Pregnancy exposure to atmospheric pollution and meteorological conditions and placental DNA methylation.

BACKGROUND: Air pollution exposure represents a major health threat to the developing foetus. DNA methylation is one of the most well-known molecular determinants of the epigenetic status of cells. Blood DNA methylation has been proven sensitive to air pollutants, but the molecular impact of air pollution on new-borns has so far received little attention. OBJECTIVES: We investigated whether nitrogen dioxide (NO2), particulate matter (PM10), temperature and humidity during pregnancy are associated with differences in placental DNA methylation levels. METHODS: Whole-genome DNA-methylation was measured using the Illumina's Infinium HumanMethylation450 BeadChip in the placenta of 668 newborns from the EDEN cohort. We designed an original strategy using a priori biological information to focus on candidate genes with a specific expression pattern in placenta (active or silent) combined with an agnostic epigenome-wide association study (EWAS). We used robust linear regression to identify CpGs and differentially methylated regions (DMR) associated with each exposure during short- and long-term time-windows. RESULTS: The candidate genes approach identified nine CpGs mapping to 9 genes associated with prenatal NO2 and PM10 exposure [false discovery rate (FDR) p < 0.05]. Among these, the methylation level of 2 CpGs located in ADORA2B remained significantly associated with NO2 exposure during the 2nd trimester and whole pregnancy in the EWAS (FDR p < 0.05). EWAS further revealed associations between the environmental exposures under study and variations of DNA methylation of 4 other CpGs. We further identified 27 DMRs significantly (FDR p < 0.05) associated with air pollutants exposure and 13 DMRs with meteorological conditions. CONCLUSIONS: The methylation of ADORA2B, a gene whose expression was previously associated with hypoxia and pre-eclampsia, was consistently found here sensitive to atmospheric pollutants. In addition, air pollutants were associated to DMRs pointing towards genes previously implicated in preeclampsia, hypertensive and metabolic disorders. These findings demonstrate that air pollutants exposure at levels commonly experienced in the European population are associated with placental gene methylation and provide some mechanistic insight into some of the reported effects of air pollutants on preeclampsia.

Health effects of ambient air pollution: do different methods for estimating exposure lead to different results?

BACKGROUND: Spatially resolved exposure models are increasingly used in epidemiology. We previously reported that, although exhibiting a moderate correlation, pregnancy nitrogen dioxide (NO2) levels estimated by the nearest air quality monitoring station (AQMS) model and a geostatistical model, showed similar associations with infant birth weight. OBJECTIVES: We extended this study by comparing a total of four exposure models, including two highly spatially resolved models: a land-use regression (LUR) model and a dispersion model. Comparisons were made in terms of predicted NO2 and particle (aerodynamic diameter<10 µm, PM10) exposure and adjusted association with birth weight. METHODS: The four exposure models were implemented in two French metropolitan areas where 1026 pregnant women were followed as part of the EDEN mother-child cohort. RESULTS: Correlations between model predictions were high (≥ 0.70), except for NO2 between the AQMS and both the LUR (r = 0.54) and dispersion models (r = 0.63). Spatial variations as estimated by the AQMS model were greater for NO2 (95%) than for PM10 (22%). The direction of effect estimates of NO2 on birth weight varied according to the exposure model, while PM10 effect estimates were more consistent across exposure models. CONCLUSIONS: For PM10, highly spatially resolved exposure model agreed with the poor spatial resolution AQMS model in terms of estimated pollutant levels and health effects. For more spatially heterogeneous pollutants like NO2, although predicted levels from spatially resolved models (all but AQMS) agreed with each other, our results suggest that some may disagree with each other as well as with the AQMS regarding the direction of the estimated health effects.

Does consideration of larger study areas yield more accurate estimates of air pollution health effects? An illustration of the bias-variance trade-off in air pollution epidemiology.

BACKGROUND: Spatially-resolved air pollution models can be developed in large areas. The resulting increased exposure contrasts and population size offer opportunities to better characterize the effect of atmospheric pollutants on respiratory health. However the heterogeneity of these areas may also enhance the potential for confounding. We aimed to discuss some analytical approaches to handle this trade-off. METHODS: We modeled NO2 and PM10 concentrations at the home addresses of 1082 pregnant mothers from EDEN cohort living in and around urban areas, using ADMS dispersion model. Simulations were performed to identify the best strategy to limit confounding by unmeasured factors varying with area type. We examined the relation between modeled concentrations and respiratory health in infants using regression models with and without adjustment or interaction terms with area type. RESULTS: Simulations indicated that adjustment for area limited the bias due to unmeasured confounders varying with area at the costs of a slight decrease in statistical power. In our cohort, rural and urban areas differed for air pollution levels and for many factors associated with respiratory health and exposure. Area tended to modify effect measures of air pollution on respiratory health. CONCLUSIONS: Increasing the size of the study area also increases the potential for residual confounding. Our simulations suggest that adjusting for type of area is a good option to limit residual confounding due to area-associated factors without restricting the area size. Other statistical approaches developed in the field of spatial epidemiology are an alternative to control for poorly-measured spatially-varying confounders.

Pregnancy exposure to atmospheric pollutants and placental weight: an approach relying on a dispersion model.

BACKGROUND: Epidemiologic studies suggest an association between air pollution exposure and foetal growth. The possible underlying biological mechanisms have little been studied in humans, but animal studies suggest an impact of atmospheric pollutants on placental function. OBJECTIVES: Our aim was to investigate the association between exposure to atmospheric pollutants' levels during pregnancy and placental weight, birth weight and the placental to foetal weights ratio (PFR). For comparison purposes, the effects of active smoking on the same measures at birth have also been estimated. METHODS: The study relies on women from Eden mother-child cohort recruited in the middle-sized cities of Poitiers and Nancy (France). Nitrogen dioxide (NO(2)) and particulate matter with diameter <10 µm (PM10) home address levels during pregnancy were assessed using ADMS-Urban dispersion model. We characterized associations of NO(2), PM10 levels and active smoking with placental, birth weights and PFR by distinct linear regression models. RESULTS: Air pollution levels were higher and had greater variability in Nancy (5th-95th centiles, 19.9-27.9 µg/m(3) for PM10) than in Poitiers (5th-95th centiles, 14.3-17.8 µg/m(3)). Associations differed by study area: in Nancy (355 births), air pollution levels were associated with decreased placental weight and PFR, while in Poitiers (446 births), opposite or null associations were observed. Cigarette smoking was not associated with placental weight while it was associated with a decrease in birth weight and an increase in PFR. CONCLUSION: Results regarding air pollution estimated effects were not similar in both study areas and should therefore be taken with caution. The placental weight decrease observed with air pollutants in the more polluted area of Nancy is consistent with a recent epidemiological study. In this area, maternal active smoking and PM10 levels tended to have opposite effects on the PFR, suggesting different mechanisms of action of both pollutants on foetal growth.

Maternal exposure to nitrogen dioxide during pregnancy and offspring birth weight: comparison of two exposure models.

BACKGROUND: Studies of the effects of air pollutants on birth weight often assess exposure with networks of permanent air quality monitoring stations (AQMSs), which have a poor spatial resolution. OBJECTIVE: We aimed to compare the exposure model based on the nearest AQMS and a temporally adjusted geostatistical (TAG) model with a finer spatial resolution, for use in pregnancy studies. METHODS: The AQMS and TAG exposure models were implemented in two areas surrounding medium-size cities in which 776 pregnant women were followed as part of the EDEN mother-child cohort. The exposure models were compared in terms of estimated nitrogen dioxide (NO2) levels and of their association with birth weight. RESULTS: The correlations between the two estimates of exposure during the first trimester of pregnancy were r = 0.67, 0.70, and 0.83 for women living within 5, 2, and 1 km of an AQMS, respectively. Exposure patterns displayed greater spatial than temporal variations. Exposure during the first trimester of pregnancy was most strongly associated with birth weight for women living < 2 km away from an AQMS: a 10-µg/m3 increase in NO2 exposure was associated with an adjusted difference in birth weight of -37 g [95% confidence interval (CI), -75 to 1 g] for the nearest-AQMS model and of -51 g (95% CI, -128 to 26 g) for the TAG model. The association was less strong (higher p-value) for women living within 5 or 1 km of an AQMS. CONCLUSIONS: The two exposure models tended to give consistent results in terms of association with birth weight, despite the moderate concordance between exposure estimates.