Gene–environment interaction in dyslipidemia
Bibliographic record
Abstract
PURPOSE OF REVIEW: Recent genome-wide association studies have identified numerous common genetic variants associated with plasma lipid traits and have provided new insights into the regulation of lipoprotein metabolism including the identification of novel biological processes. These findings add to a body of existing data on dietary and environmental factors affecting plasma lipids. Here we explore how interactions between genetic risk factors and other phenotypes may explain some of the missing heritability of plasma lipid traits. RECENT FINDINGS: Recent studies have identified true statistical interaction between several environmental and genetic risk factors and their effects on plasma lipid fractions. These include interactions between behaviors such as smoking or exercise as well as specific dietary nutrients and the effect size of specific genetic variants on plasma lipid traits risk and modifying effects of measures of adiposity on the cumulative impact of a number of common genetic variants on each of plasma triglycerides and HDL cholesterol. SUMMARY: Interactions between genetic risk factors and clinical phenotypes may account for some of the unexplained heritability of plasma lipid traits. Recent studies provide biological insight into specific genetic associations and may aid in the identification of dyslipidemic patients for whom specific lifestyle interventions are likely to be most effective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".