Is there a relationship between tea intake and maternal whole blood heavy metal concentrations?
Bibliographic record
Abstract
The aim of this analysis was to examine the association between tea intake during pregnancy and maternal and infant metal exposures. Data from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a pan-Canadian pregnancy cohort, were used. All participants with a gestational age of ⩾20 weeks (n=1954) with available biomarkers were included. Geometric means (GMs) for lead, arsenic, mercury, cadmium and manganese in maternal (first and third trimesters) and cord blood, as well as speciated arsenic in maternal urine in the first trimester, were calculated for participants who drank regular, green or herbal tea and for those who did not. Differences between groups were examined using chi-square tests. Adjusted least squares geometric means (LSGMs) were estimated by tea intake, controlling for factors such as country of birth, coffee intake and maternal smoking. Concentrations of all metals were above the limits of detection in most participants in the first trimester: lead (GM): 0.62 μg/dl), mercury (GM: 2.99 nmol/l); cadmium (GM 1.93 nmol/l), arsenic (GM 9.75 nmol/l) and manganese (GM 160.1 nmol/l). Adjusted LSGMs for lead in the first trimester were higher for tea drinkers than for those who were non-tea drinkers (LSGM 0.65 μg/dl, 95%CI: 0.62, 0.69 and 0.61 μg/dl, 95%CI: 0.59, 0.62), and there was evidence of a dose-response relationship for green and herbal tea. Those who consumed herbal tea in the third trimester had significantly higher third trimester maternal and cord blood lead concentrations than non-herbal tea drinkers. This study provides evidence of an association between blood lead concentrations and green or herbal tea consumption. However, the GM blood lead concentrations of the highest tea consumers were still less than 1 μg/dl and within the normal range of blood lead concentrations in the Canadian population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".