1.Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review
Narendra N KHANNA ; Manasvi SINGH ; Mahesh MAINDARKAR ; Ashish KUMAR ; Amer M. JOHRI ; Laura MENTELLA ; John R LAIRD ; Kosmas I. PARASKEVAS ; Zoltan RUZSA ; Narpinder SINGH ; Mannudeep K. KALRA ; Jose Fernandes E. FERNANDES ; Seemant CHATURVEDI ; Andrew NICOLAIDES ; Vijay RATHORE ; Inder SINGH ; Jagjit S. TEJI ; Mostafa AL-MAINI ; Esma R. ISENOVIC ; Vijay VISWANATHAN ; Puneet KHANNA ; Mostafa M. FOUDA ; Luca SABA ; Jasjit S. SURI
Journal of Korean Medical Science 2023;38(46):e395-
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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