The association between metals, vitamin D and preterm birth in the MIREC Cohort Study
Bibliographic record
Abstract
BACKGROUND Toxic metals, like lead, are risk factors for preterm birth (PTB), but few studies have explored this association at the low levels currently found in the Canadian population. Conversely, studies suggest that vitamin D may have antioxidant activity and protect against PTB. In this study, we sought to investigate associations between toxic metals (lead, mercury, cadmium and arsenic) and PTB, and to determine if maternal plasma vitamin D concentrations modify these associations. METHODS We investigated whether metals in whole blood measured in early and late pregnancy were associated with PTB (<37 weeks) and spontaneous PTB (a subset of PTBs with spontaneous labour) in 1,851 pregnancies resulting in live births from the Maternal-Infant Research on Environmental Chemicals Study (MIREC) using discrete time survival analysis. In addition, we investigated whether the risk of PTB was modified by plasma 25OHD concentration measured in the 1st trimester. RESULTS Six percent (n=117) of live births in the MIREC study were PTBs; 4% (n=89) were spontaneous PTBs. A 1 μg/dL increase in blood lead concentrations during pregnancy was associated with increased risk of PTB (OR=1.5, 95% CI: 1.0, 2.3) and spontaneous PTB (OR=1.7, 95% CI: 1.1, 2.8). In stratified analysis, the risk of PTB (OR=2.7, 95% CI: 1.0, 7.2) and spontaneous PTB (OR=3.4, 95% CI: 1.1, 10.8) was higher in women with insufficient vitamin D concentrations (25OHD <50 nmol/L). However, a statistical interaction was not seen. Arsenic was associated with a higher risk of PTB (OR=1.1, 95% CI: 1.0, 1.2) and spontaneous PTB (OR=1.1, 95% CI: 1.0, 1.2) per µg/L, but these associations were not modified by vitamin D. CONCLUSIONS Low levels of lead and arsenic may increase the risk of PTB and spontaneous PTB; the association with lead was stronger in participants with insufficient plasma vitamin D. Replication of these findings is warranted.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".