Electronic cigarette use vs Non-smokers: IL-8 level
The review reports that GCF evidence was limited and may show a potential association.
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Electronic cigarette use vs Non-smokers IL-8 level · People who use electronic cigarettes · Gingival crevicular fluid, GCF The review reports that GCF evidence was limited and may show a potential association. The effects of electronic smoking on dental caries and proinflammatory markers: a systematic review and… (2025) All outcomes | no estimate extracted | 1 study | GRADE not reported |
Electronic cigarette use vs Non-smokers IL-8 level · People who use electronic cigarettes · Saliva, Saliva The review reports no significant e-cigarette effect on salivary IL-8. The effects of electronic smoking on dental caries and proinflammatory markers: a systematic review and… (2025) All outcomes | Cohen's d -0.32 [-0.99, 0.35] | 5 studies | GRADE not reported |
Electronic cigarette use vs Non-smokers IL-8 level · People who use electronic cigarettes · Overall The review reports no e-cigarette effect on salivary IL-8, while findings for GCF may differ. 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 |
Studies behind the findings
None of these reviews ties its included studies to this outcome in the data here. Cochrane reviews and reviews with headline outcomes only do not list their studies yet.
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.