Human Early Life Exposome (HELIX) study: a European population-based exposome cohort

PURPOSE

Essential to exposome research is the collection of data on many environmental exposures from different domains in the same subjects. The aim of the Human Early Life Exposome (HELIX) study was to measure and describe multiple environmental exposures during early life (pregnancy and childhood) in a prospective cohort and associate these exposures with molecular omics signatures and child health outcomes. Here, we describe recruitment, measurements available and baseline data of the HELIX study populations.

PARTICIPANTS

The HELIX study represents a collaborative project across six established and ongoing longitudinal population-based birth cohort studies in six European countries (France, Greece, Lithuania, Norway, Spain and the UK). HELIX used a multilevel study design with the entire study population totalling 31 472 mother-child pairs, recruited during pregnancy, in the six existing cohorts (first level); a subcohort of 1301 mother-child pairs where biomarkers, omics signatures and child health outcomes were measured at age 6-11 years (second level) and repeat-sampling panel studies with around 150 children and 150 pregnant women aimed at collecting personal exposure data (third level).

FINDINGS TO DATE

Cohort data include urban environment, hazardous substances and lifestyle-related exposures for women during pregnancy and their offspring from birth until 6-11 years. Common, standardised protocols were used to collect biological samples, measure exposure biomarkers and omics signatures and assess child health across the six cohorts. Baseline data of the cohort show substantial variation in health outcomes and determinants between the six countries, for example, in family affluence levels, tobacco smoking, physical activity, dietary habits and prevalence of childhood obesity, asthma, allergies and attention deficit hyperactivity disorder.

FUTURE PLANS

HELIX study results will inform on the early life exposome and its association with molecular omics signatures and child health outcomes. Cohort data are accessible for future research involving researchers external to the project.

A Systematic Comparison of Linear Regression-Based Statistical Methods to Assess Exposome-Health Associations

BACKGROUND

The exposome constitutes a promising framework to improve understanding of the effects of environmental exposures on health by explicitly considering multiple testing and avoiding selective reporting. However, exposome studies are challenged by the simultaneous consideration of many correlated exposures.

OBJECTIVES

We compared the performances of linear regression-based statistical methods in assessing exposome-health associations.

METHODS

In a simulation study, we generated 237 exposure covariates with a realistic correlation structure and with a health outcome linearly related to 0 to 25 of these covariates. Statistical methods were compared primarily in terms of false discovery proportion (FDP) and sensitivity.

RESULTS

On average over all simulation settings, the elastic net and sparse partial least-squares regression showed a sensitivity of 76% and an FDP of 44%; Graphical Unit Evolutionary Stochastic Search (GUESS) and the deletion/substitution/addition (DSA) algorithm revealed a sensitivity of 81% and an FDP of 34%. The environment-wide association study (EWAS) underperformed these methods in terms of FDP (average FDP, 86%) despite a higher sensitivity. Performances decreased considerably when assuming an exposome exposure matrix with high levels of correlation between covariates.

CONCLUSIONS

Correlation between exposures is a challenge for exposome research, and the statistical methods investigated in this study were limited in their ability to efficiently differentiate true predictors from correlated covariates in a realistic exposome context. Although GUESS and DSA provided a marginally better balance between sensitivity and FDP, they did not outperform the other multivariate methods across all scenarios and properties examined, and computational complexity and flexibility should also be considered when choosing between these methods. Citation: Agier L, Portengen L, Chadeau-Hyam M, Basagaña X, Giorgis-Allemand L, Siroux V, Robinson O, Vlaanderen J, González JR, Nieuwenhuijsen MJ, Vineis P, Vrijheid M, Slama R, Vermeulen R. 2016. A systematic comparison of linear regression-based statistical methods to assess exposome-health associations. Environ Health Perspect 124:1848-1856; http://dx.doi.org/10.1289/EHP172.

Variability of urinary concentrations of non-persistent chemicals in pregnant women and school-aged children

BACKGROUND

Exposome studies are challenged by exposure misclassification for non-persistent chemicals, whose temporal variability contributes to bias in dose-response functions.

OBJECTIVES

We evaluated the variability of urinary concentrations of 24 non-persistent chemicals: 10 phthalate metabolites, 7 phenols, 6 organophosphate (OP) pesticide metabolites, and cotinine, between weeks from different pregnancy trimesters in pregnant women, and between days and between seasons in children.

METHODS

154 pregnant women and 152 children from six European countries were enrolled in 2014-2015. Pregnant women provided three urine samples over a day (morning, midday, and night), for one week in the 2nd and 3rd pregnancy trimesters. Children provided two urines a day (morning and night), over two one-week periods, six months apart. We pooled all samples for a given subject that were collected within a week. In children, we also made four daily pools (combining morning and night voids) during the last four days of the first follow-up week. Pools were analyzed for all 24 metabolites of interest. We calculated intraclass-correlation coefficients (ICC) and estimated the number of pools needed to obtain an ICC above 0.80.

RESULTS

All phthalate metabolites and phenols were detected in >90% of pools whereas certain OP pesticide metabolites and cotinine were detected in <43% of pools. We observed fair (ICC = 0.40-0.59) to good (0.60-0.74) between-day reliability of the pools of two samples in children for all chemicals. Reliability was poor (<0.40) to fair between trimesters in pregnant women and between seasons in children. For most chemicals, three daily pools of two urines each (for weekly exposure windows) and four weekly pools of 15-20 urines each would be necessary to obtain an ICC above 0.80.

CONCLUSIONS

This quantification of the variability of biomarker measurements of many non-persistent chemicals during several time windows shows that for many of these compounds a few dozen samples are required to accurately assess exposure over periods encompassing several trimesters or months.

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.

Air Pollution Exposure during Pregnancy and Childhood Autistic Traits in Four European Population-Based Cohort Studies: The ESCAPE Project

BACKGROUND

Prenatal exposure to air pollutants has been suggested as a possible etiologic factor for the occurrence of autism spectrum disorder.

OBJECTIVES

We aimed to assess whether prenatal air pollution exposure is associated with childhood autistic traits in the general population.

METHODS

Ours was a collaborative study of four European population-based birth/child cohorts-CATSS (Sweden), Generation R (the Netherlands), GASPII (Italy), and INMA (Spain). Nitrogen oxides (NO2, NOx) and particulate matter (PM) with diameters of ≤ 2.5 μm (PM2.5), ≤ 10 μm (PM10), and between 2.5 and 10 μm (PM(coarse)), and PM2.5 absorbance were estimated for birth addresses by land-use regression models based on monitoring campaigns performed between 2008 and 2011. Levels were extrapolated back in time to exact pregnancy periods. We quantitatively assessed autistic traits when the child was between 4 and 10 years of age. Children were classified with autistic traits within the borderline/clinical range and within the clinical range using validated cut-offs. Adjusted cohort-specific effect estimates were combined using random-effects meta-analysis.

RESULTS

A total of 8,079 children were included. Prenatal air pollution exposure was not associated with autistic traits within the borderline/clinical range (odds ratio = 0.94; 95% CI: 0.81, 1.10 per each 10-μg/m3 increase in NO2 pregnancy levels). Similar results were observed in the different cohorts, for the other pollutants, and in assessments of children with autistic traits within the clinical range or children with autistic traits as a quantitative score.

CONCLUSIONS

Prenatal exposure to NO2 and PM was not associated with autistic traits in children from 4 to 10 years of age in four European population-based birth/child cohort studies.

CITATION

Guxens M, Ghassabian A, Gong T, Garcia-Esteban R, Porta D, Giorgis-Allemand L, Almqvist C, Aranbarri A, Beelen R, Badaloni C, Cesaroni G, de Nazelle A, Estarlich M, Forastiere F, Forns J, Gehring U, Ibarluzea J, Jaddoe VW, Korek M, Lichtenstein P, Nieuwenhuijsen MJ, Rebagliato M, Slama R, Tiemeier H, Verhulst FC, Volk HE, Pershagen G, Brunekreef B, Sunyer J. 2016. Air pollution exposure during pregnancy and childhood autistic traits in four European population-based cohort studies: the ESCAPE Project. Environ Health Perspect 124:133-140; http://dx.doi.org/10.1289/ehp.1408483.

The Urban Exposome during Pregnancy and Its Socioeconomic Determinants

BACKGROUND

The urban exposome is the set of environmental factors that are experienced in the outdoor urban environment and that may influence child development.

OBJECTIVE

The authors' goal was to describe the urban exposome among European pregnant women and understand its socioeconomic determinants.

METHODS

Using geographic information systems, remote sensing and spatio-temporal modeling we estimated exposure during pregnancy to 28 environmental indicators in almost 30,000 women from six population-based birth cohorts, in nine urban areas from across Europe. Exposures included meteorological factors, air pollutants, traffic noise, traffic indicators, natural space, the built environment, public transport, facilities, and walkability. Socioeconomic position (SEP), assessed at both the area and individual level, was related to the exposome through an exposome-wide association study and principal component (PC) analysis.

RESULTS

Mean±standard deviation (SD) NO2 levels ranged from 13.6±5.1 μg/m3 (in Heraklion, Crete) to 43.2±11 μg/m3 (in Sabadell, Spain), mean±SD walkability score ranged from 0.22±0.04 (Kaunas, Lithuania) to 0.32±0.07 (Valencia, Spain) and mean±SD Normalized Difference Vegetation Index ranged from 0.21±0.05 in Heraklion to 0.51±0.1 in Oslo, Norway. Four PCs explained more than half of variation in the urban exposome. There was considerable heterogeneity in social patterning of the urban exposome across cities. For example, high-SEP (based on family education) women lived in greener, less noisy, and less polluted areas in Bradford, UK (0.39 higher PC1 score, 95% confidence interval (CI): 0.31, 0.47), but the reverse was observed in Oslo (-0.57 PC1 score, 95% CI: -0.73, -0.41). For most cities, effects were stronger when SEP was assessed at the area level: In Bradford, women living in high SEP areas had a 1.34 higher average PC1 score (95% CI: 1.21, 1.48).

CONCLUSIONS

The urban exposome showed considerable variability across Europe. Pregnant women of low SEP were exposed to higher levels of environmental hazards in some cities, but not others, which may contribute to inequities in child health and development. https://doi.org/10.1289/EHP2862.

Influence of the Urban Exposome on Birth Weight

BACKGROUND

The exposome is defined as the totality of environmental exposures from conception onwards. It calls for providing a holistic view of environmental exposures and their effects on human health by evaluating multiple environmental exposures simultaneously during critical periods of life.

OBJECTIVE

We evaluated the association of the urban exposome with birth weight.

METHODS

We estimated exposure to the urban exposome, including the built environment, air pollution, road traffic noise, meteorology, natural space, and road traffic (corresponding to 24 environmental indicators and 60 exposures) for nearly 32,000 pregnant women from six European birth cohorts. To evaluate associations with either continuous birth weight or term low birth weight (TLBW) risk, we primarily relied on the Deletion-Substitution-Addition (DSA) algorithm, which is an extension of the stepwise variable selection method. Second, we used an exposure-by-exposure exposome-wide association studies (ExWAS) method accounting for multiple hypotheses testing to report associations not adjusted for coexposures.

RESULTS

The most consistent statistically significant associations were observed between increasing green space exposure estimated as Normalized Difference Vegetation Index (NDVI) and increased birth weight and decreased TLBW risk. Furthermore, we observed statistically significant associations among presence of public bus line, land use Shannon's Evenness Index, and traffic density and birth weight in our DSA analysis.

CONCLUSION

This investigation is the first large urban exposome study of birth weight that tests many environmental urban exposures. It confirmed previously reported associations for NDVI and generated new hypotheses for a number of built-environment exposures. https://doi.org/10.1289/EHP3971.

In Utero Exposure to Select Phenols and Phthalates and Respiratory Health in Five-Year-Old Boys: A Prospective Study

BACKGROUND

Phenols and phthalates may have immunomodulatory and proinflammatory effects and thereby adversely affect respiratory health.

OBJECTIVE

We estimated the associations between gestational exposure to select phthalates and phenols and respiratory health in boys.

METHODS

Among 587 pregnant women from the EDEN (Etude des Déterminants pré et post natals du développement et de la santé de l'Enfant) cohort who delivered a boy, 9 phenols and 11 phthalates metabolites were quantified in spot pregnancy urine samples. Respiratory outcomes were followed up by questionnaires until age 5, when forced expiratory volume in 1 s (FEV1) was measured by spirometry. Adjusted associations of urinary metabolites log-transformed concentrations with respiratory outcomes and FEV1 in percent predicted (FEV1%) were estimated by survival and linear regression models, respectively.

RESULTS

No phenol or phthalate metabolite exhibited clear deleterious associations simultaneously with several respiratory outcomes. Ethyl-paraben was associated with increased asthma rate [hazard rate (HR)=1.10; 95% confidence interval (CI): 1.00, 1.21] and tended to be negatively associated with FEV1% (beta=-0.59; 95% CI: -1.24, 0.05); bisphenol A tended to be associated with increased rates of asthma diagnosis (HR=1.23; 95% CI: 0.97, 1.55) and bronchiolitis/bronchitis (HR=1.13; 95% CI: 0.99, 1.30). Isolated trends for deleterious associations were also observed between 2,5-dichlorophenol and wheezing, and between monocarboxynonyl phthalate, a metabolite of di-isodecyl phthalate (DIDP), and wheezing.

CONCLUSION

Ethyl-paraben, bisphenol A, 2,5-dichlorophenol, and DIDP tended to be associated with altered respiratory health, with ethyl-paraben and bisphenol A exhibiting some consistency across respiratory outcomes. The trends between bisphenol A pregnancy level and increased asthma and bronchiolitis/bronchitis rates in childhood were consistent with a previous cohort study. https://doi.org/10.1289/EHP1015.

Elemental Constituents of Particulate Matter and Newborn's Size in Eight European Cohorts

BACKGROUND

The health effects of suspended particulate matter (PM) may depend on its chemical composition. Associations between maternal exposure to chemical constituents of PM and newborn's size have been little examined.

OBJECTIVE

We aimed to investigate the associations of exposure to elemental constituents of PM with term low birth weight (LBW; weight < 2,500 g among births after 37 weeks of gestation), mean birth weight, and head circumference, relying on standardized fine-scale exposure assessment and with extensive control for potential confounders.

METHODS

We pooled data from eight European cohorts comprising 34,923 singleton births in 1994-2008. Annual average concentrations of elemental constituents of PM ≤ 2.5 and ≤ 10 μm (PM2.5 and PM10) at maternal home addresses during pregnancy were estimated using land-use regression models. Adjusted associations between each birth measurement and concentrations of eight elements (copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc) were calculated using random-effects regression on pooled data.

RESULTS

A 200-ng/m3 increase in sulfur in PM2.5 was associated with an increased risk of LBW (adjusted odds ratio = 1.36; 95% confidence interval: 1.17, 1.58). Increased nickel and zinc in PM2.5 concentrations were also associated with an increased risk of LBW. Head circumference was reduced at higher exposure to all elements except potassium. All associations with sulfur were most robust to adjustment for PM2.5 mass concentration. All results were similar for PM10.

CONCLUSION

Sulfur, reflecting secondary combustion particles in this study, may adversely affect LBW and head circumference, independently of particle mass.

CITATION

Pedersen M, Gehring U, Beelen R, Wang M, Giorgis-Allemand L, Andersen AM, Basagaña X, Bernard C, Cirach M, Forastiere F, de Hoogh K, Gražulevičienė R, Gruzieva O, Hoek G, Jedynska A, Klümper C, Kooter IM, Krämer U, Kukkonen J, Porta D, Postma DS, Raaschou-Nielsen O, van Rossem L, Sunyer J, Sørensen M, Tsai MY, Vrijkotte TG, Wilhelm M, Nieuwenhuijsen MJ, Pershagen G, Brunekreef B, Kogevinas M, Slama R. 2016. Elemental constituents of particulate matter and newborn's size in eight European cohorts. Environ Health Perspect 124:141-150; http://dx.doi.org/10.1289/ehp.1409546.

A systematic comparison of statistical methods to detect interactions in exposome-health associations

BACKGROUND

There is growing interest in examining the simultaneous effects of multiple exposures and, more generally, the effects of mixtures of exposures, as part of the exposome concept (being defined as the totality of human environmental exposures from conception onwards). Uncovering such combined effects is challenging owing to the large number of exposures, several of them being highly correlated. We performed a simulation study in an exposome context to compare the performance of several statistical methods that have been proposed to detect statistical interactions.

METHODS

Simulations were based on an exposome including 237 exposures with a realistic correlation structure. We considered several statistical regression-based methods, including two-step Environment-Wide Association Study (EWAS₂), the Deletion/Substitution/Addition (DSA) algorithm, the Least Absolute Shrinkage and Selection Operator (LASSO), Group-Lasso INTERaction-NET (GLINTERNET), a three-step method based on regression trees and finally Boosted Regression Trees (BRT). We assessed the performance of each method in terms of model size, predictive ability, sensitivity and false discovery rate.

RESULTS

GLINTERNET and DSA had better overall performance than the other methods, with GLINTERNET having better properties in terms of selecting the true predictors (sensitivity) and of predictive ability, while DSA had a lower number of false positives. In terms of ability to capture interaction terms, GLINTERNET and DSA had again the best performances, with the same trade-off between sensitivity and false discovery proportion. When GLINTERNET and DSA failed to select an exposure truly associated with the outcome, they tended to select a highly correlated one. When interactions were not present in the data, using variable selection methods that allowed for interactions had only slight costs in performance compared to methods that only searched for main effects.

CONCLUSIONS

GLINTERNET and DSA provided better performance in detecting two-way interactions, compared to other existing methods.

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.

Estimation of exposure to atmospheric pollutants during pregnancy integrating space-time activity and indoor air levels: Does it make a difference?

Studies of air pollution effects during pregnancy generally only consider exposure in the outdoor air at the home address. We aimed to compare exposure models differing in their ability to account for the spatial resolution of pollutants, space-time activity and indoor air pollution levels. We recruited 40 pregnant women in the Grenoble urban area, France, who carried a Global Positioning System (GPS) during up to 3 weeks; in a subgroup, indoor measurements of fine particles (PM2.5) were conducted at home (n=9) and personal exposure to nitrogen dioxide (NO2) was assessed using passive air samplers (n=10). Outdoor concentrations of NO2, and PM2.5 were estimated from a dispersion model with a fine spatial resolution. Women spent on average 16 h per day at home. Considering only outdoor levels, for estimates at the home address, the correlation between the estimate using the nearest background air monitoring station and the estimate from the dispersion model was high (r=0.93) for PM2.5 and moderate (r=0.67) for NO2. The model incorporating clean GPS data was less correlated with the estimate relying on raw GPS data (r=0.77) than the model ignoring space-time activity (r=0.93). PM2.5 outdoor levels were not to moderately correlated with estimates from the model incorporating indoor measurements and space-time activity (r=-0.10 to 0.47), while NO2 personal levels were not correlated with outdoor levels (r=-0.42 to 0.03). In this urban area, accounting for space-time activity little influenced exposure estimates; in a subgroup of subjects (n=9), incorporating indoor pollution levels seemed to strongly modify them.

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.

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

DISCUSSION

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

Association between the pregnancy exposome and fetal growth

BACKGROUND

Several environmental contaminants were shown to possibly influence fetal growth, generally from single exposure family studies, which are prone to publication bias and confounding by co-exposures. The exposome paradigm offers perspectives to avoid selective reporting of findings and to control for confounding by co-exposures. We aimed to characterize associations of fetal growth with the pregnancy chemical and external exposomes.

METHODS

Within the Human Early-Life Exposome project, 131 prenatal exposures were assessed using biomarkers and environmental models in 1287 mother-child pairs from six European cohorts. We investigated their associations with fetal growth using a deletion-substitution-addition (DSA) algorithm considering all exposures simultaneously, and an exposome-wide association study (ExWAS) considering each exposure independently. We corrected for exposure measurement error and tested for exposure-exposure and sex-exposure interactions.

RESULTS

The DSA model identified lead blood level, which was associated with a 97 g birth weight decrease for each doubling in lead concentration. No exposure passed the multiple testing-corrected significance threshold of ExWAS; without multiple testing correction, this model was in favour of negative associations of lead, fine particulate matter concentration and absorbance with birth weight, and of a positive sex-specific association of parabens with birth weight in boys. No two-way interaction between exposure variables was identified.

CONCLUSIONS

This first large-scale exposome study of fetal growth simultaneously considered >100 environmental exposures. Compared with single exposure studies, our approach allowed making all tests (usually reported in successive publications) explicit. Lead exposure is still a health concern in Europe and parabens health effects warrant further investigation.

The Influence of Meteorological Factors and Atmospheric Pollutants on the Risk of Preterm Birth

Atmospheric pollutants and meteorological conditions are suspected to be causes of preterm birth. We aimed to characterize their possible association with the risk of preterm birth (defined as birth occurring before 37 completed gestational weeks). We pooled individual data from 13 birth cohorts in 11 European countries (71,493 births from the period 1994-2011, European Study of Cohorts for Air Pollution Effects (ESCAPE)). City-specific meteorological data from routine monitors were averaged over time windows spanning from 1 week to the whole pregnancy. Atmospheric pollution measurements (nitrogen oxides and particulate matter) were combined with data from permanent monitors and land-use data into seasonally adjusted land-use regression models. Preterm birth risks associated with air pollution and meteorological factors were estimated using adjusted discrete-time Cox models. The frequency of preterm birth was 5.0%. Preterm birth risk tended to increase with first-trimester average atmospheric pressure (odds ratio per 5-mbar increase = 1.06, 95% confidence interval: 1.01, 1.11), which could not be distinguished from altitude. There was also some evidence of an increase in preterm birth risk with first-trimester average temperature in the -5°C to 15°C range, with a plateau afterwards (spline coding, P = 0.08). No evidence of adverse association with atmospheric pollutants was observed. Our study lends support for an increase in preterm birth risk with atmospheric pressure.

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.

Incident hypertension in relation to aircraft noise exposure: results of the DEBATS longitudinal study in France

BACKGROUND

Although several cross-sectional studies have shown that aircraft noise exposure was associated with an increased risk of hypertension, a limited number of longitudinal studies have addressed this issue. This study is part of the DEBATS (Discussion on the health effect of aircraft noise) research programme and aimed to investigate the association between aircraft noise exposure and the incidence of hypertension.

METHODS

In 2013, 1244 adults living near three major French airports were included in this longitudinal study. Systolic and diastolic blood pressure, as well as demographic and lifestyle factors, were collected at baseline and after 2 and 4 years of follow-up during face-to-face interviews. Exposure to aircraft noise was estimated for each participant's home address using noise maps. Statistical analyses were performed using mixed Poisson and linear regression models adjusted for potential confounding factors.

RESULTS

A 10 dB(A) increase in aircraft noise levels in terms of L_den was associated with a higher incidence of hypertension (incidence rate ratio (IRR)=1.36, 95% CI 1.02 to 1.82). The association was also significant for L_day (IRR 1.41, 95% CI 1.07; to 1.85) and L_night (IRR 1.31, 95% CI 1.01 to 1.71). Systolic and diastolic blood pressure increased with all noise indicators.

CONCLUSION

These results strengthen those obtained from the cross-sectional analysis of the data collected at the time of inclusion in DEBATS, as well as those from previous studies conducted in other countries. Hence, they support the hypothesis that aircraft noise exposure may be considered as a risk factor for hypertension.

Can atmospheric pollutants influence menstrual cycle function?

A few experimental studies suggest that atmospheric pollutants could affect the endocrine system, and in particular stress hormones and the hypothalamic-hypophyseal-ovarian axis, which could in turn influence menstrual cycle function. We aimed to study the possible short-term effects of atmospheric pollutants on the length of the follicular and luteal phases and on the duration of the menstrual cycle in humans. To do so, from a nation-wide study on couples' fecundity, we recruited 184 women not using contraception who collected urine samples at least every other day during one menstrual cycle, from which a progesterone metabolite was assayed, allowing estimation of the duration of the follicular and luteal phases of the cycle. Atmospheric pollution (nitrogen dioxide and particulate matter with an aerodynamical diameter below 10 μm, PM₁₀) levels were estimated from a dispersion model with a 1-km resolution combined with permanent monitoring stations measurements, allowing to estimate exposures in the 30-day, 1-10 and 11-30-day periods before the start of the menstrual cycle. Regression models allowed to quantify the change in cycle duration associated with atmospheric pollutants and adjusted for potential confounders. Follicular phase duration increased on average by 0.7 day (95% confidence interval, CI, 0.2; 1.3) for each increase by 10 μg/m³ in NO₂ concentration averaged over the 30 days before the cycle and by 1.6 day (95% CI, 0.3; 2.9) for each increase by 10 μg/m³ in PM₁₀. There was no strong evidence of associations of exposures in this time window with luteal phase or with total menstrual cycle durations (p > 0.2). Exposures in the 1-10 day period before the cycle start were also associated with increased follicular phase duration. This study is one of the first prospective studies to suggest short-term alterations in follicular phase duration following atmospheric pollutants exposure.

The Role of Noise Annoyance and Noise Sensitivity in the Effect of Aircraft Noise on Self-Reported Health: The Results of the DEBATS Longitudinal Study in France

Background

Transportation noise seems to impair self-reported health status (SRHS). However, only a few studies have considered the role of noise annoyance and noise sensitivity in this deleterious effect. This study aims investigating mediator and moderator roles of noise annoyance and noise sensitivity.

Methods

In 2013, the DEBATS longitudinal study included 1244 participants aged over 18 years and living around three French airports. These participants were followed up in 2015 and 2017. They self-reported their perceived health status, aircraft noise annoyance, and their noise sensitivity via a questionnaire during the three visits. Noise maps were used to estimate aircraft noise levels at the facade of participants' residence. Generalized linear mixed models with a random intercept at the participant level were used.

Results

Aircraft noise levels were associated with severe annoyance. Severe annoyance tent to be associated with impaired SRHS. Aircraft noise levels were associated with impaired SRHS only in men (odds ratio [OR] = 1.47, 95% confidence interval [CI] = [1.02, 2.11], for a 10-dBA L_den increase in aircraft noise levels) with a weaker association adjusted for annoyance (OR = 1.36, 95% CI = [0.94, 1.98]). The association was stronger in men who reported high noise sensitivity (OR = 1.84, 95% CI = [0.92, 3.70], versus OR = 1.39, 95% CI = [0.90, 2.14], for men who were not highly sensitive to noise).

Conclusion

From our results, the deleterious effect of aircraft noise on SRHS could be mediated by noise annoyance and moderated by noise sensitivity. Further studies using causal inference methods are needed for identifying causal effect of exposure, mediator, and moderator.

Quantification of Sound Exposure from Wind Turbines in France

The WHO guidelines on environmental noise highlight that evidence on the health effects of wind turbine sound levels is either non-existent or of poor quality. In this context, a feasibility study was conducted in France in 2017. The objective was to suggest a methodology for calculating wind turbine sound levels in order to quantify the number of windfarms' residents exposed to this sound. Based on a literature review, the Harmonoise model was selected for sound exposure calculation. It was validated by quantifying its uncertainties, and finally used to estimate the population exposed to wind turbine sound in metropolitan France. Compared to other environmental noise sources (e.g., transportation), sound exposure is very moderate, with more than 80% of the exposed people exposed to sound levels below 40 dBA. The total number of people exposed to more than 30 dBA is about 686,000 and 722,000 people for typical daytime and night-time meteorological conditions respectively, i.e., about 1% of the French population in 2017. These results represent the first ever assessment of sound exposure from wind turbines at the scale of the entire metropolitan France.