Evidence map›Paper›PMID 35839910›Full record

ArticleEnvironmental research2022

Assessing urinary phenol and paraben mixtures in pregnant women with and without gestational diabetes mellitus: A case-control study.

Wei-Jen Chen, Candace Robledo, Erin M Davis, Jean R Goodman, Chao Xu, Jooyeon Hwang, Amanda E Janitz, Tabitha Garwe, Antonia M Calafat, Jennifer D Peck

Open access · greenAbstract read
In one paragraph

Article in Environmental research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
2.9field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.

  1. Pooled it
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  10. International journal of molecular sciences · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 6 institutions in 1 country.

Wei-Jen ChenDepartment of Biostatistics and Epidemiology, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA; Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX, USA. Electronic address: Wei-Jen.Chen@bcm.edu.
Candace RobledoDepartment of Population Health and Biostatistics, School of Medicine, University of Texas Rio Grande Valley, Edinburg, TX, USA.
Erin M DavisDepartment of Anatomical Sciences and Neurobiology, University of Louisville, Louisville, KY, USA.
Jean R GoodmanDivision of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of Missouri, Columbia, MO, USA.
Chao XuDepartment of Biostatistics and Epidemiology, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
Jooyeon HwangDepartment of Occupational and Environmental Health, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
Amanda E JanitzDepartment of Biostatistics and Epidemiology, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
Tabitha GarweDepartment of Biostatistics and Epidemiology, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
Antonia M CalafatDivision of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Jennifer D PeckDepartment of Biostatistics and Epidemiology, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
University of Oklahoma Health Sciences Center · USBaylor College of Medicine · USCenters for Disease Control and Prevention · USThe University of Texas Rio Grande Valley · USUniversity of Louisville · USUniversity of Missouri · US

Funding

Tracking and Evaluation CoreU54GM104938 · NIGMS · UNIVERSITY OF OKLAHOMA HLTH SCIENCES CTR · PI Robert Hal Scofield · 2013 to 2026
$68.2M
South Texas Alzheimer's Disease Research CenterP30AG066546 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI Mohamad Habes · 2021 to 2026
$24.0M
NIA NIH HHS P30 AG066546NIGMS NIH HHS U54 GM104938
6 · The paper itself

Abstract

Prior studies have identified the associations between environmental phenol and paraben exposures and increased risk of gestational diabetes mellitus (GDM), but no study addressed these exposures as mixtures. As methods have emerged to better assess exposures to multiple chemicals, our study aimed to apply Bayesian kernel machine regression (BKMR) to evaluate the association between phenol and paraben mixtures and GDM. This study included 64 GDM cases and 237 obstetric patient controls from the University of Oklahoma Medical Center. Mid-pregnancy spot urine samples were collected to quantify concentrations of bisphenol A (BPA), benzophenone-3, triclosan, 2,4-dichlorophenol, 2,5-dichlorophenol, butylparaben, methylparaben, and propylparaben. Multivariable logistic regression was used to evaluate the associations between individual chemical biomarkers and GDM while controlling for confounding. We used probit implementation of BKMR with hierarchical variable selection to estimate the mean difference in GDM probability for each component of the phenol and paraben mixtures while controlling for the correlation among the chemical biomarkers. When analyzing individual chemicals using logistic regression, benzophenone-3 was positively associated with GDM [adjusted odds ratio (aOR) per interquartile range (IQR) = 1.54, 95% confidence interval (CI) 1.15, 2.08], while BPA was negatively associated with GDM (aOR 0.61, 95% CI 0.37, 0.99). In probit-BKMR analysis, an increase in z-score transformed log urinary concentrations of benzophenone-3 from the 10th to 90th percentile was associated with an increase in the estimated difference in the probability of GDM (0.67, 95% Credible Interval 0.04, 1.30), holding other chemicals fixed at their medians. No associations were identified between other chemical biomarkers and GDM in the BKMR analyses. We observed that the association of BPA and GDM was attenuated when accounting for correlated phenols and parabens, suggesting the importance of addressing chemical mixtures in perinatal environmental exposure studies. Additional prospective investigations will increase the understanding of the relationship between benzophenone-3 exposure and GDM development.

Indexed as

Diabetes, GestationalParabensBayes TheoremBiomarkersCase-Control StudiesFemaleHumansPhenolPhenolsPregnancyPregnant PeopleProspective StudiesBiomarkersParabensPhenolPhenolsBenzophenone-3Bisphenol AGestational diabetesMixturesParabenPhenol

Identifiers

PMID35839910
PMCPMC9514543
OpenAlexW4285585273

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.