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Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review
Narendra N Khanna1,2, Manasvi Singh3,4, Mahesh Maindarkar2,3,5
1Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India.
Insights
Artificial intelligence (AI)-based polygenic risk scores (PRS) show improved accuracy in predicting cardiovascular disease (CVD) risk compared to traditional methods. AI models integrate more genetic and environmental factors for precise, individualized CVD risk assessment and management.
Area of Science:
- Genomics
- Cardiovascular Medicine
- Artificial Intelligence
Background:
- Cardiovascular disease (CVD) poses a significant societal burden.
- The interplay between genetic predisposition and environmental factors in CVD risk is not fully understood.
- Polygenic risk scores (PRS) are emerging tools for assessing genetic susceptibility to complex diseases like CVD.
Purpose of the Study:
- To review and compare artificial intelligence (AI)-based PRS models with conventional approaches for CVD risk prediction.
- To evaluate the potential of AI in enhancing the accuracy and personalization of CVD risk assessment.
- To propose hypotheses regarding AI's role in improving CVD risk prediction by integrating diverse data types and reducing dimensionality.
Main Methods:
- Systematic literature review using the PRISMA search method.
- Analysis and comparison of conventional PRS calculators versus AI-based PRS models.
- Evaluation of AI's capability to incorporate multiple genetic and non-genetic risk factors.
Main Results:
- AI-based PRS models demonstrated superior performance over traditional PRS calculators in predicting CVD risk.
- AI facilitates the integration of a wider array of genetic and non-genetic factors for more precise risk estimation.
- AI approaches effectively reduce the dimensionality of large genomic datasets, enhancing model accuracy and efficiency.
Conclusions:
- AI-based PRS offers a more accurate and personalized approach to cardiovascular disease risk prediction.
- The integration of AI in PRS development has significant implications for individualized CVD prevention and treatment strategies.
- AI enhances the predictive power of PRS by leveraging comprehensive genetic and environmental data.
Abstract:
Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.
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