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Record W4309485618 · doi:10.1016/j.envres.2022.114842

Urinary concentrations and determinants of glyphosate and glufosinate in pregnant Canadian participants in the MIREC study

2022· article· en· W4309485618 on OpenAlexafffundabout
Jillian Ashley‐Martin, Rong Huang, Susan MacPherson, Orly Brion, James Owen, Éric Gaudreau, Jean-François Bienvenu, Mandy Fisher, Michael M. Borghese, Maryse F. Bouchard, Bruce P. Lanphear, Warren G. Foster, Tye E. Arbuckle

Bibliographic record

VenueEnvironmental Research · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsMcMaster UniversityHealth CanadaInstitut National de Santé Publique du QuébecSimon Fraser UniversityUniversité de MontréalMcGill University Health Centre
FundersCanadian Institutes of Health ResearchHealth CanadaMinistère de l’Environnement, de la Protection de la nature et des Parcs
KeywordsGlufosinateGlyphosateBiomonitoringUrinePesticideUrinary systemPregnancyAnimal scienceToxicologyChemistryEnvironmental chemistryBiologyMedicineBiotechnologyAgronomyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Glyphosate is the most widely applied herbicide in agriculture. Glufosinate is a broad spectrum herbicide used to manage glyphosate-resistant weeds. Despite the widespread use of these herbicides, biomonitoring data - which inform risk assessment and management - are sparse. OBJECTIVES: To identify determinants of urinary concentrations of these herbicides and their metabolites in pregnancy. METHODS: We measured urinary concentrations of glyphosate, glufosinate, and their primary metabolites aminomethylphosphonic acid (AMPA) and 3-methylphosphinicopropionic acid (3-MPPA) in a single spot urine specimen collected during the first trimester of pregnancy from the Maternal-Infant Research on Environmental Chemicals (MIREC) study. MIREC recruited about 2000 pregnant women from 10 Canadian cities between 2008 and 2011. We used UItra-Performance Liquid Chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) with sensitive limits of detection to quantify analyte concentrations. We examined urinary concentrations according to maternal sociodemographics, sample collection characteristics, reported pesticide use, and consumption of fruits, vegetables, legumes, and grain products. We used ANOVA models with specific gravity-standardized chemical concentrations as the dependent variable to determine associations with maternal and sample determinants. RESULTS: Among women with biobanked urine samples (n = 1829-1854), 74% and 72% had detectable concentrations of glyphosate and AMPA, respectively. In contrast, one and six percent of women had detectable concentrations of glufosinate and 3-MPPA, respectively. The specific gravity-standardized geometric mean (95% CI) concentrations of glyphosate and AMPA were 0.112 (0.099-0.127) μg/L and 0.159 (0.147-0.172) μg/L, respectively. We observed a dose-response relationship between consumption of whole grain bread and higher urinary glyphosate concentrations. Season of urine collection and self-reported pesticide use were not associated with increased concentrations of any analyte. CONCLUSIONS: We detected glyphosate and AMPA in the majority of pregnant women from this predominantly urban Canadian cohort. Diet was a probable route of 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 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)0.001
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 score0.079
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.068
GPT teacher head0.336
Teacher spread0.267 · 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

Citations47
Published2022
Admission routes3
Has abstractyes

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