Bioinformatic platforms for clinical stratification of natural history of atherosclerotic cardiovascular diseases

生命银行 医学 疾病 弗雷明翰风险评分 生物信息学 PCSK9 弗雷明翰心脏研究 表观遗传学 内科学 肿瘤科 胆固醇 DNA甲基化 生物 遗传学 基因 脂蛋白 基因表达 低密度脂蛋白受体
作者
Giuditta Benincasa,Rosa Suades,Teresa Padró,Lina Badimon,Claudio Napoli
出处
期刊:European Heart Journal - Cardiovascular Pharmacotherapy [Oxford University Press]
卷期号:9 (8): 758-769 被引量:1
标识
DOI:10.1093/ehjcvp/pvad059
摘要

Abstract Although bioinformatic methods gained a lot of attention in the latest years, their use in real-world studies for primary and secondary prevention of atherosclerotic cardiovascular diseases (ASCVD) is still lacking. Bioinformatic resources have been applied to thousands of individuals from the Framingham Heart Study as well as health care-associated biobanks such as the UK Biobank, the Million Veteran Program, and the CARDIoGRAMplusC4D Consortium and randomized controlled trials (i.e. ODYSSEY, FOURIER, ASPREE, and PREDIMED). These studies contributed to the development of polygenic risk scores (PRS), which emerged as novel potent genetic-oriented tools, able to calculate the individual risk of ASCVD and to predict the individual response to therapies such as statins and proprotein convertase subtilisin/kexin type 9 inhibitor. ASCVD are the first cause of death around the world including coronary heart disease (CHD), peripheral artery disease, and stroke. To achieve the goal of precision medicine and personalized therapy, advanced bioinformatic platforms are set to link clinically useful indices to heterogeneous molecular data, mainly epigenomics, transcriptomics, metabolomics, and proteomics. The DIANA study found that differential methylation of ABCA1, TCF7, PDGFA, and PRKCZ significantly discriminated patients with acute coronary syndrome from healthy subjects and their expression levels positively associated with CK-MB serum concentrations. The ARIC Study revealed several plasma proteins, acting or not in lipid metabolism, with a potential role in determining the different pleiotropic effects of statins in each subject. The implementation of molecular high-throughput studies and bioinformatic techniques into traditional cardiovascular risk prediction scores is emerging as a more accurate practice to stratify patients earlier in life and to favour timely and tailored risk reduction strategies. Of note, radiogenomics aims to combine imaging features extracted for instance by coronary computed tomography angiography and molecular biomarkers to create CHD diagnostic algorithms useful to characterize atherosclerotic lesions and myocardial abnormalities. The current view is that such platforms could be of clinical value for prevention, risk stratification, and treatment of ASCVD.
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