Abstract
目的
Graves眼病是一种复杂的器官特异性自身免疫性疾病,发病机制尚不明确。补体系统中的核心成分5/5a(component 5/5a,C5/C5a)可能在该疾病的病理过程中发挥重要作用。本研究旨在利用孟德尔随机化(Mendelian randomization,MR)方法探究C5/C5a与Graves眼病的因果关系,以期为Graves眼病的诊断与治疗提供新的理论依据。
方法
基于全基因组关联分析(genome-wide association study,GWAS)的汇总数据,以补体C5/C5a作为暴露因素,Graves眼病作为结局因素,分析补体C5/C5a与Graves眼病的因果关系,利用共定位分析得出假设的后验概率(posterior probability of hypothesis,PPH),进一步验证补体C5与Graves眼病的遗传关联。
结果
Wald比率法模型显示补体C5与Graves眼病呈显著正相关(OR=4.109,95% CI 1.990~8.486,P<0.001),逆方差加权法(inverse variance weighted,IVW)模型显示C5a与Graves眼病同样呈正相关(OR=2.901,95% CI 1.225~6.869,P=0.015),共定位分析显示补体C5与Graves眼病在给定的遗传窗口内共享同一个单核苷酸多态性(single nucleotide polymorphism,SNP)rs7036980,PPH4为0.81(>0.80为概率高)。
结论
补体C5/C5a的高水平显著增加Graves眼病的发生风险,通过靶向补体C5的抑制剂可以有效降低Graves眼病的发生风险。
Keywords: 补体C5, 补体C5a, Graves眼病, 孟德尔随机化, 共定位分析
Abstract
Objective
Graves’ ophthalmopathy is a complex organ-specific autoimmune disease with an unclear pathogenesis. Complement component 5/5a (C5/C5a), a key element of the component system, may play a significant role in the disease’s pathological process. This study aims to investigate the causal relationship between C5/C5a and Graves’ ophthalmopathy using Mendelian randomization (MR) to provide new theoretical insights for its diagnosis and treatment.
Methods
Utilizing summary data from genome-wide association study (GWAS), C5/C5a was designated as the exposure factor and Graves’ ophthalmopathy as the outcome. The causal relationship between C5/C5a and Graves’ ophthalmopathy was analyzed, and colocalization analysis was performed to determine the posterior probability of hypothesis (PPH) and verify the genetic association between C5 and Graves’ ophthalmopathy.
Results
The Wald ratio model showed a significant positive correlation between C5 and Graves’ ophthalmopathy (OR=4.109, 95% CI 1.990 to 8.486, P<0.001). Similarly, the inverse variance weighted (IVW) model showed a positive correlation between C5a and Graves’ ophthalmopathy (OR=2.901, 95% CI 1.225 to 6.869, P=0.015). Colocalization analysis showed that C5 and Graves’ ophthalmopathy share a single nucleotide polymorphism (SNP), rs7036980, within the specified genetic window, with a PPH4 value of 0.81 (a value >0.80 indicates high probability).
Conclusion
Elevated levels of C5/C5a significantly increase the risk of developing Graves’ ophthalmopathy. Targeting complement C5 with inhibitors may effectively reduce the risk of Graves’ ophthalmopathy.
Keywords: complement C5, complement C5a, Graves’ ophthalmopathy, Mendelian randomization, colocalization analysis
Graves眼病(Graves’ ophthalmopathy,GO)又称甲状腺相关性眼病(thyroid-associated ophthalmopathy,TAO),是一种复杂的器官特异性自身免疫性疾病,是全身性甲状腺相关性自身免疫性疾病的眼部表现,即Graves病(Graves’ disease,GD)最常见的甲状腺外表现,可导致眼球突出、眼部软组织红肿、眼睑退缩、复视、暴露性角膜病变和压迫性视神经病变,严重者可引起视力丧失[1-2]。据2021年欧洲甲状腺学会报道,女性GO的发病率为每年(2.67~3.30)/10万,男性为每年(0.54~0.90)/10万[3]。GO的发病机制尚未完全阐明,普遍认为是一种由免疫、遗传、环境等多因素参与的疾病[4]。近年来,许多遗传学研究[5]和分子研究[6]表明补体系统在GO进展中起至关重要的作用,包括遗传变异、替代途径的过度激活、炎症、氧化应激等。目前,针对补体核心成分3(component 3,C3)和成分5(component 5,C5)及补体调节因子[补体因子D(complement factor D,FD)和补体因子I(complement factor I,FI)等]的抑制剂已超过14种,这些补体抑制剂在年龄相关性黄斑变性中已经完成或正在进行的临床试验近40项[7-10]。
孟德尔随机化(Mendelian randomization,MR)是一种基于全基因组关联分析(genome-wide association study,GWAS)数据,利用单核苷酸多态性(single nucleotide polymorphism,SNP)作为工具变量(instrumental variable,IV),用于揭示因果关系的新型流行病学方法,效力等同于随机对照试验,其优势在于规避了先前观察性研究的缺点,可最大限度地减少反向因果关系和混淆偏倚的机会[11]。本研究利用MR方法,探究补体C5/C5a与GO的因果关联,以期寻找GO的早期生物标志物及治疗靶点,为其诊断与治疗提供新的理论依据。
1. 资料与方法
1.1. 研究设计
用于MR分析的IVs需满足3个主要假设:1)从IV(Z)到暴露因素X再到结局变量Y的路径存在强关联,混杂变量C分别与暴露因素X和结局变量Y相关。2)混杂变量C与IV(Z)不存在路径关系。3)从IV(Z)到结果变量Y除了通过暴露外没有其他路径[12-13]。选择血浆蛋白补体C5/C5a作为暴露因素,GO作为结局因素,使用两样本双向MR方法进行因果关联分析,通过共定位分析检测补体C5与GO的遗传关联(图1)。
图1.
蛋白质组学MR分析的主要设计
Figure 1 Main design of proteomic MR analysis
MR: Mendelian randomization; GWAS: Genome-wide association study; pQTLs: Protein quantitative trait loci; UKB-PPP: UK Biobank Pharma Proteomics Project.
1.2. 数据来源
补体C5/C5a蛋白质数量性状位点(protein quantitative trait loci,pQTL)的GWAS汇总数据分别来源于英国生物样本库药物蛋白质组学项目(UK Biobank Pharma Proteomics Project,UKB-PPP)和deCODE数据库。UKB-PPP机构使用基于抗体的 Olink蛋白质组学测定法测试了1 610万个序列变异与54 306名英国生物样本库(UK Biobank)参与者血浆中2 922种蛋白质水平的遗传关联[14],补体C5测定所纳入的有效样本量为33 995;deCODE机构使用基于Soma scan的蛋白质组学测定法测试了2 700万个序列变异与35 559名冰岛人血浆中4 719种蛋白质水平的遗传关联[15],C5a测定所纳入的有效样本量为35 376。GO的汇总数据来自FinnGenR10数据库,共有691例欧洲血统病例和411 490例欧洲血统对照参与了本次GWAS(表1)。
表1.
表型来源与特征
Table 1 Phenotypic origin and characteristics
| 表型 | 机构 | 病例人数 | 对照人数 | PMID | 调整 |
|---|---|---|---|---|---|
| C5 | UKB-PPP | 33 995 | ─ | 37 794 186 | 年龄、性别、批次、UKB中心、UKB基因阵列、血液采样和测量之间的时间以及前20个遗传主成分 |
| C5a | deCODE | 35 376 | ─ | 34 857 953 | 年龄、性别等前20个遗传主成分 |
| GO | FinnGenR10 | 691 | 411 490 | ─ | 年龄、性别等前20个遗传主成分 |
GO:Graves眼病;UKB-PPP:英国生物样本库药物蛋白质组学项目;FinnGenR10:芬兰基因组计划第十版;PMID:PubMed唯一识别码。
本研究采用的所有GWAS汇总数据都是公开的,因此不需要额外的伦理审批。
1.3. IV
首先,选择与补体C5/C5a显著性相关的cis-pQTL作为IV(P<5×10-8)[16-17]。cis-pQTL指的是距离编码对应蛋白质基因的上下游1 Mb区域内的SNP。使用1000Genomes欧洲人群去除存在连锁不平衡的SNP(clump_kb=10 000,r 2=0.001)。其次,使用PhenoScanner数据库去除混杂SNP,使用MR-PRESSO测试以识别和去除具有多效性的SNP。最后,为了避免弱IV,所有IV的F值>10,计算公式为F=β 2/SE 2,式中β为SNP对暴露的效应量,SE为β的标准误[18-20]。最终纳入UKB-PPP来源的1个SNP、deCODE来源的2个SNP用于MR分析(表2)。
表2.
工具变量的基本信息
Table 2 Basic information of the instrumental variables
| SNP | 蛋白质 | Chr: Pos | EA | OA | EAF | β | SE | P | F |
|---|---|---|---|---|---|---|---|---|---|
| rs4837810 | UKB-PPP-C5 | 9:121118921 | C | T | 0.577 | -0.143 | 0.008 | 6.55×10-78 | 319.515 |
| rs17220750 | deCODE-C5a | 9:121025721 | A | G | 0.107 | 0.186 | 0.013 | 3.78×10-10 | 204.710 |
| rs928406 | deCODE-C5a | 9:120365967 | G | C | 0.394 | 0.051 | 0.008 | 4.11×10-48 | 40.641 |
SNP:单核苷酸多态性;Chr:染色体;Pos:位置;EA:效应等位基因;OA:其他等位基因;EAF:效应等位基因频率;β:效应值;SE:标准误;UKB-PPP:英国生物样本库药物蛋白质组学项目。
1.4. MR分析
对单个SNP使用Wald比率法(Wald ratio)作为主要分析方法。对2个及以上的SNP使用逆方差加权法(inverse variance weighted,IVW)作为主要分析方法[21-23],如果检测到异质性,则选择IVW的随机效应作为结果;否则,IVW的固定效应是首选。对3个及以上的SNP,采用MR-Egger法、加权中位数法(weighted median estimator,WME)作为IVW的补充方法,当MR-Egger、WME的β值与IVW的β值方向一致时,提示IVW结果稳定可靠。此外,本研究采用了最新的cML-MA,cML-MA是一种基于约束最大似然和模型平均的MR新方法,用于控制相关和不相关的多效性效应,能够提供更可靠和稳健的因果效应估计,比MR-Egger更为强大[24-25]。
1.5. 敏感性分析
1.6. 反向MR分析
将GO作为暴露因素,补体C5/C5a作为结局因素,进行反向MR分析,旨在排除补体C5/C5a与GO之间反向因果关联的影响,增加结果的可靠性。
1.7. 共定位分析
采用colocR软件包进行共定位分析,测试补体C5与GO之间的关联是否由连锁不平衡驱动[29]。默认先验概率为:P1=1E-5,P2=1E-5,P12=1E-5。P1、P2和P12是预定义的概率,即测试区域中的SNP与补体C5表达、GO风险或两者有实质性联系。从共定位分析得出假设的后验概率(posterior probability of hypothesis,PPH),对应如下[30]:1)PPH0、SNP与任何一个表型都无关;2)PPH1、SNP与补体C5表达相关,但与GO风险无关;3)PPH2、SNP与GO风险相关,但与补体C5表达无关;4)PPH3、SNP与GO风险和补体C5表达均相关,但2种表型由不同的SNP驱动;5)PPH4、SNP与GO风险和补体C5表达均相关,且2种表型由共同的SNP驱动。共定位的显著性阈值设定为PPH4>0.80[31],与GO共定位被视为潜在的药物靶蛋白。
1.8. 统计学处理
所有数据分析和可视化均在R 4.3.2软件中进行,本研究使用了R软件包“TwoSampleMR”“MRPRESSO”“coloc”“ggplot2”“locuscomparer”模块。检验水准α=0.05,P<0.05为差异有统计学意义。
2. 结 果
2.1. MR分析结果
Wald比率法模型显示补体C5与GO存在显著正向因果关联(OR=4.109,95% CI 1.990~8.486,P=1.34×10-4),IVW模型显示C5a与GO同样存在正向因果关联(OR=2.901, 95% CI 1.225~6.869,P=0.015);cML-MA法也观察到了一致的因果效应(P<0.05,表3)。
表3.
正向 MR分析结果
Table 3 Forward MR analysis results
| 暴露 | 结局 | SNP | 方法 | OR | 95% CI | P |
|---|---|---|---|---|---|---|
| C5 | GO | 1 | Wald ratio | 4.109 | 1.990~8.486 | 1.34×10-4 |
| 1 | cML-MA | 4.108 | 2.078~8.120 | 4.81×10-5 | ||
| C5a | GO | 2 | IVW | 2.901 | 1.225~6.869 | 0.015 |
| 2 | cML-MA | 2.755 | 1.132~6.705 | 0.025 |
MR:孟德尔随机化;GO:Graves眼病;SNP:单核苷酸多态性;IVW:逆方差加权;cML-MA:约束最大似然与模型平均法;OR:比值比;CI:置信区间。
2.2. 敏感性分析结果
Steiger方向性检验显示:补体C5与GO(P=6.99×10-64)、C5a与GO(P=1.13×10-47)的因果关系均为正向。补体C5作为暴露时,仅有1个SNP纳入分析,未进行Cochran Q检验;C5a作为暴露时,Cochran Q检验未发现SNP间存在异质性(P=0.440)。
2.3. 反向MR分析结果
以GO作为暴露因素,补体C5/C5a作为结局因素,最终纳入3个SNP用于反向MR分析(表4)。IVW分析结果提示GO与补体C5(P=0.063)和C5a(P=0.340)均不具有因果关系(表5)。
表 4.
反向MR分析工具变量
Table 4 Reverse MR analysis instrumental variables
| SNP | Chr: Pos | EA | OA | EAF | β | SE | P | F |
|---|---|---|---|---|---|---|---|---|
| rs12198492 | 6:33900632 | C | G | 0.059 | 0.581 | 0.094 | 5.53×10-10 | 38.203 |
| rs143190692 | 7:9088452 | A | G | 0.012 | 0.984 | 0.180 | 4.39×10-8 | 29.884 |
| rs1794530 | 6:32704023 | G | T | 0.101 | 0.784 | 0.068 | 1.70×10-30 | 132.927 |
MR:孟德尔随机化;SNP:单核苷酸多态性;Chr:染色体;Pos:位置;EA:效应等位基因;OA:其他等位基因;EAF:效应等位基因频率;β:效应值;SE:标准误。
表5.
反向MR分析结果
Table 5 Reverse MR analysis results
| 暴露 | 结局 | SNP | 方法 | OR | 95% CI | P |
|---|---|---|---|---|---|---|
| GO | C5 | 3 | IVW | 0.979 | 0.957~1.001 | 0.063 |
| 3 | MR Egger | 0.999 | 0.858~1.162 | 0.988 | ||
| 3 | Weighted median | 0.978 | 0.955~1.002 | 0.072 | ||
| GO | C5a | 3 | IVW | 1.014 | 0.985~1.044 | 0.340 |
| 3 | MR Egger | 0.954 | 0.799~1.140 | 0.697 | ||
| 3 | Weighted median | 1.011 | 0.979~1.044 | 0.502 |
MR:孟德尔随机化;GO:Graves眼病;SNP:单核苷酸多态性;IVW:逆方差加权;OR:比值比;CI:置信区间。
2.4. 共定位分析
共定位分析有强有力的证据(PPH4=0.81)表明补体C5和GO在给定的基因区域内(1 000 kb)共享同一个因果变异(rs7036980),加强了MR分析中确定的致病作用,提示补体C5在GO的发病机制中起重要作用,是GO的潜在药物靶点(图2)。
图2.
共定位分析结果
Figure 2 Results of colocalization analysis
GRAVES_OPHT: Graves’ ophthalmopathy; C5: Component 5; UKB_PPP: UK Biobank Pharma Proteomics Project; PPH4: Posterior probability of hypothesis 4; Chr: Chromosome.
3. 讨 论
本研究利用两样本MR方法,基于GWAS数据探究补体C5/C5a与GO的因果关联,发现补体C5/C5a与GO存在正向因果关联,共定位分析结果显示补体C5与GO在给定的遗传窗口内共享同一个SNP(rs7036980),预示补体C5有望成为GO早期生物标志物及治疗靶点。
迄今为止,在GO的治疗中出现了许多针对补体成分的创新治疗尝试[32],但仍然缺乏关于补体系统参与GO发病机制的全面研究。补体系统由Jules Bordet和Paul Ehrlich在19世纪后期首次描述为辅助系统,是一个由50多种血清蛋白组成的复杂系统,在先天免疫和适应性免疫中发挥重要作用[33-34],而免疫失调和炎症反应是GO发病机制的潜在介质,故补体系统可能参与GO的发病过程[35]。补体级联反应通过经典(抗体依赖性)、替代性(抗体非依赖性)和凝集素途径激活,3个级联反应汇聚产生补体 C3 转化酶,该酶切割补体C3进而产生补体C5转化酶,该转化酶将补体C5切割为C5a和C5b,这是补体级联反应的关键末端效应子成分,可导致细胞死亡[36]。C5是补体级联反应的核心成分,抑制补体级联反应中的这一关键步骤可防止关键末端片段(C5a和C5b)的形成[37]。C5a可能在炎症小体启动和激活中发挥重要作用,导致细胞焦亡和细胞死亡;C5b可能参与膜攻击复合物(C5b-9)的形成,导致细胞裂解和细胞死亡[38-39]。抑制这些C5介导的复合物活性可能具有减缓GO进展的潜力。Jaffe等[40]的一项II期临床试验表明特异性靶向补体C5的抑制剂Avacincaptad pegol(一种聚乙二醇化的RNA适配体)减缓了年龄相关性黄斑变性患者12个月内地理萎缩的进展,与C3抑制治疗的效果相比,补体C5抑制治疗效果起效更早,这可能是由于补体级联反应在C5水平的下游得到进一步抑制,且C5抑制理论上保留了C3活性,因此可能提供了额外的安全优势。此外,传统的药物开发的主要问题是由于II期和III期缺乏疗效而导致的失败[41],而MR研究的结果为蛋白质药物靶点与疾病之间的因果关系提供了令人信服的证据,增加了候选药物在III期临床试验中成功的可能性[42]。在一些情况下,MR研究在临床试验测试之前可预测候选药物的治疗效果。
本研究存在一定的局限性:首先,本研究使用的GWAS数据均来自于欧洲人群,结论推广到其他人群中需谨慎;其次,本研究中使用的pQTL是使用Olink和Somascan 2个不同的平台获得的,可能会引入异质性;最后,本研究仅从遗传学角度推断补体C5/C5a与GO存在因果关系,具体的生物学机制有待进一步研究。
综上所述,本研究利用MR方法证明了补体C5/C5a与GO存在正向因果关系,补体C5/C5a的高水平显著增加GO的发生风险;共定位分析结果表明补体C5是GO的潜在靶蛋白,通过靶向补体C5的抑制剂可以有效降低GO的发生风险。未来仍需要大样本、多中心的随机对照研究进一步证实补体C5抑制剂治疗GO的有效性和安全性。
基金资助
国家自然科学基金(82071006)。This work was supported by the National Natural Science Foundation, China (82071006).
利益冲突声明
作者声称无任何利益冲突。
作者贡献
朱敏 资料收集,软件操作,统计分析,论文撰写与修改;吴冰萱、陈赵昌辞、陈海燕 协助统计分析,论文撰写;熊炜 研究设计,论文修改与审阅。所有作者阅读并同意最终的文本。
Footnotes
http://dx.chinadoi.cn/10.11817/j.issn.1672-7347.2024.240062
原文网址
http://xbyxb.csu.edu.cn/xbwk/fileup/PDF/2024101633.pdf
参考文献
- 1. Eckstein A, Dekowski D, Führer-Sakel D, et al. Endokrine Orbitopathie [Graves’ ophthalmopathy][J]. Ophthalmologe, 2016, 113(4): 349-364, 465-466. 10.1007/s00347-016-0239-3. [DOI] [PubMed] [Google Scholar]
- 2. Bahn RS. Graves’ ophthalmopathy[J]. N Engl J Med, 2010, 362(8): 726-738. 10.1056/NEJMra0905750. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Bartalena L, Kahaly GJ, Baldeschi L, et al. The 2021 European Group on Graves’ orbitopathy (EUGOGO) clinical practice guidelines for the medical management of Graves’ orbitopathy[J]. Eur J Endocrinol, 2021, 185(4): G43-G67. 10.1530/EJE-21-0479. [DOI] [PubMed] [Google Scholar]
- 4. Zhang X, Zhao Q, Li B. Current and promising therapies based on the pathogenesis of Graves’ ophthalmopathy[J]. Front Pharmacol, 2023, 14: 1217253. 10.3389/fphar.2023.1217253. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Cruz-Pimentel M, Wu L. Complement inhibitors for advanced dry age-related macular degeneration (geographic atrophy): some light at the end of the tunnel?[J]. J Clin Med, 2023, 12(15): 5131. 10.3390/jcm12155131. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Williams MA, McKay GJ, Chakravarthy U. Complement inhibitors for age-related macular degeneration[J]. Cochrane Database Syst Rev, 2014, 2014(1): CD009300. 10.1002/14651858.CD009300.pub2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Heesterbeek TJ, Lechanteur YTE, Lorés-Motta L, et al. Complement activation levels are related to disease stage in AMD[J]. Invest Ophthalmol Vis Sci, 2020, 61(3): 18. 10.1167/iovs.61.3.18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Altay L, Sitnilska V, Schick T, et al. Early local activation of complement in aqueous humour of patients with age-related macular degeneration[J]. Eye, 2019, 33(12): 1859-1864. 10.1038/s41433-019-0501-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Schick T, Steinhauer M, Aslanidis A, et al. Local complement activation in aqueous humor in patients with age-related macular degeneration[J]. Eye, 2017, 31(5): 810-813. 10.1038/eye.2016.328. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Tomany SC, Wang JJ, van Leeuwen R, et al. Risk factors for incident age-related macular degeneration: pooled findings from 3 continents[J]. Ophthalmology, 2004, 111(7): 1280-1287. 10.1016/j.ophtha.2003.11.010. [DOI] [PubMed] [Google Scholar]
- 11. Spiller W, Slichter D, Bowden J, et al. Detecting and correcting for bias in Mendelian randomization analyses using Gene-by-Environment interactions[J]. Int J Epidemiol, 2019, 48(3): 702-712. 10.1093/ije/dyy204. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data[J]. Int J Epidemiol, 2017, 46(6): 1734-1739. 10.1093/ije/dyx034. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Burgess S, Bowden J, Fall T, et al. Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants[J]. Epidemiology, 2017, 28(1): 30-42. 10.1097/EDE.0000000000000559. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Sun BB, Chiou J, Traylor M, et al. Plasma proteomic associations with genetics and health in the UK Biobank[J]. Nature, 2023, 622(7982): 329-338. 10.1038/s41586-023-06592-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Ferkingstad E, Sulem P, Atlason BA, et al. Large-scale integration of the plasma proteome with genetics and disease[J]. Nat Genet, 2021, 53(12): 1712-1721. 10.1038/s41588-021-00978-w. [DOI] [PubMed] [Google Scholar]
- 16. Hu T, Su P, Yang F, et al. Circulating cytokines and venous thromboembolism: a bidirectional two-sample Mendelian randomization study[J]. Thromb Haemost, 2024, 124(5): 471-481. 10.1055/s-0043-1777351. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Shin H. XGBoost regression of the most significant photoplethysmogram features for assessing vascular aging[J]. IEEE J Biomed Health Inform, 2022, 26(7): 3354-3361. 10.1109/JBHI.2022.3151091. [DOI] [PubMed] [Google Scholar]
- 18. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians[J]. BMJ, 2018, 362: k601. 10.1136/bmj.k601. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. 滕梦豪, 苏啸尘, 郭梦, 等. 孟德尔随机化探究白细胞介素与便秘的因果效应[J]. 西安交通大学学报(医学版), 2023, 44(5): 737-745. 10.7652/jdyxb202305012. [DOI] [Google Scholar]; TENG Menghao, SU Xiaochen, GUO Meng, et al. Causal association between interleukin and constipation: a Mendelian randomization analysis[J]. Journal of Xi’an Jiaotong University. Medical Sciences, 2023, 44(5): 737-745. 10.7652/jdyxb202305012. [DOI] [Google Scholar]
- 20. 牛晓亚, 熊雅俊, 蔡梦婷, 等. 胃食管反流病增加慢性阻塞性肺疾病的患病风险: 一项两样本双向孟德尔随机化研究[J]. 重庆医科大学学报, 2023, 48(12): 1439-1445. 10.13406/j.cnki.cyxb.003396. [DOI] [Google Scholar]; NIU Xiaoya, XIONG Yajun, CAI Mengting, et al. Gastroesophageal reflux disease causes an increased risk of chronic obstructive pulmonary disease: a two-sample bidirectional Mendelian randomization study[J]. Journal of Chongqing Medical University, 2023, 48(12): 1439-1445. 10.13406/j.cnki.cyxb.003396. [DOI] [Google Scholar]
- 21. Yang H, Chen L, Liu Y. A large-scale plasma proteome Mendelian randomization study identifies novel causal plasma proteins related to primary biliary cholangitis[J]. Front Immunol, 2023, 14: 1052616. 10.3389/fimmu.2023.1052616. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Yang H, Chen L, Liu Y. Novel causal plasma proteins for hypothyroidism: a large-scale plasma proteome Mendelian randomization analysis[J]. J Clin Endocrinol Metab, 2023, 108(2): 433-442. 10.1210/clinem/dgac575. [DOI] [PubMed] [Google Scholar]
- 23. Yuan S, Xu F, Li X, et al. Plasma proteins and onset of type 2 diabetes and diabetic complications: Proteome-wide Mendelian randomization and colocalization analyses[J]. Cell Rep Med, 2023, 4(9): 101174. 10.1016/j.xcrm.2023.101174. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Yin Q, Zhu L. Does co-localization analysis reinforce the results of Mendelian randomization?[J/OL]. Brain, 2024, 147(1): e7-e8[2024-01-12]. 10.1093/brain/awad295. [DOI] [PubMed] [Google Scholar]
- 25. Li P, Wang H, Guo L, et al. Association between gut microbiota and preeclampsia-eclampsia: a two-sample Mendelian randomization study[J]. BMC Med, 2022, 20(1): 443. 10.1186/s12916-022-02657-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Jiang M, Yan W, Li X, et al. Calcium homeostasis and psychiatric disorders: a Mendelian randomization study[J]. Nutrients, 2023, 15(18): 4051. 10.3390/nu15184051. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Lu L, Wan B, Li L, et al. Hypothyroidism has a protective causal association with hepatocellular carcinoma: a two-sample Mendelian randomization study[J]. Front Endocrinol, 2022, 13: 987401. 10.3389/fendo.2022.987401. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Jiang M, Yan W, Zhang Y, et al. Phosphodiesterase and psychiatric disorders: a two-sample Mendelian randomization study[J]. J Transl Med, 2023, 21(1): 560. 10.1186/s12967-023-04368-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Giambartolomei C, Vukcevic D, Schadt EE, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics[J/OL]. PLoS Genet, 2014, 10(5): e1004383[2024-01-21]. 10.1371/journal.pgen.1004383. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Foley CN, Staley JR, Breen PG, et al. A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits[J]. Nat Commun, 2021, 12(1): 764. 10.1038/s41467-020-20885-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Chen J, Xu F, Ruan X, et al. Therapeutic targets for inflammatory bowel disease: proteome-wide Mendelian randomization and colocalization analyses[J]. EBioMedicine, 2023, 89: 104494. 10.1016/j.ebiom.2023.104494. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Mastellos DC, Ricklin D, Lambris JD. Clinical promise of next-generation complement therapeutics[J]. Nat Rev Drug Discov, 2019, 18(9): 707-729. 10.1038/s41573-019-0031-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Reis ES, Mastellos DC, Hajishengallis G, et al. New insights into the immune functions of complement[J]. Nat Rev Immunol, 2019, 19(8): 503-516. 10.1038/s41577-019-0168-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Bruno V, Mühlig AK, Oh J, et al. New insights into the immune functions of podocytes: the role of complement[J]. Mol Cell Pediatr, 2023, 10(1): 3. 10.1186/s40348-023-00157-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Kang S, Hamed Azzam S, Minakaran N, et al. Rituximab for thyroid-associated ophthalmopathy[J]. Cochrane Database Syst Rev, 2022, 6(6): CD009226. 10.1002/14651858.CD009226.pub3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Kolev M, Le Friec G, Kemper C. Complement: tapping into new sites and effector systems[J]. Nat Rev Immunol, 2014, 14(12): 811-820. 10.1038/nri3761. [DOI] [PubMed] [Google Scholar]
- 37. Swanson KV, Deng M, Ting JPY. The NLRP3 inflammasome: molecular activation and regulation to therapeutics[J]. Nat Rev Immunol, 2019, 19(8): 477-489. 10.1038/s41577-019-0165-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Mulfaul K, Mullin NK, Giacalone JC, et al. Local factor H production by human choroidal endothelial cells mitigates complement deposition: implications for macular degeneration[J]. J Pathol, 2022, 257(1): 29-38. 10.1002/path.5867. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Georgiannakis A, Burgoyne T, Lueck K, et al. Retinal pigment epithelial cells mitigate the effects of complement attack by endocytosis of C5b-9[J]. J Immunol, 2015, 195(7): 3382-3389. 10.4049/jimmunol.1500937. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Jaffe GJ, Westby K, Csaky KG, et al. C5 inhibitor avacincaptad pegol for geographic atrophy due to age-related macular degeneration: a randomized pivotal phase 2/3 trial[J]. Ophthalmology, 2021, 128(4): 576-586. 10.1016/j.ophtha.2020.08.027. [DOI] [PubMed] [Google Scholar]
- 41. Holmes MV, Richardson TG, Ference BA, et al. Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development[J]. Nat Rev Cardiol, 2021, 18(6): 435-453. 10.1038/s41569-020-00493-1. [DOI] [PubMed] [Google Scholar]
- 42. Nelson MR, Tipney H, Painter JL, et al. The support of human genetic evidence for approved drug indications[J]. Nat Genet, 2015, 47(8): 856-860. 10.1038/ng.3314. [DOI] [PubMed] [Google Scholar]


