Cannabis Vapor Alters Metabolism in Human Bronchial Epithelial Cells
Bibliographic record
Abstract
Abstract Since its legalization, approximately 27% of Canadians aged 16 and older report using cannabis, coinciding with widespread adoption of vaping amongst youth as an alternative to smoking, largely driven by the perception that it is a safer alternative to smoking. This shift is reshaping adolescent substance use. Although inhalation remains the primary method of cannabis consumption, vaping is steadily replacing smoking. Cannabis vaping involves inhaling an aerosol from heated cannabis products, available in both distillate and dry flower cartridges. However, we have previously demonstrated that cannabis vapor still contains carcinogens, teratogens, and respiratory toxicants that can interfere with cellular metabolism. While tobacco smoking is known to disrupt cellular metabolism, the effects of cannabis vapor on lung cell metabolism remain underexplored. Cellular metabolism disruptions are often overlooked as contributors to pulmonary pathophysiology. Disruptions in lung epithelial cell metabolism can impair tissue repair, compromise barrier integrity, and are associated with pulmonary diseases such as lung cancer and fibrosis. To investigate the metabolic effects of cannabis vapor, we exposed immortalized human lung epithelial cells to high-THC cannabis vapor and performed targeted metabolomic analysis via LC/MS. We found significant changes in metabolites related to lipid and membrane metabolism, with increased O-phosphorylethanolamine suggesting enhanced membrane turnover. We found elevated levels of oxidized glutathione, indicative of oxidative stress, and observed changes in energy production and nucleotide pathways suggesting potential impairments in cellular repair and function. Cannabis vapor exposure also increased levels of 5-hydroxymethylcytosine (5hmC), a marker of active DNA demethylation, suggesting the induction of epigenetic changes. Taken together, these findings indicate that cannabis vapor disrupts pulmonary epithelial cellular metabolism, possibly inducing epigenetic changes. These results underscore the need for more research on the respiratory effects of cannabis vaping and challenge its perception as a risk-free alternative to smoking.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 0.001 |
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".