基于多组学数据的药物靶点孟德尔随机化及其在心脑血管疾病防治研究中的应用Drug-Target Mendelian Randomization Based on Multi-omics Data and Its Application in the Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases

Abstract

心脑血管疾病是全球死亡和伤残的主要原因，需要持续研发药物以减轻社会和医疗保健系统沉重的疾病负担。药物靶点孟德尔随机化采用顺式蛋白质数量性状基因座作为工具变量，在观察性研究数据中探究蛋白靶点与疾病结局的因果关系，为药物研发提供了可靠的临床前证据。在心脑血管疾病领域，该方法已广泛用于靶点筛选、不良反应评估及药物再利用等。本文系统阐述了药物靶点孟德尔随机化分析框架（包括工具变量选择、效应估计、敏感性分析、不良反应和中介分析），总结了该方法在心脑血管疾病治疗中降脂、抗炎等药物靶点方面的研究进展，并探讨了其在专病队列研究中的局限性和未来展望，以期为心脑血管疾病的药物靶点开发提供思路。Cardiovascular and cerebrovascular diseases are leading causes of death and disability worldwide, necessitating sustained drug development efforts to alleviate the substantial burden on society and healthcare systems. Drug‑target Mendelian randomization (MR) employs cis protein quantitative trait loci as instrumental variables to investigate causal relationships between protein targets and disease outcomes in observational studies, thereby providing robust preclinical evidence for drug development. In the field of cardiovascular and cerebrovascular diseases, this approach has been widely applied for target screening, adverse reaction assessment, and drug repurposing. This article systematically elaborates the analytical framework of drug‑target MR (including instrumental variable selection, effect estimation, sensitivity analysis, adverse reactions, and mediation analysis), summarizes the research progress of drug targets such as lipid‑lowering and anti‑inflammatory in the treatment of cardiovascular and cerebrovascular diseases and discusses its limitations and future directions in specialized disease cohort studies. These insights aim to inform the discovery of novel drug targets for cardiovascular and cerebrovascular diseases.

Keywords

• 心脑血管疾病
• 药物靶点
• 因果推断
• 孟德尔随机化
• cardiovascular and cerebrovascular disease
• drug target
• causal inference
• mendelian randomization