Glycerol contained in vaping liquids affects the liver and aspects of energy homeostasis in a sex‐dependent manner
Bibliographic record
Abstract
Vaping is increasingly popular among the young and adult population. Vaping liquids contained in electronic cigarettes (e-cigarettes) are mainly composed of propylene glycol and glycerol, to which nicotine and flavors are added. Among several biological processes, glycerol is a metabolic substrate used for lipid synthesis in fed state as well as glucose synthesis in fasting state. We aimed to investigate the effects of glycerol e-cigarette aerosol exposure on the aspects of glycerol and glucose homeostasis. Adult and young male and female mice were exposed to e-cigarette aerosols with glycerol as vaping liquid using an established whole-body exposure system. Mice were exposed acutely (single 2-h exposure) or chronically (2 h/day, 5 days/week for 9 weeks). Circulating glycerol and glucose levels were assessed and glycerol as well as glucose tolerance tests were performed. The liver was also investigated to assess changes in the histology, lipid content, inflammation, and stress markers. Lung functions were also assessed as well as hepatic mRNA expression of genes controlling the circadian rhythm. Acute exposure to glycerol aerosols generated by an e-cigarette increased circulating glycerol levels in female mice. Increased hepatic triglyceride and phosphatidylcholine concentrations were observed in female mice with no increase in circulating alanine aminotransferase or evidence of inflammation, fibrosis, or endoplasmic reticulum stress. Chronic exposure to glycerol e-cigarette aerosols mildly impacted glucose tolerance test in young female and male mice. Fasting glycerol, glucose, and insulin remained unchanged. Increased pulmonary resistance was observed in young male mice. Taken together, this study shows that the glycerol contained in vaping liquids can affect the liver as well as the aspects of glucose and glycerol homeostasis. Additional work is required to translate these observations to humans and determine the biological and potential pathological impacts of these findings.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".