Genetic risk and its role in primary prevention of CAD
Bibliographic record
Abstract
Coronary artery disease (CAD) is a pandemic disease and the number one cause of death in the world. Predisposition to CAD is about 50% acquired and 50% genetic. CAD prevention has been proven in randomized clinical trials with statin therapy. However, primary prevention is limited by the lack of biomarkers to detect asymptomatic young individuals at risk. Traditional risk factors (TRFs) such as hypertension or Type 2 Diabetes are age-dependent and often not present until the sixth or seventh decade. In contrast, genetic risk determined at conception is potentially a biomarker to detect young individuals at risk for CAD. The first genetic risk variant for CAD (9p21) was discovered in 2007, and subsequently, over 200 risk variants for CAD were discovered. A genetic risk score (GRS) based on the genetic risk variants for CAD was evaluated in over one million individuals. Retrospective analysis of clinical trials assessing the effect of statin therapy showed that individuals with the highest GRS had the highest risk for cardiac events and also the most benefit from lowering cholesterol. In a recent study of 55,685 individuals, those with the highest GRS (20%) had a 91% higher risk for cardiac events. Furthermore, those with high genetic risk on a favorable lifestyle had 46% fewer cardiac events than those with an unfavorable lifestyle. The GRS is superior and independent of TRFs. Incorporation into clinical practice will be a paradigm shift in preventing this pandemic.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".