Multiomics for Risk Stratification in Atherosclerotic Cardiovascular Disease.

Area of Science:

• Cardiovascular Disease Research
• Genomics and Multiomics
• Biomarker Discovery

Background:

• Atherosclerotic cardiovascular disease (ASCVD) is a major global health concern.
• Current ASCVD risk prediction models have limitations, especially for younger individuals and women.
• Early detection and accurate risk stratification are crucial for preventing ASCVD events.

Purpose of the Study:

• To explore the potential of multiomics technologies for improving ASCVD risk stratification.
• To address the limitations of current risk prediction models.
• To discuss how integrating multiomic data can enhance patient management and reduce ASCVD burden.

Main Methods:

• Review of current literature on multiomics applications in ASCVD.
• Exploration of genomics, epigenomics, transcriptomics, proteomics, and metabolomics.
• Discussion of computational modeling for integrating diverse omic data.

Main Results:

• Polygenic risk scores can enhance ASCVD risk classification.
• Epigenomics reflects environmental influences on ASCVD risk.
• Proteomics, transcriptomics, and metabolomics provide insights into ASCVD mechanisms and outcomes.
• Multiomics data can identify distinct molecular subgroups of atherosclerotic lesions.

Conclusions:

• Integrating multiple omic layers holds significant promise for enhanced ASCVD patient stratification.
• Multiomics can optimize clinical management strategies for ASCVD.
• This integrated approach has the potential to reduce the global burden of atherosclerotic cardiovascular disease.