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

doi:10.3969/j.issn.1673-5765.2025.06.005

摘要/Abstract

摘要： 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。

文章导读： 药物靶点孟德尔随机化方法使用顺式遗传变异模拟药物靶点扰动探究其与结局的因果关系，从而促进疾病药物靶点的识别与开发。

关键词: 心脑血管疾病; 药物靶点; 因果推断; 孟德尔随机化

Abstract: 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.

Key words: Cardiovascular and cerebrovascular disease; Drug target; Causal inference; Mendelian randomization

中图分类号:

樊哲, 姜明慧, 程丝, 刘斯洋. 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[J]. 中国卒中杂志, 2025, 20(6): 699-709.

FAN Zhe, JIANG Minghui, CHENG Si, LIU Siyang. Drug-Target Mendelian Randomization Based on Multi-omics Data and Its Application in the Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases[J]. Chinese Journal of Stroke, 2025, 20(6): 699-709.

参考文献

[1] GBD 2021 Causes of Death Collaborators. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations，1990—2021：a systematic analysis for the global burden of disease study 2021[J]. Lancet，2024，403（10440）：2100-2132.
[2] MENSAH G A，ROTH G A，FUSTER V. The global burden of cardiovascular diseases and risk factors：2020 and beyond[J]. J Am Coll Cardiol，2019，74（20）：2529-2532.
[3] KHERA A V，KATHIRESAN S. Genetics of coronary artery disease：discovery，biology and clinical translation[J]. Nat Rev Genet，2017，18（6）：331-344.
[4] GBD 2021 Diseases and Injuries Collaborators. Global incidence，prevalence，years lived with disability（YLDs），disability-adjusted life-years（DALYs），and healthy life expectancy（HALE）for 371 diseases and injuries in 204 countries and territories and 811 subnational locations，1990—2021：a systematic analysis for the global burden of disease study 2021[J]. Lancet，2024，403（10440）：2133-2161.
[5] DOWDEN H，MUNRO J. Trends in clinical success rates and therapeutic focus[J]. Nat Rev Drug Discov，2019，18（7）：495-496.
[6] ALTERI E，GUIZZARO L. Be open about drug failures to speed up research[J]. Nature，2018，563（7731）：317-319.
[7] DAGHLAS I，GILL D. Mendelian randomization as a tool to inform drug development using human genetics[J/OL]. Camb Prisms Precis Med，2023，1：e16[2025-05-29]. https://doi.org/10.1017/pcm.2023.5.
[8] PLENGE R M，SCOLNICK E M，ALTSHULER D. Validating therapeutic targets through human genetics[J]. Nat Rev Drug Discov，2013，12（8）：581-594.
[9] THANASSOULIS G，O’DONNELL C J. Mendelian randomization：nature’s randomized trial in the post-genome era？[J]. JAMA，2009，301（22）：2386-2388.
[10] SMITH G D，EBRAHIM S.‘Mendelian randomization’：can genetic epidemiology contribute to understanding environmental determinants of disease？[J]. Int J Epidemiol，2003，32（1）：1-22.
[11] KING E A，DAVIS J W，DEGNER J F. Are drug targets with genetic support twice as likely to be approved？Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval[J/OL]. PLoS Genet，2019，15（12）：e1008489[2025-05-29]. https://doi.org/10.1371/journal.pgen.1008489.
[12] NELSON M R，TIPNEY H，PAINTER J L，et al. The support of human genetic evidence for approved drug indications[J]. Nat Genet，2015，47（8）：856-860.
[13] THOMAS D C，CONTI D V. Commentary：the concept of‘Mendelian randomization’[J]. Int J Epidemiol，2004，33（1）：21-25.
[14] HARBORD R M，DIDELEZ V，PALMER T M，et al. Severity of bias of a simple estimator of the causal odds ratio in Mendelian randomization studies[J]. Stat Med，2013，32（7）：1246-1258.
[15] BURGESS S，BUTTERWORTH A，THOMPSON S G. Mendelian randomization analysis with multiple genetic variants using summarized data[J]. Genet Epidemiol，2013，37（7）：658-665.
[16] SANDERSON E，GLYMOUR M M，HOLMES M V，et al. Mendelian randomization[J/OL]. Nat Rev Methods Primers，2022，2：6[2025-05-29]. https://doi.org/10.1038/s43586-021-00092-5.
[17] HOLMES M V，RICHARDSON T G，FERENCE B A，et al. Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development[J]. Nat Rev Cardiol，2021，18（6）：435-453.
[18] SAID S，PAZOKI R，KARHUNEN V，et al. Genetic analysis of over half a million people characterises C-reactive protein loci[J/OL]. Nat Commun，2022，13（1）：2198[2025-05-29]. https://doi.org/10.1038/s41467-022-29650-5.
[19] SWERDLOW D I，KUCHENBAECKER K B，SHAH S，et al. Selecting instruments for Mendelian randomization in the wake of genome-wide association studies[J]. Int J Epidemiol，2016，45（5）：1600-1616.
[20] PIETZNER M，WHEELER E，CARRASCO-ZANINI J，et al. Mapping the proteo-genomic convergence of human diseases[J/OL]. Science，2021，374（6569）：eabj1541[2025-05-29]. https://doi.org/10.1126/science.abj1541.
[21] BURGESS S，MASON A M，GRANT A J，et al. Using genetic association data to guide drug discovery and development：review of methods and applications[J]. Am J Hum Genet，2023，110（2）：195-214.
[22] HORMOZDIARI F，VAN DE BUNT M，SEGRÈ A V，et al. Colocalization of GWAS and eQTL signals detects target genes[J]. Am J Hum Genet，2016，99（6）：1245-1260.
[23] GIAMBARTOLOMEI C，VUKCEVIC D，SCHADT E E，et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics[J/OL]. PLoS Genet，2014，10（5）：e1004383[2025-05-29]. https://doi.org/10.1371/journal.pgen.1004383.
[24] ZUBER V，GRINBERG N F，GILL D，et al. Combining evidence from Mendelian randomization and colocalization：review and comparison of approaches[J]. Am J Hum Genet，2022，109（5）：767-782.
[25] FRIEDEN T R. Evidence for health decision making—beyond randomized，controlled trials[J]. N Engl J Med，2017，377（5）：465-475.
[26] TSANG R，COLLEY L，LYND L D. Inadequate statistical power to detect clinically significant differences in adverse event rates in randomized controlled trials[J]. J Clin Epidemiol，2009，62（6）：609-616.
[27] ZHENG J，HABERLAND V，BAIRD D，et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases[J]. Nat Genet，2020，52（10）：1122-1131.
[28] DENNY J C，RITCHIE M D，BASFORD M A，et al. PheWAS：demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations[J]. Bioinformatics，2010，26（9）：1205-1210.
[29] MILLARD L A C，DAVIES N M，TILLING K，et al. Searching for the causal effects of body mass index in over 300 000 participants in UK biobank，using Mendelian randomization[J/OL]. PLoS Genet，2019，15（2）：e1007951[2025-05-29]. https://doi.org/10.1371/journal.pgen.1007951.
[30] BURGESS S，THOMPSON S G. Multivariable Mendelian randomization：the use of pleiotropic genetic variants to estimate causal effects[J]. Am J Epidemiol，2015，181（4）：251-260.
[31] PORCU E，RÜEGER S，LEPIK K，et al. Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits[J/OL]. Nat Commun，2019，10（1）：3300[2025-05-29]. https://doi.org/10.1038/s41467-019-10936-0.
[32] SANDERSON E，DAVEY SMITH G，WINDMEIJER F，et al. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings[J]. Int J Epidemiol，2019，48（3）：713-727.
[33] PATEL A，GILL D，SHUNGIN D，et al. Robust use of phenotypic heterogeneity at drug target genes for mechanistic insights：application of cis-multivariable Mendelian randomization to GLP1R gene region[J]. Genet Epidemiol，2024，48（4）：151-163.
[34] CARTER A R，SANDERSON E，HAMMERTON G，et al. Mendelian randomisation for mediation analysis：current methods and challenges for implementation[J]. Eur J Epidemiol，2021，36（5）：465-478.
[35] BURGESS S，THOMPSON D J，REES J M B，et al. Dissecting causal pathways using Mendelian randomization with summarized genetic data：application to age at menarche and risk of breast cancer[J]. Genetics，2017，207（2）：481-487.
[36] SABATINE M S，GIUGLIANO R P，KEECH A C，et al. Evolocumab and clinical outcomes in patients with cardiovascular disease[J]. N Engl J Med，2017，376（18）：1713-1722.
[37] IL6R Genetics Consortium Emerging Risk Factors Collaboration. Interleukin-6 receptor pathways in coronary heart disease：a collaborative meta-analysis of 82 studies[J]. Lancet，2012，379（9822）：1205-1213.
[38] The Interleukin-6 Receptor Mendelian Randomisation Analysis（IL6R MR）Consortium. The interleukin-6 receptor as a target for prevention of coronary heart disease：a Mendelian randomisation analysis[J]. Lancet，2012，379（9822）：1214-1224.
[39] GILL D，GEORGAKIS M K，LAFFAN M，et al. Genetically determined FⅪ（factor Ⅺ）levels and risk of stroke[J]. Stroke，2018，49（11）：2761-2763.
[40] YU Z，ZHANG L J，ZHANG G，et al. Lipids，apolipoproteins，statins，and intracerebral hemorrhage：a Mendelian randomization study[J]. Ann Neurol，2022，92（3）：390-399.
[41] HALL S S. Genetics：a gene of rare effect[J]. Nature，2013，496（7444）：152-155.
[42] COHEN J，PERTSEMLIDIS A，KOTOWSKI I K，et al. Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in PCSK9[J]. Nat Genet，2005，37（2）：161-165.
[43] COHEN J C，BOERWINKLE E，MOSLEY T H，et al. Sequence variations in PCSK9，low LDL，and protection against coronary heart disease[J]. N Engl J Med，2006，354（12）：1264-1272.
[44] HOOPER A J，MARAIS A D，TANYANYIWA D M，et al. The C679X mutation in PCSK9 is present and lowers blood cholesterol in a Southern African population[J]. Atherosclerosis，2007，193（2）：445-448.
[45] FASANO T，CEFALÙ A B，DI LEO E，et al. A novel loss of function mutation of PCSK9 gene in white subjects with low-plasma low-density lipoprotein cholesterol[J]. Arterioscler Thromb Vasc Biol，2007，27（3）：677-681.
[46] SHAPIRO M D，TAVORI H，FAZIO S. PCSK9：from basic science discoveries to clinical trials[J]. Circ Res，2018，122（10）：1420-1438.
[47] BENN M，NORDESTGAARD B G，FRIKKE-SCHMIDT R，et al. Low LDL cholesterol，PCSK9 and HMGCR genetic variation，and risk of Alzheimer’s disease and Parkinson’s disease：Mendelian randomisation study[J/OL]. BMJ，2017，357：j1648[2025-05-29]. https://doi.org/10.1136/bmj.j1648.
[48] BOVIJN J，LINDGREN C M，HOLMES M V. Genetic variants mimicking therapeutic inhibition of IL-6 receptor signaling and risk of COVID-19[J/OL]. Lancet Rheumatol，2020，2（11）：e658-e659[2025-05-29]. https://doi.org/10.1016/S2665-9913 (20) 30345-3.
[49] LARSSON S C，BURGESS S，GILL D. Genetically proxied interleukin-6 receptor inhibition：opposing associations with COVID-19 and pneumonia[J/OL]. Eur Respir J，2021，57（1）：2003545[2025-05-29]. https://doi.org/10.1183/13993003.03545-2020.
[50] RAJASUNDARAM S，BURGESS S，GILL D. Treatment of severe COVID-19 with interleukin 6 receptor inhibition[J/OL]. BMJ Med，2022，1（1）：e000144[2025-05-29]. https://doi.org/10.1136/bmjmed-2022-000144.
[51] RUBIN E J，LONGO D L，BADEN L R. Interleukin-6 receptor inhibition in COVID-19—cooling the inflammatory soup[J]. N Engl J Med，2021，384（16）：1564-1565.
[52] ZHAO S S，GILL D. Genetically proxied IL-6 receptor inhibition and risk of polymyalgia rheumatica[J]. Ann Rheum Dis，2022，81（10）：1480-1482.
[53] DEVAUCHELLE-PENSEC V，CARVAJAL-ALEGRIA G，DERNIS E，et al. Effect of tocilizumab on disease activity in patients with active polymyalgia rheumatica receiving glucocorticoid therapy：a randomized clinical trial[J]. JAMA，2022，328（11）：1053-1062.
[54] RIDKER P M，MACFADYEN J G，THUREN T，et al. Residual inflammatory risk associated with interleukin-18 and interleukin-6 after successful interleukin-1β inhibition with canakinumab：further rationale for the development of targeted anti-cytokine therapies for the treatment of atherothrombosis[J]. Eur Heart J，2020，41（23）：2153-2163.
[55] KIM S C，SOLOMON D H，ROGERS J R，et al. No difference in cardiovascular risk of tocilizumab versus abatacept for rheumatoid arthritis：a multi-database cohort study[J]. Semin Arthritis Rheum，2018，48（3）：399-405.
[56] GEORGAKIS M K，MALIK R，LI X，et al. Genetically downregulated interleukin-6 signaling is associated with a favorable cardiometabolic profile：a phenome-wide association study[J]. Circulation，2021，143（11）：1177-1180.
[57] CHEN L Y，PETERS J E，PRINS B，et al. Systematic Mendelian randomization using the human plasma proteome to discover potential therapeutic targets for stroke[J/OL]. Nat Commun，2022，13（1）：6143[2025-05-29]. https://doi.org/10.1038/s41467-022-33675-1.
[58] PRAPIADOU S，ŽIVKOVIĆ L，THORAND B，et al. Proteogenomic data integration reveals CXCL10 as a potentially downstream causal mediator for IL-6 signaling on atherosclerosis[J]. Circulation，2024，149（9）：669-683.
[59] GTEx Consortium. The genotype-tissue expression（GTEx）project[J]. Nat Genet，2013，45（6）：580-585.
[60] VÕSA U，CLARINGBOULD A，WESTRA H J，et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression[J]. Nat Genet，2021，53（9）：1300-1310.
[61] SUN B B，CHIOU J，TRAYLOR M，et al. Plasma proteomic associations with genetics and health in the UK biobank[J]. Nature，2023，622（7982）：329-338.
[62] ZHANG J N，DUTTA D，KÖTTGEN A，et al. Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies[J]. Nat Genet，2022，54（5）：593-602.
[63] HAKONARSON H，GULCHER J R，STEFANSSON K. deCODE genetics，Inc.[J]. Pharmacogenomics，2003，4（2）：209-215.
[64] CEREZO M，SOLLIS E，JI Y，et al. The NHGRI-EBI GWAS Catalog：standards for reusability，sustainability and diversity[J/OL]. Nucleic Acids Res，2025，53（D1）：D998-D1005[2025-05-29]. https://doi.org/10.1093/nar/gkae1070.
[65] ELSWORTH B，LYON M，ALEXANDER T，et al. The MRC IEU OpenGWAS data infrastructure[J/OL]. bioRxiv，2020[2025-05-29]. https://doi.org/10.1101/2020.08.10.244293.
[66] PATERNOSTER L，TILLING K，DAVEY SMITH G. Genetic epidemiology and Mendelian randomization for informing disease therapeutics：conceptual and methodological challenges[J/OL]. PLoS Genet，2017，13（10）：e1006944[2025-05-29]. https://doi.org/10.1371/journal.pgen.1006944.
[67] MAHMOUD O，DUDBRIDGE F，DAVEY SMITH G，et al. A robust method for collider bias correction in conditional genome-wide association studies[J/OL]. Nat Commun，2022，13（1）：619[2025-05-29]. https://doi.org/10.1038/s41467-022-28119-9.
[68] SEAMAN S R，WHITE I R. Review of inverse probability weighting for dealing with missing data[J]. Stat Methods Med Res，2013，22（3）：278-295.
[69] ZAFRIR B，JAFFE R，RUBINSHTEIN R，et al. Influence of body mass index on long-term survival after cardiac catheterization[J]. Am J Cardiol，2018，121（1）：113-119.
[70] MITCHELL R E，PATERNOSTER L，DAVEY SMITH G. Mendelian randomization in case only studies：a promising approach to be applied with caution[J]. Am J Cardiol，2018，122（12）：2169-2171.
[71] HERNÁN M A，ROBINS J M. Causal inference：what if[M]. Boca Raton：Chapman & Hall/CRC，2020.
[72] GRIFFITH G J，MORRIS T T，TUDBALL M J，et al. Collider bias undermines our understanding of COVID-19 disease risk and severity[J/OL]. Nat Commun，2020，11（1）：5749[2025-05-29]. https://doi.org/10.1038/s41467-020-19478-2.
[73] DUDBRIDGE F，ALLEN R J，SHEEHAN N A，et al. Adjustment for index event bias in genome-wide association studies of subsequent events[J/OL]. Nat Commun，2019，10（1）：1561[2025-05-29]. https://doi.org/10.1038/s41467-019-09381-w.
[74] PEARL J. Causality[M]. Cambridge：Cambridge University Press，2009.
[75] SPIRTES P，GLYMOUR C，SCHEINES R. Causation，prediction，and search[M]. Cambridge：MIT Press，2001.
[76] HOWEY R，SHIN S Y，RELTON C，et al. Bayesian network analysis incorporating genetic anchors complements conventional Mendelian randomization approaches for exploratory analysis of causal relationships in complex data[J/OL]. PLoS Genet，2020，16（3）：e1008198[2025-05-29]. https://doi.org/10.1371/journal.pgen.1008198.
[77] BADSHA M B，FU A Q. Learning causal biological networks with the principle of Mendelian randomization[J/OL]. Front Genet，2019，10：460[2025-05-29]. https://doi.org/10.3389/fgene.2019.00460.
[78] AMAR D，SINNOTT-ARMSTRONG N，ASHLEY E A，et al. Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks[J/OL]. Nat Commun，2021，12（1）：350[2025-05-29]. https://doi.org/10.1038/s41467-020-20516-2.
[79] HOU L，GENG Z，YUAN Z S，et al. MRSL：a causal network pruning algorithm based on GWAS summary data[J/OL]. Brief Bioinform，2024，25（2）：bbae086[2025-05-29]. https://doi.org/10.1093/bib/bbae086.
[80] FERENCE B A，GINSBERG H N，GRAHAM I，et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic，epidemiologic，and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel[J]. Eur Heart J，2017，38（32）：2459-2472.
[81] LAWLOR D A，HARBORD R M，STERNE J A C，et al. Mendelian randomization：using genes as instruments for making causal inferences in epidemiology[J]. Stat Med，2008，27（8）：1133-1163.
[82] VERBANCK M，CHEN C Y，NEALE B，et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases[J]. Nat Genet，2018，50（5）：693-698.
[83] SLOB E A W，BURGESS S. A comparison of robust Mendelian randomization methods using summary data[J]. Genet Epidemiol，2020，44（4）：313-329.
[84] GILL D，DIB M J，CRONJÉ H T，et al. Common pitfalls in drug target Mendelian randomization and how to avoid them[J/OL]. BMC Med，2024，22（1）：473[2025-05-29]. https://doi.org/10.1186/s12916-024-03700-9.