The value of studying gene-environment interactions in culturally diverse populations
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the leading cause of death and disability in the world. It is anticipated that CVD will reach pandemic proportions by the year 2020. Although the major causes of CVD are well documented and explain the majority of cardiovascular deaths, the prevalence of conventional cardiovascular risk factors vary substantially across diverse cultural groups. These differences are attributed to cultural or genetic differences or to interactions between genes and environmental factors. Substantial efforts have been invested in determining the genetic influences on CVD development, and it is unlikely that a single gene is responsible for the development of atherosclerotic CVD or its classical risk factors such as blood pressure or plasma lipids. It is more plausible that multiple genes, acting either alone or in concert with one another, which display effect modification in the presence of certain environmental factors, are modestly associated with CVD or its main risk factors. Following this hypothesis, studying populations with diversity in environmental factors may increase the discovery potential of gene-environmental interactions. In this brief review, the advantage of studying gene-environment interactions across heterogeneous groups with diverse lifestyles is discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".