Clinical utility and implementation of polygenic risk scores for predicting cardiovascular disease
Bibliographic record
Abstract
Genome-wide association studies have revealed hundreds of genetic variants associated with cardiovascular diseases (CVD). Polygenic risk scores (PRS) can capture this information in a single metric and hold promise for use in CVD risk prediction. Importantly, PRS information can reflect the causally mediated risk to which the individual is exposed throughout life. Although European Society of Cardiology guidelines do not currently advocate their use in routine clinical practice, PRS are commercially available and increasingly sought by clinicians, health systems, and members of the public to inform personalized health care decision-making. This clinical consensus statement provides an overview of the scientific basis of PRS and evidence to date on their role in CVD risk prediction for the purposes of disease prevention. It provides the reader with a summary of the opportunities and challenges for implementation and identifies current gaps in supporting evidence. The document also lays out a potential roadmap by which the scientific and clinical community can navigate any future transition of PRS into routine clinical care. Finally, clinical scenarios are presented where information from PRS may hold most value and discuss organizational frameworks to enable responsible use of PRS testing while more evidence is being generated by clinical studies.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".