# Early-life exposure to mixtures of Endocrine-Disrupting Chemicals and a Multi-domain Health Score in preschool children

Ines Amine, Alicia Guillien, Sam Bayat, Sarah Lyon-Caen, Marion Ouidir, Azemira Sabaredzovic, Amrit Sakhi, Cathrine Thomsen, Séverine Valmary-Degano, Claire Philippat, et al.

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## To cite this version:

Ines Amine, Alicia Guillien, Sam Bayat, Sarah Lyon-Caen, Marion Ouidir, et al. Early-life exposure to mixtures of Endocrine-Disrupting Chemicals and a Multi-domain Health Score in preschool children. Environmental Research, 2025, 272, pp.121173. 10.1016/j.envres.2025.121173. hal-05125173

HAL Id: hal-05125173

https://hal.science/hal-05125173v1

Submitted on 23 Jun 2025

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Early-life exposure to mixtures of
Endocrine-Disrupting Chemicals and a
Multi-domain Health Score in
preschool children

Ines Amine¹, Alicia Guillien¹, Sam Bayat², Sarah Lyon-Caen¹, Marion Ouidir¹, Azemira

Sabaredzovic³, Amrit K. Sakhi³, Cathrine Thomsen³, Séverine Valmary-Degano⁴, Claire

Philippat¹, Valérie Siroux¹

1 - Université Grenoble Alpes, Inserm U 1209, CNRS UMR 5309, Team of Environmental

Epidemiology Applied to the Development and Respiratory Health, Institute for Advanced

Biosciences, 38700 La Tronche, France

2 - Department of Pulmonology and Physiology, CHU Grenoble Alpes, Grenoble, France

3 - Department of Food Safety, Norwegian Institute of Public Health, 0213 Oslo, Norway

4 - BB-0033-00069 Biobank, Univ. Grenoble Alpes, Inserm U1209, CNRS UMR5309,
Institute for Advanced Biosciences, CHU Grenoble-Alpes, F-38000 Grenoble, France.

Corresponding author: Ines Amine, ines.amine@univ-grenoble-alpes.fr, Institute for
Advanced Biosciences, Site Santé, Allée des Alpes, 384700 La Tronche

Last document submitted to Environmental Research, Accepted in March 2025.

Tables and figure are displayed at the end of the manuscript (after references).

Abstract:

**Background:** Early-life exposure to endocrine-disrupting chemicals, such as phenols and phthalates, is suspected to impact various dimensions of child health. Using a multi-outcome approach, this study aimed to estimate their cumulative effect on the child cardiometabolic, respiratory and neurodevelopmental health.

**Methods:** In 373 children of 3 years old from the SEPAGES cohort, a multi-domain health score was built from twenty-three health parameters. Fourteen metabolites of parabens, phenols, and phthalate/DINCH were measured several times during pregnancy (trimester 2
and 3) and infancy (2 and 12 months of age). Two mixture models, quantile g computation (q-gcomp) and Bayesian Kernel Machine Regression (BKMR), estimated associations between increased concentration of parabens, phenols, and phthalates/DINCH and the child health score.

**Results:** Q-gcomp showed that the paraben mixture and the phthalate mixture were associated with a poorer health score ($\beta$ = -0.11, 95% Confidence Interval (CI): -0.22, 0.00; $\beta$ = -0.14, 95% CI: -0.27, -0.01, respectively), while no significant association was found for the mixture of phenols ($\beta$ = -0.06, 95% CI: -0.18, 0.06). A trend for an association was observed between the whole mixture (parabens, phenols and phthalates combined) with a poorer health score ($\beta$ = -0.14, 95% CI: -0.32, 0.04). Similar patterns of association, while subject to large uncertainty, have been observed with BKMR.

**Discussion:** This study provides further evidence for the adverse health effects of early-life exposure to parabens and phthalates. Based on their potential impact on multiple areas of child health, public health policies targeting these chemical compounds are recommended.

**Key words (1 to 7):** endocrine disruptors, chemical mixtures, outcome-wide, Holistic Health, pregnancy, childhood.
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# 1. Introduction

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Early-life exposure to endocrine-disrupting compounds (EDCs) is a growing concern due to their influence on hormone regulation (1) and broader health impact (2–4). EDCs are metabolized into different compounds, classified into families and subfamilies based on their chemical properties and sources (e.g. phenols, phthalates, per- and polyfluoroalkyl substances (PFAS)). Among EDCs, phenols and phthalates stand out due to their widespread presence and short half-lives. Synthetic phenols and phthalates are commonly found in plastic products (e.g. food packaging, PVC), food items, personal care items and building materials. These substances are used by many manufacturers, with new compounds regularly coming onto the market, such as the phthalate alternative 1,2-cyclohexane dicarboxylic acid, diisononyl ester (DINCH). Despite their short half-lives, the omnipresence of phenols and phthalates leads to heightened concerns about their potential health effects. These non-persistent chemical compounds may disrupt thyroid (5,6) and steroid hormones (7,8). Chronic exposure during critical life stages, such as pregnancy and infancy, may impact child development and increase chronic disease risks (9).

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Most of the existing literature has focused on estimating the effect of exposure to EDCs on single health outcome or domain. Extensive research was conducted on reproductive and developmental health (10), and more recently on metabolic disorders, asthma, and allergies (11,12). Adopting a more holistic approach, it is now of interest to evaluate the impact of chemical compounds across multiple health domains within a single model. This approach could identify the chemical compounds with pleiotropic health effects, i.e. effects on multiple health outcomes, and support the development of more targeted and effective public health recommendations (13). Recent studies have explored different “multi-outcome” strategies to study the overall health impact of EDCs, such as outcome-wide models (14), multimorbidity indicators (15,16), and composite health scores combining clinical and subclinical parameters (17). Under the assumption that exposure to EDCs negatively affects each health parameter considered, which is supported by the literature (4), the composite health score is particularly advantageous in studies on young children, who have not yet developed clinical multimorbidity (18). In addition, compared to an outcome-wide analysis, this approach minimizes biases associated with multiple testing through the use of a single metric.
73 Studying phenols and phthalates poses challenges, such as the accurate assessment of
74 exposure and the estimation of their mixture effect. Given their short half-lives (6 to 48
75 hours), frequent measurements are essential for representative exposure estimates. Many
76 studies have relied on single urine samples, despite evidence that it may lead to significant
77 attenuation bias in health effect estimates, ranging from 40 to 80% according to the chemical
78 temporal variability (19). In contrast, pooling urine samples collected over multiple
79 consecutive days offers a more effective solution, reducing this bias to below 20% and
80 improving the representativeness of exposure measurement across the full week (19,20).
81 Furthermore, exposure may vary weekly due to factors like seasonal product use (e.g.
82 sunscreens in summer) and lifestyle changes, underscoring the importance of measuring
83 EDCs at multiple time points over the study period. Another challenge in studying EDCs is
84 their "cocktail" or "mixture" effects, i.e. the joint effects of multiple chemical compounds
85 (21). Most research to date has used single-pollutant models, which primarily inform per-
86 compound regulation and fall short in preventing the use of substitutes with unknown toxicity
87 (22,23). By estimating the effects of chemical mixtures, we could identify groups of
88 compounds that collectively impact health. This approach is especially relevant for chemical
89 compounds from common sources, such as parabens which are mostly found in personal care
90 products, as it could support per-family public health strategies.

91 The current study aimed to estimate the association between early-life exposure to mixtures of
92 parabens, mixtures of phenols and mixtures of phthalate/DINCH metabolites and a multi-
93 domain health score covering the cardiometabolic, respiratory and neurodevelopmental health
94 in 3-year-old children.

## 2. Methods

96 The methodology used for this project is illustrated in Figure 1 and detailed below.

97 a. Study population

98 This analysis is based on the SEPAGES cohort (Suivi de l'Exposition à la Pollution
99 Atmosphérique durant la Grossesse et Effet sur la Santé) (24). Recruitment, conducted
100 between July 2014 and 2017, included 484 pregnant women from ultrasonography practices
101 in the Grenoble area, France. Participants met the following criteria: less than 19 weeks of
102 gestation, over 18 years of age, expecting a single child, planning to deliver at a Grenoble
103 maternity clinic, and residing within the study area. Ethical approvals were obtained from
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relevant committees (CNIL: Comité Nationale de l'Informatique et des Libertés; CPP: Comité de Protection des Personnes Sud-Est V), and participants provided written consent for themselves and their children.

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The study population includes 373 children who participated in the 3-year visit and had at least one cardiometabolic, one respiratory/allergic, and one neurodevelopmental parameter available (see the list of parameters in part 2.c) along with urine samples collected for at least one time point for chemical concentrations (see part 2.b). Main analyses were conducted on n=318 children (phenols) or n=330 children (phthalates), with available chemical measurements at all time points (see the flow chart in Figure S1 of the supplementary materials). Notably, excluded mother-child pairs had lower parental education than included ones (see Table S1).

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## b. Synthetic parabens, phenols, phthalates and DINCH

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As listed in **Table 1**, eight phenol metabolites (including four parabens), eleven phthalate metabolites, and two non-phthalate plasticizers (DINCH) were measured in maternal and infant urine samples. Non-fasting urine samples were collected twice during pregnancy (at 18 and 34 gestational weeks) and during infancy (at 2 and 12 months). During seven consecutive days, mothers were instructed to collect three spot urine samples per day (morning, midday, and evening), and weekly within-subject pools were created (see sampling variability in Table S2). A similar protocol was followed for children, with one spot urine sample collected per day for seven days (see Table S2). Assessment of phenols, phthalate, and DINCH metabolites involved on-line column switching ultra-high performance liquid chromatography (for phenols) or high-performance liquid chromatography (for phthalates and DINCH), both coupled with tandem mass spectrometry (UPLC-MS-MS or HPLC-MS-MS), as described elsewhere (25,26). Additionally, urine specific gravity was measured in each pool using a handheld Atago PAL 10-S refractometer (Atago). Quality control, included check for possible contamination in child measurements, can be found in eMethod 1.

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Compounds with more than 30% below the Limit Of Detection (LOD) were excluded from the analysis (butylparaben and bisphenol S at all time points, and propylparaben at 2 months), as well as phthalates metabolites at 2 months measured for only part of the samples due to budget constraints. For other compounds, chemical concentrations below the LOD or Limit of Quantification (LOQ) were singly imputed using the compound probability distribution (27).
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Standardization for sampling conditions (sample transport time, thawing time during the pooling) and the analytical batch was then performed (28,29) when these conditions affected the measured concentrations (p-values < 0.2), based on regression models. Finally, molar sum of Di (2-ethylhexyl) phthalate (DEHP), Di-isononyl phthalate (DiNP) and DINCH metabolites were estimated, and all concentrations were log-10 transformed.

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### c. Health data

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As listed in Table 2, twenty-three health parameters were assessed in children, mostly at the 3-year follow-up, covering cardiometabolic, respiratory, and neurodevelopmental health (see eMethod 2 for details).

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**Cardiometabolic health** was assessed by a cohort clinical research assistant, including diastolic and systolic blood pressure (BP), heart rate (measured with a SureSigns-VSi monitor VSi, Philips, Amsterdam, Netherlands), and body fat estimated by bioelectrical impedance (with an empty bladder using the BodyStat 1500 MDD, Isle of Man, UK) coupled with skinfold thickness measurements (subscapular and triceps skinfolds using a skinfold caliper) and weight measurement. Fat mass percentage was derived from Goran's equation for improved accuracy in children (30).

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**Respiratory and allergy-related health** was assessed through lung function using oscillometry (resistance at 7 Hz (R7), frequency-dependence of resistance (R7-19), area of the reactance curve (AX), within-breath volume-dependence of resistance (ΔR)) (32), skin prick allergy tests (5 allergens tested), and parent-reported questionnaires administered at 2 months, 12 months, 2 years, and 3 years, asking whether the child had ever had doctor-diagnosed asthma (yes/no), doctor-diagnosed eczema (yes/no), wheezing (yes/no), bronchiolitis (yes/no) and bronchitis (yes/no) since birth.

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**Neurodevelopmental** assessments included cognitive testing (Wechsler Preschool and Primary Scale of Intelligence, Fourth Edition (WPPSI), measuring verbal and visuospatial comprehension, and working memory) and three parent questionnaires (the 99-item Child Behaviour Checklist (CBLC), the Behaviour Rating Inventory of Executive Function – Preschool version (BRIEF-P), the Social Responsiveness Scale (SRS)). These questionnaires provided scores on internalizing/externalizing behaviors (CBCL), executive functions (BRIEF-P: Emergent Metacognition index, Inhibitory Self-Control index, Flexibility index),

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and social impairment within the autism spectrum disorders (SRS total score). All measures
were performed at the 3-year follow-up, except CBCL, assessed at 2 years.

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d. Covariates

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A set of covariates was defined with the main confounders between the exposures and the
multi-domain health score as well as the main predictors of each health subdomain, as
detailed in the Directed Acyclic Graph (DAG) in Figure S2 of the supplementary materials.

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Because of the moderate sample size, we built a parsimonious model excluding covariates
with no sign of association with the outcome in our data (regression p-value>0.2). The final
set of covariates used included: **1) parental characteristics and health history:** highest
parental education (below Master degree vs Master degree or higher), parental history of
asthma or allergy (none vs at least one parent) assessed during pregnancy, maternal
depression and anxiety during pregnancy (total score from the Hospital Anxiety and
Depression scale), **2) details on pregnancy:** season of conception, maternal tobacco (active
or passive vs none) during pregnancy, **3) child characteristics and environment at 3 years:**
child sex, child height, child postnatal exposure to tobacco (yes/no), child stimulation score
(total scale of the Home Observation Measurement of the Environment (HOME)).

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e. Pre-treatment of the data

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Imputation of the missing values on covariates and the 23 health parameters was done using
the method of chained equations (31) (R package *mice* v3.16.0.). Before, we checked that the
percentage of missingness was below 30%, and we log-transformed covariates with a skewed
distribution. It generated 20 imputed datasets, which were checked (see *eMethods* 3) and then
used to compute the multi-domain health score and perform the association studies.

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f. Creation of the multi-domain health score

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Similarly as done in Amine et al. (2023), a **multi-domain health score** (also named as
“general health score” or simply “health score”) averaged three subscores, each representing a
specific health domain (cardiometabolic, respiratory/allergy, and neurodevelopmental health).

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Beforehand, each continuous parameter was transformed in a z-score, using a Box-Cox
transformation (R package *MASS* v7.3-57) plus centering and scaling. The cardiometabolic
subscore was defined as (-z fat mass) + (- z heart rate) + (- z diastolic BP – z systolic BP)/2,
similar to the metabolic score used in the Helix population (32). In the same way, the
neurodevelopmental subscore was defined as (z WPPSI – z SRS – z CBLC – z BRIEF)/4,

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where each test z-score is the average of the z-scores of its considered indexes. Following the approach of Eisenmann (33) the respiratory/allergic subscore was defined as the first principal component of a multiple factorial analysis (R package *FactoMineR* v.2.9.), able to deal with categorical outcomes.

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Given the challenge of prioritizing one health domain over another, we assigned equal weights to each subscore when creating the multi-domain health score. While alternative weighting schemes could be based on disease burden metrics (e.g., DALYs), economic costs, or long-term impact, determining an appropriate approach requires further research. We thus opted for an equal-weight method as a straightforward and unbiased choice.

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By construct, the multi-domain health score is low-to-moderate for children who exhibit low-to-moderate health in cardiometabolic, respiratory/allergy and neurodevelopmental domains simultaneously, as well as for children who are severely affected in one health domain while being unaffected or poorly affected in the other two. The multi-domain health score determinants were described using a multivariable linear regression model using all covariates (listed in part 2.d).

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g. Strategy of analysis for the association study

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The association between mixtures of EDCs and the multi-domain health score was performed using two complementary statistical approaches: quantile g-computation (q-gcomp) (34) and Bayesian Kernel Machine Regression (BKMR) (35). These two models allow for an effect estimation of increasing all exposures simultaneously, with additive linear effects for q-gcomp, and possible interactions and non-linearity for BKMR. Because each model (q-gcomp and BKMR) has its own assumptions, comparing their results provides additional support to validate the findings (36).

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In **q-gcomp**, continuous exposures are transformed in quartiles, and the overall mixture effect is estimated as a weighted sum of the effects of the quantized exposures (*qgcomp* R package, v2.15.2). This approach evaluates the impact of simultaneously increasing all exposures by one quartile. Mathematically, if $Y_i$ represents the continuous outcome for individual i, the regression model is expressed as $Y_i = \beta_0 + \psi S_i + \beta C_i + \epsilon_i$, where: $\beta_0$ is the intercept, $S_i$ is the exposure index (defined below), $\psi$ represent the overall mixture effect, $C_i$ is the matrix of covariates with corresponding vector of coefficients $\beta$, and $\epsilon_i$ is the error term. The exposure index is defined as $S_i = \sum w_j X_{ij}^q$, where $w_j$ is the weight assigned to jth exposure and $X_{ij}^q$ is
the quantized version of the jth exposure for individual i. While the weights can be used to assess individual chemical contributions, these estimates should be interpreted cautiously, especially when correlated exposures are considered (34). Due to the moderate sample size, we did not include non-linear terms nor pairwise interactions in q-gcomp.

On the other hand, **BKMR** represents a non-parametric Bayesian approach using a kernel function to estimate the relationship between several exposures and an outcome (R package *bkmr* v.0.2.2.). Mathematically, if $Y_i$ represents the continuous outcome for individual i and $\mu_i = E(Y_i)$, the modeling framework is given by $\mu_i = h(X_{i1},...,X_{iM}) + \beta C_i$, where h is a flexible function of the exposures $X_{ij}$, and $C_i$ is the matrix of covariates with corresponding vector of coefficient $\beta$ (linear assumption). The exposure-response function $h(.)$ is characterized using a Gaussian kernel, expressed as $K(X, X') = \exp\{-\sum_m r_m (X_m - X'_m)^2\}$, where X and X' represent vectors of exposures from two different individuals, and $r_m \ge 0$ are tuning parameters. This kernel function shrinks the estimated effects of two individuals with similar exposure mixtures towards each other. The mixture overall effect is computed for all chemical compounds fixed to a given quantile, compared to when they are all fixed to the 10th quantile. The inclusion of 0 within the credible interval is used to evaluate the certainty of the mixture effect. As this model performs variable selection at each iteration ($r_m$ can be equal to 0), the contribution of each chemical compound in the mixture can be estimated through the mean Posterior Inclusion Probabilities (PIP).

One separate model was fitted for each family of compounds (parabens, phenols, phthalates/DINCH) and the whole mixture (parabens, phenols and phthalates combined). As the outcome was assessed only once (at 3 years of age), we did not perform a repeated measures analysis, but rather integrated chemical measurements from all time points as separate predictors in the model. To handle the multiplicity of predictors in the model including all chemical families (parabens, phenols and phthalates) in BKMR, the hierarchical selection was used (“h-BKMR”) considering one group for each compound. All models were adjusted for the covariates listed in part 2.d, and fitted for each imputed dataset before combining the results, using Rubin's rule for q-gcomp and merging all simulations for BKMR (37,38).

Several complementary analyses were performed to deepen the association study, including 1) the investigation of windows-specific effects by fitting separate models for each time point
(trimester 2, trimester 3, 2 months (parabens and phenols only) and 12 months), 2) the investigation of potential sex-specific association, by fitting sex-stratified analyses, 3) the description of domain-specific health effects, by investigating the association separately for each subscore (cardiometabolic, respiratory, neurodevelopmental), 4) the description of chemical-specific associations, by fitting adjusted linear regressions between each single chemical compound with the multi-domain health score (“uni-polluant” models).

Various sensitivity analyses were used to further assess the robustness of the findings to 1) extreme values, using winsorization on the 2% lowest and 2% highest values for the multi-domain health score (39), 2) adjustment factors, by considering the minimal adjustment set (i.e. excluding predictors of the outcome, see the DAG in Figure S2), 3) removal of children with potential measurements error in blood pressure (remaining n=359), 4) the use of concentrations corrected for urine specific gravity, even if this approach is discussed as it may bias health effect estimates (40), 5) the use of the conjugated form of the compound when contamination was suspected (bisphenol A at 2 and 12 months and methylparaben at 12 months, see eMethods 1), for the uni-pollutant analysis only.

Analyses were done with R version 4.2.1.

## 3. Results

### a. Description of the population

As shown in **Table 3**, the study population comprised 373 children, with a median age of 3.07 years (percentile 25; 75: 3.04; 3.13) at the time of the clinical visit, and 46% of the participants were female. At birth, mothers had a median age of 32 years (percentile 25; 75: 30; 35), and for 74% of families, at least one parent had a high degree of education (Master degree or higher). Chemical concentration levels (before log10-transformation) are summarized in **Figure 2** and detailed in Table S3 and S4, with additional information available in previous studies (41,42). Between-compound concentrations in a given time point (e.g. different phthalate compounds at 12 months) were more strongly correlated that within-compound concentrations across time (i.e. measurements of a given phthalate compound at 2 and 12 months of age) (Figure S3). A table describing each health parameter is available in the supplementary materials (Table S5). Before imputation, half covariates and health parameters had less than 2% of missing values, and the highest missingness was reached for parameters of lung function (18-23%) (Table 3 and S5).
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## b. Description of the multi-domain health score

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For the respiratory/allergic subscore, we observed a similar contribution from each outcome, although ever-asthma diagnosis had a slightly greater weight (see Table S6 and Figure S4).

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For neurodevelopmental and cardiometabolic subscores, each outcome contributed equally by design, as they were based on an unweighted mean of z-scores. Although the three subscores (cardiometabolic, respiratory/allergic, and neurodevelopmental) were poorly correlated (rho<0.1, see Table S7), we observed that the multi-domain health score significantly increased with all health parameters included (Table S8).

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As shown in **Figure 3**, the multi-domain health score built had a normal distribution (Shapiro test p-value = 0.42) with a median (percentiles 25; 75) of 0.02 (-0.42; 0.42) (see also Table S9). The following characteristics were associated with a better multi-domain health score: higher child height, higher child stimulation score, and male sex, while a poorer score was associated with parental history of asthma or rhinitis and higher maternal anxiety and depression score (Table S10).

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## c. Chemical mixtures associated with the multi-domain health score

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As shown in **Figure 4** and Table S11, the q-gcomp mixture model showed that higher concentrations to mixtures of parabens and phthalates/DINCH were associated with a poorer multi-domain health score. A one-quartile increase in all chemical compounds from the phthalate/DINCH mixture was associated with a poorer health score ($\beta$ =-0.14, 95% Confidence Interval (CI): -0.27; -0.01), similarly as increased concentration of the paraben mixture ($\beta$ =-0.11, 95% CI: -0.22; 0.00). On the other hand, no significant association was found for the phenol mixture ($\beta$ =-0.06, 95% CI: -0.18; 0.06). When parabens, phenols and phthalates were studied in a combined mixture ("whole mixture"), a trend for an association was observed with a poorer health score ($\beta$ =-0.14, 95% CI: -0.27; 0.01). The results of the BKMR models were more uncertain, with wide credibility intervals that crossed the null (see Figure 4). Therefore, we interpreted these findings according to the direction of the association only. Consistently, the results pointed to poorer health scores with increased exposure to mixtures, providing support for the q-gcomp findings.

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In the analysis evaluating each time window separately (see Figures S5 and S6), we found that the highlighted associations with a poorer health score in q-gcomp were replicated in trimester 2 of pregnancy for the phthalates/DINCH mixture ($\beta$ = -0.10; 95% CI: -0.19; 0.00),

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and at 12 months for the paraben mixture (β = -0.05; 95% CI: -0.11; 0.01). In addition, significant associations for the phenol mixture and the whole mixture with a poorer health score, which were not observed in the main models, were observed specifically in trimester 2 (β =-0.08; 95%, CI: -0.15; 0.00, β =-0.11; 95%CI: -0.23; 0.01, respectively).

Using q-gcomp, the observed associations showed similar patterns of association in girls and boys for parabens and phthalates/DINCH (Figure S7). Additional associations were observed specifically in girls, but not in the whole population, for the phenol mixture and for the whole mixture (parabens, phenols and phthalates). BKMR confirmed the girl-specific association for the phenol mixture and suggested another girl-specific association for the phthalates/DINCH mixture (Figure S8). However, the wide credible intervals obtained in BKMR contained 0.

Analysis on specific health domains confirmed that the associations observed with the multi-domain health score were also reflected on several health subscores (cardiometabolic, respiratory/allergic, neurodevelopmental), although these associations were not always statistically significant (see Figure 5). Specifically, the association observed for the paraben mixture with the multi-domain health score was mainly driven by the neurodevelopmental subscore (β = -0.21, 95% CI: -0.40, -0.02), but it was also supported by the respiratory/allergic subscore (β = -0.11, 95% CI: -0.31, 0.09). Regarding phthalates, associations were mainly driven by the neurodevelopmental subscore (β =-0.23, 95% CI: -0.45, -0.01) and supported by the cardiometabolic subscore (β = -0.13, 95% CI: -0.35, 0.09).

The chemical compounds highlighted by the uni-pollutant analysis (Figure S9 and Tables S12-S13) were predominantly those contributing most to the mixtures using q-gcomp and BKMR (Figure S10 and Tables S14-S15) and included maternal concentration of monoethyl phthalate (MEP) (trimester 2 and 3), monobenzyl phthalate (MBzP) (trimester 2) and ethylparaben (trimester 3), as well as the infant concentration of propylparaben (12 months).

Overall, the findings of q-gcomp remained unchanged in the sensitivity analyses (see Figure S11 online). Similar results were obtained after removing children with potential measurement errors in blood pressure and after winsorizing extreme values on the outcome. Models restricted to the minimal set of confounders confirmed the results with overall stronger effect sizes as compared to the main model. When using chemical concentrations corrected for urine specific gravity, the mixture effect of phthalates was shifted towards the null, while the other significant findings remained unchanged. Using conjugated forms for
bisphenol A (2 and 12 months) and methylparaben (12 months) did not change the results in the uni-pollutant analysis (null associations), as displayed in Figure S9 online.

## 4. Discussion

This novel study estimates the overall effect of early-life exposure to parabens, phenols and phthalates on child health through a comprehensive approach using: 1) a multi-outcome strategy to estimate the cumulative effects across multiple health domains (cardiometabolic, respiratory and neurodevelopmental), 2) mixture models to estimate the joint effects of chemical compounds coming from similar sources, and 3) accurate and representative measures of overall chemical exposure via repeated measurements from early pregnancy to the first year of life. Our findings indicate that increased concentrations to mixtures of parabens and phthalates in early-life were associated with a poorer health score in children at 3 years of age. This study provides additional evidence to support adverse health effects of early-life exposure to parabens and phthalates on multiple areas of child health.

### a. Main findings

Our results showed associations between higher concentrations of parabens and phthalates and a poorer multi-domain health score in children. To our knowledge, only one study from the Human Early Life Exposome (HELIX) project used a similar composite health score to explore associations with EDCs (17). While a comparable finding was found between methylparaben and poorer child health, limited evidence was found with other non-persistent chemical compounds, likely due to reliance on single urine samples for exposure measurement. Using a different approach, two multi-outcome studies have also examined the effects of phthalates on child health (14,15); one with results on respiratory outcomes only, while the other found varying directional effects across domains, thus lacking evidence of a broad, unidirectional effect.

Our results align with existing literature on the associations between parabens and phthalates with individual outcomes of the three health domains covered: cardiometabolic, respiratory and neurodevelopmental health. Several studies suggests that early-life exposure to parabens and phthalates (especially DEHP and MEP) may be associated with adverse metabolic outcomes, including adiposity-related measures and increased blood pressure in children (43,44). Regarding respiratory health and allergies, several reviews or meta-analyses suggested associations between parabens, several phthalates (especially MBzP and DEHP)
and increased risk of asthma and eczema (45–48). In regards to neurodevelopmental health, some studies suggested that several outcomes were associated with exposure to parabens and DEHP (49–53), though other studies remained less conclusive (54,55). Using a multi-domain health score, our findings strengthen the hypothesis that phthalates and parabens could broadly affect multiple health areas in childhood. This is further supported by supplementary analyses which indicate suggestive associations with neurodevelopmental and respiratory/allergic subscores for parabens, and with neurodevelopmental and cardiometabolic subscores for phthalates/DINCH.

Among the suspected mechanisms of action of phenols and phthalates, key processes like endocrine dysregulation, oxidative stress, inflammation and epigenetic modifications, may contribute to the development of various chronic diseases across the three health domains studied. Many phenols and phthalates are classified as endocrine-disrupting chemicals (1,56) and may interfere with thyroid function and other hormones (7,8), which are critical for the brain development (57) and child growth (58), and might impact the onset of later chronic disorders (59–61). Hormonal effects may explain the girl-specific association observed for phenols, though stratification further reduces statistical power, warranting cautious interpretation of these sex-specific findings. To our knowledge, the epidemiological literature on sex-specific effects remains unclear. For example, several studies suggest that BPA exposure impacts neurobehavioral functioning differently by sex, but studies are inconsistent in regards to which sex is more affected (62). Additionally, phenols and phthalates may generate oxidative stress (63) and trigger inflammatory responses (64,65), which are strongly associated with allergy-related diseases (66,67), metabolic disorders (68,69) and mental health disorders like depression (70). Lastly, exposure to phenols and phthalates may induce epigenetic alterations, altering gene expression patterns critical to processes like fetal development or immune responses, potentially leading to wide-ranging health effects (71,72). Taken together, these findings suggest that exposure to phenols and phthalates may impact biological pathways common to multiple health domains, potentially explaining their pleiotropic effects (i.e. simultaneous effects on multiple health outcomes) (13).

By integrating multiple measures of exposures to EDCs during pregnancy and infancy, this study aimed to model their health effects more comprehensively. Given that phenols and phthalates are non-persistent chemicals, with half-lives ranging from approximately 6 to 30 hours in the human body (73,74), studies relying on a single urine sample may be subject to
attenuation bias in the estimated health effects (20,75). Our first strategy to mitigate
attenuation bias in the association study was to use pooled urine samples collected over an
entire week (20,24). As a complementary approach, we proposed to integrate concentrations
measured at several time points (trimester 2 and 3 of pregnancy, 2 and 12 months of age) in a
unified model to better model effects of the overall exposure. This method resulted in slightly
larger effect sizes compared to when studying specific exposure windows, especially for
phthalates and paraben when using q-gcomp, and for phenols when using BKMR. Thus, these
results support the hypothesis of a cumulative health effect of chemical compounds over time.

To assess the robustness of the results, we performed a series of sensitivity analyses, and used
two complementary statistical models (q-gcomp and BKMR). Overall, the sensitivity analyses
confirmed the robustness of the results. However, we observed that the association with
phthalates/DINCH was not robust in models using urine concentrations corrected for specific
gravity. Accounting for urine dilution in such analysis is debated as it may introduce bias
(40). Therefore, these supplementary findings do not fully undermine the credibility of the
main results. In addition, both q-gcomp and BKMR models showed similar association
trends, but BKMR yielded greater uncertainty, with wide credible intervals including zero.
This large uncertainty may arise from its complex design, which can model interactions and
non-linearity. In this context, BKMR might require larger samples sizes or effect sizes
compared to q-gcomp, which is based on a simpler linear regression model. Alternative
mixture models, such as WQS, non-parametric models such as Bayesian additive regression
trees (BART), and multivariate adaptive regression splines (MARS), could have further
validated our results. However, WQS unidirectionality assumption could introduce bias (34),
and simulations have shown that most non-parametric mixture models (including BKMR)
exhibit similar performance (76). Therefore, to simplify the analysis, we selected one
quantile-based model (q-gcomp) and one non-parametric model (BKMR), as commonly used
in similar studies (77,78).

b. Strengths and weaknesses

This study has several strengths. First, from a public health perspective, our original results
relying on a multi-domain health score enabled us to identify key risk factors impacting
multiple health domains simultaneously. Using this score helps mitigate the false discovery
rate, as the number of tests is divided by the number of outcomes included, i.e. by twenty-
three. Moreover, we employed a by-family approach to model mixtures, which allowed us to
15
target specific sources of chemical compounds. This targeted method may facilitate more
effective per-family regulation of endocrine-disrupting chemicals. A further asset relates to
the precise and repeated measurements of EDC exposure from weekly-pooled urine samples,
conducted twice during pregnancy and twice during infancy. This strategy allowed us to
better characterize the overall chemical exposure during this critical developmental period and
its potential health effect. Moreover, robustness of our findings was evaluated using two
mixture models, q-gcomp and BKMR, which are based on similar hypotheses but apply
different methodology to model mixtures.

However, our study has some limitations. The moderate sample size (n=318 to 330) and the
use of imputations for individual health parameters may have reduced the statistical power to
detect associations, although this should not have biased the results (79). Moreover, the use of
a multi-domain health score limited the results to compounds showing a uni-directional
association across outcomes and may also have obscured effects occurring on a single health
domain. Additionally, we cannot exclude residual confounding from unmeasured variables
such as diet, or selection bias, without the possibility of using techniques such as Inverse
Probability Weighting in all models (especially in BKMR).

c. Public health impact

This study provides additional evidence that early-life exposure to parabens and phthalates
may impact multiple health domains in children, emphasizing the need for prioritized public
health policies. These findings support public health policies to reduce exposure to these
chemical compounds, especially stricter regulations since interventions targeting
manufacturing have been shown to be more effective than those relying on individual
behavior (80). At the same time, labeling requirements of products and educational initiatives
remain essential to raise public awareness and promote safer consumer choices. In addition,
the findings related to specific chemical mixtures (parabens and phthalates) support a shift
toward per-family regulation of chemical compounds, moving away from the traditional
approach of regulating individual compounds.

Conclusion

We introduced a novel multi-outcome approach that integrate multiple health parameters
covering cardiometabolic, respiratory and neurodevelopmental health domains into a multi-
domain health score. This study extends prior findings by suggesting that early-life exposure
477 to mixture of parabens and mixture of phthalates may jointly impact multiple health aspects of
478 child health. Thus, it advocates for public health policies targeting these chemical families.

479 Acknowledgments

480 We thank the Sepages study group: A. Licinia (Groupe Hospitalier Mutualiste, Grenoble), S.
481 Bayat, P. Hoffmann, E. Hullo, (Grenoble Alpes University Hospital, La Tronche), X. Morin
482 (Clinique des Cèdres, Echirolles), A. Morlot (Clinique Belledonne, Saint-Martin d'Hères), J.
483 Lepeule, S. Lyon-Caen, M.Ouidir, C. Philippat, I. Pin, J. Quentin, V. Siroux, R. Slama
484 (Grenoble Alpes University, Inserm, CNRS, IAB).

485 SEPAGES biospecimens are stored at Grenoble University Hospital (CHU-GA) biobank (bb-
486 0033-00069); we would like to thank the whole CRB team, led by Pr. P. Mossuz and Mr. P.
487 Lorimier, and in particular the technicians for the huge work of biospecimens processing and
488 pooling: Mrs. W. Jayar, Mrs. L. Than, Mrs. J.Lafond, Mrs. C.Chalazon, Mr. T.Caffaratti, Mrs.
489 N.Francini and as well as Mr. G. Schummer

490 Many thanks to Dr I. Pin who was the Sepages PI from 2012 until 2022. We also thank Mr.
491 Y. Gioria, and Mrs. A. Guerrache-Siebert, clinical research assistants; Mrs. M. Marceau,
492 nurses; Mrs. E. Charvet, midwives; Mrs. M. Graca, Mrs. K. Gridel, Mrs. C. Pelini, Mrs. M.
493 Barbagallo fieldworkers; Mrs. A. Bossant, K. Guichardet, J-T Iltis, C. Martel, E. Quinteiro,
494 neuropsychologists; Mrs. K. Supernant, A.Boudier, and Matthieu Rolland for data
495 management; the staff from Grenoble Center for Clinical Investigation (CIC): Prof. J.-L.
496 Cracowski, Dr. C. Cracowski, Dr. E. Hodaj, Mrs. D. Abry, Mr. N. Gonnet and Mrs. A.
497 Tournier. A warm thank you also to Dr. M. Althuser, Dr. F. Camus-Chauvet, Mr. P.
498 Dusonchet, Mrs. S. Dusonchet, Mrs. A. Royannais, Dr. D. Tournadre, Mr. P. Viossat, and
499 clinicians from Grenoble University Hospital for their support in the recruitment of the study
500 volunteers. We also thank Mrs. SF. Caraby, Mrs. J. Dujourdil, and all midwives from the four
501 maternity wards of Grenoble urban areas. SEPAGES data are stored thanks to the Inserm RE-
502 CO-NAI platform funded by Commissariat Général à l'Investissement, with the implication
503 of Sophie de Visme (Inserm DSI). Many thanks to Dr. M.A. Charles, RE-CO-NAI
504 coordinator, for her support.

505 Finally, and importantly, we would like to express our sincere thanks to the participants of the
506 Sepages study.
507 Fundings

508 The study received funding from the H2020-EU.3.1.2. - Preventing Disease Programme (grant agreement no 874583 - ATHLETE project). The SEPAGES cohort was supported by the European Research Council (N°311765-E-DOHaD), the European Community's Seventh Framework Programme (FP7/2007-2013 - N°308333-892 HELIX), the European Union's Horizon 2020 research and innovation programme (N°825712 OBERON Project), the French Research Agency - ANR (PAPER project ANR-12-PDOC-0029-01, SHALCOH project ANR-14-CE21-0007, ANR-15-IDEX-02 and ANR-15-IDEX5, GUMME project ANR-17-CE34-0013, ETAPE project ANR-18-CE36-0005 - EDeN project ANR-19-CE36-0003-01 - MEMORI project ANR 21-CE34-0022, ORANDANI project ANR-22-CE36-0018), the French Agency for Food, Environmental and Occupational Health & Safety - ANSES (CNAP project EST-2016-121, PENDORE project EST-2018-1-264, HyPAxE project EST-2019/1/039, PENDALIRE project EST-2022-169), the Plan Cancer (Canc'Air project), the French Cancer Research Foundation Association de Recherche sur le Cancer - ARC, the French Endowment Fund AGIR for chronic diseases - APMC (projects PRENAPAR, LCI-FOT, DysCard), the French Endowment Fund for Respiratory Health, the French Fund - Fondation de France (CLIMATHES - 00081169, SEPAGES 5 - 00099903, ELEMENTUM - 00124527).

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737 75. Casas M, Basagaña X, Sakhi AK, Haug LS, Philippat C, Granum B, et al. Variability of urinary concentrations of non-persistent chemicals in pregnant women and school-aged children. Environ Int. 2018 Dec;121(Pt 1):561-73.

740 76. Lazarevic N, Knibbs LD, Sly PD, Barnett AG. Performance of variable and function selection methods for estimating the nonlinear health effects of correlated chemical mixtures: A simulation study. Stat Med. 2020 Nov 30;39(27):3947-67.

743 77. Gu L, Wang Z, Pan Y, Wang H, Sun L, Liu L, et al. Associations between mixed urinary phenols and parabens metabolites and bone mineral density: Four statistical models. Chemosphere. 2023 Jan;311(Pt 2):137065.

746 78. Génard-Walton M, McGee G, Williams PL, Souter I, Ford JB, Chavarro JE, et al. Mixtures of urinary concentrations of phenols and phthalate biomarkers in relation to the ovarian reserve among women attending a fertility clinic. Sci Total Environ. 2023 Nov 10;898:165536.

750 79. Little RJA. Regression with Missing X's: A Review. J Am Stat Assoc. 1992 Dec 1;87(420):1227-37.

752 80. Yang TC, Jovanovic N, Chong F, Worcester M, Sakhi AK, Thomsen C, et al. Interventions to Reduce Exposure to Synthetic Phenols and Phthalates from Dietary Intake and Personal Care Products: a Scoping Review. Curr Environ Health Rep. 2023 Jun 1;10(2):184-214.

756
<table><thead><tr><th>Synthetic phenols</th><th>Phthalates/DINCH</th></tr></thead><tbody><tr><td>Parabens:</td><td></td></tr><tr><td>- Methylparaben (MEPA)</td><td>- Monoethyl phthalate (MEP)</td></tr><tr><td>- Ethylparaben (ETPA)</td><td>- Mono-n-butyl phthalate (MnBP)</td></tr><tr><td>- Propylparaben (PRPA)</td><td>- Mono-isobutyl phthalate (MiBP)</td></tr><tr><td>- Buthylparaben (BUPA)</td><td>- Monobenzyl phthalate (MBzP)</td></tr><tr><td>Other phenols:</td><td>- 6-Hydroxy Monopropylheptyl phthalate (ohMPHP)</td></tr><tr><td>- Benzophenone-3 / Oxybenzophenone (OXBE)</td><td>- Di-isononyl phthalate (DiNP)*</td></tr><tr><td>- Tricolosan (TRCS)</td><td>- Di (2-ethylhexyl) phthalate (DEHP)*</td></tr><tr><td>- Bisphenol A (BPA)</td><td>- 1,2-cyclohexanedicarboxylic acid, diisopropyl ester (DINCH)*</td></tr><tr><td>- Bisphenol S (BPS)</td><td></td></tr></tbody></table>

This table lists all chemical compounds considered for this study, by chemical families.

* DINP, DINCH, and DEHP measures were estimated through molar sums of their metabolites.

DEHP: molar sum of Mono(2-ethyl-5oxohexyl) phthalate (MEOHP), Mono(2-ethyl-5-carboxypentyl) phthalate (MECPP), Mono(2-ethylhexyl) phthalate (MEHP), Mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), Mono-2-carboxymethyl hexyl phthalate (MMCHP).

DiNP: molar sum of Mono-4-methyl-7-hydroxyoctyl phthalate (oh-MiNP), Mono-4-methyl-7-oxooctyl phthalate (oxo-MiNP) and Mono-4-methyl-7-carboxyoctyl phthalate (cx-MiNP)

DINCH: molar sum of 2-(((Hydroxy-4-methyloctyl)oxy)carbonyl)cyclohexanedicarboxylic acid (oh-MINCH) and 2-(((4-Methyl-7-oxyoctyl)oxy)carbonyl)cyclohexane carboxylic acid (oxo-MINCH)
<table><caption>Table 2 – Health parameters included in the multi-domain health score in children aged 3 years</caption><thead><tr><th>Cardiometabolic health</th><th>Respiratory health and allergies</th><th>Neurodevelopment</th></tr></thead><tbody><tr><td>- Blood pressure: systolic and diastolic<br/>- Heart rate<br/>- Percentage of fat mass (bioimpedance and skinfold)</td><td>- Lung function: intra-breath and within-breath oscillometry parameters<br/>- Asthma and allergy-related diseases: ever-asthma*, ever-wheezing, ever-eczema*, ever-bronchiolitis, ever-bronchitis (questionnaires)<br/>- Allergies (skin prick tests)</td><td>- Cognitive abilities (WPPSI)<br/>- Behavioural disorders (CBCL)<br/>- Behavioral manifestations of executive functions (BRIEF-P)<br/>- Social impairment within the autism spectrum disorders (SRS)</td></tr></tbody></table>

This table lists all health parameters considered for the creation of the multi-domain health score. All parameters were measured at the 3-year visit, except for the CBCL which was assessed at 2 years.

* Doctor diagnosed

779 BRIEF-P: Behaviour Rating Inventory of Executive Function – Preschool version, CBCL:
780 Child Behaviour CheckList, SRS: Social Responsiveness Scale, WPPSI: Wechsler Preschool and Primary Scale of Intelligence, Fourth Edition.
<table><caption>Table 3 - Description of the study population</caption><thead><tr><th>Variable</th><th>N (%)</th><th>n (%) or Median (percentile 25, 75)</th></tr></thead><tbody><tr><td colspan="3"><strong>PARENTAL CHARACTERISTICS AND HEALTH HISTORY</strong></td></tr><tr><td>Highest parental education</td><td>373 (100%)</td><td></td></tr><tr><td>Below master degree</td><td></td><td>98 (26%)</td></tr><tr><td>Master degree or above</td><td></td><td>275 (74%)</td></tr><tr><td>Maternal age at conception (years)</td><td>373 (100%)</td><td>32.4 (30.2, 35.4)</td></tr><tr><td>Maternal parity</td><td>373 (100%)</td><td></td></tr><tr><td>No child</td><td></td><td>166 (45%)</td></tr><tr><td>1 child</td><td></td><td>168 (45%)</td></tr><tr><td>2 children or more</td><td></td><td>39 (10%)</td></tr><tr><td>Parental history of asthma or rhinitis</td><td>355 (95%)</td><td></td></tr><tr><td>None</td><td></td><td>118 (33%)</td></tr><tr><td>At least one parent</td><td></td><td>237 (67%)</td></tr><tr><td>Maternal anxiety and depression score</td><td>360 (97%)</td><td>10.0 (7.0, 13.0)</td></tr><tr><td colspan="3"><strong>PREGNANCY AND EARLY LIFE</strong></td></tr><tr><td>Season conception</td><td>373 (100%)</td><td></td></tr><tr><td>January-March</td><td></td><td>108 (29%)</td></tr><tr><td>April-June</td><td></td><td>74 (20%)</td></tr><tr><td>July-September</td><td></td><td>87 (23%)</td></tr><tr><td>October-December</td><td></td><td>104 (28%)</td></tr><tr><td>Pregnancy Tobacco</td><td>348 (93%)</td><td></td></tr><tr><td>No Tobacco</td><td></td><td>268 (77%)</td></tr></tbody></table>
Active or passive tobacco 80 (23%)

CHILD AT THE 3-YEARS FOLLOW UP

<table><tr><td>Child sex</td><td>373 (100%)</td><td></td></tr><tr><td>Female</td><td></td><td>171 (46%)</td></tr><tr><td>Male</td><td></td><td>202 (54%)</td></tr><tr><td>Child age at the follow-up (years)</td><td>372 (100%)</td><td>3.07 (3.04, 3.13)</td></tr><tr><td>Child height (cm)</td><td>373 (100%)</td><td>95.3 (92.8, 97.5)</td></tr><tr><td>Child passive smoking (ever)</td><td>370 (99%)</td><td></td></tr><tr><td>Never exposed</td><td></td><td>285 (77%)</td></tr><tr><td>Exposed</td><td></td><td>85 (23%)</td></tr><tr><td>Child stimulation score (HOME)</td><td>364 (98%)</td><td>25.00 (23.00, 26.00)</td></tr></table>

784

BMI: Body Mass Index, HOME: Home Observation Measurement of the Environment,

WAIS: Wechsler Adult Intelligence Scale – Fourth Edition.
787 Figures

788 **Figure 1 – Methodological workflow**

789 This figure describe the methodology used for this project.

790

791 **Figure 2 – Description of the exposure to parabens, phenols, phthalates, and non-phthalate plasticizers in the study population**

792 Distribution of the chemical concentrations measured in the study population (n=373), along with Limit of Detection (LOD) and Limit of Quantification (LOQ). When multiple values of LOD/LOQ were used for a given compound according to the sample (different batches), the minimum value was plotted. For molar sums (DiNP, DEHP, DINCH), LOQ and LOD were artificially computed by using the same molar weights. This plot displays the standardized, imputed, non-log transformed values, at the exception of butylparaben and bisphenol S for which raw machine values were used (>30 % below limit of detection).

800 DEHP: Di (2-ethylhexyl) phthalate, DiNP: Di-isononyl phthalate, DINCH: 1,2-cyclohexanedicarboxylic acid, diisononyl ester, MBzP: Monobenzyl phthalate, MEPA: Methylparaben, MEP: Monoethyl phthalate, MiBP: Mono-isobutyl phthalate, MnBP: Mono-butyl phthalate, oh-MPHP: 6-Hydroxy Monopropylheptylphthalate.

804 **Figure 3 - Description of the cardiometabolic (A), respiratory/allergy (B), and mental (C) health subscores and the multi-domain health score (D).**

806 Distribution of the cardiometabolic subscore (panel A), respiratory subscore (panel B), neurodevelopmental subscore (panel C) and the multi-domain health score (panel D), i.e. the average of the three subscores. All twenty imputed datasets were used.

809 **Figure 4 – Associations between chemical mixtures and the multi-domain health score, integrating chemical measurements at all time points, estimated by q-gcomp and BKMR**

810 This figure represents the association between each chemical mixture and the multi-domain health score estimated by quantile g-computation (q-gcomp) model (panel A) and Bayesian Kernel Model Regression (BKMR) (panel B). For q-gcomp, the graph shows the overall effect of each mixture on the multi-domain health score, i.e. the effect for a quartile increase in all chemical concentrations, with 95% Confidence Interval (CI). For BKMR, it represents the change in the multi-domain health score for each 10% quantile increase in exposure to chemical mixture. For both models, each graph represents an independent model (one model by exposure family), built from chemical measurement from all time points (n=318 to 330).
Models were applied to each of the 20 imputed datasets and results were combined using the Rubin's rule (q-gcomp) or by combining all simulations (BKMR). Adjustment was made on parental education, parental history of asthma or rhinitis, maternal depression and anxiety during pregnancy, season of conception, maternal tobacco during pregnancy, child sex, child height, child exposure to tobacco, child stimulation score.

**Figure 5 - Associations between chemical mixtures and each health subscore estimated by q-gcomp**

This figure represents the results of the quantile g-computation (q-gcomp) models on each subscores (cardiometabolic, respiratory, neurodevelopmental), using chemical concentrations at all time points. It shows the overall effect of each mixture on each subscore, i.e. the effect for a quartile increase in all chemical concentrations, with 95% Confidence Interval (CI). One estimate represents an independent model (one model by exposure family), built from chemical measurement from all time points (n=318 to 330). Models were applied to each of the 20 imputed datasets and results were combined using the Rubin's rule. Adjustment was made on parental education, parental history of asthma or rhinitis, maternal depression and anxiety during pregnancy, season of conception, maternal tobacco during pregnancy, child sex, child height, child exposure to tobacco, child stimulation score.