Genes, Fat Intake, and Cardiovascular Disease Risk Factors in the Quebec Family Study
Bibliographic record
Abstract
Abstract Objective: The aim of this study was to assess gene‐diet interaction effects on cardiovascular disease (CVD) risk factors (waist circumference, plasma triacylglycerol, high‐density lipoprotein‐cholesterol and fasting glucose concentrations, and diastolic and systolic blood pressure) in the Quebec Family Study cohort. Design: Sixty‐four polymorphisms from 45 candidate genes were studied in 645 subjects. Dietary fat intake was obtained from a 3‐day weighted food record. Results: We observed 18 significant interactions at a p value ≤ 0.01. Among them, the Pro12Ala polymorphism in peroxisome proliferator‐activated receptor γ, alone or in interaction with fat intake, significantly modulated waist circumference (p = 0.0005 for both effects). Additionally, the apolipoprotein E genotype in interaction with fat intake was significantly associated with diastolic and systolic blood pressure (p = 0.01 and p = 0.001, respectively). The ghrelin Leu72Met polymorphism also interacted with dietary fat in its relation to waist circumference and triacylglycerol concentrations (p = 0.0004 and p = 0.005). Discussion: These results suggest that several alleles at candidate genes interact with dietary fat intake to modulate well‐known CVD risk factors. The identification of gene‐diet interaction effects is likely to provide useful information concerning the etiology of CVD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".