Skip to main content
Download PDF
Download ePub

Neuromotor effects of early-life exposure to a mixture of endocrine disruptors in Belgian preschool children

Environmental Health volume 24, Article number: 11 (2025) Cite this article

Abstract

Objective

Children gradually develop motor skills that enable them to move efficiently in various daily activities such as self-care, academics and sports. The impact of prenatal exposure to endocrine disruptors (EDCs) on these performances remains understudied and current results are inconsistent. This study aims at examining the neuromotor function of Belgian preschoolers exposed in utero to a mixture of some of these chemicals.

Methods

From 2014 to 2016, 66 children (35 boys and 31 girls) were recruited for a longitudinal cohort study. Two polychlorinated biphenyls (PCBs) and four perfluoroalkyl substances (PFASs) were measured in cord serum. A standardized motor evaluation, the Movement Assessment Battery for Children II (MABC-II), and a clinical sensori-motor assessment examining minor neurological dysfunction were administered at 6 years of age. The impact of the mixture of EDCs on neuromotor outcome measures was evaluated using two validated statistical models. Sex-specific analyses were also conducted.

Results

Using a principal component analysis, a negative association was identified between a mixture of PCB-153 and − 180 and the Total Clinical examination score in the whole population (β (95% CI) = -15.8 (-26.51; -5.09), p = 0.005). After stratification by sex, negative associations were observed between the Gross Motor score of the MABC-II test and prenatal exposure to a mixture of PFASs and PCB-180, specifically in boys. This association was consistent across both the weighted quantile sum regression model (β (95% CI) = -2.36 (-3.42; -0.62), p = 0.023) and the principal component approach (β (95% CI) = -1.09 (-2.15; -0.13), p = 0.044).

Conclusion

Our findings suggest that the neuromotor function of young children is adversely influenced by prenatal exposure to toxicants in a sex-specific manner.

Peer Review reports

Introduction

The effects of endocrine disruptors (EDCs) on neurodevelopment represent a growing concern in modern societies. Among these toxics, persistent organic pollutants are man-made chemicals historically used for a wide variety of industrial purposes such as flame-retardants, solvents, and pesticides. They are widespread and stable environmental toxicants, highly resistant to biotransformation and environmental degradation. Despite the fact that the production and use of these chemicals has been banned or limited by law, exposure to many of these compounds such as polychlorinated biphenyls (PCBs) and perfluoroalkyl substances (PFASs) persists [1, 2]. More importantly, these contaminants cross the placenta during pregnancy and bioaccumulate in foetal tissues [3].

According to the Developmental Origins of Health and Disease (DOHaD) hypothesis [4], environmental exposure to EDCs may lead to potential health effects, including a range of deficiencies in motor development, The fetal and early popstnatal periods are particularly vulnerable to such effects due to rapid brain development [5]. During the first weeks of life, neurological assessment is primarily based on the evaluation of active and passive muscle tone. Infants with hypotonic phenotypes are known to have lower psychomotor development at 2–3 years of age [6]. In school-aged children, motor skills range from gross motor coordination and balance to fine motor performance, which are necessary for daily personal care, academic achievements and athletic activities [7]. Consequently, poor movement performance often leads to reduced participation to educational activities and learning abilities, with long-term detrimental impacts on self-confidence and academic success [8, 9]. In this context, a neuromotor assessment looking for minor neurological dysfunction (MND) has been systematically carried out in some countries to identify children at-risk of motor developmental disorders as early as possible. MND is defined as neurological dysfunctions, such as choreiform dyskinesia, mild diffuse hypotonia, or mild impairment of fine manipulative ability, which is not attributable to a brain injury such as cerebral palsy [10]. MND is well predictive of increased neurodevelopmental disorder. Furthermore, MND in school-aged children is associated with neurodevelopmental disorders, such as developmental coordination disorder or learning disabilities [11,12,13]. As it is widely accepted that performance at the beginning of primary school partly predicts later school careers, this evaluation usually takes place before entering elementary school.

Some EDCs are suspected to alter neuromotor development. Studies in different animal models have shown an impairment of motor function associated with prenatal exposure of EDCs [14,15,16] and suggested possible sex-related differences that could make males particularly vulnerable [17,18,19]. In humans, results are less consistent. The neurotoxicity of EDCs was first recognized through accidental poisoning incidents in Yusho (Japan 1968) and Yu-Cheng (Taiwan 1979): babies exposed to PBCs in utero exhibited slowness, lack of endurance, hypotonia, jerkiness, and clumsy movement [20]. The children of Yu-Cheng were closely monitored, and a number of adverse outcomes were associated with PCBs poisoning, including lower Bayley Scales of Infant Development (BSID) psychomotor scores, which assesses body control, large muscle coordination, fine hands and fingers movements, and dynamic movement [21]. Compared to unexposed infants they also showed delay on 32 of 33 developmental milestones, including turning pages, holding pencils, imitating drawn circles, and catching a ball [22]. At 7–12 years of age, Chen et al. 1994 [23] did not report anomalies on the standard neurological examination but observed subtle signs consisting of mirror movements, mild to moderate deficits in finger-thumb opposition, and choreiform movements. In the years following the Yusho and Yu-Cheng poisonings, several epidemiological studies were conducted to investigate the impact of antenatal environmental exposure to PCBs and other EDCs on motor functioning. Although most studies reported either null or inverse associations, the epidemiological findings to date remained inconclusive [24,25,26,27]. These inconsistencies might be explained, to some extent, by the different methodological approaches and population-specific effects, potentially resulting from dose-dependent responses [26]. Various assessment tools were used, including neurological examination, neurodevelopmental evaluations as well as more specific motor tests. Furthermore, humans are typically exposed to mixtures of environmental endocrine–disrupting chemicals simultaneously, yet most studies focused on single chemicals or a class of similar chemicals. By neglecting the influence of EDC mixtures, prior research may have failed to capture the synergic and/or cumulative health effects of EDCs, due to correlated co-pollutants [28]. Finally, some authors investigated sex-specific associations between prenatal exposure to EDCs and neurodevelopmental outcomes in children [29,30,31,32,33].

The main goal of this study was to evaluate the neuromotor effects of early-life exposure to a mixture of EDCs in preschool children using two validated statistical approaches. Given the evidence of sex-specific effect reported in the literature, analyses were performed both on the whole population and within sex-stratified subgroups.

Methods

Study participants

Participants were part of the EPOPEE (Effet des Polluants Organiques Persistants sur l’Evolution des Enfants) study described elsewhere [34].

Briefly, mother-child pairs from the general population were recruited through the maternity of the University Hospital of Liege (Belgium) between 2014 and 2016. Umbilical cord blood samples were collected, centrifugated and stored at -80 °C immediately after delivery. Inclusion criteria include the absence of ante/perinatal disease, prematurity and chronic affection.

Of the 212 original participants, 77 gave their consent to participate in the motor and clinical evaluation at 6 years of age, before starting elementary school. Finally, only 66 of them had enough cord blood to perform a dosage of all EDCs studied and were selected for the present study. This study was approved by the local biomedical Ethics Committee of the University Hospital of Liege.

Exposure

EDCs in cord blood were measured using a detailed analytical method described in Dufour et al. [35]. Briefly, four organochlorine pesticides, namely β-hexachlorohexane (β-HCH), hexachlorobenzene (HCB), trans-nonachlor and 4,4’-dichlorodiphenyldichloroethylene (4,4’-DDE), 4 PCBs (PCBs-118, -138, -153, and − 180) were analysed following the methodology outlined by Pirard et al. [36]. The concentrations of seven PFASs ((perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), perfluorohexane sulfonate (PFHxS), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), perfluoroheptanoic acid (PFHpA) and perfluoroundecanoic acid (PFUdA)) were determined based on the protocol described by Karrman et al. [37]. The analytical methods were validated according the total error approach in order to meet the ISO17025 standards and the guidelines of the French Society of Pharmaceutical Science and Technique [38]. For each EDC, the limit of quantification (LOQ) was determined during the validation process and defined as the lowest concentration measurable with a maximal uncertainty not exceeding 40%.

Only EDCs with a detection rate above the LOQ in more than 50% of sample were included in the analysis [39]. This resulted in the selection of two PCBs (PCBs-138 and − 180) and four PFASs (PFOS, PFOA, PFHxS and PFNA). As recommended by Lubin et al. [40], values below the detection limit were replaced using multiple imputation techniques to create five imputed datasets (K = 5) [41, 42].

Finally, a logarithmic transformation was applied to reduce the dispersion and satisfy normality assumptions of our models.

Motor outcome

Motor outcome was assessed using the Movement Assessment Battery for Children II (MABC-II), a standardized test of motor skills for children 4 to 12 years of age [43]. This test, widely used in practice and in research, yields a score for total movement performance based on separate scores for fine motor skills (manual dexterity), ball skills (object control), and static and dynamic balance (postural control). Standardized scores for each subtest as well as the total score range from 0 to 20, with higher score indicating better performance. In our study, the last two subtests were combined to have a global measure of gross motor skills. The MABC-II assesses motor skills in children aged 3 to 6 years through a set of age-appropriate tasks. In addition, each child was tested for signs of minor neurological dysfunction using the standardized protocol developed by Willems G. et al. [44] (Appendix C). This clinical sensori-motor assessment, inspired by the works of Kalverboer A.F [45]. and Bax M [46], was specifically created to offer a comprehensive school-entrant medical examination and to highlight children at risk of developing motor or learning disorders. The Willems examination consists of 89 items including motor tasks, as well as sensory evaluation. Each item receives a score of 2, 1 or 0 depending on whether the test was passed, hesitant, or failed. Medical elements were also considered and scored from 2 to 0 according to their deviation from the norm. This assessment gives an overall score out of 500 points which is predictive of learning disabilities, a lower score indicating a higher risk of learning difficulties, mainly when entering primary school. In our study, a motor-specific clinical score which focused only on motor items (max score = 150) was also taken into consideration.

The MABC-II and the clinical evaluation were performed in a standardized manner by the first author, blinded to EDC exposure.

Covariates

Data regarding the parents and child were obtained through a general questionnaire completed during the first study visit. Additional information was collected from hospital medical records. These data included maternal age at delivery, parity, duration of breastfeeding, parental education (categorized as having a diploma higher than high school or not), parental smoking status, maternal alcohol consumption during pregnancy, gestational age, child birth weight, age during testing and child’s sex. Children’s thyroid function was assessed by measuring thyroid stimulating hormone (TSH) levels in a dried blood spot collected three days after birth.

The main confounders were selected based on prior knowledge, according to Directed Acyclic Graph theory [47] (maternal and paternal education, maternal age, maternal and paternal smoking, multiparity, gestational age, birth weight and gender) (Appendix D). Furthermore, models were adjusted for child’s age as this parameter greatly influences the outcome. Some parameters (peri/antenatal disease and the presence of a chronic affection), used as exclusion criteria, were not included in the models. Finally, other covariates (alcohol consumption during pregnancy, thyroid function, breastfeeding) were selected a priori based on previous literature as they greatly influence the development of cognitive functions [48, 49].

The associations between each variable and the outcome were then evaluated: Student tests were used to test group differences for binary variables, while univariate regression models were carried out to assess the effects of continuous variables on outcome measures (Appendix E). Variables associated with motor scores at a significant level of p < 0.20 were included as covariates in the final analysis [50]. The results indicated that all tests were influenced by parental educational level and smoking status. There was an effect of age on the Total and Motor specific clinical scores of the clinical examination, as well as the Global and the Fine Motor scores of the MABC-II. Additionally, the Total score of the Clinical examination and all scores of the MABC-II were influenced by the age of the mother at delivery. The sex of the child and the breast-feeding duration impacted the Fine Motor score of the MABC-II. Lastly, only the Global score of the MABC-II was influenced by multiparity (Table 1).

Table 1 Selected covariates for each cognitive test; student tests were used for binary variables, while univariate regression models were carried out for continuous variables
Full size table

Statistical analysis

We computed descriptive statistics for exposure and outcome variables, as well as model covariates.

Two different statistical models were employed to explore the association between MABC-II and Clinical examination scores and prenatal exposure to a mixture of EDCs. Given that some chemicals are correlated, classic regression models were rejected because they are subject to dimensionality and collinearity issues.

So, we first specifically used Principal Components Approach (PCA) to reduce the the set of original variables and to extract a smaller number of principal components (Comp). Components explaining at least 50% of the variance cumulatively were selected [51]. Multiple linear regression models were then used to investigate the association between each component and Global Motor and Clinical examination scores while controlling for relevant covariates [26].

Secondly, generalized Weighted Quantile Sum (WQS) regression was used to quantify the cumulative effect of EDCs exposures and to estimate the relative contribution of individual components of the mixture to the outcomes of interest [52]. To achieve this, the overall exposure to the mixture of EDCs was summarized by estimating a body burden index (the WQS index). We used quartiles of exposures with 100 bootstrap samples, a 60% validation dataset, and a negative coefficient constraint. The final index was then included in a regression model to evaluate the overall effect of the mixture on the outcomes of interest. To identify the chemicals most strongly associated with the outcome, significant components of the WQS index were determined by comparing the average weight for each component against a sectioned threshold parameter, τ. In our analysis conducted with six components, we used τ = 1/6 = 0.167. To ensure the stability of our data and to approximate the repeated holdout strategy described by Tanner et al. [53], the standard WQS analysis was repeated (rh = 100) to simulate a distribution of validated results from the underlying population. The mean and confidence intervals (95% CI) for the WQS index β coefficient and the chemical weights in relevant situation were considered (Appendix F).

Finally, given the concern about the hormonally active property of some EDCs [54] and the sex-specific effects observed in animal models [17, 19], analyses were conducted on both the total and sex-stratified populations.

All analyses were performed using R software version 4.1.2 [55]. The miWQS package was used for WQS regression analysis [56]. The MIPCA package [57] was applied as a preliminary step to perform multiple imputation before running PCA model with the FactoMineR package [58]. Statistical significance was set at a p-value of 0.05.

Results

Descriptive analyses

General characteristics of the study sample are summarized in Table 2. In total, 66 participants were included in the analysis, with a slightly larger proportion of boys (53%). All children were born at full term with an average weight of 3307 g. Half of them were the first child of their mother, and a majority were breastfed (80%). The average age of the children at testing was 5 years and 9 months. The average maternal age was 30 years old at delivery. Mother generally did not smoke or consume alcohol during pregnancy. More than half of the mothers (67%) and the fathers (58%) had education attainment higher than high school.

Table 2 presents an overview of children’s motor and clinical outcomes. The mean (± SD) for Total Clinical examination score and Motor specific clinical score were 342 (± 51) and 94 (± 17) respectively. Only one child had a Total score under 200. Concerning the motor evaluation with the MABC-II test, 55% of the population had a Global score in the normal range [7,8,9,10,11,12,13]; 36% and 9% of the participants were respectively under vs. above the average range. The mean (± SD) for global MABC-II score, Fine Motor score and Gross Motor score were 8 (± 5), 9 (± 3) and 17 (± 8) respectively. Note that there was a significant correlation between the Motor specific clinical score and all scores of the MABC-II test (p < 0.001).

Table 2 Demographic characteristics and cognitive scores
Full size table

Six chemicals were detectable in more than 50% of the study population. The median concentration, geometric mean concentration, and distribution of these chemicals are shown in Table 3. PCBs were detected in 83% of the samples and included PCB-153 and − 180 with a detection rate of 55% and 77% respectively. Finally, in 98% of the samples, at least one PFAS was found. PFOA was the most represented PFASs (mean: 0.79 ng/mL), followed by PFNA (mean: 0.18 ng/mL), PFOS (mean: 1.07 ng/mL) and PFHxS (mean: 0.20 ng/mL).

No significant differences between girls and boys were found regarding exposure to EDCs and neuromotor scores (Appendix G).

Table 3 Detection rate above the limit of quantification (LOQ), geometric mean concentrations, standard deviation (SD) and quartiles of the EDCs considered in the study. Concentrations were expressed in ng/mL
Full size table

EDCs and neuromotor outcomes

Principal components approach

Pairwise Pearson correlations among cord blood concentrations of chemicals are shown in Fig. 1A. Analyses indicated that several EDC concentrations are moderately correlated.

PCA identified two main components accounting respectively for the 40.9% and 23.2% of the total variance. Loading factors for each chemical on each component are presented in Fig. 1B. The first component (Comp1) was characterized by significant loading factors for all PFASs and PCB-180. The second component (Comp2) had high loading factors for PCBs-153 and − 180.

Fig. 1
figure 1

Pairwise Pearson’s correlation coefficients between individual EDCs (A) and between the two components and EDCs (B); positive correlations are highlighted in blue and negative correlations in red

Full size image

Adjusted multivariate regression analysis were conducted to examine the relationship between PCA components and motor outcomes (clinical evaluation and MABC-II assessment). In boys and girls taken together, Comp2 was negatively associated with the Total clinical examination score (β (95% CI) = -15.8 (-26.51; -5.09), p = 0.005). Additionally, sex-specific analyses revealed negative association in boys only, specifically on the Gross Motor score of the MABC-II ((β (95% CI) = -1.09 (-2.15; -0.13), p = 0.044) (Table 4).

Table 4 Adjusted multivariate regression coefficient β (95% confidence intervals (CI)) between PCA components and the clinical examination score or MABC-II test in preschool children. Component 1 (Comp1) included all PFASs and PCB-180; component 2 (Comp2) included PCBs-153 and − 180. Statistically significant results (p < 0.05) are indicated in bold
Full size table

Weighted quantile sum model

The results of the WQS model are presented in Table 5. No correlation was found between the WQS indexes and either the scores of the clinical examination or the MABC-II in the whole population. After stratification by sex, significant negative association was observed with the Gross Motor score of the MABC-II, but only in boys (β (95% CI) = -2.36 (-3.42; -0.62), p = 0.023). Among the six chemicals included, PFOA, PFNA and PCB-180 were the primary contributors to the negative effect, collectively accounting for more 50% of the observed association with the motor outcomes (Table 6).

Table 5 Adjusted associations (β coefficient and 95% confidence intervals (CI)) between WQS index and scores of the clinical examination and the MABC-II test performed in preschool children. Statistically significant results (p < 0.05) are reported in bold
Full size table
Table 6 Weights from weighted quantile sum regression for pollutant index and risk of lower Gross Motor score of the MABC-II test in boys. Weights above 0.167 are in bold
Full size table

Discussion

We assessed cord blood concentrations of six EDCs and investigated their combined effects on child neuromotor development at 6 years of age.

PCA revealed a negative association between the second component (Comp2) and the Total score of the clinical examination in the whole population. Comp2 was characterized by high loading factors for PCB-153 and − 180.

Sex-specific analysis showed a negative association between exposure to a mixture of EDCs and the Gross Motor score of the MABC-II in boys, as identified by both PCA and WQS. PCA highlighted PFASs and PCB-180 as significant loading factors, while WQS identified PFOA, PFNA, and PCB-180 as the chemicals with the greatest impact.

Impact of EDC mixtures on motor skills in children

Effects of antenatal exposure to PCBs or PFASs on the development of motor function are inconsistently reported in literature (Appendix A and B).

Prenatal PCB exposure has been shown to be associated with altered neonatal motor performance and reflexes [59,60,61,62] in some studies but not others [63,64,65,66,67]. In toddlers, associations between antenatal exposure to some PCB and more minor neurological dysfunctions were reported [29, 68,69,70]. However, no or positive correlations [29, 66, 71,72,73] were found in other similar studies.

Using developmental assessment instruments, mostly the psychomotor index of the BSID, some studies demonstrated a negative association [74,75,76,77,78]. However, 13 other studies, overall more recent and carried out on a smaller population, did not confirm this findings [27, 30, 66, 79,80,81,82,83,84,85,86,87,88]. Higher levels of organochlorine pollutants were, however, linked to significant motor delay in one of them [30]. Most studies using quantitative motor or visuo-motor tests, such as the Motor domain area of the McCarthy Scales of Children’s Ability test, failed to identify specific impairment [69, 73, 89,90,91,92,93,94,95,96,97].

Interestingly, scores worsened at 7 years of age when parental and home characteristics were less optimal [98]. More recently, Forns et al. reported poorer fine motor skills associated with greater prenatal exposure to PCB-153, but not to other PCBs such as -118, -138, or -180, at 4 years of age [94]. Additionally, one single Dutch study identified detrimental influence of antenatal exposure to PCB-183 on ball skills in 14-year-olds assessed with the MABC test [99]. No other article has examined motor function in adolescents.

Only one study analysed the impact of prenatal exposure to PFASs on clinical examination using the Neonatal Network Neurobehavioral Scale. This study found no association with any of the 11 outcomes [100], although a 10-fold increase in prenatal PFOA exposure increased the odds of hypotonia. Among studies evaluating motor development in infants, seven of them showed negative correlations [31, 32, 48, 101,102,103,104] while three reported no association [105,106,107]. In older children, specific motor tests or questionnaires did not reveal negative impacts on fine or global motor functioning [95, 102, 108]. However, a positive correlation was found between antenatal PFOA exposition and the Wide Range Assessment of Visual Motor Abilities (WRA-VMA) which assesses visual-motor (drawing subtest), fine motor (pegboard subtest) and visuospatial (matching subtest) skills [49].

The effects of PCBs and PFASs had already studied together in large cohort studies including the Sapporo/Hokkaido Study [33, 83, 84, 106], the INMA Project [78, 94, 102], and the INUENDO cohort [95]. However, in these prospectives studies, each chemical was analysed individually. Considering the interaction of various exposures, single-pollutant models were less interpretable when studying correlated chemical pollutants. Studies analyzing the effect of prenatal exposure to PFAS mixtures demonstrated negative associations with motor development in children aged 1 to 3 years [32, 48, 101]. However, these combined effects were not investigated in older children. Evidence regarding the negative impact of PFASs on motor development in older children remained inconclusive to date [95, 102, 108]. To our knowledge, no prior study had analyzed the combined effects of PCBs and PFASs on motor development.

Our results for the whole population were consistent with studies reporting negative effects of antenatal PCBs exposure on neuromotor development. Furthermore, consistent with previous research, PFASs did not appear to have significant negative effect on motor function in preschool children when boys and girls were analyzed together. Interestingly, the negative correlation observed in our clinical examination data, which does not seem to be fully explained by motor deficit, leads us to suspect other cognitive impairments. Indeed, this test aims to briefly evaluate the basic skills predictive of learning disabilities in primary school. It not only includes clinical assessment of motor acquisitions, but also other prerequisites for learnings such as attentional or language, all of which would deserve a more detailed analysis.

Sex impact

In sex-specific analyses, the negative association between Comp2 and the Total clinical examination score was not significant. However, we showed a negative association between antenatal exposure to a mixture of EDCs and the Gross Motor score of the MABC-II, as observed in both the PCA and WQS models. In both approaches, a mixture of PCB-180 and PFASs was implicated.

Although our results had to be interpreted with caution due to the small sample size, they were consistent with the existing literature. Indeed, most studies investigating sex-specific associations between prenatal PCBs or PFASs exposure and child’s neurodevelopment also suggested that boys were more vulnerable to environmental chemicals than girls. Regarding psychomotor development at six months, Kishi et al. reported that higher exposure to five PCB congeners was associated with a lower developmental score in boys, compared with two PCB congeners in girls [33]. Similarly, Berghuis et al. found that higher exposure to PCBs was associated with more optimal scores in girls [29]. The adverse effects of antenatal PFAS exposure on motor development were also found to be larger in boys than in girls [31, 32, 101]. In the Flemish Environment and Health Study, prenatal exposure to higher organochlorine pollutants, including PCBs, were associated with a significant motor delay, an effects more pronounced in boys than in girls [30]. However, some studies did not identify any sex-specific correlations when boys and girls were analysed separately [48, 49, 102, 106]. Finally, better motor skills at 4 years in boys more exposed to PFOA in utero were reported in only one study, which assessed motor function using a simple questionnaire [107].

Although the exact mechanisms are unknown, some hypotheses underlying the sex-specific effects of EDCs on motor development have been put forward. First of all, pharmacokinetic modelling in animal studies has shown longer half-lives and a greater tissue accumulation of some EDCs in male than in female rats [109,110,111]. Chemical exposure during gestation may alter foetal thyroid and sex hormone levels, which can adversely affect cognitive functions in later life, in a sexually dimorphic manner [5, 112]. Furthermore, EDCs can have a direct deleterious effect on specific neurons. For example, Nguon et al., who examined sex effects of a certain mixture of PCBs on cerebellar development and motor functions in rat neonates, found a reduced cerebellar mass in pups, more pronounced in male than female pups [113]. The cerebellum plays an essential role not only in motor control (including balance and coordination) but also in motor learning and cognition. The development of the cerebellum takes place from the early embryonic period until the first years of life making this brain area particularly vulnerable to environmental insults [114]. Another explanation for sex-specific effects can be the differences in interference with the expression of genes, particularly those coding for hormonal receptors [115]. All these data are consistent with our findings that prenatal EDCs mixture exposure interferes with motor outcome mainly in boys. However, more studies should be conducted to elucidate the sex differences of EDCs effect on neurodevelopment.

Strengths and limitations

Strengths of this study include the prospective design and the use of complementary clinical methods to assess different components of neuromotor development. The selected instruments were adapted for preschool-age children and more sensitive than traditional questionnaires for measuring pre-clinical alterations in motor functions. All children were seen by the same examiner to minimize the risk for bias due to interobserver variation. Moreover, the main EDCs of three families of EDCs were collected at birth in blood cord. The long half-life of these compounds in humans indicates a steady exposure condition during pregnancy. As such, a single cord blood measure of EDCs levels has been suggested as a good indicator of foetal exposure [116]. Moreover, the ante- and perinatal periods constitute critical moments for the development of the central nervous system. Finally, two validated statistical approaches were used to evaluate the impact of a mixture of EDCs on neuromotor function, with analyses adjusted for covariates collected prospectively.

However, some potential limitations of the current study should be mentioned. The main limitation of this study includes the relatively small number of participants. There was considerable loss to follow-up for neurodevelopmental assessment during the study period, which increased the risk of selection bias. However, the characteristics of subjects in the original cohort were similar to those in the final sample [35]. Other toxicants such as lead, which adverse effects on the developing brain have long been studied [117], could have been added to the analyses. A choice had to be made taking into account the limited volume of the blood sample. Additionally, the relationship between EDCs and developmental measures may have been confounded by postnatal environmental influences that were not evaluated in the present study. For example, environmental encouragement has been found to correlate with the pace of motor skills development [118], but this kind of stimulation is difficult to calibrate. Moreover, as mentioned by Torres-Sanchez et al. [119], children who were most exposed to chemicals in utero seems to be those who are also exposed to less stimulating developmental environment. In our population, the motor performance of participating children was rather poor. This is in line with previous studies showing secular declines in physical activity in children [120]. Moreover, our results of clinical examination and movement performance showed a trend towards improvement with increasing age, as previous studies have shown improvement in manual dexterity [121], aiming and catching [122] and balance [123] with age. Children around the age of 6 normally spend their time performing fine and gross motor activities and the time spent practicing positively influence the mastery of specific prehension skills [124]. Concerning the clinical significance of the neuromotor developmental level observed in this cohort of preschool-aged children, it is difficult to predict later motor development or school functioning. Subtle neuromotor deficits are likely to interfere with different skills depending on the motor function, such as the acquisition of writing skills. Also, it remains to be determined whether and how the subtle motor deficits observed here persists and have an impact on the acquisition of subsequent motor skills during development. Finally, comparisons of results between different studies must be made carefully. Even on a same toxicants family, compounds might exert opposite effects, and their actions can also vary depending on factors such as the level of exposure, sex, or the presence of other pollutant(s) [18].

Conclusion

There is growing concern that exposure to some endocrine-disrupting chemicals during critical periods of human development could increase the risk of neurodevelopmental disorders, including motors disabilities.

This study aimed to assess the neuromotor function of preschool children exposed in utero to a mixture of PCBs and PFASs using two validated statistical approaches. Our findings strengthen existing evidence that environmental toxicants adversely affect the neuromotor development of young children, with boys appearing to be particularly vulnerable.

Further longitudinal research is needed to elucidate the long-term impacts of prenatal exposure to chemical mixtures on motor development and related developmental disorders.

Data availability

No datasets were generated or analysed during the current study.

References

  1. Barletta M, Lima ARA, Costa MF. Distribution, sources and consequences of nutrients, persistent organic pollutants, metals and microplastics in south American estuaries. Sci Total Environ févr. 2019;651:1199–218.

    Article  CAS  Google Scholar 

  2. Liu Y, Li A, Buchanan S, Liu W. Exposure characteristics for congeners, isomers, and enantiomers of perfluoroalkyl substances in mothers and infants. Environ Int Nov. 2020;144:106012.

    Article  CAS  Google Scholar 

  3. Soechitram SD, Athanasiadou M, Hovander L, Bergman A, Sauer PJJ. Fetal exposure to PCBs and their hydroxylated metabolites in a Dutch cohort. Environ Health Perspect août. 2004;112(11):1208–12.

    Article  CAS  Google Scholar 

  4. Barker DJP, Osmond C, Forsén TJ, Kajantie E, Eriksson JG. Trajectories of growth among children who have coronary events as adults. N Engl J Med. oct 2005;27(17):1802–9.

  5. Roegge CS, Schantz SL. Motor function following developmental exposure to PCBS and/or MEHG. Neurotoxicol Teratol mars. 2006;28(2):260–77.

    Article  CAS  Google Scholar 

  6. Sucharew H, Khoury JC, Xu Y, Succop P, Yolton K. NICU Network Neurobehavioral Scale Profiles Predict Developmental Outcomes in a low-risk sample. Paediatr Perinat Epidemiol Juill. 2012;26(4):344–52.

    Article  Google Scholar 

  7. Mitsiou M, Giagazoglou P, Sidiropoulou M, Kotsikas G, Tsimaras V, Fotiadou E. Static Balance Ability in Children with Developmental Coordination Disorder. Eur J Phys Educ Sport [Internet]. 12 mars 2016 [cité 9 déc 2024];11(1). Disponible sur: https://ejpes.cherkasgu.press/journals_n/1460057229.pdf

  8. Van Der Linde BW, Van Netten JJ, Otten B, Postema K, Geuze RH, Schoemaker MM. Activities of Daily Living in Children with Developmental Coordination Disorder: performance, Learning, and participation. Phys Ther 1 nov. 2015;95(11):1496–506.

    Article  Google Scholar 

  9. Mancini VO, Rigoli D, Heritage B, Roberts LD, Piek JP. The relationship between Motor skills, Perceived Social Support, and internalizing problems in a community adolescent sample. Front Psychol. 2016;7:543.

    Article  Google Scholar 

  10. Hadders-Algra M. Two distinct forms of minor neurological dysfunction: perspectives emerging from a review of data of the Groningen Perinatal Project. Dev Med Child Neurol [Internet]. août 2002 [cité 9 déc 2024];44(08). Disponible sur: https://doiorg.publicaciones.saludcastillayleon.es/10.1017/S0012162201002560

  11. Peters LH, Maathuis CG, Hadders-Algra M. Limited motor performance and minor neurological dysfunction at school age. Acta Paediatr févr. 2011;100(2):271–8.

    Article  Google Scholar 

  12. Hadders-Algra M. Neurological examination of the child with minor neurological dysfunction. 3rd ed. London: Mac Keith; 2014. p. 161.

    Google Scholar 

  13. Schendelaar P, Seggers J, Heineman MJ, Hadders-Algra M. Neurological condition assessed with the Hempel examination and cognition and behaviour at 4 years. Early Hum Dev sept. 2017;112:9–13.

    Article  Google Scholar 

  14. Zhang L, Li Y, yuan, Chen T, Xia W, Zhou Y, Wan Y, jian, et al. Abnormal development of motor neurons in perfluorooctane sulphonate exposed zebrafish embryos. Ecotoxicol juin. 2011;20(4):643–52.

    Article  CAS  Google Scholar 

  15. Harada T, Takeda M, Kojima S, Tomiyama N. Toxicity and carcinogenicity of dichlorodiphenyltrichloroethane (DDT). Toxicol Res 31 janv. 2016;32(1):21–33.

    Article  CAS  Google Scholar 

  16. Tilson HA, Jacobson JL, Rogan WJ. Polychlorinated biphenyls and the developing nervous system: cross-species comparisons. Neurotoxicol Teratol Mai. 1990;12(3):239–48.

    Article  CAS  Google Scholar 

  17. Onishchenko N, Fischer C, Wan Ibrahim WN, Negri S, Spulber S, Cottica D, et al. Prenatal exposure to PFOS or PFOA alters motor function in mice in a sex-related manner. Neurotox Res avr. 2011;19(3):452–61.

    Article  CAS  Google Scholar 

  18. Cauli O, Piedrafita B, Llansola M, Felipo V. Gender differential effects of developmental exposure to methyl-mercury, polychlorinated biphenyls 126 or 153, or its combinations on motor activity and coordination. Toxicol Sept. 2013;311(1–2):61–8.

    Article  CAS  Google Scholar 

  19. Sobolewski M, Conrad K, Allen JL, Weston H, Martin K, Lawrence BP, et al. Sex-specific enhanced behavioral toxicity induced by maternal exposure to a mixture of low dose endocrine-disrupting chemicals. NeuroToxicology déc. 2014;45:121–30.

    Article  CAS  Google Scholar 

  20. Hadara M. Intrauterine poisoning. Bull Inst Const Med Jumamoto Univ. 1976;25:38–61.

    Google Scholar 

  21. Bradley-Johnson S. Cognitive Assessment for the Youngest children: a critical review of tests. J Psychoeduc Assess mars. 2001;19(1):19–44.

    Article  Google Scholar 

  22. Rogan WJ, Gladen BC, Hung KL, Koong SL, Shih LY, Taylor JS, et al. Congenital poisoning by Polychlorinated biphenyls and their contaminants in Taiwan. Sci 15 Juill. 1988;241(4863):334–6.

    CAS  Google Scholar 

  23. Chen Y, Hsu C. Effects of prenatal exposure to PCBs on the neurological function of children: a neuropsychological and neurophysiology study. Dev Med Child Neurol avr. 1994;36(4):312–20.

    Article  CAS  Google Scholar 

  24. Berghuis SA, Bos AF, Sauer PJJ, Roze E. Developmental neurotoxicity of persistent organic pollutants: an update on childhood outcome. Arch Toxicol Mai. 2015;89(5):687–709.

    Article  CAS  Google Scholar 

  25. Jurewicz J, Polańska K, Hanke W. Chemical exposure early in life and the neurodevelopment of children–an overview of current epidemiological evidence. Ann Agric Environ Med AAEM. 2013;20(3):465–86.

    Google Scholar 

  26. Liew Z, Goudarzi H, Oulhote Y. Developmental exposures to Perfluoroalkyl substances (PFASs): an update of Associated Health outcomes. Curr Environ Health Rep mars. 2018;5(1):1–19.

    Article  CAS  Google Scholar 

  27. Ruel MVM, Bos AF, Soechitram SD, Meijer L, Sauer PJJ, Berghuis SA. Prenatal exposure to organohalogen compounds and children’s mental and motor development at 18 and 30 months of age. NeuroToxicology Mai. 2019;72:6–14.

    Article  CAS  Google Scholar 

  28. Lazarevic N, Barnett AG, Sly PD, Knibbs LD. Statistical methodology in studies of prenatal exposure to mixtures of endocrine-disrupting chemicals: a review of existing approaches and New Alternatives. Environ Health Perspect févr. 2019;127(2):026001.

    Article  CAS  Google Scholar 

  29. Berghuis SA, Soechitram SD, Sauer PJJ, Bos AF. Prenatal exposure to Polychlorinated biphenyls and their hydroxylated metabolites is Associated with neurological functioning in 3-Month-Old infants. Toxicol Sci déc. 2014;142(2):455–62.

    Article  CAS  Google Scholar 

  30. Vermeir G, Covaci A, Van Larebeke N, Schoeters G, Nelen V, Koppen G, et al. Neurobehavioural and cognitive effects of prenatal exposure to organochlorine compounds in three year old children. BMC Pediatr déc. 2021;21(1):99.

    Article  CAS  Google Scholar 

  31. Chen MH, Ha EH, Liao HF, Jeng SF, Su YN, Wen TW, et al. Perfluorinated compound levels in cord blood and neurodevelopment at 2 years of age. Epidemiol Nov. 2013;24(6):800–8.

    Article  Google Scholar 

  32. Zhou Y, Li Q, Wang P, Li J, Zhao W, Zhang L, et al. Associations of prenatal PFAS exposure and early childhood neurodevelopment: evidence from the Shanghai maternal-child pairs cohort. Environ Int mars. 2023;173:107850.

    Article  CAS  Google Scholar 

  33. Kishi R, Kobayashi S, Ikeno T, Araki A, Miyashita C, Itoh S, et al. Ten years of progress in the Hokkaido birth cohort study on environment and children’s health: cohort profile—updated 2013. Environ Health Prev Med Nov. 2013;18(6):429–50.

    Article  Google Scholar 

  34. Barrea C, Dufour P, Catherine P, Charlier C, Brevers F, Rousselle L, et al. Impact of antenatal exposure to a mixture of persistent organic pollutants on intellectual development. Int J Hyg Environ Health août. 2024;261:114422.

    Article  CAS  Google Scholar 

  35. Dufour P, Pirard C, Seghaye MC, Charlier C. Association between organohalogenated pollutants in cord blood and thyroid function in newborns and mothers from Belgian population. Environ Pollut Juill. 2018;238:389–96.

    Article  CAS  Google Scholar 

  36. Pirard C, Compere S, Firquet K, Charlier C. The current environmental levels of endocrine disruptors (mercury, cadmium, organochlorine pesticides and PCBs) in a Belgian adult population and their predictors of exposure. Int J Hyg Environ Health mars. 2018;221(2):211–22.

    Article  CAS  Google Scholar 

  37. Kärrman A, Ericson I, Van Bavel B, Darnerud PO, Aune M, Glynn A, et al. Exposure of Perfluorinated Chemicals through Lactation: levels of Matched Human milk and serum and a temporal Trend, 1996–2004, in Sweden. Environ Health Perspect févr. 2007;115(2):226–30.

    Article  Google Scholar 

  38. Hubert Ph, Nguyen-Huu JJ, Boulanger B, Chapuzet E, Cohen N, Compagnon PA, et al. Harmonization of strategies for the validation of quantitative analytical procedures. J Pharm Biomed Anal sept. 2007;45(1):82–96.

    Article  Google Scholar 

  39. Van Den Dries MA, Ferguson KK, Keil AP, Pronk A, Spaan S, Ghassabian A, et al. Prenatal exposure to Nonpersistent Chemical Mixtures and offspring IQ and emotional and behavioral problems. Environ Sci Technol 21 déc. 2021;55(24):16502–14.

    Article  Google Scholar 

  40. Lubin JH, Colt JS, Camann D, Davis S, Cerhan JR, Severson RK, et al. Epidemiologic Evaluation of Measurement Data in the Presence of detection limits. Environ Health Perspect déc. 2004;112(17):1691–6.

    Article  CAS  Google Scholar 

  41. Josse J, Pagès J, Husson F. Multiple imputation in principal component analysis. Adv Data Anal Classif oct. 2011;5(3):231–46.

    Article  Google Scholar 

  42. Hargarten PM, Wheeler DC. Accounting for the uncertainty due to chemicals below the detection limit in mixture analysis. Environ Res Juill. 2020;186:109466.

    Article  CAS  Google Scholar 

  43. Henderson S, Sugden D, Barnett A. Movement Assessment Battery for Children-2. Second edition (MABC-2). Examiner’s manual. Pearson; 2007.

  44. Willems G, Noël A, Evrard P. Les troubles de l’apprentissage scolaire: examen neuropédiatrique des fonctions d’apprentissages de l’enfant en âge préscolaire. Paris: Doin Editeurs; 1979.

    Google Scholar 

  45. Kalverboer AF. A study of « minimal brain dysfunction ». Dev Med Child Neurol févr. 1969;11(1):115–6.

    CAS  Google Scholar 

  46. Bax M, Whitmore K. Neurodevelopmental screening in the school-entrant medical examination. Lancet août. 1973;302(7825):368–70.

    Article  Google Scholar 

  47. VanderWeele TJ, Hernán MA, Robins JM. Causal Directed Acyclic Graphs and the Direction of Unmeasured Confounding Bias. Epidemiol Sept. 2008;19(5):720–8.

    Article  Google Scholar 

  48. Luo F, Chen Q, Yu G, Huo X, Wang H, Nian M, et al. Exposure to perfluoroalkyl substances and neurodevelopment in 2-year-old children: a prospective cohort study. Environ Int août. 2022;166:107384.

    Article  CAS  Google Scholar 

  49. Harris MH, Oken E, Rifas-Shiman SL, Calafat AM, Ye X, Bellinger DC, et al. Prenatal and childhood exposure to per- and polyfluoroalkyl substances (PFASs) and child cognition. Environ Int juin. 2018;115:358–69.

    Article  CAS  Google Scholar 

  50. Maldonado G, Greenland S. Simulation Study of Confounder-Selection strategies. Am J Epidemiol 1 déc. 1993;138(11):923–36.

    Article  CAS  Google Scholar 

  51. Kalloo G, Wellenius GA, McCandless L, Calafat AM, Sjodin A, Romano ME, et al. Exposures to chemical mixtures during pregnancy and neonatal outcomes: the HOME study. Environ Int janv. 2020;134:105219.

    Article  CAS  Google Scholar 

  52. Carrico C, Gennings C, Wheeler DC, Factor-Litvak P. Characterization of Weighted Quantile Sum Regression for highly correlated data in a risk analysis setting. J Agric Biol Environ Stat mars. 2015;20(1):100–20.

    Article  Google Scholar 

  53. Tanner EM, Hallerbäck MU, Wikström S, Lindh C, Kiviranta H, Gennings C, et al. Early prenatal exposure to suspected endocrine disruptor mixtures is associated with lower IQ at age seven. Environ Int janv. 2020;134:105185.

    Article  CAS  Google Scholar 

  54. White SS, Fenton SE, Hines EP. Endocrine disrupting properties of perfluorooctanoic acid. J Steroid Biochem Mol Biol oct. 2011;127(1–2):16–26.

    Article  CAS  Google Scholar 

  55. R Core Team. R: a language and environment for statistical computing [Computer software] [Internet]. Vienna: RFoundation for Statistical Computing; 2021. Disponible sur:. http://www.R-project.org.

    Google Scholar 

  56. Hargarten PM, Wheeler DC. Multiple Imputation Using Weighted Quantile Sum Regression. Vol. R Package Version 0.4.4. 2021.

  57. Priti K, Shakya KS, Kumar P. Selection of statistical technique for imputation of single site-univariate and multisite–multivariate methods for particulate pollutants time series data with long gaps and high missing percentage. Environ Sci Pollut Res 23 mai. 2023;30(30):75469–88.

    Article  Google Scholar 

  58. Francois Husson J, Josse S, Le JM. FactoMineR: Multivariate Exploratory Data Analysis and Data Mining [Internet]. 2006 [cité 9 déc 2024]. p. 2.11. Disponible sur: https://CRAN.R-project.org/package=FactoMineR

  59. Jacobson SW, Fein GG, Jacobson JL, Schwartz PM, Dowler JK. The effect of intrauterine PCB exposure on visual recognition memory. Child Dev août. 1985;56(4):853–60.

    Article  CAS  Google Scholar 

  60. Rogan WJ, Gladen BC, McKinney JD, Carreras N, Hardy P, Thullen J, et al. Neonatal effects of transplacental exposure to PCBs and DDE. J Pediatr août. 1986;109(2):335–41.

    Article  CAS  Google Scholar 

  61. Stewart P, Reihman J, Lonky E, Darvill T, Pagano J. Prenatal PCB exposure and neonatal behavioral assessment scale (NBAS) performance. Neurotoxicol Teratol janv. 2000;22(1):21–9.

    Article  CAS  Google Scholar 

  62. Sagiv SK, Nugent JK, Brazelton TB, Choi AL, Tolbert PE, Altshul LM, et al. Prenatal Organochlorine exposure and measures of Behavior in Infancy using the neonatal behavioral Assessment Scale (NBAS). Environ Health Perspect Mai. 2008;116(5):666–73.

    Article  CAS  Google Scholar 

  63. Engel SM, Berkowitz GS, Barr DB, Teitelbaum SL, Siskind J, Meisel SJ, et al. Prenatal Organophosphate metabolite and organochlorine levels and performance on the Brazelton Neonatal Behavioral Assessment Scale in a multiethnic pregnancy cohort. Am J Epidemiol 15 juin. 2007;165(12):1397–404.

    Article  Google Scholar 

  64. Suzuki K, Nakai K, Sugawara T, Nakamura T, Ohba T, Shimada M, et al. Neurobehavioral effects of prenatal exposure to methylmercury and PCBs, and seafood intake: neonatal behavioral assessment scale results of Tohoku study of child development. Environ Res oct. 2010;110(7):699–704.

    Article  CAS  Google Scholar 

  65. Prechtl HFR, Beintema DJ, Prechtl HFR. Die neurologische Untersuchung des reifen Neugeborenen. 2., überarbeitete Auflage. Stuttgart: Thieme; 1976. 104 p. (Flexibles Taschenbuch).

  66. Wilhelm M, Wittsiepe J, Lemm F, Ranft U, Kramer U, Furst P, et al. The Duisburg birth cohort study: influence of the prenatal exposure to PCDD/Fs and dioxin-like PCBs on thyroid hormone status in newborns and neurodevelopment of infants until the age of 24 months. Mutat Res Mutat Res Juill. 2008;659(1–2):83–92.

    Article  CAS  Google Scholar 

  67. Huisman M, Koopman-Esseboom C, Fidler V, Hadders-Algra M, Van Der Paauw CG, Tuinstra LGMT, et al. Perinatal exposure to polychlorinated biphenyls and dioxins and its effect on neonatal neurological development. Early Hum Dev avr. 1995;41(2):111–27.

    Article  CAS  Google Scholar 

  68. Huisman M, Koopman-Esseboom C, Lanting CI, Van Der Paauw CG, Tuinstra LGMTh, Fidler V, et al. Neurological condition in 18-month-old children perinatally exposed to polychlorinated biphenyls and dioxins. Early Hum Dev oct. 1995;43(2):165–76.

    Article  CAS  Google Scholar 

  69. Despres C, Beuter A, Richer F, Poitras K, Veilleux A, Ayotte P, et al. Neuromotor functions in Inuit preschool children exposed to pb, PCBs, and hg. Neurotoxicol Teratol mars. 2005;27(2):245–57.

    Article  CAS  Google Scholar 

  70. Hempel MS. Neurological development during toddling age in normal children and children at risk of developmental disorders. Early Hum Dev sept. 1993;34(1–2):47–57.

    Article  CAS  Google Scholar 

  71. Lanting CI, Patandin S, Fidler V, Weisglas-Kuperus N, Sauer PJJ, Boersma ER, et al. Neurological condition in 42-month-old children in relation to pre- and postnatal exposure to polychlorinated biphenyls and dioxins. Early Hum Dev févr. 1998;50(3):283–92.

    Article  CAS  Google Scholar 

  72. Boersma ER. Environmental exposure to polychlorinated biphenyls (PCBs) and dioxins: Consequences for longterm neurological and cognitive development of the child. A Review. APMIS [Internet]. juill 2001 [cité 9 déc 2024];109(S103). Disponible sur: https://onlinelibrary.wiley.com/doi/https://doiorg.publicaciones.saludcastillayleon.es/10.1111/j.1600-0463.2001.tb05773.x

  73. Roze E, Meijer L, Bakker A, Van Braeckel KNJA, Sauer PJJ, Bos AF. Prenatal exposure to Organohalogens, including Brominated Flame retardants, influences Motor, Cognitive, and behavioral performance at School Age. Environ Health Perspect déc. 2009;117(12):1953–8.

    Article  CAS  Google Scholar 

  74. Gladen BC, Rogan WJ, Hardy P, Thullen J, Tingelstad J, Tully M. Development after exposure to polychlorinated biphenyls and dichlorodiphenyl dichloroethene transplacentally and through human milk. J Pediatr déc. 1988;113(6):991–5.

    Article  CAS  Google Scholar 

  75. Rogan WJ, Gladen BC. PCBs, DDE, and child development at 18 and 24 months. Ann Epidemiol août. 1991;1(5):407–13.

    Article  CAS  Google Scholar 

  76. Koopman-Esseboom C, Weisglas-Kuperus N, de Ridder MA, Van der Paauw CG, Tuinstra LG, Sauer PJ. Effects of polychlorinated biphenyl/dioxin exposure and feeding type on infants’ mental and psychomotor development. Pediatr Mai. 1996;97(5):700–6.

    Article  CAS  Google Scholar 

  77. Park HY, Hertz-Picciotto I, Sovcikova E, Kocan A, Drobna B, Trnovec T. Neurodevelopmental toxicity of prenatal polychlorinated biphenyls (PCBs) by chemical structure and activity: a birth cohort study. Environ Health déc. 2010;9(1):51.

    Article  Google Scholar 

  78. Forns J, Lertxundi N, Aranbarri A, Murcia M, Gascon M, Martinez D, et al. Prenatal exposure to organochlorine compounds and neuropsychological development up to two years of life. Environ Int sept. 2012;45:72–7.

    Article  CAS  Google Scholar 

  79. Winneke G, Bucholski A, Heinzow B, Krämer U, Schmidt E, Walkowiak J, et al. Developmental neurotoxicity of polychlorinated biphenyls (PCBS): cognitive and psychomotor functions in 7-month old children. Toxicol Lett déc. 1998;102–103:423–8.

    Article  Google Scholar 

  80. Walkowiak J, Wiener JA, Fastabend A, Heinzow B, Krämer U, Schmidt E, et al. Environmental exposure to polychlorinated biphenyls and quality of the home environment: effects on psychodevelopment in early childhood. Lancet Nov. 2001;358(9293):1602–7.

    Article  CAS  Google Scholar 

  81. Daniels JL. Prenatal exposure to low-level Polychlorinated biphenyls in Relation to Mental and Motor Development at 8 months. Am J Epidemiol 15 mars. 2003;157(6):485–92.

    Article  Google Scholar 

  82. Ribas-Fitó N, Cardo E, Sala M, Eulàlia De Muga M, Mazón C, Verdú A, et al. Breastfeeding, exposure to Organochlorine compounds, and Neurodevelopment in infants. Pediatr 1 mai. 2003;111(5):e580–5.

    Article  Google Scholar 

  83. Nakajima S, Saijo Y, Kato S, Sasaki S, Uno A, Kanagami N, et al. Effects of prenatal exposure to Polychlorinated biphenyls and dioxins on Mental and Motor Development in Japanese Children at 6 months of age. Environ Health Perspect Mai. 2006;114(5):773–8.

    Article  CAS  Google Scholar 

  84. Nakajima S, Saijo Y, Miyashita C, Ikeno T, Sasaki S, Kajiwara J, et al. Sex-specific differences in effect of prenatal exposure to dioxin-like compounds on neurodevelopment in Japanese children: Sapporo cohort study. Environ Res Nov. 2017;159:222–31.

    Article  CAS  Google Scholar 

  85. Lynch CD, Jackson LW, Kostyniak PJ, McGuinness BM, Buck Louis GM. The effect of prenatal and postnatal exposure to polychlorinated biphenyls and child neurodevelopment at age twenty four months. Reprod Toxicol Nov. 2012;34(3):451–6.

    Article  CAS  Google Scholar 

  86. Doi H, Nishitani S, Fujisawa TX, Nagai T, Kakeyama M, Maeda T et al. Prenatal Exposure to a Polychlorinated Biphenyl (PCB) Congener Influences Fixation Duration on Biological Motion at 4-Months-Old: A Preliminary Study. Pant AB, éditeur. PLoS ONE. 28 mars. 2013;8(3):e59196.

  87. Boucher O, Muckle G, Jacobson JL, Carter RC, Kaplan-Estrin M, Ayotte P, et al. Domain-specific effects of prenatal exposure to PCBs, Mercury, and lead on Infant Cognition: results from the Environmental contaminants and Child Development Study in Nunavik. Environ Health Perspect mars. 2014;122(3):310–6.

    Article  CAS  Google Scholar 

  88. Kim S, Eom S, Kim HJ, Lee JJ, Choi G, Choi S, et al. Association between maternal exposure to major phthalates, heavy metals, and persistent organic pollutants, and the neurodevelopmental performances of their children at 1 to 2 years of age- CHECK cohort study. Sci Total Environ Mai. 2018;624:377–84.

    Article  CAS  Google Scholar 

  89. Jacobson JL, Jacobson SW, Humphrey HEB. Effects of in utero exposure to polychlorinated biphenyls and related contaminants on cognitive functioning in young children. J Pediatr janv. 1990;116(1):38–45.

    Article  CAS  Google Scholar 

  90. Gladen BC, Rogan WJ. Effects of perinatal polychlorinated biphenyls and dichlorodiphenyl dichloroethene on later development. J Pediatr Juill. 1991;119(1):58–63.

    Article  CAS  Google Scholar 

  91. Grandjean P, Weihe P, Burse VW, Needham LL, Storr-Hansen E, Heinzow B, et al. Neurobehavioral deficits associated with PCB in 7-year-old children prenatally exposed to seafood neurotoxicants. Neurotoxicol Teratol Juill. 2001;23(4):305–17.

    Article  CAS  Google Scholar 

  92. Stewart PW, Reihman J, Lonky EI, Darvill TJ, Pagano J. Cognitive development in preschool children prenatally exposed to PCBs and MeHg. Neurotoxicol Teratol janv. 2003;25(1):11–22.

    Article  CAS  Google Scholar 

  93. Vreugdenhil HJI, Mulder PGH, Emmen HH, Weisglas-Kuperus N. Effects of Perinatal exposure to PCBs on neuropsychological functions in the Rotterdam Cohort at 9 years of age. Neuropsychology. 2004;18(1):185–93.

    Article  Google Scholar 

  94. Forns J, Torrent M, Garcia-Esteban R, Grellier J, Gascon M, Julvez J, et al. Prenatal exposure to polychlorinated biphenyls and child neuropsychological development in 4-year-olds: an analysis per congener and specific cognitive domain. Sci Total Environ août. 2012;432:338–43.

    Article  CAS  Google Scholar 

  95. Høyer BB, Ramlau-Hansen CH, Obel C, Pedersen HS, Hernik A, Ogniev V, et al. Pregnancy serum concentrations of perfluorinated alkyl substances and offspring behaviour and motor development at age 5–9 years – a prospective study. Environ Health déc. 2015;14(1):2.

    Article  Google Scholar 

  96. Boucher O, Muckle G, Ayotte P, Dewailly E, Jacobson SW, Jacobson JL. Altered fine motor function at school age in Inuit children exposed to PCBs, methylmercury, and lead. Environ Int oct. 2016;95:144–51.

    Article  CAS  Google Scholar 

  97. Kyriklaki A, Vafeiadi M, Kampouri M, Koutra K, Roumeliotaki T, Chalkiadaki G, et al. Prenatal exposure to persistent organic pollutants in association with offspring neuropsychological development at 4years of age: the Rhea mother-child cohort, Crete, Greece. Environ Int déc. 2016;97:204–11.

    Article  CAS  Google Scholar 

  98. Vreugdenhil HJI, Lanting CI, Mulder PGH, Boersma ER, Weisglas-Kuperus N. Effects of prenatal PCB and dioxin background exposure on cognitive and motor abilities in Dutch children at school age. J Pediatr janv. 2002;140(1):48–56.

    Article  CAS  Google Scholar 

  99. Berghuis SA, Van Braeckel KNJA, Sauer PJJ, Bos AF. Prenatal exposure to persistent organic pollutants and cognition and motor performance in adolescence. Environ Int déc. 2018;121:13–22.

    Article  CAS  Google Scholar 

  100. Donauer S, Chen A, Xu Y, Calafat AM, Sjodin A, Yolton K. Prenatal exposure to Polybrominated Diphenyl Ethers and Polyfluoroalkyl Chemicals and Infant Neurobehavior. J Pediatr mars. 2015;166(3):736–42.

    Article  CAS  Google Scholar 

  101. Spratlen MJ, Perera FP, Lederman SA, Rauh VA, Robinson M, Kannan K, et al. The association between prenatal exposure to perfluoroalkyl substances and childhood neurodevelopment. Environ Pollut août. 2020;263:114444.

    Article  CAS  Google Scholar 

  102. Carrizosa C, Murcia M, Ballesteros V, Costa O, Manzano-Salgado CB, Ibarluzea J, et al. Prenatal perfluoroalkyl substance exposure and neuropsychological development throughout childhood: the INMA Project. J Hazard Mater août. 2021;416:125185.

    Article  CAS  Google Scholar 

  103. Oh J, Schmidt RJ, Tancredi D, Calafat AM, Roa DL, Hertz-Picciotto I, et al. Prenatal exposure to per- and polyfluoroalkyl substances and cognitive development in infancy and toddlerhood. Environ Res Mai. 2021;196:110939.

    Article  CAS  Google Scholar 

  104. Yao Q, Vinturache A, Lei X, Wang Z, Pan C, Shi R, et al. Prenatal exposure to per- and polyfluoroalkyl substances, fetal thyroid hormones, and infant neurodevelopment. Environ Res avr. 2022;206:112561.

    Article  CAS  Google Scholar 

  105. Fei C, McLaughlin JK, Lipworth L, Olsen J. Prenatal exposure to Perfluorooctanoate (PFOA) and perfluorooctanesulfonate (PFOS) and maternally reported Developmental milestones in Infancy. Environ Health Perspect oct. 2008;116(10):1391–5.

    Article  CAS  Google Scholar 

  106. Goudarzi H, Nakajima S, Ikeno T, Sasaki S, Kobayashi S, Miyashita C, et al. Prenatal exposure to perfluorinated chemicals and neurodevelopment in early infancy: the Hokkaido Study. Sci Total Environ janv. 2016;541:1002–10.

    Article  CAS  Google Scholar 

  107. Niu J, Liang H, Tian Y, Yuan W, Xiao H, Hu H, et al. Prenatal plasma concentrations of Perfluoroalkyl and polyfluoroalkyl substances and neuropsychological development in children at four years of age. Environ Health déc. 2019;18(1):53.

    Article  Google Scholar 

  108. Fei C, Olsen J. Prenatal exposure to Perfluorinated Chemicals and behavioral or coordination problems at Age 7 years. Environ Health Perspect avr. 2011;119(4):573–8.

    Article  CAS  Google Scholar 

  109. Dzierlenga AL, Robinson VG, Waidyanatha S, DeVito MJ, Eifrid MA, Gibbs ST, et al. Toxicokinetics of perfluorohexanoic acid (PFHxA), perfluorooctanoic acid (PFOA) and perfluorodecanoic acid (PFDA) in male and female hsd:Sprague Dawley SD rats following intravenous or gavage administration. Xenobiotica 2 juin. 2020;50(6):722–32.

    Article  CAS  Google Scholar 

  110. Choi GW, Choi EJ, Kim JH, Kang DW, Lee YB, Cho HY. Gender differences in pharmacokinetics of perfluoropentanoic acid using non-linear mixed-effect modeling in rats. Arch Toxicol Mai. 2020;94(5):1601–12.

    Article  CAS  Google Scholar 

  111. Kern JK, Geier DA, Homme KG, King PG, Bjørklund G, Chirumbolo S, et al. Developmental neurotoxicants and the vulnerable male brain: a systematic review of suspected neurotoxicants that disproportionally affect males. Acta Neurobiol Exp (Warsz). 2017;77(4):269–96.

    Article  Google Scholar 

  112. Nian M, Luo K, Luo F, Aimuzi R, Huo X, Chen Q, et al. Association between prenatal exposure to PFAS and fetal sex hormones: are the short-Chain PFAS Safer? Environ Sci Technol 7 Juill. 2020;54(13):8291–9.

    Article  CAS  Google Scholar 

  113. Nguon K, Baxter MG, Sajdel-Sulkowska EM. Perinatal exposure to polychlorinated biphenyls differentially affects cerebellar development and motor functions in male and female rat neonates. Cerebellum 1 juin. 2005;4(2):112–22.

    Article  CAS  Google Scholar 

  114. Ten Donkelaar HJ, Lammens M, Wesseling P, Thijssen HO, Renier WO. Development and developmental disorders of the human cerebellum. J Neurol 1 sept. 2003;250(9):1025–36.

    Article  CAS  Google Scholar 

  115. Bell MR, Dryden A, Will R, Gore AC. Sex differences in effects of gestational polychlorinated biphenyl exposure on hypothalamic neuroimmune and neuromodulator systems in neonatal rats. Toxicol Appl Pharmacol août. 2018;353:55–66.

    Article  CAS  Google Scholar 

  116. Olsen GW, Burris JM, Ehresman DJ, Froehlich JW, Seacat AM, Butenhoff JL, et al. Half-life of serum elimination of Perfluorooctanesulfonate,Perfluorohexanesulfonate, and Perfluorooctanoate in Retired Fluorochemical Production workers. Environ Health Perspect Sept. 2007;115(9):1298–305.

    Article  CAS  Google Scholar 

  117. Leão LKR, Bittencourt LO, Oliveira ACA, Nascimento PC, Ferreira MKM, Miranda GHN, et al. Lead-Induced Motor Dysfunction is Associated with oxidative stress, Proteome Modulation, and Neurodegeneration in Motor cortex of rats. Ciobica A, éditeur. Oxid Med Cell Longev janv. 2021;2021(1):5595047.

    Article  Google Scholar 

  118. Abbott AL, Bartlett DJ, Fanning JEK, Kramer J. Infant Motor Development and aspects of the Home Environment. Pediatr Phys Ther. 2000;12(2):62–7.

    Article  Google Scholar 

  119. Torres-Sánchez L, Schnaas L, Cebrián ME, Hernández MDC, Valencia EO, García Hernández RM, et al. Prenatal dichlorodiphenyldichloroethylene (DDE) exposure and neurodevelopment: a follow-up from 12 to 30 months of age. NeuroToxicology Nov. 2009;30(6):1162–5.

    Article  Google Scholar 

  120. Sedlak P, Pařízková J, Daniš R, Dvořáková H, Vignerová J. Secular changes of Adiposity and Motor Development in Czech Preschool children: Lifestyle Changes in fifty-five year retrospective study. BioMed Res Int. 2015;2015:1–9.

    Article  Google Scholar 

  121. Satiansukpong N, Punyanon T, Sasat D. Developing Hand Dexterity Test for Children aged 6–12 years. Acad Psychiatry Psychol J. 2019;35(1):34–47.

    Google Scholar 

  122. Kita Y, Suzuki K, Hirata S, Sakihara K, Inagaki M, Nakai A. Applicability of the Movement Assessment Battery for Children-Second Edition to Japanese children: a study of the Age Band 2. Brain Dev sept. 2016;38(8):706–13.

    Article  Google Scholar 

  123. Condon C, Cremin K. Static balance norms in children: balance, temporal, stance, Tandem, single Leg. Physiother Res Int mars. 2014;19(1):1–7.

    Article  Google Scholar 

  124. Cech D, Martin ST. Functional Movement Development Across the Life Span [Internet]. Elsevier; 2012 [cité 9 déc 2024]. Disponible sur: https://linkinghub.elsevier.com/retrieve/pii/C20090607303

Download references

Author information

Author notes

  1. Anne-Simone Parent and Laurence Rousselle contributed equally to this work.

Authors and Affiliations

  1. Department of Paediatrics, University of Liege (ULg), CHU, Liege, 4000, Belgium

    Christophe Barrea & Anne-Simone Parent

  2. GIGA Neurosciences, Neuroendocrinology Unit, University of Liege (ULg), CHU, Liege, 4000, Belgium

    Christophe Barrea & Anne-Simone Parent

  3. Laboratory of Clinical, Forensic and Environmental Toxicology, University of Liege (ULg), CHU, Liege, 4000, Belgium

    Patrice Dufour, Pirard Catherine & Corinne Charlier

  4. Research Unit for a Life-Course Perspective on Health and Education, University of Liege (ULg), Liege, 4000, Belgium

    Fanny Brevers & Laurence Rousselle

  5. CHU University of Liege, 1, Avenue de l’Hôpital, Liège, 4000, Belgium

    Christophe Barrea

Authors
  1. Christophe Barrea
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Patrice Dufour
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Pirard Catherine
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Corinne Charlier
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Fanny Brevers
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Anne-Simone Parent
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Laurence Rousselle
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

C.B. designed the work, performed analysis and interpretation of the results, and wrote the main manuscript textP.D., C. P., C.C. and F.B. made some analysis and revised the workA-S.P. and L.R. designed the work, performed interpretation of data and revised the workAll of the authors have read and approved the paper. This article has not been published previously and is not considered by any other peer-reviewed journal.The authors have no competing interests to declareFunding sources supporting this work are provided by the Léon Fredericq Foundation of Liege. This study was approved by collegial decision from the university hospital-faculty medical Ethics Committee of Liege (Nr 2019 / 67).

Corresponding author

Correspondence to Christophe Barrea.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Barrea, C., Dufour, P., Catherine, P. et al. Neuromotor effects of early-life exposure to a mixture of endocrine disruptors in Belgian preschool children. Environ Health 24, 11 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-025-01156-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-025-01156-9

Keywords

Environmental Health

ISSN: 1476-069X