Alcohol consumption and DNA methylation: an epigenome-wide association study within the French E3N cohort
Bibliographic record
Abstract
BACKGROUND: Alcohol consumption can have harmful effects on health, depending on the quantity and frequency. Understanding the underlying molecular mechanisms is essential to grasp its health consequences. The study aimed to assess the association between alcohol consumption and blood DNA methylation, an epigenetic mechanism that controls gene expression. METHODS: The epigenome-wide association study (EWAS) included 1,538 women from a case-cohort study within the French E3N cohort. Weighted linear mixed-effects models were used to assess the associations between self-reported alcohol consumption (in g/day in 1993) and DNA methylation at 715,986 CpGs measured with the HumanMethylationEPIC Beadchip. Women were cancer-free at blood collection in 1995-1999. RESULTS: Of the 715,986 sites analyzed, 19,255 were associated with alcohol consumption (FDR < 0.05). Over-representation analysis highlighted enrichment of genes involved in cancer, the nervous system and aging. Of these 19,255 sites, 1,528 were replicated in an independent case-control study, with 85 also identified in other EWAS. Notably, at least six studies reported sites in SLC7A11, ANP32B, MCM2, HNRNPA1, SNORD30, and TRA2B genes. CONCLUSIONS: Several potential methylation markers for alcohol consumption, documented prior to blood sampling, have been identified. The link between these sites and chronic diseases should be investigated to understand the molecular mechanisms underlying the harmful effects of alcohol consumption on health.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".