Alcohol Consumption and Mortality From Coronary Heart Disease: An Updated Meta-Analysis of Cohort Studies
Bibliographic record
Abstract
OBJECTIVE: Previous meta-analyses estimate that low-volume alcohol consumption protects against coronary heart disease (CHD). Potential errors in studies include systematic misclassification of drinkers as abstainers, inadequate measurement, and selection bias across the life course. METHOD: Prospective studies of alcohol consumption and CHD mortality were identified in scholarly databases and reference lists. Studies were coded for potential abstainer biases and other study characteristics. The alcohol-CHD risk relationship was estimated in mixed models with controls for potential biases. Stratified analyses were performed based on variables identified as potential effect modifiers. RESULTS: Fully adjusted meta-analysis of all 45 studies found significantly reduced CHD mortality for current low-volume drinkers (relative risk [RR] = 0.80, 95% CI [0.69, 0.93]) and all current drinkers (RR = 0.88, 95% CI [0.78, 0.99]). There was evidence of effect modification by cohort age, gender, ethnicity, and heart health at baseline. In stratified analyses, low-volume consumption was not significantly protective for cohorts ages 55 years or younger at baseline (RR = 0.95, 95% CI [0.75, 1.21]), for studies controlling for heart health (RR = 0.87, 95% CI [0.71, 1.06]), or for higher quality studies (RR = 0.86, 95% CI [0.68, 1.09]). In studies in which the mean age was 55 years or younger at baseline, there were significantly increased RRs for both former (RR = 1.45, 95% CI [1.08, 1.95]) and occasional drinkers (RR = 1.44, 95% CI [1.09, 1.89]) compared with abstainers. CONCLUSIONS: Pooled analysis of all identified studies suggested an association between alcohol use and reduced CHD risk. However, this association was not observed in studies of those age 55 years or younger at baseline, in higher quality studies, or in studies that controlled for heart health. The appearance of cardio-protection among older people may reflect systematic selection biases that accumulate over the life course.
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.061 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".