Coronary artery disease remains the leading cause of premature mortality worldwide, placing a heavy burden on modern healthcare systems. To tackle this crisis, clinicians increasingly examine genomic tools to detect subclinical disease before adverse clinical events occur. Recent advances have introduced polygenic risk scores to capture inherited susceptibility across millions of common genetic variants. Although researchers originally calibrated these scores to predict clinical events, compelling evidence now demonstrates their power to detect early anatomical atherosclerosis.

The Expanding Clinical Role of Polygenic Risk Scores in Cardiovascular Care

Cardiovascular risk assessment has long relied on traditional clinical risk calculators. Standard algorithms, such as the Framingham Risk Score and Pooled Cohort Equations, evaluate factors like age, blood pressure, cholesterol levels, and smoking habits. However, these conventional frameworks often miss young individuals who harbor substantial silent plaque. Furthermore, up to a quarter of acute myocardial infarctions occur in patients without conventional modifiable risk factors. This critical diagnostic blind spot underscores the need for biology-driven stratifiers in primary prevention.

Polygenic risk scores quantify the cumulative impact of hundreds or thousands of single nucleotide polymorphisms distributed throughout the genome. Unlike circulating lipid biomarkers, an individual’s inherited genetic architecture remains constant from conception. Consequently, genetic profiling offers an enduring estimate of lifetime cardiovascular risk. Early studies proved that elevated scores correlate strongly with hard coronary events, including myocardial infarction and revascularization. Nevertheless, clinicians frequently question whether these mathematical models predict actual arterial calcification. Recent investigations now show that elevated scores directly reflect anatomical disease, bridging genomic data with observable pathology.

Evaluating Polygenic Risk Scores Against Coronary Artery Calcification

Coronary artery calcium scoring represents an established non-invasive imaging modality that directly quantifies subclinical calcified plaque. Healthcare providers frequently utilize computed tomography to calculate the Agatston score, which accurately predicts future major adverse cardiovascular events. Current preventive guidelines recommend calcium scoring to resolve clinical uncertainty in intermediate-risk patients. However, performing computed tomography on every asymptomatic individual remains impractical due to radiation exposure and healthcare costs. Therefore, researchers seek efficient non-invasive triage mechanisms to identify individuals who warrant targeted imaging.

To address this clinical question, investigators evaluated whether publicly available polygenic algorithms could forecast clinically significant calcium scores. The study examined participants from the BioHEART-CT Discovery cohort who underwent clinically indicated computed tomography coronary angiography. Researchers assessed two distinct endpoints defining clinically actionable calcification: an absolute Agatston score of 100 or greater, and a percentile score reaching or exceeding the 75th age- and sex-adjusted percentile. By testing 53 distinct polygenic algorithms, the investigators evaluated the consistency and robustness of genetic tools in predicting coronary calcification.

Robust Performance and Odds Ratios Across Multiple Polygenic Models

The BioHEART-CT analysis evaluated 803 participants, among whom 487 individuals displayed detectable coronary artery calcium. Most evaluated models demonstrated a statistically significant association with calcium outcomes. When analysts evaluated the genetic score as a continuous variable, the association became particularly pronounced. Specifically, 94.3 percent of the 53 evaluated tools showed significant associations with high percentile calcification after comprehensive adjustment for genetic ancestry and modifiable risk factors. The median odds ratio per standard deviation increase reached 1.41, confirming that higher genetic burdens consistently align with elevated calcification.

Furthermore, quintile-based stratification revealed striking gradients of risk. Individuals in the top quintile exhibited up to a 7.99-fold increase in the odds of exceeding the 75th calcium percentile compared to the bottom quintile. This profound difference persisted even after adjusting for traditional variables such as hypertension, diabetes, and dyslipidemia. Consequently, the data demonstrate that polygenic scores capture unique biological determinants of coronary calcification that standard risk markers completely overlook.

Triaging Low-Risk Patients for Coronary Artery Calcium Screening

The most compelling clinical insight emerged when researchers stratified participants according to baseline Framingham Risk Scores. In clinical practice, low-risk patients rarely qualify for advanced diagnostic imaging or aggressive lipid-lowering therapy. However, within this low Framingham group, incorporating genetic scoring dramatically improved risk discrimination. Specifically, a high genetic score nearly doubled the pre-test probability of significant coronary calcification, increasing it from 14 percent to 26 percent. Thus, genetic profiling unmasks hidden vascular vulnerability in individuals whom conventional scores categorize as completely healthy.

This finding supports the role of genetic screening as an upstream triage mechanism in primary care. Instead of subjecting entire populations to indiscriminate computed tomography, physicians can utilize saliva or blood genomic tests to select appropriate candidates. Consequently, patients flagged with elevated genetic scores can proceed directly to targeted calcium imaging. Conversely, individuals displaying low genetic risk alongside low clinical risk can avoid unnecessary radiation and imaging expenses. This selective approach maximizes healthcare efficiency while directing preventive pharmacotherapy to patients who will benefit most.

Translating Genomic Stratification into Primary Prevention Practice

Modern preventive cardiology emphasizes early, lifetime risk management over reactive late-stage intervention. Because atherosclerosis progresses silently over decades, identifying vulnerable individuals during early adulthood offers the greatest opportunity to prevent adverse cardiovascular events. Genetic risk scores provide an actionable foundation because clinicians can assess them early in life. When a young adult demonstrates high genetic liability, clinicians can recommend aggressive dietary optimization, sustained physical activity, and strict blood pressure control. Furthermore, clinicians can initiate low-dose statin therapy before irreversible plaque calcification occurs.

Additionally, demonstrating objective arterial calcium provides powerful psychological motivation for lifestyle modification. Studies consistently reveal that patients adhere more strictly to preventive regimens when imaging confirms coronary plaque. Therefore, pairing genomic assessment with targeted computed tomography creates an effective clinical continuum. Physicians first identify elevated biological vulnerability through genetic tests, then confirm active calcification with imaging, and finally personalize cardioprotective therapies. As genomic testing costs continue to decline, this combined paradigm promises to transform primary cardiovascular prevention across diverse clinical settings.

Clinical Considerations and Future Implementation Challenges

Despite these promising findings, clinicians must consider several practical hurdles before universal adoption. Most publicly available algorithms were developed and validated in cohorts of predominantly European ancestry. Consequently, their predictive precision can decline when applied to other ethnicities, such as South Asian or African populations. Researchers must prioritize the development of multi-ancestry polygenic models to ensure equitable clinical benefits across all patient demographics. Furthermore, clinicians require standardized reporting frameworks that present complex genomic calculations in intuitive, clinically actionable formats.

Moreover, physicians must recognize that genetic susceptibility does not represent immutable destiny. Healthy lifestyle habits and pharmacotherapy substantially mitigate inherited cardiovascular risk. When discussing genetic results with patients, healthcare providers must communicate that genetic liability is modifiable through early intervention. Training programs must equip general practitioners and internists with genomic literacy to guide shared decision-making effectively. By integrating polygenic tools prudently into routine workflows, modern medicine can advance toward truly individualized cardiovascular protection.

Can polygenic risk scores replace coronary artery calcium scanning?

No, polygenic risk scores cannot replace coronary artery calcium scanning because they measure distinct biological parameters. While genetic scores capture an individual's lifetime inherited susceptibility from birth, calcium scans directly quantify established anatomical plaque burden. Therefore, clinicians should view genetic tools as upstream risk stratification instruments. Consequently, these scores help physicians identify candidates who will benefit most from targeted imaging, rather than acting as a surrogate for anatomical calcium assessment.

How do these findings alter primary prevention strategies for younger patients?

Traditional risk calculators frequently misclassify younger adults because age heavily drives absolute risk calculations. However, polygenic risk scores remain stable throughout life and reveal elevated cardiovascular risk decades before clinical events occur. Therefore, incorporating genetic profiling allows clinicians to reclassify younger patients who appear healthy into higher surveillance categories. Consequently, clinicians can initiate timely lifestyle interventions, prescribe lipid-lowering therapies earlier, and order selective calcium scoring before extensive vascular damage develops.

What are the primary limitations of applying polygenic risk scores universally?

The principal limitation involves ancestral bias in underlying genetic datasets, as most genome-wide association studies reflect populations of European ancestry. Therefore, score performance may diminish when applied to ethnically diverse cohorts, including South Asian populations. Furthermore, genetic scores measure lifetime susceptibility rather than immediate disease activity or plaque stability. Consequently, clinicians must always interpret genetic data alongside clinical evaluations, family histories, and conventional laboratory biomarkers to avoid premature or inappropriate interventions.

Disclaimer: This content is for informational and educational purposes only and should not be considered medical advice. Healthcare providers must exercise independent clinical judgment when interpreting risk models. Refer to the latest local and national guidelines for clinical practice.

References

1. Gray MP et al. Comparison of Performance of Publicly Available Polygenic Risk Scores to Predict Clinically Actionable Coronary Artery Calcium Scores: The BioHEART-CT Cohort. Eur J Prev Cardiol. 2026 Sep 16. doi: undefined. PMID: 42747424.


2. Khan SS, Post WS, Guo X, et al. Coronary Artery Calcium Score and Polygenic Risk Score for the Prediction of Coronary Heart Disease Events. JAMA. 2023;329(20):1768-1777.


3. Patel AP, Wang M, Ruan Y, et al. A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease. Nat Med. 2023;29(7):1793-1803.