Genetic Polymorphisms And Hair, Blood And Urine Mercury Levels: A Gene Environment Study Of Mercury In The American Dental Association (ADA) Study
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Bibliographic record
Abstract
Background/Aims: Mercury (Hg) is a potent toxicant of concern to the general public. Recent studies suggest that several genes that mediate mercury metabolism are polymorphic. We hypothesize that single nucleotide polymorphisms (SNPs) in such genes may underline inter-individual differences in exposure biomarkers. Methods: Dental professionals (n =908) were recruited during the American Dental Association (ADA) 2012 Annual Meeting. Samples of hair, blood, and urine were collected for inorganic/organic mercury levels and genotyping (119 SNPs). Questionnaires were administrated for demographics and fish consumption. ANOVA and linear regressions were used for statistical analysis. Results: Mean (geometric) mercury levels in hair (hHg), blood (bHg), urine (uHg) and the average mercury intake from fish were 0.62µg/g, 3.75µg/L, 1.32µg/L, and 0.12µg/kg/d, respectively. Out of 119 SNPs genotyped, 89 SNPs were eligible for further analysis after screening. Hg biomarker levels differed by genotype for 14 SNPs. Five SNPs, mostly in transporter genes, showed specific group differences for hHg and bHg ratio. When the associations between Hg contributors (base model) and biomarkers were analyzed with respect to SNPs, many main and gene-environment interactions were significant. Out of 89 SNPs evaluated, 21, 24, and 5 SNPs showed significant main effects for hHg, bHg and uHg level, respectively. Similarly, 20, 10, and 4 gene-environment interactions showed significant interaction effects for hHg, bHg and uHg level, respectively. Conclusion: The findings suggest that polymorphisms in environmentally-responsive genes can influence Hg biomarker levels. Hence, consideration of such gene-environment factors may improve our ability to assess the health risks of Hg more precisely.
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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 | 0.001 | 0.000 |
| 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 it