Independent and joint effects of neighborhood-level environmental and socioeconomic exposures on body mass index in early childhood: The environmental influences on child health outcomes (ECHO) cohort.

Past studies support the hypothesis that the prenatal period influences childhood growth. However, few studies explore the joint effects of exposures that occur simultaneously during pregnancy. To explore the feasibility of using mixtures methods with neighborhood-level environmental exposures, we assessed the effects of multiple prenatal exposures on body mass index (BMI) from birth to age 24 months. We used data from two cohorts: Healthy Start (n = 977) and Maternal and Developmental Risks from Environmental and Social Stressors (MADRES; n = 303). BMI was measured at delivery and 6, 12, and 24 months and standardized as z-scores. We included variables for air pollutants, built and natural environments, food access, and neighborhood socioeconomic status (SES). We used two complementary statistical approaches: single-exposure linear regression and quantile-based g-computation. Models were fit separately for each cohort and time point and were adjusted for relevant covariates. Single-exposure models identified negative associations between NO2 and distance to parks and positive associations between low neighborhood SES and BMI z-scores for Healthy Start participants; for MADRES participants, we observed negative associations between O3 and distance to parks and BMI z-scores. G-computations models produced comparable results for each cohort: higher exposures were generally associated with lower BMI, although results were not significant. Results from the g-computation models, which do not require a priori knowledge of the direction of associations, indicated that the direction of associations between mixture components and BMI varied by cohort and time point. Our study highlights challenges in assessing mixtures effects at the neighborhood level and in harmonizing exposure data across cohorts. For example, geospatial data of neighborhood-level exposures may not fully capture the qualities that might influence health behavior. Studies aiming to harmonize geospatial data from different geographical regions should consider contextual factors when operationalizing exposure variables.

Multiple Prenatal Exposures and Acute Care Clinical Encounters for Asthma among Children Born to Mothers Living near a Superfund Site.

Prenatal exposures are associated with childhood asthma, and risk may increase with simultaneous exposures. Pregnant women living in lower-income communities tend to have elevated exposures to a range of potential asthma risk factors, which may interact in complex ways. We examined the association between prenatal exposures and the risk of childhood asthma acute care clinical encounters (hospitalization, emergency department visit, observational stay) using conditional logistic regression with a multivariable smooth to model the interaction between continuous variables, adjusted for maternal characteristics, and stratified by sex. All births near the New Bedford Harbor (NBH) Superfund site (2000-2006) were followed through 2011 using the Massachusetts Pregnancy to Early Life Longitudinal data system to identify children ages 5-11 with asthma acute care clinical encounters (265 cases among 7,787 with follow-up). Hazard ratios (HRs) were higher for children living closer to the NBH with higher cord blood Pb levels than children living further away from the NBH with lower Pb levels (P<0.001). HRs were highest for girls (HR=4.17, 95% CI: 3.60, 4.82) compared to boys (HR=1.72, 95% CI: 1.46, 2.02). Our results suggest that prenatal Pb exposure in combination with residential proximity to the NBH is associated with childhood asthma acute care clinical encounters.

Prenatal environmental exposures and associations with teen births.

BACKGROUND: Children's prenatal exposure to multiple environmental chemicals may contribute to subsequent deficits in impulse control, predisposing them to risk-taking. OBJECTIVE: Our goal was to investigate associations between prenatal exposure mixtures and risk of teen birth, a manifestation of high-risk sexual activity, among 5865 girls (1st generation) born in southeast Massachusetts from 1992-1998. METHODS: Exposures included prenatal modeled polychlorinated biphenyls (PCBs), ρ,ρ'-dichlorodiphenyl dichloroethylene (DDE), hexachlorobenzene (HCB), lead (Pb), and mercury (Hg). We fit adjusted generalized additive models with multivariable smooths of exposure mixtures, 1st generation infant's birth year, and maternal age at 1st generation birth. Predicted odds ratios (ORs) for teen birth were mapped as a function of joint exposures. We also conducted sensitivity analyses among 1st generation girls with measured exposure biomarkers (n = 371). RESULTS: The highest teen birth risk was associated with a mixture of high prenatal HCB, Hg, Pb, and PCB, but low DDE exposure, with similar associations in sensitivity analyses. The highest OR predicted for girls born in 1995 to mothers of median age (26 years) was at the 95th percentile of the HCB and PCB exposure distributions (OR = 3.09; 95% confidence interval: 0.29, 32.4). Additionally, girls born earlier in the study period or to teen mothers were at increased risk of teen birth. SIGNIFICANCE: Prenatal environmental chemical exposures and sociodemographic characteristics may interact to substantially increase risk of teen births.

Using Birth Cohort Data to Estimate Prenatal Chemical Exposures for All Births around the New Bedford Harbor Superfund Site in Massachusetts.

BACKGROUND: Children born near New Bedford, Massachusetts, have been prenatally exposed to multiple environmental chemicals, in part due to an older housing stock, maternal diet, and proximity to the New Bedford Harbor (NBH) Superfund site. Chemical exposure measures are not available for all births, limiting epidemiologic investigations and potential interventions. OBJECTIVE: We linked biomonitoring data from the New Bedford Cohort (NBC) and birth record data to predict prenatal exposures for all contemporaneous area births. METHODS: We used prenatal exposure biomarker data from the NBC, a population-based cohort of 788 mother-infant pairs born during 1993­1998 to mothers living near the NBH, linked to their corresponding Massachusetts birth record data, to build predictive models for cord serum polychlorinated biphenyls (expressed as a sum, [Formula: see text]), [Formula: see text] (DDE), hexachlorobenzene (HCB), cord blood lead (Pb), and maternal hair mercury (Hg). We applied the best fit models (highest pseudo [Formula: see text]), with multivariable smooths of continuous variables, to predict exposure biomarkers for all 10,270 births during 1993­1998 around the NBH. We used 10-fold cross validation to validate the exposure models and the bootstrap method to characterize sampling variability in the exposure predictions. RESULTS: The 10-fold cross-validated [Formula: see text] for the [Formula: see text], DDE, HCB, Pb, and Hg exposure models were 0.54, 0.40, 0.34, 0.46, and 0.40, respectively. For each exposure model, multivariable smooths of continuous variables improved the fit compared with linear models. Other variables with significant effects on exposure estimates were paternal education, maternal race/ethnicity, and maternal ancestry. The resulting exposure predictions for all births had variability consistent with the NBC measured exposures. CONCLUSIONS: Predictive models using multivariable smoothing explained reasonable amounts of variance in prenatal exposure biomarkers. Our analyses suggest that prenatal chemical exposures can be predicted for all contemporaneous births in the same geographic area by modeling available biomarker data for a subset of that population. https://doi.org/10.1289/EHP4849.

Correction to: Early-life exposure to PM2.5 and risk of acute asthma clinical encounters among children in Massachusetts: a case-crossover analysis.

After publication of the article [1], it was brought to our attention that a number in Table 1 is incorrect.

Early-life exposure to PM2.5 and risk of acute asthma clinical encounters among children in Massachusetts: a case-crossover analysis.

BACKGROUND: Associations between ambient particulate matter < 2.5 µm (PM2.5) and asthma morbidity have been suggested in previous epidemiologic studies but results are inconsistent for areas with lower PM2.5 levels. We estimated the associations between early-life short-term PM2.5 exposure and the risk of asthma or wheeze clinical encounters among Massachusetts children in the innovative Pregnancy to Early Life Longitudinal (PELL) cohort data linkage system. METHODS: We used a semi-bidirectional case-crossover study design with short-term exposure lags for asthma exacerbation using data from the PELL system. Cases included children up to 9 years of age who had a hospitalization, observational stay, or emergency department visit for asthma or wheeze between January 2001 and September 2009 (n = 33,387). Daily PM2.5 concentrations were estimated at a 4-km resolution using satellite remote sensing, land use, and meteorological data. We applied conditional logistic regression models to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CI). We also stratified by potential effect modifiers. RESULTS: The median PM2.5 concentration among participants was 7.8 µg/m3 with an interquartile range of 5.9 µg/m3. Overall, associations between PM2.5 exposure and asthma clinical encounters among children at lags 0, 1 and 2 were close to the null value of OR = 1.0. Evidence of effect modification was observed by birthweight for lags 0, 1 and 2 (p < 0.05), and season of clinical encounter for lags 0 and 1 (p < 0.05). Children with low birthweight (LBW) (< 2500 g) had increased odds of having an asthma clinical encounter due to higher PM2.5 exposure for lag 1 (OR: 1.08 per interquartile range (IQR) increase in PM2.5; 95% CI: 1.01, 1.15). CONCLUSION: Asthma or wheeze exacerbations among LBW children were associated with short-term increases in PM2.5 concentrations at low levels in Massachusetts.

Monomethylarsonous acid, but not inorganic arsenic, is a mitochondria-specific toxicant in vascular smooth muscle cells.

Arsenic exposure has been implicated as a risk factor for cardiovascular diseases, metabolic disorders, and cancer, yet the role mitochondrial dysfunction plays in the cellular mechanisms of pathology is largely unknown. To investigate arsenic-induced mitochondrial dysfunction in vascular smooth muscle cells (VSMCs), we exposed rat aortic smooth muscle cells (A7r5) to inorganic arsenic (iAs(III)) and its metabolite monomethylarsonous acid (MMA(III)) and compared their effects on mitochondrial function and oxidative stress. Our results indicate that MMA(III) is significantly more toxic to mitochondria than iAs(III). Exposure of VSMCs to MMA(III), but not iAs(III), significantly decreased basal and maximal oxygen consumption rates and concomitantly increased compensatory extracellular acidification rates, a proxy for glycolysis. Treatment with MMA(III) significantly increased hydrogen peroxide and superoxide levels compared to iAs(III). Exposure to MMA(III) resulted in significant decreases in mitochondrial ATP, aberrant perinuclear clustering of mitochondria, and decreased mitochondrial content. Mechanistically, we observed that mitochondrial superoxide and hydrogen peroxide contribute to mitochondrial toxicity, as treatment of cells with MnTBAP (a mitochondrial superoxide dismutase mimetic) and catalase significantly reduced mitochondrial respiration deficits and cell death induced by both arsenic compounds. Overall, our data demonstrates that MMA(III) is a mitochondria-specific toxicant that elevates mitochondrial and non-mitochondrial sources of ROS.