Electronic cigarettes and cardiovascular diseases: An updated systematic review and network meta-analysis
Bibliographic record
Abstract
INTRODUCTION: The association between electronic cigarettes (e-cigarettes) and the risk of cardiovascular disease (CVD) remains inconclusive. This study aims to compare CVD risk from the use of e-cigarettes, cigarettes, combined cigarette and e-cigarette use, and non-use. METHODS: This study is a systematic review and network meta-analysis (NMA). MEDLINE and Scopus databases (through February 2024) were used to identify eligible studies. Observational studies that investigated the effect of e-cigarettes on the risk of composite CVD, myocardial infarction (MI), or stroke, compared to cigarette, dual use, or non-use, were included. NMA was applied to estimate relative effects (i.e. adjusted odds ratio, AOR) of e-cigarette, cigarette, and dual use, on composite CVD, MI, and stroke outcomes. Risk of bias was assessed using the Joanna Briggs Institute tool for surveys and the Newcastle-Ottawa scale for cohort studies. RESULTS: Eleven adult population studies were eligible for review. E-cigarette, cigarette, and dual use were significantly associated with composite CVD outcomes. Pooled AORs (95% CI) were 1.31 (1.05-1.62) for e-cigarette, 1.57 (1.30-1.88) for cigarette, and 1.67 (1.37-2.03) for dual use. Additionally, former cigarette and former dual use significantly increased the risk of composite CVD outcomes, compared to non-use. The pooled AORs (95% CI) were 1.29 (1.05-1.59) for former cigarette, and 1.46 (1.03-2.08) for former dual use, while former e-cigarette use was not significantly associated with composite CVD endpoints. For MI and stroke outcomes, only cigarette and dual use were significantly associated with these events. CONCLUSIONS: Current e-cigarette, cigarette, and dual use were significantly associated with increased risk of composite CVD outcomes, while only cigarette and dual use significantly increased the risk of MI and stroke, compared to non-use. However, these findings were primarily based on cross-sectional data limiting the temporality of effect; additional prospective cohort studies are needed to confirm our 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.000 | 0.002 |
| 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".