Association of Vitamin D Levels with Incident All-Cause Dementia in Longitudinal Observational Studies: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: The role of vitamin D is not only limited to bone health and pathogenesis of chronic diseases. Evidence now suggests that it is also involved in the development of various dementias and Alzheimer's disease (AD). OBJECTIVE: To carry out a systematic review and meta-analysis to evaluate the association between vitamin D levels and increased risk of incident all-cause dementia in longitudinal studies. DESIGN: We conducted a systematic review and meta-analysis using the electronic bibliographic databases PubMed and Scopus. SETTING: Prospective cohort studies. PARTICIPANTS: Community-dwelling older adults. MEASUREMENTS: Vitamin D serum concentrations were categorized in three groups: normal levels (>50 nmol/L), insufficient levels (25 - 49.9 nmol/L), and deficient levels (<25 nmol/L). We performed a meta-analysis using the general inverse variance method to calculate the pooled risk of AD and all-cause dementia according to vitamin D levels. Random-effects or fixed-effect model were used to calculate the pooled risk based on the heterogeneity analysis. RESULTS: Five studies were included in the meta-analysis. The pooled risk of all-cause dementia and AD was significantly higher in those with deficient serum vitamin D level compared to those with normal level (1.33, CI95% [1.15, 1.54], and 1.87, CI95% [1.03, 3.41], respectively). Those with insufficient level also had a higher pooled risk of all-cause dementia and AD, but the strength of association was less robust (1.14 CI95% [1.02, 1.27] and 1.25, CI95% [1.04 - 1.51], respectively). CONCLUSION: We found a gradient effect for the risk of all-cause dementia and AD according to the vitamin D level, with higher risk in those in the deficient levels group and intermediate risk in those with insufficient levels. Our findings were limited by the relatively small number of studies included in the meta-analysis and their geographic restriction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.046 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".