Tea consumption in pregnancy as a predictor of pesticide exposure and adverse birth outcomes: The MIREC Study
Bibliographic record
Abstract
INTRODUCTION: Pesticide residues in tea may contribute to exposure during pregnancy; however, the impact on maternal and infant health is not well understood. The aim of this study was to determine whether tea intake in the first trimester was associated with elevated concentrations of various pesticides in maternal blood or urine. Further, we examined the relationship between tea consumption and adverse birth outcomes. METHODS: Data from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a pan-Canada pregnancy cohort, were used. All singleton, live births (n=1898) with available biomarkers were included in the analyses. Descriptive statistics were used to characterize the population. The geometric means (GM) of organochlorine (OC) pesticide constituents or metabolites in maternal plasma (lipid adjusted) and organophosphate (OP) pesticide metabolites (adjusted for specific gravity) in maternal urine were calculated for participants who drank regular, green or herbal tea in the first trimester and for those who did not. Differences between groups were examined using chi-square or t-tests. Associations between frequency of drinking tea and adverse birth outcomes were examined using logistic regression (preterm birth and small-for-gestational-age) or generalized linear models (birthweight decile and head circumference). RESULTS: The GM of the OC pesticide constituent trans-nonachlor was 2.74 mg/g lipid, and for metabolites oxychlordane and p,p'-DDE this was 1.94 ng/g lipid and 55.8 ng/g lipid, respectively. OP pesticide metabolite concentrations adjusted for specific gravity, were dimethylphosphate (GM: 3.19 µg/L), dimethylthiophosphate (GM: 3.29 µg/L), dimethyldithiophosphate (GM: 0.48 µg/L), diethlphosphate (GM: 2.46), and diethylthiophosphate (GM: 0.67 µg/L). There was no significant difference in mean concentrations for these OC or OP pesticide constituents or metabolites between tea drinkers - of any type - and non-tea drinkers. Further, no association was found between tea intake and adverse birth outcomes. CONCLUSIONS: Pesticide concentrations did not differ by tea intake. Further, tea intake in the first trimester was not associated with adverse birth outcomes. In this study population, there was no evidence for concern about tea intake being a source of the OP or OC pesticide metabolites measured or adversely affecting birth outcomes; however, tea intake was lower than national Canadian data for women of reproductive age.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".