Cannabis smoking is associated with persistent epigenome-wide disruptions despite smoking cessation
Bibliographic record
Abstract
Abstract Background The use of cannabis has been associated with both therapeutic and harmful effects. As with cigarette smoking, cannabis smoking may affect the epigenetic regulation (e.g., DNA methylation) of gene expression which could result in long term health effects. The study of DNA methylation in cannabis smoking has to date been restricted to young adults and there remains yet no evaluation of whether cannabis smoking cessation can reverse epigenetic disturbances. Here, we aimed to investigate the relationship between genome-wide DNA methylation and cannabis smoking. Methods We used peripheral blood from a subset of older adults within the Canadian Cohort of Obstructive Lung Disease (CanCOLD) cohort (n = 93) to conduct an epigenome-wide DNA methylation analysis that identified differential methylated positions (DMPs) associated with cannabis smoking at a false discovery rate < 0.05. Using these DMPs, we then identified differentially methylated genes (DMGs) that enriched pathways associated with both former and current cannabis smoking status. Results We found DMPs corresponding to 12,115 DMGs and 10,806 DMGs that distinguished the current and former cannabis smoking groups, respectively, from the never cannabis smoking group. 5,915 of these DMGs were shared between the current and former cannabis smoking groups. 50 enriched pathways were also shared between the current and former cannabis smoking groups, which were heavily represented by multiple aging- and cancer-related pathways. Conclusions Our findings indicate that in older adults, cannabis smoking is linked with epigenome-wide disruptions, many of which persist despite cannabis smoking cessation. Epigenetic modulation of genes associated with aging and cancer that remains even after quitting cannabis should serve as a caution that there may be long-lasting epigenetic injury with cannabis smoking. Trial registration NCT00920348.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.317 | 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 teacher head, 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".