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. Author manuscript; available in PMC: 2023 May 8.
Published in final edited form as: Environ Int. 2023 Apr 10;174:107922. doi: 10.1016/j.envint.2023.107922

Maternal urinary bisphenols and phthalates in relation to estimated fetal weight across mid to late pregnancy

Whitney Cowell a,b,*, Melanie H Jacobson a, Sara E Long a, Yuyan Wang b, Linda G Kahn a,b, Akhgar Ghassabian a,b, Mrudula Naidu a, Ghazaleh Doostparast Torshizi a, Yelena Afanasyeva a, Mengling Liu b, Shilpi S Mehta-Lee c, Sara G Brubaker c, Kurunthachalam Kannan a, Leonardo Trasande a,b,d,e
PMCID: PMC10165618  NIHMSID: NIHMS1895275  PMID: 37075581

Abstract

Background:

Bisphenols and phthalates are high production volume chemicals used as additives in a variety of plastic consumer products leading to near ubiquitous human exposure. These chemicals have established endocrine disrupting properties and have been linked to a range of adverse reproductive and developmental outcomes. Here, we investigated exposure in relation to fetal growth.

Methods:

Participants included 855 mother-fetal pairs enrolled in the population-based New York University Children’s Health and Environment Study (NYU CHES). Bisphenols and phthalates were measured in maternal urine collected repeatedly during pregnancy. Analyses included 15 phthalate metabolites and 2 bisphenols that were detected in 50 % of participants or more. Fetal biometry data were extracted from electronic ultrasonography records and estimated fetal weight (EFW) was predicted for all fetuses at 20, 30, and 36 weeks gestation. We used quantile regression adjusted for covariates to model exposure-outcome relations across percentiles of fetal weight at each gestational timepoint. We examined sex differences using stratified models.

Results:

Few statistically significant associations were observed across chemicals, gestational time periods, percentiles, and sexes. However, within gestational timepoints, we found that among females, the molar sums of the phthalates DiNP and DnOP were generally associated with decreases in EFW among smaller babies and increases in EFW among larger babies. Among males, the opposite trend was observed. However, confidence intervals were generally wide at the tails of the distribution.

Conclusion:

In this sample, exposure to bisphenols and phthalates was associated with small sex-specific shifts in fetal growth; however, few associations were observed at the median of fetal weight and confidence intervals in the tails were wide. Findings were strongest for DiNP and DnOP, which are increasingly used as replacements for DEHP, supporting the need for future research on these contaminants.

Keywords: Pregnancy, Fetal growth, Fetal weight, Ultrasound, Bisphenols, BPA, Phthalates, DEHP

1. Introduction

High and low deviations from average fetal growth increase the risk of adverse health outcomes for the mother and newborn and have been linked to a range of co-morbidities across the life course (Gluckman and Hanson, 2004; Hales and Barker, 1992; Robinson, 2001). Fetuses that have restricted growth or low birthweight are at increased risk for neonatal complications, altered childhood growth, neurodevelopmental disorders, and long-term cardiometabolic morbidity (Harrild and Marx, 2017; Malhotra et al., 2019; McIntire et al., 1999; Miller et al., 2016). On the other hand, newborns that are large for gestational age present challenges during labor and delivery and are also at increased risk for developing a range of cardiometabolic outcomes, such as high blood pressure, diabetes, and obesity (Boulet et al., 2003; Raio et al., 2003).

Fetal growth and developmental processes are dependent on placental exchange of nutrients, gases, and other essential substrates between the mother and fetus. The placenta also serves as a site of active steroidogenesis during pregnancy and is characterized by a high density of steroid receptors, making it sensitive to perturbations of the intrauterine hormonal milieu (Chatuphonprasert et al., 2018; Gingrich et al., 2020; Pasqualini and Chetrite, 2016; Albrecht et al., 2021). Maternal exposure to environmental toxicants that interfere with intrinsic endocrine activity may thus have consequences for fetal growth through placenta-mediated pathways.

Bisphenol A (BPA) is a high-production synthetic chemical used as an additive in polycarbonate plastics and as a component of thermal papers, epoxy resins present in food containers, and other commonly used consumer products (Metcalfe et al., 2022). With time, BPA is released from base polymers and can migrate into food, beverages, and other environmental media resulting in widespread human exposure with detectable levels in nearly all of the general United States (US) population (Calafat et al., 2008). Due to concerns regarding health risks, BPA has been increasingly replaced over the past decade with analogs, such as bisphenol S (BPS), for which little is known regarding human toxicity (Pelch et al., 2019). Like bisphenols, phthalates are synthetic chemicals used in a range of consumer products as plasticizers and fragrance stabilizers. Phthalates with a high molecular weight (HMW) are commonly used in PVC flooring, food packaging, and medical devices, while low molecular weight (LMW) phthalates are primarily applied to pharmaceuticals and personal care products, such as perfumes, lotions, and cosmetics (Metcalfe et al., 2022). Like BPA, phthalates readily leach from consumer products resulting in near ubiquitous human exposure (Phthalates NRCUCotHRo, 2008). Due to concerns of adverse health effects resulting from exposure, the most prevalent HMW phthalate, di-(2-ethylhexyl) phthalate (DEHP), is being phased out of many products, with alternative phthalates such as di-n-octyl phthalate (DnOP) and diisononyl phthalate (DiNP) increasing used as replacements.

Phthalates and bisphenols can cross the placenta and have been detected in amniotic fluid, placenta tissue, and umbilical cord blood (Gely et al., 2021; Katsikantami et al., 2020; Mose et al., 2007; Zbucka-Kretowska et al., 2019). These chemicals are classified as endocrine disrupting chemicals (EDCs) that act as weak ligands capable of interfering with estrogen, androgen, thyroid hormone, glucocorticoid, and peroxisome proliferator-activated receptors (PPARs), which regulate important developmental processes during pregnancy (Phthalates NRCUCotHRo, 2008; Schock et al., 2016; Vrachnis et al., 2021). In addition to dysregulating hormone signaling, bisphenols and phthalates have been shown to induce oxidative stress, epigenetic modifications, and inflammation, supporting their potential to disturb fetal growth (Dutta et al., 2020; Ferguson et al., 2019; van T Erve et al., 2019).

Most previous investigations examining maternal urinary concentrations of bisphenols or phthalate metabolites during pregnancy in relation to fetal growth have focused on birthweight as an endpoint (Gaston et al., 2020; Golestanzadeh et al., 2019; Hu et al., 2018; Marie et al., 2015; Pergialiotis et al., 2018; Yaghjyan et al., 2016; Zarean et al., 2016; Zhong et al., 2020; Zhou et al., 2019), which may fail to capture pathologic processes occurring at different points across gestation (Mayer and Joseph, 2013). Several prior studies have also investigated associations in relation to ultrasound measures of fetal growth, most of which have identified inverse associations between at least one exposure and estimated fetal weight or biometric indicators of fetal size (Botton et al., 2016; Casas et al., 2016; Ferguson et al., 2016; Lee et al., 2018; Li et al., 2021; Santos et al., 2021; Snijder et al., 2013; Stevens et al., 2022; van den Dries et al., 2021; Zhou et al., 2020). However, few studies have considered bisphenol and phthalates that are increasingly used as replacements for BPA and DEHP, respectively. Furthermore, despite the greater clinical significance of deviations in fetal growth in the tails of the distribution (i.e., among small and large fetuses) (Boulet et al., 2003; Chauhan et al., 2017; Zeve et al., 2016), no prior studies have examined associations across the distribution of fetal weight. In the present analysis, we examined urinary bisphenols and phthalate metabolites measured repeatedly over the course of gestation in relation to ultra-sound measures of fetal weight at three time points during mid- to late gestation among 855 mother-fetal pairs enrolled in an ongoing, prospective pre-birth cohort. Rather than focusing on changes at the mean, we used quantile regression to examine exposure-outcome relationships across percentiles of fetal weight, which allowed us to examine whether exposure was more impactful among smaller or larger babies. Finally, given established sex differences in fetal development (Rosenfeld, 2015), as well as known sex-differential effects of bisphenols and phthalates on fetal and placental endpoints, we examined moderation by fetal sex (Casas et al., 2016; Strakovsky and Schantz, 2018; Warner et al., 2021).

2. Methods

Study sample.

Participants included pregnant individuals enrolled in the population-based New York University Children’s Health and Environment Study (NYU CHES), which is an ongoing, prospective pre-birth cohort that has been previously described in detail (Trasande et al., 2020). Briefly, participants were recruited from the prenatal clinics of three NYU-affiliated hospitals beginning in 2016. Eligibility criteria included 18 years of age or older, <18 weeks gestational age at the time of enrollment, and fluency in English, Spanish or Chinese. Participants completed questionnaires during study visits conducted throughout pregnancy and information on maternal health characteristics and fetal growth metrics was abstracted from electronic medical records (EMRs). The sample included 910 participants for whom urinary bisphenol and phthalate metabolite concentrations were measured (69.7 % with repeated chemical data at three timepoints). From these participants, we excluded those who were missing all ultrasound information (n = 44), were missing fetal sex information (n = 1) or were missing covariate data (n = 10), resulting in an analytic sample of 855 mother-fetal pairs. The study was approved by the Institutional Review Board of the NYU Grossman School of Medicine; all participants provided written informed consent at the time of enrollment.

Bisphenol and phthalate measurement.

Spot urine samples were collected during clinical care visits during early (<18 gestational weeks, mean ± SD: 76 ± 23 gestational days), mid- (18–25 weeks, 146 ± 15), and late (greater than 25 weeks, 207 ± 26) pregnancy. Of participants included in the analytic sample, 598 (70 %) had exposure biomarkers measured in urine collected at all three time points, 175 (20 %) at two time points, and 82 (10 %) at one time point. Supplemental Figure 1 provides the distribution of gestational age at urine collection. Samples were collected in polyethylene containers, aliquoted into bisphenol- and phthalate-free tubes, and stored at −80C until chemical measurement by the NYU Human and Environmental Exposure Analysis Laboratory (HEAL). Once absorbed, phthalates are metabolized into several monoesters that, along with bisphenols, have short half-lives and are excreted primarily in urine (Wang et al., 2019). Urinary levels of these chemicals are thus considered reliable markers of internal exposure for human biomonitoring. Phthalate metabolites (n = 22) were measured using enzymatic deconjugation of glucuronidated and sulfated phthalate monoesters followed by solid phase extraction (SPE) coupled with high-performance liquid chromatography electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS) as previously described in detail (Gaylord et al., 2022; Liu et al., 2022). Of the 22 phthalate metabolites measured, we included 15 that were detected in 50 % of participants or more. We categorized metabolites into low (∑LMW, <250 Da) and high (∑HMW, >250 Da) molecular weight groups, which reflects structural similarity and their application in consumer products. We also categorized metabolites into three groups (∑DEHP, ∑DnOP, ∑DiNP) on the basis of their parent diester. For each group, we calculated the molar sum of metabolite components (nmol/mL); ∑DiNP had only one metabolite detected in over 50 % of participants. We also examined phthalic acid, which is the final common metabolite of phthalate esters and reflects overall phthalate exposure (Bang du et al., 2011). Bisphenols (n = 8) were also measured by HPLC-ESI-MS/MS as previously described (Gaylord et al., 2022). Inferential analyses focused on the molar sum of BPA and BPS (∑BP), which were the only two of the eight detected in more than 50 % of participants. Table 2 provides an overview of all bisphenol and phthalate groupings and provides full chemical names. We assigned chemical values below the limit of detection (LOD) with LOD/sqrt(2) (Hornung and Reed, 1989). Urinary creatinine (Cr) was measured by HPLC-MS/MS following a standard protocol as previously described (Martinez and Kannan, 2018). To account for variability in urine dilution, we corrected for creatinine using a variation of the approach popularized by Boeniger et al. (Boeniger et al., 1993; Kuiper et al., 2021). Specifically, chemical concentrations were multiplied by the batch and time point specific median creatinine value divided by the creatinine value of the sample. We used the timepoint and batch-specific Cr values to account for small differences in Cr between timepoints and batches arising from non-biological laboratory variation. We natural log-transformed the exposure data to account for right-skewed distributions. Across phthalate metabolites, the median (range) ICC over the three sampling time points was 0.32 (0.23–0.64); ICCs for BPA (0.28) and BPS (0.30) were similar (Gaylord et al., 2022). Given this variability and the short biological half-lives of the exposures, we averaged concentrations across the three sampling time points to arrive at the most representative index of exposure across gestation that we could calculate with our data. Finally, because of differences in distributions across bisphenol and phthalate metabolites, we standardized the data by calculating z-scores for the creatinine-corrected, natural log-transformed average exposure concentrations.

Table 2.

Median and interquartile ranges (IQR) of pregnancy-averaged bisphenols and phthalate metabolites (n = 855).

% <LOD Median (IQR)
∑Bisphenols (∑BP)b – 0.006 (0.007)
Bisphenol A (BPA)a 4.1 0.84 (0.88)
Bisphenol S (BPS)a 5.7 0.51 (0.87)
Low molecular weight phthalates (∑LMW)b – 0.36 (0.55)
Mono-ethyl phthalate (mEP)a 0.0 41.95 (81.37)
Mono-n-butyl phthalate (mnBP)a 0.5 12.03 (17.65)
Mono-isobutyl phthalate (mIBP)a 0.7 7.76 (11.52)
High molecular weight phthalates (∑HMW)b – 0.13 (0.17)
Di-(2-ethylhexyl) phthalate (∑DEHP)b – 0.08 (0.10)
Mono-(2-ethylhexyl) phthalate (mEHP)a 16.3 1.66 (3.09)
Mono-(2-ethyl-5-oxohexyl) phthalate (mEOHP)a 0.0 4.04 (5.03)
Mono-(2-ethyl-5-hydroxyhexyl) phthalate (mEHHP)a 0.0 6.61 (7.89)
Mono-(2-carboxymethyl) phthalate (mCMHP)a 6.3 2.9 (4.02)
Mono-(2-ethyl-5-carboxypentyl) phthalate (mECPP)a 0.0 6.76 (8.60)
Di-n-octyl phthalate (∑DnOP)b – 0.006 (0.010)
Mono-(3-carboxypropyl) phthalate (mCPP)a 21.3 0.74 (1.18)
Mono-(7-carboxyheptyl) phthalate (mCHpP)a 31.7 0.35 (1.69)
Diisononyl phthalate (∑DiNP)b – 0.006 (0.010)
Mono-(carboxyisooctyl) phthalate (mCiOP)a 0.6 1.98 (3.10)
Other HMWb
Mono-(carboxyisononyl) phthalate (mCiNP)a 1.5 1.89 (3.27)
Mono-benzyl phthalate (mBzP)a 13.5 2.62 (6.41)
Mono-hexyl phthalate (mHxP)a 46.1 0.02 (0.05)
Phthalic acida 2.1 19.17 (21.81)
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a

Units of ng chemical/mL urine,

b

Units of nmol chemical/mL urine

Fetal growth.

We abstracted fetal biometry data from electronic ultrasonography records. All ultrasounds were conducted by licensed sonographers at NYU-affiliated prenatal care sites. Measurements, in millimeters, included biparietal diameter (BPD), femur length (FL), head circumference (HC), and abdominal circumference (AC). We estimated fetal weight (EFW) at each ultrasound exam from these measurements using the Hadlock-III formula (Hadlock et al., 1985). Consistent with American College of Obstetricians and Gynecologists guidelines (Committee opinion No. 700: Methods for estimating the due date, 2017), we used the last menstrual period for pregnancy dating unless it was unavailable or differed by more than 5 days from ultrasound dating assessed before 9 + 0 weeks gestation or 7 days from ultrasound dating assessed between 9 and 14 + 0 weeks gestation. In these cases, ultra-sound dating based on crown-rump length was used. The mean ± SD number of ultrasound examinations was 4.5 ± 2.0. To account for variability in the number and timing of ultrasound examinations, we used a linear mixed model with cubic splines for gestational age to predict EFW for all fetuses at 20 weeks, 30 weeks, and 36 weeks, which were common times at which clinical ultrasounds were conducted in the sample (18–22 weeks: n = 786, 91.9 %; 28–32 weeks: n = 353, 41.3 %; 34–38 weeks: n = 543, 63.5 %). This prediction model included all 2414 participants enrolled in the cohort with available ultrasound data. Notably, while it is routine to perform an anatomy ultrasound at approximately 20 weeks, later ultrasounds are more likely to be performed among patients who are older or have a clinically significant comorbidity such as obesity, diabetes, or hypertension. As such, predictions at later timepoints may not generalize to a healthy sample of optimally progressing pregnancies. Models were fit with random intercepts and slopes; knots were placed at 20 and 27 weeks in order to align with the anatomy scan and the start of the third trimester, respectively (Ultrasound in pregnancy, 2016). Model fit was assessed using AIC/BIC and by comparing predicted to observed values. We chose to focus on separate ultrasound timepoints across mid- to late pregnancy, rather than performing a longitudinal analysis, as mid-pregnancy scans are useful for observing gross deviations from typical growth whereas later scans have the potential to inform more subtle differences in growth. In addition, a recent review of this topic highlighted the importance of measurements from the second half of pregnancy to capture the period when most growth occurs (Kamai et al., 2019). While we focus on EFW as an overall metric of fetal size, we similarly used a linear mixed model with cubic splines for gestational age to predict AC, HC, FL, and BPD at 20 weeks, 30 weeks, and 36 weeks for all fetuses.

Covariates.

Extensive information on participant sociodemographics, lifestyle characteristics, and health history was collected by questionnaire during prenatal study visits. When possible, data that were missing from questionnaires were filled in using information recorded in the EMR. In addition, age at enrollment, pre-pregnancy height and weight, and parity were obtained from EMRs; height and weight were used to calculate pre-pregnancy body mass index (BMI). Urinary cotinine was analyzed in the same maternal urine samples used for bisphenol and phthalate measurement by HPLC-MS/MS (Honda et al., 2018) and the maximum value across repeated pregnancy samples was used. We identified potential confounders based on prior literature and constructed a Directed Acyclic Graph (DAG) to select covariates for inclusion in adjusted models (Supplemental Figure 2). These included: maternal age (continuous in years), pre-pregnancy BMI (continuous in kg/m2), natural-log transformed and creatinine-adjusted urinary cotinine levels (continuous in ng/mL), maternal education (high school or less vs some college/associate degree vs bachelor degree vs postgraduate degree), maternal race/ethnicity (Hispanic vs non-Hispanic White vs non-Hispanic Black vs non-Hispanic Asian vs other/multiple), and relationship status (married/living with a partner vs single). Similar to other studies of EDCs (Stevens et al., 2022), we conceptualized race as a social construct that is included as a proxy for culturally-driven variation in personal care product use (Chan et al., 2021), diet (Pacyga et al., 2019), and other unmeasured social factors (e.g., racism/discrimination) that may influence both exposure to EDCs and fetal growth (Dominguez et al., 2008; Larrabee Sonderlund et al., 2021). We included parity (nulliparous vs parous) as a precision variable associated with the outcome.

Statistical analysis.

We calculated summary statistics for all covariates and examined distributions of continuous covariates using boxplots and histograms. For each exposure, we calculated the percent of samples below the LOD and visually inspected distributions. We calculated medians and interquartile ranges (IQR) for each bisphenol and phthalate metabolite (ng/mL urine) and each grouping (nmol/mL urine). Detailed information on the distribution of bisphenols and phthalates in this sample has been previously described (Gaylord et al., 2022; Liu et al., 2022). Likewise, we examined the distribution of predicted EFW at each gestational time period (20 weeks, 30 weeks, 36 weeks) and evaluated differences by sex using Student’s t-tests. We used multivariable quantile regression to assess the associations between chemical groupings and predicted EFW at each gestational time period in separate models and output estimates at the 10th, 25th, 50th, 75th and 90th percentiles of EFW. All continuous covariates were centered before inclusion in the model. Models for EFW at 30 weeks excluded two newborns born before 30 weeks and those for EFW at 36 weeks excluded 41 newborns born before 36 weeks. Effect estimates at each output percentile were reported for a one standard deviation (SD) increase in the natural log-transformed, creatinine-adjusted chemical exposure. We performed quantile regression in the sample overall and stratified by sex. We used the effective number of tests approach to control for the family-wise error rate (FWER). This is an extension of Bonferroni adjustment that accounts for a lack of full independence between exposures (Li et al., 2012). We defined the threshold of significance as p-value < 0.01, which was obtained by dividing 0.05 by the effective number of tests based on eigenvalues of the correlation matrix among pairs of the main phthalate (∑DEHP, ∑DiNP, ∑DnOP, ∑LMW) and bisphenol groups. We did not consider ∑HMW phthalates in our calculation of the adjusted p-value because of substantial overlap among components of the HMW group with ∑DEHP, ∑DiNP and ∑DnOP. Similar to the primary results focused on EFW, we also used multivariable quantile regression to assess associations between chemical groupings and predicted AC, HC, FL, and BPD at each gestational time period in separate models and again output estimates at the 10th, 25th, 50th, 75th and 90th percentiles of these fetal measurements. All statistical analyses were performed using R version 4.0.0 or SAS version 9.4 (SAS Institute, Cary, NC).

Sensitivity analyses.

We performed two sets of sensitivity analyses to evaluate the robustness of results to modeling decisions. First, we examined models that only included exposure biomarkers measured before gestational week 18 (n = 801 participants). While a single spot urine sample may be less representative of an individual’s typical exposure, this approach allowed for assessing temporality of the exposure-outcome relationship. Second, we ran models excluding participants (n = 244, 28.5 %) diagnosed with gestational diabetes, preeclampsia or eclampsia during pregnancy, which are clinical complications that are known to affect fetal growth.

3. Results

Demographic characteristics of participants included in this study are presented in Table 1. Participants had a mean age of 32 years old at delivery with a mean pre-pregnancy BMI of 25 kg/m2, 89 % were married or partnered, 33 % had a high school education or less, and 50 % were nulliparous. The sample is multiethnic with 50 % of participants identifying as Hispanic; among non-Hispanic participants, 33 % identified as White, 6 % as Black, 8 % as Asian, and 3 % as other or multiple race. Participants included in the analytic sample did not vary from those excluded (Supplemental Table 1). Mean (SD) estimated fetal weight at 20, 30, and 36 weeks gestation in grams was 339.1 (8.6), 1556.5 (123.7), and 2814.5 (241.6), respectively. At each period, EFW significantly varied by sex, with males presenting as larger (20 weeks: 340.6 g, 30 weeks: 1578.5 g, 36 weeks: 2853.2 g) compared to females (20 weeks: 337.6 g, 30 weeks: 1535.0 g, 36 weeks: 2776.7 g; all p-values < 0.05). Table 2 presents detection frequencies, medians and IQRs for each bisphenol and phthalate group, as well as values for each individual component. A description of how EDCs vary by participant characteristics in this cohort has been previously published (Liu et al., 2022). Supplemental Figure 3 provides a heat map illustrating correlations between individual bisphenols and phthalate metabolites.

Table 1.

Characteristics of CHES study participants included in the analytic sample (n = 855). Values are N (%) or mean (SD).

Age (years) 31.8 (5.6)
Race/ethnicity
Hispanic 427 (49.9)
Non-Hispanic White 284 (33.2)
Non-Hispanic Black 50 (5.9)
Non-Hispanic Asian 70 (8.2)
Non-Hispanic other/multiple 24 (2.8)
Education
High school or less 283 (33.1)
Some college or Associate’s degree 140 (16.4)
Bachelor’s degree 200 (23.4)
Postgraduate degree 232 (27.1)
Parity
Nulliparous 429 (50.2)
Parous 426 (49.8)
Marital status
Married/living with a partner 764 (89.4)
Single 91 (10.6)
Pre-pregnancy BMI (kg/m2)a 24.8 (6.6)
Urine cotinine (ng/mL)a,b 0.01 (0.40)
Fetal sex
Male 422 (49.4)
Female 433 (50.6)
Gestational age at birth (weeks)a 39.4 (1.7)
Estimated fetal weight (grams)
20 weeks gestation 339.1 (8.6)
30 weeks gestation 1556.5 (123.7)
36 weeks gestation 2814.5 (241.6)
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Not all percentages add up to 100% due to rounding.

a

Values are median (interquartile range).

b

Maximum cotinine measurement during pregnancy.

Fig. 1 presents effect estimates and 95 % confidence intervals (CIs) at each output percentile for associations between each chemical grouping and EFW predicted at 20 and 36 weeks stratified by fetal sex. Results at 30 weeks were generally consistent and, for brevity, are presented in the supplemental material given the observed similarity (Supplemental Material Figure 4). Tables presenting effect estimates, 95 % CIs, and p-values adjusted for multiple comparisons, are also presented in the Supplemental Material for the sample overall (Supplemental Table 2), among females (Supplemental Table 3) and among males (Supplemental Table 4). At the median, we observed no statistically significant associations after accounting for multiple comparisons. Further, few statistically significant associations were consistently observed across chemicals, gestational time periods, percentiles, and sexes. However, an intriguing pattern emerged that was relatively constant across gestation, such that among females, exposure to ∑DiNP and ∑DnOP was generally associated with decreases in fetal weight among smaller babies (i.e., 10th percentile) and increases in fetal weight among larger babies (i.e., 90th percentile). Among males, the opposite trend was observed, such that fetal weight decreased with increasing exposure to these molar sums among larger babies and increased with increasing exposure among smaller babies. For example, at 36 weeks gestation a 1-SD increase in ∑DiNP phthalate exposure was associated with a 31.3 g decrease in EFW (95 % CI: −69.4, 6.8) at the 10th percentile of estimated fetal weight for females and a 10.3 g increase in EFW (95 % CI: −14.8, 35.3) at the 10th percentile for males. At the same gestational time period, a 1-SD increase in ∑DiNP phthalate exposure was associated with a 37.7 g increase in EFW (95 % CI: 3.0, 72.4) among females at the 90th percentile and a 24.0 g decrease in EFW (95 % CI: −60.4, 30.5) among males at the 90th percentile. However, we highlight that associations at the tails of the fetal weight distribution were imprecise with wide confidence intervals. With regard to models examining fetal biometry data, no single measurement (AC, HC, FL, or BPD) was identified as driving findings with EFW (see Supplemental Figures 5–13).

Fig. 1.

Fig. 1.

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Change in estimated fetal weight for a 1-SD increase in bisphenol and phthalate exposure at 20 and 36 weeks gestation stratified by fetal sex (n = 855). Estimates are provided at the 10th, 25th, 50th, 75th, and 90th percentiles of estimated fetal weight.

All exposures are corrected for creatinine, log-transformed, and averaged across pregnancy before z-scores were calculated. Full chemical names are provided in Table 2. Models adjusted for: maternal age, pre-pregnancy BMI, parity, cotinine, maternal education, maternal race/ethnicity, and marital status. Star (*) symbols indicate p-value ≤ 0.01 which was the threshold for significance after accounting for multiple comparison using the effective number of tests method.

Sensitivity analyses.

Results of models examining exposure bio-markers measured before gestational week 18 were generally similar to models including the average of up to three biomarker concentrations across pregnancy except that, among females, less of a trend was observed for bisphenols (Supplemental Figures 14–16). Results from models that excluded participants diagnosed with gestational diabetes, pre-eclampsia or eclampsia were not meaningfully different from models including the entire sample (Supplemental Figures 17–19).

4. Discussion

In this study, we used quantile regression to examine associations between bisphenol and phthalate exposure averaged across the course of pregnancy in relation to EFW during mid- to late pregnancy. When considering trends across percentiles, we observed that exposure to ∑DiNP and ∑DnOP molar sums was generally associated with reductions in fetal weight among small female fetuses and increases in weight among large females. Opposite trends were observed for males, although associations were imprecise at the tails of the distribution regardless of sex. Findings were consistent across the three gestational periods considered (20 weeks, 30 weeks, 36 weeks), suggesting that exposure-related disturbances to fetal growth may be occurring by or during the second trimester and are carried through until birth.

We identified 12 prior epidemiologic studies that have investigated associations between prenatal bisphenol and/or phthalate exposure and ultrasound measures of fetal growth. Most found that BPA was associated with reductions in growth, as determined by analysis of EFW or specific biometric measurements (Lee et al., 2018; Snijder et al., 2013; Zhou et al., 2020). Specifically, in cross-sectional analyses, the Korean-based MOCEH birth cohort (n = 788) found that increasing BPA levels were associated with decreased FL in the third trimester. In the Dutch Generation R cohort (n = 219), BPA measured repeatedly across pregnancy was associated with reduced EFW and HC, although associations were attenuated among participants with fewer repeated measures of exposure. A more recent study based in the same cohort (n = 1379) measured BPA, BPS and bisphenol-F (BPF) at three timepoints in pregnancy and examined associations with ultrasound-based measures of fetal growth during the second and third trimesters. The findings revealed a positive association between BPS and HC; however, the other bisphenols were not consistently associated with fetal size outcomes (Sol et al., 2021). A birth cohort study based in Wuhan, China (n = 322) examined cross sectional associations of the same three bisphenols in relation to measures of fetal size in late pregnancy and found increasing BPA, but not BPS or BPF, was associated with sex-specific changes in HC (Zhou et al., 2020). The Boston-based LIFECODES cohort (n = 476) detected no significant associations between BPS and ultrasound measures of fetal growth during late pregnancy (Ferguson et al., 2018).

With regard to phthalates, most studies have observed negative associations with fetal growth. In the French-based EDEN birth cohort (n = 520), 11 phthalate metabolites were measured in mid-pregnancy and examined in relation to ultrasound measures of fetal size assessed 2–3 times during pregnancy. HMW phthalates were negatively associated with EFW, however, the study notably included only male fetuses (Botton et al., 2016). In an analysis conducted in LIFECODES (n = 482), inverse associations were detected between DEHP averaged across four collection time points during pregnancy and measures of fetal size (EFW and biometry data) ascertained during the second half of pregnancy (Ferguson et al., 2016). Similarly, in Generation R (n = 1379), phthalate metabolites averaged across three time points in pregnancy were associated with lower EFW across gestation, with the strongest associations observed for later pregnancy (Santos et al., 2021). In a separate analysis of the Generation R cohort (n = 776), mixtures of bisphenols, phthalates and organophosphates were also associated with smaller fetal growth parameters (van den Dries et al., 2021). A study based in Wuhan, China (n = 814) measured DEHP metabolites at three time points in pregnancy in relation to repeated measures of fetal size and found first trimester DEHP was negatively associated with size (EFW, AC, HC, FL) among males (Li et al., 2021). Finally, a study based in the Spanish-based INMA-Sabadell cohort (n = 488) examined BPA and several phthalates averaged across two measurements during the 1st and 3rd trimesters in relation to fetal growth biometry data from at least two ultrasounds. No associations were detected for BPA or metabolites of DEHP, however, MBzP was positively associated with FL at 20–34 weeks and MnBP was negatively associated with HC at 12–20 weeks (Casas et al., 2016). Notably, none of these prior studies used quantile regression to investigate associations across the distribution of fetal weight, making specific comparisons with our findings difficult. Additionally, none of the 6 prior studies examining phthalates and fetal growth considered metabolites of DiNP, and only one considered metabolites of DnOP (Santos et al., 2021), both of which are increasingly used as alternative plasticizers in place of DEHP. We detected the strongest trends with these two metabolites, including significant associations among females at 20 weeks, supporting the need for future research on the reproductive developmental toxicity of these phthalates.

Multiple biologic mechanisms could plausibly underlie our findings. The placenta serves as the primary interface between the mother and fetus and is responsible for the production of hormones, transport of essential nutrients and gases, and removal of waste, among other functions critical to fetal growth and development. A number of in vitro and murine studies have examined the effects of exposure to bisphenols and/or phthalates on placental structure and function and collectively support that exposure to these EDCs may lead to suboptimal fetal growth through placenta-mediated pathways (Warner et al., 2021; Yang et al., 2019). For example, bisphenols and phthalates have both been directly linked with reduced placental growth (Barberio et al., 2021; Zong et al., 2015). Bisphenols have also been shown to inhibit proliferation and invasion of human trophoblast cells (Basak et al., 2018; Gingrich et al., 2018; Tait et al., 2015; Ye et al., 2019; Muller et al., 2018) and modulate receptivity and secretory functions in the decidua (Mannelli et al., 2015). Similarly, phthalates have been associated with altered methylation and expression of genes essential for fetal growth (Strakovsky and Schantz, 2018), disrupted placental thyroid signaling, which is involved in early growth regulation (Romano et al., 2018; Yu et al., 2018), and the inhibition of placental enzymes that protect the fetus from excess glucocorticoid exposure - a risk factor for fetal growth restriction (Zhao et al., 2010; Ma et al., 2011). Pro-inflammatory mechanisms and oxidative stress pathways could also be at play. Phthalates have been shown to alter the balance of fatty acids and induce oxidative stress (Biri et al., 2007; Ferguson et al., 2015; Hong et al., 2009; Tetz et al., 2013) and inflammation (Xu et al., 2008), including in the placenta (Tetz et al., 2013; Xu et al., 2005). In turn, oxidative stress and inflammation are linked to fetal growth restriction (Kamai et al., 2019; Biri et al., 2007).

Evidence also supports impaired placental angiogenesis as a relevant pathophysiological mechanism. Bisphenols and phthalates have been associated with biomarkers of placental angiogenesis and are hypothe-sized to act on the placental vasculature to disrupt uterine-placental blood flow resulting in poor fetal growth secondary to an inadequate nutrient supply (Basak et al., 2018; Ferguson et al., 2015; Conde-Agudelo et al., 2013). However, it is also mechanistically plausible that EDCs could play a protective role by promoting angiogenesis. It is well established that estrogen stimulates angiogenesis and acts in a vasodilatory fashion, as has been demonstrated in animal models in which injection of estrogen directly into the uterine arteries produces striking increases in blood flow (Albrecht and Pepe, 2010; Tal and Taylor, 2000). While not investigated experimentally to our knowledge, it is possible that bisphenols lead to an upregulation of angiogenesis by acting as weak estrogen receptor ligands. Moreover, phthalates are associated with altered steroidogenesis resulting in decreased levels of androgens and increased levels of estrogens (Kolatorova et al., 2018; Lee and Koo, 2007), which could further promote placental vascular remodeling. In turn, hypervascularization can lead to excessive nutrient exchange and macrosomia (Vambergue and Fajardy, 2011). Testosterone is also associated with other changes in the placenta that cause impaired fetal growth (Abbott et al., 2005; Carlsen et al., 2005), suggesting that the anti-androgenic effects of phthalates could contribute to an upregulation of fetal development. Both in vitro and murine research have also shown that BPA exposure during pregnancy upregulates placental GLUT-1 expression and activity, leading to an increase in glucose uptake, the primary energy substrate for the fetus (Benincasa et al., 2020). Taken together, these findings suggest that different mechanisms may be operating concurrently and may be exerting competing pressures on fetal growth that could contribute to the different exposure-outcome relationships we observed across sexes and the distribution of fetal weight. It is also notable that nearly all mechanistic research has been conducted using murine models or human cell lines. Future research focused on the effects of these exposures in placenta tissue collected from the general population will advance our understanding of the pathways through which EDCs disrupt pregnancy physiology in the community setting.

The findings presented here should be considered with regard to the study’s strengths and weaknesses. This is one of only a few studies that have examined repeated prenatal measures of bisphenol and phthalate exposure in relation to ultrasound measures of fetal growth at multiple gestational time points, and the only to examine exposure–response associations across the distribution of fetal weight. Our sample size was large and all ultrasounds were conducted at one of three affiliate hospitals by licensed sonographers. However, we acknowledge the potential for measurement error in ultrasound-based estimates of fetal biometric parameters (Wright et al., 2020; Dudley, 2005), which we anticipate would be non-differential with regard to exposure. Importantly, it should also be noted that formula-based estimates of fetal weight have been shown to have greater error among fetuses at the tails of the size spectrum, where we observed the strongest associations (Dudley, 2005). Additionally, all ultrasound data were collected from clinical prenatal care visits, rather than as part of the study protocol. As such, participants with a later gestation ultrasound may have been more likely to be older or have a co-morbidity that could affect fetal growth. In turn, our predicted values based on these later data points could be biased high or low, although the direction of any bias is unclear as co-morbidities may be a risk factor for both larger (e.g., gestational diabetes, obesity) or smaller (e.g., hypertension, pre-eclampsia) babies. We also note that the Hadlock formula that we used to estimate fetal weight was developed based on a small sample (n = 276) of healthy pregnancies from the mid-1980 s (Hadlock et al., 1985), which may not generalize to more diverse samples. Yet, despite the passage of time, small size, and limited diversity of the sample, a 2018 study of several thousand patients found that accuracy of the formula could not be further improved upon and that it performed well compared to 70 other models developed to predict EFW (Hammami et al., 2018). We highlight that 80 % of participants had at least one later (28–38 weeks) ultrasound and that the associations we detected during later pregnancy were consistent with those detected during early pregnancy. As with all human biomonitoring studies, there is a potential for exposure misclassification. This is especially relevant to studies of EDCs in which exposure is routine and episodic in nature, yet half-lives are short. We addressed this by collecting urine, the preferred matrix for measuring these chemicals, at three time points across gestation and averaging concentrations in order to arrive at the most representative measure of exposure within the constraints of our available data (Braun et al., 2012). However, this approach ignores temporality of the exposure-outcome relationship, and as such the causal association between EDC exposure and fetal growth cannot be established. However, we highlight that the observed trends at the last ultrasound time point (36 weeks gestation), which largely occurred after the last bisphenol and phthalate measurement, were similar to exposure-outcome trends observed earlier in gestation and that results of sensitivity analyses using early pregnancy exposure only were similar to overall results. We analyzed bisphenols and phthalate metabolites as molar sums that reflect parent compounds and shared use in consumer products. Yet, there remains a potential for co-pollutant confounding by the molar sum groupings, which displayed weak to moderate correlations with each other (Spearman’s rho ranging from: 0.14 to 0.31). Future directions should consider treating exposure to these EDCs as a mixture of chemicals that may have synergistic or cumulative health effects. Furthermore, maternal urinary concentrations of other environmental toxicants, including organophosphate pesticides and poly-cyclic aromatic hydrocarbons, that may affect fetal growth (Choi et al., 2006; Ferguson et al., 2019; Rauch et al., 2012) have been measured in this cohort or are currently being measured (Liu et al., 2021). Future analyses that consider the mixture of exposures across chemical groupings is an important next step as mixtures may better reflect an individual’s true exposure and associated health risks. Finally residual confounding due to unmeasured lifestyle factors, such as diet, could also be present.

In the prospective CHES pregnancy cohort, exposure to bisphenols and phthalates, especially ∑DiNP and ∑DnOP, was associated with sexspecific shifts in fetal growth, with the strongest associations observed among small and large babies. Few associations were observed at the median of fetal weight and few associations were statistically significant. These findings support the use of quantile regression and suggest that studies that estimate only mean fetal weight may miss important trends. While our findings are difficult to interpret clinically given that effect estimates are small and imprecise, we note that even incremental changes in fetal size have been associated with a range of health outcomes, with the greatest clinical significance at the ends of the fetal size continuum (Boulet et al., 2003; Chauhan et al., 2017; Zeve et al., 2016).

Supplementary Material

1

Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Whitney Cowell reports financial support was provided by National Institutes of Health. Linda Kahn reports financial support was provided by National Institutes of Health. Leonardo Trasande reports financial support was provided by National Institutes of Health].

Funding

NYU CHES is supported by institutional funds of NYU Grossman School of Medicine as well as the NIH Office of the Director (UG3/UH3OD023305). During preparation of this manuscript, WC was supported by R00ES032029 and LGK was supported by R00ES030403.

Footnotes

CRediT authorship contribution statement

Whitney Cowell: Conceptualization, Writing – original draft, Visualization, Supervision. Melanie H. Jacobson: Conceptualization, Writing – original draft, Data curation. Sara E. Long: Conceptualization, Methodology, Formal analysis, Data curation, Writing – review & editing. Yuyan Wang: Data curation, Methodology, Formal analysis, Writing – review & editing. Linda G. Kahn: Conceptualization, Writing – review & editing. Akhgar Ghassabian: Conceptualization, Writing – review & editing. Mrudula Naidu: Conceptualization, Writing – review & editing. Ghazaleh Doostparast Torshizi: Conceptualization, Writing – review & editing. Yelena Afanasyeva: Data curation. Mengling Liu: Supervision, Writing – review & editing. Shilpi S. Mehta-Lee: Supervision, Writing – review & editing. Sara G. Brubaker: Supervision, Writing – review & editing. Kurunthachalam Kannan: Resources, Data curation, Writing – review & editing. Leonardo Trasande: Supervision, Funding acquisition, Project administration, Writing – review & editing.

Appendix A. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envint.2023.107922.

Data availability

The data that has been used is confidential.

References

  1. Abbott DH, Barnett DK, Bruns CM, Dumesic DA, 2005. Androgen excess fetal programming of female reproduction: a developmental aetiology for polycystic ovary syndrome? Hum. Reprod. Update Jul-Aug 11 (4), 357–374. 10.1093/humupd/dmi013. [DOI] [PubMed] [Google Scholar]
  2. Albrecht ED, Pepe GJ, 2010. Estrogen regulation of placental angiogenesis and fetal ovarian development during primate pregnancy. Int. J. Dev. Biol 54 (2–3), 397–408. 10.1387/ijdb.082758ea. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Albrecht ED, Babischkin JS, Aberdeen GW, Burch MG, Pepe GJ, 2021. Maternal systemic vascular dysfunction in a primate model of defective uterine spiral artery remodeling. Am. J. Phys. Heart Circ. Phys 320 (4), H1712–H1723. 10.1152/ajpheart.00613.2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bang du Y, Lee IK, Lee BM, Dec 2011. Toxicological characterization of phthalic Acid. Toxicol. Res 27 (4), 191–203. 10.5487/TR.2011.27.4.191. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Barberio L, Paulesu L, Canesi L, Grasselli E, Mandala M. Bisphenol a Interferes with Uterine Artery Features and Impairs Rat Feto-Placental Growth. Int J Mol Sci Jun 27 2021;22(13)doi: 10.3390/ijms22136912. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Basak S, Srinivas V, Duttaroy AK, Dec 2018. Bisphenol-A impairs cellular function and alters DNA methylation of stress pathway genes in first trimester trophoblast cells. Reprod. Toxicol 82, 72–79. 10.1016/j.reprotox.2018.10.009. [DOI] [PubMed] [Google Scholar]
  7. Benincasa L, Mandala M, Paulesu L, Barberio L, Ietta F. Prenatal Nutrition Containing Bisphenol A Affects Placenta Glucose Transfer: Evidence in Rats and Human Trophoblast. Nutrients. May 11 2020;12(5)doi: 10.3390/nu12051375. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Biri A, Bozkurt N, Turp A, Kavutcu M, Himmetoglu O, Durak I, 2007. Role of oxidative stress in intrauterine growth restriction. Gynecol. Obstet. Invest 64 (4), 187–192. 10.1159/000106488. [DOI] [PubMed] [Google Scholar]
  9. Boeniger MF, Lowry LK, Rosenberg J, Oct 1993. Interpretation of urine results used to assess chemical exposure with emphasis on creatinine adjustments: a review. Am. Ind. Hyg. Assoc. J 54 (10), 615–627. 10.1080/15298669391355134. [DOI] [PubMed] [Google Scholar]
  10. Botton J, Philippat C, Calafat AM, et al. , Nov 2016. Phthalate pregnancy exposure and male offspring growth from the intra-uterine period to five years of age. Environ. Res 151, 601–609. 10.1016/j.envres.2016.08.033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Boulet SL, Alexander GR, Salihu HM, Pass M, May 2003. Macrosomic births in the united states: determinants, outcomes, and proposed grades of risk. Am. J. Obstet. Gynecol 188 (5), 1372–1378. 10.1067/mob.2003.302. [DOI] [PubMed] [Google Scholar]
  12. Braun JM, Smith KW, Williams PL, et al. , May 2012. Variability of urinary phthalate metabolite and bisphenol A concentrations before and during pregnancy. Environ. Health Perspect 120 (5), 739–745. 10.1289/ehp.1104139. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Calafat AM, Ye X, Wong LY, Reidy JA, Needham LL, Jan 2008. Exposure of the U.S. population to bisphenol A and 4-tertiary-octylphenol: 2003–2004. Environ. Health Perspect 116 (1), 39–44. 10.1289/ehp.10753. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Carlsen SM, Romundstad P, Jacobsen G, Feb 2005. Early second-trimester maternal hyperandrogenemia and subsequent preeclampsia: a prospective study. Acta Obstet. Gynecol. Scand 84 (2), 117–121. 10.1111/j.0001-6349.2005.00493.x. [DOI] [PubMed] [Google Scholar]
  15. Casas M, Valvi D, Ballesteros-Gomez A, et al. , Apr 2016. Exposure to Bisphenol A and Phthalates during Pregnancy and Ultrasound Measures of Fetal Growth in the INMA-Sabadell Cohort. Environ. Health Perspect 124 (4), 521–528. 10.1289/ehp.1409190. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Chan M, Mita C, Bellavia A, Parker M, James-Todd T, Jun 2021. Racial/ethnic disparities in pregnancy and prenatal exposure to endocrine-disrupting chemicals commonly used in personal care products. Curr. Environ. Health Rep 8 (2), 98–112. 10.1007/s40572-021-00317-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Chatuphonprasert W, Jarukamjorn K, Ellinger I, 2018. Physiology and Pathophysiology of Steroid Biosynthesis, Transport and Metabolism in the Human Placenta. Front. Pharmacol 9, 1027. 10.3389/fphar.2018.01027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Chauhan SP, Rice MM, Grobman WA, et al. , Sep 2017. Neonatal Morbidity of Small- and Large-for-Gestational-Age Neonates Born at Term in Uncomplicated Pregnancies. Obstet. Gynecol 130 (3), 511–519. 10.1097/AOG.0000000000002199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Choi H, Jedrychowski W, Spengler J, et al. , Nov 2006. International studies of prenatal exposure to polycyclic aromatic hydrocarbons and fetal growth. Environ. Health Perspect 114 (11), 1744–1750. 10.1289/ehp.8982. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Committee opinion No. 700: Methods for estimating the due date. Obstet Gynecol. 2017; 129 5 e150–e154. [DOI] [PubMed] [Google Scholar]
  21. Conde-Agudelo A, Papageorghiou AT, Kennedy SH, Villar J, May 2013. Novel biomarkers for predicting intrauterine growth restriction: a systematic review and meta-analysis. BJOG 120 (6), 681–694. 10.1111/1471-0528.12172. [DOI] [PubMed] [Google Scholar]
  22. Dominguez TP, Dunkel-Schetter C, Glynn LM, Hobel C, Sandman CA, Mar 2008. Racial differences in birth outcomes: the role of general, pregnancy, and racism stress. Health Psychol. 27 (2), 194–203. 10.1037/0278-6133.27.2.194. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Dudley NJ, Jan 2005. A systematic review of the ultrasound estimation of fetal weight. Ultrasound Obstet. Gynecol 25 (1), 80–89. 10.1002/uog.1751. [DOI] [PubMed] [Google Scholar]
  24. Dutta S, Haggerty DK, Rappolee DA, Ruden DM, 2020. Phthalate exposure and long-term epigenomic consequences: a review. Front. Genet 11, 405. 10.3389/fgene.2020.00405. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Ferguson KK, McElrath TF, Cantonwine DE, Mukherjee B, Meeker JD, Jun 2015. Phthalate metabolites and bisphenol-A in association with circulating angiogenic biomarkers across pregnancy. Placenta 36 (6), 699–703. 10.1016/j.placenta.2015.04.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Ferguson KK, McElrath TF, Chen YH, Mukherjee B, Meeker JD, Mar 2015. Urinary phthalate metabolites and biomarkers of oxidative stress in pregnant women: a repeated measures analysis. Environ. Health Perspect 123 (3), 210–216. 10.1289/ehp.1307996. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Ferguson KK, Meeker JD, Cantonwine DE, Chen YH, Mukherjee B, McElrath TF, Sep 2016. Urinary phthalate metabolite and bisphenol A associations with ultrasound and delivery indices of fetal growth. Environ. Int 94, 531–537. 10.1016/j.envint.2016.06.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Ferguson KK, Meeker JD, Cantonwine DE, et al. , Mar 2018. Environmental phenol associations with ultrasound and delivery measures of fetal growth. Environ. Int 112, 243–250. 10.1016/j.envint.2017.12.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Ferguson KK, van den Dries MA, Gaillard R, et al. , Aug 2019. Organophosphate pesticide exposure in pregnancy in association with ultrasound and delivery measures of fetal growth. Environ. Health Perspect 127 (8), 87005. 10.1289/EHP4858. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Ferguson KK, Lan Z, Yu Y, Mukherjee B, McElrath TF, Meeker JD, Oct 2019. Urinary concentrations of phenols in association with biomarkers of oxidative stress in pregnancy: assessment of effects independent of phthalates. Environ. Int 131, 104903 10.1016/j.envint.2019.104903. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Gaston SA, Birnbaum LS, Jackson CL, Mar 2020. Synthetic chemicals and cardiometabolic health across the life course among vulnerable populations: a Review of the Literature from 2018 to 2019. Curr. Environ. Health Rep 7 (1), 30–47. 10.1007/s40572-020-00265-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Gaylord A, Kannan K, Lakuleswaran M, et al. , Sep 15 2022. Variability and correlations of synthetic chemicals in urine from a New York City-based cohort of pregnant women. Environ. Pollut 309, 119774 10.1016/j.envpol.2022.119774. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Gely CA, Lacroix MZ, Morin M, Vayssiere C, Gayrard V, Picard-Hagen N, Aug 2021. Comparison of the materno-fetal transfer of fifteen structurally related bisphenol analogues using an ex vivo human placental perfusion model. Chemosphere 276, 130213. 10.1016/j.chemosphere.2021.130213. [DOI] [PubMed] [Google Scholar]
  34. Gingrich J, Pu Y, Roberts J, et al. , May 2018. Gestational bisphenol S impairs placental endocrine function and the fusogenic trophoblast signaling pathway. Arch. Toxicol 92 (5), 1861–1876. 10.1007/s00204-018-2191-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Gingrich J, Ticiani E, Veiga-Lopez A, Jul 2020. Placenta disrupted: endocrine disrupting chemicals and pregnancy. Trends Endocrinol. Metab 31 (7), 508–524. 10.1016/j.tem.2020.03.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Gluckman PD, Hanson MA, 2004. Living with the past: evolution, development, and patterns of disease. Science 305 (5691), 1733–1736. 10.1126/science.1095292. [DOI] [PubMed] [Google Scholar]
  37. Golestanzadeh M, Riahi R, Kelishadi R, Dec 2019. Association of exposure to phthalates with cardiometabolic risk factors in children and adolescents: a systematic review and meta-analysis. Environ. Sci. Pollut. Res. Int 26 (35), 35670–35686. 10.1007/s11356-019-06589-7. [DOI] [PubMed] [Google Scholar]
  38. Hadlock FP, Harrist RB, Sharman RS, Deter RL, Park SK, 1985. Estimation of fetal weight with the use of head, body, and femur measurements–a prospective study. Am. J. Obstet. Gynecol 151 (3), 333–337. 10.1016/0002-9378(85)90298-4. [DOI] [PubMed] [Google Scholar]
  39. Hales CN, Barker DJ, Jul 1992. Type 2 (non-insulin-dependent) diabetes mellitus: the thrifty phenotype hypothesis. Diabetologia 35 (7), 595–601. 10.1007/BF00400248. [DOI] [PubMed] [Google Scholar]
  40. Hammami A, Mazer Zumaeta A, Syngelaki A, Akolekar R, Nicolaides KH, Jul 2018. Ultrasonographic estimation of fetal weight: development of new model and assessment of performance of previous models. Ultrasound Obstet. Gynecol 52 (1), 35–43. 10.1002/uog.19066. [DOI] [PubMed] [Google Scholar]
  41. Harrild DM, Marx GR, Jan 2017. Fetal growth restriction and long-term cardiovascular risk: is it time to get with the program? Circ. Cardiovasc. Imaging 10 (1). 10.1161/CIRCIMAGING.116.005731. [DOI] [PubMed] [Google Scholar]
  42. Honda M, Robinson M, Kannan K, 2018. A rapid method for the analysis of perfluorinated alkyl substances in serum by hybrid solid-phase extraction. Environ. Chem 15 (2), 92–99. [Google Scholar]
  43. Hong YC, Park EY, Park MS, et al. , 2009. Community level exposure to chemicals and oxidative stress in adult population. Toxicol. Lett 184 (2), 139–144. 10.1016/j.toxlet.2008.11.001. [DOI] [PubMed] [Google Scholar]
  44. Hornung R, Reed L, 1989. Estimation of average concentration in the presence of nondetectable values. Appl. Occup. Environ. Hyg 5 (1), 46–51. [Google Scholar]
  45. Hu CY, Li FL, Hua XG, Jiang W, Mao C, Zhang XJ, Aug 2018. The association between prenatal bisphenol A exposure and birth weight: a meta-analysis. Reprod. Toxicol 79, 21–31. 10.1016/j.reprotox.2018.04.013. [DOI] [PubMed] [Google Scholar]
  46. Kamai EM, McElrath TF, Ferguson KK, May 8 2019. Fetal growth in environmental epidemiology: mechanisms, limitations, and a review of associations with biomarkers of non-persistent chemical exposures during pregnancy. Environ. Health 18 (1), 43. 10.1186/s12940-019-0480-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Katsikantami I, Tzatzarakis MN, Alegakis AK, et al. , 2020. Phthalate metabolites concentrations in amniotic fluid and maternal urine: cumulative exposure and risk assessment. Toxicol. Rep 7, 529–538. 10.1016/j.toxrep.2020.04.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Kolatorova L, Vitku J, Vavrous A, et al. , 2018. Phthalate metabolites in maternal and cord plasma and their relations to other selected endocrine disruptors and steroids. Physiol. Res 67 (Suppl 3), S473–S487. 10.33549/physiolres.933962. [DOI] [PubMed] [Google Scholar]
  49. Kuiper JR, O’Brien KM, Ferguson KK, Buckley JP, Nov 2021. Urinary specific gravity measures in the U.S. population: implications for the adjustment of nonpersistent chemical urinary biomarker data. Environ. Int 156, 106656 10.1016/j.envint.2021.106656. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Larrabee Sonderlund A, Schoenthaler A, Thilsing T. The Association between Maternal Experiences of Interpersonal Discrimination and Adverse Birth Outcomes: a Systematic Review of the Evidence. Int J Environ Res Public Health. Feb 4 2021;18(4) doi: 10.3390/ijerph18041465. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Lee YM, Hong YC, Ha M, et al. , 2018. Prenatal Bisphenol-A exposure affects fetal length growth by maternal glutathione transferase polymorphisms, and neonatal exposure affects child volume growth by sex: from multiregional prospective birth cohort MOCEH study. Sci. Total Environ 612, 1433–1441. 10.1016/j.scitotenv.2017.08.317. [DOI] [PubMed] [Google Scholar]
  52. Lee BM, Koo HJ, Aug 2007. Hershberger assay for antiandrogenic effects of phthalates. J. Toxic. Environ. Health A 70 (15–16), 1365–1370. 10.1080/15287390701432285. [DOI] [PubMed] [Google Scholar]
  53. Li J, Qian X, Zhou Y, et al. , Jul 10 2021. Trimester-specific and sex-specific effects of prenatal exposure to di(2-ethylhexyl) phthalate on fetal growth, birth size, and early-childhood growth: A longitudinal prospective cohort study. Sci. Total Environ 777, 146146 10.1016/j.scitotenv.2021.146146. [DOI] [PubMed] [Google Scholar]
  54. Li MX, Yeung JM, Cherny SS, Sham PC, May 2012. Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Hum. Genet 131 (5), 747–756. 10.1007/s00439-011-1118-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Liu H, Campana AM, Wang Y, et al. Organophosphate pesticide exposure: Demographic and dietary predictors in an urban pregnancy cohort. Environ Pollut. Aug 15 2021; 283:116920. doi: 10.1016/j.envpol.2021.116920. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Liu H, Wang Y, Kannan K, et al. , Sep 2022. Determinants of phthalate exposures in pregnant women in New York City. Environ. Res 212 (Pt A), 113203 10.1016/j.envres.2022.113203. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Ma X, Lian QQ, Dong Q, Ge RS, 2011. Environmental inhibitors of 11beta-hydroxysteroid dehydrogenase type 2. Toxicology 285 (3), 83–89. 10.1016/j.tox.2011.04.007. [DOI] [PubMed] [Google Scholar]
  58. Malhotra A, Miller SL, Jenkin G, et al. , Jul 2019. Fetal growth restriction is associated with an altered cardiopulmonary and cerebral hemodynamic response to surfactant therapy in preterm lambs. Pediatr. Res 86 (1), 47–54. 10.1038/s41390-019-0398-4. [DOI] [PubMed] [Google Scholar]
  59. Mannelli C, Szostek AZ, Lukasik K, et al. , Aug 2015. Bisphenol A modulates receptivity and secretory function of human decidual cells: an in vitro study. Reproduction 150 (2), 115–125. 10.1530/REP-14-0601. [DOI] [PubMed] [Google Scholar]
  60. Marie C, Vendittelli F, Sauvant-Rochat MP, Oct 2015. Obstetrical outcomes and biomarkers to assess exposure to phthalates: a review. Environ. Int 83, 116–136. 10.1016/j.envint.2015.06.003. [DOI] [PubMed] [Google Scholar]
  61. Martinez MP, Kannan K, 2018. Simultaneous analysis of seven biomarkers of oxidative damage to lipids, proteins, and DNA in Urine. Environ. Sci. Tech 52 (11), 6647–6655. 10.1021/acs.est.8b00883. [DOI] [PubMed] [Google Scholar]
  62. Mayer C, Joseph KS, Feb 2013. Fetal growth: a review of terms, concepts and issues relevant to obstetrics. Ultrasound Obstet. Gynecol 41 (2), 136–145. 10.1002/uog.11204. [DOI] [PubMed] [Google Scholar]
  63. McIntire DD, Bloom SL, Casey BM, Leveno KJ, 1999. Birth weight in relation to morbidity and mortality among newborn infants. N. Engl. J. Med 340 (16), 1234–1238. 10.1056/NEJM199904223401603. [DOI] [PubMed] [Google Scholar]
  64. Metcalfe CD, Bayen S, Desrosiers M, Munoz G, Sauve S, Yargeau V, May 1 2022. An introduction to the sources, fate, occurrence and effects of endocrine disrupting chemicals released into the environment. Environ. Res 207, 112658 10.1016/j.envres.2021.112658. [DOI] [PubMed] [Google Scholar]
  65. Miller SL, Huppi PS, Mallard C, 2016. The consequences of fetal growth restriction on brain structure and neurodevelopmental outcome. J. Physiol 594 (4), 807–823. 10.1113/JP271402. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Mose T, Mortensen GK, Hedegaard M, Knudsen LE, Jan 2007. Phthalate monoesters in perfusate from a dual placenta perfusion system, the placenta tissue and umbilical cord blood. Reprod. Toxicol 23 (1), 83–91. 10.1016/j.reprotox.2006.08.006. [DOI] [PubMed] [Google Scholar]
  67. Muller JE, Meyer N, Santamaria CG, et al. Bisphenol A exposure during early pregnancy impairs uterine spiral artery remodeling and provokes intrauterine growth restriction in mice. Sci Rep. Jun 15 2018;8(1):9196. doi: 10.1038/s41598-018-27575-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Pacyga DC, Sathyanarayana S, Strakovsky RS, 2019. Dietary Predictors of Phthalate and Bisphenol Exposures in Pregnant Women. Adv. Nutr 10 (5), 803–815. 10.1093/advances/nmz029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Pasqualini JR, Chetrite GS, 2016. The formation and transformation of hormones in maternal, placental and fetal compartments: biological implications. Horm. Mol. Biol. Clin. Investig 27 (1), 11–28. 10.1515/hmbci-2016-0036. [DOI] [PubMed] [Google Scholar]
  70. Pelch K, Wignall JA, Goldstone AE, et al. , Aug 1 2019. A scoping review of the health and toxicological activity of bisphenol A (BPA) structural analogues and functional alternatives. Toxicology 424, 152235. 10.1016/j.tox.2019.06.006. [DOI] [PubMed] [Google Scholar]
  71. Pergialiotis V, Kotrogianni P, Christopoulos-Timogiannakis E, Koutaki D, Daskalakis G, Papantoniou N, Dec 2018. Bisphenol A and adverse pregnancy outcomes: a systematic review of the literature. J. Matern. Fetal Neonatal Med 31 (24), 3320–3327. 10.1080/14767058.2017.1368076. [DOI] [PubMed] [Google Scholar]
  72. Phthalates NRCUCotHRo, 2008. Phthalates and Cumulative Risk Assessment: The Tasks Ahead. National Research Council (US) Committee on the Health Risks of Phthalates. Washington (DC): National Academies Press; (US: ). [PubMed] [Google Scholar]
  73. Raio L, Ghezzi F, Di Naro E, et al. , 2003. Perinatal outcome of fetuses with a birth weight greater than 4500 g: an analysis of 3356 cases. Eur. J. Obstet. Gynecol. Reprod. Biol 109 (2), 160–165. 10.1016/s0301-2115(03)00045-9. [DOI] [PubMed] [Google Scholar]
  74. Rauch SA, Braun JM, Barr DB, et al. , Jul 2012. Associations of prenatal exposure to organophosphate pesticide metabolites with gestational age and birth weight. Environ. Health Perspect 120 (7), 1055–1060. 10.1289/ehp.1104615. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Robinson R, 2001. The fetal origins of adult disease. BMJ 322 (7283), 375–376. 10.1136/bmj.322.7283.375. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Romano ME, Eliot MN, Zoeller RT, et al. , May 2018. Maternal urinary phthalate metabolites during pregnancy and thyroid hormone concentrations in maternal and cord sera: The HOME Study. Int. J. Hyg. Environ. Health 221 (4), 623–631. 10.1016/j.ijheh.2018.03.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Rosenfeld CS, Oct 2015. Sex-Specific Placental Responses in Fetal Development. Endocrinology 156 (10), 3422–3434. 10.1210/en.2015-1227. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Santos S, Sol CM, van Zwol-Janssens C, et al. Maternal phthalate urine concentrations, fetal growth and adverse birth outcomes. A population-based prospective cohort study. Environ Int. Jun 2021;151:106443. doi: 10.1016/j.envint.2021.106443. [DOI] [PubMed] [Google Scholar]
  79. Schock H, Zeleniuch-Jacquotte A, Lundin E, et al. , 2016. Hormone concentrations throughout uncomplicated pregnancies: a longitudinal study. BMC Pregnancy Childbirth 16 (1), 146. 10.1186/s12884-016-0937-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Snijder CA, Heederik D, Pierik FH, et al. , Mar 2013. Fetal growth and prenatal exposure to bisphenol A: the generation R study. Environ. Health Perspect 121 (3), 393–398. 10.1289/ehp.1205296. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Sol CM, van Zwol-Janssens C, Philips EM, et al. , May 15 2021. Maternal bisphenol urine concentrations, fetal growth and adverse birth outcomes: A population-based prospective cohort. Environ. Health 20 (1), 60. 10.1186/s12940-021-00747-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Stevens DR, Bommarito PA, Keil AP, et al. , May 2022. Urinary phthalate metabolite mixtures in pregnancy and fetal growth: Findings from the infant development and the environment study. Environ. Int 163, 107235 10.1016/j.envint.2022.107235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Strakovsky RS, Schantz SL, Jul 2018. Impacts of bisphenol A (BPA) and phthalate exposures on epigenetic outcomes in the human placenta. Environ. Epigenet 4(3): dvy022 10.1093/eep/dvy022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Tait S, Tassinari R, Maranghi F, Mantovani A, Nov 2015. Bisphenol A affects placental layers morphology and angiogenesis during early pregnancy phase in mice. J. Appl. Toxicol 35 (11), 1278–1291. 10.1002/jat.3176. [DOI] [PubMed] [Google Scholar]
  85. Tal R, Taylor HS, 2000. Endocrinology of Pregnancy. 2021 Mar 18. In: Endotext [Internet]. South Dartmouth (MA): MDText.com, Inc. [Google Scholar]
  86. Tetz LM, Cheng AA, Korte CS, et al. , 2013. Mono-2-ethylhexyl phthalate induces oxidative stress responses in human placental cells in vitro. Toxicol. Appl. Pharmacol 268 (1), 47–54. 10.1016/j.taap.2013.01.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Trasande L, Ghassabian A, Kahn LG, et al. , Mar 2020. The NYU Children’s Health and Environment Study. Eur. J. Epidemiol 35 (3), 305–320. 10.1007/s10654-020-00623-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Ultrasound in pregnancy. Practice Bulletin No. 175. Obstet Gynecol. 2016;128:e241–56. [DOI] [PubMed] [Google Scholar]
  89. Vambergue A, Fajardy I, 2011. Consequences of gestational and pregestational diabetes on placental function and birth weight. World J. Diabetes 2 (11), 196–203. 10.4239/wjd.v2.i11.196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. van den Dries MA, Keil AP, Tiemeier H, et al. , Nov 2021. Prenatal exposure to nonpersistent chemical mixtures and fetal growth: a population-based study. Environ. Health Perspect 129 (11), 117008 10.1289/EHP9178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. van T Erve TJ, Rosen EM, Barrett ES, et al. , 2019. Phthalates and Phthalate Alternatives Have Diverse Associations with Oxidative Stress and Inflammation in Pregnant Women. Environ Sci Technol 53 (6), 3258–3267. 10.1021/acs.est.8b05729. [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Vrachnis N, Loukas N, Vrachnis D, et al. , 2021. A Systematic Review of Bisphenol A from Dietary and Non-Dietary Sources during Pregnancy and Its Possible Connection with Fetal Growth Restriction: Investigating Its Potential Effects and the Window of Fetal Vulnerability. Nutrients 13 (7). 10.3390/nu13072426. [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Wang Y, Zhu H, Kannan K. A Review of Biomonitoring of Phthalate Exposures. Toxics. Apr 5 2019;7(2)doi: 10.3390/toxics7020021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Warner GR, Dettogni RS, Bagchi IC, Flaws JA, Graceli JB, Aug 2021. Placental outcomes of phthalate exposure. Reprod. Toxicol 103, 1–17. 10.1016/j.reprotox.2021.05.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Wright D, Wright A, Smith E, Nicolaides KH, Feb 2020. Impact of biometric measurement error on identification of small- and large-for-gestational-age fetuses. Ultrasound Obstet. Gynecol 55 (2), 170–176. 10.1002/uog.21909. [DOI] [PMC free article] [PubMed] [Google Scholar]
  96. Xu Y, Cook TJ, Knipp GT, Apr 2005. Effects of di-(2-ethylhexyl)-phthalate (DEHP) and its metabolites on fatty acid homeostasis regulating proteins in rat placental HRP-1 trophoblast cells. Toxicol. Sci 84 (2), 287–300. 10.1093/toxsci/kfi083. [DOI] [PubMed] [Google Scholar]
  97. Xu Y, Agrawal S, Cook TJ, Knipp GT, Nov 2008. Maternal di-(2-ethylhexyl)-phthalate exposure influences essential fatty acid homeostasis in rat placenta. Placenta 29 (11), 962–969. 10.1016/j.placenta.2008.08.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  98. Yaghjyan L, Ghita GL, Dumont-Driscoll M, Yost RA, Chang SH, Oct 2016. Maternal exposure to di-2-ethylhexylphthalate and adverse delivery outcomes: a systematic review. Reprod. Toxicol 65, 76–86. 10.1016/j.reprotox.2016.07.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  99. Yang C, Song G, Lim W, Sep 2019. A mechanism for the effect of endocrine disrupting chemicals on placentation. Chemosphere 231, 326–336. 10.1016/j.chemosphere.2019.05.133. [DOI] [PubMed] [Google Scholar]
  100. Ye Y, Tang Y, Xiong Y, Feng L, Li X, Feb 2019. Bisphenol A exposure alters placentation and causes preeclampsia-like features in pregnant mice involved in reprogramming of DNA methylation of WNT2. FASEB J. 33 (2), 2732–2742. 10.1096/fj.201800934RRR. [DOI] [PMC free article] [PubMed] [Google Scholar]
  101. Yu Z, Han Y, Shen R, et al. , 2018. Gestational di-(2-ethylhexyl) phthalate exposure causes fetal intrauterine growth restriction through disturbing placental thyroid hormone receptor signaling. Toxicol. Lett 294, 1–10. 10.1016/j.toxlet.2018.05.013. [DOI] [PubMed] [Google Scholar]
  102. Zarean M, Keikha M, Poursafa P, Khalighinejad P, Amin M, Kelishadi R, Dec 2016. A systematic review on the adverse health effects of di-2-ethylhexyl phthalate. Environ. Sci. Pollut. Res. Int 23 (24), 24642–24693. 10.1007/s11356-016-7648-3. [DOI] [PubMed] [Google Scholar]
  103. Zbucka-Kretowska M, Lazarek U, Miltyk W, et al. , 2019. Simultaneous analysis of bisphenol A fractions in maternal and fetal compartments in early second trimester of pregnancy. J. Perinat. Med 47 (7), 765–770. 10.1515/jpm-2019-0040. [DOI] [PubMed] [Google Scholar]
  104. Zeve D, Regelmann MO, Holzman IR, Rapaport R, 2016. Small at Birth, but How Small? The Definition of SGA Revisited. Horm. Res. Paediatr 86 (5), 357–360. 10.1159/000449275. [DOI] [PubMed] [Google Scholar]
  105. Zhao B, Chu Y, Huang Y, Hardy DO, Lin S, Ge RS, 2010. Structure-dependent inhibition of human and rat 11beta-hydroxysteroid dehydrogenase 2 activities by phthalates. Chem. Biol. Interact 183 (1), 79–84. 10.1016/j.cbi.2009.09.014. [DOI] [PubMed] [Google Scholar]
  106. Zhong Q, Peng M, He J, Yang W, Huang F. Association of prenatal exposure to phenols and parabens with birth size: A systematic review and meta-analysis. Sci Total Environ. Feb 10 2020;703:134720. doi: 10.1016/j.scitotenv.2019.134720. [DOI] [PubMed] [Google Scholar]
  107. Zhou Z, Lei Y, Wei W, et al. , Nov 2019. Association between prenatal exposure to bisphenol a and birth outcomes: a systematic review with meta-analysis. Medicine (Baltimore) 98 (44). [DOI] [PMC free article] [PubMed] [Google Scholar]
  108. Zhou B, Yang P, Deng YL, Zeng Q, Lu WQ, Mei SR, May 2020. Prenatal exposure to bisphenol a and its analogues (bisphenol F and S) and ultrasound parameters of fetal growth. Chemosphere 246, 125805. 10.1016/j.chemosphere.2019.125805. [DOI] [PubMed] [Google Scholar]
  109. Zong T, Lai L, Hu J, et al. , 2015. Maternal exposure to di-(2-ethylhexyl) phthalate disrupts placental growth and development in pregnant mice. J. Hazard. Mater 297, 25–33. 10.1016/j.jhazmat.2015.04.065. [DOI] [PubMed] [Google Scholar]

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