Alcohol consumption and risk of dementia: a systematic review and meta‐analysis
Bibliographic record
Abstract
To examine the relationship between alcohol intake and the risk of developing dementia, we conducted a comprehensive search of PubMed, Embase, Cochrane Library and Web of Science databases for relevant studies up to July 22, 2024. The quality of the original studies was appraised utilising the Newcastle-Ottawa Scale (NOS). The link between alcohol intake and the risk of dementia was presented using relative risks (RRs) and their corresponding 95% confidence intervals (CIs). Subgroup analyses were implemented based on the level of alcohol consumption, geographic region and age. All statistical analyses were executed utilising Stata 15.0. The meta-analysis unveiled no significant link between alcohol exposure and the risk of developing dementia, including all-cause dementia (ACD) (RR = 1.03, 95% CI: 0.84-1.27), Alzheimer disease (AD) (RR = 0.97, 95% CI: 0.86-1.08), vascular dementia (VD) (RR = 1.09, 95% CI: 0.95-1.26) and other dementia (RR = 0.62, 95% CI: 0.33-1.15). In the subgroup analysis by drinking levels, light to moderate alcohol intake was linked to a decreased risk of ACD and AD (RR = 0.88, 95% CI: 0.81-0.96; RR = 0.88, 95% CI: 0.79-0.97, respectively). Nonetheless, heavy alcohol consumption significantly increased the risk of developing all types of dementia (ACD, RR = 1.18, 95% CI: 1.02-1.36; AD, RR = 1.29, 95% CI: 1.21-1.36; VD, RR = 1.25, 95% CI: 1.11-1.40). Further subgroup analyses indicated that light to moderate drinking's protective effect was stronger in Europe and among individuals aged 60 to 69 years. Light to moderate drinking may protect against dementia, while heavy drinking or alcohol use disorders raises dementia risk.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".