Maternal exposure to metals and persistent pollutants and cord blood immune system biomarkers
Bibliographic record
Abstract
BACKGROUND: The fetal time period is a critical window of immune system development and resulting heightened susceptibility to the adverse effects of environmental exposures. Epidemiologists and toxicologists have hypothesized that persistent organic pollutants, pesticides and metals have immunotoxic properties. Immunotoxic effects may manifest as an altered immune system profile at birth. Immunoglobulin E, thymic stromal lymphopoietin (TSLP), and interleukin-33 (IL-33) may be implicated in the etiology of childhood allergy and are detectable at birth. The objective of this study was to examine the potential relationship between maternal concentrations of metals, persistent organic pollutants, and pesticides and elevated umbilical cord blood concentrations of IgE, TSLP, and IL-33 in a Canadian birth cohort. METHODS: This study utilized data collected in the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a trans-Canada cohort study of 2,001 pregnant women. Of these women, 1258 had a singleton, term birth and cord blood sample. Logistic regression was used to determine associations between log-transformed continuous variables and immune system biomarkers. RESULTS: Inverse relationships were observed between lead, DDE, PCB-118, and a summary index of organophosphorous metabolites and jointly elevated concentrations of IL-33 and TSLP. None of the environmental contaminants were associated with increased odds of a high cord blood immune system biomarker concentration. CONCLUSIONS: In this primarily urban Canadian population of pregnant women and their newborns, maternal blood or urine concentrations of persistent organic pollutants, pesticides, and metals were not associated with immunotoxic effects that manifest as increased odds of elevated concentrations of IgE, TSLP or IL-33.
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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) | 0.002 | 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".