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Descriptive analysis of organophosphate ester metabolites in a pan-Canadian pregnancy cohort

2023· article· en· W4362699733 on OpenAlexafffundabout
Jillian Ashley‐Martin, Susan MacPherson, Zhao Zhao, Éric Gaudreau, Gilles Provencher, Mandy Fisher, Michael M. Borghese, Maryse F. Bouchard, Linda Booij, Tye E. Arbuckle

Bibliographic record

VenueThe Science of The Total Environment · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsMcGill University Health CentreCentre Hospitalier Universitaire Sainte-JustineHealth CanadaInstitut National de Santé Publique du QuébecInstitut National de la Recherche ScientifiqueMcGill UniversityDouglas Mental Health University InstituteStatistics Canada
FundersHealth Canada
KeywordsMetaboliteChemistryBiomonitoringOrganophosphateUrinePopulationChromatographyGas chromatography–mass spectrometryEnvironmental chemistryLiquid chromatography–mass spectrometryMass spectrometryBiologyMedicinePesticideBiochemistryEnvironmental health

Abstract

fetched live from OpenAlex

Organophosphate esters (OPEs) are widely used in numerous consumer products for their flame retardant and plasticizing properties. Despite potential widespread exposure, biomonitoring data during critical windows of development are scarce and limited to the most widely studied metabolites. We quantified urinary concentrations of multiple OPE metabolites in a vulnerable Canadian population. Using data and biobanked specimens from the Maternal-Infant Research on Environmental Chemicals (MIREC) study (2008-2011), we measured first trimester urinary concentrations of 15 OPE metabolites as well as one flame retardant metabolite and quantified associations with sociodemographic and sample collection characteristics in 1865 pregnant participants. We applied 2 different analytical methods to quantify OPEs, one using UItra-Performance Liquid Chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) and the other using Atmospheric Pressure Gas Chromatography coupled to mass spectrometry (APGC-MS/MS) with sensitive limits of detection (0.008-0.1 μg/L). We modelled associations between sociodemographic and sample collection characteristics and specific gravity-standardized chemical concentrations. Six OPE metabolites were detected in the majority (68.1-97.4 %) of participants. Bis-(2-chloroethyl) hydrogen phosphate had the highest detection rate (97.4 %). Diphenyl phosphate had the highest geometric mean concentration (0.657 μg/L). Metabolites of tricresyl phosphate were detected in few participants. Associations between sociodemographic characteristics varied according to each OPE metabolite. Pre-pregnancy body mass index tended to be positively associated with OPE metabolite concentrations whereas age tended to be inversely associated with OPE concentrations. OPE concentrations were, on average, higher in urine samples collected in the summer than other seasons the winter. We present the largest biomonitoring study of OPE metabolites in pregnant people to date. These findings demonstrate widespread exposure to OPEs and their metabolites and identify subpopulations who may experience heightened exposure.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.010
GPT teacher head0.210
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations14
Published2023
Admission routes3
Has abstractyes

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