Reassessing alcohol consumption and cardiovascular disease by addressing bias: the Multi-Ethnic Study of Atherosclerosis
Bibliographic record
Abstract
Abstract Background Alcohol consumption and its relationship with cardiovascular disease (CVD) remains controversial. Observational studies have reported J-shaped associations suggesting cardioprotective effects of moderate drinking, while other observational and Mendelian Randomization studies have found dose-response harm. Biases such as abstainer misclassification, sick quitter effects, and residual confounding may obscure true associations. This study aimed to reassess alcohol’s impact on CVD using novel methods to minimize bias. Purpose We evaluated the relationship between alcohol and subclinical (coronary artery calcium [CAC]) and clinical (myocardial infarction [MI], stroke, cardiovascular death, major adverse cardiovascular events [MACE], and heart failure) outcomes in the Multi-Ethnic Study of Atherosclerosis (MESA). We compared conventional models with those using (1) occasional drinkers as the reference group instead of lifetime abstainers and (2) intention-to-treat (ITT) reallocation of former drinkers based on past drinking patterns. Methods MESA is a prospective cohort study of 6,814 participants aged 45-84 years without baseline CVD. Alcohol use was classified as lifetime abstainer, former drinker, or current drinker (occasional [≤1 drink/week], light [2-7 drinks/week], moderate [8-14 drinks/week], heavy [>14 drinks/week]). We used multinomial logistic regression for CAC and Cox proportional hazards models for clinical outcomes across four models: (1) non-drinker reference, (2) lifetime abstainer reference, (3) occasional drinker reference, and (4) occasional drinker reference with ITT reallocation of former drinkers to their previous drinking levels. Results Increasing alcohol consumption correlated with a dose-response increase in CAC. Heavy drinking was consistently associated with severe CAC (>300). In fully adjusted models, light (OR: 1.23, 95% CI: 1.02-1.48), moderate (OR: 1.27, 95% CI: 1.04-1.55), and heavy drinking (OR: 1.42, 95% CI: 1.12-1.78) were associated with higher CAC scores compared to occasional drinkers. Traditional models replicated protective effects of moderate drinking for MI and cardiovascular mortality. However, these associations attenuated in adjusted models. Moderate drinking showed an attenuated, non-significant benefit for MI (HR: 0.64, 95% CI: 0.38-1.06) and cardiovascular mortality (HR: 0.81, 95% CI: 0.53-1.25) in fully adjusted models. Light drinking shifted from null to increased risk for MI (HR: 1.46, 95% CI: 1.11-1.92), stroke (HR: 1.54, 95% CI: 1.17-2.03), MACE (HR: 1.33, 95% CI: 1.12-1.57), and heart failure (HR: 1.42, 95% CI: 1.10-1.84). Conclusion Adjusting for abstainer and sick quitter biases altered the observed relationship between alcohol and CVD, showing increased harm with light drinking, attenuated protective effects of moderate drinking, and a dose-response relationship for CAC in bias-adjusted models. These findings illustrate the importance of methodology in alcohol-CVD relationships.
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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 | 0.134 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".