Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.
Artificial Intelligence
Cardiovascular Disease
Genomics
Polygenic Risk Score
Precision Medicine
Journal
Journal of Korean medical science
ISSN: 1598-6357
Titre abrégé: J Korean Med Sci
Pays: Korea (South)
ID NLM: 8703518
Informations de publication
Date de publication:
27 Nov 2023
27 Nov 2023
Historique:
received:
31
07
2023
accepted:
15
10
2023
medline:
29
11
2023
pubmed:
28
11
2023
entrez:
28
11
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 38013648
pii: 38.e395
doi: 10.3346/jkms.2023.38.e395
pmc: PMC10681845
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
e395Informations de copyright
© 2023 The Korean Academy of Medical Sciences.
Déclaration de conflit d'intérêts
The authors have no potential conflicts of interest to disclose.
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