Proteomics and lipidomics in atherosclerotic cardiovascular disease risk prediction

Abstract

Given the limited accuracy of clinically used risk scores such as the Systematic COronary Risk Evaluation 2 system and the Second Manifestations of ARTerial disease 2 risk scores, novel risk algorithms determining an individual's susceptibility of future incident or recurrent atherosclerotic cardiovascular disease (ASCVD) risk are urgently needed. Due to 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. More than a dozen of 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. Prospective studies are needed to determine the clinical utility of a biomarker panel in clinical ASCVD risk stratification. 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.

Keywords: ASCVD; Lipidomics; Multiomics; Proteomics; Risk score.

Graphical Abstract

Proteomics and lipidomics improve traditional ASCVD risk prediction. Plasma proteomics and lipidomics hold a major promise in improving ASCVD risk prediction offering high-throughput assessment using different techniques. Albeit retrospectively, large proteomic and lipidomic studies have consistently demonstrated improved ASCVD risk prediction in terms of discrimination and reclassification benefit compared to risk scoring with clinical characteristics. Future studies into clinical utility are needed for widespread clinical implementation. ASCVD, atherosclerotic cardiovascular disease; AUC, area under the receiver operating curve; non-HDL-C, non-high-density lipoprotein cholesterol; SCORE2, Systematic COronary Risk Evaluation 2 system; SMART2, Second Manifestations of ARTerial disease 2; LC-MS, liquid chromatography–mass spectrometry; MS, mass spectrometry.

Figure 1

Methods for performing plasma proteomics and lipidomics. Characteristics of the most frequently used analysing methods for plasma proteomics (A) and plasma lipidomics (B) in large-scale studies. Shown are differences in proteins/lipid species, target/discovery, and strengths/limitations. The numbers shown reflect the maximum number of proteins/lipid species of the used techniques in the clinical studies (Tables 1 and 2) included in the current review. Created with BioRender.com. MS, mass spectrometry.

Figure 2

A one-stop shop for future ASCVD risk prediction. A personalized atherosclerotic cardiovascular disease 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. In addition, the most ‘relevant’ pathways contributing to the cardiovascular risk in specific individuals could be identified and subsequently treated with specific therapies, depending on the specific risk factor signature (e.g. anti-thrombotic, anti-inflammatory, and other). Created with BioRender.com. ASCVD, atherosclerotic cardiovascular disease; CCTA, coronary computed tomography angiography.