Benincasa Giuditta, Suades Rosa, Padró Teresa, Badimon Lina, Napoli Claudio
Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania 'Luigi Vanvitelli', 80138 Naples, Italy.
Cardiovascular Program ICCC, Research Institute of Hospital Santa Creu i Sant Pau, IIB Sant Pau, Avinguda Sant Antoni Maria Claret 167, Pavelló 11 (Antic Convent), 08049 Barcelona, Spain.
Eur Heart J Cardiovasc Pharmacother. 2023 Dec 14;9(8):758-769. doi: 10.1093/ehjcvp/pvad059.
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.
尽管生物信息学方法在最近几年受到了广泛关注，但它们在动脉粥样硬化性心血管疾病（ASCVD）一级和二级预防的实际研究中的应用仍然不足。生物信息学资源已应用于来自弗雷明汉心脏研究的数千名个体，以及与医疗保健相关的生物样本库，如英国生物样本库、百万退伍军人计划和CARDIoGRAMplusC4D联盟以及随机对照试验（即ODYSSEY、FOURIER、ASPREE和PREDIMED）。这些研究推动了多基因风险评分（PRS）的发展，PRS作为一种新型的强大的基因导向工具出现，能够计算个体患ASCVD的风险，并预测个体对他汀类药物和前蛋白转化酶枯草溶菌素/kexin 9型抑制剂等治疗的反应。ASCVD是全球首要死因，包括冠心病（CHD）、外周动脉疾病和中风。为了实现精准医学和个性化治疗的目标，先进的生物信息学平台旨在将临床有用指标与异质分子数据（主要是表观基因组学、转录组学、代谢组学和蛋白质组学）联系起来。DIANA研究发现，ABCA1、TCF7、PDGFA和PRKCZ的差异甲基化显著区分了急性冠状动脉综合征患者和健康受试者，并且它们的表达水平与CK-MB血清浓度呈正相关。ARIC研究揭示了几种血浆蛋白，无论是否参与脂质代谢，在确定他汀类药物对每个受试者的不同多效性作用方面都具有潜在作用。将分子高通量研究和生物信息学技术应用于传统心血管风险预测评分，正成为一种更准确的做法，以便在生命早期对患者进行分层，并支持及时和量身定制的风险降低策略。值得注意的是，放射基因组学旨在将例如通过冠状动脉计算机断层扫描血管造影提取的成像特征与分子生物标志物相结合，以创建有助于表征动脉粥样硬化病变和心肌异常的CHD诊断算法。目前的观点是，这样的平台对于ASCVD的预防、风险分层和治疗可能具有临床价值。
