Józefczuk Ewelina, Guzik Tomasz J, Siedlinski Mateusz
Department of Internal and Agricultural Medicine, Jagiellonian University Medical College, Kraków, Poland.
Center for Medical Genomics OMICRON, Jagiellonian University Medical College, Kraków, Poland.
Kardiol Pol. 2023;81(3):221-231. doi: 10.33963/KP.a2023.0037. Epub 2023 Feb 5.
Hypertension (HT) is a modifiable risk factor for life-threatening cardiovascular diseases (CVDs) including coronary artery disease, heart failure, or stroke. Despite significant progress in understanding the pathophysiological mechanisms of the disease, the molecular pathways targeted by HT treatment remain largely unchanged. This warrants the need for finding novel biomarkers, which are causally related to persistent high blood pressure (BP) and may be pharmacologically targeted. Analytical output derived from large-scale biobanks, containing high-throughput genetic and biochemical data, such as OLINK and SomaScan-based proteomics or Nuclear Magnetic Resonance-based metabolomics, as well as novel analytical tools including the Mendelian randomization (MR) approach, enabling genetic causal inference, may create new treatment opportunities for HT and related CVDs. MR analysis may constitute additional evidence for observational studies and facilitate selection of drug targets for clinical testing and has been already used to nominate potentially causal biomarkers for HT and CVDs such as circulating glycine, branched-chain amino acids, lipoprotein(a), insulin-like growth factor 1, or fibronectin 1. Using the MR framework, genetic proxies for targets of already known drugs, such as statins, PCSK9, and ACE inhibitors, may additionally be informative about potential side effects and eventually contribute to more personalized medicine. Finally, genetic causal inference may disentangle independent direct effects of correlated traits such as lipid classes or markers of inflammation on cardiovascular clinical outcomes such as atherosclerosis and HT. While several novel HT-targeting drugs are currently under clinical investigation (e.g. brain renin-angiotensin-aldosterone system inhibitors or endothelin-1 receptor antagonists), analysis of high-throughput proteomic and metabolomic data from well-powered studies may deliver novel druggable molecular targets for HT and associated CVDs.
高血压（HT）是包括冠状动脉疾病、心力衰竭或中风在内的危及生命的心血管疾病（CVD）的一个可改变的风险因素。尽管在理解该疾病的病理生理机制方面取得了重大进展，但HT治疗所针对的分子途径在很大程度上仍未改变。这就需要寻找新的生物标志物，这些生物标志物与持续性高血压（BP）存在因果关系，并且可能成为药物作用靶点。来自大型生物样本库的分析结果，包含高通量遗传和生化数据，如基于OLINK和SomaScan的蛋白质组学或基于核磁共振的代谢组学，以及包括孟德尔随机化（MR）方法在内的新型分析工具，能够进行遗传因果推断，可能为HT及相关CVD创造新的治疗机会。MR分析可为观察性研究提供额外证据，并有助于选择用于临床试验的药物靶点，并且已经被用于确定HT和CVD的潜在因果生物标志物，如循环甘氨酸、支链氨基酸、脂蛋白(a)、胰岛素样生长因子1或纤连蛋白1。使用MR框架，已知药物（如他汀类药物、前蛋白转化酶枯草溶菌素9和血管紧张素转换酶抑制剂）靶点的遗传代理，可能还能提供有关潜在副作用的信息，并最终有助于实现更个性化的医疗。最后，遗传因果推断可能会厘清相关性状（如脂质类别或炎症标志物）对心血管临床结局（如动脉粥样硬化和HT）的独立直接影响。虽然目前有几种新型HT靶向药物正在进行临床研究（例如脑肾素 - 血管紧张素 - 醛固酮系统抑制剂或内皮素 - 1受体拮抗剂），但对来自样本量充足研究的高通量蛋白质组学和代谢组学数据进行分析，可能会为HT及相关CVD提供新的可成药分子靶点。
