Heavy metals, organic solvents, and multiple sclerosis: An exploratory look at gene-environment interactions
Bibliographic record
Abstract
Exposure to heavy metals and organic solvents are potential etiologic factors for multiple sclerosis (MS), but their interaction with MS-associated genes is under-studied. The authors explored the relationship between environmental exposure to lead, mercury, and solvents and 58 single-nucleotide polymorphisms (SNPs) in MS-associated genes. Data from a population-based case-control study of 217 prevalent MS cases and 496 age-, race-, gender-, and geographically matched controls were used to fit conditional logistic regression models of the association between the chemical, gene, and MS, adjusting for education and ancestry. MS cases were more likely than controls to report lead (odds ratio [OR] = 2.03; 95% confidence interval [CI]: 1.07, 3.86) and mercury exposure (OR = 2.06; 95% CI: 1.08, 3.91). Findings of potential gene-environment interactions between SNPs in TNF-α, TNF-β, TCA-β, VDR, MBP, and APOE, and lead, mercury, or solvents should be considered cautiously due to limited sample size.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".