Polygenic Risk Scores Identify High Susceptibility for Eight Cardiovascular Traits

Standardizing Genetic Risk Assessment in Clinical Cardiology

Cardiovascular disease remains the leading cause of morbidity globally, yet traditional risk assessment tools often fail to capture the full spectrum of inherited susceptibility [1]. While clinical guidelines emphasize managing modifiable factors like lipids and blood pressure, the underlying genetic architecture of these conditions points to a need for more individualized diagnostic tools [2, 3]. Polygenic risk scores, which aggregate the effects of thousands of genetic variants to quantify inherited disease risk, have emerged as a potential method to refine risk stratification beyond conventional models [4]. However, the transition from research biobanks to actionable clinical reports has been hindered by a lack of standardized implementation frameworks and real-world validation [5, 6]. To bridge this gap, researchers have developed and validated an integrated reporting system that evaluates genetic susceptibility across eight distinct cardiovascular pathologies.

Development and Validation Across Large Patient Cohorts

The researchers sought to develop and validate integrated polygenic risk scores for eight cardiovascular conditions while establishing a standardized framework for clinical reporting. The study first analyzed genotype and clinical data from a discovery cohort of 245,394 participants enrolled in the All of Us Research Program. The investigation focused on coronary artery disease, atrial fibrillation, type 2 diabetes, venous thromboembolism, thoracic aortic aneurysm, extreme hypertension, severe hypercholesterolemia, and elevated lipoprotein(a). To optimize predictive power, the authors combined publicly available polygenic risk scores for these traits using PRSmix, an elastic-net approach (a statistical method that merges multiple predictive models while penalizing less relevant variables to prevent overfitting). This integrated tool was then subjected to external validation using a separate cohort of 53,306 genotyped participants from the Mass General Brigham Biobank. This validation group, which had a mean age of 53 plus or minus 17 years and was 55.6 percent women, provided a robust dataset to test the performance of the scores across different demographic profiles. By testing the scores in a real-world biobank population, the researchers demonstrated that this framework can effectively categorize patients based on their inherited susceptibility, moving the tool closer to integration into daily clinical workflows.

Quantifying Risk for Common and Rare Pathologies

To ensure clinical validity, the researchers performed a validation analysis using logistic regression, adjusting for age, sex, and ancestry to isolate the independent contribution of genetic factors to disease risk. The study established a standardized framework for risk stratification by defining high genetic risk as the top 10 percent of the polygenic risk score distribution for most conditions. For these traits, average risk was defined as the 26th to 75th percentiles. The clinical impact of this stratification was most pronounced in common metabolic and vascular conditions. For coronary artery disease, patients in the high genetic risk group demonstrated an odds ratio of 3.7 (95% CI: 3.4 to 4.1) compared to those with average risk. Similarly, the inherited risk for type 2 diabetes was significantly elevated with an odds ratio of 3.1 (95% CI: 2.8 to 3.3), while atrial fibrillation showed an odds ratio of 3.0 (95% CI: 2.7 to 3.3). These data points suggest that polygenic risk scores can identify a subset of the population with a threefold or greater susceptibility to these major cardiovascular drivers, potentially warranting earlier screening in primary care. The study also quantified the genetic burden for primary risk factors that often precede major adverse events. For severe hypercholesterolemia, the high-risk group had an odds ratio of 4.1 (95% CI: 3.7 to 4.5). Extreme hypertension also showed a clear genetic signal with an odds ratio of 2.1 (95% CI: 1.8 to 2.3). For less common conditions, the researchers adjusted the thresholds to maintain statistical power, defining high genetic risk for venous thromboembolism and thoracic aortic aneurysm as the top 20 percent of the distribution, with average risk spanning the 21st to 80th percentiles. The analysis for venous thromboembolism yielded an odds ratio of 1.9 (95% CI: 1.6 to 2.0), and the risk for thoracic aortic aneurysm was 1.7 (95% CI: 1.5 to 1.9).

Clinical Performance and Lipoprotein(a) Extremes

The most substantial finding in the study involved the genetic assessment of elevated lipoprotein(a), a known independent risk factor for atherosclerotic cardiovascular disease that is often under-screened in routine practice. When comparing individuals in the high genetic risk group to those with average risk, the researchers found an odds ratio of 41.0 (95% CI: 27.0 to 62.2) for elevated lipoprotein(a). This magnitude of risk suggests that polygenic risk scores can identify genetic outliers with extreme susceptibility who might otherwise be missed by standard lipid panels. Across all eight cardiovascular traits, the integrated polygenic risk scores demonstrated robust discrimination (the ability of the test to correctly distinguish between those with and without the condition) and appropriate calibration (the degree to which the predicted risk aligns with the observed frequency of the disease). Beyond identifying high-risk individuals, the study evaluated how these genetic tools integrate with existing diagnostic workflows. The researchers found that incorporating integrated polygenic risk scores into clinical models improved risk classification, providing a more granular assessment of a patient's true susceptibility than traditional risk factors alone. This improvement is particularly relevant for primary prevention, where refining risk estimates can guide the initiation of pharmacotherapy or lifestyle interventions. Furthermore, prospective analyses confirmed significant associations between the polygenic risk scores and incident cardiovascular outcomes, meaning the scores successfully predicted the development of new-onset disease over time.

Implementation in Routine Practice

The transition of polygenic risk scores from theoretical research to bedside application is marked by the current availability of this tool for practitioners. The researchers emphasize that the integrated polygenic risk score is now available as a clinically orderable test, providing a standardized framework for reporting genetic susceptibility. This allows clinicians to move beyond traditional family history, which is often incomplete or qualitative, and instead utilize a quantitative report that stratifies risk for eight distinct conditions. By identifying patients in the top 10 percent or 20 percent of the genetic risk distribution, physicians can pinpoint individuals with significantly elevated odds for pathologies such as coronary artery disease or severe hypercholesterolemia before clinical symptoms manifest. Despite the immediate availability of these tests, the authors maintain a cautious approach regarding their long-term integration into standard care guidelines. They conclude that broader prospective validation studies are needed to further establish clinical utility, particularly to determine how these scores should influence specific treatment thresholds or long-term patient outcomes. While the current data from the All of Us Research Program and the Mass General Brigham Biobank demonstrate strong predictive value, future research must confirm if early intervention based on these scores leads to a measurable reduction in cardiovascular events. For the practicing physician, these scores currently serve as a supplemental tool to refine risk assessment in patients who may otherwise appear to be at average risk based on conventional clinical models.

References

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3. Brundel BJ, Ai X, Hills MT, Kuipers MF, Lip GY, Groot NMD. Atrial fibrillation. Nature Reviews Disease Primers. 2022. doi:10.1038/s41572-022-00347-9

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6. Russo L, Proto L, Farina S, et al. Polygenic Risk Scores disclosure for cardiovascular prevention: Protocol of the Personalized HeartCare (PHC) trial.. PloS one. 2026. doi:10.1371/journal.pone.0345294