Current State and Future of Polygenic Risk Scores in Cardiometabolic Disease: A Scoping Review
Bibliographic record
Abstract
A polygenic risk score (PRS) is derived from a genome-wide association study and represents an aggregate of thousands of single-nucleotide polymorphisms that provide a baseline estimate of an individual's genetic risk for a specific disease or trait at birth. However, it remains unclear how PRSs can be used in clinical practice. We provide an overview of the PRSs related to cardiometabolic disease and discuss the evidence supporting their clinical applications and limitations. The Preferred Reporting Items For Systematic Reviews and Meta-Analysis Extension for Scoping Reviews protocol was used to conduct a scoping review of the MEDLINE, EMBASE, and CENTRAL databases. Across the 4863 studies screened, 82 articles met the inclusion criteria. The most common PRS related to coronary artery disease, followed by hypertension and cerebrovascular disease. Limited ancestral diversity was observed in the study sample populations. Most studies included only individuals of European ancestry. The predictive performance of most PRSs was similar to or superior to traditional risk factors. More than half of the included studies reported an integrated risk model combining a derived PRS and clinical risk tools such as the Framingham Risk Score and Pooled Cohort Equations. The inclusion of a PRS into a clinical risk model tended to improve predictive accuracy consistently. This scoping review is the first of its kind and reports strong evidence for the clinical utility of PRSs in coronary artery disease, hypertension, cerebrovascular disease, and atrial fibrillation. However, most PRSs are generated in cohorts of European ancestry, which likely contributes to a lack of PRS transferability across different ancestral groups. Future prospective studies should focus on further establishing the clinical utility of PRSs and ensuring diversity is incorporated into genome-wide association study cohorts.
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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 | 0.033 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".