Key result
Adding multiomics to clinical scores shows promise for improving ASCVD risk prediction.
Why the study?
A large proportion of preventable ASCVD cases is not discovered early, as current risk prediction models relate to 10-year rather than lifetime risk and underdiagnose risk in women and the young.
Does integrating multiomics approaches improve risk stratification and prediction of atherosclerotic cardiovascular disease compared to traditional clinical risk models?
Does integrating multiomics approaches improve risk stratification and prediction of atherosclerotic cardiovascular disease compared to traditional clinical risk models?
Integrating multiomics data (genomics, epigenomics, transcriptomics, proteomics, and metabolomics) via machine learning offers a promising avenue to overcome the limitations of traditional clinical risk scores and improve personalized risk stratification in atherosclerotic cardiovascular disease.
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Multiomics may refine ASCVD stratification beyond SCORE-2; leaves open prospective outcome validation before clinical adoption.
Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of morbidity and mortality worldwide. Preventing ASCVD is of utmost importance; however, a large proportion of preventable cases is not discovered early enough to initiate relevant treatment. Risk stratification for ASCVD includes classical risk factors, such as sex, age, smoking habits, blood pressure, cholesterol levels, and diabetes. Current risk prediction models, including the Systematic Coronary Risk Evaluation 2 algorithms, are designed for individuals aged 40 to 69 years and relate to 10-year risk and not to lifetime risk, thereby being inaccurate for the young. Another problem is the underdiagnosis of events in women, thereby underestimating risk. Multiomics, encompassing genomics, epigenomics, transcriptomics, epitranscriptomics, proteomics, and metabolomics, offers new opportunities. Polygenic risk scores derived from genomic data may improve ASCVD risk classification. While genomic risk is established at inception, epigenomics captures the influence of environmental exposures over the lifespan through dynamic DNA modifications that regulate gene expression. Proteomics-based prediction reflects interactions between genetic inheritance, and modifiable and nonmodifiable influences. Transcriptomic analyses of carotid plaques have clustered human atherosclerotic lesions into distinct molecular subgroups, and changes in RNA methylation of circulating blood cells have been linked to clinical outcomes after ASCVD. Metabolomics identifies metabolic signatures, including lipid subclass alterations, amino acid imbalances, and inflammatory markers, all associated with cardiovascular disease incidence. In this review, we highlight current challenges, explore potential solutions, and discuss how integrating multiple omic layers through computational modeling (multiomics) could enhance patient stratification, optimize clinical management, and reduce the global burden of ASCVD.
Nordestgaard et al. (2026) conducted a review in Atherosclerotic cardiovascular disease (ASCVD). Multiomics risk stratification (genomics, epigenomics, transcriptomics, epitranscriptomics, proteomics, and metabolomics) vs. Traditional clinical risk prediction models (e.g., SCORE-2, Framingham Risk Score) was evaluated. Multiomics integration shows promise for improving ASCVD risk stratification over clinical scores like SCORE-2 (C-index 0.67-0.81), with polygenic risk scores improving discrimination by 0.4%-7%.
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