The following are key points to remember from a state-of-the-art review on proteomics and lipidomics in atherosclerotic cardiovascular disease (ASCVD) risk prediction:

1. Identification of patients at greatest risk of ASCVD events poses a major challenge in both primary and secondary prevention.
2. To effectively reduce the increasing global burden of ASCVD, improvement in risk stratification is of essence. Currently used clinical risk algorithms, including the Framingham risk score, the Systematic COronary Risk Evaluation 2 (SCORE2) system, and the Second Manifestations of ARTerial disease 2 (SMART2), are based on traditional risk factors for CVD and predict future events with limited accuracy.
3. The current clinical risk scores include risk factors such as smoking, hypertension, diabetes, and hypercholesterolemia that cannot encompass the multitude of pathophysiological processes contributing to the onset and progression of ASCVD, nor do these scores incorporate the heterogeneity in interindividual atherogenic vulnerability.
4. With major improvements in assay techniques, multimarker proteomic and lipidomic panels hold the promise to be reliably assessed in a high-throughput routine. Novel machine learning-based approaches have facilitated the use of this high-dimensional data resulting from these analyses for ASCVD risk prediction.
5. More than a dozen large-scale retrospective studies using different sets of biomarkers and different statistical methods have consistently demonstrated the additive prognostic value of these panels over traditionally used clinical risk scores for ASCVD risk.
6. When combined with the genetic predisposition captured with polygenic risk scores and the actual ASCVD phenotype observed with coronary artery imaging, proteomics and lipidomics can advance understanding of the complex multifactorial causes underlying an individual’s ASCVD risk.
7. However, rigorous prospective studies are needed to determine the clinical utility of a biomarker panel in clinical ASCVD risk stratification.
8. If validated in rigorous clinical studies, prognostic plasma biomarkers could be combined with genetic risk scores and coronary artery imaging to capture the complex, multidimensional atherosclerosis process underlying an individual’s ASCVD risk.
9. A personalized ASCVD risk prediction in a one-stop shop can in addition to clinical risk factors incorporate a patient’s genetic predisposition, capture environmental and lifestyle factors in interaction with genetics using plasma biomarkers, and can define the actual phenotype of disease using coronary computed tomography angiography imaging.
10. Finally, once the most ‘relevant’ pathways contributing to the CV risk in specific individuals is identified, targeted treatment with specific therapies, depending on the specific risk factor signature (e.g., antithrombotic, anti-inflammatory, and more) can be implemented.

Clinical Topics: Arrhythmias and Clinical EP, Cardiovascular Care Team, Diabetes and Cardiometabolic Disease, Dyslipidemia, Invasive Cardiovascular Angiography and Intervention, Noninvasive Imaging, Prevention, Genetic Arrhythmic Conditions, Homozygous Familial Hypercholesterolemia, Interventions and Imaging, Angiography, Computed Tomography, Nuclear Imaging, Hypertension, Smoking

Keywords: Angiography, Atherosclerosis, Biomarkers, Computed Tomography Angiography, Diabetes Mellitus, Diagnostic Imaging, Dyslipidemias, Fibrinolytic Agents, Genetics, Hypercholesterolemia, Hypertension, Lipidomics, Phenotype, Primary Prevention, Proteomics, Risk Factors, Secondary Prevention, Smoking, Vascular Diseases

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