# Association of exposure to mixture of chemicals during pregnancy with cognitive abilities and fine motor function of children

Pavla Brennan Kearns, Michiel A van den Dries, Jordi Julvez, Mariza Kampouri, Mónica López-Vicente, Lea Maitre, Claire Philippat, Line Småstuen Haug, Marina Vafeiadi, Cathrine Thomsen, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Pavla Brennan Kearns, Michiel A van den Dries, Jordi Julvez, Mariza Kampouri, Mónica López-Vicente, et al. Association of exposure to mixture of chemicals during pregnancy with cognitive abilities and fine motor function of children. Environment International, 2024, 185, pp.108490. 10.1016/j.envint.2024.108490. hal-04662261

HAL Id: hal-04662261

https://hal.science/hal-04662261v1

Submitted on 25 Jul 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

Full length article

# Association of exposure to mixture of chemicals during pregnancy with cognitive abilities and fine motor function of children

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

Pavla Brennan Kearns<sup>a</sup>, Michiel A. van den Dries<sup>b</sup>, Jordi Julvez<sup>c,d,e,f</sup>, Mariza Kampouri<sup>g,h</sup>, Mónica López-Vicente<sup>c,d,e</sup>, Lea Maitre<sup>c,d,e</sup>, Claire Philippat<sup>i</sup>, Line Småstuen Haug<sup>j</sup>, Marina Vafeiadi<sup>g</sup>, Cathrine Thomsen<sup>j</sup>, Tiffany C Yang<sup>k</sup>, Martine Vrijheid<sup>c,d,e</sup>, Henning Tiemeier<sup>b,l</sup>, Mònica Guxens<sup>b,c,d,e,*</sup>

ARTICLE INFO

Handling Editor: Adrian Covaci

Keywords:

Chemical mixtures

Prenatal exposures

Neurodevelopment

Fine motor function

Cognitive abilities

ABSTRACT

Chemical exposures often occur in mixtures and exposures during pregnancy may lead to adverse effects on the fetal brain, potentially reducing lower cognitive abilities and fine motor function of the child. We investigated the association of mothers exposure to a mixture of chemicals during pregnancy (i.e., organochlorine compounds, per- and polyfluoroalkyl substances, phenols, phthalates, organophosphate pesticides) with cognitive abilities and fine motor function in their children. We studied 1097 mother–child pairs from five European cohorts participating in the Human Early Life Exposome study (HELIX). Measurement of 26 biomarkers of exposure to chemicals was performed on urine or blood samples of pregnant women (mean age 31 years). Cognitive abilities and fine motor function were assessed in their children (mean age 8 years) with a battery of computerized tests administered in person (Raveńs Coloured Progressive Matrices, Attention Network Test, N-back Test, Trail Making Test, Finger Tapping Test). We estimated the joint effect of prenatal exposure to chemicals on cognitive abilities and fine motor function using the quantile-based g-computation method, adjusting for sociodemographic characteristics. A quartile increase in all the chemicals in the overall mixture was associated with worse fine motor function, specifically lower scores in the Finger Tapping Test [-8.5 points, 95 % confidence interval (CI) –13.6 to –3.4; –14.5 points, 95 % CI –22.4 to –6.6, and –18.0 points, 95 % CI –28.6 to –7.4) for the second, third and fourth quartile of the overall mixture, respectively, when compared to the first quartile]. Organochlorine compounds, phthalates, and per- and polyfluoroalkyl substances contributed most to this association. We did not find a relationship with cognitive abilities. We conclude that exposure to chemical mixtures during pregnancy may influence neurodevelopment, impacting fine motor function of the offspring.

<footnote><sup>a</sup> Department of Epidemiology, Second Faculty of Medicine, Charles University, Prague, Czechia</footnote>

<footnote><sup>b</sup> Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Centre, Rotterdam, the Netherlands</footnote>

<footnote><sup>c</sup> ISGlobal, Barcelona, Spain</footnote>

<footnote><sup>d</sup> Universitat Pompeu Fabra, Barcelona, Spain</footnote>

<footnote><sup>e</sup> Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Spain</footnote>

<footnote><sup>f</sup> Clinical and Epidemiological Neuroscience (NeuroEpi), Institut d'Investigació Sanitària Pere Virgili (IISPV), 43204 Reus, Spain</footnote>

<footnote><sup>g</sup> University of Crete, Heraklion, Greece</footnote>

<footnote><sup>h</sup> Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden</footnote>

<footnote><sup>i</sup> University Grenoble Alpes, Inserm U 1209, CNRS UMR 5309, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Institute for Advanced Biosciences, Grenoble, France</footnote>

<footnote><sup>j</sup> Norwegian Institute of Public Health, Department of Food Safety, Oslo, Norway</footnote>

<footnote><sup>k</sup> Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK</footnote>

<footnote><sup>l</sup> Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, USA</footnote>

<footnote>* Corresponding author at: Mònica Guxens, ISGlobal Barcelona Biomedical Research Park (PRBB). Doctor Aiguader, 88 08003 Barcelona, Spain.
E-mail address: Monica.guxens@isglobal.org (M. Guxens).</footnote>
# 1. Introduction

Cognitive abilities, such as general intelligence, memory, attention, or cognitive flexibility, are among the strongest predictors of healthy and successful lives (Lövdén et al., 2020, Mashburn et al., 2023). An increasing number of findings suggest that cognitive abilities are grounded in sensorimotor experiences and that cognitive development is strongly related to fine motor function (Martzog et al., 2019), which is a strong predictor of mental and physical functioning during the life-course (Austin et al., 2011). As the developing fetus is particularly susceptible to environmental toxins, there is a growing concern to protect future children from such toxins (Lanphear et al., 2005). Humans can be exposed to thousands of chemicals, which are currently being produced (Landrigan et al., 2018, Landrigan and Goldman, 2011, Grandjean and Landrigan, 2006) and many of them cross the placenta (Mose et al., 2007, Porpora et al., 2013) and can adversely affect development of the fetal brain (Zoeller et al., 2012).

It is well known that prenatal exposure to organochlorine (OC) compounds, per - and polyfluoroalkyl substances (PFAS), phenols, phthalates, and organophosphate (OP) pesticides may be associated with altered infant brain development, which could manifest as lower cognitive abilities and worse fine motor function (Braun, 2017, Vrijheid et al., 2016). These chemicals are widely present as legacy chemicals in nature or currently used in households, food and many consumer products. Many studies have evaluated health effects of single chemicals or small groups of chemicals, despite the fact that humans are exposed to mixtures of chemicals simultaneously (Zoeller et al., 2012). The effects of multiple simultaneous exposures during early life on cognitive abilities and fine motor function in childhood have been investigated using approaches that simply correct for multiple testing or identify the best predictors through variable selection (Julvez et al., 2021). However, new methods have been developed to investigate mixtures that aim to account for the joint effect of combinations of chemicals (Lazarevic et al., 2019). The simultaneous exposure to mixtures of chemicals may elicit effects that would otherwise be ignored in an analysis using a single-pollutant approach, such as additive or synergistic effects, and can lower the threshold for the harmful effects of individual exposures (Zoeller et al., 2012, Kortenkamp, 2014).

Some recent publications that assessed the exposure to a mixture of chemicals using these novel approaches on offspring's cognitive abilities (Tanner et al., 2020, Loftus et al., 2021, van den Dries et al., 2021, Luo et al., 2022, Lee et al., 2021, Yim et al., 2022) and fine motor function (Daniel et al., 2020, Luo et al., 2022) provided mixed results. Some found strong associations, others only with some outcomes, in some population subgroups or no associations at all. However, the previous studies mainly focused on a selected group of chemicals, such as phthalates (Daniel et al., 2020, Loftus et al., 2021, Day et al., 2021, Li et al., 2020), phenols and phthalates (Guilbert et al., 2021), PFAS (Luo et al., 2022, Skogheim et al., 2021), persistent organic pollutants (Yim et al., 2022), and nonpersistent chemicals (van den Dries et al., 2021), while assessment of a mixture to a wider range of chemical groups is scarce (Tanner et al., 2020). However, in the real world, for example commonly in households, one is often exposed to a broad group of chemicals at the same time. All of the previous studies were based only on a cohort from one country, which limits their generalizability. Capitalizing on an exposome cohort covering various regions of Europe, our objective was to study the association of mother's exposure to a mixture of chemicals during pregnancy (OC compounds, PFAS, phenols, phthalates, and OP pesticides) with cognitive abilities and fine motor function in children.

# 2. Material and methods

## 2.1. Participants

We utilized data from the Human Early Life Exposome (HELIX) study

(Maitre et al., 2018), a collaborative project across six established longitudinal population-based birth cohorts in six European countries: the Born in Bradford (BiB) study in the UK (Wright et al., 2013), the Étude des Déterminants pré et postnatals du développement et de la santé de l'Enfant (EDEN) study in France (Heude et al., 2016), the INFancia y Medio Ambiente (INMA) Project in Spain (Guxens et al., 2012), the Kaunus cohort (KANC) in Lithuania, the Norwegian Mother, Father and Child Cohort Study (MoBa) (Magnus et al., 2016) and the RHEA Mother Child Cohort study in Crete, Greece (Chatzi et al., 2017). As KANC had missing data on most of the chemicals in the prenatal samples analyzed in this study, we did not include it.

A subcohort of 1097 mother-child pairs were recruited for a detailed investigation including neuropsychological assessment at 6-12 years old. They were selected from the entire cohorts so that there were approximately the same number of mother-child pairs from each cohort (specifically 205 in BiB, 198 in EDEN, 272 in MoBa, 199 in RHEA and 223 in INMA). Inclusion and exclusion criteria varied between cohorts and are described elsewhere (Maitre et al., 2018).

## 2.2. Exposure to chemicals

Maternal samples for the assessment of exposure to chemicals were collected in the first and third trimester for INMA, first trimester for RHEA, during the first and the second trimester for EDEN and MoBa, and during the second and the third trimester for BiB. In the present study, we analyzed 26 biomarkers of exposure to chemicals, which were assessed in all five cohorts: 5 OC compounds, 4 PFAS, 5 phenols, 7 phthalates, and 5 OP pesticides. These chemicals were selected as they belong to groups, within which individual members share biological properties and are often repleacable for one another. Complete methodology including specific values of limit of quantification and limit of detection are available elsewhere (Haug et al., 2018, Tamayo-Uria et al., 2019). Briefly, measurements of most chemicals were performed at the Department of Food Safety at the Norwegian Institute of Public Health (NIPH) in Norway. Some INMA samples were analyzed at the Laboratory of Public Health in Guipuzcoa in Spain (OC compounds) (Goñi et al., 2007), Institute for Occupational Medicine in Aachen in Germany (PFAS) (Manzano-Salgado et al., 2015), and Hospital del Mar Medical Research Institute in Barcelona in Spain (phthalates) (Valvi et al., 2015). Some RHEA samples were assessed at the National Institute for Health and Welfare in Kuopio, Finland (OC compounds) (Koponen et al., 2013) and some EDEN samples at the Center for Disease Control and Prevention in the US (phenols) (Philippat et al., 2012). For all assessments at the NIPH or their contract laboratories, concentrations were also reported below limit of quantification whenever a signal was observable. For samples where the concentrations were below limit of detection, singly imputed values were obtained using a quantile regression approach.

The concentration of OC compounds was determined in serum or plasma in most cases using methods reported by Caspersen (Caspersen et al., 2016), but another method was used in INMA (Goñi et al., 2007). The concentrations of OC compounds were adjusted for lipids (ng/g of lipids), which were determined in the Fürst Medical Analysis Laboratory in Norway in serum or plasma using the FS kit from DiaSys (phospholipids) or the ADVIA® Chemistry XPT System (other lipids). In INMA and RHEA, total cholesterol and triglycerides were determined with the Cobas Mira self-analyzer and phospholipids were calculated based on a formula (Covaci et al., 2006). Total fat percentage was calculated according to Grimvall (Grimvall et al., 1997).

Concentration of PFAS was determined in serum or plasma using the method by Haug (Haug et al., 2009). In INMA maternal plasma samples, concentrations of PFAS were determined according to Manzano-Salgado (Manzano-Salgado et al., 2015). Assessment of the comparability between labs has been previously described (Haug et al., 2018).

In urine, analysis of phenols (Sakhi et al., 2018), OP pesticides (Cequier et al., 2016) and phthalates (Sabaredzovic et al., 2015) was
performed. In the majority of INMA maternal samples, phthalates were determined according to Valvi (Valvi et al., 2015). The urinary concentrations of these three chemical groups were adjusted for creatinine (µg/g of creatinine). Measurements of concentration of creatinine in urine in most samples were performed on an AU680 Chemistry System form Beckman Coulter using DRI® Creatinine-Detect® Test at Fürst Medisinsk Laboratorium, Norway. Creatinine in maternal samples of INMA was determined by the Jaffé method - Beckman Coulter® AU5400, while an enzymatic reaction using a Roche Hitachi 912 chemistry analyzer was utilized in EDEN.

## 2.3. Cognitive abilities and fine motor function

Cognitive abilities and fine motor function were assessed at the average age of 8 years old (range 6–12 years) with a battery of stand-ardised, non-linguistic, and culturally blind computerized tests. In the present study, we investigated six individual outcomes: general non-verbal intelligence, sustained attention, working memory, cognitive flexibility (task switching and task shifting), and fine motor speed.

General non-verbal intelligence was assessed by Raven's Coloured Progressive Matrices (Raven and Raven, 1998). Participants had to apply logical reasoning and detect an organizing principle in visual materials, requiring participants to recognize spatial, design, and nu-merical relationships. The outcome was the total number of correct re-sponses, where higher values indicated higher non-verbal intelligence.

Sustained attention was assessed with the Attention Network Test (Rueda et al., 2004). It consisted of the presentation of a row of five fish appearing either above or below a fixation point and participants had to indicate the direction, in which the central fish was pointing, ignoring distractions. The outcome that we used was standard error of the hit reaction time, where higher values indicate inattentiveness.

Working memory was assessed with the N-back Test (Vuontela et al., 2003). Participants were required to monitor a series of stimuli (colours and numbers) presented in the centre of the screen and had to respond whenever a given stimulus was the same as the one presented in pre-vious trials (1-, 2-, and 3-back). We selected 3-back results since they require higher working memory demands. The outcome used was d prime, a measure derived from signal detection theory, with higher levels indicating better working memory (Forns et al., 2014).

The Trail Making Test (Lezak et al., 2004) was used to assess task switching and task shifting, which both indicate cognitive flexibility. In part A, participants had to draw lines connecting encircled numbers in a numerical order, and in part B, they had to draw lines alternating be-tween numbers and letters in an ascending sequence. The outcomes were task switching (unconscious shift in attention) and task shifting (conscious shift in attention), with higher values indicating lower cognitive flexibility. Task switching was the time to complete part B. Task shifting was calculated as follows: (time to complete part A minus time to complete part B) divided by time to complete part A.

Fine motor function was assessed with fine motor speed, which was measured by the Finger Tapping Test (Lezak et al., 2004). Participants had to tap a key for 15 s, twice with their preferred hand and twice with their non-preferred hand. The average value of the sums of taps for the preferred hand and the non-preferred hand was used as the outcome, with higher values indicating higher fine motor speed.

## 2.4. Confounders

We selected confounding variables based on a directed acyclic graph (DAG) as factors that are antecedent of the exposure and have an effect on the outcome (Supplement, Figure S1). Data was obtained from questionnaires administered during pregnancy, which included mother's age at the time of exposure assessment, mother's education (low / middle / high), father's education (low / middle / high), mother's country of origin (born in the country of the cohort / not born in the country of the cohort), mother's pre-pregnancy body mass index

(calculated from self-reported weight and measured height, kg/m²; underweight or normal weight / overweight / obesity), mother's parity (no previous child / one previous child / two or more previous children) and mother's active smoking during pregnancy (yes / no).

In addition, we adjusted for offspring's sex (male / female), which was obtained from hospital records, offspring's age at the time of outcome assessment, which was calculated based on the visit date. Both these variables are strong determinants of the outcomes, as cognitive abilities and fine motor function differ for boys and girls and increase with age. Moreover, as there were differences in characteristics of participants between individual subcohorts (Supplement, Table S1 and S2) and there could be also batch effects, we also adjusted for subcohort (BiB / EDEN / INMA / MoBa / RHEA).

## 2.5. Statistical analysis

Prior to the analysis, we used the expectation-maximization algorithm to impute the missing data on confounders (7.7 % for father's education, <5% for other confounders), using the package *Amelia* in R. To estimate the joint effect of all chemicals on cognitive abilities and fine motor function of the offspring, we used the quantile-based g-computation method in R, which has been previously described in detail (Keil et al., 2020). The quantile g-computation estimates the effect on an outcome by simultaneously increasing all chemicals within the mixture by a quartile. It decreases type one error and deals with co-pollutant confounding, as it allows assessment of the joint effect of the overall mixture as well as a specific chemical group (e.g. phthalates), while adjusting for confounding by other chemical groups. We adjusted all models for confounders detailed above.

We first investigated the overall mixture effects (all chemicals in the model) on each of the outcomes, fitting a linear model. Further, we explored non-linearity the following way: For each outcome, we repeatedly fit the full model, adding one by one each individual chemical squared, and tested if this model has a better fit than the full linear model, using Akaike information criterion (AIC). If the AIC was lower for the non-linear model than for the linear one, we compared their fit, utilizing likelihood ratio (LR) test. In case the p value from LR test was below 0.05, we considered this as evidence of non-linearity for the specific chemical tested. If more chemicals individually showed a better fit with a quadratic term for a specific outcome, we combined these quadratic terms into one final model, with the rest of chemicals as linear. We then assessed with the LR test, if the final model reached a better fit than the fully linear one. If the p value from the LR test was < 0.05, we chose the final model with the combined quadratic terms.

The models with the outcomes non-verbal intelligence and sustained attention had a better fit as linear for all chemicals. The model with the outcome working memory included hexachlorobenzene (HCB) with a quadratic term. The models with the three remaining outcomes fit better when quadratic terms for more chemicals were included. For all of them, the final models with the combined quadratic terms had a better fit than the fully linear one. The model with the outcome task switching had the best fit with quadratic terms for perfluorohexane sulfonate (PFHxS), perfluorooctane sulfonate (PFOS), and mono-2-ethyl-5-oxohexyl phthalate (MEOHP). The model with the outcome task shifting included quadratic terms for PFHxS, PFOS, mono-n-butyl phthalate (MnBP), and dimethyl phosphate (DMP). For the outcome fine motor speed, the model included quadratic terms for perfluorooctanoate (PFOA), HCB, and oxybenzone (OXBE). As we tested six outcomes, we applied Bonferroni correction and accepted results as statistically significant if the p value was < 0.008.

When we identified an association between the overall mixture and a specific outcome, we investigated the exposure effect within each chemical group on the identified outcome, while adjusting for the other chemical groups (one summary variable for each group). In addition, we estimated which chemicals within the mixture were contributing the most to the overall mixture effect for the model. In line with a previous
study (van den Dries et al., 2021), we assumed linearity in this model and did not include the quadratic terms. This analysis generates percentage of positive or negative weight corresponding to the extent, to which each chemical contributes to the association. Data analysis was performed using R statistical programming language, version 4.0.5 (Team, 2021).

## 3. Results

We studied 1097 mother-child pairs (see Tables 1 and 2 and Supplement, Figures S2 and S3 for their descriptive characteristics). Mothers were on average 31 years old during pregnancy. Half of the cohort were mothers with university education, 12 % of them were obese before pregnancy, and 17 % of them smoked when pregnant.

Fig. 1 presents differences in estimated outcomes for each exposure quartile compared to the lowest quartile in all chemicals within all chemical groups. The overall mixture of all chemicals was associated only with lower fine motor speed for the second, third, and fourth quartile (-8.5 points, 95 % confidence interval (CI) -13.6 to -3.4; -14.5 points, 95 % CI -22.4 to -6.6; and -18.0 points, 95 % CI -28.6 to -7.4) of the overall mixture, respectively, when compared to the first quartile (p value for the overall model 0.003). When Bonferroni correction was applied, no associations were observed between the overall mixture and general non-verbal intelligence, sustained attention, working memory, task shifting, or task switching (Supplement, Table S3).

Further analyses on the exposure effect within each group of chemicals, while adjusting for the other chemical groups, were ran for fine motor speed (Fig. 2 and Supplement, Table S4). A quartile increase in the OC compounds mixture was associated with lower scores in fine motor speed (-4.4 points, 95 % CI -7.3 to -1.5 for the second quartile; -6.7 points, 95 % CI -10.9 to -2.4 for the third quartile; and -6.7 points, 95 % CI -12.4 to -1.1 for the fourth quartile, when compared to the first quartile). Similarly, a quartile increase in the phthalates mixture was associated with lower fine motor speed as well (-3.1 points, 95 % CI -4.9 to -1.2 for the second quartile; -6.2 points, 95 % CI -9.9 to -2.5

<table><caption>**Table 1**

Characteristics of participants (n = 1097).</caption><thead><tr><th></th><th>Distribution</th></tr></thead><tbody><tr><td>Offspring's age at outcome assessment, years</td><td>8.2 ± 1.6</td></tr><tr><td>Offspring's sex (female vs. male)</td><td>497 (45.3)</td></tr><tr><td>Mother's age at exposure assessment, years</td><td>31.1 ± 4.8</td></tr><tr><td>Mother's educational level</td><td></td></tr><tr><td>Low</td><td>161 (15.2)</td></tr><tr><td>Middle</td><td>362 (34.2)</td></tr><tr><td>High</td><td>535 (50.6)</td></tr><tr><td>Father's educational level</td><td></td></tr><tr><td>Low</td><td>192 (19.0)</td></tr><tr><td>Middle</td><td>374 (36.9)</td></tr><tr><td>High</td><td>447 (44.1)</td></tr><tr><td>Mother's prepregnancy body mass index</td><td></td></tr><tr><td>Underweight or normal weight</td><td>719 (66.7)</td></tr><tr><td>Overweight</td><td>229 (21.2)</td></tr><tr><td>Obesity</td><td>130 (12.1)</td></tr><tr><td>Mother's country of birth (country of the cohort vs. others)</td><td>910 (84.0)</td></tr><tr><td>Mother's parity</td><td></td></tr><tr><td>No child</td><td>497 (46.4)</td></tr><tr><td>One child</td><td>401 (37.4)</td></tr><tr><td>Two or more children</td><td>173 (16.2)</td></tr><tr><td>Mother's smoking during pregnancy (yes vs. no)</td><td>178 (16.8)</td></tr><tr><td>Cognitive abilities and fine motor function in offspring</td><td></td></tr><tr><td>Non-verbal intelligence</td><td>26.9 ± 6.4</td></tr><tr><td>Sustained attention</td><td>0.3 ± 0.1</td></tr><tr><td>Working memory</td><td>1.2 ± 1.1</td></tr><tr><td>Task switching (minutes)</td><td>1.5 ± 0.9</td></tr><tr><td>Task shifting</td><td>0.1 ± 0.4</td></tr><tr><td>Fine motor function</td><td>126.5 ± 23.2</td></tr></tbody></table>

Note: Values are frequency numbers and percentages for categorical variables, mean and standard deviation for continuous variables. Missing data on covariates were not imputed.

<table><caption>**Table 2**

Distribution of prenatal exposures to chemicals.</caption><thead><tr><th></th><th>Median (Q1; Q3)</th><th>Mean ± SD</th></tr></thead><tbody><tr><td><strong>Organochlorine compounds</strong>, ng/g lipids</td><td></td><td></td></tr><tr><td>Dichlorodiphenyldichloroethylene (DDE)</td><td>104.7 (51.8; 222.1)</td><td>203.1 ± 307.1</td></tr><tr><td>Hexachlorobenzene (HCB)</td><td>16.3 (11.2; 26.0)</td><td>25.6 ± 32.0</td></tr><tr><td>Polychlorinated biphenyl (PCB) -138</td><td>18.2 (11.1; 32.3)</td><td>25.2 ± 25.2</td></tr><tr><td>Polychlorinated biphenyl (PCB) -153</td><td>35.3 (20.8; 61.2)</td><td>49.3 ± 58.9</td></tr><tr><td>Polychlorinated biphenyl (PCB) -180</td><td>20.8 (11.6; 37.2)</td><td>33.1 ± 47.6</td></tr><tr><td><strong>Per- and polyfluoroalkyl substances</strong>, µg/l</td><td></td><td></td></tr><tr><td>Perfluorooctanoate (PFOA)</td><td>2.6 (1.7; 3.5)</td><td>2.8 ± 1.8</td></tr><tr><td>Perfluorononanoate (PFNA)</td><td>0.7 (0.4; 1.1)</td><td>0.8 ± 0.6</td></tr><tr><td>Perfluorohexane sulfonate (PFHxS)</td><td>0.6 (0.4; 1.0)</td><td>0.8 ± 1.1</td></tr><tr><td>Perfluorooctane sulfonate (PFOS)</td><td>7.1 (4.6; 10.7)</td><td>8.6 ± 6.2</td></tr><tr><td><strong>Phenols</strong>, µg/g creatinine</td><td></td><td></td></tr><tr><td>Propyl paraben (PRPA)</td><td>44.2 (8.9; 134.2)</td><td>193.1 ± 778.1</td></tr><tr><td>Bisphenol A (BPA)</td><td>2.8 (1.6; 6.6)</td><td>7.2 ± 12.9</td></tr><tr><td>B-butyl paraben (BUPA)</td><td>3.4 (0.4; 14.4)</td><td>13.1 ± 26.6</td></tr><tr><td>Oxybenzone (OXBE)</td><td>4.9 (1.5; 27.5)</td><td>146.2 ± 701.4</td></tr><tr><td>Triclosan (TRCS)</td><td>6.3 (1.5; 79.9)</td><td>101.8 ± 215.5</td></tr><tr><td><strong>Phthalates</strong>, µg/g creatinine</td><td></td><td></td></tr><tr><td>Monoethyl phthalate (MEP)</td><td>178.9 (72.0; 468.5)</td><td>447.9 ± 964.6</td></tr><tr><td>Mono-iso-butyl phthalate (MiBP)</td><td>38.7 (23.3; 60.7)</td><td>54.5 ± 61.6</td></tr><tr><td>Mono-n-butyl phthalate (MnBP)</td><td>29.6 (18.3; 47.3)</td><td>57.7 ± 236.7</td></tr><tr><td>Mono benzyl phthalate (MBzP)</td><td>7.3 (3.6; 15.3)</td><td>17.8 ± 48.2</td></tr><tr><td>Mono-2-ethylhexyl (MEHP)</td><td>8.7 (4.4; 15.3)</td><td>13.9 ± 22.2</td></tr><tr><td>Mono-2-ethyl-5-hydroxyhexyl phthalate (MEHHP)</td><td>18.2 (10.5; 31.2)</td><td>31.3 ± 61.1</td></tr><tr><td>Mono-2-ethyl-5-oxohexyl phthalate (MEOHP)</td><td>14.1 (8.3; 23.7)</td><td>23.0 ± 43.6</td></tr><tr><td><strong>Organophosphate pesticides</strong>, µg/g creatinine</td><td></td><td></td></tr><tr><td>Dimethyl phosphate (DMP)</td><td>8.4 (4.1; 16.4)</td><td>14.2 ± 20.8</td></tr><tr><td>Dimethyl thiophosphate (DMTP)</td><td>5.0 (2.1; 12.4)</td><td>12.1 ± 22.3</td></tr><tr><td>Dimethyl dithiophosphate (DMDTP)</td><td>0.6 (0.2; 4.6)</td><td>6.3 ± 21.1</td></tr><tr><td>Diethyl phosphate (DEP)</td><td>3.3 (1.9; 6.4)</td><td>5.6 ± 9.5</td></tr><tr><td>Diethyl thiophosphate (DETP)</td><td>0.6 (0.1; 2.6)</td><td>2.6 ± 5.1</td></tr></tbody></table>

Note: Q1, first quartile; Q3, third quartile; SD, standard deviation.

for the third quartile; and -9.3 points, -14.8 to -3.7 for the fourth quartile, when compared to the first quartile). To a smaller extent, we observed an association between the PFAS mixture and fine motor speed, however, the pattern of the association did not show a dose-response relation and only the second and third quartile were significantly different from the first quartile (-3.2 points, 95 % CI -5.5 to -1.0 for the second quartile; -4.0 points, 95 % CI -7.2 to -0.7 for the third quartile; when compared to the first quartile). Mixtures of phenols and OP pesticides were not associated with fine motor speed.

Table 3 presents the contributed weight for each chemical included in the overall mixture on fine motor speed. Overall, the total contribution of chemical concentration in the negative direction (-19.2 points of lower fine motor speed) was greater than for the positive direction (+9.0 points of higher fine motor speed). Considering each group of chemicals, OC compounds, PFAS, and phthalates had a major negative weight (36 %, 34 %, and 24 %, respectively). Concerning individual chemicals, PFOA, HCB, and MEOHP had the greatest negative weight (28 %, 20 %, and 12 %, respectively). Other chemicals had a negative weight lower than 10 %.

## 4. DISCUSSION

In the present study, capitalizing on more than 1000 mother-child pairs from 5 European countries, we found that prenatal exposure to a mixture of 26 chemicals, in particular OC compounds, phthalates, and PFAS, was associated with lower fine motor function, as assessed by fine motor speed, in 8 year old children. We did not find any association of
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Predicted differences in estimated outcomes for each exposure quartile compared to the lowest quartile in all biomarkers of exposure within all chemical groups Models adjusted for cohort, offspring's sex, offspring's age at the time of outcome assessment, mother's age at the time of exposure assessment, mother's educational level, father's educational level, mother's country of birth, mother's pre-pregnancy body mass index, mother's parity, and mother's smoking during pregnancy. Higher values indicate better performance in non-verbal intelligence, working memory and fine motor speed, while worse performance in sustained attention, task switching, and task shifting. The models with the outcomes non-verbal intelligence and sustained attention are linear. The model with the outcome working memory includes a quadratic term for hexachlorobenzene. The model with the outcome task switching includes quadratic terms for perfluorohexane sulfonate, perfluorooctane sulfonate, and mono-2-ethyl-5-oxohexyl phthalate. The model with the outcome task shifting includes quadratic terms for perfluorohexane sulfonate, perfluorooctane sulfonate, mono-n-butyl phthalate, and dimethyl phosphate. The model with the outcome fine motor speed includes quadratic terms for perfluorooctanoate, hexachlorobenzene, and oxybenzone.</figcaption></figure>

prenatal exposure to this mixture of chemicals with cognitive abilities (i.e., general non-verbal intelligence, sustained attention, working memory, and cognitive flexibility).

Previous literature, which did not take into account chemical mixtures, indicates that prenatal exposure to OC compounds may be related to lower early motor development (Forns et al., 2012). To the best of our knowledge, no study assessed the association of the mixture of OC compounds with fine motor function. Our finding that exposure to a mixture of phthalates may influence fine motor function in children is in accordance with some (Kim et al., 2011, Daniel et al., 2020, Polanska et al., 2014), but not all (Gascon et al., 2015) previous studies; others found an association only in females and not males (Balalian et al., 2019, Doherty et al., 2017). Daniel et al. assessed the exposure to the mixtures of seven phthalate metabolites in more than 200 mother–child pairs residing in New York (Daniel et al., 2020). They utilized Weighted Quantile Sum (WQS) to estimate the association of the mixtures with fine motor function in 11 years old children and found a significant association with worse fine motor function only among females (Daniel et al., 2020). Luo et al. investigated the effects of a mixture of 10 PFAS in two year old children based on more than 2000 mother–child pairs from Shanghai, using a measure of motor development, which also included assessment of fine motor function. Utilizing quantile g-computation, they found that the mixture of PFAS was associated with worse motor function, when the outcome was dichotomized. However, the

association was not present when the score was utilized as a continuous outcome. Their findings are not directly comparable to our study as they included largely different compounds within the PFAS mixture (Luo et al., 2022).

The potential negative effects of prenatal exposure to chemical mixtures on fine motor function in children is of concern. Fine motor function, which can be defined as small muscle movements requiring close eye-hand coordination, requires a complex communication between the premotor and motor cortex, cerebellum, basal ganglia, corticospinal tracts, and peripheral nerves, not to mention visuospatial, sensory, and executive function processing (Burr and Choudhury, 2020). Previous studies showed that better fine motor function in children is predictive of greater reading as well as mathematical skills and general academic success (Fischer et al., 2020). Other results indicated that fine motor function but not gross motor skills in childhood predicted reasoning in adolescence and that this association mediated the relationship between early fine motor function and later reading and math ability (Cortes et al., 2022). As imaging studies on effects of chemicals on the structure and function of the brain are scarce, the neural mechanisms underlying the detected association with fine motor function are not known. As brain regions underlying the development of fine motor function and cognitive abilities differ and have different critical windows, it is possible that the timing of the exposures is crucial in influencing the impact on the cognitive or motor domain.
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Predicted differences in fine motor function for each exposure quartile compared to the lowest quartile in biomarkers of exposure within individual chemical groups *Models adjusted for cohort, offspring's sex, offspring's age at the time of outcome assessment, mother's age at the time of exposure assessment, mother's educational level, father's educational level, mother's country of birth, mother's pre-pregnancy body mass index, mother's parity, mother's smoking during pregnancy, and all the other chemical groups. The models include quadratic terms for perfluorooctanoate, hexachlorobenzene, and oxybenzone.*</figcaption></figure>

As opposed to less frequently investigated fine motor function, more epidemiological studies were perfomed with the outcome of cognitive abilities (Martínez-Martínez et al., 2021). Our findings on cognitive abilities were null, which is not in line with several previous studies (Luo et al., 2022, Tanner et al., 2020, van den Dries et al., 2021), although comparison is difficult because of the use of different approaches to investigate mixture effects and different chemical mixture combinations. Using quantile g-computation, in the aforementioned study by Luo et al., the authors found that a mixtures of PFAS was associated with decreased cognitive and language skills in two years old children (Luo et al., 2022). Studying more than 700 mother–child pairs residing in Sweden, Tanner et al. utilized WQS and found that prenatal exposure to a mixture of 26 potential endocrine-disrupting chemicals was related to lower cognitive abilities among 7-year old boys, and the effect was mostly driven by bisphenol compounds (Tanner et al., 2020). Utilizing quantile g-computation on more than 700 mother–child pairs from Rotterdam, Van den Dries et al. discovered that prenatal exposure to the mixture of phthalates, OP pesticides, and bisphenol was associated with lower non-verbal IQ in children; the association was driven mainly by phthalates (van den Dries et al., 2021). On the contrary, two other mixture studies did not provide evidence of effects on cognitive abilities. Yim et al. assessed the exposure to a mixture of persistent organic pollutants using more than 500 mother–child pairs from a Japanese birth cohort (Yim et al., 2022). Utilizing Bayesian kernel machine regression as well as quantile g-computation, they did not find effects on psychomotor and mental development in 6 months old children (Yim et al., 2022). Loftus et al. used multivariable WQS regression to estimate the

prenatal exposure to the mixture of 22 phthalate metabolites on more than 1000 mother–child pairs from an American cohort and they observed predominantly null associations between mixtures of prenatal phthalates and language skills and IQ at 3 to 6 years old (Loftus et al., 2021).

The heterogeneous results across studies could be partially attributed to differences in the exposure levels of the studied chemicals due to varying policies, industries, and behaviors in individual countries. When compared to our pooled cohort, the exposure levels of phthalates in the Columbia Center for Children’s Environmental Health birth cohort (Daniel et al., 2020) were highly variable. For example, the median exposure level was five times lower for mono-*iso*-butyl phthalate (MiBP) and slightly lower for monoethyl phthalate (MEP), but almost twice higher for mono benzyl phthalate (MBzP), slightly higher for MEOHP, and similar for mono-2-ethyl-5-hydroxyhexyl phthalate (MEHHP) (Balalian et al., 2019). In the Shanghai birth cohort study, participants were prenatally exposed to greater levels of PFAS (the median exposure levels were almost five times higher for PFOA, twice for perfluoronanoate (PFNA), and slightly higher for PFOS), except for PFHxS, where the exposure was similar to our cohort (Luo et al., 2022). In the Dutch Generation R Study, the median exposure level to bisphenol A was half the amount in our pooled cohort. Participants from the Generation R Study were also exposed to a smaller extent to phthalates (the median levels were approximately three times lower for MiBP, and twice for MnBP, MBzP, MEHHP, MEOHP, and MEP). However, they were exposed to a greater extent to OC pesticides (median levels three times higher for dimethyl thiophosphate (DMTP), twice for DMP,
<caption>**Table 3**</caption>

Adjusted effect contribution for each chemical included in the overall mixture model on fine motor function.

<table><thead><tr><th>Biomarker of exposure</th><th>Negative effect contribution B (95 % CI)</th><th>Weight (%)</th><th>Positive effect contribution B (95 % CI)</th><th>Weight (%)</th></tr></thead><tbody><tr><td><strong>Organochlorine compounds</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>DDE</td><td>-0.2 (-1.6; 1.2)</td><td>1</td><td></td><td></td></tr><tr><td>HCB</td><td>-3.8 (-7.6; 0.9)</td><td>20</td><td></td><td></td></tr><tr><td>PCB-138</td><td></td><td></td><td>1.8 (-1.2; 4.8)</td><td>20</td></tr><tr><td>PCB-153</td><td>-1.5 (-5.0; 2.0)</td><td>8</td><td></td><td></td></tr><tr><td>PCB-180</td><td>-1.3 (-4.1; 1.4)</td><td>7</td><td></td><td></td></tr><tr><td><strong>Total</strong></td><td></td><td>36</td><td></td><td>20</td></tr><tr><td><strong>Per- and polyfluoroalkyl substances</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>PFOA</td><td>-5.3 (-9.0; -1.6)</td><td>28</td><td></td><td></td></tr><tr><td>PFNA</td><td>-0.9 (-2.7; 0.9)</td><td>5</td><td></td><td></td></tr><tr><td>PFHxS</td><td></td><td></td><td>0.7 (-0.9; 2.2)</td><td>7</td></tr><tr><td>PFOS</td><td>-0.2 (-1.8; 1.5)</td><td>1</td><td></td><td></td></tr><tr><td><strong>Total</strong></td><td></td><td>34</td><td></td><td>7</td></tr><tr><td><strong>Phenols</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>PRPA</td><td>-0.1 (-1.1; 1.0)</td><td>0</td><td></td><td></td></tr><tr><td>BPA</td><td>-0.3 (-1.4; 0.9)</td><td>1</td><td></td><td></td></tr><tr><td>BUPA</td><td>-0.3 (-1.5; 0.8)</td><td>2</td><td></td><td></td></tr><tr><td>OXBE</td><td></td><td></td><td>4.1 (0.7; 7.5)</td><td>46</td></tr><tr><td>TRCS</td><td></td><td></td><td>0.1 (-1.0; 1.1)</td><td>1</td></tr><tr><td><strong>Total</strong></td><td></td><td>3</td><td></td><td>47</td></tr><tr><td><strong>Phthalates</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>MEP</td><td>-0.8 (-1.8; 0.2)</td><td>4</td><td></td><td></td></tr><tr><td>MiBP</td><td>-0.3 (-1.4; 0.9)</td><td>1</td><td></td><td></td></tr><tr><td>MnBP</td><td>-1.4 (-2.7; -0.1)</td><td>7</td><td>0.0 (-1.2; 1.3)</td><td>0</td></tr><tr><td>MBzP</td><td>-0.1 (-1.6; 1.4)</td><td>0</td><td></td><td></td></tr><tr><td>MEHP</td><td></td><td></td><td></td><td></td></tr><tr><td>MEHHP</td><td>-2.3 (-4.7; 0.1)</td><td>12</td><td>1.7 (-0.8; 4.2)</td><td>19</td></tr><tr><td><strong>Total</strong></td><td></td><td>24</td><td></td><td>19</td></tr><tr><td><strong>Organophosphate pesticides</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>DMP</td><td>-0.1 (-1.5; 1.3)</td><td>0</td><td></td><td></td></tr><tr><td>DMTP</td><td>-0.3 (-1.8; 1.2)</td><td>2</td><td></td><td></td></tr><tr><td>DMDTP</td><td></td><td></td><td>0.6 (-0.6; 1.8)</td><td>7</td></tr><tr><td>DEP</td><td>-0.1 (-1.3; 1.0)</td><td>1</td><td></td><td></td></tr><tr><td>DETP</td><td>-0.3 (-1.2; 1.1)</td><td>0</td><td></td><td></td></tr><tr><td><strong>Total</strong></td><td></td><td>3</td><td></td><td>7</td></tr></tbody></table>

Note: BPA = Bisphenol A; BUPA = B-butyl paraben; DDE = Dichlorodiphenyldichloroethylene; DEP = Diethyl phosphate; DETP = Diethyl thiophosphate; DMDTP = Dimethyl dithiophosphate; DMP = Dimethyl phosphate; DMTP = Dimethyl thiophosphate; HCB = Hexachlorobenzene; MBzP = Mono benzyl phthalate; MEHHP = Mono-2-ethyl-5-hydroxyhexyl phthalate; MEHP = Mono-2-ethylhexyl; MEOHP = Mono-2-ethyl-5-oxohexyl phthalate; MEP = Monoethyl phthalate; MiBP = Mono-iso-butyl phthalate; MnBP = Mono-n-butyl phthalate; OXBE = Oxybenzone; PCB = Polychlorinated biphenyl; PFHxS = Perfluorohexane sulfonate; PFOA = Perfluorooctanoate; PFNA = Perfluoronanoate; PFOS = Perfluorooctane sulfonate; PRPA = Propyl paraben; TRCS = Triclosan.

diethyl thiophosphate (DETP), and diethyl phosphate (DEP)), except for dimethyl dithiophosphate (DMDTP), where the median level of exposure was 1.5 times lower than in our cohort (van den Dries et al., 2021).

Our study is strengthened by a large sample size of mother-child pairs residing in five different European countries. We used the quantile g computation, which allowed us to investigate non-linear, complex combinations of chemicals, which approximates real world exposures. However, several limitations need to be mentioned. The cohorts included in our analysis differed in the pregnancy period when data on exposure to chemicals was assessed. Specifically, exposure was assessed in INMA in the first and the third trimester, in RHEA in the first trimester, in EDEN and MoBa in the first or the second trimester and BiB in the second or the third trimester. Previous studies suggest unique periods of susceptibility to chemical exposure and the consequent adverse effects during brain development, given that the developmental periods and neurodevelopmental processes vary between trimesters (Zhu et al., 2020). However, the relatively small sample sizes of participant in individual cohorts in our study did not allow us to determine trimester-specific associations to reveal effects of the sample collection timing. Next, the exposure to chemicals was measured only at one time point, which may result in imprecise estimates, in particular for the compounds with short elimination half-lives. Furthermore, as older and more educated mothers tend to be overrepresented in our sample, we cannot fully generalize our findings. In addition, residual confounding may be present. Specifically, our adjustment for parental education and country of birth might not have reduced all confounding effects of socioeconomic status. Furthermore, other confounding factors may be present, such as mother's cognitive ability, mental health, prenatal stress or quality of the home environment.

Even though the quantile-based g-computation method has many strengths, some weaknesses should be mentioned. The mixture under consideration only includes a subset of the measured exposures while controlling for other measured exposures. Such conditional mixture effects may be of interest in some settings where exposure sources influence the levels of some, but not all components of the mixture. Quantile g-computation forces to specify a parametric non-linear model, rather than assuming non-linearity. It estimates the parameters of a joint marginal structural model, which quantify the average effects of modifying all exposures simultaneously, with the limitation that the underlying model is not smooth and may not adequately capture the dose-response function. When the non-linearities are known, quantile g-computation is unbiased, however, whether this holds in realistic settings when non-linearities are unknown is currently unclear. In the end, the utility of quantized exposures depends on the context, it may suffer from difficulty in extrapolating results to other populations and reduced power when a linear model fits the data well.

To conclude, our study shows that prenatal exposure to a mixture of chemicals, mainly driven by OC compounds, phthalates, and PFAS, is related to worse fine motor function in children but not cognitive abilities. This suggests that these chemicals may affect motor abilities through mechanisms that are distinct from effects on cognition. Future studies need to reproduce our findings and further investigations should clarify the mediating role of structural and functional brain alterations to understand the neural pathways that may be influenced. Longer, prospective studies are necessary to reveal if the association to worse fine motor function remains until adulthood and how it impacts global functioning of affected individuals.

### CRediT authorship contribution statement

**Pavla Brennan Kearns:** Data curation, Formal analysis, Writing – original draft, Conceptualization. **Michiel A. van den Dries:** Methodology, Software, Writing – review & editing. **Jordi Julvez:** Writing – review & editing. **Mariza Kampouri:** Writing – review & editing. **Mónica López-Vicente:** Writing – review & editing. **Lea Maitre:** Writing – review & editing. **Claire Philippat:** Writing – review & editing.
**editing.** **Line Småstuen Haug:** Writing – review & editing. **Marina Vafeiadi:** Writing – review & editing. **Cathrine Thomsen:** Writing – review & editing. **Tiffany C Yang:** Writing – review & editing. **Martine Vrijheid:** Funding acquisition, Resources, Writing – review & editing. **Henning Tiemeier:** Writing – review & editing. **Mónica Guxens:** Conceptualization, Supervision, Writing – review & editing.

## Declaration of competing 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.

## Data availability

Data will be made available on request.

## Acknowledgments

The study was supported by the European Community's Seventh Framework Programme [FP7/2007-2013] under grant agreement no. 308333 [the HELIX project], and by the European Union's Horizon 2020 research and innovation programme under grant agreements no. 874583 [ATHLETE], and no. 733032 [HBM4EU]. We further acknowledge funding 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, CPII8/00018), 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, Juan de la Cierva-Incorporación fellowship IJC2018-035394-I), Agence Nationale de Securite Sanitaire de l'Alimentation de l'Environnement et du Travail (1262C0010), EU Commission (261357, 603794 and 634453), project IJC2020-045355-I, funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. We also acknowledge support from the grant CEX2018-000806-S funded by MCIN/AEI/ 10.13039/501100011033, the European Union NextGenerationEU/PRTR and support from the Generalitat de Catalunya through the CERCA Program. BiB receives funding from by 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]. 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. The authors acknowledge that BiB is only possible because of the enthusiasm and commitment of the children and parents in BiB. We are grateful to all participants, health professionals and researchers who have made BiB happen. MoBa is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research. EDEN 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), 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). RHEA was supported by the General

Secretariat for Research and Technology in Greece and the Research Committee of the University of Crete, Greece. We acknowledge the input of the entire HELIX consortium. We are grateful to all the participating families in the cohorts that took part in this study and all fieldworkers. The funding sources had no role in the conceptualization of this manuscript.

## Appendix A. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envint.2024.108490.

## References

Austin, D., Jimison, H., Hayes, T., Mattek, N., Kaye, J., Pavel, M., 2011. Measuring motor speed through typing: a surrogate for the finger tapping test. Behav. Res. Methods 43, 903–909.

Balalian, A.A., Whyatt, R.M., Liu, X., Insel, B.J., Rauh, V.A., Herbstman, J., Factor-Litvak, P., 2019. Prenatal and childhood exposure to phthalates and motor skills at age 11 years. Environ. Res. 171, 416–427.

Braun, J.M., 2017. Early-life exposure to EDCs: role in childhood obesity and neurodevelopment. Nat. Rev. Endocrinol. 13, 161–173.

Burr, P., Choudhury, P., 2020. Fine motor disability.

Caspersen, I.H., Kvalem, H.E., Haugen, M., Brantsæter, A.L., Meltzer, H.M., Alexander, J., Thomsen, C., Froshaug, M., Bremnes, N.M.B., Broadwell, S.L., 2016. Determinants of plasma PCB, brominated flame retardants, and organochlorine pesticides in pregnant women and 3 year old children in The Norwegian Mother and Child Cohort Study. Environ. Res. 146, 136–144.

Cequier, E., Sakhi, A.K., Haug, L.S., Thomsen, C., 2016. Development of an ion-pair liquid chromatography–high resolution mass spectrometry method for determination of organophosphate pesticide metabolites in large-scale biomonitoring studies. J. Chromatogr. A 1454, 32–41.

Chatzi, L., Leventakou, V., Vafeiadi, M., Koutra, K., Roumeliotaki, T., Chalkiadaki, G., Karachaliou, M., Daraki, V., Kyriklaki, A., Kampouri, M., 2017. Cohort profile: the mother-child cohort in Crete, Greece (Rhea Study). Int. J. Epidemiol. 46, 1392–1393k.

Cortes, R.A., Green, A.E., Barr, R.F., Ryan, R.M., 2022. Fine motor skills during early childhood predict visuospatial deductive reasoning in adolescence. Dev. Psychol. 58, 1264.

Covaci, A., Voorspoels, S., Thomsen, C., van Bavel, B., Neels, H., 2006. Evaluation of total lipids using enzymatic methods for the normalization of persistent organic pollutant levels in serum. Sci. Total Environ. 366, 361–366.

Daniel, S., Balalian, A.A., Whyatt, R.M., Liu, X., Rauh, V., Herbstman, J., Factor-Litvak, P., 2020. Perinatal phthalates exposure decreases fine-motor functions in 11-year-old girls: Results from weighted Quantile sum regression. Environ Int 136, 105424.

Day, D.B., Collett, B.R., Barrett, E.S., Bush, N.R., Swan, S.H., Nguyen, R.H.N., Szpiro, A. A., Sathyanarayana, S., 2021. Phthalate mixtures in pregnancy, autistic traits, and adverse childhood behavioral outcomes. Environ. Int. 147, 106330.

Doherty, B.T., Engel, S.M., Buckley, J.P., Silva, M.J., Calafat, A.M., Wolff, M.S., 2017. Prenatal phthalate biomarker concentrations and performance on the Bayley Scales of Infant Development-II in a population of young urban children. Environ. Res. 152, 51–58.

Fischer, U., Suggate, S.P., Stoeger, H., 2020. The implicit contribution of fine motor skills to mathematical insight in early childhood. Front. Psychol. 11, 1143.

Forns, J., Lertxundi, N., Aranbarri, A., Murcia, M., Gascon, M., Martinez, D., Grellier, J., Lertxundi, A., Julvez, J., Fano, E., Goni, F., Grimalt, J.O., Ballester, F., Sunyer, J., Ibarluzea, J., 2012. Prenatal exposure to organochlorine compounds and neuropsychological development up to two years of life. Environ. Int. 45, 72–77.

Forns, J., Esnaola, M., López-Vicente, M., Suades-González, E., Alvarez-Pedrerol, M., Julvez, J., Grellier, J., Sebastián-Gallés, N., Sunyer, J., 2014. The n-back Test and the Attentional Network Task as measures of child neuropsychological development in epidemiological studies. Neuropsychology 28, 519.

Gascon, M., Valdi, D., Forns, J., Casas, M., Martínez, D., Julvez, J., Monfort, N., Ventura, R., Sunyer, J., Vrijheid, M., 2015. Prenatal exposure to phthalates and neuropsychological development during childhood. Int. J. Hyg. Environ. Health 218, 550–558.

Goni, F., López, R., Etxeandia, A., Millán, E., Amiano, P., 2007. High throughput method for the determination of organochlorine pesticides and polychlorinated biphenyls in human serum. J. Chromatogr. B 852, 15–21.

Grandjean, P., Landrigan, P.J., 2006. Developmental neurotoxicity of industrial chemicals. Lancet 368, 2167–2178.

Grimvall, E., Rylander, L., Nilsson-Ehle, P., Nilsson, U., Strömberg, U., Hagmar, L., Ostman, C., 1997. Monitoring of polychlorinated biphenyls in human blood plasma: methodological developments and influence of age, lactation, and fish consumption. Arch. Environ. Contam. Toxicol. 32, 329–336.

Guilbert, A., Rolland, M., Pin, I., Thomsen, C., Sakhi, A.K., Sabaredzovic, A., Slama, R., Guichardet, K., Philippat, C., 2021. Associations between a mixture of phenols and phthalates and child behaviour in a French mother–child cohort with repeated assessment of exposure. Environ. Int. 156, 106697.

Guxens, M., Ballester, F., Espada, M., Fernández, M.F., Grimalt, J.O., Ibarluzea, J., Olea, N., Rebagliato, M., Tardón, A., Torrent, M., Vioque, J., Vrijheid, M., Sunyer, J.,
2012. Cohort Profile: the INMA-INfancia y Medio Ambiente-(Environment and Childhood) Project. Int J Epidemiol 41, 930-940.

Haug, L.S., Thomsen, C., Becher, G., 2009. A sensitive method for determination of a broad range of perfluorinated compounds in serum suitable for large-scale human biomonitoring. J. Chromatogr. A 1216, 385-393.

Haug, L.S., Sakhi, A.K., Cequier, E., Casas, M., Maitre, L., Basagana, X., Andrusaityte, S., Chalkiadaki, G., Chatzi, L., Coen, M., 2018. In-utero and childhood chemical exposome in six European mother-child cohorts. Environ. Int. 121, 751-763.

Heude, B., Forhan, A., Slama, R., Douhaud, L., Bedel, S., Saurel-Cubizolles, M.-J., Hankard, R., Thiebaugeorges, O., de Agostini, M., Annesi-Maesano, I., 2016. Cohort Profile: The EDEN mother-child cohort on the prenatal and early postnatal determinants of child health and development. Int. J. Epidemiol. 45, 353-363.

Julvez, J., López-Vicente, M., Warembourg, C., Maitre, L., Philippat, C., Gützkow, K.B., Guxens, M., Evandt, J., Andrusaityte, S., Burgaleta, M., 2021. Early life multiple exposures and child cognitive function: A multi-centric birth cohort study in six European countries. Environ. Pollut. 284, 117404.

Keil, A.P., Buckley, J.P., O'Brien, K.M., Ferguson, K.K., Zhao, S., White, A.J., 2020. A Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures. Environ Health Perspect 128, 47004.

Kim, Y., Ha, E.H., Kim, E.J., Park, H., Ha, M., Kim, J.H., Hong, Y.C., Chang, N., Kim, B.N., 2011. Prenatal exposure to phthalates and infant development at 6 months: prospective Mothers and Children's Environmental Health (MOCEH) study. Environ Health Perspect 119, 1495-1500.

Koponen, J., Rantakokko, P., Airaksinen, R., Kiviranta, H., 2013. Determination of selected perfluorinated alkyl acids and persistent organic pollutants from a small volume human serum sample relevant for epidemiological studies. J. Chromatogr. A 1309, 48-55.

Kortenkamp, A., 2014. Low dose mixture effects of endocrine disrupters and their implications for regulatory thresholds in chemical risk assessment. Curr. Opin. Pharmacol. 19, 105-111.

Landrigan, P.J., Goldman, L.R., 2011. Children's vulnerability to toxic chemicals: a challenge and opportunity to strengthen health and environmental policy. Health Aff. 30, 842-850.

Landrigan, P.J., Fuller, R., Acosta, N.J., Adeyi, O., Arnold, R., Baldé, A.B., Bertollini, R., Bose-O'reilly, S., Boufford, J.I., Breysse, P.N., 2018. The Lancet Commission on pollution and health. Lancet 391, 462-512.

Lanphear, B.P., Vorhees, C.V., Bellinger, D.C., 2005. Protecting children from environmental toxins. PLoS Med. 2, e61.

Lazarevic, N., Barnett, A.G., Sly, P.D., Knibbs, L.D., 2019. Statistical methodology in studies of prenatal exposure to mixtures of endocrine-disrupting chemicals: a review of existing approaches and new alternatives. Environ. Health Perspect. 127, 026001.

Lee, K.-S., Kim, K.-N., Ahn, Y.D., Choi, Y.-J., Cho, J., Jang, Y., Lim, Y.-H., Kim, J.I., Shin, C.H., Lee, Y.A., 2021. Prenatal and postnatal exposures to four metals mixture and IQ in 6-year-old children: a prospective cohort study in South Korea. Environ. Int. 157, 106798.

Lezak, M., Howieson, D., Loring, D., Fischer, J., 2004. Neuropsychological assessment. Oxford University Press, USA.

Li, N., Papandonatos, G.D., Calafat, A.M., Yolton, K., Lanphear, B.P., Chen, A., Braun, J. M., 2020. Gestational and childhood exposure to phthalates and child behavior. Environ. Int. 144, 106036.

Loftus, C.T., Bush, N.R., Day, D.B., Ni, Y., Tylavsky, F.A., Karr, C.J., Kannan, K., Barrett, E.S., Szpiro, A.A., Sathyanarayana, S., Lewinn, K.Z., 2021. Exposure to prenatal phthalate mixtures and neurodevelopment in the Conditions Affecting Neurocognitive Development and Learning in Early childhood (CANDLE) study. Environ. Int. 150, 106409.

Lövdén, M., Fratiglioni, L., Glymour, M.M., Lindenberger, U., Tucker-Drob, E.M., 2020. Education and Cognitive Functioning Across the Life Span. Psychol Sci Public Interest 21, 6-41.

Luo, F., Chen, Q., Yu, G., Huo, X., Wang, H., Nian, M., Tian, Y., Xu, J., Zhang, J., Zhang, J., 2022. Exposure to perfluoroalkyl substances and neurodevelopment in 2-year-old children: A prospective cohort study. Environ. Int. 166, 107384.

Magnus, P., Birke, C., Vejrup, K., Haugan, A., Alsaker, E., Daltveit, A.K., Handal, M., Haugen, M., Høiseth, G., Knudsen, G.P., 2016. Cohort profile update: the Norwegian mother and child cohort study (MoBa). Int. J. Epidemiol. 45, 382-388.

Maitre, I., De bont, J., Casas, M., Robinson, O., Aasvang, G.M., Agier, I., Andrusaityte, S., Ballester, F., Basagana, X., Borràs, E., Brochot, C., Bustamante, M., Carracedo, A., De Castro, M., Dedele, A., Donaire-gonzalez, D., Estivill, X., Evandt, J., Fossati, S., Giorgis-allemand, I., r Gonzalez, J., Granum, B., Grazuleviciene, R., Bjerve gützkow, K., Småstuen haug, I., Hernandez-ferrer, C., Heude, B., Ibarluzea, J., Julvez, J., Karachaliou, M., Keun, H.C., Hjertager krog, N., Lau, c.-h. e., leventakou, v., Iyoncaen, s., manzano, c., mason, d., mceachan, r., meltzer, h. m., petraviciene, i., quentin, j., roumeliotaki, t., sabido, e., saulnier, p.-j., siskos, a.p., Siroux, v., Sunyer, J., Tamayo, I., Urquiza, J., Vafeiadi, M., Van gent, D., Vives-Usano, M., Waiblinger, D., Warembourg, C., Chatzi, l., Coen, M., Van den hazel, P., Nieuwenhuijsen, m.j., Slama, R., Thomsen, C., Wright, J., Vrijheid, M., 2018. Human Early Life Exposome (HELIX) study: a European population-based exposome cohort. *BMJ Open*, 8, e021311.

Manzano-Salgado, C.B., Casas, M., Lopez-Espinosa, M.-J., Ballester, F., Basterrechea, M., Grimalt, J.O., Jiménez, A.-M., Kraus, T., Schettgen, T., Sunyer, J., 2015. Transfer of

perfluoroalkyl substances from mother to fetus in a Spanish birth cohort. Environ. Res. 142, 471-478.

Martínez-Martínez, M.I., Alegre-Martínez, A., Cauli, O., 2021. Prenatal exposure to phthalates and its effects upon cognitive and motor functions: A systematic review. Toxicology 463, 152980.

Martzog, P., Stoeger, H., Suggate, S., 2019. Relations between Preschool Children's Fine Motor Skills and General Cognitive Abilities. J. Cogn. Dev. 20, 443-465.

Mashburn, C.A., Burgoyne, A.P., Engle, R. W. 2023. 149C7Working memory, intelligence, and life success: Examining relations to academic achievement: job performance, physical health, mortality, and psychological well-being. In: Logie, R., Cowan, N., Gathercole, S., Engle, R. & Wen, Z. (eds.) Memory in Science for Society: There is nothing as practical as a good theory. Oxford University Press.

Mose, T., Knudsen, L.E., Hedegaard, M., Mortensen, G.K., 2007. Transplacental transfer of monomethyl phthalate and mono (2-ethylhexyl) phthalate in a human placenta perfusion system. Int. J. Toxicol. 26, 221-229.

Philippat, C., Mortamais, M., Chevrier, C., Petit, C., Calafat, A.M., Ye, X., Silva, M.J., Brambilla, C., Pin, I., Charles, M.-A., 2012. Exposure to phthalates and phenols during pregnancy and offspring size at birth. Environ. Health Perspect. 120, 464-470.

Polanska, K., Ligocka, D., Sobala, W., Hanke, W., 2014. Phthalate exposure and child development: the Polish Mother and Child Cohort Study. Early Hum. Dev. 90, 477-485.

Porpora, M.G., Lucchini, R., Abballe, A., Ingelido, A.M., Valentini, S., Fuggetta, E., Cardi, V., Ticino, A., Marra, V., Fulgenzi, A.R., 2013. Placental transfer of persistent organic pollutants: a preliminary study on mother-newborn pairs. Int. J. Environ. Res. Public Health 10, 699-711.

Raven, J., Raven, J., 1998. Progressive matrices couleur/colored progressive matrices. Centre de Psychologie Appliquée, Paris.

Rueda, M.R., Fan, J., McCandliss, B.D., Halparin, J.D., Gruber, D.B., Lercari, L.P., Posner, M.I., 2004. Development of attentional networks in childhood. Neuropsychologia 42, 1029-1040.

Sabaredzovic, A., Sakhi, A.K., Brantsæter, A.L., Thomsen, C., 2015. Determination of 12 urinary phthalate metabolites in Norwegian pregnant women by core-shell high performance liquid chromatography with on-line solid-phase extraction, column switching and tandem mass spectrometry. J. Chromatogr. B 1002, 343-352.

Sakhi, A.K., Sabaredzovic, A., Papadopoulou, E., Cequier, E., Thomsen, C., 2018. Levels, variability and determinants of environmental phenols in pairs of Norwegian mothers and children. Environ. Int. 114, 242-251.

Skogheim, T.S., Weyde, K.V.F., Aase, H., Engel, S.M., Surén, P., Øie, M.G., Biele, G., Reichborn-Kjennerud, T., Brantsæter, A.L., Haug, L.S., 2021. Prenatal exposure to per-and polyfluoroalkyl substances (PFAS) and associations with attention-deficit/ hyperactivity disorder and autism spectrum disorder in children. Environ. Res. 202, 111692.

Tamayo-Uria, I., Maitre, L., Thomsen, C., Nieuwenhuijsen, M.J., Chatzi, L., Siroux, V., Aasvang, G.M., Agier, L., Andrusaityte, S., Casas, M., 2019. The early-life exposome: description and patterns in six European countries. Environ. Int. 123, 189-200.

Tanner, E.M., Hallerbäck, M.U., Wikström, S., Lindh, C., Kiviranta, H., Gennings, C., Bornehag, C.G., 2020. Early prenatal exposure to suspected endocrine disruptor mixtures is associated with lower IQ at age seven. Environ Int 134, 105185.

TEAM, R. C., 2021. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. (No Title).

Valvi, D., Monfort, N., Ventura, R., Casas, M., Casas, L., Sunyer, J., Vrijheid, M., 2015. Variability and predictors of urinary phthalate metabolites in Spanish pregnant women. Int. J. Hyg. Environ. Health 218, 220-231.

van den Dries, M.A., Ferguson, K.K., Keil, A.P., Pronk, A., Spaan, S., Ghassabian, A., Santos, S., Jaddoe, W.V.W., Trasande, L., Tiemeier, H., Guxens, M., 2021. Prenatal Exposure to Nonpersistent Chemical Mixtures and Offspring IQ and Emotional and Behavioral Problems. Environ. Sci. Tech. 55, 16502-16514.

Vrijheid, M., Casas, M., Gascon, M., Valvi, D., Nieuwenhuijsen, M., 2016. Environmental pollutants and child health—a review of recent concerns. Int. J. Hyg. Environ. Health 219, 331-342.

Vuontela, V., Steenari, M.-R., Carlson, S., Koivisto, J., Fjällberg, M., Aronen, E.T., 2003. Audiospatial and visuospatial working memory in 6-13 year old school children. Learn. Mem. 10, 74-81.

Wright, J., Small, N., Raynor, P., Tuffnell, D., Bhopal, R., Cameron, N., Fairley, L., Lawlor, D.A., Parslow, R., Petherick, E.S., 2013. Cohort profile: the Born in Bradford multi-ethnic family cohort study. Int. J. Epidemiol. 42, 978-991.

Yim, G., Minatoya, M., Kioumourtzoglou, M.-A., Bellavia, A., Weisskopf, M., Ikeda-Araki, A., Miyashita, C., Kishi, R., 2022. The associations of prenatal exposure to dioxins and polychlorinated biphenyls with neurodevelopment at 6 Months of age: Multi-pollutant approaches. Environ. Res. 209, 112757.

Zhu, Y.-D., Wu, X.-Y., Yan, S.-Q., Huang, K., Tong, J., Gao, H., Xie, Y., Tao, S.-M., Ding, P., Zhu, P., 2020. Domain-and trimester-specific effect of prenatal phthalate exposure on preschooler cognitive development in the Ma'anshan Birth Cohort (MABC) study. Environ. Int. 142, 105882.

Zoeller, R.T., Brown, T.R., Doan, L.L., Gore, A.C., Skakkebaek, N.E., Soto, A.M., Woodruff, T., vom Saal, F., 2012. Endocrine-disrupting chemicals and public health protection: a statement of principles from The Endocrine Society. Endocrinology 153, 4097-4110.