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Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
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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

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Journal of Korean Medical Science
|November 28, 2023
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
Artificial IntelligenceCardiovascular DiseaseGenomicsPolygenic Risk ScorePrecision Medicine

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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.