Multiomics for Risk Stratification in Atherosclerotic Cardiovascular Disease - PubMed
3 hours ago
- #cardiovascular disease
- #multiomics
- #risk stratification
- Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of morbidity and mortality worldwide.
- Current risk prediction models like Systematic Coronary Risk Evaluation 2 are limited to individuals aged 40-69 and focus on 10-year risk, not lifetime risk.
- Multiomics (genomics, epigenomics, transcriptomics, proteomics, metabolomics) offers new opportunities for better ASCVD risk stratification.
- Polygenic risk scores from genomic data can improve ASCVD risk classification.
- Epigenomics captures environmental influences on gene expression through DNA modifications.
- Proteomics reflects interactions between genetic and environmental factors.
- Transcriptomic analyses have identified molecular subgroups in atherosclerotic lesions.
- Metabolomics identifies metabolic signatures linked to cardiovascular disease.
- Integrating multiomics through computational modeling could enhance patient stratification and reduce ASCVD burden.