Skip to main content
Open access
Research
9 November 2022

Phenol and Phthalate Effects on Thyroid Hormone Levels during Pregnancy: Relying on In Vitro Assays and Adverse Outcome Pathways to Inform an Epidemiological Analysis

Publication: Environmental Health Perspectives
Volume 130, Issue 11
CID: 117004

Abstract

Background:

Studies characterizing associations between phenols, phthalates and thyroid hormones during pregnancy produce inconsistent results. This divergence may be partly attributable to false positives due to multiple comparison testing of large numbers of chemicals, and measurement error as studies rely on small numbers of biospecimens despite high intra-individual variability in urinary chemical metabolite concentrations.

Objectives:

This study employs a priori chemical filtering and expanded urinary biomonitoring to evaluate associations between phenol/phthalate exposures and serum thyroid hormones assessed during pregnancy.

Methods:

A two-tiered approach was implemented: a) In vitro high-throughput screening results from the ToxCast/Tox21 database, as informed by a thyroid Adverse Outcome Pathway network, were evaluated to select phenols/phthalates with activity on known and putative molecular initiating events in the thyroid pathway; and b) Adjusted linear regressions were used to study associations between filtered compounds and serum thyroid hormones measured in 437 pregnant women recruited in Grenoble area (France) between 2014 and 2017. Phenol/phthalate metabolites were measured in repeated spot urine sample pools (median: 21 samples/women).

Results:

The ToxCast/Tox21 screening reduced the chemical set from 16 to 13 and the associated number of statistical comparisons by 19%. Parabens were negatively associated with free triiodothyronine (T3) and the T3/T4 (total thyroxine) ratio. Monobenzyl phthalate was positively associated with total T4 and negatively with the T3/T4 ratio. Effect modification by iodine status was detected for several compounds (among them ΣDEHP and mono-n-butyl phthalate) that were associated with some hormones among women with normal iodine levels.

Conclusion:

For these chemicals, screening for compounds with an increased likelihood for thyroid-related effects and relying on repeated urine samples to assess exposures improved the overall performance of multichemical analyses of thyroid disruption. This approach may improve future evaluations of human data for the thyroid pathway with implication for fetal health and may serve as a model for evaluating other toxicity outcomes. https://doi.org/10.1289/EHP10239

Introduction

During pregnancy, euthyroidism is crucial for normal fetal growth and development.1 Even subtle alterations of thyroid hormone homeostasis can negatively impact the growing fetus and postnatal health.2 Aside from iodine deficiency and preexisting thyroid diseases, exposure to environmental contaminants, specifically endocrine disruptors such as synthetic phenols and phthalates, are suspected to contribute to thyroid hormone dysregulation.3,4 In vivo and in vitro data suggest that these compounds may disrupt thyroid hormone signaling by perturbing hormone biosynthesis, metabolic activation/inactivation, and associated negative feedbacks with the central hypothalamic-pituitary-thyroid (HPT) axis (as reviewed by Bruker-Davis,5 Murk et al.,6 Noyes et al.,7 and Zoeller8).
Several epidemiological studies have explored associations between exposures to phenols and phthalates and thyroid hormone homeostasis during pregnancy. However, drawing conclusions from these studies is not straightforward, because results often differ. For example, urinary DEHP metabolites were shown to be associated with decreased thyroid stimulating hormone (TSH) and increased thyroxine (T4) concentrations in 2,521 pregnant women,9 whereas opposing results (increased TSH and decreased T4) have been reported elsewhere (n=439).10 Result discrepancies across studies could be partly explained by differences in study designs, such as trimester of urine and blood collection; differences in participant’s characteristics, such as iodine levels; and difference in exposures. In addition, due to their short half-lives and temporal variability in sources of exposure (e.g., diet, use of personal care products) high intra-individual variability in urinary concentrations has been reported for some of the studied compounds [e.g., intraclass correlation coefficients of about 0.2 for bisphenol A (BPA)1113]. Studies often rely on a limited number of urine specimens to assess exposure, which is unlikely to be sufficient to reflect exposure over the full pregnancy term. That approach leads to classical measurement error and effect estimates biased toward the null that explain null findings.14,15 Finally, due to the high number of hypotheses tested (several thyroid hormones and exposures assessed, sometimes at repeated time points) family-wise error rate (FWER; probability of making one or more false discoveries) is likely to be elevated in these studies,16 which may also explain result discrepancies across studies.
Herein, we relied on a two-tier approach. A thyroid adverse outcome pathway (AOP) network published by Noyes et al.7 and results from in vitro higher-throughput screening (HTS) assays in the ToxCast/Tox21 database17 were evaluated to select phenols and phthalates predicted to be bioactive modulators of molecular initiating events (MIEs) in thyroid toxicity pathway. We then studied associations between this restricted set of compounds and thyroid hormone concentrations in maternal blood. In comparison with a purely agnostic approach, our hypothesis-driven approach focused on compounds with a higher a priori likelihood for effects on thyroid hormone homeostasis and reduced the number of statistical tests performed to help mitigate the probability for false positive findings.

Methodology

Study Population

The prospective SEPAGES [Suivi de l’Exposition à la Pollution Atmosphérique durant la Grossesse et Effets sur la Santé (Assessment of air pollution exposure during pregnancy and effect on health)] cohort recruited 484 pregnant women from eight obstetrical ultrasonography practices located in Grenoble area of France, between July 2014 and July 2017.18 Women were included based on the following criteria: 18y of age, being pregnant for 19 gestational weeks or less, having a singleton pregnancy, residing in the study area, and planning to give birth in one of the four maternity clinics from the Grenoble area that were near the SEPAGES biobank.
Ethical agreements were obtained from the Comité de Protection des Personnes Sud – Est V (CPP) and the Comité Nationale de l’Informatique et des Libertés (CNIL), the French data privacy institution. All participating women gave written consent.
This analysis was restricted to the 437 pregnant women who did not report taking medication for any thyroid diseases (questionnaire completed during the first trimester that specifically asked about thyroid disorders) and had blood and urine samples collected during pregnancy (See flowchart in Supplemental Material, Figure S1).

Biospecimen Collection

Urine samples were collected over a week in the second trimester (median 17.7 gestational weeks (GW); 5th and 95th percentiles: 14.4, 20.0 wk, respectively), during which time women were requested to collect three spot urine samples per day (in the morning, midday, and evening). Samples were collected in 60mL polypropylene tubes and stored in the participants’ freezer (20°C). At the end of the collection week, samples were transported on ice by a study fieldworker to the certified biobank of Grenoble University Hospital (bb-0033-00069). Samples were thawed overnight at 4°C, and for each subject a pool of the same volume of all the spots collected over the week were made following a previously validated protocol.19 Although assessments of biomarker concentrations did not formally account for urine dilution of individual samples, biomarker concentrations assessed in such equal volume pools have been shown to correlate well with those assessed in a pool of all urine volume collected over 24 h or a week.20 For each woman, this pool was aliquoted and stored at 80°C. Daily pools (equal volume pools of all samples collected over a day) were also made.

Assessments of Phenol and Phthalate Metabolite Concentrations

Pools of urine samples collected over a week were sent on dry ice with a temperature sensor to the Norwegian Institute of Public Health (NIPH), where measurements of phenol and phthalate metabolite concentrations were carried out (see Table 1 for a detailed list of biomarkers assessed). Phthalate and di(isononyl)cyclohexane-1,2-dicarboxylate (DINCH) biomarkers were analyzed and quantified using high-performance liquid chromatography coupled to mass spectrometry (HPLC-MS-MS).21 Phenols were analyzed and quantified using ultra high-performance liquid chromatography coupled to mass spectrometry (UPLC-MS-MS).22 The free and conjugated forms of phenol biomarkers were preliminarily measured in samples from 50 women. These preliminary measurements did not suggest external contamination,23 so for the remaining participating women, we relied on analysis of the total form (free+conjugated). Bisphenols AF, B, F, and triclocarban were detected in <5% of the pooled samples and were not considered in our analysis.
Table 1 List of metabolites and parent compounds assessed in SEPAGES.
Parent compoundsBiomarkers assessed in SEPAGES urine samplesBiomarkers excluded from the statistical analysis due to low frequency of detection (<5%)Biomarkers excluded from the statistical analysis because not identified as bioactive on relevant MIEs
Phenols
 MethylparabenMethylparabenX
 EthylparabenEthylparabenX
 PropylparabenPropylparaben
 ButylparabenButylparaben
 Bisphenol ABisphenol A
 Bisphenol SBisphenol S
 Bisphenol FBisphenol FX
 Bisphenol BBisphenol BX
 Bisphenol AFBisphenol AFX
 Benzophenone-3Benzophenone-3
 TriclosanTriclosan
 TriclocarbanTriclocarbanX
Phthalates
 Diethyl phthalate (DEP)Monoethyl phthalate (MEP)X
 Diisobutyl phthalate (DiBP)Monoisobutyl phthalate (MiBP)
 Dibutyl phthalate (DBP)Mono-n-butyl phthalate (MnBP)
 Butyl-benzyl phthalate (BBzP)Monobenzyl phthalate monobutyl phthalate (minor) (MBzP)
 Di(2-propylheptyl) phthalate (DPHP)6-hydroxy-mono-propyl-heptyl phthalate (oh-MPHP)
 Di(2-ethylhexyl) phthalate (DEHP)Mono(2-ethylhexyl) phthalate (MEHP)
Mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP)
Mono(2-ethyl-5-oxohexyl) phthalate (MEOHP)
Mono(2-ethyl-5-carboxypentyl) phthalate (MECPP)
Mono(2-methylcarboxyhexyl) phthalate (MMCHP)
 Diisononyl phthalate (DiNP)Mono(4-methyl-7-hydroxy-octyl) phthalate (OH-MiNP)
Mono(4-methyl-7-oxo-octyl) phthalate (oxo-MiNP)
Mono(4-methyl-7-carboxy-heptyl) phthalate (cx-MiNP)
Nonphthalate plasticizers
 Di(isononyl)cyclohexane-1,2-dicarboxylate (DINCH)2-(((hydroxy-4-methyloctyl)oxy)carbonyl)cyclohexanecarboxylic acid (oh-MINCH)
2-(((4-methyl-7-oxyooctyl)oxy)carbonyl)cyclohexanecarboxylic acid (oxo-MINCH)
Note: —, included in our statistical analysis; MIE, molecular initiating event; X, excluded from our statistical analysis.

Collection of Maternal Blood

Nonfasting maternal blood was collected by trained SEPAGES fieldworkers during a study visit at the participants’ homes. For 85% of the women, blood was collected at the end of the urine collection week, and blood for the other 15% was collected several weeks after urine (median 9 wk, 5th and 95th percentiles: 6, 12, respectively). After collection, samples were transported on ice to the biobank of Grenoble University Hospital; there, blood was processed, and serum aliquots were stored at 80°C.

Measurements of Thyroid Hormone, Selenium, and Iodine Concentrations

TSH was quantified in maternal sera by LOCI Chemiluminescence on Dimension Vista analyzer (Siemens).24 Serum concentrations of protein-bound and free T4 and 3, 5, 3′-triiodothyronine (T3) were quantified by RIA-Gnost (CisBio Bioassays). Maternal total T3 and T4 were obtained by summing the free and protein-bound concentrations. The ratio of total T3 to T4, an indicator of T4 deiodination into the bioactive T3 form, was then calculated. Selenium, an essential micronutrient required for biosynthesis of selenoproteins involved in the peripheral conversion of free T4 to free T3, as well as being vital antioxidants in the thyroid gland, was also measured in maternal sera using inductively coupled plasma mass spectrometry (ICP-MS).25
Iodine, an essential element in the synthesis of thyroid hormones, was measured in daily pooled samples of maternal urine using inductively coupled plasma mass spectrometry (ICP-MS).26

Tier 1: Relying on a Thyroid AOP Network and ToxCast/Tox21 In Vitro HTS Database to Select Phenols and Phthalates

The research effort herein relied on existing AOP networks that map the causal and putative MIEs in the thyroid pathway that have been demonstrated or hypothesized to be the chemical targets. To this end, there have been a number of efforts in the development and evolution of the thyroid AOP network, including an effort recently by Noyes et al.7 Several ToxCast/Tox21 in vitro HTS assays were evaluated for chemical interactions with MIEs in the thyroid pathway.17 MIEs targeted are highlighted in the Supplemental Material, Figure S2 (thick, green border in left-hand column) and include those involved in thyroid hormone biosynthesis in the thyroid gland (Na+/I symporter [NIS,27,28 thyroperoxidase (TPO29)]; receptor-based interactions [thyroid hormone (TR) receptors (TRα, TRβ30) thyroid stimulating hormone (TSH) receptor,31 and thyrotropin releasing hormone (TRH) receptor]; thyroid hormone peripheral tissue metabolism [iodothyronine deiodinases (DIO1, DIO2, DIO332,33)], and activation of hepatic T4 catabolism [e.g., constitutive androstane receptor (CAR), pregnane X receptor (PXR), uridine diphosphate glucuronosyl transferases (UDPGTs)]. The MIEs in the thyroid pathway have been described in detail elsewhere and readers are referred to reviews for additional background.6,7,34
ToxCast/Tox21 database outputs are typically presented as positive (hitcall=1) or negative (hitcall=0) with associated half-maximal activity concentration (AC50) values and efficacy values (cutoff and maximum responses) for bioactive substances. To derive point estimates, raw chemical screening data in assay tests are processed through the ToxCast data analysis pipeline involving several steps in data normalization and dose–response modeling. ToxCast applies three dose–response models in its data evaluations: a) constant model with a constant value of zero response and only one parameter, the scale term; b) Hill model that is a constrained three parameter model; and c) Gain-Loss model that is a constrained five-parameter model. For assay results to be considered bioactive, the modeled concentration–response curves must meet three criteria: a) Hill or Gain-Loss curve fit models must be the winning models; b) the modeled curve fit top must exceed the efficacy cutoff for at least one dose; and c) the median response must exceed the efficacy cutoff. Automated flags identify potentially anomalous outputs. To further limit potential false positives, we also considered only ToxCast positive hitcalls that matched the following criteria: a) concentration–response curves had no more than three flags; b) response curve fits displayed a sigmoidal shape; c) more than one data point was above the efficacy cutoff; and d) data for low concentrations were present (i.e., AC50 should not have been extrapolated35,36). Concentration–response curves and associated data were extracted from the ToxCast chemistry dashboard for each chemical.37 Those not meeting these criteria and excluded from the analysis are displayed in Supplemental Material, Figure S3.

Tier-2: Statistical Analyses

Exposure biomarker concentrations below the limit of detection (LOD) and between the limit of detection and the limit of quantification (LOQ) were singly imputed by values randomly selected between 0 and LOD and between LOD and LOQ, respectively, based on the estimated underlying distribution.38,39 To limit the impact of between-subject variations in conditions related to urine processing (i.e., sample transport time from participant’s home to the biobank, time during which the individual samples were thawed at 4°C during the pooling procedure) and assay (analytical batches), we standardized the measured phenol and phthalate metabolite concentrations when needed. We first estimated the associations between each biomarker concentration assessed in pools (natural log-transformed) and the factors above-mentioned using adjusted linear regression. If processing/assay conditions were identified as associated with the biomarker urine concentrations (p<0.2), we then used the measured biomarker concentrations and the estimated effects of the processing/assay conditions to predict standardized concentrations (i.e., concentrations that would have been observed if all samples had been processed under the same conditions and assayed in the same analytical batch).40,41 We used these standardized concentrations in our statistical analyses.
To limit the impact of extreme values, thyroid hormone concentrations, as well as the T3/T4 ratio, were ln-transformed. Phenol and phthalate metabolite concentrations were considered as continuous (ln-transformed) variables, as well as categorized into tertiles, except for compounds detected in <70% of the samples that were dichotomized (detected/undetected). We computed the molar sum of metabolites from the same parent [e.g., di(2-ethylhexyl) phthalate (DEHP), diisononyl phthalate (DiNP) and DINCH parents, Table 1]. We used adjusted linear regression to assess the associations between each selected phenol and phthalate biomarker and thyroid hormone (TSH, T4, T3) or the T3/T4 ratio.
Adjustment factors for our statistical analyses were selected a priori and included variables likely to be common causes of both the exposures and the thyroid hormones without being likely consequences thereof and factors that were possible predictors of the thyroid hormones only,42,43 such as maternal age (quadratic terms), body mass index (BMI, kilograms per square meter) before pregnancy (continuous), education level (three categories: 2y after high school, 3–4 y after high school, and postgraduate or 5y after high school), maternal smoking during the first trimester of pregnancy (Yes/No), parity (nulliparous and parous), gestational age at serum collection (continuous, weeks), hour of serum collection (categorized), maternal urinary iodine concentrations (ln-transformed, micrograms per liter), and selenium concentrations in sera during pregnancy (tertiles, micromoles per liter). Models were also adjusted for analytical batch for all hormones except TSH, for which no batch effect was detected. A directed acyclic graph representing these relationships is displayed in Supplemental Material, Figure S4. Missing values for covariates were handled using multiple imputation (20 imputed data sets). Effect estimates were reported as percentage change in the hormone concentrations for a doubling of urinary biomarker concentrations. For bisphenol S (BPS) and butylparaben, which were dichotomized, effect estimates represent percentage change in hormone concentrations between those with detected and undetected concentrations.
Betas were expressed as percentage change in outcomes for each doubling of urinary biomarker concentrations except for butylparaben and BPS, for which effect estimates represent percentage change in outcomes between those with undetected and detected urinary concentrations. These percentage changes were obtained from the original betas using the following formulae: (2β1)×100 for continuous exposures and (eβ1)×100 for categorial exposures (butylparaben and BPS). We report in the “Results” section all associations with p<0.05. Associations with p--values between 0.05 and 0.10 were also reported as a trend when the biomarker was associated (p<0.05) with another hormone.

Additional Analyses

Iodine status might modify the associations between chemical exposure and thyroid hormone levels.44 For this reason, in sensitivity analyses, we explored modification by adding an interaction term between iodine levels and urinary phenol and phthalate metabolite concentrations. When an effect modification was suggested (pinteraction <0.10), stratified analyses on iodine status were performed. For this analysis, we dichotomized iodine levels according to the World Health Organization (WHO) threshold for iodine deficiency in pregnant women (150μg/L45).
Although serum samples were collected at the end of the urine collection week for most women, serum samples were collected for 15% of participants several weeks after the urine samples (median 9 wk, 5th and 95th percentiles: 6, 12, respectively). For this reason, we ran an additional analysis restricted to the 373 women for whom serum samples were collected at the end of the urine collection week.
For the main models, we plotted residuals to visually identify influential individuals. If any, we run a sensitivity analysis without these individuals to assess result’s robustness.
Finally, we estimated the joint effects of the selected phenols and phthalates on each hormone using adjusted Bayesian Kernel Machine Regressions (BKMR; R package: bkmr). Such modeling allows estimation of mixture effect and identification of the important components of the mixture. It also accommodates for nonlinear relationships and correlated exposures.46,47 Categorical exposures (BPS and butylparaben) were not considered in this analysis. Each exposure biomarker concentration was standardized [i.e., divided by their standard deviation (SD)]. For each model, we ran 50,000 iterations, dropped the first 25,000, and kept every fifth iteration among the last 25,000 for inference. The overall mixture effect was given by a figure showing the expected change in hormone concentration with concomitant increase quantiles of all exposure biomarkers, relative to when they are fixed at their 25th percentile. When this graph was suggestive of an effect of the mixture, we provided the posterior inclusion probability (PIP) for each biomarker and plotted the estimated effect of an increase from the 25th to 75th percentile in a single biomarker concentration when all other biomarker concentrations were fixed at either their 25th, 50th, or 75th percentiles.
Analyses were carried out with Stata/SE (version 15.1; StataCorp LLC) and R (version 4.0.4; R Development Core Team).

Results

Characteristics of Study Participants

Median of maternal age at recruitment was 32.5 y old (Table 2). The majority (55%) already had a child and were highly educated (56% had pursued education for 5y after high school). Most (74%) had a BMI in normal range (18.525kg/m2), and 6% reported smoking during their first trimester of the pregnancy (after they knew they were pregnant). Median of gestational age at birth was 40 wk, and 54% of the infants were males. Median (percentiles: 5th and 95th) number of samples in each pool was 21 (17 and 21).
Table 2 Characteristics of pregnant women included in this study (n=437, SEPAGES cohort, 2014–2017).
Characteristicsn (%)Median (5th–95th percentiles)
Maternal age (years)43732.2 (26.5–39.0)
Gestational age at serum collection (weeks)43719.1 (15.9–28.0)
Gestational age at delivery (weeks)43640.0 (37.1–41.4)
Number of urine samples in pools43721 (17–21)
Education level
  2y after high school75 (17)
  3–4 y after high school116 (27)
  5y after high school245 (56)
  Missing1
Parity
  Nulliparous198 (45)
  Parous239 (55)
Maternal prepregnancy BMI (kg/m2)
  <18.527 (6)
  18.5 to <25324 (75)
  2582 (19)
  Missing4
Child sex
  Male235 (54)
  Female200 (46)
  Missing2
Vitamin use during pregnancy
  Yes387 (91)
  No37 (9)
  Missing13
Smoking (first trimester)
  No377 (94)
  Yes23 (6)
  Missing37
Note: —, no data; BMI, body mass index.

Distribution of Maternal Urinary Phenol and Phthalate Concentrations

Phenol and phthalate metabolite concentrations have been described elsewhere.23,48 Briefly, bisphenols AF, B, F, and triclocarban were detected in <5% of the pooled samples and were not considered in our analysis. Except for butylparaben and BPS detected in 25% of the samples, frequencies of detection for the other compounds were above 83% (Table 3).
Table 3 Distribution of phenol and phthalate metabolite concentrations in a pool of repeated urine samples collected over a week [median (percentiles 5 and 95) number of samples in each pool: 21 (17–21); n=437 pregnant women from the SEPAGES cohort, 2014–2017].
MetabolitesLOD/LOQPercentage >LODPercentage >LOQStandardizeda,b concentrationsMeasured concentrationsaRhoc
PercentilesPercentiles
5th50th95th5th50th95th
Phenols
 Bisphenol A0.04/0.199.599.30.631.868.540.672.088.930.95
 Bisphenol S0.1/0.425.220.6<LOD<LOD2.9
 Benzophenone-30.04/0.110098.60.180.8625.20.251.2035.60.97
 Triclosan0.04/0.198.298.20.210.911890.210.911891.00
 Methylparaben0.04/0.11001002.1510.62342.5312.02780.94
 Ethylparaben0.04/0.199.799.80.270.8938.20.240.7132.30.93
 Propylparaben0.04/0.18467.30.000.3455.70.010.4571.50.95
 Butylparaben0.07/0.224.711<LOD<LOD0.72
Phthalate metabolites
 MEP0.2/0.51001006.4224.11306.5224.51410.97
 MiBP0.2/0.5100106.3415.147.16.6118.357.50.87
 MnBP0.2/0.51001004.6810.632.85.4112.741.30.97
 MBzP0.07/0.21001001.464.4416.31.544.7717.31.00
 oh-MPHP0.07/0.210099.50.500.862.770.410.872.920.82
 MEHP0.2/0.510099.10.742.368.470.742.338.540.97
 MEHHP0.2/0.51001003.217.0424.23.177.1024.00.97
 MEOHP0.2/0.51001002.264.9617.42.255.2517.310.97
 MECPP0.7/210099.85.119.9627.55.2310.631.20.95
 MMCHP0.7/299.399.14.177.5619.84.989.3525.00.91
ΣDEHP0.050.110.320.060.120.350.97
 oh-MiNP0.1/0.251001001.704.8728.91.704.8728.91.00
 oxo-MiNP0.1/0.2510099.50.832.1714.80.892.2617.90.96
 cx-MiNP0.4/11001002.584.6826.82.534.6528.60.90
ΣDiNP0.020.040.210.020.040.220.98
Nonphthalate plasticizer
 oh-MINCH0.07/0.21001000.751.7718.60.451.5016.30.87
 oxo-MINCH0.07/0.299.899.80.631.5113.50.371.1313.30.89
ΣDINCH0.000.010.110.000.010.100.88
Note: Bisphenols AF, B, F, and triclocarban were detected in <5% of the pooled samples and were not displayed in this table. —, no data; cx-MiNP, mono-4-methyl-7-carboxyoctyl phthalate; DEHP, di(2-ethylhexyl) phthalate; DINCH, di(isononyl)cyclohexane-1,2-dicarboxylate; DiNP, diisononyl phthalate; LOD, limit of detection; LOQ, limit of quantification; MBzP, monobenzyl phthalate; MECPP, mono(2-ethyl-5-carboxypentyl) phthalate; MEHHP, mono(2-ethyl-5-hydroxyhexyl) phthalate; MEHP, mono(2-ethylhexyl) phthalate; MEOHP, mono(2-ethyl-5-oxohexyl) phthalate; MMCHP, mono-2-carboxymethyl hexyl phthalate; MEP, monoethyl phthalate; MiBP, monoisobutyl phthalate; MiNP, monoisononyl phthalate; MnBP, mono-n-butyl phthalate; oh-MINCH, 2-(((Hydroxy-4-methyloctyl) oxy) carbonyl) cyclohexanecarboxylic acid; oh-MiNP, mono-4-methyl-7-hydroxyoctyl phthalate; oxo-MiNP, mono-4-methyl-7-oxooctyl phthalate; oxo-MINCH, 2-(((4-Methyl-7-oxooctyl) oxy) carbonyl) cyclohexanecarboxylic acid; oh-MPHP, mono-6-hydroxy-propylheptyl phthalate; ΣDEHP, molar sum of the five DEHP metabolites; ΣDiNP, molar sum of the three DiNP metabolites; ΣDINCH, molar sum of the two DINCH metabolites.
a
Concentrations in micrograms per liter, except for ΣDEHP, ΣDiNP and ΣDINCH, which are provided in micromoles per liter.
b
bConcentrations were standardized for sample transport time from participant’s home to the biobank, during which time the individual samples were thawed at 4°C during the pooling procedure or analytical batches when these variables were associated with the measured biomarker concentrations (p<0.2).
c
Spearman correlation coefficient between measured and standardized concentrations.

Distribution of Serum Thyroid Hormones

For TSH, 95% of the women (n=414) were within the reference range defined for pregnant women by the French Health Authority (0.358 to 2.500 mUI/L for samples collected during the first trimester and 0.358 to 3.000 mUI/L for samples collected later in pregnancy). Median iodine urinary concentration assessed in urine daily pools was relatively low (89μg/L; Table 4), and 80% of the women had a iodine urinary concentration lower than the WHO guideline for pregnant women.45
Table 4 Distributions of serum thyroid hormone concentrations, selenium and iodine assessed in serum or urine (iodine) of pregnant women of the SEPAGES cohort.
 naPercentiles
5th25th50th75th95th
Total T4 (ng/mL)43775.585.495.6105.3122.9
Free T4 (pg/mL)a4355.46.47.28.410.4
Total T3 (ng/mL)a4050.91.01.21.31.7
Free T3 (pg/mL)4371.71.92.12.32.6
TSH (mUI/L)4370.60.91.31.82.6
Iodine (μg/L)43731.656.789.3134.5271.6
Selenium (μmol/L)3640.80.91.01.11.2
Note: T3, triiodothyronine; T4, thyroxine; TSH, thyroid stimulating hormone.
a
Lower n for free T4, total T3, and selenium are due to insufficient serum quantity to perform all the assessments for a few women.

Tier 1: Relying on a Thyroid AOP Network and an Existing In Vitro HTS Database to Select Phenols and Phthalates

Three compounds (methylparaben, ethylparaben, and diethyl phthalate) were not bioactive at any of the thyroid-related MIEs evaluated and so were not included in the statistical analysis (Table 5; Supplemental Material, Excel Table S1). Among the phenols and phthalates assessed in SEPAGES, one phthalate, [bis(2-propylheptyl) phthalate (DPHP)] was not screened for thyroid activity in ToxCast. We nevertheless retained its metabolite in the tier-2 statistical analysis, based on thyroid toxicity (i.e., thyroid gland hypertrophy/hyperplasia) reported in adult male rats in a two-generation reproductive toxicity study.49
Table 5 Summary of information extracted from the U.S. EPA ToxCast screening library (https://comptox.epa.gov/dashboard/chemical_lists/TOXCAST, version 3.3; accessed November 2020).
CompoundPeripheral TH metabolismaTH synthesisInduction of xenobiotic receptor (liver)Hypothalamic-pituitary feedbackTR transactivation
DIO inhibitionaTSHR bindingTPO inhibitionNIS inhibitionCARAhRPPARPXRUDPGT1A1TRβTRHRTRαTRβ
TriclosanXXXXXXXXbXcXb
MethylparabenNANANA
EthylparabenNANANA
ButylparabenNANAXXX
PropylparabenNANANAXXX
Benzophenone-3NANAXXXXNA
Bisphenol SNANAXNA
Bisphenol ANAXdXXXXXbXb
Diethyl phthalateNANANA
Benzylbutyl phthalateNAXXXX
Di(2-ethylhexyl)phthalateNANANAXX
Dibutyl phthalateNANAXXXb
Diisobutyl phthalateNANAXX
Diisononylcyclohe1ane-1,2-dicarbo1ylateNANANAX
Diisononyl phthalateNANANAXNA
Bis(2-propylheptyl) phthalateNANANANANANANANANANANANANA
Note: ToxCast positive hitcalls were only considered if they matched the following criteria: Curves should have less than three flags; curves should display a sigmoidal shape; there should be more than one isolated data point above efficacy cutoff; and data for low concentrations should be present (i.e., AC50 should not have been extrapolated35,36). —, compound identified as non-bioactive in vitro; AC50, activity concentration, 50%; AhR, aryl hydrocarbon receptor; CAR, constitutive androstane receptor; DIO, iodothyronine deiodinase; MIE, molecular initiating event; NA, compound not assessed in ToxCast for this MIE; NIS, sodium–iodide symporter; PPAR, peroxisome proliferator–activated receptor; PXR, pregnane X receptor, TH, thyroid hormone; TPO, thyroperoxidase; TR, thyroid hormone receptor; TRHR, thyrotropin releasing hormone receptor; TSHR, thyroid stimulating hormone receptor; UDPGT, uridine diphosphate glucuronosyltransferase; U.S. EPA, U.S. Environmental Protection Agency; X, compound identified as bioactive in vitro (either antagonist or agonist activity).
a
Includes inhibition of the three DIO isoforms (1, 2, and 3).
b
Antagonists.
c
Both agonist and antagonist.
d
Agonist.
This selection step allowed us to restrict our set of 16 (13 individual compounds and 3 molar sums) initially considered chemicals to 13, leading to a reduction in the number of tests performed in our main analysis (Table 6) by approximatively 19%.
Table 6 Adjusted associations between phenol and phthalate metabolite concentrations and thyroid hormones (ln-transformed) in the SEPAGES cohort.
 TSH (mUI/L, n=437)Total T4 (ng/mL, n=437)Free T4 (pg/mL, n=435)Total T3 (ng/mL, n=405)Free T3 (pg/mL, n=437)
Ratio T3/T4 (n=405)
βa95% CIp-Valueβa95% CIp-Valueβa95% CIp-Valueβa95% CIp-Valueβa95% CIp-Valueβa95% CIp-Value
Bisphenol A4.3[8.9, 0.6]0.080.2[0.9, 1.4]0.730.0[1.2, 1.3]0.950.7[0.7, 2.1]0.350.1[1.2, 0.9]0.820.1[1.2, 1.5]0.84
Bisphenol Sb1.7[11.4, 16.7]0.810.2[2.9, 3.4]0.921.6[1.9, 5.1]0.372.4[1.6, 6.5]0.241.4[1.4, 4.4]0.321.4[2.4, 5.3]0.47
Triclosan0.0[2.1, 2.1]0.990.1[0.6, 0.4]0.730.2[0.7, 0.4]0.560.3[0.9, 0.3]0.280.0[0.4, 0.5]0.890.2[0.8, 0.4]0.49
Propyl paraben1.4[2.8, 0.1]0.070.3[0.1, 0.6]0.150.2[0.1, 0.6]0.200.2[0.6, 0.3]0.430.3[0.6, 0.0]0.070.5[0.9, 0.1]0.03
Butylparabenb2.7[15.4, 11.9]0.701.1[2.1, 4.4]0.520.2[3.7, 3.5]0.931.6[5.5, 2.5]0.452.0[4.8, 1.0]0.193.2[6.9, 0.7]0.10
Benzophenone 30.2[2.9, 2.5]0.870.1[0.5, 0.8]0.660.2[0.9, 0.5]0.550.1[0.9, 0.6]0.710.5[1.1, 0.1]0.080.2[1.0, 0.5]0.57
MBzP1.0[6.5, 4.7]0.721.3[0.0, 2.6]0.040.7[0.7, 2.1]0.340.2[1.4, 1.9]0.820.3[1.4, 0.9]0.651.4[2.9, 0.2]0.08
MiBP1.5[8.1, 5.6]0.670.9[0.7, 2.5]0.280.3[1.4, 2.0]0.740.2[1.8, 2.2]0.860.4[1.0, 1.8]0.590.6[2.5, 1.3]0.50
MnBP3.7[10.5, 3.6]0.311.1[0.5, 2.8]0.180.0[1.8, 1.8]1.000.7[1.4, 2.8]0.520.9[0.6, 2.5]0.250.6[2.6, 1.5]0.58
oh-MPHP7.4[13.8, 0.4]0.040.2[1.9, 1.4]0.780.4[1.4, 2.3]0.650.1[2.1, 1.9]0.890.1[1.6, 1.4]0.870.3[2.2, 1.6]0.75
ΣDiNP2.6[7.6, 2.6]0.320.0[1.2, 1.2]0.990.7[0.6, 2.1]0.280.3[1.1, 1.9]0.650.5[0.5, 1.6]0.330.2[1.3, 1.6]0.83
ΣDINCH1.3[5.6, 3.1]0.550.4[1.4, 0.6]0.430.2[0.9, 1.3]0.730.1[1.1, 1.4]0.860.2[1.1, 0.7]0.640.3[0.9, 1.5]0.64
ΣDEHP5.3[11.7, 1.5]0.120.5[1.1, 2.1]0.570.8[1.0, 2.6]0.390.2[1.7, 2.3]0.820.6[0.8, 2.1]0.410.6[2.5, 1.3]0.53
Note: Analyses were adjusted for maternal age, BMI before pregnancy, education level, maternal smoking during the first trimester of pregnancy, parity, gestational age at serum collection, time of serum collection, and maternal urinary iodine concentrations and selenium concentrations in sera during pregnancy. Models were also adjusted for analytical batch for all hormones but TSH, for which no batch effect was detected. Effect estimates represent percent change in outcomes for each doubling of urinary biomarker concentrations except for butylparaben and BPS, for which effect estimates represent percent change in outcomes between those with undetected and detected urinary concentrations. Percentage changes were obtained from the original betas using the following formulae: (2β1)×100 for continuous exposures and (eβ1)×100 for categorial exposures (butylparaben and BPS). BMI, body mass index; BPS, bisphenol S; CI, confidence interval; DEHP, di(2-ethylhexyl) phthalate; DINCH, di(isononyl)cyclohexane-1,2-dicarboxylate; DiNP, diisononyl phthalate; MBzP, Monobenzyl phthalate; MiBP, monoisobutyl phthalate; MnBP, mono-n-butyl phthalate; oh-MPHP, mono-6-hydroxy-propylheptyl phthalate, ΣDEHP, molar sum of DEHP metabolites; ΣDiNP, molar sum of DiNP metabolites; ΣDINCH, molar sum of DINCH metabolites; T3, triiodothyronine; T4, thyroxine; TSH, thyroid stimulating hormone.
a
Expressed as percentage change in the studied outcome.
b
Categorized as follows: undetected/detected.

Tier 2: Associations of Selected Phenol and Phthalate Metabolites with Thyroid Hormone Concentrations

Parabens.

Propylparaben was negatively associated with the T3/T4 ratio [β=0.5% (95% confidence interval (CI): 0.9, 0.1) for each doubling in propylparaben concentration]. This compound also tended to be negatively associated with TSH (β=1.4%; 95% CI: 2.8, 0.1) and free T3 (β=0.3%; 95% CI: 0.6, 0.0). Based on model residuals, we identified three individuals with low TSH concentrations (0.2 mUI/L) that may drive the association with this hormone. Their exclusion indeed led to an attenuated effect estimate for the association between propylparaben and TSH: β=0.9%; 95% CI: 2.1, 0.4, Supplemental Material, Table S1.

Other phenols.

The analysis relying on exposure biomarkers categorized in tertiles showed a negative association between BPA and TSH that decreased by 6.8% (95% CI: 19.5, 7.8) and 16.3% (95% CI: 27.8, 3.0) in the second and third concentration tertiles in comparison with the first (Table 7). For triclosan, estimates were suggestive of a U-shaped association with TSH, that decreased by 21.3% (95% CI: 31.7, 9.4) and 9.1% (95% CI: 21.4, 5.1) in the second and third triclosan concentration tertiles, respectively in comparison with the first.
Table 7 Adjusted associations between phenol and phthalate metabolite concentrations coded in tertiles and thyroid hormones (ln-transformed) in the SEPAGES cohort.
 TSH (mUI/L, n=437)Total T4 (ng/mL, n=437)Free T4 (ng/mL, n=435)Total T3 (ng/mL, n=405)Free T3 (ng/mL, n=437)
Ratio T3/T4 (n=405)
βa95% CIp-Valueβa95% CIp-Valueβa95% CIp-Valueβa95% CIp-Valueβa95% CIp-Valueβa95% CIp-Value
Bisphenol A
 Tertile 1000000
 Tertile 26.8[19.5, 7.8]0.340.7[2.6, 4.1]0.703.7[0.0, 7.6]0.052.7[1.5, 7.1]0.210.2[2.8, 3.3]0.901.3[2.7, 5.5]0.52
 Tertile 316.3[27.8, 3.0]0.021.9[1.5, 5.4]0.282.7[1.0, 6.7]0.162.6[1.7, 7.1]0.241.2[1.9, 4.4]0.440.3[4.3, 3.9]0.88
Triclosan
 Tertile 1000000
 Tertile 221.3[31.7, 9.4]0.000.1[3.3, 3.2]0.962.0[1.6, 5.7]0.281.1[2.9, 5.4]0.592.0[1.0, 5.1]0.180.9[3.0, 5.0]0.64
 Tertile 39.1[21.4, 5.1]0.200.1[3.2, 3.5]0.950.3[3.3, 4.1]0.871.6[5.6, 2.6]0.450.8[2.2, 3.9]0.601.4[5.3, 2.7]0.49
Propylparaben
 Tertile 1000000
 Tertile 23.4[10.6, 19.6]0.652.6[0.7, 6.1]0.123.3[0.4, 7.2]0.081.7[2.4, 6.0]0.421.2[4.1, 1.9]0.451.4[5.2, 2.6]0.50
 Tertile 313.7[25.6, 0.2]0.052.5[1.0, 6.2]0.162.4[1.4, 6.4]0.221.8[6.0, 2.6]0.422.8[5.8, 0.3]0.084.3[8.3, 0.2]0.04
Benzophenone 3
 Tertile 1000000
 Tertile 211.2[23.3, 2.8]0.111.5[1.8, 4.9]0.392.9[0.8, 6.7]0.121.6[2.6, 5.9]0.462.4[0.7, 5.5]0.131.5[2.5, 5.6]0.48
 Tertile 35.4[18.3, 9.5]0.460.6[2.7, 4.0]0.730.5[4.1, 3.2]0.780.3[3.9, 4.6]0.900.8[3.7, 2.3]0.610.1[4.1, 4.1]0.97
MBzP
 Tertile 1000000
 Tertile 24.3[9.9, 20.6]0.571.2[2.1, 4.6]0.461.6[2.1, 5.3]0.401.2[5.2, 3.0]0.560.8[2.2, 3.9]0.592.5[6.3, 1.4]0.21
 Tertile 31.3[14.6, 14.1]0.862.8[0.5, 6.3]0.103.7[0.0, 7.6]0.051.7[5.7, 2.5]0.430.0[3.0, 3.1]0.995.0[8.7, 1.1]0.01
MiBP
 Tertile 1000000
 Tertile 26.8[7.8, 23.8]0.381.3[2.0, 4.8]0.431.6[2.1, 5.5]0.391.0[5.1, 3.3]0.640.6[3.6, 2.5]0.721.8[5.7, 2.3]0.38
 Tertile 30.1[13.8, 16.1]0.991.4[2.0, 4.9]0.421.2[2.5, 5.0]0.542.1[6.2, 2.1]0.320.1[3.0, 3.2]0.952.9[6.8, 1.1]0.15
MnBP
 Tertile 1000000
 Tertile 25.4[9.0, 22.1]0.483.2[0.2, 6.7]0.061.0[4.6, 2.7]0.580.3[3.8, 4.6]0.881.0[4.0, 2.0]0.511.9[5.8, 2.1]0.35
 Tertile 30.7[13.2, 16.9]0.931.3[2.1, 4.7]0.470.5[4.1, 3.3]0.810.9[3.4, 5.3]0.700.8[2.3, 4.0]0.620.5[4.5, 3.7]0.81
oh-MPHP
 Tertile 1000000
 Tertile 27.7[20.4, 6.9]0.281.3[4.6, 2.1]0.431.1[2.6, 4.9]0.583.0[1.2, 7.5]0.162.2[0.9, 5.4]0.173.4[0.7, 7.7]0.11
 Tertile 38.8[21.3, 5.8]0.221.3[4.6, 2.1]0.440.7[3.0, 4.5]0.720.9[3.3, 5.2]0.701.6[1.4, 4.8]0.300.8[3.2, 5.0]0.70
ΣDiNP
 Tertile 1000000
 Tertile 20.9[14.4, 14.7]0.900.5[2.8, 3.9]0.790.8[2.8, 4.6]0.670.5[4.6, 3.7]0.802.0[1.0, 5.2]0.191.1[5.0, 3.0]0.60
 Tertile 31.5[15.0, 14.3]0.850.3[3.6, 3.2]0.881.6[2.1, 5.4]0.410.0[4.2, 4.4]0.992.1[1.0, 5.3]0.190.1[4.0, 4.3]0.98
ΣDINCH
 Tertile 1000000
 Tertile 23.2[10.9, 19.5]0.670.4[3.7, 3.1]0.841.5[5.1, 2.2]0.410.7[4.9, 3.6]0.740.5[3.5, 2.6]0.740.2[4.3, 4.0]0.92
 Tertile 36.9[19.8, 7.9]0.340.5[3.8, 2.9]0.771.0[2.7, 4.8]0.590.2[4.0, 4.6]0.920.5[2.5, 3.7]0.740.0[4.0, 4.2]1.00
ΣDEHP
 Tertile 1000000
 Tertile 22.5[11.7, 18.9]0.750.0[3.3, 3.5]1.002.0[1.8, 5.9]0.312.1[2.1, 6.5]0.340.0[3.1, 3.1]0.990.3[3.7, 4.5]0.87
 Tertile 35.0[17.9, 9.9]0.490.1[3.4, 3.4]0.972.0[1.7, 5.9]0.290.5[3.7, 4.8]0.821.2[1.9, 4.3]0.450.8[4.8, 3.3]0.70
Note: Effect estimates represent percent change in outcomes. Percentage changes were obtained from the original betas using the following formula: (eβ1)×100. Analyses were adjusted for maternal age, BMI before pregnancy, education level, maternal smoking during the first trimester of pregnancy, parity, gestational age at serum collection, time of serum collection, maternal urinary iodine concentrations and selenium concentrations in sera during pregnancy. Models were also adjusted for analytical batch for all hormones but TSH for which no batch effect was detected. —, reference; BMI, body mass index; CI, confidence interval; DEHP, di(2-ethylhexyl) phthalate; DINCH, di(isononyl)cyclohexane-1,2-dicarboxylate; DiNP, diisononyl phthalate; MBzP, monobenzyl phthalate; MiBP, monoisobutyl phthalate; MnBP, mono-n-butyl phthalate; oh-MPHP, mono-6-hydroxy-propylheptyl phthalate; ΣDEHP, molar sum of DEHP metabolites; ΣDiNP, molar sum of DiNP metabolites; ΣDINCH, molar sum of DINCH metabolites; T3, triiodothyronine; T4, thyroxine; TSH, thyroid stimulating hormone.
a
Expressed as percent change in the studied outcome.
We did not observe associations between benzophenone-3, butylparaben, BPS, and thyroid hormone concentrations in our main analysis.

Phthalates.

Monobenzyl phthalate monobutyl phthalate (minor) (MBzP), a metabolite of butylbenzyl phthalate (BBzP), was positively associated with total T4 (β=1.3%, 95% CI: 0.0, 2.6). This metabolite also tended to be negatively associated with the T3/T4 ratio that on average decreased by 1.4% (95% CI: 2.9, 0.2) for each doubling in MBzP urinary concentration (Table 6). Mono-6-hydroxy-propylheptyl phthalate (oh-MPHP), a metabolite of DPHP, was negatively associated with TSH (β=7.4%, 95% CI: 13.8, 0.4). However as for propylparaben, this association was driven by the three individuals with the lowest TSH values [β of 2.6% (95% CI: 8.3, 3.4) after exclusion]. No other phthalate metabolite was associated with thyroid hormones in our main analysis (Table 6).

Additional analysis restricted to women for which serum sample was collected at the end of urine collection.

Restricting our analysis to the 373 women who had their blood withdrawn at the end of the urine collection week did not strongly impact the results. As expected by the sample size decrease, p-values increased slightly, but effect sizes generally were similar except for butylparaben, which was negatively associated with the T3/T4 ratio (β=4.1%; 95% CI: 8.0, 0.0) and free T3 (β=2.9%; 95% CI: 6.0, 0.3). A negative association between benzophenone-3 and free T3 also appeared (β=0.6%; 95% CI: 1.2, 0.0) (Supplemental Material, Table S2).

Interaction with iodine levels.

Interaction with iodine levels (pinteraction<0.10) was observed for several exposure–thyroid hormone pairs (Supplemental Material, Table S3). After stratification for iodine status, a negative association between ΣDEHP and TSH emerged among women with normal iodine concentration (Table 8). In the normal iodine group, we also observed a positive association between MnBP and total T3 (β=5.6%; 95% CI: 0.1, 11.7) and free T3 (β=3.9% ; 95% CI: 0.5, 8.4; Table 8).
Table 8 Adjusted associations between phenols, phthalate metabolites, and thyroid hormones (ln-transformed) stratified for iodine urinary levels.
 TSH (mUI/L)Total T4 (ng/mL)Total T3 (ng/mL)Free T3 (ng/mL)Ratio T3/T4
βa95% CIp-Valueβa95% CIp-Valueβa95% CIp-Valueβa95% CIp-Valueβa95% CIp-Value
Triclosan
Iodine<150μg/L0.3[0.89, 0.19]0.21
Iodine150μg/L1.0[0.09, 2.15]0.07
MBzP
Iodine<150μg/L0.8[2.55, 1.02]0.391.0[2.25, 0.27]0.12
Iodine150μg/L1.7[2.77, 6.39]0.451.6[1.79, 5.12]0.35
MnBP
Iodine<150μg/L0.6[2.85, 1.79]0.640.2[1.48, 1.83]0.851.6[3.83, 0.62]0.15
Iodine150μg/L5.6[0.08, 11.70]0.053.9[0.48, 8.38]0.082.0[3.23, 7.62]0.45
ΣDEHP
Iodine<150μg/L2.6[9.98, 5.44]0.52
Iodine150μg/L20.5[32.80, 6.02]0.01
Note: Stratified analyses were only conducted for the biomarker outcome pairs for which an interaction with iodine status was detected (p-values for the interaction term biomarker – iodine <0.01; all p-values for interaction are displayed in Supplemental Material, Table S3). —, not computed; CI, confidence interval; ΣDEHP, molar sum of DEHP metabolites; MBzP, monobenzyl phthalate; MnBP, mono-n-butyl phthalate; T3, triiodothyronine; T4, thyroxine; TSH, thyroid stimulating hormone.
a
Expressed as percentage change in the studied outcome.

Joint effect.

Analyses relying on BKMR suggested a negative association between the mixture of the 13 selected chemicals and the T3/T4 ratio (Figure 1; Supplemental Material, Table S4). These negative associations seemed to be driven by MBzP and propylparaben, the two compounds with the highest PIP (Figure 2; Supplemental Material, Table S5). They were both negatively associated with this ratio in the unipollutant model. Most of the other compounds were considered has noninfluential by BKMR (PIP and effect estimates of 0; Figure 2; Supplemental Material, Table S5). No association with the mixture was highlighted for the other hormones (Figure 1).
Figure 1. Expected changes and 95% CIs in (A) TSH, (B) total T4, (C) free T4, (D) Total T3, (E) free T3, and (F) T3/T4 ratio associated with concurrently increasing quantiles of all exposure biomarkers, relative to when all concentrations are fixed at their 25th percentile. Note: Numerical value of effect estimates and 95% CIs are reported in Supplemental Material, Table S4. Analyses were adjusted for maternal age, BMI before pregnancy, education level, maternal smoking during the first trimester of pregnancy, parity, gestational age at serum collection, time of serum collection, maternal urinary iodine concentrations and selenium concentrations in sera during pregnancy. Models were also adjusted for analytical batch for all hormones but TSH for which no batch effect was detected. BMI, body mass index; CI, confidence interval; T3, triiodothyronine; T4, thyroxine; TSH, thyroid stimulating hormone.
Figure 2. Estimated effect and 95% CI of an increase from the 25th to 75th percentile in a single biomarker concentration on T3/T4 ratio when all other exposure biomarkers are fixed at either the 25th, 50th, or 75th percentiles. Note: Numerical value of effect estimates and 95% CI are reported in Supplemental Material, Table S5. Analyses were adjusted for maternal age, BMI before pregnancy, education level, maternal smoking during the first trimester of pregnancy, parity, gestational age at serum collection, time of serum collection, maternal urinary iodine concentrations and selenium concentrations in sera during pregnancy. Models were also adjusted for analytical batch for all hormones but TSH for which no batch effect was detected. BMI, body mass index; CI, confidence interval; DEHP, di(2-ethylhexyl) phthalate, DINCH, di(isononyl)cyclohexane-1,2-dicarboxylate; DiNP, Diisononyl phthalate; ΣDEHP, molar sum of the five DEHP metabolites; ΣDINCH, molar sum of the two DINCH metabolites; ΣDiNP, molar sum of the three DiNP metabolites; MBzP, monobenzyl phthalate; MiBP, monoisobutyl phthalate; MnBP, mono-n-butyl phthalate; oh-MPHP, mono-6-hydroxy-propylheptyl phthalate; T3, triiodothyronine; T4, thyroxine.

Discussion

Compound selection based on in vitro bioactivity allowed us to reduce the number of tests performed in our main analysis by approximatively 19% (from 16 to 13 compounds) and provides a biologically based screen to help limit the propagation of chance findings and false positives. Relying on within-subject pools of repeated urine samples collected during pregnancy, we highlight associations between individual prenatal exposures to several phenols and phthalates, as well as the mixture, and maternal thyroid hormone concentrations. Associations were mainly seen with serum TSH, free T3, and the T3/T4 ratio. Most of the observed associations were negative (either monotonic decrease or U-shaped associations). A few associations with ΣDEHP and MnBP were modulated by urinary iodine concentrations and observed only among participants with iodine concentrations above 150μg/L. However, careful interpretation is required due to the relatively small numbers of women in this group (n=87).

Parabens

Propylparaben was negatively associated with free T3 and the T3/T4 ratio. A similar pattern was observed for butylparaben in our analysis restricted to the 373 women for whom serum samples were collected at the end of the urine collection week. Propylparaben was also identified as a major contributor of the negative association observed between the mixture and T3/T4 ratio. Among the few studies that have explored associations between parabens and thyroid hormone concentrations during pregnancy,50,51 only one assessed the T3/T4 ratio and did not report association for propylparaben and butylparaben.50 To our knowledge, no study assessed free T3, limiting comparison with our results. Although studies in animal are also scarce, exposure to butylparaben has been shown to increase TPO activity and reduce DIO activity.52 Regarding other potential mechanisms by which parabens may affect thyroid hormone homeostasis, the in vitro HTS data indicated activation of xenobiotic nuclear receptors (e.g., CAR, PXR) regulating expression of genes encoding metabolizing enzymes, which could in turn enhance thyroid hormone (Table 5).

Other Phenols

When categorized in tertiles, BPA was negatively associated with TSH. This result was in line with those of two previous studies,53,54 whereas for one the association was seen only among women with a prepregnancy BMI>23kg/m2.54 Five other human studies reported null associations with TSH.50,5558 We did not observe any association between BPA and the other hormones assessed nor with the T3/T4 ratio, whereas a few previous studies did.50,55,57 Changes in thyroid hormone concentrations have also been reported in pregnant females (and/or their offspring) following low-dose exposures in experimental animal models.5961 These results, along with ToxCast data indicating BPA bioactivity on several relevant MIEs in the thyroid pathway, strengthen the overall body of evidence of its potential to perturb thyroid hormone signaling during sensitive developmental periods.
We did not observe any association for BPS. One study, with a bigger sample size and a higher frequency of detection than ours, reported a positive association between BPS and total T4.56
We observed a non-monotonic decrease in TSH concentrations with increased triclosan urinary concentrations. Wang et al. also reported a U-shape association with TSH,62 whereas other studies report no association with TSH50,51,57; however, only one has explored nonmonotonic associations.57 Mechanisms by which triclosan may affect thyroid hormone homeostasis include inhibiting DIO, TPO, and NIS, binding TRβ, and activating xenobiotic nuclear receptors (Table 5), which generally aligns with other mechanistic evidence.6368 Additionally, an in vitro screening assay indicated some capacity for triclosan to inhibit iodotyrosine deiodinase involved in iodide recycling in the thyroid gland.69

Phthalates

MBzP, a metabolite of BBzP, was positively associated with total T4 and negatively with the T3/T4 ratio. MBzP was also one of the major contributors of the negative association observed between the mixture and T3/T4 ratio. To the best of our knowledge, associations with the T3/T4 ratio has been evaluated in only two studies.10,70 Consistent with our results, one reported a negative association,70 whereas the other did not.10 Our results for total T4 are not aligned with previous studies that reported a negative9 or no association with T4.10,43,70 BBzP can up-regulate the transcriptional activity of NIS.71 Consistent with results herein, it is possible up-regulation of NIS may prompt increased iodine intake into the thyroid and increased thyroid hormone production. The fact that we observed a positive association only with total T4 and not total T3 might partly be related to difference in half-lives across hormones with T3 (1 d) having shorter half-life than T4 (5 to 7 d).
Effect modification by iodine concentrations were observed for two phthalates, ΣDEHP and MnBP. Only Villanger et al. examined interactions with iodine concentrations.44 That study relied on a factor analysis and reported interactions between iodine status and the factor containing MnBP along with MiBP and MBzP. However, associations were observed for TSH and total and free T4, whereas we observed associations with total and free T3. Villanger et al. did not report interactions with the factor containing ΣDEHP.

Use of AOP Network and In Vitro HTS to Select Phenols and Phthalates

In comparison with an agnostic approach, the hypothesis-driven selection of phenols and phthalates using the thyroid AOP network and in vitro HTS assays allowed for the reduction of the number of statistical tests performed and limited FWER. However, the complexity of the thyroid system that includes tightly controlled compensatory signaling, and differences in pharmacokinetic parameters in vivo and in vitro, make predictions with in vitro screening data a challenge. Adaptive responses at higher levels of biological organization also may impart some protection to chemicals, with in vitro assays typically predicting effects at lower dose levels (more sensitive). This complexity may explain why the direction of associations observed in our study are not always aligned with those described in AOPs for the targeted MIEs. In addition, although several of the phthalates and phenols evaluated in this study activated xenobiotic nuclear receptors (and the mechanistic literature suggests a role for up-regulated T4 catabolism pathways), the performance of these and other assays in predicting effects at putative targets in the thyroid hormone conjugation and excretion pathway is an area of ongoing study. In addition, the ToxCast in vitro HTS assays rely on testing individual chemicals, so the potential role of chemical mixtures in affecting thyroid regulation was not considered in chemical selection. Finally, it is possible that a direct chemical effect on another biological target (e.g., immune system/inflammation72) may prompt secondary effects on thyroid regulation that in turn elicit effects elsewhere in the body, depending on the life stage, timing, severity, and duration of the hormonal disruption. By filtering for compounds with bioactivity at thyroid MIEs, we may have excluded chemicals that elicit thyroid disruption by secondary pathways. Nonetheless, although beyond the scope of this study, integration of biological pathway models such as the AOP network approaches used herein provide a framework from which to begin capturing some of these more complex modes of action and effects of thyroid hormone reductions in pregnant women exposed to environmental chemicals. The approach herein allowed for the selection of chemicals with increased biological plausibility for interacting with MIEs in the thyroid pathway and thus provided a useful application in mixture and exposome studies that test larger sets of chemicals.

Other Strengths and Limitations

We relied on within-subject pools of repeated urine samples collected over a week to assess exposure. This approach is of importance for compounds with high intra-individual variability, such as BPA and DEHP metabolites (intraclass correlation coefficients of about 0.2–0.31113). For such compounds, reliance on multiple urine samples during relevant time windows should lead to decreased measurement error and, for a given sample size, increased power.15 SEPAGES correlation coefficients between two non-consecutive weeks of pregnancy was still relatively low for some compounds,23,48 highlighting the need to collect repeated biospecimens in sensitive time windows. For most women (85%), urine samples were collected the week preceding blood draws, allowing for evaluation of short-term effects of phenols and phthalates. In addition to thyroid hormone concentrations, we also explored associations with the T3/T4 ratio. Although clinically relevant, this indicator, which was negatively associated with MBzP, propylparaben, and butylparaben, as well as the mixture, has not been extensively studied in association with prenatal exposure to phenols and phthalates. In our study population, 80% of the women were below the WHO threshold for iodine deficiency. Such low iodine concentrations have been previously described among pregnant women from France73,74 and other European countries.75 Further studies should consider this essential element, given results here and among other researchers44 that effects of some phthalates and phenols on the thyroid may be modulated by iodine status.
Although we accounted for many potential confounders, residual confounding cannot be ruled out. There are indeed other synthetic chemicals known to disrupt thyroid functioning that were not assessed in our study, and that may have confounded the observed associations (e.g., halogenated BPA compounds such as tetrabromobisphenol A). In addition, we did not assess thyroid antibodies, which might be important predictors of thyroid hormone concentrations. We also did not recruit women early enough to assess thyroid hormones during the first trimester of pregnancy, a key period during which the fetal thyroid gland is immature and the fetus is dependent on maternal sources of thyroid hormone. Despite our chemicals’ a priori selection, the number of associations tested was still high. We did not apply any formal correction for multiple comparisons. This and the fact that in vitro data may not always be predictive of in vivo biological events suggest that part of the associations we observed may still have resulted from chance findings and thus should be interpreted cautiously.

Conclusion

Relying on pools of multiple urine samples and on a novel hypothesis-driven method to reduce false positives and chance findings, we observed negative associations between several phenols and phthalates and TSH, free T3, and the T3/T4 ratio. Given widespread exposure to these compounds in the general population and the crucial role of thyroid hormones in development, the impact on fetal and child health might be substantial.

Acknowledgments

The SEPAGES study group includes: E. Eyriey, A. Licinia, A. Vellement (Groupe Hospitalier Mutualiste, Grenoble), I. Pin, P. Hoffmann, E. Hullo, C. Llerena (Grenoble University Hospital, La Tronche), X. Morin (Clinique des Cèdres, Echirolles), A. Morlot (Clinique Belledonne, Saint-Martin d’Hères), J. Lepeule, S.L.-C., C.P., I.P., J. Quentin, V. Siroux, and R.S. (Inserm, CNRS, University Grenoble Alpes IAB research center).
The authors acknowledge M. Rolland and K. Supernant for data management; P. Jedynak, O. Coiffier, and M. Ouidir for their help with the implementation of the BKMR mixture model; L. Bajard for her help accessing and interpreting ToxCast data; and M. Barbagallo for her help downloading data from the ToxCast chemistry dashboard. The authors are also thankful to I. Druwe and H. Ru of the U.S. Environmental Protection Agency (U.S. EPA) for their helpful reviews of the manuscript.
This work was funded by ANSES (CNAP, EST-2016-121). D.N. was supported by a doctoral grant from Université Grenoble Alpes. The SEPAGES cohort was supported by the European Research Council (N°311765-E-DOHaD), the European Community’s Seventh Framework Programme (FP7/2007-206 – N 308333-892 HELIX), the French Research Agency – ANR (ANR-19-CE36-0003-01; ANR-12-PDOC-0029-01, ANR-14-CE21-0007, ANR-18-CE36-005, ANR-15-IDEX-02, ANR-15-IDEX5 and GUMME project), the French Agency for Food, Environmental and Occupational Health & Safety – ANSES (EST-2019/1/039, EST-2016-121), the Plan Cancer (Canc’Air project), the French Cancer Research Foundation Association de Recherche sur le Cancer – ARC, the French Endowment Fund AGIR for chronic diseases – APMC, PRENAPAR), the French Endowment Fund for Respiratory Health and the French Fund – Fondation de France (CLIMATHES – 00081169).

Article Notes

All authors declare they have no actual or potential competing financial interests.
Data used for this study is confidential. It can be provided on reasonable request toward the SEPAGES study comity.
The views expressed in this manuscript are those of the authors and do not necessarily reflect the views or policies of the U.S. EPA.

Supplementary Material

File (ehp10239.smcontents.508.pdf)
File (ehp10239.s001.acco.pdf)
File (ehp10239.s002.codeanddata.acco.zip)

References

1.
Forhead AJ, Fowden AL. 2014. Thyroid hormones in fetal growth and prepartum maturation. J Endocrinol 221(3):R87–R103. https://pubmed.ncbi.nlm.nih.gov/24648121/, https://doi.org/10.1530/JOE-14-0025.
2.
Korevaar TIM, Muetzel R, Medici M, Chaker L, Jaddoe VWV, de Rijke YB, et al. 2016. Association of maternal thyroid function during early pregnancy with offspring IQ and brain morphology in childhood: a population-based prospective cohort study. Lancet Diabetes Endocrinol 4(1):35–43. https://pubmed.ncbi.nlm.nih.gov/26497402/, https://doi.org/10.1016/S2213-8587(15)00327-7.
3.
Gorini F, Bustaffa E, Coi A, Iervasi G, Bianchi F. 2020. Bisphenols as environmental triggers of thyroid dysfunction: clues and evidence. Int J Environ Res Public Health 17(8):2654. https://pubmed.ncbi.nlm.nih.gov/32294918/, https://doi.org/10.3390/ijerph17082654.
4.
Kim MJ, Moon S, Oh BC, Jung D, Choi K, Park YJ. 2019. Association between diethylhexyl phthalate exposure and thyroid function: a meta-analysis. Thyroid 29(2):183–192. https://pubmed.ncbi.nlm.nih.gov/30588877/, https://doi.org/10.1089/thy.2018.0051.
5.
Brucker-Davis F. 1998. Effects of environmental synthetic chemicals on thyroid function. Thyroid 8(9):827–856. https://pubmed.ncbi.nlm.nih.gov/9777756/, https://doi.org/10.1089/thy.1998.8.827.
6.
Murk AJ, Rijntjes E, Blaauboer BJ, Clewell R, Crofton KM, Dingemans MML, et al. 2013. Mechanism-based testing strategy using in vitro approaches for identification of thyroid hormone disrupting chemicals. Toxicol In Vitro 27(4):1320–1346. https://pubmed.ncbi.nlm.nih.gov/23453986/, https://doi.org/10.1016/j.tiv.2013.02.012.
7.
Noyes PD, Friedman KP, Browne P, Haselman JT, Gilbert ME, Hornung MW, et al. 2019. Evaluating chemicals for thyroid disruption: opportunities and challenges with in vitro testing and adverse outcome pathway approaches. Environ Health Perspect 127(9):95001. https://pubmed.ncbi.nlm.nih.gov/31487205/, https://doi.org/10.1289/EHP5297.
8.
Zoeller RT. 2005. Environmental chemicals as thyroid hormone analogues: new studies indicate that thyroid hormone receptors are targets of industrial chemicals? Mol Cell Endocrinol 242(1–2):10–15. https://pubmed.ncbi.nlm.nih.gov/16150534/, https://doi.org/10.1016/j.mce.2005.07.006.
9.
Yao H-Y, Han Y, Gao H, Huang K, Ge X, Xu Y-Y, et al. 2016. Maternal phthalate exposure during the first trimester and serum thyroid hormones in pregnant women and their newborns. Chemosphere 157:42–48. https://pubmed.ncbi.nlm.nih.gov/27208644/, https://doi.org/10.1016/j.chemosphere.2016.05.023.
10.
Johns LE, Ferguson KK, McElrath TF, Mukherjee B, Meeker JD. 2016. Associations between repeated measures of maternal urinary phthalate metabolites and thyroid hormone parameters during pregnancy. Environ Health Perspect 124(11):1808–1815. https://pubmed.ncbi.nlm.nih.gov/27152641/, https://doi.org/10.1289/EHP170.
11.
Braun JM, Kalkbrenner AE, Calafat AM, Bernert JT, Ye X, Silva MJ, et al. 2011. Variability and predictors of urinary bisphenol A concentrations during pregnancy. Environ Health Perspect 119(1):131–137. https://pubmed.ncbi.nlm.nih.gov/21205581/, https://doi.org/10.1289/ehp.1002366.
12.
Casas M, Basagaña X, Sakhi AK, Haug LS, Philippat C, Granum B, et al. 2018. Variability of urinary concentrations of non-persistent chemicals in pregnant women and school-aged children. Environ Int 121(Pt 1):561–573. https://pubmed.ncbi.nlm.nih.gov/30300814/, https://doi.org/10.1016/j.envint.2018.09.046.
13.
Philippat C, Wolff MS, Calafat AM, Ye X, Bausell R, Meadows M, et al. 2013. Prenatal exposure to environmental phenols: concentrations in amniotic fluid and variability in urinary concentrations during pregnancy. Environ Health Perspect 121(10):1225–1231. https://pubmed.ncbi.nlm.nih.gov/23942273/, https://doi.org/10.1289/ehp.1206335.
14.
de Klerk NH, English DR, Armstrong BK. 1989. A review of the effects of random measurement error on relative risk estimates in epidemiological studies. Int J Epidemiol 18(3):705–712. https://pubmed.ncbi.nlm.nih.gov/2807678/, https://doi.org/10.1093/ije/18.3.705.
15.
Perrier F, Giorgis-Allemand L, Slama R, Philippat C. 2016. Within-subject pooling of biological samples to reduce exposure misclassification in biomarker-based studies. Epidemiology 27(3):378–388. https://pubmed.ncbi.nlm.nih.gov/27035688/, https://doi.org/10.1097/EDE.0000000000000460.
16.
Agier L, Portengen L, Chadeau-Hyam M, Basagaña X, Giorgis-Allemand L, Siroux V, et al. 2016. A systematic comparison of linear regression-based statistical methods to assess exposome–health associations. Environ Health Perspect 124(12):1848–1856. https://pubmed.ncbi.nlm.nih.gov/27219331/, https://doi.org/10.1289/EHP172.
17.
U.S. EPA (U.S. Environmental Protection Agency). 2020. Toxcast & Tox21 Summary files from invitrodb_v3_3. Retrieved from https://www.epa.gov/chemical-research/toxicity-forecaster-toxcasttm-data [accessed 21 November 2020].
18.
Lyon-Caen S, Siroux V, Lepeule J, et al. 2019. Deciphering the impact of Early-Life exposures to highly variable environmental factors on foetal and child health: design of SEPAGES couple-child cohort. Int J Environ Res Public Health 16(20):3888. https://pubmed.ncbi.nlm.nih.gov/31615055/, https://doi.org/10.3390/ijerph16203888.
19.
Vernet C, Philippat C, Agier L, Calafat AM, Ye X, Lyon-Caen S, et al. 2019. An empirical validation of the within-subject biospecimens pooling approach to minimize exposure misclassification in biomarker-based studies. Epidemiology 30(5):756–767. https://pubmed.ncbi.nlm.nih.gov/31373935/, https://doi.org/10.1097/EDE.0000000000001056.
20.
Philippat C, Calafat AM. 2021. Comparison of strategies to efficiently combine repeated urine samples in biomarker-based studies. Environ Res 192:110275. https://pubmed.ncbi.nlm.nih.gov/33022216/, https://doi.org/10.1016/j.envres.2020.110275.
21.
Sabaredzovic A, Sakhi AK, Brantsæter AL, Thomsen C. 2015. Determination of 12 urinary phthalate metabolites in Norwegian pregnant women by core-shell high performance liquid chromatography with on-line solid-phase extraction, column switching and tandem mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci 1002:343–352. https://pubmed.ncbi.nlm.nih.gov/26355271/, https://doi.org/10.1016/j.jchromb.2015.08.040.
22.
Sakhi AK, Sabaredzovic A, Papadopoulou E, Cequier E, Thomsen C. 2018. Levels, variability and determinants of environmental phenols in pairs of Norwegian mothers and children. Environ Int 114:242–251. https://pubmed.ncbi.nlm.nih.gov/29524920/, https://doi.org/10.1016/j.envint.2018.02.037.
23.
Rolland M, Lyon-Caen S, Sakhi AK, Pin I, Sabaredzovic A, Thomsen C, et al. 2020. Exposure to phenols during pregnancy and the first year of life in a new type of couple-child cohort relying on repeated urine biospecimens. Environ Int 139:105678. https://pubmed.ncbi.nlm.nih.gov/32248023/, https://doi.org/10.1016/j.envint.2020.105678.
24.
Monneret D, Guergour D, Vergnaud S, Laporte F, Faure P, Gauchez AS. 2013. Evaluation of LOCI technology-based thyroid blood tests on the dimension vista analyzer. Clin Biochem 46(13–14):1290–1297. https://pubmed.ncbi.nlm.nih.gov/23195135/, https://doi.org/10.1016/j.clinbiochem.2012.11.011.
25.
Stürup S, Hayes RB, Peters U. 2005. Development and application of a simple routine method for the determination of selenium in serum by octopole reaction system ICPMS. Anal Bioanal Chem 381(3):686–694. https://pubmed.ncbi.nlm.nih.gov/15702311/, https://doi.org/10.1007/s00216-004-2946-x.
26.
Allain P, Mauras Y, Dougé C, Jaunault L, Delaporte T, Beaugrand C. 1990. Determination of iodine and bromine in plasma and urine by inductively coupled plasma mass spectrometry. Analyst 115(6):813–815. https://pubmed.ncbi.nlm.nih.gov/2393085/, https://doi.org/10.1039/an9901500813.
27.
Hallinger DR, Murr AS, Buckalew AR, Simmons SO, Stoker TE, Laws SC. 2017. Development of a screening approach to detect thyroid disrupting chemicals that inhibit the human sodium iodide symporter (NIS). Toxicol In Vitro 40:66–78. https://pubmed.ncbi.nlm.nih.gov/27979590/, https://doi.org/10.1016/j.tiv.2016.12.006.
28.
Wang J, Hallinger DR, Murr AS, Buckalew AR, Simmons SO, Laws SC, et al. 2018. High-Throughput screening and quantitative chemical ranking for sodium-iodide symporter inhibitors in ToxCast phase I chemical library. Environ Sci Technol 52(9):5417–5426. https://pubmed.ncbi.nlm.nih.gov/29611697/, https://doi.org/10.1021/acs.est.7b06145.
29.
Paul Friedman K, Watt ED, Hornung MW, Hedge JM, Judson RS, Crofton KM, et al. 2016. Tiered high-throughput screening approach to identify thyroperoxidase inhibitors within the ToxCast phase I and II chemical libraries. Toxicol Sci 151(1):160–180. https://pubmed.ncbi.nlm.nih.gov/26884060/, https://doi.org/10.1093/toxsci/kfw034.
30.
Paul-Friedman K, Martin M, Crofton KM, Hsu C-W, Sakamuru S, Zhao J, et al. 2019. Limited chemical structural diversity found to modulate thyroid hormone receptor in the Tox21 chemical library. Environ Health Perspect 127(9):97009. https://pubmed.ncbi.nlm.nih.gov/31566444/, https://doi.org/10.1289/EHP5314.
31.
Paul-Friedman K, Zhao J, Huang R, Xia M, Crofton K, Houck K. 2017. Screening the Tox21 10K Library for Thyroid Stimulating Hormone Receptor Agonist and Antagonist Activity. https://doi.org/10.23645/epacomptox.5176897.v1
32.
Hornung MW, Korte JJ, Olker JH, Denny JS, Knutsen C, Hartig PC, et al. 2018. Screening the ToxCast phase 1 chemical library for inhibition of deiodinase type 1 activity. Toxicol Sci 162(2):570–581. https://pubmed.ncbi.nlm.nih.gov/29228274/, https://doi.org/10.1093/toxsci/kfx279.
33.
Olker JH, Korte JJ, Denny JS, Hartig PC, Cardon MC, Knutsen CN, et al. 2019. Screening the ToxCast phase 1, phase 2, and e1k chemical libraries for inhibitors of iodothyronine deiodinases. Toxicol Sci 168(2):430–442. https://pubmed.ncbi.nlm.nih.gov/30561685/, https://doi.org/10.1093/toxsci/kfy302.
34.
OECD (Organisation for Economic Co-operation and Development). 2017. New Scoping Document on In Vitro and Ex Vivo Assays for the Identification of Modulators of Thyroid Hormone Signalling. Paris, France: OECD https://doi.org/10.1787/9789264274716-en.
35.
Bajard L, Melymuk L, Blaha L. 2019. Prioritization of hazards of novel flame retardants using the mechanistic toxicology information from ToxCast and Adverse Outcome Pathways. Environ Sci Eur 31(1):14, https://doi.org/10.1186/s12302-019-0195-z.
36.
Paul Friedman K, Gagne M, Loo L-H, Karamertzanis P, Netzeva T, Sobanski T, et al. 2020. Utility of in vitro bioactivity as a lower bound estimate of in vivo adverse effect levels and in risk-based prioritization. Toxicol Sci 173(1):202–225. https://pubmed.ncbi.nlm.nih.gov/31532525/, https://doi.org/10.1093/toxsci/kfz201.
37.
Williams AJ, Grulke CM, Edwards J, McEachran AD, Mansouri K, Baker NC, et al. 2017. The CompTox chemistry dashboard: a community data resource for environmental chemistry. J Cheminform 9(1):61. https://pubmed.ncbi.nlm.nih.gov/29185060/, https://doi.org/10.1186/s13321-017-0247-6.
38.
Helsel DR. 2011. Statistics for Censored Environmental Data Using Minitab and R, vol 77. Hoboken, NJ: John Wiley & Sons.
39.
Lubin JH, Colt JS, Camann D, Davis S, Cerhan JR, Severson RK, et al. 2004. Epidemiologic evaluation of measurement data in the presence of detection limits. Environ Health Perspect 112(17):1691–1696. https://pubmed.ncbi.nlm.nih.gov/15579415/, https://doi.org/10.1289/ehp.7199.
40.
Mortamais M, Chevrier C, Philippat C, Petit C, Calafat AM, Ye X, et al. 2012. Correcting for the influence of sampling conditions on biomarkers of exposure to phenols and phthalates: a 2-step standardization method based on regression residuals. Environ Health 11(1):29. https://pubmed.ncbi.nlm.nih.gov/22537080/, https://doi.org/10.1186/1476-069X-11-29.
41.
Philippat C, Mortamais M, Chevrier C, Petit C, Calafat AM, Ye X, et al. 2012. Exposure to phthalates and phenols during pregnancy and offspring size at birth. Environ Health Perspect 120(3):464–470. https://pubmed.ncbi.nlm.nih.gov/21900077/, https://doi.org/10.1289/ehp.1103634.
42.
Herbstman J, Apelberg BJ, Witter FR, Panny S, Goldman LR. 2008. Maternal, infant, and delivery factors associated with neonatal thyroid hormone status. Thyroid 18(1):67–76. https://pubmed.ncbi.nlm.nih.gov/18302520/, https://doi.org/10.1089/thy.2007.0180.
43.
Romano ME, Eliot MN, Zoeller RT, Hoofnagle AN, Calafat AM, Karagas MR, et al. 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. https://pubmed.ncbi.nlm.nih.gov/29606598/, https://doi.org/10.1016/j.ijheh.2018.03.010.
44.
Villanger GD, Drover SSM, Nethery RC, Thomsen C, Sakhi AK, Øvergaard KR, et al. 2020. Associations between urine phthalate metabolites and thyroid function in pregnant women and the influence of iodine status. Environ Int 137:105509. https://pubmed.ncbi.nlm.nih.gov/32044443/, https://doi.org/10.1016/j.envint.2020.105509.
45.
World Health Organization. 2013. Urinary Iodine Concentrations for Determining Iodine Status in Populations. Geneva, Switzerland: World Health Organization.
46.
Lazarevic N, Barnett AG, Sly PD, Knibbs LD. 2019. Statistical methodology in studies of prenatal exposure to mixtures of endocrine-disrupting chemicals: a review of existing approaches and new alternatives. Environ Health Perspect 127(2):26001. https://pubmed.ncbi.nlm.nih.gov/30720337/, https://doi.org/10.1289/EHP2207.
47.
Bobb JF, Valeri L, Claus Henn B, Christiani DC, Wright RO, Mazumdar M, et al. 2015. Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures. Biostatistics 16(3):493–508. https://pubmed.ncbi.nlm.nih.gov/25532525/, https://doi.org/10.1093/biostatistics/kxu058.
48.
Philippat C, Rolland M, Lyon-Caen S, Pin I, Sakhi AK, Sabaredzovic A, et al. 2021. Pre- and early post-natal exposure to phthalates and DINCH in a new type of mother-child cohort relying on within-subject pools of repeated urine samples. Environ Pollut 287:117650. https://pubmed.ncbi.nlm.nih.gov/34435564/, https://doi.org/10.1016/j.envpol.2021.117650.
49.
Bhat VS, Durham JL, English JC. 2014. Derivation of an oral reference dose (RfD) for the plasticizer, di-(2-propylheptyl)phthalate (palatinol® 10-P). Regul Toxicol Pharmacol 70(1):65–74. https://pubmed.ncbi.nlm.nih.gov/24925829/, https://doi.org/10.1016/j.yrtph.2014.06.002.
50.
Aker AM, Ferguson KK, Rosario ZY, Mukherjee B, Alshawabkeh AN, Calafat AM, et al. 2019. A repeated measures study of phenol, paraben and triclocarban urinary biomarkers and circulating maternal hormones during gestation in the Puerto Rico PROTECT cohort. Environ Health 18(1):28. https://pubmed.ncbi.nlm.nih.gov/30940137/, https://doi.org/10.1186/s12940-019-0459-5.
51.
Berger K, Gunier RB, Chevrier J, Calafat AM, Ye X, Eskenazi B, et al. 2018. Associations of maternal exposure to triclosan, parabens, and other phenols with prenatal maternal and neonatal thyroid hormone levels. Environ Res 165:379–386. https://pubmed.ncbi.nlm.nih.gov/29803919/, https://doi.org/10.1016/j.envres.2018.05.005.
52.
Gogoi P, Kalita JC. 2020. Effects of butylparaben exposure on thyroid peroxidase (TPO) and type 1 iodothyronine deiodinase (D1) in female Wistar rats. Toxicology 443:152562. https://pubmed.ncbi.nlm.nih.gov/32798586/, https://doi.org/10.1016/j.tox.2020.152562.
53.
Aung MT, Johns LE, Ferguson KK, Mukherjee B, McElrath TF, Meeker JD. 2017. Thyroid hormone parameters during pregnancy in relation to urinary bisphenol A concentrations: a repeated measures study. Environ Int 104:33–40. https://pubmed.ncbi.nlm.nih.gov/28410473/, https://doi.org/10.1016/j.envint.2017.04.001.
54.
Wang X, Tang N, Nakayama SF, Fan P, Liu Z, Zhang J, et al. 2020. Maternal urinary bisphenol a concentration and thyroid hormone levels of Chinese mothers and newborns by maternal body mass index. Environ Sci Pollut Res Int 27(10):10939–10949. https://pubmed.ncbi.nlm.nih.gov/31953761/, https://doi.org/10.1007/s11356-020-07705-8.
55.
Chevrier J, Gunier RB, Bradman A, et al. 2013. Maternal urinary bisphenol a during pregnancy and maternal and neonatal thyroid function in the CHAMACOS study. Environ Health Perspect 121(1):138–144. https://pubmed.ncbi.nlm.nih.gov/23052180/, https://doi.org/10.1289/ehp.1205092.
56.
Derakhshan A, Philips EM, Ghassabian A, Santos S, Asimakopoulos AG, Kannan K, et al. 2021. Association of urinary bisphenols during pregnancy with maternal, cord blood and childhood thyroid function. Environ Int 146:106160. https://pubmed.ncbi.nlm.nih.gov/33068853/, https://doi.org/10.1016/j.envint.2020.106160.
57.
Derakhshan A, Shu H, Peeters RP, Kortenkamp A, Lindh CH, Demeneix B, et al. 2019. Association of urinary bisphenols and triclosan with thyroid function during early pregnancy. Environ Int 133(Pt A):105123. https://pubmed.ncbi.nlm.nih.gov/31521814/, https://doi.org/10.1016/j.envint.2019.105123.
58.
Romano ME, Webster GM, Vuong AM, et al. 2015. Gestational urinary bisphenol A and maternal and newborn thyroid hormone concentrations: The HOME Study. Environ Res 138. https://pubmed.ncbi.nlm.nih.gov/25794847/, https://doi.org/10.1016/j.envres.2015.03.003.
59.
Guignard D, Gayrard V, Lacroix MZ, Puel S, Picard-Hagen N, Viguié C. 2017. Evidence for bisphenol A-induced disruption of maternal thyroid homeostasis in the pregnant ewe at low level representative of human exposure. Chemosphere 182:458–467. https://pubmed.ncbi.nlm.nih.gov/28521160/, https://doi.org/10.1016/j.chemosphere.2017.05.028.
60.
Viguié C, Collet SH, Gayrard V, Picard-Hagen N, Puel S, Roques BB, et al. 2013. Maternal and fetal exposure to bisphenol A is associated with alterations of thyroid function in pregnant ewes and their newborn lambs. Endocrinology 154(1):521–528. https://pubmed.ncbi.nlm.nih.gov/23150491/, https://doi.org/10.1210/en.2012-1401.
61.
Xu X, Liu Y, Sadamatsu M, Tsutsumi S, Akaike M, Ushijima H, et al. 2007. Perinatal bisphenol A affects the behavior and SRC-1 expression of male pups but does not influence on the thyroid hormone receptors and its responsive gene. Neurosci Res 58(2):149–155. https://pubmed.ncbi.nlm.nih.gov/17412439/, https://doi.org/10.1016/j.neures.2007.02.011.
62.
Wang X, Ouyang F, Feng L, Wang X, Liu Z, Zhang J. 2017. Maternal urinary triclosan concentration in relation to maternal and neonatal thyroid hormone levels: a prospective study. Environ Health Perspect 125(6):067017. https://pubmed.ncbi.nlm.nih.gov/28669941/, https://doi.org/10.1289/EHP500.
63.
Butt CM, Stapleton HM. 2013. Inhibition of thyroid hormone sulfotransferase activity by brominated flame retardants and halogenated phenolics. Chem Res Toxicol 26(11):1692–1702. https://pubmed.ncbi.nlm.nih.gov/24089703/, https://doi.org/10.1021/tx400342k.
64.
Moriyama K, Tagami T, Akamizu T, Usui T, Saijo M, Kanamoto N, et al. 2002. Thyroid hormone action is disrupted by bisphenol a as an antagonist. J Clin Endocrinol Metab 87(11):5185–5190. https://pubmed.ncbi.nlm.nih.gov/12414890/, https://doi.org/10.1210/jc.2002-020209.
65.
Paul KB, Thompson JT, Simmons SO, Vanden Heuvel JP, Crofton KM. 2013. Evidence for triclosan-induced activation of human and rodent xenobiotic nuclear receptors. Toxicol In Vitro 27(7):2049–2060. https://pubmed.ncbi.nlm.nih.gov/23899473/, https://doi.org/10.1016/j.tiv.2013.07.008.
66.
Paul KB, Hedge JM, DeVito MJ, Crofton KM. 2010. Short-term exposure to triclosan decreases thyroxine in vivo via upregulation of hepatic catabolism in young Long-Evans rats. Toxicol Sci 113(2):367–379. https://pubmed.ncbi.nlm.nih.gov/19910387/, https://doi.org/10.1093/toxsci/kfp271.
67.
Terasaki M, Kosaka K, Kunikane S, Makino M, Shiraishi F. 2011. Assessment of thyroid hormone activity of halogenated bisphenol A using a yeast two-hybrid assay. Chemosphere 84(10):1527–1530. https://pubmed.ncbi.nlm.nih.gov/21550628/, https://doi.org/10.1016/j.chemosphere.2011.04.045.
68.
Zoeller RT, Bansal R, Parris C. 2005. Bisphenol-A, an environmental contaminant that acts as a thyroid hormone receptor antagonist in vitro, increases serum thyroxine, and alters RC3/neurogranin expression in the developing rat brain. Endocrinology 146(2):607–612. https://pubmed.ncbi.nlm.nih.gov/15498886/, https://doi.org/10.1210/en.2004-1018.
69.
Olker JH, Korte JJ, Denny JS, Haselman JT, Hartig PC, Cardon MC, et al. 2021. In vitro screening for chemical inhibition of the iodide recycling enzyme, iodotyrosine deiodinase. Toxicol In Vitro 71:105073. https://pubmed.ncbi.nlm.nih.gov/33352258/, https://doi.org/10.1016/j.tiv.2020.105073.
70.
Derakhshan A, Shu H, Broeren MAC, Lindh CH, Peeters RP, Kortenkamp A, et al. 2021. Association of phthalate exposure with thyroid function during pregnancy. Environ Int 157:106795. https://pubmed.ncbi.nlm.nih.gov/34358912/, https://doi.org/10.1016/j.envint.2021.106795.
71.
Breous E, Wenzel A, Loos U. 2005. The promoter of the human sodium/iodide symporter responds to certain phthalate plasticisers. Mol Cell Endocrinol 244(1–2):75–78. https://pubmed.ncbi.nlm.nih.gov/16257484/, https://doi.org/10.1016/j.mce.2005.06.009.
72.
De Luca R, Davis PJ, Lin H-Y, Gionfra F, Percario ZA, Affabris E, et al. 2020. Thyroid hormones interaction with immune response, inflammation and non-thyroidal illness syndrome. Front Cell Dev Biol 8:614030. https://pubmed.ncbi.nlm.nih.gov/33553149/, https://doi.org/10.3389/fcell.2020.614030.
73.
Luton D, Alberti C, Vuillard E, Ducarme G, Oury JF, Guibourdenche J. 2011. Iodine deficiency in northern Paris area: impact on fetal thyroid mensuration. PLoS One 6(2):e14707. https://pubmed.ncbi.nlm.nih.gov/21359193/, https://doi.org/10.1371/journal.pone.0014707.
74.
Raverot V, Bournaud C, Sassolas G, Orgiazzi J, Claustrat F, Gaucherand P, et al. 2012. Pregnant French women living in the Lyon area are iodine deficient and have elevated serum thyroglobulin concentrations. Thyroid 22(5):522–528. https://pubmed.ncbi.nlm.nih.gov/22468941/, https://doi.org/10.1089/thy.2011.0184.
75.
Zimmermann M, Delange F. 2004. Iodine supplementation of pregnant women in Europe: a review and recommendations. Eur J Clin Nutr 58(7):979–984. https://pubmed.ncbi.nlm.nih.gov/15220938/, https://doi.org/10.1038/sj.ejcn.1601933.

Information & Authors

Information

Published In

Environmental Health Perspectives
Volume 130Issue 11November 2022
PubMed: 36350136

History

Received: 1 September 2021
Revision received: 1 August 2022
Accepted: 7 October 2022
Published online: 9 November 2022

Authors

Affiliations

Dorothy Nakiwala
Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB), Institut national de la santé et de la recherche médicale (Inserm) U1209, Centre national de la recherche scientifique (CNRS) UMR 5309, Université Grenoble Alpes, Grenoble, France
Pamela D. Noyes
Center for Public Health and Environmental Assessment, Office of Research and Development (ORD), U.S. Environmental Protection Agency, Washington, District of Columbia, USA
Patrice Faure
Service de Biochimie SB2TE, Institut de Biologie et Pathologie CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France
Benoît Chovelon
Service de Biochimie SB2TE, Institut de Biologie et Pathologie CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France
Département de Pharmacochimie Moleculaire, CNRS, UMR 5063, Université Grenoble Alpes, Grenoble, France
Service de Biochimie SB2TE, Institut de Biologie et Pathologie CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France
Anne Sophie Gauchez
Service de Biochimie SB2TE, Institut de Biologie et Pathologie CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France
Dorra Guergour
Service de Biochimie SB2TE, Institut de Biologie et Pathologie CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France
Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB), Institut national de la santé et de la recherche médicale (Inserm) U1209, Centre national de la recherche scientifique (CNRS) UMR 5309, Université Grenoble Alpes, Grenoble, France
Department of Food Safety, Norwegian Institute of Public Health, Oslo, Norway
Azemira Sabaredzovic
Department of Food Safety, Norwegian Institute of Public Health, Oslo, Norway
Department of Food Safety, Norwegian Institute of Public Health, Oslo, Norway
Isabelle Pin
Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB), Institut national de la santé et de la recherche médicale (Inserm) U1209, Centre national de la recherche scientifique (CNRS) UMR 5309, Université Grenoble Alpes, Grenoble, France
Pediatric Department, Grenoble University Hospital, La Tronche, France
Rémy Slama
Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB), Institut national de la santé et de la recherche médicale (Inserm) U1209, Centre national de la recherche scientifique (CNRS) UMR 5309, Université Grenoble Alpes, Grenoble, France
Claire Philippat https://orcid.org/0000-0002-4959-6648 on behalf of and the SEPAGES Study Group
Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB), Institut national de la santé et de la recherche médicale (Inserm) U1209, Centre national de la recherche scientifique (CNRS) UMR 5309, Université Grenoble Alpes, Grenoble, France

Notes

Address correspondence to Claire Philippat, Institute for Advanced Bioscience (IAB), Site Santé, Allée des Alpes, 38700, La Tronche, France. Telephone: +33 4 76 54 94 51. Email: [email protected]

Metrics & Citations

Metrics

About Article Metrics


Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click DOWNLOAD.

Cited by

  • Trimester-specific exposure to triclocarban during pregnancy: Associations with oxidative stress and size at birth, Science of The Total Environment, 10.1016/j.scitotenv.2023.168100, 907, (168100), (2024).
  • Analyzing the impact of phthalate and DINCH exposure on fetal growth in a cohort with repeated urine collection, Environment International, 10.1016/j.envint.2024.108584, 186, (108584), (2024).
  • Associations of urinary non-persistent endocrine disrupting chemical biomarkers with early-to-mid pregnancy plasma sex-steroid and thyroid hormones, Environment International, 10.1016/j.envint.2024.108433, 183, (108433), (2024).
  • Associations between Phthalate Metabolite Concentrations in Follicular Fluid and Reproductive Outcomes among Women Undergoing in Vitro Fertilization/Intracytoplasmic Sperm Injection Treatment, Environmental Health Perspectives, 10.1289/EHP11998, 131, 12, (2023).
  • Association of exposure to perfluoroalkyl substances (PFAS) and phthalates with thyroid hormones in adolescents from HBM4EU aligned studies, Environmental Research, 10.1016/j.envres.2023.116897, 237, (116897), (2023).
  • Exposure to a mixture of non-persistent environmental chemicals and neonatal thyroid function in a cohort with improved exposure assessment, Environment International, 10.1016/j.envint.2023.107840, 173, (107840), (2023).
  • BMI-specific inflammatory response to phthalate exposure in early pregnancy: findings from the TMCHESC study, Environmental Science and Pollution Research, 10.1007/s11356-023-30922-w, 30, 59, (123383-123395), (2023).
  • Invited Perspective: Inroads to Biology-Informed Exposomics, Environmental Health Perspectives, 10.1289/EHP12224, 130, 11, (2022).

View Options

View options

PDF

View PDF

Get Access

Restore your content access

Enter your email address to restore your content access:

Note: This functionality works only for purchases done as a guest. If you already have an account, log in to access the content to which you are entitled.

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share on social media