Polygenic Risk Score Implementation into Clinical Practice for Primary Prevention of Cardiometabolic Disease
Bibliographic record
Abstract
Background: Cardiovascular disease is a leading cause of mortality globally and a major contributor to disability. Traditional risk factors, as initially established in the FRAMINGHAM study, have helped to stratify populations and identify patients for early intervention. Incorporating genetic factors enhances risk stratification tools, enabling the earlier identification of individuals at increased risk and facilitating more targeted and effective risk factor modifications. While monogenic risk variants are present in a minority of the population, polygenic risk scores (PRS) are collections of multiple single-nucleotide variants that collectively provide summative risk and capture a more accurate risk score for a greater number of people. PRS have demonstrated clear utility in cardiometabolic diseases by predicting onset, progression, and therapeutic response. Methods: A structured and exploratory hybrid search strategy was employed, combining keyword-based database searches and supplementary techniques to comprehensively synthesize the literature on PRS implementation in clinical practice. Discussion: A comprehensive overview of PRS in cardiometabolic diseases and their potential avenues for integration into primary care is discussed. First, we examine the implementation of genetic screening, risk communication, and intervention strategies through the lens of the American Heart Association’s implementation criteria, focusing on their efficacy, minimization of harm, and logistical considerations. Then, we explores how the varied perceptions of patients and practitioners towards PRS can influence both adoption and utilization. Lastly, we addresses the need for the development of clear guidelines and regulations to support this process, ensuring PRS integration is both scientifically sound and ethically responsible. Future directions: Initiatives aimed at advancing personalized approaches to disease prevention will enhance health outcomes. Developing guidelines for the responsible use of PRS by establishing benefits, while mitigating risk, will a key factor in implementation for clinical utility. Conclusions: For integration into clinical practice, we must address both patient and provider concerns and experience. Standardized guidelines and training will help to effectively implement PRS into clinical practice. Developing these resources will be essential for PRS to fulfill its potential in personalized, patient-centered care.
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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 | 0.110 | 0.307 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".