Higher circulating 25-hydroxyvitamin D (25(OH)D) concentration vs Lower circulating 25-hydroxyvitamin D (25(OH)D) concentration: All cause mortality
- Population
- Adults (general population)
- Intervention
- Higher circulating 25-hydroxyvitamin D (25(OH)D) concentration
- Comparison
- Lower circulating 25-hydroxyvitamin D (25(OH)D) concentration
- Outcome
- All cause mortality
Meta-analysis of observational studies (higher vs lower 25(OH)D) for All cause mortality: HR 0.72 (95% CI 0.66 to 0.78), statistically significant. Umbrella review outcome-level evidence grade: No conclusion.
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Higher circulating 25-hydroxyvitamin D (25(OH)D) concentration vs Lower circulating 25-hydroxyvitamin D (25(OH)D) concentration All cause mortality · Adults (general population) Meta-analysis of observational studies (higher vs lower 25(OH)D) for All cause mortality: HR 0.72 (95% CI 0.66 to 0.78), statistically significant. Umbrella review outcome-level evidence grade: No conclusion. Vitamin D and multiple health outcomes: umbrella review of systematic reviews and meta-analyses of… (2014) All outcomes | HR 0.72 [0.66, 0.78] | 18 studies · 77155 participants | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Vitamin D and multiple health outcomes: umbrella review of systematic reviews and meta-analyses of observational studies and randomised… 2014 All outcomes | 11 October 2013 | 55 | Not applicable |
Related questions
The same intervention and outcome, asked in another population or against another comparator.
- Higher circulating 25-hydroxyvitamin D (25(OH)D) concentration vs Lower circulating 25-hydroxyvitamin D (25(OH)D) concentration · people with chronic kidney disease (predialysis (GFR 15 to 60 mL/min/1.73 m²), HD, PD,… · 1 review
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.