Investigating the Effects of Vaping on Lung Structure-Function With 129Xe MRI and CT
Bibliographic record
Abstract
Abstract RATIONALE: Over the past decade, vaping rates among young Canadians have skyrocketed.1 Vaping devices use a heating element to vaporize liquid containing nicotine, tetrahydrocannabinol (THC) or cannabidiol (CBD), and other chemicals, into an inhaled aerosol.2 Traditional pulmonary function tests (PFT) are not sensitive enough to detect early-stage pulmonary abnormalities that may manifest in the small airways,3 however hyperpolarized xenon-129 magnetic resonance imaging (129XeMRI) has detected ventilation and gas exchange abnormalities in asymptomatic cigarette smokers despite normal PFTs.4 We aimed to investigate lung structure-function in people who vape using 129XeMRI and chest computed tomography (CT). METHODS: Participants who reported current vaping nicotine, THC or CBD with <3 former cigarette-pack years and <5 cannabis joint-years and healthy never-smoking age-matched controls underwent pre-bronchodilator PFTs, the Chronic Obstructive Pulmonary Disease Assessment Test (CAT), the St.George's Respiratory Questionnaire (SGRQ), inspiratory chest CT and 129XeMRI. CT was reviewed by a radiologist and quantitatively analyzed (VIDA Insights) for mean lung density. 129XeMRI gas exchange was performed per guidelines5 to measure whole lung ratios of Membrane (Mem)/Gas, red blood cell (RBC)/Membrane, RBC/Gas and ventilation deficit (defect+low ventilation percent).6 Independent samples t-test and Wilcoxon rank-sum tests were used to evaluate measurements between groups. RESULTS: We evaluated 22 vaping (28±4 years, 14-female) and 22 healthy, never-vaping controls (27±6 years, 17-female). Vaping frequency ranged from 1 to 300 times per day, for a duration of 0.25 to 13 years and vaping substance varied (14 nicotine, 5 THC/CBD, 3 both). Vaping participants reported significantly greater CAT score (p=0.021) and SGRQ total score (p=0.025). There were no significant differences in PFT measurements (p>0.1) and negligible qualitative CT patterns. Quantitative CT mean lung density was significantly lower in the vaping group (p=0.01) and ventilation deficit trended higher for the total vaping group (p=0.082). Mem/Gas, RBC/Gas and RBC/Mem were not significantly different (p>0.1). A subset of high-exposure participants (n=14, vaping >8 times per day [median]) revealed a significantly greater ventilation deficit (p=0.021) and lower mean lung density p<0.001). CONCLUSIONS: These pilot findings reveal ventilation abnormalities and decreased CT lung density in participants who vape, potentially due to early small airway dysfunction. Enrollment of vaping participants is ongoing, and longitudinal follow-up will provide insight into potential disease trajectories. To better understand the clinical relationship, further work includes creating a multivariable model to determine lung structure-function predictors of respiratory symptoms. REFERENCES:[1]CTNS(2021).[2]Traboulsi.Int.J.Mol.Sci.(2020).[3]He.IntJChronObstructPulmonDis.(2021). [4]Rao.EurRadiol(2024). [5] Niedbalski.Magn.Reson.Med.(2021).[6]Myc.Thorax.(2021).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".