Phthalate exposure during pregnancy and its association with thyroid hormones: A prospective cohort study
Bibliographic record
Abstract
Phthalate esters (PAEs) possess endocrine-disrupting properties. Studies in humans have indicated that in utero phthalate exposure affects maternal thyroid hormones, which are essential for fetal growth and development. However, these studies also reported inconsistent results on the relationship between phthalates and thyroid hormones. This prospective cohort study aimed to assess phthalate exposure across the three trimesters of pregnancy and its association with thyroid hormone levels. From 2019 to 2022, we recruited 672 pregnant women, and two urine samples and one blood sample were collected from each participant during the pregnancy. We examined the urine samples from 663, 335, and 294 women in the first, second, and third trimester, respectively, for the following seven phthalate metabolites: monoethyl phthalate (MEP) from diethyl phthalate (DEP); mono-n-butyl phthalate (MnBP) and mono-iso-butyl phthalate (MiBP) from dibutyl phthalate (DBP); monobenzyl phthalate (MBzP) from butyl benzyl phthalate; and three di(2-ethylhexyl) phthalate (DEHP) metabolites, mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP), and mono-(2-ethyl-5-carboxypentyl) phthalate (MECPP). Additionally, we examined the levels of free thyroxine (FT4), thyroid-stimulating hormone (TSH), and total triiodothyronine (TT3) in the serum samples of the following participants: 596, 627, and 576 in the first trimester; 292, 293, and 282 in the second trimester; and 250, 250, and 248 in the third trimester, respectively. Other than MBzP, which was detected in 25%–33% of the samples, other metabolites were detectable in >86% of urine samples, indicating widespread exposure to DEP, DBP, and DEHP. The detected phthalate exposure levels in our cohort were significantly higher than those reported in other countries. Metabolite levels varied across the trimesters, implying changes in exposure and metabolism throughout pregnancy. The observed variability in urinary concentrations of phthalate metabolites, which ranged from poor to moderate, underscores the importance of taking multiple measurements during pregnancy for precise exposure assessment. Using a linear mixed model, we analyzed the effects of repeated phthalate exposure on thyroid hormone levels while adjusting for potential confounders. We observed significant linear trends in FT4, TSH, and, to a lesser extent, TT3 across quartiles of specific phthalate metabolites. Comparing the highest to the lowest quartiles, we found a significant increase in FT4 levels, ranging from 2 to 3.7%, associated with MEP; MECPP; MEHHP; and the sum of seven metabolites (∑7PAE), three DEHP metabolites (∑3DEHP), two DBP metabolites (∑DBP), and both low molecular weight (∑LMW) and high molecular weight metabolites. Increased TSH levels (5%–16%) were observed for all phthalate metabolites (except MEHHP) and their molar sums, including ∑7PAE. For TT3, a significant increase was observed with MEP (2.2%) and a decrease was observed with ∑DBP (−2.7%). A higher TSH/FT4 ratio was observed with the highest quartiles (third or fourth) of several phthalate metabolites: MEP (8.8%), MiBP (8.7%), MnBP (22.2%), ∑7PAE (15.3%), ∑DBP (16.4%), and ∑LMW (18.6%). These hormonal alterations, most notably in the second and third trimesters, suggest that phthalate exposure may impact fetal growth and development by affecting maternal thyroid function.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".