Genetics - Predisposition and Application to Primary Preventions of CAD
Bibliographic record
Abstract
The risk of Coronary Artery Disease (CAD) has been recognized to be approximately 50% due to genetic predisposition and the remainder due to lifestyle and acquired causes. The first genetic risk variant was discovered in 2007 and since that time over 200 genetic risk variants predisposing to CAD have been discovered. These risk variants have been encrypted on to a microarray in preparation for their evaluation as a means to predict one’s risk for CAD. The Polygenic Risk Score (PRS) derived from these variants provides a single number for the total genetic risk burden. The PRS has been evaluated in several studies, totaling over 1 million individuals. Individuals categorized as high genetic risk for CAD based on PRS stratification show 2-3 fold increase risk for cardiac events. Retrospective analysis of several clinical trials showed lowering plasma LDL cholesterol is associated with decreased genetic risk and the frequency of cardiac events. A prospective study showed a favorable lifestyle to be associated with 47% reduction in the high genetic risk group and a similar reduction of 50% from physical activity in another prospective study. The PRS unlike acquired factors is not age-dependent but determined at conception and does not change throughout one’s lifetime. The several ethical, legal, and social implications associated with the clinical use of a PRS for CAD is fully discussed. The routine clinical application of the PRS for early primary prevention of CAD has the potential to be a paradigm shift in the prevention of this pandemic disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 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".