Maternal blood metal levels and fetal markers of metabolic function
Bibliographic record
Abstract
Exposure to metals commonly found in the environment has been hypothesized to be associated with measures of fetal growth but the epidemiological literature is limited. The Maternal-Infant Research on Environmental Chemicals (MIREC) study recruited 2001 women during the first trimester of pregnancy from 10 Canadian sites. Our objective was to assess the association between prenatal exposure to metals (lead, arsenic, cadmium, and mercury) and fetal metabolic function. Average maternal metal concentrations in 1st and 3rd trimester blood samples were used to represent prenatal metals exposure. Leptin and adiponectin were measured in 1363 cord blood samples and served as markers of fetal metabolic function. Polytomous logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between metals and both high (≥ 90%) and low (≤ 10%) fetal adiponectin and leptin levels. Leptin levels were significantly higher in female infants compared to males. A significant relationship between maternal blood cadmium and odds of high leptin was observed among males but not females in adjusted models. When adjusting for birth weight z-score, lead was associated with an increased odd of high leptin. No other significant associations were found at the top or bottom 10th percentile in either leptin or adiponectin models. This study supports the proposition that maternal levels of cadmium influence cord blood adipokine levels in a sex-dependent manner. Further investigation is required to confirm these findings and to determine how such findings at birth will translate into childhood anthropometric measures.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".