Genetic Polymorphisms And Hair, Blood And Urine Mercury Levels: A Gene Environment Study Of Mercury In The American Dental Association (ADA) Study
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".