17-24 remaining teeth vs 25-32 remaining teeth: Atherosclerotic cardiovascular disease (ACVD)-related events (morbidity and mortality)
- Population
- Adults (any age) with a known number of remaining teeth, followed prospectively in cohort studies
- Intervention
- 17-24 remaining teeth
- Comparison
- 25-32 remaining teeth
- Outcome
- Atherosclerotic cardiovascular disease (ACVD)-related events (morbidity and mortality)
In prospective cohorts, adults with 17-24 teeth had a higher cumulative incidence of ACVD-related events than adults with 25-32 teeth (RR 1.73, 95% CI 1.22 to 2.45; 4 cohort studies, I2=87%); GRADE per-analysis ratings reported in Supplementary File 7 (not available in provided sources).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
17-24 remaining teeth vs 25-32 remaining teeth Atherosclerotic cardiovascular disease (ACVD)-related events (morbidity and mortality) · Adults (any age) with a known number of remaining teeth, followed prospectively in cohort studies · Community-based and clinical cohorts worldwide (all continents); events ascertained from medical records, death certificates/registers, or self-report In prospective cohorts, adults with 17-24 teeth had a higher cumulative incidence of ACVD-related events than adults with 25-32 teeth (RR 1.73, 95% CI 1.22 to 2.45; 4 cohort studies, I2=87%); GRADE per-analysis ratings reported in Supplementary File 7 (not available in provided sources). Lower Number of Teeth Is Related to Higher Risks for ACVD and Death-Systematic Review and Meta-Analyses of… (2021) All outcomes | RR 1.73 [1.22, 2.45] | 4 studies · 213202 participants | High |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Lower Number of Teeth Is Related to Higher Risks for ACVD and Death-Systematic Review and Meta-Analyses of Survival Data. 2021 All outcomes | Before 17 June 2020 | 140 | 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.