Association between toxic metals, vitamin D and preterm birth in the<scp>Maternal–Infant</scp>research on environmental chemicals study
Bibliographic record
Abstract
BACKGROUND: Toxic metals, like lead, are risk factors for preterm birth (PTB), but few studies have examined low levels found in most Canadians. Vitamin D, which may have antioxidant activity, protects against PTB. OBJECTIVES: In this study, we investigated the impact of toxic metals (lead, mercury, cadmium and arsenic) on PTB and examined if maternal plasma vitamin D concentrations modify these associations. METHODS: We investigated whether concentrations of metals in whole blood measured in early and late pregnancy were associated with PTB (<37 weeks) and spontaneous PTB in 1851 live births from the Maternal-Infant Research on Environmental Chemicals Study using discrete time survival analysis. We also investigated whether the risk of PTB was modified by first-trimester plasma 25-hydroxyvitamin D (25OHD) concentrations. RESULTS: Of 1851 live births, 6.1% (n = 113) were PTBs and 4.9% (n = 89) were spontaneous PTB. A 1 μg/dL increase in blood lead concentrations during pregnancy was associated with an increased risk of PTB (relative risk [RR] 1.48, 95% confidence interval [CI] 1.00, 2.20) and spontaneous PTB (RR 1.71, 95% CI 1.13, 2.60). The risk was higher in women with insufficient vitamin D concentrations (25OHD <50 nmol/L) for both PTB (RR 2.42, 95% CI 1.01, 5.79) and spontaneous PTB (RR 3.04, 95% CI 1.15, 8.04). However, an interaction on the additive scale was not present. Arsenic was associated with a higher risk of PTB (RR 1.10, 95% CI 1.02, 1.19) and spontaneous PTB (RR 1.11, 95% CI 1.03, 1.20) per 1 μg/L. CONCLUSIONS: Gestational exposure to low levels of lead and arsenic may increase the risk of PTB and spontaneous PTB; individuals with insufficient vitamin D may be more susceptible to the adverse effects of lead. Given our relatively small number of cases, we encourage testing of this hypothesis in other cohorts, especially those with vitamin D-deficient populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".