# Ambient air pollution and childhood obesity from infancy to late childhood: An individual participant data meta-analysis of 10 European birth cohorts

Sarah Warkentin, Serena Fossati, Sandra Marquez, Anne-Marie Nybo

Andersen, Sandra Andrusaityte, Demetris Avraam, Ferran Ballester, Tim

Cadman, Maribel Casas, Montserrat de Castro, et al.

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Sarah Warkentin, Serena Fossati, Sandra Marquez, Anne-Marie Nybo Andersen, Sandra Andrusaityte, et al.. Ambient air pollution and childhood obesity from infancy to late childhood: An individual participant data meta-analysis of 10 European birth cohorts. Environment International, 2025, 200 (6), pp.737-754. 10.1016/j.envint.2025.109527. hal-05075860

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Ambient air pollution and childhood obesity from infancy to late childhood: An individual participant data meta-analysis of 10 European birth cohorts

Sarah Warkentin, Serena Fossati, Sandra Marquez, Anne-Marie Nybo
Andersen, Sandra Andrusaityte, Demetris Avraam, Ferran Ballester, Tim
Cadman, Maribel Casas, Montserrat de Castro, Leda Chatzi, Ahmed
Elhakeem, Antonio d'Errico, Mònica Guxens, Regina Grazuleviciene,
Jennifer R. Harris, Carmen Iñiguez Hernandez, Barbara Heude, Elena
Isaevska, Vincent W.V. Jaddoe, Marianna Karachaliou, Aitana Lertxundi,
Johanna Lepeule, Rosemary R.C. McEachan, Johanna L. Thorbjørnsrud
Nader, Marie Pedersen, Susana Santos, Mariska Slofstra, Euripides G.
Stephanou, Morris A. Swertz, Tanja Vrijkotte, Tiffany C. Yang, Mark
Nieuwenhuijsen, Martine Vrijheid

PII: S0160-4120(25)00278-8

DOI: https://doi.org/10.1016/j.envint.2025.109527

Reference: EI 109527

To appear in: *Environment International*

Received Date: 27 November 2024

Revised Date: 6 May 2025

Accepted Date: 9 May 2025

Please cite this article as: S. Warkentin, S. Fossati, S. Marquez, A.N. Andersen, S. Andrusaityte, D. Avraam, F. Ballester, T. Cadman, M. Casas, M. de Castro, L. Chatzi, A. Elhakeem, A. d'Errico, M. Guxens, R. Grazuleviciene, J.R. Harris, C.I. Hernandez, B. Heude, E. Isaevska, V.W.V. Jaddoe, M. Karachaliou, A. Lertxundi, J. Lepeule, R.R.C. McEachan, J.L. Thorbjørnsrud Nader, M. Pedersen, S. Santos, M. Slofstra, E.G. Stephanou, M.A. Swertz, T. Vrijkotte, T.C. Yang, M. Nieuwenhuijsen, M. Vrijheid, Ambient air pollution and childhood obesity from infancy to late childhood: An individual participant data meta-analysis of 10 European birth cohorts, *Environment International* (2025), doi: https://doi.org/10.1016/j.envint.2025.109527

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# Ambient air pollution and childhood obesity from infancy to late childhood: An individual participant data meta-analysis of 10 European birth cohorts

Sarah Warkentin¹⁻³*, Serena Fossati¹⁻³*, Sandra Marquez¹⁻³, Anne-Marie Nybo Andersen⁴, Sandra Andrusaityte⁵, Demetris Avraam⁴, Ferran Ballester⁶, Tim Cadman¹,⁷, Maribel Casas¹⁻³, Montserrat de Castro¹⁻³, Leda Chatzi⁸, Ahmed Elhakeem⁹, Antonio d'Errico¹⁰,¹¹, Mònica Guxens¹⁻³,¹², Regina Grazuleviciene⁵, Jennifer R Harris¹³, Carmen Iñiguez Hernandez³,¹⁴, Barbara Heude¹⁵, Elena Isaevska¹⁰,¹², Vincent WV Jaddoe¹⁶,¹⁷, Marianna Karachaliou¹,¹⁸, Aitana Lertxundi³,¹⁹,²⁰, Johanna Lepeule²¹, Rosemary RC McEachan²²,²³, Johanna L Thorbjørnsrud Nader²⁴, Marie Pedersen⁴, Susana Santos¹⁶,¹⁷,²⁵,²⁶, Mariska Slofstra⁷, Euripides G. Stephanou²⁷, Morris A. Swertz⁷, Tanja Vrijkotte²⁸, Tiffany C Yang²²,²³, Mark Nieuwenhuijsen¹⁻³, Martine Vrijheid¹⁻³

*Joint co-authorship

1. ISGlobal, Barcelona, Spain

2. Universitat Pompeu Fabra (UPF), Barcelona, Spain

3. Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain

4. Department of Public Health, University of Copenhagen, Copenhagen, Denmark

5. Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania

6. FISABIO-University Jaume I-Universitat de València, Valencia, Spain

7. Department of Genetics and Genomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands

8. University of South California, Los Angeles, USA

9. University of Bristol, Bristol, UK

10. Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy

11. Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht, the Netherlands

12. Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, the Netherlands

13. Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway

14. Department of Statistics and Operational Research, Universitat de València, Valencia, Spain

15. Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), F-75004 Paris, France

16. Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
17. Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands

18. Clinic of Preventive and Social Medicine, Medical School, University of Crete, Greece

19. University of the Basque Country, Bilbao, Spain

20. Biogipuzkoa Health Research Institute, Donostia-San Sebastian, Spain

21. Université Grenoble Alpes, Inserm, CNRS, Team of Environmental Epidemiology applied to Development and Respiratory Health, IAB, Grenoble, France

22. Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK

23. Public Health Improvement, UK

24. Department of Genetics and Bioinformatics, Division of Health Data and Digitalisation, Norwegian Institute of Public Health, Oslo, Norway

25. EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal

26. Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), Universidade do Porto, Porto, Portugal

27. Department of Chemistry, University of Crete, Greece

28. University of Amsterdam, Amsterdam, the Netherlands

**Corresponding author:** Sarah Warkentin. ISGlobal - Barcelona Institute for Global Health. C/ Doctor Aiguader, 88. 08003 - Barcelona. +34 93 214 7300. E-mail: sarah.warkentin@isglobal.org
**Abstract**

Ambient air pollution may contribute to childhood obesity through various mechanisms. However, few longitudinal studies examined the relationship between pre- and postnatal exposure to air pollution and obesity outcomes in childhood. We aimed to investigate the association between pre- and postnatal exposure to air pollution and body mass index (BMI) and the risk of overweight/obesity throughout childhood in European cohorts. This study included mother-child pairs from 10 European birth cohorts (n=37111 (prenatal), 33860 (postnatal)). Exposure to nitrogen dioxide (NO₂) and fine particulate matter with aerodynamic diameter <2.5µm (PM₂.₅) was estimated at the home addresses during pre- and postnatal periods (year prior outcome assessment). BMI z-scores (continuous) and overweight/obesity (categorical: zBMI≥+2 (<5 years) or ≥+1 (≥5 years) standard deviations) were derived at 0-2, 2-5, 5-9, 9-12 years. Associations between air pollution exposure and zBMI were estimated separately for each pollutant and cohort using linear and logistic longitudinal mixed effects models, followed by a random-effects meta-analysis. The overweight/obesity prevalence ranged from 12.3-40.5% between cohorts at 0-2 years, 16.7-35.3% at 2-5 years, 12.5-40.7% at 5-9 years, and 10.7-43.8% at 9-12 years. Results showed no robust associations between NO₂ exposure and zBMI or overweight/obesity risk. Exposure to PM₂.₅ during pregnancy was associated with 23% (95CI% 1.05;1.37) higher overweight/obesity risk across childhood, and higher zBMI and overweight/obesity risk at 9-12 years. Heterogeneity between cohorts was considerable (I²:25-
89%), with some cohort-specific associations; e.g., pre- and postnatal exposure to PM<sub>2.5</sub> was associated with lower zBMI across age periods in UK cohorts (ALSPAC and BiB), while postnatal exposure to PM<sub>2.5</sub> and NO<sub>2</sub> was associated with higher zBMI in one Dutch cohort (Generation R). Overall, this large-scale meta-analysis suggests that prenatal PM<sub>2.5</sub> exposure may be associated with adverse childhood obesity outcomes, but provides no evidence to support an effect of postnatal air pollution exposure, although cohort-specific associations were observed.

**Key-words:** Air pollution, child, pediatric obesity, urban, meta-analysis.

# 1. Introduction

The worldwide prevalence of childhood obesity has quadrupled since the 1990s, when 65.1 million girls and 94.2 million boys in school-age were estimated to have obesity, with rates in constant rise (Phelps et al., 2024). Obesity is a multifactorial condition and consequence of a complex interaction between genes, lifestyle, social and psychological determinants (An, Ji, et al., 2018; González-Muniesa et al., 2017). Overweight and obesity in early life is associated with a higher risk of developing adult obesity, poorer health and lower quality of life (World Health Organization, 2016), which may persist into later life if not prevented. The identification of modifiable risk factors of these conditions represents an important opportunity for public health interventions (World Health Organization (WHO), 2021).

Exposure to combustion related pollutants, such as particulate matter with aerodynamic diameter <2.5 µm (PM<sub>2.5</sub>) <10µm (PM<sub>10</sub>), as well as nitrogen dioxide (NO<sub>2</sub>) is widespread, with approximately 56% of the population worldwide (4.4 billion people) living in more polluted, urban areas (Worldbank, 2023). Long-term exposure to these pollutants, in particular to nitrogen oxides (NO<sub>x</sub>), NO<sub>2</sub> (Malacarne et al., 2022; Wang et al., 2021), and particulate matter (PM<sub>2.5</sub> and
PM₁₀) (Parasin et al., 2021; Zheng et al., 2024), may be associated with increased risk of overweight and obesity in children, as described in four systematic reviews published in the past four years. Several direct and indirect mechanisms have been proposed to explain these associations, including inflammation (Jerrett et al., 2014), decreased glucose utilization in skeletal muscles (Toledo-Corral et al., 2018), endocrine system disruption (Darbre, 2018), respiratory diseases and decreased lung function (Wang et al., 2019), besides changes in basal metabolism and appetite control of the central nervous system (McConnell et al., 2016). Indirectly, areas in which motorized road traffic and air pollution levels are high, with limited safe, recreational areas and/or visible smog, noise, for example, may lead to behavior changes in the population, which may discourage outdoor physical activities and, consequently, increase weight gain (An, Ji, et al., 2018; Tainio et al., 2021).

There are critical windows of exposure to air pollution, including pregnancy and birth to 1 year of age, which are associated with development disorders, cancer, obesity and other conditions in childhood (Spencer-Hwang et al., 2023); thus, it is possible that exposure to air pollutants during these periods may also have disproportionate impacts on child growth and adiposity. However, evidence is inconsistent. Prenatal exposure to ambient air pollution has been associated with fetal growth restriction and low birth weight (Bekkar et al., 2020; Health Effects Institute, 2022; Hung et al., 2022; Pedersen et al., 2013), decreased weight and a reduced risk of being in a trajectory with accelerated body mass index (BMI) gain at the age of 4 (Fossati et al., 2020). In addition, prenatal exposure has been associated with variable changes in growth rates, with increases in the growth rate from the 3rd trimester of gestation up to 3 months of infancy and decreases between 6 months and 2 years in a study from the US (Ji et al., 2023). Others, however, did not find significant associations between prenatal traffic-related air pollution exposure and childhood adiposity in the first years of life (Frondelius et al., 2018; Starling et al., 2020). Regarding postnatal air pollution exposure, several longitudinal and cross-sectional studies have reported associations between exposure to pollutants, such as NO₂, and higher weight in children and adolescents from China (Huang et al., 2019), the Netherlands (Bloemsma et al., 2019) and Spain (De Bont et al., 2019; Warkentin et al., 2023). A meta-analysis by Huang et al, using mainly cross-sectional studies, indicated that postnatal exposure to ambient air pollutants, especially particulate matter, was associated with increased risk of childhood obesity and weight gain (Huang et al., 2022); prenatal exposure was not assessed in this meta-analysis. Others have, however, reported null associations between postnatal air pollution exposure and weight outcomes in childhood (Alderete et al., 2017; Fioravanti et al., 2018).

The heterogeneous results in the literature point to the necessity of larger studies incorporating pre- and postnatal exposure windows, obesity outcomes across childhood, and different geographical settings. Previous studies on air pollution and childhood obesity were mainly conducted in single countries within a specific region or city and, up to now, no studies have evaluated and compared associations across multiple longitudinal cohorts using individual participant data. Meta-analysis of individual participant data from each trial may improve the quality and scope of derived information in comparison with meta-analysis using aggregate data from publications (Riley et al., 2023). Additionally, this method enables a better control over confounders included in the models, as these are chosen a priori and harmonized across different cohorts.

For these reasons, the aim of this study was to examine associations of pre- and postnatal air pollution exposure with childhood sex- and age-specific measures of body mass index (BMI) and overweight/obesity status, using harmonized individual participant data from 10 European birth cohorts.

## 2. Material and Methods

### 2.1. *Study population*
This study was part of the European Union-funded project LifeCycle, which aims to investigate the effect of diverse early life stressors on health throughout life. LifeCycle has established the EU Child Cohort Network, a Europe-wide network of pregnancy and childhood cohort studies with harmonized data of more than 250000 mother-child dyads. More detail on the LifeCycle project and the EU Child Cohort Network can be found elsewhere (Jaddoe et al., 2020; Pinot de Moira et al., 2021).

We analyzed data from 10 European birth cohorts. Cohorts were eligible to participate if they had (i) available harmonized data on air pollution exposure estimates, (ii) harmonized data on weight and height measurements in at least one age period of interest and, (iii) harmonized data uploaded on a local server to undergo federated analysis through the DataSHIELD platform (Gaye et al., 2014). We restricted our study population in each cohort to singletons with at least one exposure estimate (pre- or postnatal) and one outcome at any age between 0 to 12 years. The following cohorts were included: ABCD [Amsterdam Born Children and their Development, Amsterdam, the Netherlands, period of enrolment: 2003-2004] (van Eijsden et al., 2011), ALSPAC [Avon Longitudinal Study of Parents and Children, Bristol, UK, 1991-1992] (Fraser et al., 2013), BiB [Born in Bradford, Bradford, UK, 2007-2011] (McEachan et al., 2024; Wright et al., 2013), DNBC [Danish National Birth Cohort, Copenhagen greater area, Denmark, 1996-2002] (Olsen et al., 2001), EDEN [Etude des Déterminants pré et postnatals précoces du développement et de la santé de l'Enfant, Nancy and Poitiers, France, 2003-2006] (Heude et al., 2016), Gen R [the Generation R Study, Rotterdam, the Netherlands, 2002-2006] (Kooijman et al., 2016), INMA [Infancia y Medio Ambiente, Sabadell, Gipuzkoa, Valencia, Spain, 2003-2008] (Guxens et al., 2012), MoBa [Norwegian Mother, Father and Child Cohort Study, Oslo urban area, Norway, 1999-2008] (Magnus et al., 2016), NINFEA [Nascita e INFanzia: gli Effetti dell'Ambiente, Florence, Rome, Turin, Italy, 2005-2016] (Richiardi et al., 2007) and RHEA [RHEA Mother & Child Cohort Study, Crete, Greece, 2007-2008] (Chatzi et al., 2017) (Figure 1, Table 1). Before enrolment, all participants signed an informed consent in accordance with each center's ethics committee (cohort-specific ethical approvals can be found in Supplementary Text 1). The flowchart of cohorts and participants can be found in Supplementary Figure 1.

<table><caption>**Table 1.** Summary of the cohort characteristics.</caption><thead><tr><th rowspan="2">Cohort</th><th rowspan="2">Country</th><th rowspan="2">City/area</th><th rowspan="2">Birth years</th><th rowspan="2">Age range of included children<sup>a</sup></th><th colspan="2">Number of included children<sup>b</sup></th></tr><tr><th>Prenatal</th><th>Postnatal</th></tr></thead><tbody><tr><td>ABCD [Amsterdam Born Children and their Development]</td><td>the Netherlands</td><td>Amsterdam</td><td>2003-2004</td><td>0-12 years</td><td>3753 (NO<sub>2</sub>)<br>3753 (PM<sub>2.5</sub>)</td><td>2690 (NO<sub>2</sub>)<br>2690 (PM<sub>2.5</sub>)</td></tr><tr><td>ALSPAC [Avon Longitudinal Study of Parents and Children]</td><td>UK</td><td>Bristol</td><td>1991-1992</td><td>0-12 years</td><td>7874 (NO<sub>2</sub>)<br>7874 (PM<sub>2.5</sub>)</td><td>7874 (NO<sub>2</sub>)<br>7874 (PM<sub>2.5</sub>)</td></tr></tbody></table>
<table><tr><td>BiB [Born in Bradford]</td><td>UK</td><td>Bradford</td><td>2007-2011</td><td>0-5 years</td><td>4029 (NO<sub>2</sub>)<br>4031 (PM<sub>2.5</sub>)</td><td>4006 (NO<sub>2</sub>)<br>4020 (PM<sub>2.5</sub>)</td></tr><tr><td>DNBC [Danish National Birth Cohort]</td><td>Denmark</td><td>Copenhagen greater area</td><td>1996-2002</td><td>0-2, 5-12 years</td><td>4340 (NO<sub>2</sub>)<br>4340 (PM<sub>2.5</sub>)</td><td>3146 (NO<sub>2</sub>)<br>3146 (PM<sub>2.5</sub>)</td></tr><tr><td>EDEN [Etude des Déterminants pré et postnatals précoces du développement et de la santé de l'Enfant]</td><td>France</td><td>Nancy and Poitiers</td><td>2003-2006</td><td>0-9 years</td><td>1186 (NO<sub>2</sub>)<br>1580 (PM<sub>2.5</sub>)</td><td>1185 (NO<sub>2</sub>)<br>1578 (PM<sub>2.5</sub>)</td></tr><tr><td>Gen R [the Generation R Study]</td><td>the Netherlands</td><td>Rotterdam</td><td>2002-2006</td><td>0-12 years</td><td>4345 (NO<sub>2</sub>)<br>4345 (PM<sub>2.5</sub>)</td><td>4334 (NO<sub>2</sub>)<br>4334 (PM<sub>2.5</sub>)</td></tr><tr><td>INMA [Infancia y Medio Ambiente]</td><td>Spain</td><td>Sabadell, Gipuzkoa, Valencia</td><td>2003-2008</td><td>0-12 years</td><td>1817 (NO<sub>2</sub>)<br>1812 (PM<sub>2.5</sub>)</td><td>1815 (NO<sub>2</sub>)<br>1802 (PM<sub>2.5</sub>)</td></tr><tr><td>MoBa [Norwegian Mother, Father and Child Cohort Study]</td><td>Norway</td><td>Oslo urban area</td><td>1999-2008</td><td>0-9 years</td><td>7028 (NO<sub>2</sub>)<br>7053 (PM<sub>2.5</sub>)</td><td>6282 (NO<sub>2</sub>)<br>6282 (PM<sub>2.5</sub>)</td></tr><tr><td>NINFEA [Nascita e INFanzia: gli Effetti dell'Ambiente]</td><td>Italy</td><td>Florence, Rome, Turin</td><td>2005-2016</td><td>0-12 years</td><td>1961 (NO<sub>2</sub>)<br>1961 (PM<sub>2.5</sub>)</td><td>1765 (NO<sub>2</sub>)<br>1765 (PM<sub>2.5</sub>)</td></tr><tr><td>RHEA [RHEA Mother & Child Cohort Study]</td><td>Greece</td><td>Crete</td><td>2007-2008</td><td>0-5 years</td><td>357 (NO<sub>2</sub>)<br>357 (PM<sub>2.5</sub>)</td><td>356 (NO<sub>2</sub>)<br>356 (PM<sub>2.5</sub>)</td></tr></table>

## 2.2. Air pollution exposure assessment

<footnote>a Children with available harmonized data on air pollution exposure estimates and harmonized data on weight and height measurements in at least one age period of interest; b Children included in the analyses (complete cases, with no missing covariates), per cohort, by period and pollutant, with some exposure and outcome data.</footnote>
The assessment of air pollution exposure was performed at the mother's residential address during pregnancy and the address at the time of each outcome assessment in each cohort. This was done in accordance with a standardized protocol for harmonized urban environment stressor data across all cohorts, developed as part of the LifeCycle project (Jaddoe et al., 2020).

Mean annual concentrations of NO₂ (in µg/m³) and PM₂.₅ (in µg/m³) were calculated using spatial land-use regression (LUR) models developed as part of the European Study of Cohorts for Air Pollution Effects (ESCAPE) project (Eeftens et al., 2012; Sellier et al., 2014) for cohorts where local ESCAPE models were available: ABCD, BiB, EDEN (NO₂), Gen R, INMA, MoBa (Oslo region), NINFEA and RHEA cohorts. The median LUR model variance (R²) for PM₂.₅ in ESCAPE project was 71%, with variations in different regions (e.g., Oslo, Norway – 74% and Catalunya, Spain - 86%), which was considered high to moderate, with differences due to local particularities, especially the data availability at each region. For NO₂ (EDEN) seasonalized LUR models accounted for 99% of the variance of exposure (Sellier et al., 2014). For cohorts where local ESCAPE models were not available, we used air pollution models developed as part of the Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) project (de Hoogh et al., 2018): ALSPAC, DNBC (Copenhagen greater area) and EDEN (PM₂.₅) cohorts. In ELAPSE, LUR model variance for PM₂.₅ on all available monitoring sites explained 62% of spatial variation, and the NO₂ model on all available sites explained 59% of the spatial variation of the measured air pollution concentrations. The ESCAPE and ELAPSE models followed a very similar procedure and thus were comparable. ESCAPE model was applied directly on the participants locations allowing estimates at point resolution. The ELAPSE model was mapped at a 100 × 100m resolution across Europe (de Hoogh et al., 2018).

To obtain estimates for each relevant exposure period in each cohort and for each participant, temporal adjustment was conducted using background routine monitoring stations. Briefly, for each participant the nearest routine air quality monitoring from the home address was selected and daily time-series data was retrieved to fully cover the target period. The adjustment factor was calculated as the ratio of the pollutant concentration from the routine monitor of each day and the annual average during 2009 and 2010 (baseline years of ESCAPE and ELAPSE models, respectively), following the common methodology developed within ESCAPE project (Alemany et al., 2021; Ballvé et al., 2024; Cruells et al., 2024; Kogevinas et al., 2023; Pedersen et al., 2013).

In the LifeCycle project, average annual exposure to each pollutant was calculated for the entire pregnancy period (prenatal exposure) and for every year of life, from birth up to 12 years of age. For prenatal exposures, we considered whole-pregnancy exposure, not trimester-specific exposures, since there are no indications in the literature for trimester specific effects (e.g. (Laurent et al., 2013; Ng et al., 2017)) and since not all cohorts had available trimester-specific exposure data at the time of the analysis.

Postnatal exposure was defined as the weighted average exposure in the year preceding the outcome assessment (postnatal exposure), as in **Supplementary Text 2**.

## 2.3. BMI and weight status outcomes

We used two weight-related outcomes based on BMI: (i) continuous BMI z-scores (zBMI) and (ii) categorical variables indicating weight status (normal weight (including underweight), overweight/obesity). Child height (in cm), weight (in kg) and age of the offspring were either measured in the clinic/research center or self-reported by parents (**Supplementary Table 1**). BMI was calculated as weight (kg) divided by height squared (m)² (Woo & Daniels, 2019), and sex- and age-specific zBMI were estimated according to the World Health Organization (WHO) reference curves (de Onis, 2007; WHO, 2006). zBMI values ±5 standard deviations (SD) from the population median were excluded. Weight status was defined according to the WHO as follows: For children <5 years: Normal weight (inclusive underweight) for zBMI<+2SD, overweight for zBMI≥+2 and zBMI<+3SD, and obesity for zBMI≥+3SD (WHO, 2006). For
children ≥5 years: Normal weight (inclusive underweight) for zBMI<+1SD, overweight for zBMI≥1 and zBMI<+2SD, and obesity for zBMI≥+2SD (de Onis, 2007). We considered weight status as overweight/obesity vs. normal weight. The age periods used for the outcome assessment were: 0-2, 2-5, 5-9 and 9-12 years, which represent key developmental periods (infancy, pre-school age, adiposity rebound, and early puberty). In cases when the child had multiple zBMI measurements within an age period, the latest available measurement within the period was used.

## 2.4. Confounders

We defined confounders that were known or a plausible risk factor associated with exposure to air pollution and childhood zBMI. We used Directed Acyclic Graphs (DAGs) to identify these confounders in pre- and postnatal periods along with potential colliders that should not be adjusted for (Supplementary Figure 2). Prenatal analyses included child sex (male/female) and exact age (in months) at follow-up, maternal educational level at birth (categories described below) as an indicator of individual-level socioeconomic status (SES), maternal pre-pregnancy BMI (self-report or clinical measurement, in kg/m²), maternal smoking during pregnancy (yes/no), and area-level SES measured during pregnancy. In our DAGs, maternal pre-pregnancy BMI was considered to be a confounder rather than a mediator, as is common in studies with a similar research question (De Bont et al., 2020; Fioravanti et al., 2018; Fleisch et al., 2015, 2017; Frondelius et al., 2018; Nieuwenhuijsen et al., 2019). For the postnatal analyses, we further included prenatal exposure to air pollution as a confounder in the case of weak-to-moderate correlation with postnatal exposure (correlation coefficient <0.8). For the multi-city cohorts EDEN, INMA, and NINFEA, we further adjusted the models for city, and for ABCD, BiB, and Gen R, we further included self-reported mother's ethnicity in the models (Western origin/Non-western origin/Mixed). This was done based on previous findings on different levels of air pollution depending on the city in the EDEN, INMA and NINFEA cohorts and the great variability of ethnic origin within the study population of ABCD, BiB and Gen R cohorts, which may have had an effect on zBMI outcomes in these specific populations (De Hoog et al., 2011; Guxens et al., 2014; Schembari et al., 2015; West et al., 2013).

Two SES measures were included in this study: one individual-level (maternal education) and one area-level (area deprivation index). These measures, although correlated, can capture different constructs either of an individual's vulnerability to or potential for exposure to air pollution (Hajat et al., 2015). Maternal education level was based on the highest ongoing or completed education at the time of delivery, and was coded according to the International Standard Classification of Education 97 (ISCED-97) in three categories: low (no education to lower secondary; ISCED-97 categories 0-2), medium (upper and post-secondary; ISCED-97 categories 3-4) and high (degree and above; ISCED-97 categories 5-6) (UNESCO, 2006). An area deprivation index was used as an indicator of prenatal area-level SES. This index was derived from country-specific indicators categorized into tertiles, where 1 means the least deprived area and 3 means most deprived area (Supplementary Text 3). This index was not available in MoBa.

As sensitivity analyses, we further included in the models two urban characteristics: a cohort-specific walkability index and population density. These two measures were selected based on the described association between various urban characteristics on air pollution levels and childhood zBMI. Countries with higher total population exhibit higher air pollution emissions per capita (with the opposite happening when looking at density in urban areas) (Castells-Quintana et al., 2021). City density is also associated with increases in childhood BMI (De Bont et al., 2020) and walkable cities are, on the other hand, related to lower childhood BMI (Vrijheid et al., 2020), as well as lower air pollution levels (Nieuwenhuijsen, 2021). The walkability index used in the current analysis included the average of the following four components capturing differences in the physical environment: (i) Land use Shannon's Evenness Index (calculated as the proportional abundance of each land use type multiplied by that proportion, divided by the logarithm of the number of land use types within a 300 m buffer), (ii) Facility richness (calculated as the number of different facility types (e.g. restaurants, shops, schools, medical centres) within
a 300m buffer divided by the maximum (ten) potential types (range 0 – 1), (iii) Population density and (iv) Connectivity index (described as the number of street intersections within a 300m buffer) with a range of 0 (less walkable) to 1 (most walkable) (Binter et al., 2022; Frank et al., 2006). Each component was converted to deciles before entering to formula to have equal weight. These four indices were summed and divided by four, resulting in a walkability index ranging from 0 to 1. Population density was expressed as the number of inhabitants per km² at the home address and was calculated using the Global Human Settlement Layer (Global Human Settlement Layer, 2015) for all cohorts except MoBa for which local data were used (Robinson et al., 2018).

## 2.5. Statistical analysis

This study used harmonized data, which were analyzed remotely through DataSHIELD (dsBase package, version 6.1.0) (Gaye et al., 2014).

We used a two-stage individual participant data meta-analysis approach to evaluate associations between pre- and postnatal air pollution exposure and zBMI and overweight/obesity status (overweight/obesity vs. normal weight). First, associations were estimated separately for each cohort using linear and logistic mixed effects models with repeated measures of exposures and outcomes (child id was treated as random effect and confounders as fixed effects). Then, cohort-specific coefficients and standard errors were combined using random-effects meta-analysis to obtain overall association estimates. Common and random effects estimates were described for adjusted models; in the common effects model it is assumed that there is one true effect size underlying all the studies in the meta-analysis, and in the random effects model, it is allowed that true effect sizes differ from study to study (Borenstein et al., 2010). Given the heterogeneity between studies in the current meta-analysis, we have focused our results on the random effects estimates. All analyses were adjusted for the main confounders selected by the DAGs. Results are described as beta estimates (95% confidence intervals (CI)), for zBMI for every 10 µg/m³ increase in air pollution levels, and odds ratios (OR) (95% CI) for overweight/obesity status (vs. normal weight). Additionally, we examined between-study heterogeneity using the I² statistic (%). We performed longitudinal analyses on the associations on overall zBMI across all age periods and stratified by age period (0-2, 2-5, 5-9 and 9-12 years). We conducted only single-pollutant analysis; due to the moderate-to-very-strong correlations between pollutants (Supplementary Figure 3), two-pollutant models were not performed.

We performed several sensitivity analyses. First, in order to assess potential effect modification by sociodemographic characteristics, we repeated analyses stratifying by maternal education (low/medium/high), as indicator of individual-level SES, and by residential area-level deprivation index (low/medium/high) as indicator of area-level SES. Second, aiming to explore if associations between air pollution exposure and child weight outcomes are independent of urban characteristics, we further adjusted the models for walkability and population density (separately and together in the same model). Third, to detect whether highly influential cohorts affected the overall effect estimates or heterogeneity we excluded each cohort in turn using a “leave-one-out analysis” approach (Viechtbauer & Cheung, 2010).

## 3. Results

A total of 37111 (prenatal) and 33860 (postnatal) mother-child dyads in the 10 cohort studies from eight European countries were included in this study. As described in Table 2, mother-child pairs from the BiB cohort (UK) were most likely to live in a high deprivation area (84.3%), and mother-child pairs from the INMA cohort (Spain) were least likely to live in a high deprivation area (12%). Child overweight/obesity status varied between cohorts and age periods, with prevalence ranging between cohorts from 12.3 to 40.5% at 0-2 years, 16.7-35.3% at 2-5 years, 12.5-40.7% at 5-9 years, and 10.7-43.8% at 9-12 years. Full characteristics of cohorts are described in Supplementary Table 2. Full cohort characteristics showed to be very similar to the study population, with exception to maternal education, in which we see participants with lower
educational level in the full cohorts of NINFEA, MoBa and DNBC, compared to the study population. This might be explained by the fact that the study population included mainly the urban areas of each cohort, aiming to estimate urban exposures (three major Italian cities (Florence, Rome, Turin) in the NINFEA cohort, Oslo urban area in the MoBa cohort, and Copenhagen greater area in the DNBC cohort). Also, in the full cohort of EDEN we observe a lower proportion of participants from Western origin, compared to the study population (study population included the urban areas of Nancy and Poitiers). Cohort-specific information on prenatal and postnatal air pollution weighted average exposure throughout the age periods, and other urban exposures, are described in **Table 3**. During pregnancy, higher NO₂ and PM₂.₅ levels were seen in the Italian cohort (NINFEA), and lower levels were seen in Greece (RHEA) and Norway (MoBa), compared to the other cohorts (**Table 3**). Correlation plots between air pollutants, by cohort and time of exposure (pre- and postnatal), are displayed in **Supplementary Figure 3**.

Meta-analyses of the estimates from the 10 cohorts showed no evidence of an association between exposure to NO₂ during pre- and postnatal periods, and zBMI and overweight/obesity across childhood with overall estimates close to null (e.g., prenatal exposure to NO₂ and zBMI per increment of 10µg/m³: random effects β(95%CI) 0.01(-0.01; 0.02); postnatal exposure to NO₂ and zBMI per increment of 10µg/m³: β(95%CI) -0.01(-0.04; 0.02) **(Figure 2)**. The association of pre- and postnatal NO₂ exposure with zBMI and overweight/obesity status showed considerable heterogeneity between cohorts (I² range 25-74%) **(Figure 2)**. Some cohort-specific associations were observed: For prenatal NO₂ exposure, a positive association was observed in ABCD (zBMI and overweight/obesity status) and negative, but non-significant, associations were observed for ALSPAC (zBMI and overweight/obesity status). For postnatal NO₂ exposure, negative associations were observed in BiB, EDEN and INMA (zBMI), ALSPAC (overweight/obesity) and RHEA (zBMI and overweight/obesity). In contrast, the Dutch Gen R cohort reported a positive association between postnatal NO₂ exposure with zBMI (β(95%CI) 0.04(0.02; 0.07)) and overweight/obesity status (OR(95%CI) 1.14(1.02; 1.27)).

For prenatal PM₂.₅ exposure, increased exposure was associated with an increased risk of overweight/obesity (OR(95%CI) 1.23 (1.01; 1.51), whereas a small non-significant increase in zBMI was observed (β(95%CI) 0.04 (-0.03; 0.10) **(Figure 3)**. There were no associations with postnatal PM₂.₅ exposure. Associations for PM₂.₅ exposure also showed considerable heterogeneity between cohorts, with I² values ranging from 43 to 89% **(Figure 3)**. We observed cohort-specific associations in both positive and negative directions: For prenatal PM₂.₅ exposure, negative associations were found for ALSPAC (zBMI), and positive associations were seen for Gen R (zBMI and overweight/obesity). For postnatal PM₂.₅ exposure, negative associations were seen in ALSPAC, INMA and RHEA (zBMI and overweight/obesity), BiB (zBMI, only) and positive associations were seen for Gen R (zBMI and overweight/obesity).

**Figure 4** shows results of the meta-analyses stratified by age period of the outcome assessment. Associations between air pollution exposures and zBMI and overweight/obesity status were close to null in most age periods and did not reach statistical significance. In the period between 9-12 years, higher exposure to PM₂.₅ during pregnancy was associated with a statistically significant increase in zBMI per increments of 10µg/m³ (β(95%CI) 0.13(0.03; 0.24) and in the risk of overweight/obesity (OR(95%CI) 1.29(1.04; 1.60)).

Analyses stratified by individual- and area-level SES showed little difference in associations by SES, with overlapping confidence intervals between the SES strata. Nevertheless, in the most deprived area-level SES group (T3) a statistically significant association was observed between prenatal PM₂.₅ and increased zBMI per increments of 10µg/m³ (β(95%CI) 0.10(0.01; 0.18)). A weaker positive association was found between prenatal NO₂ exposure and zBMI per increments of 10µg/m³ in this SES group (β(95%CI) 0.03(0.00; 0.06)) **(Supplementary Figures 4 and 5)**. Some negative associations in specific SES groups were seen in the postnatal exposure period
(e.g., in the medium deprivation group of area-level SES for postnatal PM<sub>2.5</sub> exposure and zBMI per increments of 10µg/m<sup>3</sup> (β(95%CI) -0.19(-0.34 to -0.04)).

Models with further adjustments by walkability and population density showed similar effect estimates to our main models without these adjustments (Supplementary Figure 6). Sensitivity analyses excluding one cohort at the time mostly showed minor changes in β and OR estimates that do not change the main conclusions. An exception was the association between prenatal NO<sub>2</sub> and overweight/obesity risk which increased from 1.01 (95%CI 0.96; 1.07) (overall random-effect OR) to 1.28 (95%CI 1.04;1.58) when removing the DNBC cohort. Decreases in the heterogeneity (I<sup>2</sup>) were observed mainly for prenatal NO<sub>2</sub> exposure, especially when removing the ABCD cohort, and for prenatal PM<sub>2.5</sub> exposure, especially when removing the Generation R cohort. (Supplementary Tables 3 and 4).
<table><caption>**Table 2.** Study population characteristics - outcomes and covariates.</caption><thead><tr><th></th><th>ABCD</th><th>ALSPAC</th><th>BiB</th><th>DNBC</th><th>EDEN</th><th>Gen R</th><th>INMA</th><th>MoBa</th><th>NINFEA</th><th>RHEA</th></tr></thead><tbody><tr><td><strong>Total N</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Prenatal</td><td>3753</td><td></td><td>4029 (NO<sub>2</sub>),<br>4031 (PM<sub>2.5</sub>)</td><td>4340</td><td>1186 (NO<sub>2</sub>),<br>1580 (PM<sub>2.5</sub>)</td><td>4345</td><td>1817 (NO<sub>2</sub>),<br>1812 (PM<sub>2.5</sub>)</td><td>7028 (NO<sub>2</sub>),<br>7053 (PM<sub>2.5</sub>)</td><td>1961</td><td>357</td></tr><tr><td>Postnatal</td><td>2690</td><td>7874</td><td>4006 (NO<sub>2</sub>),<br>4020 (PM<sub>2.5</sub>)</td><td>3146</td><td>1185 (NO<sub>2</sub>),<br>1578 (PM<sub>2.5</sub>)</td><td>4334</td><td>1815 (NO<sub>2</sub>),<br>1802 (PM<sub>2.5</sub>)</td><td>6282</td><td>1765</td><td>356</td></tr><tr><td><strong>Area-level deprivation<sup>a</sup> - n (%)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>1178<br>(22.34)</td><td>2711<br>(29.98)</td><td>302 (3.05)</td><td>3459<br>(54.46)</td><td>654 (38.88)</td><td>1302<br>(15.22)</td><td>939 (48.25)</td><td>-</td><td>774 (39.25)</td><td>161<br>(42.04)</td></tr><tr><td>Medium</td><td>571<br>(10.83)</td><td>3107<br>(34.35)</td><td>1255 (12.69)</td><td>1576<br>(24.81)</td><td>429 (25.51)</td><td>1786<br>(20.87)</td><td>773 (39.72)</td><td>-</td><td>662 (33.57)</td><td>122<br>(31.85)</td></tr><tr><td>High</td><td>3524<br>(66.83)</td><td>3226<br>(35.67)</td><td>8336 (84.26)</td><td>1316<br>(20.72)</td><td>599 (35.61)</td><td>5469<br>(63.91)</td><td>234 (12.02)</td><td>-</td><td>536 (27.18)</td><td>100<br>(26.11)</td></tr><tr><td>Missing</td><td>341 (6.07)</td><td>33 (0.36)</td><td>5 (0.05)</td><td>1863<br>(22.68)</td><td>40 (2.32)</td><td>72 (0.83)</td><td>6 (0.31)</td><td>-</td><td>481 (19.61)</td><td>515<br>(57.35)</td></tr></tbody></table>

**Maternal education at birth - n (%)**
<table><tbody><tr><td rowspan="2">High</td><td>2909 (52.22)</td><td>1235 (14.46)</td><td>2528 (27.73)</td><td>4174 (68.63)</td><td>957 (55.87)</td><td>3473 (45.2)</td><td>668 (34.96)</td><td>7486 (85.71)</td><td>1665 (69.78)</td><td>283 (31.55)</td></tr><tr><td>1512 (27.14)</td><td>5906 (69.16)</td><td>1416 (15.53)</td><td>1070 (17.59)</td><td>656 (38.3)</td><td>3420 (44.51)</td><td>784 (41.03)</td><td>1169 (13.38)</td><td>641 (26.87)</td><td>458 (51.06)</td></tr><tr><td rowspan="2">Low</td><td>1150 (20.64)</td><td>1399 (16.38)</td><td>5174 (56.74)</td><td>838 (13.78)</td><td>100 (5.84)</td><td>790 (10.28)</td><td>459 (24.02)</td><td>79 (0.9)</td><td>80 (3.35)</td><td>156 (17.39)</td></tr><tr><td>43 (0.77)</td><td>537 (5.92)</td><td>780 (7.88)</td><td>2132 (25.96)</td><td>9 (0.52)</td><td>946 (10.96)</td><td>41 (2.1)</td><td>582 (6.25)</td><td>67 (2.73)</td><td>1 (0.11)</td></tr><tr><td rowspan="2">Maternal pre-pregnancy BMI (kg/m²)<sup>b</sup> - Median (P5, P95)</td><td>22.2 (18.59, 30.85)</td><td>-</td><td>27.73 (20.57, 33.51)</td><td>21.72 (18.41, 28.71)</td><td>22.1 (18.14, 31.99)</td><td>22.66 (18.69, 32.35)</td><td>22.53 (18.68, 31.9)</td><td>-</td><td>21.34 (17.92, 29.06)</td><td>23.51 (18.81, 34.59)</td></tr><tr><td>433 (7.71)</td><td>7874 (100.0)</td><td>5537 (55.94)</td><td>559 (6.81)</td><td>33 (1.92)</td><td>2088 (24.2)</td><td>18 (0.92)</td><td>9316 (100)</td><td>41 (1.67)</td><td>6 (0.67)</td></tr><tr><td colspan="12">Maternal smoking during pregnancy - n(%)</td></tr><tr><td rowspan="2">Yes</td><td>617 (10.99)</td><td>2024 (25.0)</td><td>1588 (16.07)</td><td>2083 (25.6)</td><td>433 (25.22)</td><td>1910 (25.95)</td><td>605 (31.43)</td><td>1561 (16.76)</td><td>196 (8.08)</td><td>283 (33.41)</td></tr><tr><td>2 (0.04)</td><td>980 (10.8)</td><td>19 (0.19)</td><td>76 (0.93)</td><td>5 (0.29)</td><td>1270 (14.72)</td><td>27 (1.38)</td><td>1 (0.01)</td><td>26 (1.06)</td><td>51 (5.68)</td></tr></tbody></table>
Maternal ethnicity

<table><tr><td>Western origin</td><td>3193 (57.26)</td><td>8329 (98.38)</td><td>4130 (41.7)</td><td>-</td><td>1474 (85.6)</td><td>4643 (56.98)</td><td>1829 (95.51)</td><td>-</td><td>-</td><td>891 (99.66)</td></tr><tr><td>Non-western origin</td><td>1650 (29.59)</td><td>137 (1.62)</td><td>5571 (56.3)</td><td>-</td><td>8 (0.47)</td><td>2704 (33.19)</td><td>86 (4.49)</td><td>-</td><td>-</td><td>3 (0.34)</td></tr><tr><td>Mixed</td><td>733 (13.15)</td><td>0 (0)</td><td>185 (1.87)</td><td>-</td><td>6 (0.35)</td><td>801 (9.83)</td><td>0 (0)</td><td>-</td><td>-</td><td>0 (0)</td></tr><tr><td>Missing</td><td>38 (0.68)</td><td>611 (6.73)</td><td>12 (0.12)</td><td>8214 (100)</td><td>234 (13.6)</td><td>481 (5.57)</td><td>37 (1.90)</td><td>9316 (100)</td><td>2453 (100)</td><td>4 (0.45)</td></tr></table>

Child sex - n (%)

<table><tr><td>Male</td><td>2787 (49.64)</td><td>4579 (50.45)</td><td>5094 (51.46)</td><td>4194 (51.06)</td><td>898 (52.15)</td><td>4350 (50.41)</td><td>1006 (51.54)</td><td>4727 (50.74)</td><td>1264 (51.53)</td><td>478 (53.23)</td></tr></table>

Child age (months) - Median (P5, P95)

<table><tr><td>0-2 years</td><td>14 (12, 20)</td><td>12 (12, 12)</td><td>0 (0, 23)</td><td>12 (12, 14)</td><td>12 (0, 15)</td><td>14 (4, 19)</td><td>14 (12, 18)</td><td>12 (1, 16)</td><td>12 (3, 18)</td><td>14 (2, 20)</td></tr><tr><td>2-5 years</td><td>44 (26, 54)</td><td>49 (31, 49)</td><td>51 (25, 59)</td><td>-</td><td>48 (35, 57)</td><td>37 (24, 47)</td><td>51 (48, 55)</td><td>36 (24, 36)</td><td>48 (48, 48)</td><td>49 (48, 57)</td></tr></table>
<table><tr><td rowspan="2">5-9 years</td><td>69 (61, 85)</td><td>99 (69, 103)</td><td>-</td><td>85 (79, 88)</td><td>66 (60, 70)</td><td>72 (68, 89)</td><td>93 (75, 106)</td><td>84 (60, 84)</td><td>84 (75.35, 93)</td><td>-</td></tr><tr><td>128</td><td>140</td><td>-</td><td>133</td><td>-</td><td>116</td><td>131</td><td>-</td><td>122</td><td>-</td></tr><tr><td>9-12 years</td><td>(120, 141)</td><td>(127, 141)</td><td>-</td><td>(130, 138)</td><td>-</td><td>(113, 122)</td><td>(111, 140)</td><td>-</td><td>(119, 129)</td><td>-</td></tr></table>

Child zBMI<sup>c</sup> - Median (P5, P95)

<table><tr><td rowspan="2">0-2 years</td><td>0.48</td><td>0.79</td><td>-0.37</td><td>0.3</td><td>0.36</td><td>0.55</td><td>0.42</td><td>0.25</td><td>0.05</td><td>0</td></tr><tr><td>(-1.14, 2.04)</td><td>(-0.68, 2.29)</td><td>(-2.31, 1.57)</td><td>(-1.47, 2.12)</td><td>(-1.57, 1.94)</td><td>(-1.15, 2.08)</td><td>(-1.2, 2.03)</td><td>(-1.32, 1.82)</td><td>(-2.11, 2.12)</td><td>(-1.77, 1.77)</td></tr><tr><td rowspan="2">2-5 years</td><td>0.27</td><td>0.61</td><td>0.46</td><td>-</td><td>0.14</td><td>0.37</td><td>0.51</td><td>0.43</td><td>0.08</td><td>0.59</td></tr><tr><td>(-1.26, 1.95)</td><td>(-0.84, 2.17)</td><td>(-1.18, 2.44)</td><td>-</td><td>(-1.37, 1.66)</td><td>(-1.19, 2.06)</td><td>(-0.95, 2.45)</td><td>(-1.31, 2.1)</td><td>(-1.85, 2.06)</td><td>(-1.07, 2.81)</td></tr><tr><td rowspan="2">5-9 years</td><td>0.11</td><td>0.24</td><td>-</td><td>-0.06</td><td>-0.01</td><td>0.36</td><td>0.71</td><td>-0.02</td><td>-0.02</td><td>-</td></tr><tr><td>(-1.34, 1.97)</td><td>(-1.56, 2.42)</td><td>-</td><td>(-1.61, 1.51)</td><td>(-1.39, 1.52)</td><td>(-1.04, 2.29)</td><td>(-1.03, 2.9)</td><td>(-1.74, 1.67)</td><td>(-1.9, 2.09)</td><td>-</td></tr><tr><td rowspan="2">9-12 years</td><td>0.02</td><td>0.36</td><td>-</td><td>-0.28</td><td>-</td><td>0.29</td><td>0.76</td><td>-</td><td>0.07</td><td>-</td></tr><tr><td>(-1.66, 2.16)</td><td>(-1.52, 2.4)</td><td>-</td><td>(-1.97, 1.42)</td><td>-</td><td>(-1.3, 2.31)</td><td>(-1.29, 2.71)</td><td>-</td><td>(-2.23, 1.91)</td><td>-</td></tr></table>

Child overweight/obesity status<sup>c</sup> - yes, n (%)
<table><tr><td>0-2 years</td><td>1357 (28.7)</td><td>477 (40.49)</td><td>1003 (12.33)</td><td>1122 (25.1)</td><td>418 (25.32)</td><td>2107 (31.08)</td><td>510 (26.32)</td><td>1797 (20.96)</td><td>444 (21.38)</td><td>147 (17.21)</td></tr><tr><td>2-5 years</td><td>1014 (22.05)</td><td>358 (32.05)</td><td>2315 (29.96)</td><td>-</td><td>254 (16.67)</td><td>1646 (25.5)</td><td>502 (30.91)</td><td>1577 (28.4)</td><td>374 (19.63)</td><td>281 (35.26)</td></tr><tr><td>5-9 years</td><td>782 (18.57)</td><td>2111 (25.66)</td><td>-</td><td>644 (12.49)</td><td>162 (13.81)</td><td>1709 (25.58)</td><td>581 (40.74)</td><td>1072 (15.38)</td><td>247 (21.15)</td><td>-</td></tr><tr><td>9-12 years</td><td>726 (22.39)</td><td>2504 (31.7)</td><td>-</td><td>463 (10.65)</td><td>-</td><td>1466 (26.39)</td><td>412 (43.83)</td><td>-</td><td>95 (20.08)</td><td>-</td></tr></table>

ABCD: Amsterdam Born Children and their Development; ALSPAC: Avon Longitudinal Study of Parents and Children; BiB: Born in Bradford; DNBC: Danish National Birth Cohort; EDEN: Etude des Déterminants pré et postnatals précoces du développement et de la santé de l'Enfant; Gen R: Generation R Study; INMA: Infancia y Medio Ambiente; MoBa: Norwegian Mother, Father and Child Cohort Study; NINFEA: Nascita e INFanzia: gli Effetti dell'Ambiente; RHEA: Rhea Mother & Child Cohort Study; P: Percentile; BMI: Body mass index, zBMI: Body mass index z-score.ª Area-level deprivation defined in each cohort, as described in Supplementary Text 3; b Maternal pre-pregnancy BMI defined as weight/height²; c Sex- and age- specific zBMI and child weight status were estimated according to the World Health Organization (WHO) reference curves (de Onis, 2007; WHO, 2006).

<table><caption>**Table 3.** Study population characteristics – prenatal and postnatal air pollution estimatesª and other urban characteristics.</caption><tr><td>ABCD</td><td>ALSPAC</td><td>BiB</td><td>DNBC</td><td>EDEN</td><td>Gen R</td><td>INMA</td><td>MoBa</td><td>NINFEA</td><td>RHEA</td></tr></table>
Total N

<table><tr><td>Prenatal</td><td>3753</td><td>7874</td><td>4029 (NO<sub>2</sub>),<br/>4031 (PM<sub>2.5</sub>)</td><td>4340</td><td>1186 (NO<sub>2</sub>),<br/>1580<br/>(PM<sub>2.5</sub>)</td><td>4345</td><td>1817 (NO<sub>2</sub>),<br/>1812 (PM<sub>2.5</sub>)</td><td>7028 (NO<sub>2</sub>),<br/>7053 (PM<sub>2.5</sub>)</td><td>1961</td><td>357</td></tr><tr><td>Postnatal</td><td>2690</td><td></td><td>4006 (NO<sub>2</sub>),<br/>4020 (PM<sub>2.5</sub>)</td><td>3146</td><td>1185 (NO<sub>2</sub>),<br/>1578<br/>(PM<sub>2.5</sub>)</td><td>4334</td><td>1815 (NO<sub>2</sub>),<br/>1802 (PM<sub>2.5</sub>)</td><td>6282</td><td>1765</td><td>356</td></tr></table>

Prenatal air pollution exposure (µg/m³)<sup>b</sup> - Median (P5, P95)

<table><tr><td></td><td>36.62</td><td>27.21</td><td>23.04</td><td>47.27</td><td>21.28</td><td>38.35</td><td>26.23</td><td>20.66</td><td>49.25</td><td>11.39</td></tr><tr><td>NO<sub>2</sub></td><td>(27.53,<br/>48.16)</td><td>(19.22,<br/>33.38)</td><td>(18.16,<br/>29.98)</td><td>(38.72,<br/>57.91)</td><td>(11.3, 48.92)</td><td>(30.41,<br/>49.36)</td><td>(10.13,<br/>51.65)</td><td>(10.87, 37.17)</td><td>(19.49, 76.25)</td><td>(7.9,<br/>21.89)</td></tr><tr><td>Missing</td><td>118 (2.1)</td><td>193 (2.13)</td><td>15 (0.15)</td><td>634 (7.72)</td><td>464 (27)</td><td>37 (0.43)</td><td>108 (5.53)</td><td>962 (10.33)</td><td>352 (14.35)</td><td>1 (0.11)</td></tr><tr><td>PM<sub>2.5</sub></td><td>18.47<br/>(16.78,<br/>21.02)</td><td>13.33<br/>(11.92,<br/>14.67)</td><td>10.43<br/>(8.62, 12.66)</td><td>18.22<br/>(14.89,<br/>20.9)</td><td>17.11<br/>(14.17, 21.1)</td><td>19.62<br/>(16.88,<br/>23.82)</td><td>14.72<br/>(10.89,<br/>18.89)</td><td>10.8<br/>(7.73, 14.46)</td><td>24.39<br/>(9.64, 35.71)</td><td>14.18<br/>(11.02,<br/>18.67)</td></tr><tr><td>Missing</td><td>118 (2.1)</td><td>193 (2.13)</td><td>5 (0.05)</td><td>634 (7.72)</td><td>2 (0.12)</td><td>37 (0.43)</td><td>114 (5.84)</td><td>937 (10.06)</td><td>352 (14.35)</td><td>1 (0.11)</td></tr></table>
Postnatal NO₂ - weighted average

<table><tbody><tr><td rowspan="2">0-2 years</td><td>35.01</td><td>27.18</td><td>24.21</td><td>45.12</td><td>20.57</td><td>37.36</td><td>27.53</td><td>35.16</td><td>48.67</td><td>13.65</td></tr><tr><td>(26.67, 45.04)</td><td>(19.69, 33.11)</td><td>(18.76, 34.79)</td><td>(37.14, 53.34)</td><td>(11.06, 46.91)</td><td>(30.29, 47.23)</td><td>(9.54, 50.79)</td><td>(24.62; 44.15)</td><td>(18.04, 75.18)</td><td>(10.59, 24.8)</td></tr><tr><td rowspan="2">2-5 years</td><td>34.32</td><td>27.09</td><td>25.94</td><td>-</td><td>17.92</td><td>35.08</td><td>22.25</td><td>35.19</td><td>45.41</td><td>13.92</td></tr><tr><td>(26.27, 43.86)</td><td>(19.72, 32.76)</td><td>(13.71, 33.54)</td><td>-</td><td>(10.48, 39.5)</td><td>(27.94, 43.68)</td><td>(8.54, 46.68)</td><td>(24.4, 45.44)</td><td>(16.99, 61.07)</td><td>(10.71, 25.03)</td></tr><tr><td rowspan="2">5-9 years</td><td>32.09</td><td>23.57</td><td>-</td><td>40.28</td><td>15.99</td><td>32.9</td><td>22.75</td><td>33.28</td><td>44.15</td><td>-</td></tr><tr><td>(25.01, 41.43)</td><td>(16.17, 29.57)</td><td>-</td><td>(32.12, 50.9)</td><td>(10.12, 33.73)</td><td>(24.08, 41.05)</td><td>(8.15, 46.85)</td><td>(23.27, 44.47)</td><td>(19.98, 59.05)</td><td>-</td></tr><tr><td rowspan="2">9-12 years</td><td>15.24</td><td>22.08</td><td>-</td><td>33.39</td><td>-</td><td>29.03</td><td>26.8</td><td>-</td><td>40.99</td><td>-</td></tr><tr><td>(11.15, 32.29)</td><td>(15.17, 26.9)</td><td>-</td><td>(26.78, 39.8)</td><td>-</td><td>(17.8, 36.67)</td><td>(3.04, 43.76)</td><td>-</td><td>(22.61, 51.48)</td><td>-</td></tr></tbody></table>

Postnatal PM₂.₅ - weighted average

<table><tbody><tr><td rowspan="2">0-2 years</td><td>17.56</td><td>13.37</td><td>10.7</td><td>18.44</td><td>16.67</td><td>17.99</td><td>14.89</td><td>9.84</td><td>24.44</td><td>17.29</td></tr><tr><td>(16.69, 19.48)</td><td>(12.18, 14.41)</td><td>(8.9, 14.75)</td><td>(15.19, 21.04)</td><td>(14.25, 18.93)</td><td>(16.96, 22.78)</td><td>(10.72, 18.72)</td><td>(6.80; 13.7)</td><td>(8.68, 36.61)</td><td>(14.95, 20.23)</td></tr></tbody></table>
<table><tbody><tr><td rowspan="2">2-5 years</td><td>17.71</td><td>13.39</td><td>11.87</td><td>-</td><td>15.86</td><td>17.49</td><td>13.31</td><td>9.14</td><td>22.77</td><td>17.83</td></tr><tr><td>(16.81, 19.34)</td><td>(12.23, 14.48)</td><td>(6.12, 14.1)</td><td>-</td><td>(13.5, 18.39)</td><td>(16.31, 19.28)</td><td>(10.53, 15.72)</td><td>(6.45; 12.1)</td><td>(8.32, 26.92)</td><td>(15.37, 20.69)</td></tr><tr><td rowspan="2">5-9 years</td><td>17.02</td><td>11.62</td><td>-</td><td>16.07</td><td>15.37</td><td>16.18</td><td>13.69</td><td>7.66</td><td>21.74</td><td>-</td></tr><tr><td>(16.22, 18.37)</td><td>(10.22, 13.59)</td><td>-</td><td>(13.12, 19.37)</td><td>(13.44, 17.81)</td><td>(15.53, 17.38)</td><td>(10.61, 15.85)</td><td>(5.67; 10.57)</td><td>(12.15, 26.76)</td><td>-</td></tr><tr><td rowspan="2">9-12 years</td><td>7.42</td><td>11.04</td><td>-</td><td>14.34</td><td>-</td><td>14.94</td><td>12.65</td><td>-</td><td>20.13</td><td>-</td></tr><tr><td>(7.01, 15.3)</td><td>(9.8, 11.99)</td><td>-</td><td>(11.93, 16.58)</td><td>-</td><td>(12.9, 17.42)</td><td>(9.16, 14.65)</td><td>-</td><td>(14.4, 22.84)</td><td>-</td></tr><tr><td>Population density during pregnancy (persons/km² in 100m) - Median (P5, P95)</td><td>6752.25 (3132.1, 6842.3)</td><td>4320 (775.45, 11616)</td><td>5552 (1225.04, 19312)</td><td>8233.43 (2047.07, 19800.6)</td><td>2092.98 (114.51, 7595.57)</td><td>4136.41 (2457.38, 4136.41)</td><td>6121.41 (608.85, 12049.58)</td><td>6412.18 (1934.02, 25572.41)</td><td>10202.16 (5321.16, 10202.16)</td><td>5458.87 (333.25, 6887.29)</td></tr><tr><td>Missing</td><td>126 (2.24)</td><td>210 (2.31)</td><td>49 (0.5)</td><td>503 (6.12)</td><td>147 (8.54)</td><td>952 (11.03)</td><td>165 (8.45)</td><td>1038 (11.14)</td><td>363 (14.8)</td><td>19 (2.12)</td></tr><tr><td>Walkability during pregnancy (0-1 range; 300m buffer) - Median (P5, P95)</td><td>0.38 (0.25, 0.48)</td><td>0.28 (0.2, 0.42)</td><td>0.32 (0.22, 0.45)</td><td>0.38 (0.25, 0.55)</td><td>0.28 (0.2, 0.4)</td><td>0.35 (0.28, 0.45)</td><td>0.32 (0.22, 0.48)</td><td>0.3 (0.17, 0.45)</td><td>0.38 (0.3, 0.48)</td><td>0.38 (0.2, 0.5)</td></tr></tbody></table>
Missing 115 (2.05) 301 (3.32) 9 (0.09) 503 (6.12) 404 (23.46) 957 (11.09) 133 (6.81) 2224 (23.87) 353 (14.39) 237 (26.39)

ABCD: Amsterdam Born Children and their Development; ALSPAC: Avon Longitudinal Study of Parents and Children; BiB: Born in Bradford; DNBC: Danish National Birth Cohort; EDEN: Etude des Déterminants pré et postnatals précoces du développement et de la santé de l'Enfant; Gen R: Generation R Study; INMA: Infancia y Medio Ambiente; MoBa: Norwegian Mother, Father and Child Cohort Study; NINFEA: Nascita e INFanzia: gli Effetti dell'Ambiente; RHEA: Rhea Mother & Child Cohort Study; P: Percentile. <sup>a</sup>Values are described as weighted average air pollution levels in the different age periods; <sup>b</sup>Ambient air pollution levels were estimated using land-use regression (LUR) models developed within the European Study of Cohorts for Air Pollution Effects (ESCAPE) project (Eeftens et al., 2012) and through the Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) project (de Hoogh et al., 2018).
4. Discussion

In this meta-analysis of individual participant data from 10 European cohorts and a sample size of 37111 (prenatal) and 33860 (postnatal) mother-child pairs, pre- and postnatal exposure to NO₂ and PM₂.₅ showed few associations with obesity-related outcomes across key developmental periods during childhood. Exposure to PM₂.₅ during pregnancy was associated with higher overweight/obesity risk across childhood, and specifically with higher zBMI and overweight/obesity risk in one age period (9-12 years). Considerable heterogeneity between cohorts was noted, with some cohorts showing increased zBMI or overweight/obesity risk with increased air pollution exposure, and others showing negative or null associations.

This study investigated both pre- and postnatal ambient air pollution exposure and obesity-related outcomes during childhood in a multi-cohort study using individual participant data, rather than aggregate estimates from publications. Associations between pre- and postnatal air pollution exposures and childhood obesity-related outcomes have been studied in a number of single cohort studies, but results are inconsistent. Regarding prenatal air pollution exposure, various studies have shown that exposure to pollutants during pregnancy, such as PM₂.₅, may be associated with decreased weight and BMI at birth and in first years of life (Bekkar et al., 2020; Fossati et al., 2020; Hung et al., 2022). Two studies in the US suggested a complicated and dynamic exposure-weight relationship between the exposure to air pollution in prenatal life and weight outcomes. Infants in the Project Viva cohort exposed to higher prenatal traffic-related air pollution (PM₂.₅ and black carbon) exhibited reduced fetal growth at birth and rapid postnatal weight-for-length gain between 0-6 months of age (Fleisch et al., 2015). More recently, Ji and colleagues found that those children exposed to higher levels of NO₂ had faster “catch-up growth” rates in early infancy, which may lead to increased risk of obesity later in life (Ji et al., 2023). However, other studies that investigated maternal prenatal particulate matter exposure and child weight-related outcomes, did not show significant associations with childhood BMI (Huang et al., 2019; Sears et al., 2019), cardiometabolic health (Fleisch et al., 2017) nor BMI trajectories (Fleisch et al., 2019). In the current study, we observed an association between prenatal PM₂.₅ exposure and greater overweight/obesity risk across childhood, and specifically with higher zBMI and overweight/obesity risk in one age period (9-12 years). However, this age-specific analysis was based on less cohorts than the repeated outcome analysis due to less cohorts having data in this age group, and this result needs to be interpreted with caution. Experimental studies in animals have shown that PM₂.₅ exposure during pregnancy led to decreased body and tail length, birth weight, and survival rates of the young mice (Huang et al., 2023), the development of increased body weight, altered blood pressure, and increased susceptibility to cardiovascular disease and heart failure in offspring’s adulthood (Weldy et al., 2014), besides increased risk for metabolic syndrome in the offspring (Zhang et al., 2019). Further studies are necessary to better understand how prenatal air pollution exposure may affect adiposity-related outcomes in different key developmental periods during childhood.

We found no evidence for an association between postnatal air pollution exposure and childhood zBMI and overweight/obesity status. The literature has also been inconsistent with respect to findings on the association between postnatal air pollution exposure and weight outcomes in childhood. In China, exposure to PM₁, PM₂.₅, PM₁₀ and NO₂ was associated with increased zBMI, waist circumference and waist-to-height ratio, and higher prevalence of both general and central obesity, in children and adolescents (Zhang et al., 2021). In a recent natural experiment study in Catalonia, Spain, those children and adolescents who moved to more polluted areas (NO₂, PM₁₀ and PM₂.₅) showed prospective increases in zBMI (Warkentin et al., 2023). The study by De Bont and colleagues, also in Catalonia, highlighted that ambient air pollution exposure in childhood, especially at school, was associated with 30% increased odds of having overweight or obesity (De Bont et al., 2019). Another study, using the PIAMA birth cohort in the Netherlands, showed that NO₂ exposure throughout childhood was associated with an increased odds (adjusted OR=1.40) of overweight in children aged 3-17 years (Bloemsma et al., 2019). However, others reported null findings. A study in Italy did not find a significant association between long-term exposure to air pollution (PM₂.₅, PM₁₀, NO₂, NOₓ, coarse particles, and PM₂.₅abs) and traffic density, and several body weight measures including BMI, waist circumference, waist-to-height ratio and prevalence of overweight/obesity in 4-8-year-olds
(Fioravanti et al., 2018). In another study in the south of the UK, PM₁₀ exposure in preschool-age (4-5 years) was associated with small relative risk of overweight/obesity at 10 years, however not the other pollutants (PM₂.₅, NOₓ) (Wilding et al., 2020). A recent systematic review concluded that there is strong evidence of association between NO₂ and NOₓ (which are traffic-related pollutants) and childhood obesity, but evidence is still weak for PM₁₀ and PM₂.₅ (which are driven to a lesser degree by local traffic). It is argued that the biochemical mechanisms in which NO₂ is involved in the human body might also impact the onset of obesity (Malacarne et al., 2022). Besides variability of associations between air pollution and weight by specific air pollutants, this link may also vary by sex, age group (children vs. adults) (An, Ji, et al., 2018), study design (cross-sectional vs. longitudinal), methodological disparities in body weight and air pollution measurements, and heterogeneity of study populations (Zhang et al., 2021).

Our study finds prenatal, but not postnatal, exposure to PM<sub>2.5</sub> to be associated with obesity-related outcomes in childhood. From the previous epidemiological studies, described above, there is no clear indication of which time window of exposure would be expected to be more sensitive for the development of childhood obesity. Our study results, if causal, support evidence for mechanisms during pregnancy, rather than during childhood, to be of most concern. Further studies into the critical time windows for the potential effects of air pollution on childhood obesity are needed to support this. Our study also suggests that prenatal PM<sub>2.5</sub>, but not NO<sub>2</sub>, exposure is associated with obesity-related outcomes in children. Previous studies do not provide clear evidence on whether PM<sub>2.5</sub> may be more likely to be associated with obesity than NO<sub>2</sub>. We note that NO<sub>2</sub> and PM<sub>2.5</sub> share common sources and are mainly driven by combustion-related activities, such as vehicle exhaust and agricultural emissions (Cai et al., 2020). Differences between the pollutants include that NO<sub>2</sub> has been described to be a more local, short-lived trace gas (Matandirotya & Burger, 2021), whereas PM<sub>2.5</sub> can be transported over regional and long-range transboundary distances (Jun & Gu, 2023). Our study included mainly urban areas and traffic would be the main source of both pollutants in these study areas. The two pollutants showed moderate to strong correlations in the current study, and differences in study areas can partly explain differences in correlations between cohorts.

Possible explanations for the inconsistencies and heterogeneity between cohorts found in the association between air pollution exposure, both during pregnancy and in childhood, and childhood BMI may reflect a number of factors including: (i) different levels of air pollution exposure in the different cohorts; (ii) possible differences in lifestyle behaviors of children from different countries, which may alter their exposure to ambient air pollution and have an effect on weight; and (iii) possible residual confounding by SES and other factors specific to each city environment. Residual confounding may, for example, explain the association between higher air pollution levels and lower BMI we observed in the British BiB cohort. This cohort is characterized by a high level of socioeconomic deprivation and ethnic diversity, with half of the sample of Pakistani origin, a population that lives in the most polluted and deprived parts of the city, whilst at the same time having a lower BMI (West et al., 2018) and lower susceptibility to air pollution exposure for some outcomes, such as birth weight, compared to their White British peers (Schembari et al., 2015). Our adjustment for ethnicity and SES may not have completely controlled for these effects. Also, we note that the cohorts studied were very socially diverse, and that the relationship between SES and air pollution exposure can vary between cities, with some low SES communities facing higher exposure to air pollution and other environmental hazards and others lower exposures (Hajat et al., 2015; Robinson et al., 2018). In the Spanish cohort INMA, for example, contrasting patterns were seen among cities, with highly educated families being exposed to higher levels of air pollution in Sabadell, and the opposite pattern in Valencia. In Oslo (MoBa), however, high income families were exposed to higher levels of NO₂ and in Bradford (BiB) the opposite was found (i.e., high family education and high family income were exposed to lower levels of air pollutants) (Robinson et al., 2018). To summarize, these are some examples of factors that might explain the high heterogeneity found in the current study and the complex and dynamic relation between air pollution exposure and childhood weight.

The major strength of this study is the large geographical coverage including eight countries and 10 cohorts in Europe, which enabled the study of culturally diverse populations. We were also able to
include different urban environments, besides urban characteristics that could be confounders in our analyses. Additionally, we meta-analyzed individual participant data from different cohorts, which may have improved the quality and scope of derived information (Riley et al., 2023), increased our statistical power and enabled more precise estimates compared to single cohort studies. Finally, one important practicality of the current study is the use of federated analyses, which enabled the analysis of data from multiple studies/cohorts without the need of data transfer, decreasing the administrative burden of data transfer agreements, and governance issues of physical data sharing (Cadman et al., 2023).

This study also has limitations that need to be mentioned. First, two different spatial models (ESCAPE and ELAPSE) were used to estimate the exposures. Although they were developed using sampling data from the same period and a common methodology, some differences might occur. Model year development (2009-2010) may be seen as a limitation to represent a scenario several years later than the pregnancy exposure time period and earlier than the childhood exposure periods. However, for most of our cohorts, exposure estimation and health outcome data collection fell within a time period of 10 years either side of the spatial modelling and many predictor variables in LUR models (such as building density, green spaces, distance to roads, etc.) have shown to be stable over time (Beelen et al., 2007; Eeftens et al., 2011; Gulliver et al., 2013; Johnson et al., 2010). Besides, stability of the spatial structure of the ESCAPE and ELAPSE models was confirmed and agreement in spatial variation was reported to be generally high in Europe (de Hoogh et al., 2018). One outlier is the ALSPAC cohort with years of birth in 1991-1992 and a 12-year follow-up included in this study leading to longer term back-extrapolation of the spatial models. Our sensitivity analysis leaving this cohort out showed that main conclusions remain the same. Secondly, missing data were addressed using complete cases, as imputation methods were not available within DataSHIELD at the time of the study. We recognize that missing data on covariates might introduce bias into our findings, and determining the direction of this bias presents a challenge. Aiming to explore if any of the cohorts with more missings (such as cohort BiB and cohort DNBC) could have had an effect on the overall pooled estimate, we performed sensitivity analyses using the “leave-one-out” approach, which showed no meaningful cohort-specific effect on the pooled estimates in the majority of cohorts, except when excluding the DNBC cohort. The observed changes in pooled estimates when excluding DNBC cohort might be explained by the fact that this cohort showed higher proportion of missing values, in variables such as area-level SES (22.7%) and maternal education (26%), compared to the other cohorts, and no zBMI data on the age-group 2-5 years. Besides, DNBC is one of the biggest cohorts included in our analyses, so with higher weight in our analysis compared to smaller sample size cohorts. As discussed previously, considerable heterogeneity was found in this study with included cohorts, which might explain the changes in the “leave-one-out” analysis. Third, the use of different cohorts includes harmonization challenges, with variables being harmonized in different ways and in different levels of detail, which may have resulted in between-study heterogeneity that could affect the interpretation of results. More specifically, not all cohorts had data covering all age periods by the end date of data inclusion of this study (e.g., BiB, EDEN and MoBa). Also, not all cohorts had objectively measured anthropometric data (as described in **Supplementary Table 2**), so we had to rely on parent-reported information for some cohorts (e.g., DNBC, MoBa and NINFEA). In addition, we had to harmonize variables with the least detail, which could have increased the risk of residual confounding leading to greater heterogeneity of estimates. For example, maternal education was harmonized in three categories from more granular detail variables within many of the cohorts. Similarly, maternal smoking during pregnancy was used as a yes/no answer in the current study, however was asked in more detail in several cohorts (which included, for example, the number of cigarettes and the timing of smoking). Fourth, although models were adjusted for both individual-level and area-level SES indicators, we cannot rule out the possibility of bias in our effect estimates due to residual confounding of SES. Also, regarding SES, we used as a proxy of individual-level SES maternal education, which is commonly used in epidemiological studies (Hajat et al., 2015), since household income was not available in all included cohorts. Fifth, we were not able to take into account other personal and environmental factors that may influence pediatric obesity, such as physical activity and noise levels (An, Zhang, et al., 2018; González-Muniesa et al., 2017; Jebeile et al., 2022; Wu et al., 2017) due to high missingness and absence of harmonized variables for these factors. We therefore took advantage
of availability of family characteristics (e.g., maternal education, BMI, smoking, SES) which influence childhood weight outcomes (Jebeile et al., 2022), along with alternative urban environment features, such as walkability and population density, which may be seen as noise sources due to human activities emission in the neighborhood (Yuan et al., 2019). Lastly, both indoor and outdoor air pollution may have important health effects in the population. Since we did not have data on indoor air pollution, we relied on data of outdoor air pollution only. It is worth noting that a considerable proportion of indoor air pollution comes from outdoors (Amato et al., 2014). Future studies should take into account both indoor and outdoor air pollution exposures, especially in indoor places where children spend a lot of time, such as schools (De Bont et al., 2019), aiming to increase robustness of estimates.

## 5. Conclusions

In conclusion, this large-scale meta-analysis suggest that prenatal PM₂.₅ exposure may be associated with adverse childhood obesity outcomes, but provides little evidence to support an effect of postnatal air pollution exposure although cohort-specific associations were observed.

## Funding

The LifeCycle project received funding from the European Union's Horizon 2020 research and innovation programme (Grant Agreement No. 733206 LifeCycle). This project has also received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 874583 (ATHLETE project) and No 874739 (LongITools project).

**ABCD:** Core funding of the ABCD-study was provided by the Amsterdam University Medical center, Amsterdam, the Public Health Services, Amsterdam, and the Dutch Organization for Health Research and Development (ZonMw).

**ALSPAC:** Core funding for the Avon Longitudinal Study of Parents and Children (ALSPAC) is provided by the UK Medical Research Council and Wellcome (217065/Z/19/Z) and the University of Bristol. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf).

**BiB:** Born in Bradford receives funding from a joint grant from the UK Medical Research Council (MRC) and UK Economic and Social Science Research Council (ESRC) [MR/N024391/1]; the British Heart Foundation [CS/16/4/32482]; a Wellcome Infrastructure Grant [WT101597MA]; the National Institute for Health Research under its Applied Research Collaboration for Yorkshire and Humber [NIHR200166]; and was supported by UK Research and Innovation for the Healthy Urban Places consortium (grant reference MR/Y022785/1) as part of Population Health Improvement UK (PHI-UK), a national research network which works to transform health and reduce inequalities through change at the population level. The views expressed are those of the author(s), and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

**DNBC:** The Danish National Birth Cohort was established with a significant grant from the Danish National Research Foundation. Additional support was obtained from the Danish Regional Committees, the Pharmacy Foundation, the Egmont Foundation, the March of Dimes Birth Defects Foundation, the Health Foundation and other minor grants. The DNBC Biobank has been supported by the Novo Nordisk Foundation and the Lundbeck Foundation. Follow-up of mothers and children have been supported by the Danish Medical Research Council (SSVF 0646, 271-08-0839/06-066023, O602-01042B, 0602-02738B), the Lundbeck Foundation (195/04, R100-A9193), The Innovation Fund Denmark 0603-00294B (09-067124), the Nordea Foundation (02-2013-2014), Aarhus Ideas (AU R9-A959-13-S804), University of Copenhagen Strategic Grant (IFSV 2012), and the Danish Council for Independent Research (DFF - 4183-00594 and DFF - 4183-00152).
**EDEN:** The EDEN study was supported by Foundation for medical research (FRM), National Agency for Research (ANR), National Institute for Research in Public health (IRESP: TGIR cohorte santé 2008 program), French Ministry of Health (DGS), French Ministry of Research, INSERM Bone and Joint Diseases National Research (PRO-A) and Human Nutrition National Research Programs, Paris-Sud University, Nestlé, French National Institute for Population Health Surveillance (InVS), French National Institute for Health Education (INPES), the European Union FP7 programmes (FP7/2007-2013, HELIX, ESCAPE, ENRIECO, Medall projects), Diabetes National Research Program (through a collaboration with the French Association of Diabetic Patients (AFD)), French Agency for Environmental Health Safety (now ANSES), Mutuelle Générale de l'Éducation Nationale a complementary health insurance (MGEN), French national agency for food security, French speaking association for the study of diabetes and metabolism (ALFEDIAM).

**Gen R:** The general design of the Generation R Study is made possible by financial support from the Erasmus MC, University Medical Center, Rotterdam, Erasmus University Rotterdam, Netherlands Organization for Health Research and Development (ZonMw), Netherlands Organisation for Scientific Research (NWO), Ministry of Health, Welfare and Sport and Ministry of Youth and Families. This project received funding from the European Union's Horizon 2020 research and innovation programme (LIFECYCLE, grant agreement No 733206, 2016; EUCAN-Connect grant agreement No 824989; ATHLETE, grant agreement No 874583; LongITools, grant agreement No 874739). VJ received funding from a Consolidator Grant from the European Research Council (ERC-2014-CoG-648916).

**INMA:** This study was funded by grants from the Instituto de Salud Carlos III (Red INMA G03/176) and the Generalitat de Catalunya-CIRIT (1999SGR 00241). INMA-Valencia was funded by Grants from UE (FP7-ENV-2011 cod 282957 and HEALTH.2010.2.4.5-1), Spain: ISCIII (G03/176; FIS-FEDER: PI09/02647, PI11/01007, PI11/02591, PI11/02038, PI13/1944, PI13/2032, PI14/00891, PI14/01687, and PI16/1288; Miguel Servet-FEDER CP11/00178, CP15/00025, and CPII16/00051), and Generalitat Valenciana: FISABIO (UGP 15-230, UGP-15-244, and UGP-15-249). INMA-Gipuzkoa was funded by grants from the Instituto de Salud Carlos III (FISFIS PI06/0867, FISPS09/0009) 0867, Red INMA G03/176) and the Departamento de Salud del Gobierno Vasco (2005111093 and 2009111069) and the Provincial Government of Guipúzcoa (DFG06/004 and FG08/001). INMA-Sabadell was funded by grants from Instituto de Salud Carlos III (Red INMA G03/176; CB06/02/0041; PI041436; PI081151 incl. FEDER funds; PI12/01890 incl. FEDER funds; CP13/00054 incl. FEDER funds; PI15/00118 incl. FEDER funds; CP16/00128 incl. FEDER funds; PI16/00118 incl. FEDER funds; PI16/00261 incl. FEDER funds; PI17/01340 incl. FEDER funds; PI18/00547 incl. FEDER funds), CIBERESP, Generalitat de Catalunya-CIRIT 1999SGR 00241, Generalitat de Catalunya-AGAUR (2009 SGR 501, 2014 SGR 822), Fundació La marató de TV3 (090430), Spanish Ministry of Economy and Competitiveness (SAF2012-32991 incl. FEDER funds), Agence Nationale de Securité Sanitaire de l'Alimentation de l'Environnement et du Travail (1262C0010; EST-2016 RF-21), EU Commission (261357, 308333, 603794 and 634453). Mònica Guxens was funded by a Miguel Servet II fellowship (CPII18/00018) awarded by the Spanish Institute of Health Carlos III. We acknowledge support from the grant CEX2023-0001290-S funded by MCIN/AEI/10.13039/501100011033, and support from the Generalitat de Catalunya through the CERCA Program.

**MoBa:** The Norwegian Mother, Father and Child Cohort Study is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research. We are grateful to all the participating families in Norway who take part in this on-going cohort study. This work was partly supported by the Research Council of Norway through its Centres of Excellence funding scheme, project number 262700. This study includes data from the Norwegian Mother, Father and Child Cohort Study (MoBa) conducted by the Norwegian Institute of Public Health.

**NINFEA:** The NINFEA cohort was initially funded by the Compagnia SanPaolo Foundation and the Piedmont Region. It received funding from European projects: CHICOS (FP7 grant number HEALTH-FP7-2009-241604, LifeCycle (H2020 grant number 733206), ATHLETE (H2020 grant number 874583).
**RHEA:** The "Rhea" project was financially supported by European projects (EU FP6-2003-Food-3-NewGeneris, EU FP6. STREP Hiwate, EU FP7 NV.2007.1.2.2.2. Project No 211250 Escape, EU FP7-2008-ENV-1.2.1.4 Envirogenomarkers, EU FP7-HEALTH-2009- single stage CHICOS, EU FP7 ENV.2008.1.2.1.6. Proposal No 226285 ENRIECO, EU- FP7- HEALTH-2012 Proposal No 308333 HELIX) and the Greek Ministry of Health (Program of Prevention of obesity and neurodevelopmental disorders in preschool children, in Heraklion district, Crete, Greece: 2011-2014; "Rhea Plus": Primary Prevention Program of Environmental Risk Factors for Reproductive Health, and Child Health: 2012-15).

SW acknowledges receiving the funding by the Generalitat de Catalunya (Agency for Management of University and Research Grants) with a Beatriu de Pinós post-doctoral fellowship (Ref: 2021 BP 00058).

SS is supported by the European Union's Horizon Europe Research and Innovation Programme under the Marie Skłodowska-Curie Postdoctoral Fellowship Grant Agreement No. 101109136 (URBANE).

The funders had no role in the design and conduct of the study; management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

**Conflicts of Interest**

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

**Acknowledgments**

We are grateful to all the participating children, parents, practitioners and researchers in the cohorts who took part in this study.

**ABCD:** We are grateful to all participating hospitals, obstetric clinics, and general practitioners for their assistance in implementing the ABCD study and thank all of the women who participated for their cooperation.

**ALSPAC:** We are extremely grateful to all of the families who took part in ALSPAC, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses.

**BiB:** BiB is only possible because of the enthusiasm and commitment of the children and parents in BiB. We are grateful to all the participants, health professionals, schools and researchers who have made BiB happen.

**DNBC:** The authors would like to thank the participants, the previous Principal Investigators of DNBC Prof. Jørn Olsen and Prof Anne-Marie Nybo Andersen, the scientific managerial team, and DNBC secretariat for being, establishing, developing and consolidating the Danish National Birth Cohort.

**EDEN:** The authors thank all the participants of the EDEN study and the EDEN mother-child cohort study group, whose members are I. Annesi-Maesano, J.Y. Bernard, M.A. Charles, P. Dargent-Molina, B. de Lauzon-Guillain, P. Ducimetière, M. de Agostini, B. Foliguet, A. Forhan, X. Fritel, A. Germa, V. Goua, R. Hankard, B. Heude, M. Kaminski, B. Larroquey, N. Lelong, J. Lepeule, G. Magnin, L.
Marchand, C. Nabet, F. Pierre, R. Slama, M.J. Saurel-Cubizolles, M. Schweitzer, and O. Thiebaugeorges.

Gen R: The authors gratefully acknowledge the contribution of participants, research collaborators, general practitioners, hospitals, midwives, and pharmacies in Rotterdam.

INMA Gipuzkoa, Sabadell and Valencia: The authors would particularly like to thank all the participants for their generous collaboration. A full roster of the INMA Project Investigators can be found at https://www.proyectoinma.org/en/inma-project/inma-project-researchers/.

MoBa: We are grateful to all the participating families in Norway who take part in this on-going cohort study.

NINFEA: The authors thank all families participating in the NINFEA cohort.

RHEA: We are grateful to all the participating families who took part in this cohort study.

**Data availability**

The data that has been used is confidential. Metadata describing the cohorts involved is open access available at the LifeCycle data catalogue at http://data-catalogue.molgeniscloud.org

**CRediT authorship contribution statement**

**Sarah Warkentin:** Roles/Writing - original draft, Writing - review & editing. **Serena Fossati:** Conceptualization, Methodology, Writing - review & editing. **Sandra Marquez:** Methodology, Writing - review & editing, Formal analysis. **Anne-Marie Nybo Andersen:** Writing - review & editing, Funding acquisition. **Sandra Andrusaityte:** Writing - review & editing, Funding acquisition. **Demetris Avraam:** Data curation, Writing - review & editing. **Ferran Ballester:** Writing - review & editing, Funding acquisition. **Tim Cadman:** Writing - review & editing.

**Maribel Casas:** Writing - review & editing. **Montserrat de Castro:** Data curation, Writing - review & editing. **Leda Chatzi:** Writing - review & editing, Funding acquisition. **Ahmed Elhakeem:** Writing - review & editing, Funding acquisition. **Antonio d'Errico:** Writing - review & editing, Funding acquisition. **Mònica Guxens:** Writing - review & editing, Funding acquisition.

**Regina Grazuleviciene:** Writing - review & editing, Funding acquisition. **Jennifer R Harris:** Writing - review & editing, Funding acquisition. **Carmen Iñiguez Hernandez:** Writing - review & editing, Funding acquisition. **Barbara Heude:** Writing - review & editing, Funding acquisition.

**Elena Isaevska:** Writing - review & editing. **Vincent WV Jaddoe:** Writing - review & editing, Funding acquisition. **Marianna Karachaliou:** Writing - review & editing. **Aitana Lertxundi:** Writing - review & editing, Funding acquisition. **Johanna Lepeule:** Writing - review & editing.

**Rosemary RC McEachan:** Writing - review & editing, Funding acquisition. **Johanna L Thorbjørnsrud Nader:** Writing - review & editing. **Marie Pedersen:** Conceptualization, Writing - review & editing, Funding acquisition. **Susana Santos:** Writing - review & editing.

**Mariska Slofstra:** Writing - review & editing. **Euripides G. Stephanou:** Writing - review & editing. **Morris A Swertz:** Writing - review & editing. **Tanja Vrijkotte:** Writing - review & editing, Funding acquisition. **Tiffany C Yang:** Writing - review & editing. **Mark Nieuwenhuijsen:** Conceptualization, Writing - review & editing, Funding acquisition.

**Martine Vrijheid:** Conceptualization, Methodology, Roles/Writing - original draft, Writing - review & editing, Funding acquisition, Supervision.

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## Highlights

* We analyzed harmonized data from 10 birth cohorts located in 8 European countries.

* NO₂ exposure did not show robust associations with BMI and overweight/obesity.

* Prenatal PM₂.₅ exposure was associated with 23% higher overweight/obesity risk.

* Postnatal air pollution exposure was not associated with BMI or overweight/obesity.

* High heterogeneity between and associations within specific cohorts were found.
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Map of the 10 included cohorts from the EU Child Cohort Network.</figcaption></figure>

<caption>**Figure 2.** Forest plot of associations between pre- and postnatal exposureª to NO₂ and childhood</caption>

a. NO₂ prenatal exposure (zBMI)

Study
ABCD
ALSPAC
BIB
DNBC
EDEN
GENR
INMA
MOBA
NINFEA
RHEA

15018
16162
6518
7897
1345
16381
5523
17410
4394
656

Common effect model
Random effects model

<figure><img src="image_4.png" /></figure>

Heterogeneity: I² = 25%, p = 0.21

b. NO₂ prenatal exposure (overweight/obesity)

Study
ABCD
ALSPAC
BIB
DNBC
EDEN
GENR
INMA
MOBA
NINFEA
RHEA

15018
16162
6518
7897
1345
16381
5523
17410
4394
656

Common effect model
Random effects model

<figure><img src="image_5.png" /></figure>

OR
1.19 [1.04; 1.36]
0.83 [0.66; 1.03]
0.98 [0.78; 1.23]
0.99 [0.84; 1.17]
1.10 [0.90; 1.35]
1.08 [0.96; 1.22]
0.92 [0.82; 1.03]
1.00 [0.93; 1.07]
1.01 [0.92; 1.09]
1.53 [0.66; 3.57]

Weight
11.0%
5.4%
5.2%
8.4%
6.2%
12.5%
13.7%
19.5%
17.7%
0.4%

OR
1.01 [0.97; 1.05]
1.01 [0.96; 1.07]

Heterogeneity: I² = 39%, p = 0.10

zBMI and overweight/obesity using
two-stage meta-analysis.
c. NO₂ postnatal exposureª (zBMI)

<table><thead><tr><th>Study</th><th>N</th></tr></thead><tbody><tr><td>ABCD</td><td>13385</td></tr><tr><td>ALSPAC</td><td>16162</td></tr><tr><td>BIB</td><td>6443</td></tr><tr><td>DNBC</td><td>4643</td></tr><tr><td>EDEN</td><td>1326</td></tr><tr><td>GENR</td><td>16287</td></tr><tr><td>INMA</td><td>5502</td></tr><tr><td>MOBA</td><td>13461</td></tr><tr><td>NINFEA</td><td>3535</td></tr><tr><td>RHEA</td><td>647</td></tr></tbody></table>

Common effect model
Random effects model

Heterogeneity: I² = 74%, p < 0.01

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

Heterogeneity: I² = 73%, p < 0.01

d. NO₂ postnatal exposureª (overweight/obesity)

<table><thead><tr><th>Study</th><th>N</th></tr></thead><tbody><tr><td>ABCD</td><td>13385</td></tr><tr><td>ALSPAC</td><td>16162</td></tr><tr><td>BIB</td><td>6443</td></tr><tr><td>DNBC</td><td>4643</td></tr><tr><td>EDEN</td><td>1326</td></tr><tr><td>GENR</td><td>16287</td></tr><tr><td>INMA</td><td>5502</td></tr><tr><td>MOBA</td><td>13461</td></tr><tr><td>NINFEA</td><td>3535</td></tr><tr><td>RHEA</td><td>647</td></tr></tbody></table>

Common effect model
Random effects model

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

<table><thead><tr><th>Odds Ratio</th><th>OR</th><th>Weight<br>95%-CI (random)</th></tr></thead><tbody><tr><td>1.19 [1.01; 1.39]</td><td>1.19</td><td>12.2%</td></tr><tr><td>0.73 [0.55; 0.99]</td><td>0.73</td><td>7.6%</td></tr><tr><td>0.96 [0.81; 1.13]</td><td>0.96</td><td>11.9%</td></tr><tr><td>1.21 [0.99; 1.48]</td><td>1.21</td><td>10.6%</td></tr><tr><td>0.63 [0.37; 1.06]</td><td>0.63</td><td>3.6%</td></tr><tr><td>1.14 [1.02; 1.27]</td><td>1.14</td><td>14.0%</td></tr><tr><td>0.88 [0.77; 1.00]</td><td>0.88</td><td>13.1%</td></tr><tr><td>1.00 [0.90; 1.11]</td><td>1.00</td><td>14.1%</td></tr><tr><td>1.16 [0.98; 1.37]</td><td>1.16</td><td>12.0%</td></tr><tr><td>0.21 [0.07; 0.64]</td><td>0.21</td><td>1.0%</td></tr></tbody></table>

<table><tbody><tr><td>1.03 [0.98; 1.09]</td></tr><tr><td>1.00 [0.89; 1.12]</td><td>--</td><td>100.0%</td></tr></tbody></table>

ABCD: Amsterdam Born Children and their Development; ALSPAC: Avon Longitudinal Study of Parents and Children; BiB: Born in Bradford; DNBC: Danish National Birth Cohort; EDEN: Etude des Déterminants pré et postnatals précoces du développement et de la santé de l'Enfant; Gen R: Generation R Study; INMA: Infancia y Medio Ambiente; MoBa: Norwegian Mother, Father and Child Cohort Study; NINFEA: Nascita e INFanzia: gli Effetti dell'Ambiente; RHEA: Rhea Mother & Child Cohort Study; zBMI: Body mass index z-score; OR: Odds ratio; CI: Confidence intervals. Prenatal exposure models adjusted for sex, child age (in months), individual-level SES (maternal education), pre-pregnancy BMI, smoking during pregnancy, and area-level SES. Postnatal exposure models were further adjusted for prenatal air pollution exposure. Further adjustments: INMA, NINFEA and EDEN: for city, in Gen R, ABCD and BiB: for ethnicity. I²: Test for heterogeneity (%). ª Values are described as weighted average postnatal air pollution levels in the year before zBMI measurement, in the different age periods.
<figure><img src="image_8.png" /><figcaption>**Figure 3.** Forest plot of associations between pre- and postnatal exposure<sup>a</sup> to PM<sub>2.5</sub> and childhood zBMI and overweight/obesity using two-stage meta-analysis.</figcaption></figure>

ABCD: Amsterdam Born Children and their Development; ALSPAC: Avon Longitudinal Study of Parents and Children; BiB: Born in Bradford; DNBC: Danish National Birth Cohort; EDEN: Etude des Déterminants pré et postnatals précoces du développement et de la santé de l'Enfant; Gen R: Generation R Study; INMA: Infancia y Medio Ambiente; MoBa: Norwegian Mother, Father and Child Cohort Study; NINFEA: Nascita e INFanzia: gli Effetti dell'Ambiente; RHEA: Rhea Mother & Child Cohort Study; zBMI: Body mass index z-score; OR: Odds ratio; CI: Confidence intervals. Prenatal exposure models adjusted for sex, child age (in months), individual-level SES (maternal education), pre-pregnancy BMI, smoking during pregnancy, and area-level SES. Postnatal exposure models were further adjusted for prenatal air pollution exposure. Further adjustments: In INMA, NINFEA and EDEN: for city, in Gen R, ABCD and BiB: for ethnicity. I²: Test for heterogeneity (%). <sup>a</sup> Values are described as weighted average postnatal air pollution levels in the year before zBMI measurement, in the different age periods.

<caption>**Figure 4.** Pre- and postnatal air pollution exposure<sup>a</sup> and childhood overall zBMI and overweight/obesity using two-stage meta-analysis, stratified by age period.</caption>

a. NO<sub>2</sub>
<figure><img src="image_9.png" /></figure>

b. PM<sub>2.5</sub>
<figure><img src="image_10.png" /></figure>

BMI: Body mass index; OR: Odds ratio; CI: Confidence intervals. Prenatal exposure models are adjusted for sex, child age (in months), individual-level SES (maternal education), pre-pregnancy BMI, smoking during pregnancy, and area-level SES. Postnatal exposure models were further adjusted for prenatal air pollution exposure. Further adjustments: In INMA, NINFEA and EDEN: for city, in Gen R, ABCD and BiB: for ethnicity. I²: Test for heterogeneity (%). a Values are described as weighted average postnatal air pollution levels in the year before zBMI measurement, in the different age periods.
Ambient air pollution and childhood obesity
from infancy to late childhood

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

LifeCycle

ISGlobal

Study population & Methods

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

10 European birth cohorts participating in the EU Child Cohort Network -
the LifeCycle project

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

<figure><img src="image_14.png" /></figure>

Child
weight
outcomes
at
0-2, 2-5,
5-9 and 9-
12 years

Results & Conclusions

* Results showed no robust associations between NO₂ exposure and zBMI or overweight/obesity risk

* Exposure to PM₂.₅ during pregnancy was associated with 23% (95CI% 1.05;1.37) higher overweight/obesity risk across childhood, and higher zBMI and overweight/obesity risk at 9-12 years

* Heterogeneity between cohorts was considerable

* This meta-analysis suggest that prenatal PM₂.₅ exposure may be associated with adverse childhood obesity outcomes, but provides little evidence to support an effect of postnatal air pollution exposure although cohort-specific associations were observed
**Manuscript: Ambient air pollution and childhood obesity from infancy to late childhood: An individual participant data meta-analysis of 10 European birth cohorts**

**CRediT authorship contribution statement**

**Sarah Warkentin:** Roles/Writing - original draft, Writing - review & editing. **Serena Fossati:** Conceptualization, Methodology, Writing - review & editing. **Sandra Marquez:** Methodology, Writing - review & editing, Formal analysis. **Anne-Marie Nybo Andersen:** Writing - review & editing, Funding acquisition. **Sandra Andrusaityte:** Writing - review & editing, Funding acquisition. **Demetris Avraam:** Data curation, Writing - review & editing. **Ferran Ballester:** Writing - review & editing, Funding acquisition. **Tim Cadman:** Writing - review & editing. **Maribel Casas:** Writing - review & editing. **Montserrat de Castro:** Data curation, Writing - review & editing. **Leda Chatzi:** Writing - review & editing, Funding acquisition. **Ahmed Elhakeem:** Writing - review & editing, Funding acquisition. **Antonio d'Errico:** Writing - review & editing, Funding acquisition. **Mònica Guxens:** Writing - review & editing, Funding acquisition. **Regina Grazuleviciene:** Writing - review & editing, Funding acquisition. **Carmen Iñiguez Hernandez:** Writing - review & editing, Funding acquisition. **Barbara Heude:** Writing - review & editing, Funding acquisition. **Elena Isaevska:** Writing - review & editing. **Vincent WV Jaddoe:** Writing - review & editing, Funding acquisition. **Marianna Karachaliou:** Writing - review & editing. **Aitana Lertxundi:** Writing - review & editing, Funding acquisition. **Johanna Lepeule:** Writing - review & editing. **Rosemary RC McEachan:** Writing - review & editing, Funding acquisition. **Johanna L Thorbjørnsrud Nader:** Writing - review & editing. **Marie Pedersen:** Conceptualization, Writing - review & editing, Funding acquisition. **Susana Santos:** Writing - review & editing. **Mariska Slofstra:** Writing - review & editing. **Euripides G. Stephanou:** Writing - review & editing. **Morris A Swertz:** Writing - review & editing. **Tanja Vrijkotte:** Writing - review & editing, Funding acquisition. **Tiffany C Yang:** Writing - review & editing. **Mark Nieuwenhuijsen:** Conceptualization, Writing - review & editing, Funding acquisition. **Martine Vrijheid:** Conceptualization, Methodology, Roles/Writing - original draft, Writing - review & editing, Funding acquisition, Supervision

**Declaration of interests**

[x] The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

[ ] The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: