Electronic cigarette use vs Non-smokers: Interleukin-8 (IL-8)
- Population
- Humans who use e-cigarettes (mostly adults, often with periodontal disease) assessed for oral proinflammatory markers
- Intervention
- Electronic cigarette use
- Comparison
- Non-smokers
- Outcome
- Interleukin-8 (IL-8)
Meta-analysis suggests no significant effect of e-cigarette use on the salivary level of Interleukin-8 (IL-8) (6 studies), though heterogeneity was high (I2=84%) and the pooled estimate appears only in Figure 3C (unavailable); GCF data were limited (1 study(ies)) (ungraded).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
E-cigarette use (ENDS / vaping) vs Non-smokers Interleukin-8 (IL-8) · Humans who use e-cigarettes (mostly adults, often with periodontal disease) assessed for oral proinflammatory markers · Cross-sectional (single time point) for most; a few 6-month prospective, Clinical periodontal/oral studies measuring cytokines in saliva or gingival crevicular fluid (GCF) across multiple countries, Overall (saliva + GCF) Meta-analysis suggests no significant effect of e-cigarette use on the salivary level of Interleukin-8 (IL-8) (6 studies), though heterogeneity was high (I2=84%) and the pooled estimate appears only in Figure 3C (unavailable); GCF data were limited (1 study(ies)) (ungraded). The effects of electronic smoking on dental caries and proinflammatory markers: a systematic review and… (2025) All outcomes | Cohen's d -0.43 [-1.02, 0.14] | 6 studies | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| The effects of electronic smoking on dental caries and proinflammatory markers: a systematic review and meta-analysis. 2025 All outcomes | September 2024 (initial search April 2024, updated September 2024) | 41 | Not applicable |
How reviews are matched to this question
Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.