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Record W2985699996 · doi:10.1016/j.envint.2019.105219

Exposures to chemical mixtures during pregnancy and neonatal outcomes: The HOME study

2019· article· en· W2985699996 on OpenAlexaff
Geetika Kalloo, Gregory A. Wellenius, Lawrence C. McCandless, Antonia M. Calafat, Andreas Sjödin, Megan E. Romano, Margaret R. Karagas, Aimin Chen, Kimberly Yolton, Bruce P. Lanphear, Joseph M. Braun

Bibliographic record

VenueEnvironment International · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEffects and risks of endocrine disrupting chemicals
Canadian institutionsChild and Family Research InstituteSimon Fraser University
FundersU.S. Public Health ServiceNational Institute of Environmental Health SciencesInstitute at Brown for Environment and Society, Brown UniversityCenters for Disease Control and Prevention
KeywordsPregnancyEnvironmental chemistryOrganophosphateChemistryMedicinePesticideEnvironmental healthBiology

Abstract

fetched live from OpenAlex

INTRODUCTION: Exposure to mixtures of environmental chemicals are prevalent among pregnant women and may be associated with altered fetal growth and gestational age. To date, most research regarding environmental chemicals and neonatal outcomes has focused on the effect of individual agents. METHODS: In a prospective cohort of 380 pregnant women from Cincinnati, OH (enrolled 2003-2006), we used biomarkers to estimate exposure to 43 phenols, phthalates, metals, organophosphate/pyrethroid/organochlorine pesticides, polychlorinated biphenyls, polybrominated diphenyl ethers, perfluoroalkyl substances (PFAS), and environmental tobacco smoke. Using three approaches, we estimated covariate-adjusted associations of chemical mixtures or individual chemicals with gestational-age-specific birth weight z-scores, birth length, head circumference, and gestational age: k-means clustering, principal components (PC), and one-chemical-at-a-time regression. RESULTS: We identified three chemical mixture profiles using k-means clustering. Women in cluster 1 had higher concentrations of most phenols, three phthalate metabolites, several metals, organophosphate/organochlorine pesticides, polychlorinated biphenyls, and several PFAS than women in clusters 2 and 3. On average, infants born to women in clusters 1 (-1.2 cm; 95% CI: -1.9, -0.5) and 2 (-0.5 cm; 95% CI: -1.1, 0.1) had lower birth length than infants in cluster 3. Six PCs explained 50% of the variance in biomarker concentrations and biomarkers with similar chemical structures or from shared commercial/industrial settings loaded onto commons PCs. Each standard deviation increase in PC 1 (organochlorine pesticides, some phenols) and PC 6 (cadmium, bisphenol A) was associated with 0.2 cm (95% CI: -0.4, 0.0) and 0.1 cm (95% CI: -0.4, 0.1) lower birth length, respectively. Organochlorine compounds, parabens, and cadmium were inversely associated with birth length in the one-chemical-at-a-time analysis. Cluster membership, PC scores, and individual chemicals were not associated with other birth outcomes. CONCLUSION: All three methods of characterizing multiple chemical exposures in this cohort identified inverse associations of select organochlorine compounds, phenols, and cadmium with birth length, but not other neonatal outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.005
GPT teacher head0.277
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations92
Published2019
Admission routes1
Has abstractyes

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