Characterization of Maternal Mechanisms Relevant to Metal Exposure-Mediated Infant Birth Weight Outcomes in the MIREC Study
Bibliographic record
Abstract
There is growing evidence on the association between prenatal metal exposures and adverse pregnancy outcomes. Heavy metals such as arsenic (As), cadmium (Cd), mercury (Hg) and lead (Pb) are considered as endocrine disrupting chemicals and elevated exposure to these metals during pregnancy is associated with adverse effects on maternal and infant health. Nevertheless, mechanistic understanding is required to establish biological plausibility of such associations. The objective of this study was to gain insight into metal exposure-related adverse birth outcomes by understanding maternal systemic changes at the molecular level.The Maternal-Infant Research on Environmental Chemicals (MIREC) study was employed for this purpose. Third trimester maternal plasma samples were analysed for target oxidative /nitrative stress markers (e.g 8-isoprostane, 3-nitrotyrosine) by competitive enzyme immunoassay and HPLC-Coularray as well as matrix metalloproteinases, a class of enzymes and other markers of inflammation (e.g. Cytokines, cellular adhesion molecules) were measured by affinity-based multiplex array and HPLC-Fluorescence detection methods. Pearson product moment correlations, chi-squared tests and multivariate models were used to analyse the associations among maternal blood metal (Cd, Hg, Pb, As, manganese Mn) levels, plasma biomarkers, physiological changes and birth weight. Our results revealed maternal metal exposure-specific responses (p<0.05) on markers of oxidative stress pathways (e.g 8-isoprostane) and matrix metalloproteinases (MMPs), in maternal circulation. Interestingly, statistically significant (p<0.05) correlations were seen between oxidative stress pathways, MMPs and other inflammatory mediators relevant to infant birth weight changes. Our findings imply that metal exposures potentially can mediate maternal oxidative stress pathways which can alter MMP profiles and associated inflammatory processes, thus adversely impacting on infant birth weights.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".