Multiomics in atherosclerotic cardiovascular disease

Area of Science:

• Biomedical Science
• Cardiovascular Research
• Genomics and Bioinformatics

Background:

• Atherosclerotic cardiovascular disease (ASCVD) is a leading global cause of mortality.
• Technological advancements enable the use of diverse omic datasets (genomics, epigenomics, transcriptomics, proteomics, metabolomics) for ASCVD diagnostics and treatment.
• Omics data offers a deeper understanding of ASCVD pathogenesis.

Purpose of the Study:

• To review the current literature on omic data applications in ASCVD.
• To explore the integration of multiomic data with machine learning (ML) and artificial intelligence (AI) for ASCVD management.
• To discuss the potential of these integrated approaches in advancing precision medicine.

Main Methods:

• Systematic literature review of omic data in ASCVD.
• Analysis of studies integrating omic data with ML/AI methodologies.
• Synthesis of findings on the predictive and diagnostic capabilities of different omic layers.

Main Results:

• Genomics provides polygenic risk scores (PRS) for ASCVD risk prediction.
• Epigenomics highlights DNA methylation changes in atherosclerosis.
• Transcriptomics identifies microRNAs (miRNAs) involved in atherosclerosis progression.
• Proteomics and lipidomics offer independent predictive value, outperforming traditional clinical models.
• Combined multiomic data with ML/AI shows potential for developing clinical models.

Conclusions:

• Significant effort is required to translate omic data and technologies into clinical practice.
• Development of clinically approved AI/ML algorithms is crucial for interpreting large datasets.
• These algorithms will facilitate accurate precision medicine approaches for ASCVD.
• Multiomic data integration with AI/ML holds promise for personalized ASCVD prevention and treatment.