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Published on: August 28, 2018
Association of a polygenic risk score with coronary atherosclerotic burden in clinical CT angiograms
Katherine Hartmann1, Michael Gannon2, Pradeep Natarajan3,4,5
1Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
Insights
Inherited risk for coronary artery disease (CAD) is linked to more severe coronary computed tomography angiography (CTA) findings. This study shows polygenic risk scores correlate with anatomical atherosclerotic burden, suggesting genetic risk integration into imaging assessments.
Area of Science:
- Cardiovascular Genetics
- Medical Imaging
- Computational Biology
Background:
- Polygenic risk scores (PRS) for coronary artery disease (CAD) predict cardiovascular events.
- The association between inherited risk and coronary computed tomography angiography (CTA) findings is not well understood.
Purpose of the Study:
- To evaluate the relationship between a genome-wide PRS for angiographic CAD burden and coronary CTA-derived measures of atherosclerotic severity.
- To assess these associations in a real-world clinical cohort.
Main Methods:
- Utilized data from the Penn Medicine BioBank, including genotypes and coronary CTA reports.
- Calculated a PRS for angiographic CAD burden and extracted CAD-RADS scores and coronary artery calcium (CAC) values using a large language model (Llama 3.1 8B).
- Employed Bayesian regression models to assess associations between PRS and CAD-RADS severity and CAC burden.
Main Results:
- Higher PRS was significantly associated with increased coronary atherosclerotic burden on CTA.
- Each 1-SD increase in PRS correlated with a 20% higher odds of a more severe CAD-RADS category.
- Higher PRS was also linked to greater CAC burden.
Conclusions:
- Polygenic risk for CAD is reflected in clinically reported coronary CTA measures (CAD-RADS and CAC).
- Inherited susceptibility to CAD manifests as greater anatomical atherosclerotic burden at presentation.
- Findings support integrating genetic risk into imaging-based cardiovascular risk assessment.
Background:
Polygenic risk scores (PRS) for coronary artery disease (CAD) are associated with cardiovascular events, but the relationship between inherited risk and routinely reported coronary computed tomography angiography (CTA) findings has not been studied.
Objectives:
To evaluate associations between a genome-wide PRS for angiographic coronary disease burden and coronary CTA-derived measures of atherosclerotic severity in a real-world clinical cohort.
Methods:
We studied Penn Medicine BioBank participants with available genotypes and clinically obtained coronary CTA reports. A previously published PRS for angiographic CAD burden was calculated using pgsc_calc. CAD-RADS scores and coronary artery calcium (CAC) values were extracted from radiology reports using the large language model Llama 3.1 8B. Associations between PRS and CAD-RADS severity were evaluated using Bayesian cumulative ordinal logit regression, while associations with log-transformed CAC burden were assessed using Bayesian linear regression.
Results:
Among 630 participants, median age was 59 years (IQR 49 - 68), 53% were female, 62% were genetically similar to a European reference population, and 34% to an African reference population. LLM-extracted CAD-RADS and CAC values demonstrated near-perfect agreement with manual abstraction. Higher PRS was associated with greater coronary atherosclerotic burden on CTA. Each 1-standard deviation (SD) increase in PRS was associated with a 20% higher odds of belonging to a more severe CAD-RADS category (cumulative OR 1.20, 95% credible interval 1.06-1.44). Higher PRS was also associated with greater CAC burden (β 0.38, 95% credible interval 0.15 - 0.61).
Conclusions:
Polygenic risk for angiographic coronary disease burden is reflected in clinically reported coronary CTA severity measures, including CAD-RADS and CAC. These findings demonstrate that inherited susceptibility to CAD manifests as greater anatomic atherosclerotic burden at the time of clinical presentation and support further investigation of genetic risk integration into imaging-based cardiovascular risk assessment.
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