# Placental DNA methylation signatures of prenatal air pollution exposure and potential effects on birth outcomes: an analysis of three prospective cohorts

Lucile Broseus, Ariane Guilbert, Ian Hough, Itai Kloog, Anath Chauvaud, Emie Seyve, Daniel Vaiman, Barbara Heude, Cécile Chevrier, Jörg Tost, et al.

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## ► To cite this version:

Lucile Broseus, Ariane Guilbert, Ian Hough, Itai Kloog, Anath Chauvaud, et al. Placental DNA methylation signatures of prenatal air pollution exposure and potential effects on birth outcomes: an analysis of three prospective cohorts. Lancet Planetary Health, 2024, 8 (5), pp.e297-e308. 10.1016/S2542-5196(24)00045-7. hal-04615725

HAL Id: hal-04615725

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# Placental DNA methylation signatures of prenatal air pollution exposure and potential effects on birth outcomes: an analysis of three prospective cohorts

Lucile Broséus, Ariane Guilbert, Ian Hough, Itai Kloog, Anath Chauvaud, Emie Seyve, Daniel Vaiman, Barbara Heude, Cécile Chevrier, Jörg Tost, Rémy Slama, Johanna Lepeule

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## Summary

**Background** Pregnancy air pollution exposure (PAPE) has been linked to a wide range of adverse birth and childhood outcomes, but there is a paucity of data on its influence on the placental epigenome, which can regulate the programming of physiological functions and affect child development. This study aimed to investigate the association between prenatal air pollutant exposure concentrations and changes in placental DNA methylation patterns, and to explore the potential windows of susceptibility and sex-specific alterations.

**Methods** This multi-site study used three prospective population-based mother–child cohorts: EDEN, PELAGIE, and SEPAGES, originating from four French geographical regions (Nancy, Poitiers, Brittany, and Grenoble). Pregnant women were included between 2003 and 2006 for EDEN and PELAGIE, and between 2014 and 2017 for SEPAGES. The main eligibility criteria were: being older than 18 years, having a singleton pregnancy, and living and planning to deliver in one of the maternity clinics in one of the study areas. A total of 1539 mother–child pairs were analysed, measuring placental DNA methylation using Illumina BeadChips. We used validated spatiotemporally resolved models to estimate PM₂.₅, PM₁₀, and NO₂ exposure over each trimester of pregnancy at the maternal residential address. We conducted a pooled adjusted epigenome-wide association study to identify differentially methylated 5’–C-phosphate–G–3’ (CpG) sites and regions (assessed using the Infinium HumanMethylationEPIC BeadChip array, n=871), including sex-specific and sex-linked alterations, and independently validated our results (assessed using the Infinium HumanMethylation450 BeadChip array, n=668).

**Findings** We identified four CpGs and 28 regions associated with PAPE in the total population, 469 CpGs and 87 regions in male infants, and 150 CpGs and 66 regions in female infants. We validated 35% of the CpGs available. More than 30% of the identified CpGs were related to one (or more) birth outcome and most significant alterations were enriched for neural development, immunity, and metabolism related genes. The 28 regions identified for both sexes overlapped with imprinted genes (four genes), and were associated with neurodevelopment (nine genes), immune system (seven genes), and metabolism (five genes). Most associations were observed for the third trimester for female infants (134 of 150 CpGs), and throughout pregnancy (281 of 469 CpGs) and the first trimester (237 of 469 CpGs) for male infants.

**Interpretation** These findings highlight the molecular pathways through which PAPE might affect child health in a widespread and sex-specific manner, identifying the genes involved in the major physiological functions of a developing child. Further studies are needed to elucidate whether these epigenetic changes persist and affect health later in life.

**Funding** French Agency for National Research, Fondation pour la Recherche Médicale, Fondation de France, and the Plan Cancer.

**Copyright** © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC license.

## Introduction

In 2019, 99% of the global population were living in places in which the WHO air quality guidelines were not met.¹ In the womb and during infancy, infants and children are particularly vulnerable and exposed to air pollution. In Europe, outdoor air pollution is estimated to cause the loss of more than 189 000 disability-adjusted life-years (a measure of the burden of disease caused by a risk factor) among those younger than 5 years.² Developmental

exposure to particulate matter (PM) increases the risk of a wide array of neonatal outcomes, including prematurity and impaired fetal growth and, according to the Environmental Protection Agency's integrated science assessment, is likely to be causally associated with childhood cardiometabolic, respiratory, and neurodevelopmental disorders.³⁻⁵ The myriad of health effects associated with in-utero exposure to air pollution are a source of substantial public health concern.

Lancet Planet Health 2024; 8: e297-308

Published Online
May 6, 2024
https://doi.org/10.1016/
S2542-5196(24)00045-7

Université Grenoble Alpes,
Inserm, CNRS, Team of
Environmental Epidemiology
applied to Development and
Respiratory Health, IAB,
Grenoble, France
(L Broséus PhD, A Guibert MSc,
I Hough PhD, A Chauvaud MSc,
E Seyve MSc, R Slama PhD,
J Lepeule PhD); Institute of
Environmental Geosciences,
Université Grenoble Alpes,
Grenoble, France (I Hough);
Department of Geography and
Environmental Development,
Ben-Gurion University of the
Negev, Be'er Sheva, Israel
(I Hough, I Kloog PhD);
Department of Environmental
Medicine and Climate Science,
Icahn School of Medicine at
Mount Sinai, New York, NY,
USA (I Kloog); Institut Cochin,
U1016 Inserm, Unité Mixte de
Recherche 8104, CNRS, Paris-
Descartes University, Paris,
France (D Vaiman PhD);
Université Paris Cité et
Université Sorbonne Paris
Nord, Inserm, INRAE, Centre de
Recherche en Épidémiologie et
Statistiques, Paris, France
(B Heude PhD); University of
Rennes, Inserm, Ecole des
Hautes Etudes en Santé
Publique, Institut de Recherche
en Santé, Environnement et
Travail, Unité Mixte de
Recherche 1085, Rennes,
France (C Chevrier PhD);
Laboratory for Epigenetics and
Environment, Centre National
de Recherche en Génomique
Humaine, Commissariat à
l'Energie Atomique et aux
Energies Alternatives, Institut
de Biologie François Jacob,
University Paris Saclay, Evry,
France (J Tost PhD)
Correspondence to:
Dr Lucile Broséus, Université
Grenoble Alpes, Inserm, CNRS,
Team of Environmental
Epidemiology applied to
Development and Respiratory
Health, IAB, Grenoble 38000,
France
**lucile.broseus@univ-grenoble-alpes.fr**
or
Dr Johanna Lepeule, Université
Grenoble Alpes, Inserm, CNRS,
Team of Environmental
Epidemiology applied to
Development and Respiratory
Health, IAB, Grenoble 38000,
France
**johanna.lepeule@univ-grenoble-alpes.fr**

Research in context

Evidence before this study

Outdoor air pollution exposure during pregnancy is a leading risk factor for a wide range of poor neonatal outcomes and a cause of childhood cardiometabolic, respiratory, and neurodevelopmental disorders, which are a source of substantial public health concern. In Europe, outdoor air pollution is estimated to cause the loss of more than 189 000 disability-adjusted life-years among those younger than 5 years. Although the physiological effects of air pollutant exposure during pregnancy have been explored, the molecular mechanisms, which allow the reprogramming of physiological functions and could influence neonatal and long-term health outcomes, are poorly understood.
Meta-analyses have identified NO₂ and particulate matter-associated variations in DNA methylation (DNAm) in cord blood. Between May, 2022, and August, 2023, we searched PubMed for epigenome-wide studies in English investigating the effects of prenatal exposure to air pollution on placental methylation levels. We used the search terms "air pollution" plus "methylation" plus "placenta" plus ("prenatal" or "pregnancy"). Although the placenta is a key organ for fetal development and represents a molecular memory of the prenatal environment, genome-wide placental DNAm studies are scarce, and have been limited by small sample sizes. Additionally, the only three available studies did not replicate their results, did not consider sex differences or potential DNAm alterations on chromosomes X and Y, and had a low coverage of the genome. Thus, air pollutant-associated placental DNAm changes are poorly characterised.

Added value of this study

The multiple pregnancy cohorts in this study bring in diversity,
exposure contrast, and, to the best of our knowledge, the
largest dataset on genome-wide placental DNAm, and also

allow for maintaining consistency and homogeneity in
exposure assessment methods, DNAm data processing, coding
of outcomes, and covariates across cohorts, through a pooled
approach.

This study provides robust evidence, validated in an
independent dataset, that pregnancy exposure to air pollutants
is associated with placental DNAm changes occurring at
genomic locations of high importance for the development of
a child, including imprinted genes and genes known to affect
neurodevelopment, the immune system, and metabolism; and
genomic locations, a third of which were related to birth
outcomes in our dataset. Additionally, pregnancy air pollution-
associated changes in placental DNAm were sexually dimorphic,
as well as the windows of sensitivity (which were also sexually
dimorphic), both of which occurred mostly during the third
trimester for female infants whereas male infants seemed more
vulnerable to air pollution exposure during the first trimester of
pregnancy and on average over the whole pregnancy.

Implications of all the available evidence

Air pollution particles have been shown to cross the placental
barrier, and could reach the fetal side and impair fetal
development by disrupting crucial functions of the placenta.
Our findings show that prenatal air pollution is able to affect
placental DNAm and suggest it might cause the observed
adverse health effects in the neonatal and childhood period.
Additionally, changes in placental DNAm associated with
exposure in air pollutants were sexually dimorphic. Exposure
concentrations in our study population are similar to those in
other European countries. These results argue in favour of
continuing efforts to reduce exposure to air pollutants,
particularly during pregnancy, and to study the persistence of
these epigenetic changes throughout life.

Although the physiological effects of pregnancy air
pollution exposure (PAPE) have been explored, the
epigenetic molecular mechanisms, which are able to allow
the reprogramming of physiological functions and could
influence neonatal and long-term health outcomes, are
poorly understood. Cross-cohort collaborative efforts are
a relevant approach to address the issues associated with
the large number of 5'-C-phosphate-G-3' (CpG) sites
investigated in epigenome-wide association studies. In
particular, a large meta-analysis has identified NO₂⁻ and
PM-associated variations in DNA methylation (DNAm) in
cord blood.⁶,⁷ Although the placenta is a key organ for fetal
development and represents a molecular memory of the
prenatal environment, its epigenome has not been
thoroughly studied.⁸⁻¹⁰ Air pollution particles have been
shown to cross the placental barrier, and could reach the
fetal side and impair fetal development by disrupting
crucial functions of the placenta.¹¹ Sexual dimorphisms
observed in many developmental and health disorders,

including in the health effects of air pollution, could
derive from the distinct effects of sexual hormones and
chromosomes but could also result from the distinct
transport of environmental pollutants and nutrients
through the placenta.¹²,¹³

A few studies have begun characterising the effects
of PAPE on the human placental methylome, but they
have been mostly limited by small sample sizes.¹⁴⁻¹⁶
Additionally, these studies did not replicate their results,
did not consider sex differences and potential DNAm
alterations on chromosomes X and Y, and had a limited
coverage of the genome. We aimed to address these
gaps by performing a large study examining the
associations between NO₂, PM₁₀, and PM₂.₅ exposure
during pregnancy with the placental methylome from
three independent cohorts benefiting from harmonised
and consistent processes. We examined sex-specific
modifications of placental DNAm, including chromo-
somes X and Y, and validated our results against a large
independent dataset. To gain insights into the potential biological processes affected and the relationships with fetal growth, we performed an additional association study of placental DNAm with birth outcomes and conducted further analyses for functional and phenotypic enrichment.

## Methods

### Participants

This study used three prospective population-based mother-child cohorts: EDEN,¹⁷ PELAGIE,¹⁸ and SEPAGES,¹⁹ originating from four French geographical regions, combining urban, peri-urban, and rural areas.¹⁷⁻¹⁹ Pregnant women were included between 2003 and 2006 for EDEN (Nancy and Poitiers) and PELAGIE (Brittany), and between 2014 and 2017 for SEPAGES (Grenoble). The main eligibility criteria were: being older than 18 years, having a singleton pregnancy, and living and planning to deliver in one of the maternity clinics in one of the study areas. Non-French-speaking mothers were excluded. Medical and lifestyle data were collected by midwives or by self-administered questionnaires during pregnancy and at delivery. All cohorts received approval from appropriate French ethics committees. All participants signed an informed consent form for themselves and their child. Our analyses were focused on the mother-child pairs with available placental DNAm measurements: 1042 of 2002 participants for EDEN, 94 of 3322 for PELAGIE, and 395 of 484 for SEPAGES.

### Air pollution exposure assessment

We used PM₂.₅, PM₁₀, and NO₂ as proxies for traffic-related air pollutants. Outdoor daily air pollutant exposure concentrations were assessed at participants' residential addresses using the same validated methods for all centres of recruitment and averaged for different gestational periods. PM₁₀ and PM₂.₅ were predicted at a 1 km resolution using combined data on aerosol optical depth, meteorology (temperature, air pressure, and cloud cover), vegetation, and other spatiotemporal predictors (land cover, road and railway density, elevation, and population, etc) combined using machine learning models.²⁰ The model showed excellent performances, with a mean absolute error of 4.26 µg/m³, R² of 0.71, and root mean square error (RMSE) of 6.28 for PM₁₀, and a mean absolute error of 2.72 µg/m³, R² of 0.76 and RMSE of 4.02 for PM₂.₅. NO₂ exposure was estimated at a 4 km resolution using a model from the Institut National de l'Environnement Industriel et des Risques based on the Chemistry Transport Model CHIMERE.²¹ This approach relies on a kriging procedure, which showed good performance in urban areas (with a bias of <3.5%) and average RMSE across rural and urban areas of 10.4 µg/m³.

### Placental DNAm measurement

In the three cohorts, a sample of placental tissue from the fetal side was collected by the midwives of the study

using a standardised procedure. For 668 EDEN samples, DNA was extracted using the QIAsymphony instrument (Qiagen, Hilden, Germany) and whole-genome DNAm was measured using the Infinium HumanMethylation450 (450K) BeadChip (Illumina, San Diego, CA, USA).¹⁴ An additional non-overlapping set of 382 EDEN samples and all PELAGIE and SEPAGES samples were processed with the DNeasy blood and tissue kit (Qiagen, Hilden, Germany) using the DNeasy blood and tissue handbook according to the manufacturer's instructions and whole-genome DNAm was measured using the Infinium HumanMethylationEPIC (EPIC) BeadChip (Illumina, San Diego, CA, USA). To mitigate artifactual sex-bias and include probes on chromosomes X and Y in our analyses, we normalised raw intensities using the interpolatedXY adjusted funnorm method.²² Detailed placenta collection, DNA measurement methods for each cohort, and quality controls and pre-processing steps are provided in appendix 1 (p 2).

See Online for appendix 1

### Epigenome-wide analysis

The association between air pollution concentrations and DNAm was investigated in a discovery cohort consisting of the pooled dataset of 871 participants for which placental DNAm was measured with the EPIC array. Two levels of analyses were conducted: probe-wise epigenome-wide analyses and regional analyses (figure 1). Each association study between the averaged exposure to air pollutants and DNAm was performed separately for each of the four windows of exposure: the whole pregnancy, and the three trimesters of pregnancy (first trimester: 1–13 weeks; second trimester: 14–27 weeks; and third trimester: 28 weeks to delivery).

The association between each pollutant and placental DNAm of individual CpGs was evaluated by fitting robust linear regression models adjusted for potential measured and unmeasured confounders, including: maternal age, child sex, pre-gravid BMI, maternal educational level attainment at childbirth, maternal smoking during pregnancy, and the city of the centre of recruitment (Nancy, Poitiers, Brittany, or Grenoble, France). To correct for cell-type heterogeneity, models were further adjusted for reference-based placental cell type composition estimated in silico, as previously described (appendix 1 p 3).²³ Potential residual confounding due to the non-linear effect of continuous covariates (maternal age and BMI) on DNAm was accounted for using restricted cubic splines. Additional technical effects and unmeasured confounding were estimated through latent factors. We devised a method, relying on variance decomposition and using technical replicates, to identify and exclude unreliable probes that showed a low signal-to-noise ratio and could introduce false positive hits (appendix 1 p 4; appendix 2 tab 1).²⁴ 59.5% of the probes were filtered out (appendix 2 tab 1). To account for multiple testing, p values were corrected using the Benjamini-Hochberg method. A CpG position was considered differentially methylated (DMP) when its false

See Online for appendix 2
<figure><img src="image_5.png" /><figcaption>**Figure 1: Outline of the study**</figcaption></figure>

DMP=differentially methylated position. DMR=differentially methylated region.

discovery rate (FDR) was less than 0.05. Differentially
methylated regions (DMRs) were identified using the
comb-p method.²⁵ Only DMRs with an FDR-adjusted
p value of less than 0.05 and including at least three probes
were considered significant (appendix 1 p 4).

We took advantage of our large number of samples
to conduct additional sex-stratified analyses including
sex chromosomes. A CpG found to be significant
(FDR <0.05) in one sex stratum was considered sex-
specific if its nominal p value was more than 0.05 in the
other stratum, and a significant DMR was considered
sex-specific if it contained only CpG with a nominal
p value of >0.05 in the other stratum.

Association between DNAm and indicators of fetal
growth

To further understand the potential effect of PAPE-
associated DNAm changes, we investigated the statistical
association between fetal growth and the loci identified
in our main analysis. Indicators of fetal growth included
gestational age, birthweight, birth length, and head
circumference (appendix 1 p 4).

Enrichment and functional analyses

To gain further insight into the biological effect of DNAm
changes, we tested for CpG enrichment for genomic

locations (promoter, gene body, and intergenic) and
relations to CpG islands using hypergeometric tests.
Additionally, we investigated the potential biological
functions of the differentially methylated loci by query-
ing enriched pathways (within the Kyoto Encyclopedia
of Genes and Genomes²⁶ and Reactome²⁷ databases),
and investigated enriched health outcomes and pheno-
types (within the database of genotypes and phenotypes)
among informative CpGs with nominal p values of less
than 0.001.

Robustness and validation of the results

Four sensitivity analyses were conducted to evaluate
robustness to the assumptions of our models. We
compared our main results with those obtained: (1) with-
out adjusting for gestational duration; (2) when further
adjusting for the method of delivery; (3) when further
adjusting models for the season of conception (from
January to March, from April to June, from July to
September, or from October to December); and (4) when
conducting a meta-analysis rather than a pooled analysis.
Significant DNAm changes in loci that overlapped with
the Illumina 450K array were validated in an independent
dataset of 668 placental samples from the EDEN cohort.
A CpG was considered validated if its nominal p value
was less than 0.05 with same direction of the effect in
<table><thead><tr><th></th><th>EDEN discovery set (n=382)</th><th>PELAGIE (n=94)</th><th>SEPAGES (n=395)</th><th>Pooled discovery data set (N=871)</th><th>EDEN validation set (n=668)</th></tr></thead><tbody><tr><td><strong>Child sex</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Female</td><td>170 (45%)</td><td>46 (49%)</td><td>185 (47%)</td><td>401 (46%)</td><td>320 (48%)</td></tr><tr><td>Male</td><td>212 (55%)</td><td>48 (51%)</td><td>210 (53%)</td><td>470 (54%)</td><td>348 (52%)</td></tr><tr><td><strong>Maternal age</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Age, years</td><td>30.1 (26.6-33.2)</td><td>30.5 (27.6-32.8)</td><td>32.2 (29.8-35.1)</td><td>31.2 (28.3-34.1)</td><td>29.5 (25.9-32.5)</td></tr><tr><td>Missing values</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td><strong>Parity</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No child</td><td>168 (44%)</td><td>35 (37%)</td><td>178 (45%)</td><td>381 (44%)</td><td>301 (45%)</td></tr><tr><td>At least one child</td><td>214 (56%)</td><td>59 (63%)</td><td>217 (55%)</td><td>490 (56%)</td><td>367 (55%)</td></tr><tr><td>Missing values</td><td>2</td><td>0</td><td>0</td><td>2</td><td>1</td></tr><tr><td><strong>Maternal BMI</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Maternal pre-gravid BMI, kg/m²</td><td>21.8 (20.1-24.6)</td><td>21.7 (19.6-24.4)</td><td>21.3 (19.7-23.9)</td><td>21.6 (19.8-24.3)</td><td>22.0 (20.1-24.8)</td></tr><tr><td>Missing values</td><td>11</td><td>0</td><td>4</td><td>15</td><td>14</td></tr><tr><td><strong>Mother's highest educational attainment</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>High school or lower</td><td>111 (29%)</td><td>31 (33%)</td><td>24 (6%)</td><td>166 (19%)</td><td>198 (30%)</td></tr><tr><td>Lower than bachelor degree</td><td>64 (17%)</td><td>19 (20%)</td><td>45 (11%)</td><td>128 (15%)</td><td>119 (18%)</td></tr><tr><td>Bachelor degree or higher</td><td>207 (54%)</td><td>44 (47%)</td><td>326 (83%)</td><td>577 (66%)</td><td>351 (53%)</td></tr><tr><td>Missing values</td><td>5</td><td>0</td><td>2</td><td>7</td><td>14</td></tr><tr><td><strong>Smoking status</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Non-smoker</td><td>260 (68%)</td><td>80 (85%)</td><td>367 (93%)</td><td>707 (81%)</td><td>459 (69%)</td></tr><tr><td>Smoker</td><td>122 (32%)</td><td>14 (15%)</td><td>28 (7%)</td><td>164 (19%)</td><td>209 (31%)</td></tr><tr><td>Missing values</td><td>9</td><td>0</td><td>59</td><td>68</td><td>22</td></tr><tr><td><strong>Method of delivery</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Caesarean section</td><td>56 (15%)</td><td>11 (12%)</td><td>55 (14%)</td><td>122 (14%)</td><td>92 (14%)</td></tr><tr><td>Vaginal</td><td>326 (85%)</td><td>83 (88%)</td><td>340 (86%)</td><td>749 (86%)</td><td>576 (86%)</td></tr><tr><td><strong>Gestational duration</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Duration, months</td><td>39.4 (38.4-40.4)</td><td>40.1 (39.0-41.0)</td><td>40.0 (39.0-40.0)</td><td>40.0 (39.0-40.4)</td><td>40.4 (39.4-40.4)</td></tr><tr><td><strong>Season of conception</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>January to March</td><td>86 (23%)</td><td>28 (30%)</td><td>99 (25%)</td><td>213 (24%)</td><td>169 (25%)</td></tr><tr><td>April to June</td><td>85 (22%)</td><td>28 (30%)</td><td>81 (21%)</td><td>194 (22%)</td><td>121 (18%)</td></tr><tr><td>July to September</td><td>105 (27%)</td><td>26 (28%)</td><td>95 (24%)</td><td>226 (26%)</td><td>206 (31%)</td></tr><tr><td>October to December</td><td>106 (28%)</td><td>12 (13%)</td><td>120 (30%)</td><td>238 (27%)</td><td>172 (26%)</td></tr><tr><td><strong>Population density*</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>High</td><td>72 (19%)</td><td>15 (16%)</td><td>246 (62%)</td><td>333 (38%)</td><td>350 (52%)</td></tr><tr><td>Intermediate</td><td>79 (21%)</td><td>26 (28%)</td><td>72 (18%)</td><td>177 (20%)</td><td>174 (26%)</td></tr><tr><td>Low</td><td>192 (50%)</td><td>52 (55%)</td><td>55 (14%)</td><td>299 (34%)</td><td>144 (22%)</td></tr><tr><td>Very low</td><td>37 (10%)</td><td>1 (1%)</td><td>4 (1%)</td><td>42 (5%)</td><td>0 (0%)</td></tr><tr><td>Missing values</td><td>2</td><td>0</td><td>18</td><td>20</td><td>0</td></tr><tr><td><strong>Placental cell-type proportions</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Endothelial</td><td>0.09 (0.06-0.11)</td><td>0.10 (0.08-0.12)</td><td>0.05 (0.03-0.07)</td><td>0.07 (0.04-0.10)</td><td>0.10 (0.08-0.13)</td></tr><tr><td>Hofbauer</td><td>0.05 (0.04-0.07)</td><td>0.05 (0.04-0.07)</td><td>0.05 (0.04-0.06)</td><td>0.05 (0.04-0.07)</td><td>0.03 (0.02-0.04)</td></tr><tr><td>Nucleated red blood cells</td><td>0.02 (0.01-0.02)</td><td>0.02 (0.02-0.02)</td><td>0.02 (0.01-0.02)</td><td>0.02 (0.01-0.02)</td><td>0.01 (0.01-0.01)</td></tr><tr><td>Stromal</td><td>0.12 (0.10-0.14)</td><td>0.13 (0.11-0.15)</td><td>0.10 (0.07-0.13)</td><td>0.12 (0.09-0.14)</td><td>0.14 (0.12-0.16)</td></tr><tr><td>Syncytiotrophoblast</td><td>0.60 (0.55-0.63)</td><td>0.53 (0.46-0.58)</td><td>0.68 (0.62-0.74)</td><td>0.62 (0.56-0.68)</td><td>0.63 (0.58-0.67)</td></tr></tbody></table>

Data are n (%) or median (IQR). The number of missing values that were imputed is reported for relevant covariates. The pooled discovery set includes the EDEN discovery set, the PELAGIE set, and the SEPAGES set. *Population density data are available from the Insee website.²⁸

<caption>**Table 1: Participants' characteristics**</caption>

the independent dataset, and a region was considered validated if it was detected by the comb-p algorithm with an inclusion p value of <0.05. Analyses were performed using R statistical software (version 4.2.2).

## Replication of previous studies

From a review of the literature,¹⁴⁻¹⁶ we identified 43 genic or intergenic candidate regions previously associated with prenatal exposure to air pollutants (PM₂.₅, PM₁₀, and
<table><thead><tr><th></th><th>Chromosome</th><th>Position</th><th>Gene</th><th>Location</th><th>Pollutant</th><th>Window of exposure</th><th>Direction of effect*</th><th>p value</th><th>Array type</th><th>Associated birth outcome (direction of association)</th><th>Relevant features for fetal growth</th></tr></thead><tbody><tr><td colspan="12"><strong>Whole population</strong></td></tr><tr><td>cq07268999</td><td>18</td><td>68340153</td><td>Intergenic</td><td>...</td><td>NO<sub>2</sub></td><td>Third trimester</td><td>Negative</td><td>1.55 x 10<sup>7</sup></td><td>EPIC</td><td>...</td><td>...</td></tr><tr><td>cq07268999</td><td>18</td><td>68340153</td><td>Intergenic</td><td>...</td><td>PM<sub>1.5</sub></td><td>Third trimester</td><td>Negative</td><td>7.79 x 10<sup>8</sup></td><td>EPIC</td><td>...</td><td>...</td></tr><tr><td>cq15999077</td><td>5</td><td>1407523</td><td>SLC6A3</td><td>Body</td><td>PM<sub>1.5</sub></td><td>Third trimester</td><td>Negative</td><td>7.67 x 10<sup>8</sup></td><td>450K and EPIC</td><td>Birth length (+)</td><td>...</td></tr><tr><td>cq16359142*</td><td>11</td><td>278092</td><td>NLRP6</td><td>TSS1500</td><td>NO<sub>2</sub></td><td>Third trimester</td><td>Positive</td><td>3.23 x 10<sup>7</sup></td><td>450K and EPIC</td><td>...</td><td>Encodes a protein mediating inflammatory responses</td></tr><tr><td>cq19029991</td><td>6</td><td>41996535</td><td>CCND3</td><td>5' untranslated region</td><td>PM<sub>1.5</sub></td><td>First trimester</td><td>Positive</td><td>1.41 x 10<sup>7</sup></td><td>EPIC</td><td>Birthweight (+); gestational duration (+)</td><td>...</td></tr><tr><td>cq19029991</td><td>6</td><td>41996535</td><td>CCND3</td><td>5' untranslated region</td><td>PM<sub>1.5</sub></td><td>First trimester</td><td>Positive</td><td>1.71 x 10<sup>8</sup></td><td>EPIC</td><td>Birthweight (+); gestational duration (+)</td><td>...</td></tr><tr><td colspan="12"><strong>Male population</strong></td></tr><tr><td>cq16906346</td><td>6</td><td>24646782</td><td>KIAA0319</td><td>TSS1500</td><td>PM<sub>1.5</sub></td><td>First trimester</td><td>Positive</td><td>3.91 x 10<sup>10</sup></td><td>450K and EPIC</td><td>...</td><td>Development of cerebral cortex and involved in dyslexia</td></tr><tr><td>cq23931880</td><td>10</td><td>134552766</td><td>INPP5A</td><td>Body</td><td>NO<sub>2</sub></td><td>Second trimester</td><td>Positive</td><td>1.94 x 10<sup>8</sup></td><td>EPIC</td><td>...</td><td>...</td></tr><tr><td>cq00944873</td><td>6</td><td>24646780</td><td>KIAA0319</td><td>TSS1500</td><td>PM<sub>1.5</sub></td><td>First trimester</td><td>Positive</td><td>2.75 x 10<sup>8</sup></td><td>EPIC</td><td>...</td><td>Development of cerebral cortex and involved in dyslexia.</td></tr><tr><td colspan="12"><strong>Female population</strong></td></tr><tr><td>cq23388029</td><td>2</td><td>102003306</td><td>CREG2</td><td>Body</td><td>PM<sub>1.5</sub></td><td>Second trimester</td><td>Positive</td><td>1.42 x 10<sup>8</sup></td><td>450K and EPIC</td><td>...</td><td>Oxydoreductase activity</td></tr><tr><td>cq13157793†</td><td>15</td><td>24920450</td><td>NPAP1</td><td>TSS200</td><td>PM<sub>1.5</sub></td><td>Second trimester</td><td>Negative</td><td>2.68 x 10<sup>8</sup></td><td>450K and EPIC</td><td>...</td><td>Imprinted gene with paternal allele expressed in fetal brain</td></tr><tr><td>cq04043455</td><td>10</td><td>131669461</td><td>EBF3</td><td>Body</td><td>PM<sub>1.5</sub></td><td>Third trimester</td><td>Positive</td><td>5.76 x 10<sup>8</sup></td><td>450K and EPIC</td><td>...</td><td>Involved in bone development and neurogenesis</td></tr></tbody></table>

The EPIC array was the Infinium HumanMethylationEPIC BeadChip, and the 450K array was the Infinium HumanMethylation450 BeadChip. Models were adjusted for: maternal age, child sex, pre-gravid BMI, maternal educational level attainment, maternal smoking during pregnancy, the centre of recruitment, placental cell type composition, and latent factors accounting for technical effects and unmeasured confounding. CpG-5'-C-phosphate-G-3': FDR=false discovery rate. *Appendix 2 tab 3. †Validated in the independent dataset (nominal p value of <0.05 and same direction of effect).

<caption>**Table 2:** CpGs significantly associated (FDR of <0.05) with prenatal air pollution exposure concentration in the whole-population analysis and top three differentially methylated positions (FDR of <0.05) discovered in sex-stratified analyses</caption>
$NO_2$) in the placenta. We then tested whether these loci were replicated in our population. A candidate region was considered replicated in our data if, for at least one air pollutant and one window of exposure, it overlapped with a DMR detected by the comb-p algorithm (inclusion p value <0.05 and at least two CpGs).²⁵

## Role of the funding source

The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

## Results

The main characteristics of the study population are reported in table 1. The median exposure to air pollutants during the whole pregnancy was 19·7 µg/m³ for $NO_2$, 18·1 µg/m³ for $PM_{10}$, and 12·1 µg/m³ for $PM_{2.5}$. Distributions are shown in appendix 1 (p 6). $NO_2$ exposure concentrations during the first trimester were negatively correlated with $NO_2$ exposure concentrations during the third trimester. In contrast, $PM_{10}$ and $PM_{2.5}$ exposure concentrations during the first trimester were not correlated with $PM_{10}$ and $PM_{2.5}$ concentrations during the third trimester. For the three pollutants, average concentrations over the whole pregnancy were most correlated with average concentrations during the second trimester, compared with the average concentrations during the first or third trimester. Spearman correlation of exposure concentrations between contiguous trimesters of pregnancy varied between 0·30 and 0·55 (appendix 1 p 7).

The whole-population analysis identified four distinct CpG sites significantly associated (FDR of <0.05) with at least one air pollutant in the discovery cohort (table 2). Among them, one validated CpG located near the transcription start site of the inflammatory gene *NLRP6* was validated and was significantly and positively associated with $NO_2$ concentrations during the third trimester (appendix 1 p 8; appendix 2 tab 3). When stratifying analyses by child sex, we detected 650 CpG sites significantly associated (FDR <0.05) with air pollution exposures (150 in female infants and 469 in male infants), including 92 sites specific to female infants, 196 sites specific to male infants, and 362 sites consistent in both sexes (figure 2A). Overall, 35% (66/189) of the 189 CpG sites available on the 450K array were validated. In female infants, most DMPs were associated with exposure during the third trimester of pregnancy (134 of 150 CpGs, including 120 CpGs associated with $NO_2$, 19 with $PM_{10}$, and 20 with $PM_{2.5}$) and 17–36% across air pollutant types were validated. In male infants, most CpGs were associated with the average pollution exposure over the whole pregnancy (281 of 469 CpGs, including 280 CpGs associated with $NO_2$) and the first trimester (237 of 469 CpGs, including 235 CpGs associated with $NO_2$) and 28% were validated (appendix 1 pp 9–10; appendix 2 tab 2). Both in male and female

infants, $NO_2$ was the pollutant with the greatest number of significant associations with CpG sites.

Notably, sex-linked DMPs were found exclusively in male infants, including one CpG on the Y chromosome and 22 on the X chromosome (figure 2B; appendix 2 tab 3). These DMPs were related to 13 annotated genes, among which six have been implicated in intellectual developmental disorders or the nervous system, or both, and three have been implicated in the immune system (figure 2B). We observed that, for the whole pregnancy, 18% of the most significant CpGs (p value of <0.001) were specific to $NO_2$ exposure, and 7% were specific to $PM_{2.5}$ and $PM_{10}$, and that 21–50% were specific to the first or third trimester exposure concentrations (appendix 1 p 11).

In whole-population regional analyses, we identified 28 DMRs annotated to genes that were significantly associated with at least one air pollutant (figure 3A). These encompassed nine genes involved in neurodevelopment, seven in the immune system, and five in metabolism (figure 3A; appendix 1 p 6). Furthermore, four DMRs overlapped with imprinted genes, including *FOXG1*, *DIRAS3*, *OTX1*, and *GET1*. Most DMRs (17 of 28) were consistently associated with the three air pollutants, four regions were more specifically associated with $PM_{10}$ and $PM_{2.5}$ concentrations, three regions with both $NO_2$ and $PM_{2.5}$, and five regions with $NO_2$ (figure 3A). After stratifying by child sex, we detected 87 regions associated with at least one pollutant in male infants and 66 regions in female infants, including 80 regions which were consistent between male and female infants as well as 34 male-specific and female-specific regions (figure 3B). Overall, three of 28 DMRs found in the whole-population analyses, five of 87 in male infants, and eight of 66 in female infants were independently validated (figure 3A, B; appendix 2 tab 4–5).

Birthweight and gestational duration were associated to one of the four DMPs detected in the whole-population analysis (cg19029991) annotated to the *CCND3* gene, which is known to play a role in cell cycle regulation. In sex-stratified analyses, more than 30% of the PAPE-associated DMPs were also significantly associated with at least one birth outcome (appendix 1 p 12; appendix 2 tab 6–7). In male infants, half of these DMPs were significantly associated with gestational duration (p value <0.05). In most cases, the CpGs associated with a birth outcome tended to be positively associated with PAPE and were further associated with either lower birth length, increased birthweight, or a longer gestational duration (figure 2C). In female infants, 71% of the associations with birth outcomes were related to birth length and head circumference (p value <0.05; figure 2C). We noted that the CpGs associated with increased head circumference tended to be inversely associated with PAPE, whereas CpGs associated with a higher birth length tended to be associated with increased PAPE (appendix 2 tab 7).

Regarding DMRs, nine regions were associated with at least one birth outcome in the whole population;
A Overlap between sex-stratified DMPs

92/650
(14%)

96/650
(30%)

13 male-specific X-linked DMPs

C Birth outcomes associated with DMPs

Birth outcome

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

B DNAm changes on chromosomes X and Y

Male

NO₂
P T1T2T3

PM₁₀
P T1T2T3

PM₂.₅
P T1T2T3

Female

NO₂
P T1T2T3

PM₁₀
P T1T2T3

PM₂.₅
P T1T2T3

Biological function

Neurodevelopment

Immune system

Metabolism

cg04354869 (CTPS2-S100G)

cg18763008 (FAM120C)

cg10995081 (GRIPAP1)

cg02370638 (-)

cg06483826 (-)

cg07945801 (-)

cg23929224 (-)

cg25301621 (MAGT1)

cg11585623 (XIAP)

cg27446382 (ARSH)

cg17507888 (ATP7A-PGAM4)

cg15675045 (BCORL1)

cg21435544 (BCYRN1-TAF1)

cg16716983 (DRP2)

cg27065374 (EFNB1)

cg08156396 (-)

cg11826690 (-)

cg14191874 (-)

cg16806953 (-)

cg05452887 (NDUFA1-RNF113A)

cg05097282 (UPF3B)

cg27213196 (UPF3B)

cg24211806 (PRKY)

D Enriched pathways

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /><figcaption>**Figure 2:** Overview of placental DMPs found associated with pregnancy air pollutant exposure</figcaption></figure>

(A) Venn diagram showing the overlap between DMPs detected or replicated, or both, in the male and female analyses. A CpG detected in one sex stratum (FDR of <0.05) was considered replicated in the other stratum if the nominal p value was less than 0.05. (B) Significant (FDR of <0.05) hits found on sex chromosomes X and Y. A colour of red or blue indicates that the probe was significant (p value <0.05) and stands for the direction of the effect (red, positive; blue, negative; and yellow, non-significant). A white X indicated an FDR of 0.05. (C) Proportion of associated birth outcomes among the 30% of DMPs significantly associated with at least one birth outcome (p value <0.05). (D) Kyoto Encyclopedia of Genes and Genomes and Reactome pathways significantly enriched (FDR of <0.05) in DMPs (p value <0.001). CpG=5'-C-phosphate-G-3'. DMP=differentially methylated position. DNAm=DNA methylation. FDR=false discovery rate. P=pregnancy. T1=first trimester. T2=second trimester. T3=third trimester.

ten regions were associated with at least one birth outcome in the male strata; and three regions were associated with at least one birth outcome in the female strata. These included the DMR overlapping the gene GGT6, also associated with gestational duration, and the genes *TXNDC15* and *CCDC62* (in male infants) that were associated with birthweight (figure 3C). For all CpGs included in the DMR within the gene *TXNDC15*, the average PM₂.₅ concentrations over the whole pregnancy were associated with decreased DNAm concentrations, which were further associated with a lower birthweight.

The top CpGs (p value <0.001) were enriched within genes (within the body, 3□ untranslated region, and

5□ untranslated region). These CpGs thus tended to be open sea and shelves CpGs, and were accordingly depleted in intergenic locations, promoters, CpG islands, and shores (appendix 2 tab 8). Genes harbouring DMPs were significantly enriched for targets of a set of transcription factors, including *KDM2B*, *SUZ12*, *EP300*, and *PPARG*, known to be implicated in normal fetal growth and metabolism (appendix 2 tab 9).<sup>29,30</sup> In line with this, in the database of genotypes and phenotypes, these genes were enriched for growth-associated and cardiometabolic outcomes including BMI, body height, blood pressure, stroke, hypertension, cholesterol, and obesity (appendix 2 tab 10).
A Map of DMRs across pollutants and windows

<figure><img src="image_15.png" /></figure>

**Biological function**

Neurodevelopment

Immune system

Metabolism

Related pathway

B DMRs significantly associated with fetal growth

<figure><img src="image_16.png" /></figure>

Gene TXNDC15

<figure><img src="image_17.png" /></figure>

Gene CCDC62 in males

<figure><img src="image_18.png" /></figure>

C Overlap between sex-stratified DMRs

<figure><img src="image_19.png" /><figcaption>**Figure 3:** Placental DMRs found associated with pregnancy air pollutant exposure</figcaption></figure>

(A) DMRs annotated to genes significantly associated with at least one air pollutant in the main analysis (including 28 regions). A white X indicates that the DMR was detected by the comb-p algorithm. The colour (red or blue) indicates that at least one probe was significant (p value <0.05) and indicates the direction of the effect (red, positive and blue, negative). Yellow indicates that neither the DMR nor any included CpGs were significant for the corresponding air pollutant and window of exposure. Black indicates the DMR was also significantly associated with the corresponding birth outcome in our data. (B) Effect sizes of the association between DNAm concentrations and air pollution and between DNAm and a significant birth outcome for the CpG spanning genes GGT6, TXNDC15, and CCDC62. (C) Overlap between DMRs found in sex-stratified analyses. We detected 35 male-specific regions, 34 female-specific regions, and 80 regions detected (ie, identified by the comb-p algorithm, with at least three probes and a false discovery rate-adjusted p value of <0.05) in one stratum and consistent (ie, encompassed at least one CpG with a nominal p value of <0.05) in the other stratum. Names of the genes annotated to the validated DMRs are highlighted. BL=birth length. BW=birthweight. CpG=5'-C-phosphate-G-3'. DMR=differentially methylated regions. DNAm=DNA methylation. GA=gestational duration. HC=head circumference. P=pregnancy. T1=first trimester. T2=second trimester. T3=third trimester. TSS=transcription start site. *Validated DMRs.

In the whole-population analysis, genes annotated to the top CpGs associated with NO₂ concentrations during the first trimester were significantly enriched (FDR of <0.05) for the pathways cholinergic synapse, insulin regulation, and energy metabolism. In sex-stratified analyses, we found significant enrichment for several neurodevelopmental pathways, including protein-protein interactions at synapses in both strata. In addition, we found four sex-specific pathways, including: the calcium signalling pathway (PM₁₀ concentrations during the third trimester) and neurexins and neuroligins pathways (PM₁₀ concentrations during the third and the first trimester) in female infants (appendix 1 p 13) and the neuronal system and extracellular matrix organisation

pathways in male infants (NO₂ and PM₁₀ concentrations during the second trimester; figure 2D; appendix 2 tab 11–12).

Overall, CpG-wise, regional and pathway enrichment results were robust to sensitivity analyses (appendix 1 pp 13–14; appendix 2 tab 13–14). Two candidate genes were replicated in our whole-population analyses, including one with the same negative direction of the effect (DAXX, a gene involved in hypoxia). In sex-stratified analyses, we found significant associations with 11 gene candidates, including five with the same direction of the effect (DAXX, ZNF563, LMF1, ADCK5, and BID; appendix 2 tab 15). Notably, in female infants, our analysis replicated a second hypoxia gene, ADORA2B,
but with opposite direction of the effect to a previous
analysis.¹⁴

Discussion

In this work based on the largest pool of cohorts to date, including 1539 participants recruited from three French mother–child cohort studies, we identified both site-specific and regional changes in placental DNAm in association with prenatal exposure to NO₂, PM₁₀, and PM₂.₅, some of them being specific to male or female infants and others being seen in both sexes. Overall, our results showed consistent effects of the three air pollutants on the placental methylome, and highlighted early pregnancy (in male infants) and late pregnancy (in female infants) as the windows of increased sensitivity of the placental epigenome. Furthermore, our findings suggest that PAPE might especially induce modifications in the placental methylome with regard to genes involved in the brain, in nervous and immune system development, in metabolism, and in genes associated with birth outcomes.

In both sexes, we found that the epigenetic response to
PAPE predominantly occurred in genes involved in
immunity and neural development, which supports
a growing body of studies associating PAPE to impaired
neurodevelopment and reduced cognitive capacities.⁴,³¹
But our study also suggests additional sexually dimorphic
effects of PAPE on developmental pathways and windows
of sensitivity. Significant X-linked alterations were
exclusively detected in male infants and were mostly
related to genes involved in intellectual and nervous
system development. Moreover, in male infants, overall
DNAm alterations were enriched for genes involved in
the organisation of the extracellular matrix, which is
crucial for neural development.³² This finding is in line
with some of our results in the EDEN and PELAGIE
cohorts showing that cognitive function in male infants
was more susceptible to the effects of air pollution
exposure than in female infants, and suggests that
placental DNAm changes might mediate these
associations.³¹ In female infants, DNAm modifications
were instead enriched for genes related to calcium
signalling, a pathway playing a major role in fetal devel-
opment and in the regulation of oxidative stress. Its
deregulation could thus result in a wide range of
functional alterations including developmental defects,
miscarriage, and pre-eclampsia.³³ In line with this,
especially in female infants, our results supported
previous observations that air pollution concentrations
are associated with DNAm changes at CpG sites
annotated to the genes DAXX and ADORA2B, which
have been involved in hypoxia and preeclampsia.¹⁴
Altogether, these findings point to the existence of sexual
dimorphic responses of the placental methylome to
PAPE, which could contribute to fetal sex differences
in the susceptibility to adverse developmental and
pregnancy outcomes.

Because pregnancy is a phase of rapid child
development, aberrant epigenetic changes induced by
PAPE could promote abnormal fetal growth and long-
term metabolic alterations.⁴,¹⁶ In line with this, we noted
that a significant part of PAPE-associated DNAm changes
overlapped with genes related to weight and height,
body composition, cardiometabolic outcomes, and
metabolism, including the paternally imprinted gene
DIRAS3, related to growth suppression and neonatal
diabetes. Overall, we detected four DMRs overlapping
with an imprinted gene in the whole population. In the
placenta, imprinted genes are involved in nutritional
exchanges between the mother and the fetus and their
expression could be altered by environmentally induced
DNAm modifications, as has been shown in humans.³⁴
Moreover, epigenetically altered genes are enriched for
targets of the transcription factors EP300 and PPARG,
which play pivotal roles in placental trophoblast dif-
ferentiation and fetal adipocyte and immune cells
differentiation, respectively.²⁹,³⁰ We further observed that
more than a third of PAPE-associated loci were also
associated with fetal growth indicators in our population.
These included the gene TXNDC15, which might
mediate the relationship between increased pregnancy
PM₂.₅ exposure concentrations and a lower birthweight.
Notably, this gene was recently found associated with
childhood obesity in a study conducted in the Hong Kong
Children of 1997 birth cohort.³⁵ Overall, these find-
ings provide additional insights into the molecular
mechanisms through which PAPE could dysregulate
fetal growth programming and support numerous
previous observations that PAPE can prove particularly
harmful to later-life cardiometabolism.⁴

The present study has some limitations. First, regard-
ing exposure assessment, we note that the CHIMERE
model used to estimate NO₂ exposure concentrations
showed lower performances and thus is more prone to
measurement error in rural areas (overestimation of
60–80% depending on the year of assessment due to
a low number of monitoring stations) compared with
urban areas. More generally, ambient exposures are
more prone to measurement error than personal
exposures (eg, through personal monitors) but they are
expected to be less flawed by confounding bias and to
eventually lead to more reliable detections.³⁶ Secondly,
although a substantial part of our detections were
validated in a large independent dataset, the validation
had to be limited to the probes common to the 450K
and EPIC arrays. Furthermore, all our analyses were
conducted on individuals from French cohorts and thus
would need to be replicated in populations from other
geographical areas and genetic backgrounds.

Despite these limitations, our study has crucial strengths.
First, to our knowledge, this is the first independently
validated analysis investigating and comparing the effect
of prenatal exposure to the three major air pollutants, NO₂,
PM₁₀, and PM₂.₅. Secondly, it relies on an unprecedented
large sample of participants recruited in four French regions, which included both rural and urban populations exposed to a wide spectrum of air pollution concentrations. Pooling the three cohorts brought a diverse study population and increased exposure contrasts and sample size, while maintaining a consistent and harmonised approach in exposures, outcomes, and covariate assessment methods across all cohorts. Notably, placental PAPE-associated DNAm signatures were shown to differ from those observed in more accessible matrices, such as cord blood.⁶,⁷,¹⁵ In the placenta, environmentally induced DNAm modifications are significant, although limited in magnitude, and might alter gene expression.³⁷ Notably, placental DNAm changes of imprinted genes might influence fetal growth.³⁴ Furthermore, placental DNAm changes are expected to better reflect changes occurring in the developing brain, which might have been decisive in our ability to observe salient and predominant evidence of epigenetic alterations in neurodevelopmental pathways.⁹

In conclusion, on the basis of a robust analysis framework focusing on informative DNAm signals, our results support the hypothesis that, by altering epigenetic mechanisms, PAPE might affect fetal growth and neurodevelopment through a wide range of biological pathways, and shows both sex-specific and shared effects. Further research will be needed to investigate whether these placental epigenetic changes persist into childhood and the mediating role of these compelling candidates in the effects of air pollution on child development.

### Contributors

LB and JL designed the current study and wrote the manuscript. BH, CC, and RS were the coordinators of the EDEN, PELAGIE, and SEPAGES cohorts, respectively. DV contributed to DNA extraction for the EDEN cohort. JT supervised the measurement of DNA methylation. IH, IK, ES, and AG modelled and computed the air pollution exposures. ES and AG curated the database. LB performed the statistical analyses. LB, AC, and AG assessed and verified the data. AG double-checked the statistical analyses. All authors critically reviewed and approved the manuscript. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.

### Declaration of interests

We declare no competing interests.

### Data sharing

The datasets analysed in the presented study are not publicly available because they contain information that could compromise the research participants' privacy or consent. However, they are available upon reasonable request to the corresponding authors. The code used to generate the results is available at https://github.com/lbroseus.

### Acknowledgments

This work was supported by a grant from the French Agency for National Research (grant number ETAPE, ANR-18-CE36-0005). DNA methylation measurements were obtained thanks to grants from the Fondation de France (grant numbers 2012-00031593, 2012-00031617, and 00089597), the Plan Cancer (CancAir project), and the Fondation pour la Recherche Médicale (EPImEx project). The EDEN cohort was supported by the Foundation for Medical Research, the National Agency for Research, the National Institute for Research in Public Health (National Institute for Research in Public Health: Très Grand Infrastructure de Recherche cohorte santé 2008 programme), the French Ministry of Health, the French Ministry of Research, the Inserm Bone and Joint Diseases National Research and Human Nutrition National Research Programmes,

Paris-Sud University, Nestlé, the French National Institute for Population Health Surveillance, the French National Institute for Health Education, the European Union FP7 programmes (programme number FP7/2007-2013, for the HELIX, ESCAPE, ENRIECO, and Medall projects), the Diabetes National Research Programme (through a collaboration with the French Association of Diabetic Patients), the French Agency for Environmental Health Safety, Mutuelle Générale de l'Éducation Nationale, the French National Agency for Food Security, and the French-speaking Association for the Study of Diabetes and Metabolism. The PELAGIE cohort was supported by Inserm, the French Ministry of Health, the French Ministry of Labor, the French National Agency for Research, the French Agency for Environmental Health Safety, the Fondation de France, the French National Institute for Population Health Surveillance, and the French Ministry of Ecology. The SEPAGES cohort was supported by the European Research Council (grant number 311765-E-DOHaD), the European Community's Seventh Framework Programme (FP7/2007-2016 - grant number 308,333-892 HELIX), the European Union's Horizon 2020 research and innovation programme (grant number 874,583 ATHLETE Project and grant number 825,712 OBERON Project), the French Research Agency Agence Nationale de la Recherche or French Research Agency (PAPER project ANR-12-PDOC-0029, SHALCOH project ANR-14-CE21-0007, ANR-15-IDEX-02, and ANR-15-IDEX5, GUMME project, ETAPE project ANR-18-CE36-005, and EDEn project ANR-19-CE36-0003-01), the French Agency for Food, Environmental and Occupational Health & Safety (CNAP project EST-2016-121, PENDORE project EST-2016-121, and HyPAxE project EST-2019/1/039), the Plan Cancer (CancAir project), the French Cancer Research Foundation (Association de Recherche sur le Cancer), the French Endowment Fund Agir pour les Maladies Chroniques for chronic diseases (projects PRENAPAR and LCI-FOT), the French Endowment Fund for Respiratory Health, and the French Fund of the Fondation de France (CLIMATHES-00081169 and SEPAGES 5-00099903). We acknowledge the commitment of the EDEN mother-child cohort study group. We also acknowledge the commitment of the SEPAGES study group, including: Sam Bayat, Claire Philippat, Joane Quentin, Valérie Siroux, Marion Ouidir, Sarah Lyon-Caen, and Johanna Lepeule. We thank the many people that assisted with the cohorts, especially the participants of the three cohorts. We thank the French Institute for Industrial Environment and Risks (INERIS) for making available, in open data, the air pollutant concentration data from which the NO₂ concentrations used in this study were taken.

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