⚡ Quick Summary
This systematic review explores the role of artificial intelligence (AI) in optimizing polygenic risk scores (PRS) for cardiovascular disease prediction. The findings indicate that AI-optimized PRS models significantly enhance predictive accuracy, offering a more comprehensive understanding of individual risk profiles.
🔍 Key Details
- 📊 Studies analyzed: 13 studies on AI-optimized PRS
- 🧩 Features used: Genetic variants, clinical risk factors, biomarkers, imaging
- ⚙️ Technology: Machine learning algorithms
- 🏆 Performance: AI-optimized PRS models outperform nonoptimized models
🔑 Key Takeaways
- 💡 AI-optimized PRS improves cardiovascular disease risk prediction.
- 📈 Enhanced predictive accuracy through better feature selection and data integration.
- 🔍 Comprehensive risk profiles are developed by combining multiple variables.
- 👩⚕️ Personalized prevention strategies can be guided by AI-optimized PRS.
- 🌍 Future research should explore sex differences and diverse populations.
- 💻 Integration into electronic health records is essential for practical application.
- 💰 Cost-effectiveness of AI-optimized PRS needs further assessment.
📚 Background
Cardiovascular disease remains a leading cause of morbidity and mortality worldwide. Traditional risk prediction models often fall short in identifying high-risk individuals, highlighting the necessity for more precise tools. Polygenic risk scores (PRS) offer a promising approach by quantifying genetic susceptibility through the aggregation of genetic variants. However, their practical application has faced challenges, necessitating innovative solutions.
🗒️ Study
This systematic review aimed to investigate how artificial intelligence and machine learning algorithms can enhance the utility of PRS in cardiovascular disease prediction. By analyzing 13 studies, the authors assessed the effectiveness of AI-optimized PRS models in improving predictive accuracy and understanding individual risk profiles.
📈 Results
The review revealed that AI-optimized PRS models significantly enhance predictive accuracy by improving feature selection, managing high-dimensional data, and integrating various clinical and genetic variables. These models consistently outperformed nonoptimized PRS models, providing a more nuanced understanding of individual risk factors.
🌍 Impact and Implications
The implications of this research are profound. By leveraging AI to optimize PRS, healthcare providers can better stratify patients and tailor personalized prevention strategies. This advancement could lead to improved outcomes in cardiovascular health, ultimately reducing the burden of disease on individuals and healthcare systems alike.
🔮 Conclusion
This systematic review highlights the transformative potential of artificial intelligence in the realm of cardiovascular disease risk prediction. The integration of AI-optimized PRS into clinical practice could revolutionize how we approach prevention and treatment, paving the way for more personalized healthcare solutions. Continued research in this area is essential to fully realize these benefits and address existing gaps.
💬 Your comments
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Bridging Genomics to Cardiology Clinical Practice: Artificial Intelligence in Optimizing Polygenic Risk Scores: A Systematic Review.
Abstract
Despite advances in cardiovascular disease risk stratification, traditional risk prediction models often fail to identify high-risk individuals before adverse events occur, underscoring the need for more precise tools. Polygenic risk scores (PRS) quantify genetic susceptibility by aggregating genetic variants but face challenges in practice. This systematic review investigates how artificial intelligence (AI) and machine learning algorithms can optimize PRS (AI-optimized PRS) to improve cardiovascular disease prediction. Analyzing 13 studies, we found that AI-optimized PRS models enhance predictive accuracy by improving feature selection, handling high-dimensional data, and integrating diverse variables-including clinical risk factors, biomarkers, imaging, and combining multiple PRS. These models outperform nonoptimized PRS models, providing a more comprehensive understanding of individual risk profiles. Evidence suggests that AI-optimized PRS can better stratify patients and guide personalized prevention strategies. Future research is needed to explore sex differences, include diverse populations, integrate AI-optimized PRS into electronic health records, and assess cost-effectiveness.
Author: [‘Hosseini K’, ‘Anaraki N’, ‘Dastjerdi P’, ‘Kazemian S’, ‘Hasanzad M’, ‘Alkhouli M’, ‘Alam M’, ‘Nasir K’, ‘Rana JS’, ‘Bhatt AB’]
Journal: JACC Adv
Citation: Hosseini K, et al. Bridging Genomics to Cardiology Clinical Practice: Artificial Intelligence in Optimizing Polygenic Risk Scores: A Systematic Review. Bridging Genomics to Cardiology Clinical Practice: Artificial Intelligence in Optimizing Polygenic Risk Scores: A Systematic Review. 2025; 4:101803. doi: 10.1016/j.jacadv.2025.101803