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Artificial intelligence (AI) optimizes polygenic risk scores (PRS) for better cardiovascular disease prediction. AI-optimized PRS enhance accuracy, improving patient risk stratification and personalized prevention strategies.

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Area of Science:

  • Cardiovascular disease research
  • Genetics and bioinformatics
  • Artificial intelligence in medicine

Background:

  • Traditional cardiovascular disease risk models have limitations in identifying high-risk individuals.
  • Polygenic risk scores (PRS) aggregate genetic variants to quantify genetic susceptibility but face practical challenges.
  • There is a need for more precise tools for cardiovascular disease risk stratification.

Purpose of the Study:

  • To systematically review how artificial intelligence (AI) and machine learning algorithms can optimize PRS (AI-optimized PRS) for improved cardiovascular disease prediction.
  • To assess the performance of AI-optimized PRS compared to non-optimized PRS models.
  • To evaluate the potential of AI-optimized PRS in guiding personalized prevention strategies.

Main Methods:

  • Systematic review of 13 studies investigating AI and machine learning for PRS optimization.
  • Analysis of how AI algorithms improve feature selection, data handling, and variable integration in PRS models.
  • Comparison of predictive accuracy between AI-optimized PRS and traditional PRS.

Main Results:

  • AI-optimized PRS models demonstrate enhanced predictive accuracy for cardiovascular disease.
  • These models effectively handle high-dimensional genetic data and integrate diverse variables (clinical factors, biomarkers, imaging).
  • AI-optimized PRS outperform non-optimized PRS, offering a more comprehensive risk profile and better patient stratification.

Conclusions:

  • AI-optimized PRS represent a significant advancement in cardiovascular disease risk prediction.
  • These enhanced scores can improve the identification of high-risk individuals and inform personalized prevention strategies.
  • Future research should focus on sex differences, population diversity, clinical integration, and cost-effectiveness.