to cardiovascular health. Traffic-related particles, specifically BC and PN, adversely affect cardiac autonomic function, increase systemic inflammation and thrombotic activity, elevate BP, and increase the risk of ischemic stroke. There is evidence that BC and PN are associated with greater cardiovascular risk compared to PM_2.5. Further research is needed to elucidate other health effects of traffic-related particles and assess the feasibility of regulating BC and PN or their regional and local sources.

Predicting Indoor Concentrations of Black Carbon in Residential Environments

Black carbon (BC) is a descriptive term that refers to light-absorbing particulate matter (PM) produced by incomplete combustion and is often used as a surrogate for traffic-related air pollution. Exposure to BC has been linked to adverse health effects. Penetration of ambient BC is typically the primary source of indoor BC in the developed world. Other sources of indoor BC include biomass and kerosene stoves, lit candles, and charring food during cooking. Home characteristics can influence the levels of indoor BC. As people spend most of their time indoors, human exposure to BC can be associated to a large extent with indoor environments. At the same time, due to the cost of environmental monitoring, it is often not feasible to directly measure BC inside multiple individual homes in large-scale population-based studies. Thus, a predictive model for indoor BC is needed to support risk assessment in public health. In this study, home characteristics and occupant activities that potentially modify indoor levels of BC were documented in 23 homes, and indoor and outdoor BC concentrations were measured twice. The homes were located in the Cincinnati-Kentucky-Indiana tristate region and measurements occurred from September 2015 through August 2017. A linear mixed-effect model was developed to predict BC concentration in residential environments. The measured outdoor BC concentrations and the documented home characteristics were utilized as predictors of indoor BC concentrations. After the model was developed, a leave-one-out cross-validation algorithm was deployed to assess the predictive accuracy of the output. The following home characteristics and occupant activities significantly modified the concentration of indoor BC: outdoor BC, lit candles and electrostatic or high efficiency particulate air (HEPA) filters in heating, ventilation and air conditioning (HVAC) systems. Predicted indoor BC concentrations explained 78% of the variability in the measured indoor BC concentrations. The data show that outdoor BC combined with home characteristics can be used to predict indoor BC levels with reasonable accuracy.

Extending Tests of Random Effects to Assess for Measurement Invariance in Factor Models

Factor analysis models are widely used in health research to summarize hard to measure predictor or outcome variable constructs. For example, in the ELEMENT study, factor models are used to summarize lead exposure biomarkers which are thought to indirectly measure prenatal exposure to lead. Classic latent factor models are fitted assuming that factor loadings are constant across all covariate levels (e.g., maternal age in ELEMENT); that is, measurement invariance (MI) is assumed. When the MI is not met, measurement bias is introduced. Traditionally, MI is examined by defining subgroups of the data based on covariates, fitting multi-group factor analysis, and testing differences in factor loadings across covariate groups. In this paper, we develop novel tests of measurement invariance by modeling the factor loadings as varying coeffcients, i.e., letting the factor loading vary across continuous covariate values instead of groups. These varying coeffcients are estimated using penalized splines, where spline coeffcients are penalized by treating them as random coeffcients. The test of MI is then carried out by conducting a likelihood ratio test for the null hypothesis that the variance of the random spline coeffcients equals zero. We use a Monte-Carlo EM algorithm for estimation, and obtain the likelihood using Monte-Carlo in tegration. Using simulations, we compare the Type I error and power of our testing approach and the multi-group testing method. We apply the proposed methods to to summarize data on prenatal biomarkers of lead exposure from the ELEMENT study and find violations of MI due to maternal age.

Effects of Indoor and Ambient Black Carbon and [Formula: see text] on Pulmonary Function among Individuals with COPD

BACKGROUND

Particulate matter (PM) air pollution has been associated with decreased pulmonary function, but the exposure–response relationship in chronic obstructive pulmonary disease (COPD) patients is uncertain, and most studies have only focused on exposures to ambient pollution.

OBJECTIVES

We aimed to assess associations between pulmonary function and indoor and ambient PM [Formula: see text] ([Formula: see text]) and black carbon (BC).

METHODS

Between November 2012 and December 2014, 125 patients with COPD (mean age, 73.4 y) who were not currently smoking and without known indoor BC sources were recruited. Indoor BC and [Formula: see text] were measured in each home for a week in each season, up to four times a year, followed by in-person spirometry pre- and post-bronchodilator. Ambient exposures were available from a central site monitor. Multivariable adjusted mixed effects regression models were used to assess associations scaled per interquartile range (IQR) of exposure.

RESULTS

There were 367 study visits; the median (IQR) indoor BC and [Formula: see text] were 0.19 (0.22) [Formula: see text] and 6.67 (5.80) [Formula: see text], respectively. Increasing indoor exposures to BC were associated with decreases in pre-bronchodilator forced expiratory volume in 1 s [Formula: see text] and forced vital capacity (FVC), and [Formula: see text]. For example, in multivariable adjusted models, each IQR increase in indoor BC from the weekly integrated filter was associated with a [Formula: see text] [95% confidence interval (CI): [Formula: see text], [Formula: see text]] decrease in pre-bronchodilator [Formula: see text]. Increases in indoor [Formula: see text] were associated with decreases in [Formula: see text] and FVC of smaller magnitude than those for indoor BC; however, the results were less precise. Ambient BC was not associated with pre-bronchodilator pulmonary function, ambient [Formula: see text] was only associated with decreases in FVC and increases in [Formula: see text], and neither indoor nor ambient BC or [Formula: see text] were associated with post-bronchodilator pulmonary function.

CONCLUSIONS

Low-level exposures to indoor BC and [Formula: see text], but not ambient exposures, were consistently associated with decreases in pre-bronchodilator pulmonary function. There was no association between exposures and post-bronchodilator pulmonary function. https://doi.org/10.1289/EHP3668.

Extracellular vesicle-enriched microRNAs interact in the association between long-term particulate matter and blood pressure in elderly men

BACKGROUND

Several studies have shown that exposure to particulate matter (PM) may lead to increased systemic blood pressure, but the underlying biological mechanisms remain unknown. Emerging evidence shows that extracellular vesicle-enriched miRNAs (evmiRNAs) are associated with PM exposure and cardiovascular risk. In this study, we investigated the role of evmiRNAs in the association between PM and blood pressure, as well as their epigenetic regulation by DNA methylation.

METHODS

Participants (n = 22, men) were randomly selected from the Veterans Affairs Normative Aging Study (NAS). Long-term (1-year and 6-month average) PM_2.5 exposure was estimated at 1 × 1-km resolution using spatio-temporal prediction models and BC was estimated using validated time varying land use regression models. We analyzed 31 evmiRNAs detected in ≥ 90% of all individuals and for statistical analysis, we used mixed effects models with random intercept adjusted for age, body mass index, smoking, C-reactive protein, platelets, and white blood cells.

RESULTS

We found that per each 2-standard deviations increase in 6-month PM_2.5 ambient levels, there was an increase in 0.19 mm Hg (95% Confidence Interval [95%CI]: 0.11, 0.28 mmHg; p < 0.001) in systolic blood pressure (SBP). Per each 2-standard deviations increase in 1-year PM_2.5 levels, there was an increase in 0.11 mm Hg (95% Confidence Interval [95% CI]: 0.03, 0.19 mmHg; p = 0.012) in SBP in older male individuals. We also found that both miR-199a/b (β = 6.13 mmHg; 95% CI: 0.87, 11.39; p_interaction = 0.07) and miR-223-3p (β = 30.17 mmHg; 95% CI: 11.96, 48.39 mmHg; p_interaction = 0.01) modified the association between 1-year PM_2.5 and SBP. When exploring DNA methylation as a potential mechanism that could epigenetically regulate expression of evmiRNAs, we found that PM_2.5 ambient levels were negatively associated with DNA methylation levels at CpG (cg23972892) near the enhancer region of miR-199a/b (β = -13.11; 95% CI: -17.70, -8.52; p_Bonferroni< 0.01), but not miR-223-3p.

CONCLUSIONS

Our findings suggest that expression of evmiRNAs may be regulated by DNA methylation in response to long-term PM_2.5 ambient levels and modify the magnitude of association between PM_2.5 and systolic blood pressure in older individuals.

A Bayesian Approach for Summarizing and Modeling Time-Series Exposure Data with Left Censoring

Objective

Direct reading instruments are valuable tools for measuring exposure as they provide real-time measurements for rapid decision making. However, their use is limited to general survey applications in part due to issues related to their performance. Moreover, statistical analysis of real-time data is complicated by autocorrelation among successive measurements, non-stationary time series, and the presence of left-censoring due to limit-of-detection (LOD). A Bayesian framework is proposed that accounts for non-stationary autocorrelation and LOD issues in exposure time-series data in order to model workplace factors that affect exposure and estimate summary statistics for tasks or other covariates of interest.

Method

A spline-based approach is used to model non-stationary autocorrelation with relatively few assumptions about autocorrelation structure. Left-censoring is addressed by integrating over the left tail of the distribution. The model is fit using Markov-Chain Monte Carlo within a Bayesian paradigm. The method can flexibly account for hierarchical relationships, random effects and fixed effects of covariates. The method is implemented using the rjags package in R, and is illustrated by applying it to real-time exposure data. Estimates for task means and covariates from the Bayesian model are compared to those from conventional frequentist models including linear regression, mixed-effects, and time-series models with different autocorrelation structures. Simulations studies are also conducted to evaluate method performance.

Results

Simulation studies with percent of measurements below the LOD ranging from 0 to 50% showed lowest root mean squared errors for task means and the least biased standard deviations from the Bayesian model compared to the frequentist models across all levels of LOD. In the application, task means from the Bayesian model were similar to means from the frequentist models, while the standard deviations were different. Parameter estimates for covariates were significant in some frequentist models, but in the Bayesian model their credible intervals contained zero; such discrepancies were observed in multiple datasets. Variance components from the Bayesian model reflected substantial autocorrelation, consistent with the frequentist models, except for the auto-regressive moving average model. Plots of means from the Bayesian model showed good fit to the observed data.

Conclusion

The proposed Bayesian model provides an approach for modeling non-stationary autocorrelation in a hierarchical modeling framework to estimate task means, standard deviations, quantiles, and parameter estimates for covariates that are less biased and have better performance characteristics than some of the contemporary methods.

Relation of Prenatal Air Pollutant and Nutritional Exposures with Biomarkers of Allergic Disease in Adolescence

Prenatal exposures may be critical for immune system development, with consequences for allergic disease susceptibility. We examined associations of prenatal exposures (nutrient intakes and air pollutants) with allergic disease biomarkers in adolescence. We used data from 857 mother-child pairs in Project Viva, a Massachusetts-based pre-birth cohort. Outcomes of interest at follow-up (median age 12.9 years) were fractional exhaled nitric oxide (FeNO) and total serum IgE. We applied Bayesian Kernel Machine Regression analyses to estimate multivariate exposure-response functions, allowing for exposure interactions. Exposures were expressed as z-scores of log-transformed data and we report effects in % change in FeNO or IgE z-score per increase in exposure from the 25th to 75th percentile. FeNO levels were lower with higher intakes of prenatal vitamin D (-16.15%, 95% CI: -20.38 to -2.88%), folate from foods (-3.86%, 95% CI: -8.33 to 0.83%) and n-3 PUFAs (-9.21%, 95% CI -16.81 to -0.92%). Prenatal air pollutants were associated with higher FeNO and IgE, with the strongest associations detected for PM2.5 with IgE (25.6% increase, 95% CI 9.34% to 44.29%). We identified a potential synergistic interaction (p = 0.02) between vitamin E (food + supplements) and PM2.5; this exposure combination was associated with further increases in FeNO levels.

Indoor black carbon of outdoor origin and oxidative stress biomarkers in patients with chronic obstructive pulmonary disease

OBJECTIVES

We assessed relationships between indoor black carbon (BC) exposure and urinary oxidative stress biomarkers, 8-hydroxy-2'-deoxyguanosine (8-OHdG) and malondialdehyde (MDA), in participants with chronic obstructive pulmonary disease (COPD).

METHODS

Eighty-two participants completed in-home air sampling for one week prior to providing urine samples up to four times in a year. Weekly indoor and daily outdoor concentrations were used to estimate indoor daily lags and moving averages. There were no reported in-home BC sources, thus indoor levels closely represented outdoor BC infiltration. Mixed effects regression models with a random intercept for each participant were used to assess relationships between indoor BC and 8-OHdG and MDA, adjusting for age, race, BMI, diabetes, heart disease, season, time of urine collection, urine creatinine, and outdoor humidity and temperature.

RESULTS

There were positive effects of BC on 8-OHdG and MDA, with the greatest effect the day before urine collection (6.9% increase; 95% CI 0.9-13.3%, per interquartile range: 0.22 μg/m³) for 8-OHdG and 1 to 4 days before collection (8.3% increase; 95% CI 0.03-17.3% per IQR) for MDA. Results were similar in models adjusting for PM_2.5 not associated with BC and NO₂ (10.4% increase, 95% CI: 3.5-17.9 for 8-OHdG; 8.1% increase, 95% CI: -1.1-18.1 for MDA). Effects on 8-OHdG were greater in obese participants.

CONCLUSIONS

We found positive associations between BC exposure and 8-OHdG and MDA, in which associations with 8-OHdG were stronger in obese participants. These results suggest that exposure to low levels of traffic-related pollution results in lipid peroxidation and oxidative DNA damage in individuals with COPD.

Multiscale measurement error models for aggregated small area health data

Spatial data are often aggregated from a finer (smaller) to a coarser (larger) geographical level. The process of data aggregation induces a scaling effect which smoothes the variation in the data. To address the scaling problem, multiscale models that link the convolution models at different scale levels via the shared random effect have been proposed. One of the main goals in aggregated health data is to investigate the relationship between predictors and an outcome at different geographical levels. In this paper, we extend multiscale models to examine whether a predictor effect at a finer level hold true at a coarser level. To adjust for predictor uncertainty due to aggregation, we applied measurement error models in the framework of multiscale approach. To assess the benefit of using multiscale measurement error models, we compare the performance of multiscale models with and without measurement error in both real and simulated data. We found that ignoring the measurement error in multiscale models underestimates the regression coefficient, while it overestimates the variance of the spatially structured random effect. On the other hand, accounting for the measurement error in multiscale models provides a better model fit and unbiased parameter estimates.

Cumulative exposure to environmental pollutants during early pregnancy and reduced fetal growth: the Project Viva cohort

BACKGROUND

Reduced fetal growth is associated with perinatal and later morbidity. Prenatal exposure to environmental pollutants is linked to reduced fetal growth at birth, but the impact of concomitant exposure to multiple pollutants is unclear. The purpose of this study was to examine interactions between early pregnancy exposure to cigarette smoke, traffic pollution, and select perfluoroalkyl substances (PFASs) on birth weight-for-gestational age (BW/GA).

METHODS

Among 1597 Project Viva mother-infant pairs, we assessed maternal cigarette smoking by questionnaire, traffic pollution at residential address by black carbon land use regression model, and plasma concentration of select PFASs in early pregnancy. We calculated sex-specific BW/GA z-scores, an index of fetal growth, from national reference data. We fit covariate-adjusted multi-pollutant linear regression models and examined interactions between exposures, using a likelihood-ratio test to identify a best-fit model.

RESULTS

Two hundred six (13%) mothers smoked during pregnancy. Mean [standard deviation (SD)] for black carbon was 0.8 (0.3) μg/m³, perfluorooctane sulfonate (PFOS) was 29.1 (16.5) ng/mL, and BW/GA z-score was 0.19 (0.96). In the best-fit model, BW/GA z-score was lower in infants of mothers exposed to greater black carbon [- 0.08 (95% CI: -0.15, - 0.01) per interquartile range (IQR)]. BW/GA z-score (95% CI) was also lower in infants of mothers who smoked [- 0.09 (- 0.23, 0.06)] or were exposed to greater PFOS [- 0.03 (- 0.07, 0.02) per IQR], although confidence intervals crossed the null. There were no interactions between exposures. In secondary analyses, instead of PFOS, we examined perfluorononanoate (PFNA) [mean (SD): 0.7 (0.4) ng/mL], a PFAS more closely linked to lower BW/GA in our cohort. The best-fit multi-pollutant model included positive two-way interactions between PFNA and both black carbon and smoking (p-interactions = 0.03).

CONCLUSIONS

Concurrent prenatal exposures to maternal smoking, black carbon, and PFOS are additively associated with lower fetal growth, whereas PFNA may attenuate associations of smoking and black carbon with lower fetal growth. It is important to examine interactions between multiple exposures in relation to health outcomes, as effects may not always be additive and may shed light on biological pathways.

Long-term exposure to air pollution is associated with biological aging

Long-term exposure to air pollution is associated with age-related diseases. We explored the association between accelerated biological aging and air pollution, a potential mechanism linking air pollution and health. We estimated long-term exposure to PM₁₀, PM_2.5, PM_2.5 absorbance/black carbon (BC), and NO_x via land-use regression models in individuals from the KORA F4 cohort. Accelerated biological aging was assessed using telomere length (TeloAA) and three epigenetic measures: DNA methylation age acceleration (DNAmAA), extrinsic epigenetic age acceleration (correlated with immune cell counts, EEAA), and intrinsic epigenetic age acceleration (independent of immune cell counts, IEAA). We also investigated sex-specific associations between air pollution and biological aging, given the published association between sex and aging measures. In KORA an interquartile range (0.97 μg/m³) increase in PM_2.5 was associated with a 0.33 y increase in EEAA (CI = 0.01, 0.64; P = 0.04). BC and NO_x (indicators or traffic exposure) were associated with DNAmAA and IEAA in women, while TeloAA was inversely associated with BC in men. We replicated this inverse BC-TeloAA association in the Normative Aging Study, a male cohort based in the USA. A multiple phenotype analysis in KORA F4 combining all aging measures showed that BC and PM₁₀ were broadly associated with biological aging in men. Thus, we conclude that long-term exposure to air pollution is associated with biological aging measures, potentially in a sex-specific manner. However, many of the associations were relatively weak and further replication of overall and sex-specific associations is warranted.

Alternating Gaussian Process Modulated Renewal Processes for Modeling Threshold Exceedances and Durations

It is often of interest to model the incidence and duration of threshold exceedance events for an environmental variable over a set of monitoring locations. Such data arrive over continuous time and can be considered as observations of a two-state process yielding, sequentially, a length of time in the below threshold state followed by a length of time in the above threshold state, then returning to the below threshold state, etc. We have a two-state continuous time Markov process, often referred to as an alternating renewal process. The process is observed over a truncated time window and, within this window, time in each state is modeled using a distinct cumulative intensity specification. Initially, we model each intensity over the window using a parametric regression specification. We extend the regression specification adding temporal random effects to enrich the model, using a realization of a log Gaussian process over time. With only one type of renewal, this specification is referred to as a Gaussian process modulated renewal process. Here, we introduce Gaussian process modulation to the intensity for each state. Model fitting is done within a Bayesian framework. We clarify that fitting with a customary log Gaussian process specification over a lengthy time window is computationally infeasible. The nearest neighbor Gaussian process (NNGP), which supplies sparse covariance structure, is adopted to enable tractable computation. We also propose methods for both generating data under our models and for conducting model comparison. The model is applied to hourly ozone data for four monitoring sites in different locations across the United States for the ozone season of 2014. For each site, we obtain estimated profiles of up-crossing and down-crossing intensity functions through time. In addition, we obtain inference regarding the number of exceedances, the distribution of the duration of exceedance events, and the proportion of time in the above and below threshold state for any time interval.

Spatial analysis of concentrations of multiple air pollutants using NASA DISCOVER-AQ aircraft measurements: Implications for exposure assessment

In recent years, multipollutant approaches have been employed to investigate the association with health outcomes to better represent real-world conditions than more traditional analysis that considers a single pollutant. With regard to the exposure assessment of a mixture of air pollutants, it is critical to understand the spatial variability in multipollutant relations in order to assess their potential health implications. In this study, we investigated the spatial relations of multiple pollutant concentrations (i.e., NO_x, NO_y, black carbon, carbon monoxide, acetaldehyde, formaldehyde, toluene, xylenes/ethylbenzene, ozone, water-soluble organic carbon, and aerosol extinction) observed from the P-3B aircraft in the 2011 NASA field campaign in Baltimore/Washington D.C. areas during July 2011. The between-pollutant Pearson correlations and Z-scores (calculated from log-transformed concentrations) between near-highways and non-highways and between near-urban centers and non-urban centers varied by pollutant pair and space. We found generally lower correlations between NO_x and other pollutants for near-highways (average r = 0.36) than for non-highways (average r = 0.41) and also for non-urban centers (average r = 0.37) than for near-urban centers (average r = 0.41). This indicated that the temporal associations between NO_x and health outcomes might be less affected by other pollutants, which were also related to same health outcomes, for near-highways and non-urban centers. The analysis of between-pollutant Z-scores showed varying spatial relations for popular traffic-related pollutants with the Z-score differences of 0.43 (NO_x-carbon monoxide), 0.29 (NO_x-black carbon), and 0.17 (black carbon-carbon monoxide) between near-highways and non-highways. This result exhibited heterogeneous traffic-related pollutant mixtures with the proximity to highways, potentially leading to the diverse extent of health associations. Furthermore, a mixed effects model presented pollutant-specific associations between the concentrations and the proximity to highways and urban centers, showing larger declines for NO_x, xylenes/ethylbenzene, toluene, and NO_y than those for the pollutants related to secondary pollutant formation. The model also demonstrated the different sensitivity of each pollutant to meteorological parameters, which may modify the spatial and temporal variability in the relations between the pollutants. Our findings provide insights for exposure assessment studies to better understand the cumulative health consequences associated with multiple air pollutants simultaneously.

Prenatal Exposure to Traffic Pollution and Childhood Body Mass Index Trajectory

Background: Limited evidence suggests an association between prenatal exposure to traffic pollution and greater adiposity in childhood, but the time window during which growth may be most affected is not known. Methods: We studied 1,649 children in Project Viva, a Boston-area pre-birth cohort. We used spatiotemporal models to estimate prenatal residential air pollution exposures and geographic information systems to estimate neighborhood traffic density and roadway proximity. We used weight and stature measurements at clinical and research visits to estimate a BMI trajectory for each child with mixed-effects natural cubic spline models. In primary analyses, we examined associations of residential PM_2.5 and black carbon (BC) exposures during the third trimester and neighborhood traffic density and home roadway proximity at birth address with (1) estimated BMI at 6 month intervals through 10 years of age, (2) magnitude and timing of BMI peak and rebound, and (3) overall BMI trajectory. In secondary analyses, we examined associations of residential PM_2.5 and BC exposures during the first and second trimesters with BMI outcomes. Results: Median (interquartile range; IQR) concentration of residential air pollution during the third trimester was 11.4 (1.7) μg/m³ for PM_2.5 and 0.7 (0.3) μg/m³ for BC. Participants had a median (IQR) of 13 (7) clinical or research BMI measures from 0 to 10 years of age. None of the traffic pollution exposures were significantly associated with any of the BMI outcomes in covariate-adjusted models, although effect estimates were in the hypothesized direction for neighborhood traffic density and home roadway proximity. For example, greater neighborhood traffic density [median (IQR) 857 (1,452) vehicles/day x km of road within 100 m of residential address at delivery] was associated with a higher BMI throughout childhood, with the strongest associations in early childhood [e.g., per IQR increment natural log-transformed neighborhood traffic density, BMI at 12 months of age was 0.05 (-0.03, 0.13) kg/m² higher and infancy peak BMI was 0.05 (-0.03, 0.14) kg/m² higher]. Conclusions: We found no evidence for a persistent effect of prenatal exposure to traffic pollution on BMI trajectory from birth through mid-childhood in a population exposed to modest levels of air pollution.

Maternal residential air pollution and placental imprinted gene expression

BACKGROUND

Maternal exposure to air pollution is associated with reduced fetal growth, but its relationship with expression of placental imprinted genes (important regulators of fetal growth) has not yet been studied.

OBJECTIVES

To examine relationships between maternal residential air pollution and expression of placental imprinted genes in the Rhode Island Child Health Study (RICHS).

METHODS

Women-infant pairs were enrolled following delivery between 2009 and 2013. We geocoded maternal residential addresses at delivery, estimated daily levels of fine particulate matter (PM_2.5; n=355) and black carbon (BC; n=336) using spatial-temporal models, and estimated residential distance to nearest major roadway (n=355). Using linear regression models we investigated the associations between each exposure metric and expression of nine candidate genes previously associated with infant birthweight in RICHS, with secondary analyses of a panel of 108 imprinted genes expressed in the placenta. We also explored effect measure modification by infant sex.

RESULTS

PM_2.5 and BC were associated with altered expression for seven and one candidate genes, respectively, previously linked with birthweight in this cohort. Adjusting for multiple comparisons, we found that PM_2.5 and BC were associated with changes in expression of 41 and 12 of 108 placental imprinted genes, respectively. Infant sex modified the association between PM_2.5 and expression of CHD7 and between proximity to major roadways and expression of ZDBF2.

CONCLUSIONS

We found that maternal exposure to residential PM_2.5 and BC was associated with changes in placental imprinted gene expression, which suggests a plausible line of investigation of how air pollution affects fetal growth and development.

Can dispersion modeling of air pollution be improved by land-use regression? An example from Stockholm, Sweden

Both dispersion modeling (DM) and land-use regression modeling (LUR) are often used for assessment of long-term air pollution exposure in epidemiological studies, but seldom in combination. We developed a hybrid DM-LUR model using 93 biweekly observations of NO_x at 31 sites in greater Stockholm (Sweden). The DM was based on spatially resolved topographic, physiographic and emission data, and hourly meteorological data from a diagnostic wind model. Other data were from land use, meteorology and routine monitoring of NO_x. We built a linear regression model for NO_x, using a stepwise forward selection of covariates. The resulting model predicted observed NO_x (R²=0.89) better than the DM without covariates (R²=0.68, P-interaction <0.001) and with minimal apparent bias. The model included (in descending order of importance) DM, traffic intensity on the nearest street, population (number of inhabitants) within 100 m radius, global radiation (direct sunlight plus diffuse or scattered light) and urban contribution to NO_x levels (routine urban NO_x, less routine rural NO_x). Our results indicate that there is a potential for improving estimates of air pollutant concentrations based on DM, by incorporating further spatial characteristics of the immediate surroundings, possibly accounting for imperfections in the emission data.

Association of air particulate pollution with bone loss over time and bone fracture risk: analysis of data from two independent studies

BACKGROUND

Air particulate matter (PM) is a ubiquitous environmental exposure associated with oxidation, inflammation, and age-related chronic disease. Whether PM is associated with loss of bone mineral density (BMD) and risk of bone fractures is undetermined.

METHODS

We conducted two complementary studies of: (i) long-term PM <2.5 μm (PM_2.5) levels and osteoporosis-related fracture hospital admissions among 9.2 million Medicare enrollees of the Northeast/Mid-Atlantic United States between 2003-2010; (ii) long-term black carbon [BC] and PM_2.5 levels, serum calcium homeostasis biomarkers (parathyroid hormone, calcium, and 25-hydroxyvitamin D), and annualized BMD reduction over a 8-year follow-up of 692 middle-aged (46.7±12.3 yrs), low-income BACH/Bone cohort participants.

FINDINGS

In the Medicare analysis, risk of bone fracture admissions at osteoporosis-related sites was greater in areas with higher PM_2.5 levels (Risk ratio [RR] 1.041, 95% Confidence Interval [CI], 1.030, 1.051). This risk was particularly high among low-income communities (RR 1.076; 95% CI, 1.052, 1.100). In the longitudinal BACH/Bone study, baseline BC and PM_2.5 levels were associated with lower serum PTH (Estimate for baseline one interquartile increase in 1-year average BC= -1.16, 95% CI -1.93, -0.38; Estimate for baseline one interquartile increase in 1-year average PM_2.5= -7.39; 95%CI -14.17, -0.61). BC level was associated with higher BMD loss over time at multiple anatomical sites, including femoral neck (-0.08%/year per one interquartile increase; 95% CI -0.14, -0.02%/year) and ultradistal radius (-0.06%/year per one interquartile increase; 95% CI -0.12, -0.01%/year).

INTERPRETATION

Our results suggest that poor air quality is a modifiable risk factor for bone fractures and osteoporosis, especially in low-income communities.

A spatio-temporal prediction model based on support vector machine regression: Ambient Black Carbon in three New England States

Fine ambient particulate matter has been widely associated with multiple health effects. Mitigation hinges on understanding which sources are contributing to its toxicity. Black Carbon (BC), an indicator of particles generated from traffic sources, has been associated with a number of health effects however due to its high spatial variability, its concentration is difficult to estimate. We previously fit a model estimating BC concentrations in the greater Boston area; however this model was built using limited monitoring data and could not capture the complex spatio-temporal patterns of ambient BC. In order to improve our predictive ability, we obtained more data for a total of 24,301 measurements from 368 monitors over a 12 year period in Massachusetts, Rhode Island and New Hampshire. We also used Nu-Support Vector Regression (nu-SVR) - a machine learning technique which incorporates nonlinear terms and higher order interactions, with appropriate regularization of parameter estimates. We then used a generalized additive model to refit the residuals from the nu-SVR and added the residual predictions to our earlier estimates. Both spatial and temporal predictors were included in the model which allowed us to capture the change in spatial patterns of BC over time. The 10 fold cross validated (CV) R² of the model was good in both cold (10-fold CV R² = 0.87) and warm seasons (CV R² = 0.79). We have successfully built a model that can be used to estimate short and long-term exposures to BC and will be useful for studies looking at various health outcomes in MA, RI and Southern NH.

Triggering of ST-elevation myocardial infarction by ambient wood smoke and other particulate and gaseous pollutants

We previously observed increased odds of ST-elevation myocardial infarctions (STEMIs) associated with increased ambient fine particulate matter (PM_2.5) in the previous hour. However, data are lacking on the effects of specific PM sources. Using data from 362 patients, a case-crossover design, and conditional logistic regression, we estimated the relative odds of STEMI associated with increased Delta-C (wood smoke), black carbon (BC; traffic), PM_2.5, and gaseous pollutants in the previous 1-72 h. We did not observe increased odds of STEMIs associated with increased Delta-C or BC. We did observe increased odds associated with each 7.1 μg/m³ increase in PM_2.5 (OR (95% CI): 1.17 (0.99, 1.39)) and each 19.9 p.p.b. increase in ozone (O₃; 1.27 (1.00, 1.63)) in the previous hour, and each 0.22 p.p.m. increase in 48-h carbon monoxide (CO) concentrations (1.32 (1.00, 1.73]). Larger relative odds were associated with PM_2.5 in May-October, and O₃ and CO in November-April. Increased PM_2.5, O₃, and CO, but not wood smoke or BC, were associated with increased odds of STEMI, and effects may differ by season. Studies using spatially adjusted pollution estimates are needed, as well as studies further examining O₃ and CO effects on the risk of STEMI.

Prenatal and early life exposure to traffic pollution and cardiometabolic health in childhood

BACKGROUND

Prenatal exposure to traffic pollution has been associated with faster infant weight gain, but implications for cardiometabolic health in later childhood are unknown.

METHODS

Among 1418 children in Project Viva, a Boston-area pre-birth cohort, we assessed anthropometric and biochemical parameters of cardiometabolic health in early (median age 3.3 years) and mid- (median age 7.7 years) childhood. We used spatiotemporal models to estimate prenatal and early life residential PM_2.5 and black carbon exposure as well as traffic density and roadway proximity. We performed linear regression analyses adjusted for sociodemographics.

RESULTS

Children whose mothers lived close to a major roadway at the time of delivery had higher markers of adverse cardiometabolic risk in early and mid-childhood. For example, total fat mass was 2.1 kg (95%CI: 0.8, 3.5) higher in mid-childhood for children of mothers who lived <50 m vs. ≥200 m from a major roadway. Black carbon exposure and traffic density were generally not associated with cardiometabolic parameters, and PM_2.5 exposure during the year prior was paradoxically associated with improved cardiometabolic profile.

CONCLUSIONS

Infants whose mothers lived close to a major roadway at the time of delivery may be at later risk for adverse cardiometabolic health.

Telomere Length, Long-Term Black Carbon Exposure, and Cognitive Function in a Cohort of Older Men: The VA Normative Aging Study

BACKGROUND

Long-term air pollution exposure has been associated with age-related cognitive impairment, possibly because of enhanced inflammation. Leukocytes with longer telomere length (TL) are more responsive to inflammatory stimuli, yet TL has not been evaluated in relation to air pollution and cognition.

OBJECTIVES

We assessed whether TL modifies the association of 1-year exposure to black carbon (BC), a marker of traffic-related air pollution, with cognitive function in older men, and we examined whether this modification is independent of age and of C-reactive protein (CRP), a marker of inflammation.

METHODS

Between 1999 and 2007, we conducted 1-3 cognitive examinations of 428 older men in the Veterans Affairs (VA) Normative Aging Study. We used covariate-adjusted repeated-measure logistic regression to estimate associations of 1-year BC exposure with relative odds of being a low scorer (≤ 25) on the Mini-Mental State Examination (MMSE), which is a proxy of poor cognition. Confounders included age, CRP, and lifestyle and sociodemographic factors.

RESULTS

Each doubling in BC level was associated with 1.57 (95% CI: 1.20, 2.05) times higher odds of low MMSE scores. The BC-MMSE association was greater only among individuals with longer blood TL (5th quintile) (OR = 3.23; 95% CI: 1.37, 7.59; p = 0.04 for BC-by-TL-interaction). TL and CRP were associated neither with each other nor with MMSE. However, CRP modified the BC-MMSE relationship, with stronger associations only at higher CRP (5th quintile) and reference TL level (1st quintile) (OR = 2.68; 95% CI: 1.06, 6.79; p = 0.04 for BC-by-CRP-interaction).

CONCLUSIONS

TL and CRP levels may help predict the impact of BC exposure on cognitive function in older men. Citation: Colicino E, Wilson A, Frisardi MC, Prada D, Power MC, Hoxha M, Dioni L, Spiro A III, Vokonas PS, Weisskopf MG, Schwartz JD, Baccarelli AA. 2017. Telomere length, long-term black carbon exposure, and cognitive function in a cohort of older men: the VA Normative Aging Study. Environ Health Perspect 125:76-81; http://dx.doi.org/10.1289/EHP241.

Fine-scale spatial and temporal variation in temperature and arrhythmia episodes in the VA Normative Aging Study

Many studies have demonstrated that cold and hot temperatures are associated with increased deaths and hospitalization rates; new findings indicate also an association with more specific cardiac risk factors. Most of these existing studies have relied on few weather stations to characterize exposures; few have used residence-specific estimates of temperature, or examined the exposure-response function. We investigated the association of arrhythmia episodes with spatial and temporal variation in temperature. We also evaluated the association btween monitored ambient temperature (central) and the same outcome. This longitudinal analysis included 701 older men participating in the VA Normative Aging Study. Arrhythmia episodes were measured as ventricular ectopy (VE) (bigeminy, trigeminy, or couplets episodes) by 4-min electrocardiogram (ECG) monitoring in repeated visits during 2000-2010. The outcome was defined as having or not VE episodes during a study visit. We applied a mixed-effect logistic regression model with a random intercept for subject, controlling for seasonality, weekday, medication use, smoking, diabetes status, body mass index, and age. We also examined effect modification by personal characteristics, confounding by air pollution, and the exposure-response function. For 1°C increase in the same day residence-specific temperature, the odds of having VE episodes was 1.10 (95% confidence interval [CI]: 1.04-1.17). The odds associated with 1°C increase in central temperature was 1.05 (95% CI: 1.02-1.09). The exposure-response function was nonlinear for averages of temperature, presenting a J-shaped pattern, suggesting greater risk at lower and higher temperatures. Increased warm temperature and decreased cold temperature may increase the risk of ventricular arrhythmias.

IMPLICATIONS

This is the first study to provide evidence that residence-specific temperature exposure is associated with increased risk of ventricular arrhythmias in cohort of elderly subjects without known chronic medical conditions; that the delayed effect of temperature has a nonlinear relationship; and therefore that both warm and cold temperature increase the risk of having ventricular arrhythmias. Moreover, we show that the use of residence-specific temperature data reduces downward bias due to exposure error, by comparing the estimated health effect based on our spatiotemporal exposure prediction model to those based on a single local weather monitor.

On negative outcome control of unobserved confounding as a generalization of difference-in-differences

The difference-in-differences (DID) approach is a well known strategy for estimating the effect of an exposure in the presence of unobserved confounding. The approach is most commonly used when pre-and post-exposure outcome measurements are available, and one can assume that the association of the unobserved confounder with the outcome is equal in the two exposure groups, and constant over time. Then, one recovers the treatment effect by regressing the change in outcome over time on the exposure. In this paper, we interpret the difference-in-differences as a negative outcome control (NOC) approach. We show that the pre-exposure outcome is a negative control outcome, as it cannot be influenced by the subsequent exposure, and it is affected by both observed and unobserved confounders of the exposure-outcome association of interest. The relation between DID and NOC provides simple conditions under which negative control outcomes can be used to detect and correct for confounding bias. However, for general negative control outcomes, the DID-like assumption may be overly restrictive and rarely credible, because it requires that both the outcome of interest and the control outcome are measured on the same scale. Thus, we present a scale-invariant generalization of the DID that may be used in broader NOC contexts. The proposed approach is demonstrated in simulations and on a Normative Aging Study data set, in which Body Mass Index is used for NOC of the relationship between air pollution and inflammatory outcomes.

Spatial measurement errors in the field of spatial epidemiology

Background

Spatial epidemiology has been aided by advances in geographic information systems, remote sensing, global positioning systems and the development of new statistical methodologies specifically designed for such data. Given the growing popularity of these studies, we sought to review and analyze the types of spatial measurement errors commonly encountered during spatial epidemiological analysis of spatial data.

Methods

Google Scholar, Medline, and Scopus databases were searched using a broad set of terms for papers indexed by a term indicating location (space or geography or location or position) and measurement error (measurement error or measurement inaccuracy or misclassification or uncertainty): we reviewed all papers appearing before December 20, 2014. These papers and their citations were reviewed to identify the relevance to our review.

Results

We were able to define and classify spatial measurement errors into four groups: (1) pure spatial location measurement errors, including both non-instrumental errors (multiple addresses, geocoding errors, outcome aggregations, and covariate aggregation) and instrumental errors; (2) location-based outcome measurement error (purely outcome measurement errors and missing outcome measurements); (3) location-based covariate measurement errors (address proxies); and (4) Covariate-Outcome spatial misaligned measurement errors. We propose how these four classes of errors can be unified within an integrated theoretical model and possible solutions were discussed.

Conclusion

Spatial measurement errors are ubiquitous threat to the validity of spatial epidemiological studies. We propose a systematic framework for understanding the various mechanisms which generate spatial measurement errors and present practical examples of such errors.

Prenatal and childhood traffic-related air pollution exposure and childhood executive function and behavior

BACKGROUND

Traffic-related air pollution exposure may influence brain development and function and thus be related to neurobehavioral problems in children, but little is known about windows of susceptibility.

AIMS

Examine associations of gestational and childhood exposure to traffic-related pollution with executive function and behavior problems in children.

METHODS

We studied associations of pre- and postnatal pollution exposures with neurobehavioral outcomes in 1212 children in the Project Viva pre-birth cohort followed to mid-childhood (median age 7.7years). Parents and classroom teachers completed the Behavior Rating Inventory of Executive Function (BRIEF) and the Strengths and Difficulties Questionnaire (SDQ). Using validated spatiotemporal models, we estimated exposure to black carbon (BC) and fine particulate matter (PM_2.5) in the third trimester of pregnancy, from birth to 3years, from birth to 6years, and in the year before behavioral ratings. We also measured residential distance to major roadways and near-residence traffic density at birth and in mid-childhood. We estimated associations of BC, PM_2.5, and other traffic exposure measures with BRIEF and SDQ scores, adjusted for potential confounders.

RESULTS

Higher childhood BC exposure was associated with higher teacher-rated BRIEF Behavioral Regulation Index (BRI) scores, indicating greater problems: 1.0 points (95% confidence interval (CI): 0.0, 2.1) per interquartile range (IQR) increase in birth-age 6BC, and 1.7 points (95% CI: 0.6, 2.8) for BC in the year prior to behavioral ratings. Mid-childhood residential traffic density was also associated with BRI score (0.6, 95% CI: 0.1, 1.1). Birth-age 3BC was not associated with BRIEF or SDQ scores. Third trimester BC exposure was not associated with teacher-rated BRI scores (-0.2, 95% CI: -1.1, 0.8), and predicted lower scores (fewer problems) on the BRIEF Metacognition Index (-1.2, 95% CI: -2.2, -0.2) and SDQ total difficulties (-0.9, 95% CI: -1.4, -0.4). PM_2.5 exposure was associated with teacher-rated BRIEF and SDQ scores in minimally adjusted models but associations attenuated with covariate adjustment. None of the parent-rated outcomes suggested adverse effects of greater pollution exposure at any time point.

CONCLUSIONS

Children with higher mid-childhood exposure to BC and greater near-residence traffic density in mid-childhood had greater problems with behavioral regulation as assessed by classroom teachers, but not as assessed by parents. Prenatal and early childhood exposure to traffic-related pollution did not predict greater executive function or behavior problems; third trimester BC was associated with lower scores (representing fewer problems) on measures of metacognition and behavioral problems.

Non-stationary spatio-temporal modeling of traffic-related pollutants in near-road environments

A problem often encountered in environmental epidemiological studies assessing the health effects associated with ambient exposure to air pollution is the spatial misalignment between monitors' locations and subjects' actual residential locations. Several strategies have been adopted to circumvent this problem and estimate pollutants concentrations at unsampled sites, including spatial statistical or geostatistical models that rely on the assumption of stationarity to model the spatial dependence in pollution levels. Although computationally convenient, the assumption of stationarity is often untenable for pollutants concentration, particularly in the near-road environment. Building upon the work of Fuentes (2001) and Schmidt et al. (2011), in this paper we present a non-stationary spatio-temporal model for three traffic-related pollutants in a localized near-road environment. Modeling each pollutant separately and independently, we express each pollutant's concentration as a mixture of two independent spatial processes, each equipped with a non-stationary covariance function with covariates driving the non-stationarity and the mixture weights.

Lifetime Exposure to Ambient Pollution and Lung Function in Children

RATIONALE

Few studies have examined associations between exposure to air pollution and childhood lung function after implementation of strict air quality regulations in the 1990s.

OBJECTIVES

To assess traffic-related pollution exposure and childhood lung function.

METHODS

We geocoded addresses for 614 mother-child pairs enrolled during pregnancy in the Boston area 1999-2002 and followed them until a mid-childhood visit (median age, 7.7). We calculated the proximity of the home to the nearest major roadway. We estimated first year of life, lifetime, and prior-year exposure to particulate matter with a diameter smaller than 2.5 μm (PM2.5) by a hybrid model using satellite-derived aerosol optical depth, and to black carbon (BC) by a land-use regression model.

MEASUREMENTS AND MAIN RESULTS

Residential proximity to roadway and prior-year and lifetime PM2.5 and BC exposure were all associated with lower FVC. Associations with FEV1 were also negative and proportionally similar. Pollution exposures were not associated with the FEV1/FVC ratio or bronchodilator response. Compared with distances greater than or equal to 400 m, living less than 100 m from a major roadway was associated with lower FVC (-98.6 ml; -176.3 to -21.0). Each 2 μg/m(3) increment in prior-year PM2.5 was associated with lower FVC (-21.8 ml; -43.9 to 0.2) and higher odds of FEV1 less than 80% predicted (1.41; 1.03-1.93). Each 0.2 μg/m(3) increment in prior-year BC was associated with a 38.9 ml (-70.4 to -7.3) lower FVC.

CONCLUSIONS

Estimates of long-term exposure to ambient pollution, including proximity to major roadway, PM2.5, and BC (a traffic-related PM2.5 constituent), were associated with lower lung function in this Boston-area cohort of children with relatively low pollution exposures.

Long-term ambient particle exposures and blood DNA methylation age: findings from the VA normative aging study

BACKGROUND

Ambient particles have been shown to exacerbate measures of biological aging; yet, no studies have examined their relationships with DNA methylation age (DNAm-age), an epigenome-wide DNA methylation based predictor of chronological age.

OBJECTIVE

We examined the relationship of DNAm-age with fine particulate matter (PM2.5), a measure of total inhalable particle mass, and black carbon (BC), a measure of particles from vehicular traffic.

METHODS

We used validated spatiotemporal models to generate 1-year PM2.5 and BC exposure levels at the addresses of 589 older men participating in the VA Normative Aging Study with 1-3 visits between 2000 and 2011 (n = 1032 observations). Blood DNAm-age was calculated using 353 CpG sites from the Illumina HumanMethylation450 BeadChip. We estimated associations of PM2.5 and BC with DNAm-age using linear mixed effects models adjusted for age, lifestyle/environmental factors, and aging-related diseases.

RESULTS

After adjusting for covariates, a 1-µg/m3 increase in PM2.5 (95% CI: 0.30, 0.75, P<0.0001) was significantly associated with a 0.52-year increase in DNAm-age. Adjusted BC models showed similar patterns of association (β = 3.02, 95% CI: 0.48, 5.57, P = 0.02). Only PM2.5 (β = 0.54, 95% CI: 0.24, 0.84, P = 0.0004) remained significantly associated with DNAm-age in two-particle models. Methylation levels from 20 of the 353 CpGs contributing to DNAm-age were significantly associated with PM2.5 levels in our two-particle models. Several of these CpGs mapped to genes implicated in lung pathologies including LZTFL1, PDLIM5, and ATPAF1.

CONCLUSION

Our results support an association of long-termambient particle levels with DNAm-age and suggest that DNAm-age is a biomarker of particle-related physiological processes.

Using a latent variable model with non-constant factor loadings to examine PM2.5 constituents related to secondary inorganic aerosols

Factor analysis is a commonly used method of modelling correlated multivariate exposure data. Typically, the measurement model is assumed to have constant factor loadings. However, from our preliminary analyses of the Environmental Protection Agency's (EPA's) PM2.5 fine speciation data, we have observed that the factor loadings for four constituents change considerably in stratified analyses. Since invariance of factor loadings is a prerequisite for valid comparison of the underlying latent variables, we propose a factor model that includes non-constant factor loadings that change over time and space using P-spline penalized with the generalized cross-validation (GCV) criterion. The model is implemented using the Expectation-Maximization (EM) algorithm and we select the multiple spline smoothing parameters by minimizing the GCV criterion with Newton's method during each iteration of the EM algorithm. The algorithm is applied to a one-factor model that includes four constituents. Through bootstrap confidence bands, we find that the factor loading for total nitrate changes across seasons and geographic regions.

Prenatal and postnatal stress and asthma in children: Temporal- and sex-specific associations

BACKGROUND

Temporal- and sex-specific effects of perinatal stress have not been examined for childhood asthma.

OBJECTIVES

We examined associations between prenatal and/or postnatal stress and children's asthma (n = 765) and effect modification by sex in a prospective cohort study.

METHODS

Maternal negative life events were ascertained prenatally and postpartum. Negative life event scores were categorized as 0, 1 to 2, 3 to 4, or 5 or greater to assess exposure-response relationships. We examined effects of prenatal and postnatal stress on children's asthma by age 6 years, modeling each as independent predictors, mutually adjusting for prenatal and postnatal stress, and finally considering interactions between prenatal and postnatal stress. Effect modification by sex was examined in stratified analyses and by fitting interaction terms.

RESULTS

When considering stress in each period independently, among boys, a dose-response relationship was evident for each level increase on the ordinal scale prenatally (odds ratio [OR], 1.38; 95% CI, 1.06-1.79; P value for trend = .03) and postnatally (OR, 1.53; 95% CI, 1.16-2.01; P value for trend = .001); among girls, only the postnatal trend was significant (OR, 1.60; 95% CI, 1.14-2.22; P value for trend = .005). Higher stress in both the prenatal and postnatal periods was associated with increased odds of receiving a diagnosis of asthma in girls (OR, 1.37; 95% CI, 0.98-1.91; Pinteraction = .07) but not boys (OR, 1.08; 95% CI, 0.82-1.42; Pinteraction = .61).

CONCLUSIONS

Although boys were more vulnerable to stress during the prenatal period, girls were more affected by postnatal stress and cumulative stress across both periods in relation to asthma. Understanding sex and temporal differences in response to early-life stress might provide unique insight into the cause and natural history of asthma.

Long-term exposure to black carbon, cognition and single nucleotide polymorphisms in microRNA processing genes in older men

INTRODUCTION

Air pollution exposure has been linked to impaired cognitive aging, but little is known about biomarkers modifying this association. MicroRNAs (miRNAs) control gene expression and neuronal programming. miRNA levels vary due to single nucleotide polymorphisms (SNPs) in genes processing miRNAs from precursor molecules.

OBJECTIVES

To investigate whether SNPs in miRNA-processing genes are associated with cognition and modify the relationship between black carbon (BC), marker of traffic-related pollution, and cognitive functions.

METHODS

533 Normative Aging Study men (mean±SD 72±7years) were tested ≤4 times (mean=1.7 times) using seven cognitive tests between 1995 and 2007. We tested interactions of 16 miRNA-related SNPs with 1-year average BC from a validated land-use-regression model. We used covariate-adjusted logistic regression for low (≤25) Mini-Mental tate Examination (MMSE) and mixed-effect regression for a global cognitive score combining six other tests.

RESULTS

Global cognition was negatively associated with the homozygous minor variant of rs595961 AGO1 (-0.42SD; 95%CI: (-0.71, -0.13)) relative to the major variant. BC-MMSE association was stronger in heterozygous carriers of rs11077 XPO5 (OR=1.99; 95%CI: (1.39, 2.85)) and minor variant carriers of GEMIN4 rs2740348 (OR=1.34; 95%CI: (1.05, 1.7)), compared to their major variant. The BC-global-cognition association was stronger in heterozygous carriers of GEMIN4 rs4968104 (-0.10SD; 95%CI: (-0.18, -0.02)), and GEMIN4 rs910924 (-0.09SD; 95%CI: (-0.17, -0.02)) relative to the major variant. Blood miRNA expression analyses showed associations only of XPO5 rs11077 with miR-9 and miR-96.

CONCLUSIONS

Carriers of particular miRNA-processing SNPs had higher susceptibility to BC in BC-cognition associations, possibly due to influences on miRNA expression.

Long-term exposure to ambient air pollution and serum leptin in older adults: results from the MOBILIZE Boston study

OBJECTIVE

Long-term exposure to traffic-related air pollution has been linked to increased risk of obesity and diabetes and may be associated with higher serum levels of the adipokine leptin, but this hypothesis has not been previously evaluated in humans.

METHODS

In a cohort of older adults, we estimated the association between serum leptin concentrations and two markers of long-term exposure to traffic pollution, adjusting for participant characteristics, temporal trends, socioeconomic factors, and medical history.

RESULTS

An interquartile range increase (0.11 μg/m) in annual mean residential black carbon was associated with 12% (95% confidence interval: 3%, 22%) higher leptin levels. Leptin levels were not associated with residential distance to major roadway.

CONCLUSIONS

If confirmed, these findings support the emerging evidence suggesting that certain sources of traffic pollution may be associated with adverse cardiometabolic effects.

Associations between Prenatal Exposure to Black Carbon and Memory Domains in Urban Children: Modification by Sex and Prenatal Stress

Background

Whether fetal neurodevelopment is disrupted by traffic-related air pollution is uncertain. Animal studies suggest that chemical and non-chemical stressors interact to impact neurodevelopment, and that this association is further modified by sex.

Objectives

To examine associations between prenatal traffic-related black carbon exposure, prenatal stress, and sex with children’s memory and learning.

Methods

Analyses included N = 258 mother-child dyads enrolled in a Boston, Massachusetts pregnancy cohort. Black carbon exposure was estimated using a validated spatiotemporal land-use regression model. Prenatal stress was measured using the Crisis in Family Systems-Revised survey of negative life events. The Wide Range Assessment of Memory and Learning (WRAML2) was administered at age 6 years; outcomes included the General Memory Index and its component indices [Verbal, Visual, and Attention Concentration]. Relationships between black carbon and WRAML2 index scores were examined using multivariable-adjusted linear regression including effect modification by stress and sex.

Results

Mothers were primarily minorities (60% Hispanic, 26% Black); 67% had ≤12 years of education. The main effect for black carbon was not significant for any WRAML2 index; however, in stratified analyses, among boys with high exposure to prenatal stress, Attention Concentration Index scores were on average 9.5 points lower for those with high compared to low prenatal black carbon exposure (P_{3-way interaction} = 0.04).

Conclusion

The associations between prenatal exposure to black carbon and stress with children’s memory scores were stronger in boys than in girls. Studies assessing complex interactions may more fully characterize health risks and, in particular, identify vulnerable subgroups.

Prenatal and Childhood Traffic-Related Pollution Exposure and Childhood Cognition in the Project Viva Cohort (Massachusetts, USA)

BACKGROUND

Influences of prenatal and early-life exposures to air pollution on cognition are not well understood.

OBJECTIVES

We examined associations of gestational and childhood exposure to traffic-related pollution with childhood cognition.

METHODS

We studied 1,109 mother-child pairs in Project Viva, a prospective birth cohort study in eastern Massachusetts (USA). In mid-childhood (mean age, 8.0 years), we measured verbal and nonverbal intelligence, visual motor abilities, and visual memory. For periods in late pregnancy and childhood, we estimated spatially and temporally resolved black carbon (BC) and fine particulate matter (PM2.5) exposures, residential proximity to major roadways, and near-residence traffic density. We used linear regression models to examine associations of exposures with cognitive assessment scores, adjusted for potential confounders.

RESULTS

Compared with children living ≥ 200 m from a major roadway at birth, those living < 50 m away had lower nonverbal IQ [-7.5 points; 95% confidence interval (CI): -13.1, -1.9], and somewhat lower verbal IQ (-3.8 points; 95% CI: -8.2, 0.6) and visual motor abilities (-5.3 points; 95% CI: -11.0, 0.4). Cross-sectional associations of major roadway proximity and cognition at mid-childhood were weaker. Prenatal and childhood exposure to traffic density and PM2.5 did not appear to be associated with poorer cognitive performance. Third-trimester and childhood BC exposures were associated with lower verbal IQ in minimally adjusted models; but after adjustment for socioeconomic covariates, associations were attenuated or reversed.

CONCLUSIONS

Residential proximity to major roadways during gestation and early life may affect cognitive development. Influences of pollutants and socioeconomic conditions on cognition may be difficult to disentangle.

Traffic-related air pollution and sleep in the Boston Area Community Health Survey

Little is known about environmental determinants of sleep. We investigated the association between black carbon (BC), a marker of traffic-related air pollution, and sleep measures among participants of the Boston Area Community Health Survey. We also sought to assess the impact of sociodemographic factors, health conditions, and season on associations. Residential 24-h BC was estimated from a validated land-use regression model for 3821 participants and averaged over 1-6 months and 1 year. Sleep measures included questionnaire-assessed sleep duration, sleep latency, and sleep apnea. Linear and logistic regression models controlling for confounders estimated the association between sleep measures and BC. Effect modification was tested with interaction terms. Main effects were not observed between BC and sleep measures. However, in stratified models, males experienced 0.23 h less sleep (95% CI: -0.42, -0.03) and those with low SES 0.25 h less sleep (95% CI: -0.48, -0.01) per IQR increase in annual BC (0.21 μg/m(3)). In blacks, sleep duration increased with annual BC (β=0.34 per IQR; 95% CI: 0.12, 0.57). Similar findings were observed for short sleep (≤5 h). BC was not associated with sleep apnea or sleep latency, however, long-term exposure may be associated with shorter sleep duration, particularly in men and those with low SES, and longer sleep duration in blacks.

Prenatal air pollution exposure and newborn blood pressure

BACKGROUND

Air pollution exposure has been associated with increased blood pressure in adults.

OBJECTIVE

We examined associations of antenatal exposure to ambient air pollution with newborn systolic blood pressure (SBP).

METHODS

We studied 1,131 mother-infant pairs in a Boston, Massachusetts, area pre-birth cohort. We calculated average exposures by trimester and during the 2 to 90 days before birth for temporally resolved fine particulate matter (≤ 2.5 μm; PM2.5), black carbon (BC), nitrogen oxides, nitrogen dioxide, ozone (O3), and carbon monoxide measured at stationary monitoring sites, and for spatiotemporally resolved estimates of PM2.5 and BC at the residence level. We measured SBP at a mean age of 30 ± 18 hr with an automated device. We used mixed-effects models to examine associations between air pollutant exposures and SBP, taking into account measurement circumstances; child's birth weight; mother's age, race/ethnicity, socioeconomic position, and third-trimester BP; and time trend. Estimates represent differences in SBP associated with an interquartile range (IQR) increase in each pollutant.

RESULTS

Higher mean PM2.5 and BC exposures during the third trimester were associated with higher SBP (e.g., 1.0 mmHg; 95% CI: 0.1, 1.8 for a 0.32-μg/m3 increase in mean 90-day residential BC). In contrast, O3 was negatively associated with SBP (e.g., -2.3 mmHg; 95% CI: -4.4, -0.2 for a 13.5-ppb increase during the 90 days before birth).

CONCLUSIONS

Exposures to PM2.5 and BC in late pregnancy were positively associated with newborn SBP, whereas O3 was negatively associated with SBP. Longitudinal follow-up will enable us to assess the implications of these findings for health during later childhood and adulthood.

Associations between prenatal traffic-related air pollution exposure and birth weight: Modification by sex and maternal pre-pregnancy body mass index

BACKGROUND

Prenatal traffic-related air pollution exposure is linked to adverse birth outcomes. However, modifying effects of maternal body mass index (BMI) and infant sex remain virtually unexplored.

OBJECTIVES

We examined whether associations between prenatal air pollution and birth weight differed by sex and maternal BMI in 670 urban ethnically mixed mother-child pairs.

METHODS

Black carbon (BC) levels were estimated using a validated spatio-temporal land-use regression (LUR) model; fine particulate matter (PM2.5) was estimated using a hybrid LUR model incorporating satellite-derived Aerosol Optical Depth measures. Using stratified multivariable-adjusted regression analyses, we examined whether associations between prenatal air pollution and calculated birth weight for gestational age (BWGA) z-scores varied by sex and maternal pre-pregnancy BMI.

RESULTS

Median birth weight was 3.3±0.6kg; 33% of mothers were obese (BMI ≥30kg/m(3)). In stratified analyses, the association between higher PM2.5 and lower birth weight was significant in males of obese mothers (-0.42 unit of BWGA z-score change per IQR increase in PM2.5, 95%CI: -0.79 to -0.06) ( PM2.5×sex×obesity Pinteraction=0.02). Results were similar for BC models (Pinteraction=0.002).

CONCLUSIONS

Associations of prenatal exposure to traffic-related air pollution and reduced birth weight were most evident in males born to obese mothers.

Associations between air pollution and perceived stress: the Veterans Administration Normative Aging Study

BACKGROUND

There is mixed evidence suggesting that air pollution may be associated with increased risk of developing psychiatric disorders. We aimed to investigate the association between air pollution and non-specific perceived stress, often a precursor to development of affective psychiatric disorders.

METHODS

This longitudinal analysis consisted of 987 older men participating in at least one visit for the Veterans Administration Normative Aging Study between 1995 and 2007 (n = 2,244 visits). At each visit, participants were administered the 14-item Perceived Stress Scale (PSS), which quantifies stress experienced in the previous week. Scores ranged from 0-56 with higher scores indicating increased stress. Differences in PSS score per interquartile range increase in moving average (1, 2, and 4-weeks) of air pollution exposures were estimated using linear mixed-effects regression after adjustment for age, race, education, physical activity, anti-depressant medication use, seasonality, meteorology, and day of week. We also evaluated effect modification by season (April-September and March-October for warm and cold season, respectively).

RESULTS

Fine particles (PM2.5), black carbon (BC), nitrogen dioxide, and particle number counts (PNC) at moving averages of 1, 2, and 4-weeks were associated with higher perceived stress ratings. The strongest associations were observed for PNC; for example, a 15,997 counts/cm(3) interquartile range increase in 1-week average PNC was associated with a 3.2 point (95%CI: 2.1-4.3) increase in PSS score. Season modified the associations for specific pollutants; higher PSS scores in association with PM2.5, BC, and sulfate were observed mainly in colder months.

CONCLUSIONS

Air pollution was associated with higher levels of perceived stress in this sample of older men, particularly in colder months for specific pollutants.

Prenatal exposure to traffic pollution: associations with reduced fetal growth and rapid infant weight gain

BACKGROUND

Prenatal air pollution exposure inhibits fetal growth, but implications for postnatal growth are unknown.

METHODS

We assessed weights and lengths of US infants in the Project Viva cohort at birth and 6 months. We estimated 3rd-trimester residential air pollution exposures using spatiotemporal models. We estimated neighborhood traffic density and roadway proximity at birth address using geographic information systems. We performed linear and logistic regression adjusted for sociodemographic variables, fetal growth, and gestational age at birth.

RESULTS

Mean birth weight-for-gestational age z-score (fetal growth) was 0.17 (standard deviation [SD] = 0.97; n = 2,114), 0- to 6-month weight-for-length gain was 0.23 z-units (SD = 1.11; n = 689), and 17% had weight-for-length ≥95th percentile at 6 months of age. Infants exposed to the highest (vs. lowest) quartile of neighborhood traffic density had lower fetal growth (-0.13 units [95% confidence interval (CI) = -0.25 to -0.01]), more rapid 0- to 6-month weight-for-length gain (0.25 units [95% CI = 0.01 to 0.49]), and higher odds of weight-for-length ≥95th percentile at 6 months (1.84 [95% CI = 1.11 to 3.05]). Neighborhood traffic density was additionally associated with an infant being in both the lowest quartile of fetal growth and the highest quartile of 0- to 6-month weight-for-length gain (Q4 vs. Q1, odds ratio = 3.01 [95% CI = 1.08 to 8.44]). Roadway proximity and 3rd-trimester black carbon exposure were similarly associated with growth outcomes. For 3rd-trimester particulate matter (PM2.5), effect estimates were in the same direction, but smaller and imprecise.

CONCLUSIONS

Infants exposed to higher traffic-related pollution in early life may exhibit more rapid postnatal weight gain in addition to reduced fetal growth.

NONLINEAR PREDICTIVE LATENT PROCESS MODELS FOR INTEGRATING SPATIO-TEMPORAL EXPOSURE DATA FROM MULTIPLE SOURCES

Spatio-temporal prediction of levels of an environmental exposure is an important problem in environmental epidemiology. Our work is motivated by multiple studies on the spatio-temporal distribution of mobile source, or traffic related, particles in the greater Boston area. When multiple sources of exposure information are available, a joint model that pools information across sources maximizes data coverage over both space and time, thereby reducing the prediction error. We consider a Bayesian hierarchical framework in which a joint model consists of a set of submodels, one for each data source, and a model for the latent process that serves to relate the submodels to one another. If a submodel depends on the latent process nonlinearly, inference using standard MCMC techniques can be computationally prohibitive. The implications are particularly severe when the data for each submodel are aggregated at different temporal scales. To make such problems tractable, we linearize the nonlinear components with respect to the latent process and induce sparsity in the covariance matrix of the latent process using compactly supported covariance functions. We propose an efficient MCMC scheme that takes advantage of these approximations. We use our model to address a temporal change of support problem whereby interest focuses on pooling daily and multiday black carbon readings in order to maximize the spatial coverage of the study region.

Ambient air pollution and depressive symptoms in older adults: results from the MOBILIZE Boston study

BACKGROUND

Exposure to ambient air pollution, particularly from traffic, has been associated with adverse cognitive outcomes, but the association with depressive symptoms remains unclear.

OBJECTIVES

We investigated the association between exposure to ambient air and traffic pollution and the presence of depressive symptoms among 732 Boston-area adults ≥ 65 years of age (78.1 ± 5.5 years, mean ± SD).

METHODS

We assessed depressive symptoms during home interviews using the Revised Center for Epidemiological Studies Depression Scale (CESD-R). We estimated residential distance to the nearest major roadway as a marker of long-term exposure to traffic pollution and assessed short-term exposure to ambient fine particulate matter (PM2.5), sulfates, black carbon (BC), ultrafine particles, and gaseous pollutants, averaged over the 2 weeks preceding each assessment. We used generalized estimating equations to estimate the odds ratio (OR) of a CESD-R score ≥ 16 associated with exposure, adjusting for potential confounders. In sensitivity analyses, we considered CESD-R score as a continuous outcome and mean annual residential BC as an alternate marker of long-term exposure to traffic pollution.

RESULTS

We found no evidence of a positive association between depressive symptoms and long-term exposure to traffic pollution or short-term changes in pollutant levels. For example, we found an OR of CESD-R score ≥ 16 of 0.67 (95% CI: 0.46, 0.98) per interquartile range (3.4 μg/m(3)) increase in PM2.5 over the 2 weeks preceding assessment.

CONCLUSIONS

We found no evidence suggesting that ambient air pollution is associated with depressive symptoms among older adults living in a metropolitan area in attainment of current U.S. regulatory standards.

Mitochondrial haplogroups modify the effect of black carbon on age-related cognitive impairment

BACKGROUND

Traffic-related air pollution has been linked with impaired cognition in older adults, possibly due to effects of oxidative stress on the brain. Mitochondria are the main source of cellular oxidation. Haplogroups in mitochondrial DNA (mtDNA) mark individual differences in oxidative potential and are possible determinants of neurodegeneration. The aim of this study was to investigate whether mtDNA haplogroups determined differential susceptibility to cognitive effects of long-term exposure to black carbon (BC), a marker of traffic-related air pollution.

METHODS

We investigated 582 older men (72 ± 7 years) in the VA Normative Aging Study cohort with ≤4 visits per participant (1.8 in average) between 1995-2007. Low (≤25) Mini Mental State Examination (MMSE) was used to assess impaired cognition in multiple domains. We fitted repeated-measure logistic regression using validated-LUR BC estimated in the year before their first visit at the participant's address.

RESULTS

Mitochondrial haplotyping identified nine haplogroups phylogenetically categorized in four clusters. BC showed larger effect on MMSE in Cluster 4 carriers, including I, W and X haplogroups, [OR = 2.7; 95% CI (1.3-5.6)], moderate effect in Cluster 1, including J and T haplogroups [OR = 1.6; 95% CI: (0.9-2.9)], and no effect in Cluster 2 (H and V haplogroups) [OR = 1.1; 95% CI: (0.8-1.5)] or Cluster 3 (K and U haplogroups) [OR = 1.0; 95% CI: (0.6-1.6)]. BC effect varied only moderately across the I, X, and W haplogroups or across the J and T haplogroups.

CONCLUSIONS

The association of BC with impaired cognition was worsened in carriers of phylogenetically-related mtDNA haplogroups in Cluster 4. No BC effects were detected in Cluster 2 and 3 carriers. MtDNA haplotypes may modify individual susceptibility to the particle cognitive effects.

Air pollution exposure and abnormal glucose tolerance during pregnancy: the project Viva cohort

BACKGROUND

Exposure to fine particulate matter (PM with diameter ≤ 2.5 μm; PM2.5) has been linked to type 2 diabetes mellitus, but associations with hyperglycemia in pregnancy have not been well studied.

METHODS

We studied Boston, Massachusetts-area pregnant women without known diabetes. We identified impaired glucose tolerance (IGT) and gestational diabetes mellitus (GDM) during pregnancy from clinical glucose tolerance tests at median 28.1 weeks gestation. We used residential addresses to estimate second-trimester PM2.5 and black carbon exposure via a central monitoring site and spatiotemporal models. We estimated residential traffic density and roadway proximity as surrogates for exposure to traffic-related air pollution. We performed multinomial logistic regression analyses adjusted for sociodemographic covariates, and used multiple imputation to account for missing data.

RESULTS

Of 2,093 women, 65 (3%) had IGT and 118 (6%) had GDM. Second-trimester spatiotemporal exposures ranged from 8.5 to 15.9 μg/m3 for PM2.5 and from 0.1 to 1.7 μg/m3 for black carbon. Traffic density was 0-30,860 vehicles/day × length of road (kilometers) within 100 m; 281 (13%) women lived ≤ 200 m from a major road. The prevalence of IGT was elevated in the highest (vs. lowest) quartile of exposure to spatiotemporal PM2.5 [odds ratio (OR) = 2.63; 95% CI: 1.15, 6.01] and traffic density (OR = 2.66; 95% CI: 1.24, 5.71). IGT also was positively associated with other exposure measures, although associations were not statistically significant. No pollutant exposures were positively associated with GDM.

CONCLUSIONS

Greater exposure to PM2.5 and other traffic-related pollutants during pregnancy was associated with IGT but not GDM. Air pollution may contribute to abnormal glycemia in pregnancy.

Effects of prenatal community violence and ambient air pollution on childhood wheeze in an urban population

BACKGROUND

Prenatal exposures to stress and physical toxins influence children's respiratory health, although few studies consider these factors together.

OBJECTIVES

We sought to concurrently examine the effects of prenatal community-level psychosocial (exposure to community violence [ECV]) and physical (air pollution) stressors on repeated wheeze in 708 urban children followed to age 2 years.

METHODS

Multi-item ECV reported by mothers in pregnancy was summarized into a continuous score by using Rasch modeling. Prenatal black carbon exposure was estimated by using land-use regression (LUR) modeling; particulate matter with a diameter of less than 2.5 μm (PM2.5) was estimated by using LUR modeling incorporating satellite data. Mothers reported child's wheeze every 3 months. The effects of ECV and air pollutants on repeated wheeze (≥ 2 episodes) were examined by using logistic regression. Interactions between ECV and pollutants were examined.

RESULTS

Mothers were primarily black (29%) and Hispanic (55%), with lower education (62% with ≤ 12 years); 87 (12%) children wheezed repeatedly. In models examining concurrent exposures, ECV (odds ratio [OR], 1.95; 95% CI, 1.13-3.36; highest vs lowest tertile) and black carbon (OR, 1.84; 95% CI, 1.08-3.12; median or greater vs less than median) were independently associated with wheeze adjusting for sex, birth season, maternal atopy, education, race, and cockroach antigen. Associations were similar for PM2.5 (adjusted OR, 2.02; 95% CI, 1.20-3.40). An interaction between ECV with air pollution levels was suggested.

CONCLUSIONS

These findings suggest that both prenatal community violence and air pollution can contribute to respiratory health in these urban children. Moreover, place-based psychosocial stressors might affect host resistance such that physical pollutants can have adverse effects, even at relatively lower levels.