Impact of Alcohol Intake on Measures of Lipid Metabolism Depends on Context Defined by Gender, Body Mass Index, Cigarette Smoking, and<i>Apolipoprotein E</i>Genotype
Bibliographic record
Abstract
Hyperlipidemia, smoking, and obesity are well-known risk factors for cardiovascular disease. Conversely, moderate alcohol intake is associated with lower atherosclerosis risk. However, the influence of taking alcohol on the interrelationships of these factors in a particular context has not been thoroughly investigated. In this study, we asked whether the association between plasma measures of lipid metabolism and alcohol intake is dependent on context defined by gender, age, body mass index (BMI), smoking, and apolipoprotein E (APOE) genotype. Data were obtained in a sample of 869 women and 824 men who participated in the Quebec Heart Health Survey. There was no evidence that variation among APOE genotypes influenced the association between LDL cholesterol (LDL-C) or HDL cholesterol (HDL)-C and alcohol, after adjustment for age and BMI. Further, the positive (LDL-C and BMI) and the negative (HDL-C and BMI) associations that were observed in men and women with the epsilon3/2 and epsilon3/3 genotypes were not modified by alcohol intake. However, in women with the epsilon4/3 genotype only, we found a significant influence of an alcohol by BMI interaction on the prediction of total cholesterol, LDL-C, HDL-C, apoA-I, and apoB, and this interaction was influenced by the status of smoking. Whereas the influence of an alcohol by BMI interaction on total cholesterol and LDL-C was significant in smokers, its influence on HDL-C was significant only in non-smokers. This study emphasizes the context dependency of the influence of alcohol on lipid metabolism and demonstrates how biological, environmental, and genetic factors interact to determine cardiovascular disease 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".