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July 15, 2025Biomedicines13 citationsOpen Access

Genomic and Precision Medicine Approaches in Atherosclerotic Cardiovascular Disease: From Risk Prediction to Therapy—A Review

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AMAndreas MitsisEKElina KhattabMKMichaella Kyriakou

Key Points

  • Genomic and precision medicine approaches are enhancing treatment strategies for atherosclerotic cardiovascular disease (ASCVD).
  • Key findings on genetic risk scores and variants like PCSK9 improve early risk prediction for ASCVD.
  • Emerging therapies, including PCSK9 inhibitors and CRISPR, represent breakthroughs in ASCVD management.
  • Integrative multi-omics analysis using deep learning algorithms is advancing the understanding of ASCVD mechanisms.

Abstract

Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of global morbidity and mortality, prompting significant interest in individualized prevention and treatment strategies. This review synthesizes recent advances in genomic and precision medicine approaches relevant to ASCVD, with a focus on genetic risk scores, lipid metabolism genes, and emerging gene editing techniques. A structured literature search was conducted across PubMed, Scopus, and Web of Science databases to identify key publications from the last decade addressing genomic mechanisms, therapeutic targets, and computational tools in ASCVD. Notable findings include the identification of causal genetic variants such as PCSK9 and LDLR, the development of polygenic risk scores for early prediction, and the use of deep learning algorithms for integrative multi-omics analysis. In addition, we highlight current and future therapeutic applications including PCSK9 inhibitors, RNA-based therapies, and CRISPR-based genome editing. Collectively, these advances underscore the promise of precision medicine in tailoring ASCVD prevention and treatment to individual genetic and molecular profiles.

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Cite This Study

Mitsis et al. (2025) studied this question.

synapsesocial.com/papers/689a02bce6551bb0af8cc745https://doi.org/10.3390/biomedicines13071723
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