Prenatal exposure to mixtures of phthalates and phenols and body mass index and blood pressure in Spanish preadolescents

Environment International 169 (2022) 107527 Available online 15 September 2022 0160-4120/© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Full length article Prenatal exposure to mixtures of phthalates and phenols and body mass index and blood pressure in Spanish preadolescents Nuria Güil-Oumrait a , b , c , German Cano-Sancho d , Parisa Montazeri a , b , c , Nikos Stratakis a , b , c , Charline Warembourg e , Maria-Jose Lopez-Espinosa c , f , g , Jesús Vioque c , h , Loreto Santa-Marina c , i , j , Alba Jimeno-Romero c , i , k , Rosa Ventura l , Nuria Monfort l , Martine Vrijheid a , b , c , Maribel Casas a , b , c , * a ISGlobal, Barcelona, Spain b Pompeu Fabra University (UPF), Barcelona, Spain c CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain d LABERCA, UMR1329, Oniris, INRAE, Nantes, France e Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en sant´ e, environnement et travail) - UMR_S 1085, F-35000 Rennes, France f FISABIO–Universitat Jaume I–Universitat de Valencia, Valencia, Spain g Faculty of Nursing and Chiropody, University of Valencia, Valencia, Spain h Universidad Miguel Hern´ andez, Alicante, Spain i Biodonostia, Health Research Institute, Donostia, Gipuzkoa, Spain j Department of Health of the Basque Government, Subdirectorate of Public Health of Gipuzkoa, Spain k Preventive Medicine and Public Health Department, University of the Basque Country, Leioa, Bizkaia, Spain l Catalonian Antidoping Laboratory, Doping Control Research Group, IMIM, Barcelona, Spain ARTICLE INFO Handling Editor: Adrian Covaci Keywords: Phthalates Phenols Parabens Benzophenone-3 Body mass index (BMI) Blood pressure (BP) ABSTRACT Background: Pregnant women are simultaneously exposed to several non-persistent endocrine-disrupting chemicals, which may influence the risk of childhood obesity and cardiovascular diseases later in life. Previous prospective studies have mostly examined single-chemical effects, with inconsistent findings. We assessed the association between prenatal exposure to phthalates and phenols, individually and as a mixture, and body mass index (BMI) and blood pressure (BP) in preadolescents. Methods: We used data from the Spanish INMA birth cohort study (n =1,015), where the 1st and 3rdtrimester maternal urinary concentrations of eight phthalate metabolites and six phenols were quantified. At 11 years of age, we calculated BMI z-scores and measured systolic and diastolic BP. We estimated individual chemical effects with linear mixed models and joint effects of the chemical mixture with hierarchical Bayesian kernel machine regression (BKMR). Analyses were stratified by sex and by puberty status. Results: In single-exposure models, benzophenone-3 (BP3) was nonmonotonically associated with higher BMI zscore (e.g. Quartile (Q) 3: β =0.23 [95% CI =0.03, 0.44] vs Q1) and higher diastolic BP (Q2: β =1.27 [0.00, 2.53] mmHg vs Q1). Methyl paraben (MEPA) was associated with lower systolic BP (Q4: β = − 1.67 [−3.31, −0.04] mmHg vs Q1). No consistent associations were observed for the other compounds. Results from the BKMR confirmed the single-exposure results and showed similar patterns of associations, with BP3 having the Abbreviations: BKMR, Bayesian kernel machine regression; BMI, body mass index; BP, blood pressure; BPA, bisphenol A; BP3, benzophenone-3; BUPA, butyl paraben; condPIP, conditional posterior inclusion probability; CVDs, cardiovascular diseases; DEHP, sum of di(2-ethylhexyl) phthalate metabolites; ETPA, ethyl paraben; GAMM, generalised additive mixed model; groupPIP, group posterior inclusion probability; ICC, intraclass correlation coefficient; INMA, Infancia y Medio Ambiente; IQR, interquartile range; LOD, limit of detection; MBzP, mono-benzyl phthalate; MEP, mono-ethyl phthalate; MEPA, methyl paraben; MECPP, mono-(2ethyl-5-carboxy-pentyl) phthalate; MEHHP, mono-(2-ethyl-5-hydroxyhexyl) phthalate; MEHP, mono-(2-ethylhexyl) phthalate; MEOHP, mono-(2-ethyl-5-oxohexyl) phthalate; MiBP, mono-iso-butyl phthalate; MnBP, mono-n-butyl phthalate; NIPH, Norwegian Institute of Public Health; PPARγ, peroxisome proliferator-activated receptor gamma; PRPA, propyl paraben; RAAS, rennin-angiotensin-aldosterone system; SD, standard deviation; UHPLC-MS/MS, ultra-high performance liquid chromatography coupled to tandem mass spectrometry. * Corresponding author at: Barcelona Institute for Global Health (ISGlobal), 88 Dr. Aiguader St., 08003 Barcelona, Spain. E-mail address: [email protected] (M. Casas). Contents lists available at ScienceDirect Environment International journal homepage: www.elsevier.com/locate/envint https://doi.org/10.1016/j.envint.2022.107527 Received 2 February 2022; Received in revised form 29 July 2022; Accepted 14 September 2022 Environment International 169 (2022) 107527 2 highest importance in the mixture models, especially among preadolescents who reached puberty status. No overall mixture effect was found, except for a tendency of higher BMI z-score and lower systolic BP in girls. Conclusions: Prenatal exposure to UV-filter BP3 may be associated with higher BMI and diastolic BP during preadolescence, but there is little evidence for an overall phthalate and phenol mixture effect. 1. Introduction Cardiovascular diseases (CVDs) are the leading cause of death worldwide (Roth et al., 2020). Obesity and high blood pressure (BP) are well-known risk factors that predispose individuals to the occurrence of CVDs. Children and adolescents with obesity and/or high BP levels are more likely to become obese and hypertensive as adults, manifesting the importance of health prevention and management early in life (Chen and Wang, 2008; Simmonds et al., 2016). Pregnant women are exposed to phthalates and phenols, two groups of non-persistent endocrine disrupting chemicals. These compounds are widely found in a multitude of consumer products, such as flexible (high-molecular phthalates) and hard plastics (bisphenol A (BPA)), and personal care products (low-molecular phthalates and parabens), including sunscreen agents (benzophenone-3 (BP3)) (Gore et al., 2015). Therefore, although they are quickly metabolized in the human body and excreted in the urine, their exposure is ubiquitous, multi-source, multi-route, and often chronic (Gore et al., 2015; Lu et al., 2018). These chemicals can cross the blood-placenta barrier (Gil-Solsona et al., 2021; Sch¨ onfelder et al., 2002; Silva et al., 2004), and have been hypothesized to promote metabolic changes that may compromise early fetal developmental processes and influence the risk of offspring obesity and CVDs later in life (Egusquiza and Blumberg, 2020; Haverinen et al., 2021). The disruption of steroid and thyroid hormones, the activation of peroxisome proliferator-activated receptor γ (PPARγ), and the generation of reactive oxygen species are among the mechanisms by which exposure to phthalates and phenols may alter the fetal programming of cardiovascular function and adipogenesis, as supported by experimental studies (Aboul Ezz et al., 2015; Hao et al., 2013; Hu et al., 2013; Ishihara et al., 2003; Saura et al., 2014; Shen et al., 2009; Shin et al., 2020a). Although many prospective birth cohort studies have assessed the association between prenatal phthalates and BPA with offspring BMI, and BP levels to a lesser extent, the level of evidence remains inconsistent due to mixed results, as shown in Appendix 1. Evidence is even less conclusive for other phenols of widespread use and emerging concern, such as the parabens and BP3, due to the limited research in prospective birth cohorts (see Appendix 1). For instance, only four and two studies assessed the association between prenatal exposure to parabens with BMI (Berger et al., 2021; Leppert et al., 2020; Reimann et al., 2021; Vrijheid et al., 2020) and BP levels (Montazeri et al., 2022; Warembourg et al., 2019), respectively. All of them obtained null associations except for prenatal PRPA levels linked with increased BMI (Berger et al., 2021), and prenatal ETPA exposure with a higher risk of overweight only in girls (Leppert et al., 2020). Similarly, of four studies that included BP3 among the exposures (Buckley et al., 2016; Montazeri et al., 2022; Vrijheid et al., 2020; Warembourg et al., 2019), only one reported reduced levels of body fat % in girls (Buckley et al., 2016). Most of the above-mentioned studies have only considered exposure to a single chemical at a time. The reality, however, is that phthalates and phenols are found in many consumer products, and therefore, individuals can be regularly exposed to mixtures of these chemicals. Additionally, there is experimental evidence of a joint and interactive effect of those compounds on adipogenic differentiation, involving multiple mechanisms of action, further supporting the need for mixture models (Biemann et al., 2014; V¨ olker et al., 2022). Only six prospective birth cohort studies have considered these two groups of endocrinedisrupting chemicals at the same time. In the HELIX study, including 6 European cohorts, prenatal exposure to phthalates and phenols was assessed together with a wide range of other environmental exposures in relation to obesity and BP (Vrijheid et al., 2020; Warembourg et al., 2019). Only prenatal BPA was found to be associated with increased BP levels in childhood (Warembourg et al., 2019). In these studies, exposome-wide association study and deletion-substitution-addition variable selection algorithm were used, which did not allow for assessing the effect of the chemical mixture. In the Spanish Infancia y Medio Ambiente (INMA) Sabadell cohort, (Agay-Shay et al., 2015) used principal component analysis to assess the effect of the mixture of many endocrine-disrupting chemicals and found that a component mainly characterized by several phthalates was associated with lower risk of being overweight/obese at 7 years. In this same cohort, Montazeri et al., (2021) used a Bayesian weighted quantile sum regression and found no evidence for an association between a phthalate/phenol mixture and BP at 11 years. In the US CHAMACOS cohort, using Bayesian kernel machine regression (BKMR), some associations were observed between a mixture of several phthalate metabolites and BMI at 5 and 12 years of age (Berger et al., 2021; Harley et al., 2017). Conversely, in a Mexican cohort, although some associations were found between individual phthalate metabolites and BMI trajectories, using the quantile Gcomputation modelling approach, no mixture associations were observed (Kupsco et al., 2022). These studies have been limited by their modest sample size (between 300 and 400 participants), which gives limited statistical power, especially for the estimation of sex-specific effects. Also, the whole chemical mixture of phthalates and phenols was only assessed in one study in relation to BMI at 5 years (Berger et al., 2021) and one study in relation to BP at 11 years (Montazeri et al., 2022). Moreover, as described by Perng et al., 2021), the joint effect of phthalates and phenols on metabolic disruption has rarely been assessed during preadolescence, a sensitive period characterized by the hormonal onset of puberty with endocrine changes and increases in lean and fat mass. Thus, the present study aims to fill these critical knowledge gaps by examining whether prenatal exposure to phthalates and phenols (individually and in combination) are associated with BMI and BP in a sample of around 1,000 Spanish preadolescents. 2. Methods 2.1. Study population We used data from the INMA birth cohort study including three Spanish regions: Gipuzkoa, Sabadell, and Valencia. Women presenting for prenatal care were recruited in the 1st trimester of pregnancy (weeks 10–13 of gestation) between 2003 and 2008 (n =2,122). Mothers were included if they (i) were resident in the study area, (ii) were at least 16 years old, (iii) had a singleton pregnancy, (iv) did not follow any assisted reproduction program, (v) wished to deliver in the reference hospital, and (vi) had no communication barriers (Guxens et al., 2012). The population for this analysis comprised 1,015 mother–child pairs with available information on at least one phthalate metabolite and/or phenol measured during pregnancy and BMI (n =954) or BP (n =982) measured at 11 years (except in the INMA-Valencia cohort, where BP was measured at 9 years) (Fig. S1). All participating women signed written informed consent. This study was approved by the regional ethical committees of each cohort. 2.2. Phthalates and phenols exposure assessment Phthalates and phenols levels were measured twice in urine samples N. Güil-Oumrait et al. Environment International 169 (2022) 107527 3 collected during gestation. Specifically, maternal urine samples in the 1st (mean =13.2; standard deviation (SD) =1.5 weeks) and 3rd trimesters of pregnancy (mean =33.1; SD =1.9 weeks) were collected in 100-mL polypropylene containers and then aliquoted in 10 mL polyethylene tubes and stored at −20 ◦C prior to analysis. A small number of mothers (n =98, <10% of the total) provided only one urine sample. We measured total urine concentrations of eight phthalate metabolites: MEP (mono-ethyl phthalate), MiBP (mono-iso-butyl phthalate), MnBP (monon-butyl phthalate), MBzP (mono-benzyl phthalate), and the following four DEHP (di(2-ethylhexyl) phthalate) metabolites: MEHP (mono-(2ethylhexyl) phthalate), MEHHP (mono-(2-ethyl-5-hydroxyhexyl) phthalate), MEOHP (mono-(2-ethyl-5-oxohexyl) phthalate), and MECPP (mono-(2-ethyl-5-carboxy-pentyl) phthalate). Phthalate metabolites of the Gipuzkoa and Valencia cohorts were measured at the Department of Environmental Exposure and Epidemiology at the Norwegian Institute of Public Health (NIPH) (Oslo, Norway), using pooled samples (1st and 3rd trimesters) with ultra-high performance liquid chromatography coupled to tandem mass spectrometry detection (UHPLC-MS/MS) (Sabaredzovic et al., 2015). The limits of detection (LOD) ranged between 0.07 and 0.7 ng/ml. Phthalate metabolites of the Sabadell cohort were measured at the Bioanalysis Research Group at the Hospital del Mar Medical Research Institute (Barcelona, Spain) using samples separately from the 1st and 3rd trimesters also with UHPLC-MS/MS, as described in (Valvi et al., 2015a). LODs ranged from 0.5 to 1 ng/ml. We calculated the molar sums of individual metabolites (in µmol/l) of DEHP because they occur from the same parent phthalate and thus, they were highly correlated (r >0.90). Phenols of this analysis included four parabens (MEPA (methyl paraben), ETPA (ethyl paraben), PRPA (propyl paraben), and BUPA (butyl paraben)), BP3, and BPA. Phenols in samples from the Gipuzkoa cohort were measured at Instituto de Investigaci´ on Biosanitaria ibs. GRANADA (Granada, Spain) using pooled samples (1st and 3rd trimesters) with dispersive liquid–liquid microextraction and UHPLC-MS/ MS (Vela-Soria et al., 2014); LODs ranged from 0.04 to 0.12 ng/ml. Samples from the 1st and 3rd trimesters from Sabadell and Valencia were analyzed separately at NIPH with online solid-phase extraction prior to UHPLC-MS/MS (Sakhi et al., 2018); LODs ranged from 0.03 to 0.07 ng/ml. BPA for the Sabadell cohort was measured at the Department of Analytical Chemistry laboratory at the University of Cordoba (Spain) using LC-MS/MS (Casas et al., 2013). LOD was 0.10 ng/ml. In all cohorts, creatinine urine concentrations at each trimester (1st and 3rd) were determined using the Jaff´ e method (kinetic with target measurement, compensated method). Chemical concentrations were corrected for urine creatinine concentrations to adjust for urine dilution and were expressed in μ g/g of creatinine. For pooled samples, we applied the mean of the two individual creatinine measurements; we verified in 20 urine samples from Valencia that measuring creatinine levels in the pool was highly correlated with the creatinine levels estimated by averaging the creatinine levels quantified in the two independent urine samples (r =0.97). Due to the short biological half-life of the non-persistent chemicals, we used the average of the non-pooled creatinine-adjusted concentrations measured in the 1st and 3rd trimesters to provide a better estimation of exposure throughout pregnancy. Because the phthalates and phenols creatinine-adjusted concentrations were right-skewed, they were log 2 -transformed to obtain normal distributions. 2.3. BMI and BP outcomes Child weight and height were measured at 11 years of age using standard protocols, without shoes and in light clothing. We calculated BMI (weight in kg/height in m 2 ) and ageand sex-specific BMI z-scores using the World Health Organization reference and defined overweight as a BMI z-score ≥85th percentile (De Onis et al., 2007). INMA nurses used a digital automatic monitor (OMRON 705IT) to measure systolic and diastolic BP at 11 years in Gipuzkoa and Sabadell, and 9 years in Valencia. After 5 min of rest, 3 consecutive measurements were taken with one-minute time intervals between them. We derived average BP at each age as the mean of the systolic and diastolic BP values. 2.4. Statistical analysis Concentrations of phthalates and phenols above 4 times their SD (<1%) were removed since BKMR is highly sensitive to outlying values. Concentrations below the LOD were imputed using distribution-based multiple imputations by assuming a log-normal distribution of the chemicals and conditioning the imputation to the range from 0 to the LOD (Table S1). Then, they were adjusted by creatinine and log 2 - transformed. We performed multiple imputations by chained equations of missing values in exposure (<11%) and covariate data (<2%) to avoid the loss of participants in the study (Table S2). We generated ten complete data sets by using the ice package for Stata (Royston, 2005). A detailed description of this procedure is provided in Table S3. Distributions of covariates and chemical concentrations in imputed datasets were similar to the original dataset (Table S2). Pearson correlation coefficients were calculated to estimate the bivariate correlations between chemicals (log 2 -transformed). First, we assessed potential non-linear relationships between exposures and outcomes of interest (BMI z-score, systolic and diastolic BP) using generalized additive mixed models (GAMMs; R package ‘mgcv’). If the effective degrees of freedom were equal to 1, the relationship was closer to linear. GAMM models showed evidence of linearity (Fig. S2-S4) with few exceptions indicating non-linearity: BUPA and BP3 with BMI zscore (Fig. S2), BP3 with systolic BP (Fig. S3), and PRPA and BP3 with diastolic BP (Fig. S4). Therefore, we modelled chemical concentrations as both continuous and categorical variables using quartile cut-offs. Second, we assess single exposure effects with linear mixed models using the imputed data, and the results were combined using Rubin’s combination rules (Little and Rubin, 2014). All models (GAMMs and linear mixed models) were adjusted for the region of residence (Gipuzkoa, Sabadell, Valencia) as a random intercept to account for withincohort and between-cohort effects. Third, we conducted BKMR to assess the joint effect of the exposures on BMI z-score and BP. BKMR is a non-parametric method in which the health outcome is regressed on a flexible kernel function of the mixture components (Bobb et al., 2014). It accommodates non-linearity, nonadditive effects, and interactions (Bobb et al., 2014). We used a hierarchical variable selection method to identify important chemicals in the mixture managing the high correlation between exposures within chemical groups (Bobb et al., 2014). Based on Pearson correlation coefficients and also considering their common exposure sources, we classified the chemicals into two groups: (1) Phthalate metabolites (MEP, MiBP, MnBP, MBzP, and ∑DEHP), and 2) Phenols (MEPA, ETPA, PRPA, BUPA, and BP3) (Fig. 2). Initially, because of the mild or null correlations of BPA with other chemicals (0.02–0.19) (Fig. 2) (Bobb et al., 2014), BPA was included alone in a third group showing a small contribution to the overall model. Thus, in a refinement step, we ran the models without BPA. Chemicals were scaled to their SD. We calculated the group posterior inclusion probability (groupPIP) and conditional posterior inclusion probability (condPIP), which represent the probability (from 0 to 1) that a chemical within the group is included in the model (Bobb et al., 2014). We also assessed (i) the shape and direction of the exposure–response association of each chemical in relation to the health outcome when holding the other chemicals in the mixture at their median concentrations, (ii) the overall mixture effects at different percentiles, and (iii) two-way chemical interactions while holding the other exposures at their median values. Model convergence was assessed visually using trace plots. BKMR was fitted with the R package “bkmr” using the Markov chain Monte Carlo algorithm with 10,000 iterations. We only used the first imputed dataset as it is not currently possible to use multiple imputed datasets with the BKMR function. BKMR models also included region of residence as a random intercept. N. Güil-Oumrait et al. Environment International 169 (2022) 107527 4 Potential covariates for model adjustment were selected based on a priori knowledge (Casas et al., 2013; Valvi et al., 2015b) and a directed acyclic graph approach (Fig. S5). These were obtained through interviewer-administered questionnaires answered by mothers at recruitment and included: maternal age at pregnancy (in years), prepregnancy BMI (kg/ m 2 ), educational level (low, middle, high), and smoking in pregnancy (“yes” if they smoke at the moment of recruitment/no). We additionally examined if the Mediterranean diet adherence score confounded the associations since it is an indicator of maternal quality diet that considers nutrients that may affect the health outcomes and can interact with prenatal phthalates and phenols concentrations (Fern´ andez-Barr´ es et al., 2016). Passive smoking during pregnancy (yes/no) was also assessed since previous studies have shown that it can influence levels of non-persistent chemicals in pregnant women (Casas et al., 2013; Darvishmotevalli et al., 2019; Wang et al., 2020) and childhood obesity (Vrijheid et al., 2020). As coefficient estimates did not change, these variables were not retained (data not shown). Gestational age (in weeks) and birth weight (in grams), collected by clinical records, may be mediating factors in the association of prenatal phthalates and phenols exposures and BMI/BP at preadolescence (Fig. S5). Therefore, we did not adjust associations for these variables as we were interested in the total effect of prenatal phthalates and phenols exposures on BMI and BP outcomes (VanderWeele, 2009). Models with systolic and diastolic BP outcomes were additionally adjusted for the child’s age (years), sex (male/female), and height (cm). Since phthalates and phenols can interfere with sex hormones (Braun, 2017), their potential health effects may be sex-dependent. Therefore, we stratified all models (linear mixed models and BKMR) by sex. In the linear mixed models, we tested sex-interaction by inserting crossproduct terms (exposure*sex using a p-value threshold of 0.10). In the BKMR, separate models were run for girls and boys. In sensitivity analyses, we repeated all the models with BP z-scores standardized by age, sex and height in the overall study population and stratified by sex. We also repeated all models by using the complete-case dataset (n =773). Multipollutant models adjusted for the 10 exposure variables were also performed to compare the results with the BKMR ones. Because most of the preadolescents reached puberty onset at 11 years, we stratified our analyses by puberty status (prepuberty/puberty), which was rated by their parents using the Pubertal Development Scale (Carskadon and Acebo, 1993). Furthermore, we stratified by sex after excluding participants in the prepuberty stage (approximately onethird) to assess if sex-specific associations remained the same in preadolescents who reached puberty. Data cleaning, multiple imputations, and mixed models were performed using Stata version 16 (Stata Corporation, College Station, TX, USA). Pearson correlations, GAMMs, and BKMR were conducted in R version 4.1.0 (R Foundation, Vienna, Austria). 3. Results 3.1. Study population characteristics Complete details of the characteristics of the study population (N = 1,015) are shown in Table 1. At 11 years, 42% of children were either overweight or obese. Mothers included in the analyses were slightly older, had a higher educational level, and were less likely to smoke during pregnancy (Table S4). The log 2 -transformed creatinine-adjusted prenatal non-persistent chemical concentrations are presented in Fig. 1. MEP and MEPA were the chemicals with the highest levels (median = 225.90; interquartile range (IQR) =363.20 and median =204.34; IQR =366.10 µg/g creatinine, respectively), and BUPA and BPA were those with the lowest levels (median =2.77; IQR =8.94 and median =2.82; IQR =2.91 µg/g creatinine, respectively) (Table S1). Pearson’s correlations heat map revealed low to moderate correlations within the phthalate metabolites (r =0.12–0.45), and low to high (r =0.16–0.83) within-group correlations for parabens and BP3 (Fig. 2). BPA showed low or null correlation coefficients with the other chemicals (r = 0.02–0.19) (Fig. 2). 3.2. Prenatal non-persistent chemicals in association with BMI in preadolescents Table 2 shows results from the linear mixed models for BMI z-score. All the associations were null except for BP3 that in the second and third quartile was associated with a higher BMI z-score (Quartile (Q) 2: β = 0.22 [95% CI =0.01, 0.42]; Q3: β =0.23 [95% CI =0.03, 0.44]) (Table 2). Fig. 3 shows results from the BKMR model for the BMI z-score. Holding all other chemicals at their medians, BP3 was associated with a higher BMI z-score, confirming the direction of the coefficient estimates found in the single-exposure models (Fig. 3A). We did not observe any association between the whole chemical mixture and BMI z-score (Fig. 3C) nor any bivariate interactions among the chemicals. After stratifying by sex, the nonmonotonic association reported in the mixed models between BP3 and BMI z-score was only clearly observed among girls in the BKMR univariate plots (Fig. S6 A1, B1). BP3 was also the most contributing compound in the phenols group in girls (Fig. S6 B1). The joint effect of the chemical mixture showed a slightly increasing trend for girls and decreasing for boys, when all the chemicals were at their 60th percentile or above, compared to their 50th percentile; however, credible intervals contained the null (Fig. 6A). No interactions between chemicals were observed in girls or boys. 3.3. Prenatal non-persistent chemicals in association with BP in preadolescents The single-exposure models revealed null associations between the non-persistent chemicals and systolic BP, except for a negative Table 1 Maternal and offspring characteristics of the study population. Maternal characteristics N =1,015 Region of residence – n (%) Gipuzkoa 262 (25.81%) Sabadell 424 (41.77%) Valencia 329 (32.41%) Education completed – n (%) Primary 224 (21.99%) Secondary 411 (40.46%) University 380 (37.55%) Smoking during pregnancy – n (%) None 722 (70.99%) Yes* 293 (29.01%) Age at pregnancy (years) – mean (SD) 30.93 (3.83) Pre-pregnancy BMI (kg/m 2 ) – mean (SD) 23.52 (4.21) Offspring characteristics Sex – n (%) Girls 500 (49.26%) Boys 515 (50.74%) Puberty – n (%) Prepuberty 295 (29.06%) In puberty 720 (70.94%) Gestational age (weeks) – mean (SD) 39.74 (1.37) Birth weight (g) – mean (SD) 3271.10 (435.27) Age at BMI z-score assessment (years) – mean (SD) 11.02 (0.44) BMI (kg/m 2 ) – mean (SD) 19.46 (3.56) BMI z-score – mean (SD) 0.71 (1.21) Overweight – n (%) Yes 398 (41.72%) No 556 (58.28%) Age at BP assessment (years) – mean (SD) 10.41 (0.98) Systolic BP (mmHg) - mean (SD) 103.54 (9.46) Diastolic BP (mmHg) - mean (SD) 59.60 (7.47) Abbreviations: SD (standard deviation), BMI (body mass index), BP (blood pressure), * Includes women who quit smoking during current pregnancy. N. Güil-Oumrait et al. Environment International 169 (2022) 107527 5 association with MEPA in the highest quartile (Q4: β =-1.67 [95% CI = -3.31, −0.04] mmHg vs Q1) (Table 2). Consistently, the BKMR showed an inverse association between MEPA and systolic BP when the other chemicals were fixed at their median (Fig. 4A). We did not observe any effect of the overall chemical mixture with systolic BP (Fig. 4C); also, no interactions between chemicals were observed. The negative association observed in the overall population between MEPA and systolic BP was only observed in boys and in the BKMR model (Fig. S7 A2), but this phenol presented a low condPIP (0.17) (Fig. S7 B2). In boys, ∑DEHP was also associated with a decrease in systolic BP (pinteraction =0.10) (Table S5), which was confirmed in the BKMR univariate plots (Fig. S7 A2) and by being the exposure with the highest influence in the mixture (condPIP =0.63) (Fig. S7 B2). For boys, no effect of the overall mixture was observed (Fig. 6B). In girls, the singleexposure models revealed a decrease in systolic BP linked to BUPA (pinteraction =0.05) (Table S5). This was supported by the BKMR model with BUPA showing a sharp linear decrease in systolic BP when holding all other chemicals at their medians (Fig. S7 A1), its high condPIP (0.91) (Fig. S7 B2), and the negative joint effect of the mixture, although not statistically significant (Fig. 6B). No interactions between chemicals were observed in boys and girls. With respect to diastolic BP and in the single-exposure models, BP3 was the only compound associated with this outcome showing an increase in diastolic BP but only in the second quartile of exposure (Q2: β =1.27 [95% CI =0.00, 2.53] mmHg) (Table 2). This nonlinear positive association was also observed in the BKMR model when all the other chemicals remained at their median (Fig. 5A); being the chemical with the highest contribution within the mixture (condPIP =0.74) (Fig. 5B). No overall mixture effect was found (Fig. 5C) and no interactions were suggested graphically. Fig. 1. Logarithm transformed (log 2 ) creatinine-adjusted prenatal non-persistent chemical concentrations. Abbreviations: MEP (mono-ethyl phthalate), MiBP (monoiso-butyl phthalate), MnBP (mono-n-butyl phthalate), MBzP (mono-benzyl phthalate), DEHP (sum of di(2-ethylhexyl) phthalate metabolites), MEPA (methyl paraben), ETPA (ethyl paraben), PRPA (propyl paraben), BUPA (butyl paraben), BP3 (benzophenone-3), BPA (bisphenol A). Fig. 2. Correlation heatmap using Pearson’s correlation coefficients. Groups of chemicals included in the mixture model are squared in red. Abbreviations: MEP (mono-ethyl phthalate), MiBP (mono-iso-butyl phthalate), MnBP (mono-n-butyl phthalate), MBzP (mono-benzyl phthalate), DEHP (sum of di(2-ethylhexyl) phthalate metabolites), MEPA (methyl paraben), ETPA (ethyl paraben), PRPA (propyl paraben), BUPA (butyl paraben), BP3 (benzophenone-3), BPA (bisphenol A). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) N. Güil-Oumrait et al. Environment International 169 (2022) 107527 6 No sex interaction was observed in the single-exposure models (p > 0.10; Table S5). In these models, BP3 was the only chemical associated with an increase in diastolic BP, but only in boys (Q2: β =1.86 [95% CI =0.01, 3.72] mmHg) (Table S5). This association was confirmed in BKMR models (Fig. S8 A2, B2). In girls, BP3 was the chemical with the highest importance in the mixture model (condPIP =0.76) (Fig. S8 B1) and showed a nonlinear positive association after holding the other chemicals at their medians (Fig. S8, A1). As for the overall population, no overall mixture effect was found (Fig. 6C) and no interactions between chemicals were detected. 3.4. Sensitivity analyses Similar findings were observed using the z-score of BP instead of the raw BP variables (Table S6, Figure S9, S10) and restricting all the analyses to complete cases. Multipollutant model showed similar estimates for BP3 and BMI z-score and stronger estimates for MEPA and systolic BP (Table S7). Conversely, estimates of BP3 and diastolic BP were no longer statistically significant (Table S7). Among preadolescents who reached puberty status, the associations of BP3 with a higher BMI z-score and diastolic BP were strengthened (e.g. BMI z-score Q3: β =0.30 [95% CI =0.07, 0.54]) and diastolic BP (Q2: β =1.91 [95% CI =0.40, 3.43] mmHg) (Table S8). Moreover, effect estimates of BP3 with all the outcomes were stronger in sex-stratified analyses after excluding children in prepuberty status (Table S9 and Figures S11-S13). 4. Discussion In this Spanish population of pregnant women with common exposure to phthalates and phenols, prenatal urinary concentrations of BP3 Table 2 Adjusted associations a between prenatal non-persistent chemical concentrations and BMI and BP at preadolescence in single-exposure models. BMI z-score (n ¼954) Systolic BP (n ¼982) Diastolic BP (n ¼981) β (95 %CI) β 95 %CI β 95 %CI Phthalate metabolites MEP Per log 2 increase 0.01 (-0.02, 0.05) 0.05 (-0.24, 0.35) 0.04 (-0.20, 0.23) Q2 (110.67–225.90 µg/g) 0.05 (-0.16, 0.26) −0.24 (-1.96, 1.48) 0.91 (-0.41, 2.23) Q3 (225.91–473.86 µg/g) 0.17 (-0.04, 0.38) 1.00 (-0.70, 2.69) 0.30 (-1.09, 1.69) Q4 (473.87–9379.85 µg/g) 0.05 (-0.17, 0.27) 0.19 (-1.64, 2.01) 0.40 (-1.06, 1.87) MiBP Per log 2 increase −0.03 (-0.08, 0.03) −0.11 (-0.54, 0.33) 0.10 (-0.25, 0.46) Q2 (20.37–30.22 µg/g) −0.03 (-0.24, 0.18) −0.25 (-1.84, 1.34) 0.66 (-0.67, 2.00) Q3 (30.23–45.27 µg/g) −0.06 (-0.27, 0.15) −0.61 (-2.29, 1.08) 0.13 (-1.23, 1.49) Q4 (45.28–486.99 µg/g) −0.11 (-0.33, 0.12) −0.96 (-2.63, 0.71) 0.37 (-1.02, 1.76) MnBP Per log 2 increase 0.01 (-0.03, 0.04) −0.17 (-0.51, 0.17) −0.21 (-0.49, 0.07) Q2 (17.15–27.14 µg/g) 0.13 (-0.07, 0.33) 0.18 (-1.48, 1.84) 0.76 (-0.60, 2.11) Q3 (27.15–46.66 µg/g) 0.02 (-0.18, 0.22) 0.05 (-1.74, 1.84) −0.42 (-1.93, 1.08) Q4 (46.67–2101.18 µg/g) 0.04 (-0.17, 0.25) −0.29 (-2.19, 1.60) −0.34 (-2.02, 1.34) MBzP Per log 2 increase 0.00 (-0.04, 0.04) 0.01 (-0.36, 0.38) 0.06 (-0.23, 0.35) Q2 (5.22–9.37 µg/g) 0.03 (-0.18, 0.24) −0.57 (-2.27, 1.12) −0.27 (-1.66, 1.11) Q3 (9.38–16.38 µg/g) 0.01 (-0.20, 0.23) −1.13 (-2.89, 0.63) −0.48 (-1.86, 0.89) Q4 (16.39–238.35 µg/g) 0.01 (-0.20, 0.21) −0.17 (-1.96, 1.62) 0.20 (-1.21, 1.61) ∑DEHP Per log 2 increase 0.00 (-0.05, 0.06) −0.13 (-0.58, 0.33) 0.02 (-0.35, 0.40) Q2 (55.77–88.34 µg/g) −0.02 (-0.22, 0.18) −0.44 (-2.10, 1.22) −0.19 (-1.52, 1.13) Q3 (88.35–139.74 µg/g) −0.01 (-0.21, 0.19) −0.97 (-2.78, 0.84) −0.02 (-1.44, 1.39) Q4 (139.75–941.59 µg/g) −0.01 (-0.23, 0.20) −0.37 (-2.13, 1.39) 0.11 (-1.34, 1.56) Phenols MEPA Per log 2 increase −0.01 (-0.03, 0.02) −0.12 (-0.30, 0.06) −0.08 (-0.23, 0.06) Q2 (69.54–204.34 µg/g) −0.11 (-0.32, 0.10) −0.54 (-2.14, 1.05) −0.09 (-1.39, 1.21) Q3 (204.35–435.62 µg/g) −0.05 (-0.26, 0.16) −0.58 (-2.19, 1.03) −0.31 (-1.62, 1.00) Q4 (435.63–45927.09 µg/g) −0.19 (-0.40, 0.02) ¡1.67 (-3.31, ¡0.04) −0.56 (-1.89, 0.78) ETPA Per log 2 increase −0.01 (-0.03, 0.01) 0.00 (-0.17, 0.16) −0.01 (-0.13, 0.12) Q2 (4.35–13.75 µg/g) 0.09 (-0.11, 0.30) −0.47 (-2.09, 1.16) 0.60 (-0.70, 1.90) Q3 (13.76–42.11 µg/g) −0.01 (-0.21, 0.20) −0.37 (-2.00, 1.26) 0.54 (-0.76, 1.83) Q4 (42.12–980.02 µg/g) −0.10 (-0.31, 0.10) −0.37 (-2.01, 1.26) −0.08 (-1.37, 1.21) PRPA Per log 2 increase 0.00 (-0.02, 0.02) −0.08 (-0.24, 0.08) −0.01 (-0.14, 0.11) Q2 (13.07–41.41 µg/g) −0.14 (-0.35, 0.07) −1.20 (-2.80, 0.39) −0.14 (-1.43, 1.16) Q3 (41.42–114.51 µg/g) −0.15 (-0.36, 0.06) −0.32 (-1.92, 1.29) −0.59 (-1.89, 1.48) Q4 (114.52–14132.26 µg/g) −0.03 (-0.24, 0.17) −0.89 (-2.48, 0.71) 0.19 (-1.11, 1.48) BUPA Per log 2 increase 0.00 (-0.02, 0.02) −0.07 (-0.21, 0.08) 0.01 (-0.10, 0.13) Q2 (0.54–2.77 µg/g) 0.09 (-0.12, 0.29) −0.30 (-1.89, 1.29) −0.01 (-1.30, 1.27) Q3 (2.78–9.47 µg/g) 0.05 (-0.16, 0.25) −0.82 (-2.43, 0.78) 0.27 (-1.01, 1.54) Q4 (9.48–110.00 µg/g) −0.12 (-0.33, 0.09) −0.67 (-2.28, 0.94) 0.24 (-1.06, 1.55) BP3 Per log 2 increase 0.01 (-0.01, 0.02) 0.03 (-0.10, 0.16) 0.04 (-0.07, 0.15) Q2 (1.30–4.17 µg/g) 0.22 (0.01, 0.42) 1.01 (-0.55, 2.56) 1.27 (0.00, 2.53) Q3 (4.18–23.68 µg/g) 0.23 (0.03, 0.44) 0.14 (-1.45, 1.73) 0.53 (-0.75, 1.82) Q4 (23.69–3115.95 µg/g) 0.13 (-0.07, 0.34) 0.77 (-0.81, 2.36) 0.94 (-0.35, 2.22) BPA Per log 2 increase −0.01 (-0.03, 0.03) −0.05 (-0.31, 0.21) −0.05 (-0.26, 0.16) Q2 (1.69–2.82 µg/g) 0.15 (-0.05, 0.35) 0.21 (-1.40, 1.81) 0.00 (-1.37, 1.38) Q3 (2.83–4.59 µg/g) 0.00 (-0.21, 0.20) 0.17 (-1.43 1.77) −0.14 (-1.52, 1.24) Q4 (4.60–116.06 µg/g) 0.06 (-0.14, 0.26) −0.84 (-2.48, 0.79) −0.75 (-2.07, 0.58) Abbreviations: BMI (body mass index), MEP (mono-ethyl phthalate), MiBP (mono-iso-butyl phthalate), MnBP (mono-n-butyl phthalate), MBzP (mono-benzyl phthalate), ∑DEHP (sum of di(2-ethylhexyl) phthalate metabolites), MEPA (methyl paraben), ETPA (ethyl paraben), PRPA (propyl paraben), BUPA (butyl paraben), BP3 (benzophenone-3), BPA (bisphenol A). a Linear mixed models adjusted for maternal age, education, smoking in pregnancy and pre-pregnancy BMI. Blood pressure models were additionally adjusted by child’s age, sex and height. Region of residence was included as random intercept. Second (Q2), third (Q3) or fourth quartile (Q4) compared with first. Statistically significant coefficient intervals at p <0.05 are highlighted in bold. N. Güil-Oumrait et al. Environment International 169 (2022) 107527 7 were consistently linked with increased BMI z-score and diastolic BP at 11 years across single and mixture modelling approaches. These effects were further evidenced among preadolescents who reached puberty status. We also observed that MEPA was associated with a decreased systolic BP, while BUPA and ∑DEHP were associated with decreased systolic BP in girls and boys, respectively; however, all these associations were not consistent across single and mixture models. Although no overall mixture effect nor chemical interactions were detected, in girls, we observed a tendency of higher BMI z-score and decreased systolic BP of the chemical mixture, yet non-statistically significant. Results from this study highlight the benefit of combining two complementary models, considering their advantages and disadvantages. For instance, single-pollutant models may show straightforward results with interpretable effect estimates, providing an important step in assessing complex exposure patterns. However, advanced approaches able to overcome the constraints of complex exposure data structures (e.g. correlation, high-dimensionality) may be required to address more complex questions about the effect mixtures, including their joint effects or identification of interactions (Braun et al., 2016). Only two previous studies from the CHAMACOS cohort in the US have also assessed the association between prenatal mixtures of phthalates and phenols with obesity-related outcomes in childhood (Berger et al., 2021; Harley et al., 2017). Both studies combined single-exposure models with BKMR, as we did in the present study. Harley et al., (2017) visually explored the exposure–response associations obtained from BKMR between phthalates and BMI at 12 years of age; neither PIPs nor joint mixture effects were analyzed. They observed trends of phthalates that were similar to ours, and a significant association between MEP and higher BMI that in our study was only observed in the single-exposure models and in girls (Table S5). In Berger et al., (2021), PRPA was associated with higher BMI and overweight/obesity risk at 5 years in single pollutant models and it was the most relevant chemical in the mixture. In our study, PRPA showed inconsistent results among singleexposure and BKMR models and it had a low contribution to the mixture (condPIP =0.10). In Berger et al., (2021) BP3 was also considered in the mixture but it tended to be associated with reduced BMI at 5 years in both single and mixture models and it had a low contribution (PIP =0.02). Berger et al., (2021) also reported a slightly increasing trend of BMI linked to the overall mixture, which in our study was only observed in girls. The null overall mixture effect observed in all the study populations (i.e. CHAMACOS and INMA) could be partly explained by the different sense of directions of the chemicals, which may have neutralized the joint effect. This has been observed in in vitro studies where the estrogenic activity of BPA can suppress the adipogenic effects of PPARγ activators DEHP and tributyltin during adipogenesis, for example (Biemann et al., 2014; Jeong and Yoon, 2011). We may hypothesize that inconsistencies between the CHAMACOS and the INMA studies may be due to: (i) the dissimilarity of sociodemographic and genetic background between mothers from both cohorts (i.e. most of CHAMACOS’ mothers were of Latin ethnicity who lived in the US<5 years, younger, had a low educational level and household income, and more than half were overweight), which may have contributed to differences in the use of chemical-associated personal care products in pregnancy (Perng et al., 2021; Preston et al., 2021); (ii) in relation to the previous point, the different concentrations of chemicals, which were higher in CHAMACOS for both PRPA and BP3; (iii) the sample size (~300 in CHAMACOS vs ~ 1000 in INMA); (iv) the different chemicals considered into the mixture which can modify the joint effect and the contribution of each chemical (i.e. in Berger et al., (2021) more phthalates and triclosan were included whereas BUPA and MEPA were not included); v) the distinct age at outcome examination (5 years in CHAMACOS vs 11 years in INMA); and (vi) the different statistical approaches used since Berger et al., 2021 did not consider hierarchical clustering of chemicals in the BKMR as we did, which allowed us to Chemicals GroupPIP CondPIP MEP 0.23 0.28 MiBP 0.23 0.24 MnBP 0.23 0.18 MBzP 0.23 0.12 ∑DEHP 0.23 0.17 MEPA 0.31 0.10 ETPA 0.31 0.24 PRPA 0.31 0.10 BUPA 0.31 0.37 BP3 0.31 0.20 A) B) C) Fig. 3. Summary estimates from BKMR on the association between mixtures of log 2 scaled non-persistent chemicals and BMI z-score in preadolescence adjusted by maternal age, education, smoking in pregnancy and pre-pregnancy BMI; region of residence was included as random intercept. (A) Exposure-response associations for each chemical when the others are fixed at their median. (B) Group and conditional posterior inclusion probabilities (GroupPIP and CondPIP) of each chemical in the mixture-response function for BMI z-score. (C) Joint effect of prenatal non-persistent chemicals mixture on BMI z-score at preadolescence (Credible intervals overlapping the null depicted with the broken line indicate no significant effects). Abbreviations: BMI (body mass index), BKMR (Bayesian kernel machine regression), MEP (mono-ethyl phthalate), MiBP (mono-iso-butyl phthalate), MnBP (mono-n-butyl phthalate), MBzP (mono-benzyl phthalate), DEHP (sum di(2ethylhexyl) phthalate metabolites), MEPA (methyl paraben), ETPA (ethyl paraben), PRPA (propyl paraben), BUPA (butyl paraben), BP3 (benzophenone-3). N. Güil-Oumrait et al. Environment International 169 (2022) 107527 8 group the chemicals accounting for their correlations and it can modify models’ performance and output. To our knowledge, the relationship between prenatal mixtures of phthalates and phenols with postnatal systolic and diastolic BP has only been assessed recently in the INMA-Sabadell cohort (Montazeri et al., 2022) with a sample of 416 participants, in which no evidence for single or mixture associations with BP at 11 years was found. Conversely, in this study with a larger sample size, we found that BP3, MEPA, BUPA, and ∑DEHP were associated with changes in diastolic BP, with some sex-specific associations. The improvement of statistical power may have contributed to the identification of sex-specific effects in the present study. Aside from Montazeri et al., (2021) and Warembourg et al., (2019), no previous study has assessed the individual associations of phenols other than BPA with BP (see Appendix 1). As for BMI, no overall mixture effect was found with systolic and diastolic BP. However, we did observe a sharp downward trend between the mixture and systolic BP, driven by BUPA, in girls. Because parabens have potential estrogenic activity (Nowak et al., 2018), we speculate that the decrease in systolic BP only seen in girls could be mediated by a downregulation of the rennin-angiotensin-aldosterone system (RAAS), a major regulator of systemic BP, via estrogenic pathways (Medina et al., 2020). Finally, results for prenatal ∑DEHP and lower systolic BP in boys are partly consistent with the negative association between prenatal ∑DEHP metabolites and lower systolic and diastolic BP in boys and girls at 4–6 years observed in the Greek Rhea cohort (Bowman et al., 2019; Vafeiadi et al., 2018; Valvi et al., 2015a). In our study, prenatal exposure to BP3 was associated with higher BMI and diastolic BP levels at preadolescence in both sexes, and in single and mixture modelling approaches. Only in BKMR, BP3 was also associated with higher systolic BP. These associations were further consistent and stronger in preadolescents who reached puberty onset. Puberty is considered one of the developmental windows, besides fetal and neonatal life, in which endocrine-disrupting chemicals are more likely to act at any level in the “hypothalamic-pituitary–gonadal-peripheral tissues’ axis” (Gore et al., 2015). Epidemiological studies assessing the prenatal effects of BP3 on obesity and BP are scarce. Apart from the CHAMACOS study (Berger et al., 2021), another study in the US assessed prenatal BP3 and obesity in 170 4–9 years-old children and reported a decreased body fat mass % in girls (Buckley et al., 2016). Likewise, in the HELIX subcohort, prenatal BP3 was associated neither with child BMI nor BP (Vrijheid et al., 2020; Warembourg et al., 2019). The present study is the first to assess and identify associations of prenatal BP3 alone and in a mixture of non-persistent chemicals in relation to BMI and BP at preadolescence in a big sample size (n ~ 1,000). Our results shed light on BP3 potential metabolic disrupting effects in puberty due to fetal development exposure. Benzophenones are UV light filters used in sunscreens to absorb and dissipate the UV radiation and in cosmetics to prevent damage to colour and scent, being probably the reason why higher BP3 exposure has been observed in females in biomonitoring studies (Calafat et al., 2008). Developmental effects of BP3 exposure on adipogenesis may be mediated through its estrogen-like and anti-androgenic activity (Blüthgen et al., 2012; Kerdivel et al., 2013; Kim and Choi, 2014; Majhi et al., 2020; Schlumpf et al., 2001; Schreurs et al., 2005; Wang et al., 2016; Watanabe et al., 2015), the decrease in thyroid hormone balance (Lee et al., 2018), and the upregulation of PPARγ, a direct transcriptional regulator of human adipogenesis that contributes to adipocyte differentiation and insulin sensitization (Shin et al., 2020b; Wnuk et al., 2019). The mechanisms underlying BP3 effects on blood pressure could be indirectly via BP3 obesogen-like effects, or directly through the disruption of steroid and thyroid hormone molecular pathways, which may alter RAAS (Barreto-Chaves et al., 2010). Furthermore, recent Chemicals GroupPIP CondPIP MEP 0.09 0.32 MiBP 0.09 0.17 MnBP 0.09 0.16 MBzP 0.09 0.16 ∑DEHP 0.09 0.19 MEPA 0.16 0.21 ETPA 0.16 0.15 PRPA 0.16 0.10 BUPA 0.16 0.15 BP3 0.16 0.39 A) B) C) Fig. 4. Summary estimates from BKMR on the association between mixtures of log 2 scaled non-persistent chemicals and systolic BP in preadolescence adjusted by maternal age, education, smoking in pregnancy, pre-pregnancy BMI, sex, height and age at outcome assessment; region of residence was included as random intercept. (A) Exposure-response associations for each chemical when the others are fixed at their median. (B) Group and conditional posterior inclusion probabilities (GroupPIP and CondPIP) of each chemical in the mixture-response function for systolic BP. (C) Joint effect of prenatal non-persistent chemicals mixture on systolic BP at preadolescence (Credible intervals overlapping the null depicted with the broken line indicate no significant effects). Abbreviations: BP (blood pressure), BMI (body mass index), BKMR (Bayesian kernel machine regression), MEP (mono-ethyl phthalate), MiBP (mono-iso-butyl phthalate), MnBP (mono-n-butyl phthalate), MBzP (mono-benzyl phthalate), DEHP (sum of di(2-ethylhexyl) phthalate metabolites), MEPA (methyl paraben), ETPA (ethyl paraben), PRPA (propyl paraben), BUPA (butyl paraben), BP3 (benzophenone-3). N. Güil-Oumrait et al. Environment International 169 (2022) 107527 9 studies with animal models have reported BP3 exposure to induce free radical production and the downregulation of antioxidant enzymes (Liu et al., 2015; Rodríguez-Fuentes et al., 2015), which may eventually influence the early development of the cardiovascular system (´ Avila et al., 2015). The non-monotonic exposure–response relationships found in this study suggest that the health effects of BP3 can occur even at low doses of exposure (Vandenberg et al., 2012). Of interest, the magnitude of the association between BP3 and BMI z-score (e.g. Q2: β =0.22 [95% CI =0.01, 0.42]) was very similar to associations found in adolescence of the INMA Menorca cohort in relation to prenatal exposure to hexachlorobenzene and BMI z-score (tertile 2: β =0.24 [95% CI: 0.01, 0.47]) (Güil-Oumrait et al., 2021). Although BP3 has a short biological half-life (<24 h) (Kim and Choi, 2014), it is one of the phenols with the lowest intraindividual variability (Vernet et al., 2018). However, the intraclass correlation coefficient of a spot urine sample collected during pregnancy is still low (between 0.39 (Mínguez-Alarc´ on et al., 2019) and 0.62 Chemicals GroupPIP CondPIP MEP 0.08 0.28 MiBP 0.08 0.10 MnBP 0.08 0.11 MBzP 0.08 0.25 ∑DEHP 0.08 0.25 MEPA 0.16 0.11 ETPA 0.16 0.08 PRPA 0.16 0.05 BUPA 0.16 0.03 BP3 0.16 0.74 A) B) C) Fig. 5. Summary estimates from BKMR on the association between mixtures of log 2 scaled non-persistent chemicals and diastolic BP in preadolescence adjusted for maternal age, education, smoking in pregnancy, pre-pregnancy BMI, sex, height and age at outcome assessment; region of residence was included as random intercept. (A) Exposure-response associations for each chemical when the others are fixed at their median. (B) Group and conditional posterior inclusion probabilities (GroupPIP and CondPIP) of each chemical in the mixture-response function for diastolic BP. (C) Joint effect of prenatal non-persistent chemicals mixture on diastolic BP at preadolescence (Credible intervals overlapping the null depicted with the broken line indicate no significant effects). Abbreviations: BP (blood pressure), BMI (body mass index), BKMR (Bayesian kernel machine regression), MEP (mono-ethyl phthalate), MiBP (mono-iso-butyl phthalate), MnBP (mono-n-butyl phthalate), MBzP (mono-benzyl phthalate), DEHP (sum of di(2-ethylhexyl) phthalate metabolites), MEPA (methyl paraben), ETPA (ethyl paraben), PRPA (propyl paraben), BUPA (butyl paraben), BP3 (benzophenone-3). Fig. 6. Sex-stratified analysis. Joint effect of log 2 scaled prenatal non-persistent chemicals mixture on BMI z-score (A), systolic BP (B), and diastolic BP (C) stratified in girls and boys shown in black and grey lines, respectively. 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