Twenty percent of participants were classified in both the highest and lowest 5% of heart disease risk by different polygenic risk scores, raising concerns about their reliability for individual predictions.
Summary: New research published in JAMA and presented at the American Heart Association’s Scientific Sessions evaluated the accuracy of polygenic risk scores in predicting coronary artery disease (CAD). The study, which analyzed data from over 260,000 individuals, found variability in individual-level predictions among 48 different scores. While the scores performed similarly at the population level, 20% of participants were classified in both the highest and lowest 5% of CAD risk depending on the score used. Researchers say these findings highlight the need for refinement and consistency before polygenic risk scores can be reliably used for personalized care.
Key Takeaways:
- Significant Individual-Level Variability: While polygenic risk scores showed similar performance at the population level, 20% of participants were classified in both high-risk and low-risk categories by different scores, raising concerns about their reliability for individual predictions.
- Need for Improved Methods: Researchers emphasize the importance of refining polygenic risk scores to ensure consistent and reliable results for clinical use, particularly when health decisions depend on accurate risk assessments.
- Part of a Broader Strategy: Clinicians are advised to use polygenic risk scores as one component of a broader risk assessment strategy, incorporating clinical, genetic, and lifestyle factors to provide a more holistic view of CAD risk.
Polygenic risk scores are a cutting-edge tool in genetics, combining information from genetic markers across the genome to estimate a person’s risk of developing certain diseases, such as coronary artery disease (CAD).
By analyzing a person’s DNA, polygenic risk scores offer insights into an individual’s genetic predisposition for conditions like heart disease, potentially informing a more personalized approach to healthcare. But there can be significant variability across currently available polygenic risk scores, which may limit their reliability for individual predictions, according to new research from the Perelman School of Medicine at the University of Pennsylvania published in JAMA and presented at the American Heart Association’s Scientific Sessions in Chicago.
The researchers analyzed data from more than 260,000 participants from diverse backgrounds and found that although most polygenic risk scores performed similarly when predicting CAD risk across populations, individual-level predictions varied widely. Many participants were placed in both high and low-risk categories by different polygenic risk scores, suggesting that patients could receive conflicting advice based on which score is used.
Variability Challenges Individual Risk Predictions
“Polygenic risk scores represent an exciting frontier in personalized medicine that has been gaining traction in clinics and as commercial health tests, but our findings suggest that they need to be used carefully,” says co-lead author Michael G. Levin, MD, an assistant professor of cardiovascular medicine and cardiologist at Penn and the Corporal Michael Crescenz VA Medical Center, in a release. “At the individual level, these scores can vary quite a bit, which means that the same patient could receive dramatically different risk assessments that impact how doctors make decisions about prevention and treatment.”
The research, conducted with data from the National Institute of Health’s All of Us Research Program, Penn Medicine Biobank, and UCLA ATLAS Precision Health Biobank, compared 48 different CAD polygenic risk scores using health and genetic data. While 46 of the scores provided similar population-level predictions, 20% of participants had at least one score placing them in both the highest and lowest 5% of risk, depending on which score was used.
“The goal of [polygenic risk scores] is to help identify people at higher genetic risk for diseases like heart disease,” says Scott M. Damrauer, MD, vice chair for clinical research in Penn’s department of surgery and a vascular surgeon at Penn and the Corporal Michael Crescenz VA Medical Center, in a release. “But for clinical use, it’s important that the results are consistent and reliable, especially when decisions about someone’s health is on the line.”
Need for Refinement and Broader Strategies
“Our research underscores a critical gap in our understanding of [polygenic risk scores], which has implications for their use in personalized medicine,” says the study’s lead author, Sarah Abramowitz, BA, a medical student at the Zucker School of Medicine at Hofstra/Northwell and a Sarnoff Cardiovascular Research Fellow at the Perelman School of Medicine, in a release. “While these scores show promise for population-level CAD risk assessment, we need more robust methods to quantify and communicate the uncertainty of individual-level predictions.”
The study’s findings highlight the need for more refinement before polygenic risk scores can be widely adopted in healthcare to guide an individual’s CAD risk assessment. Researchers recommend that clinicians consider potential inconsistencies and use these scores as part of a broader risk assessment strategy that considers clinical and lifestyle factors, among others.
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