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.

Distinct Lipidomic Profiles between People Living with HIV Treated with E/C/F/TAF or B/F/TAF: An Open-Label Prospective Cohort Study

“…This could be explained by the short follow-up duration (24 weeks), low median age (32.00, IQR 27.00–37.00), and small sample size (44 patients). Furthermore, it has been demonstrated that conventional blood lipid analysis including that of LDL-C cannot encompass perturbations in overall lipid profiles and may not adequately assess the heterogeneity in interindividual atherogenic vulnerability [ 25 ]. In some cases, lower LDL-C may not be associated with improved cardiovascular outcomes [ 26 ].…”

Interpretable Machine Learning Leverages Proteomics to Improve Cardiovascular Disease Risk Prediction and Biomarker Identification

“…Previous studies have used proteomics to predict cardiovascular events (Helgason, et al, 2023), type II diabetes (Gadd, et al, 2023), and chronic kidney disease (Avram, 2023). Incorporating additional information, such as clinical (Williams, et al, 2022), lipidomics (Nurmohamed, et al, 2023), and metabolomics data (Nightingale Health Biobank Collaborative Group, et al, 2023) has been demonstrated to further enhance the predictive capabilities of disease risk. Apart from predicting disease risk, some machine learning models also excel in extracting in-depth insights of feature importance and individualized risk predictions from high-dimensional and complex datasets.…”

Plasma Lipidomics Profiles Highlight the Associations of the Dual Antioxidant/Pro-oxidant Molecules Sphingomyelin and Phosphatidylcholine with Subclinical Atherosclerosis in Patients with Type 1 Diabetes

“…Conversely, lipidomics offers a novel and powerful avenue to obtain a holistic analysis of the lipid profiles associated with atherosclerosis and a better understanding of the complex interaction between atherosclerosis and T1DM [11]. Thus, in the last years there has been an increased number of studies using lipidomics approaches that investigate the role of lipids in atherosclerotic CVD risk [12]. Among them, sphingolipids, and particularly ceramides, have been consistently found to be positively associated with ACS, CHF, or a vulnerable plaque phenotype [12,13].…”

“…Thus, in the last years there has been an increased number of studies using lipidomics approaches that investigate the role of lipids in atherosclerotic CVD risk [12]. Among them, sphingolipids, and particularly ceramides, have been consistently found to be positively associated with ACS, CHF, or a vulnerable plaque phenotype [12,13]. A recent study performing a 1 H-NMR-derived lipidomics analysis in T1DM patients without CVD identified sphingomyelin as independently associated with carotid plaque presence, while w-6 fatty acids and linoleic acid were associated with a higher plaque burden [14].…”