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Article(id=1240972420857778357, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240972413354176744, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202311131, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1699545600000, receivedDateStr=2023-11-10, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773800481285, onlineDateStr=2026-03-18, pubDate=1715270400000, pubDateStr=2024-05-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773800481285, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773800481285, creator=13701087609, updateTime=1773800481285, updator=13701087609, issue=Issue{id=1240972413354176744, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='9', pageStart='1537', pageEnd='1728', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773800479495, creator=13701087609, updateTime=1773800596829, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240972905568334240, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240972413354176744, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240972905568334241, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240972413354176744, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1562, endPage=1567, ext={EN=ArticleExt(id=1240972421210099909, articleId=1240972420857778357, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Study on the causal relationship between lipoprotein (a) and cerebrovascular disease by two-sample Mendelian randomization analysis, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the causal relationship between lipoprotein (a) [Lp (a)] and cerebral small vascular disease (CSVD) by Mendelian randomization (MR) analysis.

Methods

Using genome-wide association study (GWAS) public database, the single nucleotide polymorphisms independently associated with Lp (a) (P < 5 × 10-8) were selected as instrumental variables (IVs). The main outcomes of this study were white matter hyperintensity (WMH), cerebral microhemorrhage, and lacunar infarction. Inverse variance weighted (IVW) mothod was used to evaluate the causal relationship between Lp (a) and CSVD. Sensitivity analysis was carried out by MR-Egger and MR-PRESSO to evaluate the pleiotropy of IVs. Furthermore, weighted median method, simple median method, and maximum likelihood ratio method were used to test the reliability of the results.

Results

IVW results showed that there was a positive risk relationship between Lp (a) and WMH (OR=1.083, 95%CI: 1.035-1.134, P <0.001), but Lp (a) was not correlated with cerebral microhemorrhage (P=0.568) and lacunar infarction (P=0.651). MR-Egger analysis showed that the MR analysis of Lp (a) and WMH was not affected by horizontal pleiotropy (P=0.721).

Conclusion

MR analysis shows that Lp (a) is related to the increased risk of WMH.

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目的

利用孟德尔随机化方法（Mendelian randomization，MR）来探究脂蛋白（a）[Lipoprotein(a)，Lp(a)]与脑小血管病（cerebral small vascular disease，CSVD）之间的因果关系。

方法

采用两个大样本的全基因组关联公共数据库，根据遗传位点满足相互独立，挑选出和Lp(a)相关联的单核苷酸多态性位点（P<5×10-8）作为工具变量。本研究的主要结局为脑白质高信号（white matter hyperintensity，WMH）、脑微出血以及腔隙性梗塞。使用逆方差加权法（inverse variance weighting，IVW）评估Lp(a)与CSVD的因果关系，通过MR-Egger和MR-PRESSO进行敏感性分析，评估工具变量的多效性。进一步采用加权中位数法、简单中位数法、最大似然比法等分析方法检验结果是否可靠。

结果

IVW结果表明Lp(a)与WMH之间存在正向风险关系（OR=1.083，95%CI: 1.035～1.134，P<0.001），但Lp(a)与脑微出血（P=0.568）和腔隙性梗塞（P=0.651）没有相关性。MR-Egger分析表明有关Lp(a)和WMH的MR分析结果不受水平多效性的影响（P=0.721）。

结论

MR分析显示Lp(a)与WMH发生风险升高有关。

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陆军，E-mail:
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=ZB+C9LKTI/GtfKlwGWxInQ==, magXml=w0w+4Gia3G0jt0KPYmOxBA==, pdfUrl=null, pdf=wE/x0sfmSAPOcqwfXh6Ojw==, pdfFileSize=811608, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=f1GqUIogLgYZTHc5ol7xLw==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=T/lqYspOrusqpMKY8s9tiQ==, mapNumber=null, authorCompany=null, fund=null, authors=

刘雪情（1997-），女，硕士在读，研究方向：脑血管疾病的基础和临床研究

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刘雪情（1997-），女，硕士在读，研究方向：脑血管疾病的基础和临床研究

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Summary data used in this study

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露/结局种族病例组对照组样本量SNPs数量数据公布年份（年）
Lp(a)欧洲——10 70817 652 7982020
脑微出血欧洲1 935471 578473 51324 191 2842021
腔隙性中风混合种族7 338225 258232 5966 898 4542021
WMH欧洲8 41111 734 3542018
), ArticleFig(id=1240986271565280181, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972420857778357, language=CN, label=表1, caption=

数据汇总信息

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露/结局种族病例组对照组样本量SNPs数量数据公布年份（年）
Lp(a)欧洲——10 70817 652 7982020
脑微出血欧洲1 935471 578473 51324 191 2842021
腔隙性中风混合种族7 338225 258232 5966 898 4542021
WMH欧洲8 41111 734 3542018
), ArticleFig(id=1240986271867270073, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972420857778357, language=EN, label=Table 2, caption=

Detailed information on the 5 included SNPs

, figureFileSmall=null, figureFileBig=null, tableContent=
SNPLocus NamePosChrEA OA EAFβ P值F
rs11075921LPA7213212916TC0.1530.4360.0181.76×10-135613
rs116891509LPA7210556016TC0.075-0.3880.0259.40×10-56247
rs2954021LPA1264820778GA0.505-0.0730.0132.48×10-831
rs613808LPA11671096811GA0.715-0.0830.0141.09×10-832
rs62056527LPA7191226116CT0.052-0.1950.0307.30×10-1142
), ArticleFig(id=1240986272576107452, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972420857778357, language=CN, label=表2, caption=

五个纳入的SNPs的详细信息

, figureFileSmall=null, figureFileBig=null, tableContent=
SNPLocus NamePosChrEA OA EAFβ P值F
rs11075921LPA7213212916TC0.1530.4360.0181.76×10-135613
rs116891509LPA7210556016TC0.075-0.3880.0259.40×10-56247
rs2954021LPA1264820778GA0.505-0.0730.0132.48×10-831
rs613808LPA11671096811GA0.715-0.0830.0141.09×10-832
rs62056527LPA7191226116CT0.052-0.1950.0307.30×10-1142
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Results of two-sample MR analysis, heterogeneity test, and horizontal pleiotropy test

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结局MR方法SNPsOR值（95%CI）P值异质性检验结果水平多效性检验结果
MR-Egger51.032（0.970～1.097）0.3920.7210.137
weighted median51.080（1.035～1.128）<0.001
WMH IVW51.083（1.035～1.134）<0.0010.248
simple mode51.103（1.012～1.203）0.090
weighted mode51.068（1.023～1.116）0.041
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两样本的MR分析、异质性检验及水平多效性检验的结果

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结局MR方法SNPsOR值（95%CI）P值异质性检验结果水平多效性检验结果
MR-Egger51.032（0.970～1.097）0.3920.7210.137
weighted median51.080（1.035～1.128）<0.001
WMH IVW51.083（1.035～1.134）<0.0010.248
simple mode51.103（1.012～1.203）0.090
weighted mode51.068（1.023～1.116）0.041
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基于两样本孟德尔随机化探讨脂蛋白（a）和脑小血管病的因果关系
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刘雪情 1, 2 , 宫秀群 1, 2 , 刚玉文 2 , 项心怡 2 , 李晗晓 2 , 陆军 2
现代预防医学 | 流行病与统计方法 2024,51(9): 1562-1567
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现代预防医学 | 流行病与统计方法 2024, 51(9): 1562-1567
基于两样本孟德尔随机化探讨脂蛋白（a）和脑小血管病的因果关系
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刘雪情1, 2, 宫秀群1, 2, 刚玉文2, 项心怡2, 李晗晓2, 陆军2
作者信息
  • 1.安徽理工大学第一附属医院神经内科，安徽 淮南 232001
  • 2.安徽理工大学医学院，安徽 淮南 232001

    • 刘雪情（1997-），女，硕士在读，研究方向：脑血管疾病的基础和临床研究

通讯作者:

陆军，E-mail:
Study on the causal relationship between lipoprotein (a) and cerebrovascular disease by two-sample Mendelian randomization analysis
Xue-qing LIU1, 2, Xiu-qun GONG1, 2, Yu-wen GANG2, Xin-yi XIANG2, Han-xiao LI2, Jun LU2
Affiliations
  • Department of Neurology, the First Affiliated Hospital of Anhui University of Technology, Huainan, Anhui 232001, China
出版时间: 2024-05-10 doi: 10.20043/j.cnki.MPM.202311131
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摘要
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目的

利用孟德尔随机化方法（Mendelian randomization，MR）来探究脂蛋白（a）[Lipoprotein(a)，Lp(a)]与脑小血管病（cerebral small vascular disease，CSVD）之间的因果关系。

方法

采用两个大样本的全基因组关联公共数据库，根据遗传位点满足相互独立，挑选出和Lp(a)相关联的单核苷酸多态性位点（P<5×10-8）作为工具变量。本研究的主要结局为脑白质高信号（white matter hyperintensity，WMH）、脑微出血以及腔隙性梗塞。使用逆方差加权法（inverse variance weighting，IVW）评估Lp(a)与CSVD的因果关系，通过MR-Egger和MR-PRESSO进行敏感性分析，评估工具变量的多效性。进一步采用加权中位数法、简单中位数法、最大似然比法等分析方法检验结果是否可靠。

结果

IVW结果表明Lp(a)与WMH之间存在正向风险关系（OR=1.083，95%CI: 1.035～1.134，P<0.001），但Lp(a)与脑微出血（P=0.568）和腔隙性梗塞（P=0.651）没有相关性。MR-Egger分析表明有关Lp(a)和WMH的MR分析结果不受水平多效性的影响（P=0.721）。

结论

MR分析显示Lp(a)与WMH发生风险升高有关。

脑白质高信号  /  脂蛋白（a）  /  孟德尔随机化  /  工具变量  /  因果关系
Objective

To explore the causal relationship between lipoprotein (a) [Lp (a)] and cerebral small vascular disease (CSVD) by Mendelian randomization (MR) analysis.

Methods

Using genome-wide association study (GWAS) public database, the single nucleotide polymorphisms independently associated with Lp (a) (P < 5 × 10-8) were selected as instrumental variables (IVs). The main outcomes of this study were white matter hyperintensity (WMH), cerebral microhemorrhage, and lacunar infarction. Inverse variance weighted (IVW) mothod was used to evaluate the causal relationship between Lp (a) and CSVD. Sensitivity analysis was carried out by MR-Egger and MR-PRESSO to evaluate the pleiotropy of IVs. Furthermore, weighted median method, simple median method, and maximum likelihood ratio method were used to test the reliability of the results.

Results

IVW results showed that there was a positive risk relationship between Lp (a) and WMH (OR=1.083, 95%CI: 1.035-1.134, P <0.001), but Lp (a) was not correlated with cerebral microhemorrhage (P=0.568) and lacunar infarction (P=0.651). MR-Egger analysis showed that the MR analysis of Lp (a) and WMH was not affected by horizontal pleiotropy (P=0.721).

Conclusion

MR analysis shows that Lp (a) is related to the increased risk of WMH.

White matter hypersignal  /  Lipoprotein (a)  /  Mendelian randomization  /  Instrumental variable  /  Causality
刘雪情, 宫秀群, 刚玉文, 项心怡, 李晗晓, 陆军. 基于两样本孟德尔随机化探讨脂蛋白（a）和脑小血管病的因果关系. 现代预防医学, 2024 , 51 (9) : 1562 -1567 . DOI: 10.20043/j.cnki.MPM.202311131
Xue-qing LIU, Xiu-qun GONG, Yu-wen GANG, Xin-yi XIANG, Han-xiao LI, Jun LU. Study on the causal relationship between lipoprotein (a) and cerebrovascular disease by two-sample Mendelian randomization analysis[J]. Modern Preventive Medicine, 2024 , 51 (9) : 1562 -1567 . DOI: 10.20043/j.cnki.MPM.202311131
正文
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脑小血管病（cerebral small vascular disease,CSVD）是一种最常见的病理性神经疾病，其中一种最主要表现形式为脑白质高信号（white matter hyperintensity，WMH），可导致中风和痴呆的发生[1]。近年来随着中国人口老龄化加剧，WMH的发病率逐年上升，导致老年人中风及中风后预后不良风险增加，认知功能发生不可逆性损伤障碍，给家庭及社会医疗体系带来沉重负担。人们关于WMH的发病机制尚不完全明确，最近的研究指出神经炎症在WMH的发病中起关键作用。有研究表明炎症过程与内皮功能障碍、血脑屏障（blood-brain barrier，BBB）通透性和大脑血流量密切相关，可能会影响CSVD的发生与发展[2]。既往研究证实WMH的危险因素包括年龄、高血压、吸烟等，还有其他的病因不明，无法从源头预防其发生[3]。因此需要进一步探索其发病的危险因素来预防WMH。
研究指出，血脂异常的高水平脂蛋白（a）[Lipoprotein(a)，Lp(a)]可能与脑卒中等心脑血管事件的发生有关联[4]。 Lp(a)是一种炎症相关循环蛋白[5]，通过促炎、促动脉粥样硬化和促血栓形成等病理机制引发动脉粥样硬化[6]。既往的临床研究提示，Lp(a)和CSVD之间存在一定的相关性，然而由于传统观察性研究常存在混杂和反向因果关联等偏倚，在推断因果关联上有一定局限性。
由于基因突变在怀孕过程中是随机发生的，这使得我们能够解决临床观察型研究中可能出现的混淆因素及逆向因果关系的偏差问题，避免出现错误的相关性及影响推断疾病原因。孟德尔随机化分析（Mendelian randomization，MR）遵循孟德尔遗传规律，把遗传变异作为工具变量来尽肯能消除可能存在的混杂因素和反向因果效应的分析方法，从而探究Lp(a)和CSVD之间的因果关系[7]。MR通过建立工具变量与暴露和结局之间的关联，探究暴露和疾病风险之间的因果关系或相关性。相对于回顾性和前瞻性研究而言，MR的具体优势体现在研究具有足够大的样本量及能够尽可能避免潜在混杂因素对因果关联的影响[8]。既往MR研究表明，Lp(a)水平升高与冠状动脉疾病的高风险呈正相关，但是关于Lp(a)与脑血管病的因果关系仍不完全明确。在本研究中，我们使用MR分析来评估Lp(a)与脑小血管病风险的因果关系。
1　数据与方法
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1.1　研究设计和数据来源
当确认了所有相关联的暴露变量的数据和结局变量之间的关系是完全独立的时候，我们利用Lp(a)作为一个潜在的风险指标，通过使用两样本MR进行因果关联分析评估其对于CSVD的影响程度。暴露变量Lp(a)和结局CSVD包括WMH、腔隙性中风、脑微出血的数据库来自IEU open GWAS(genome-wide association study) project（https://gwas.mrcieu.ac.uk/ datasets/ebi-a-GCST90019420/）和GWAS catalog，具体信息见表1；暴露样本量10 708例，WMH结局样本量8 411例，所有参与者均为欧洲血统，避免种族相关混杂因素造成偏差。MR分析可用于无偏差检测危险因素对疾病的因果影响，因为遗传变异在受孕时是随机分配的[9]。MR研究满足三个基本假设[10]：（1）纳入的工具变量须与暴露的危险因素Lp(a)具有密切相关性；（2）工具变量独立于观察到或未观察到的混杂因素；（3）工具变量仅通过调查的危险因素与CSVD相关。遗传变异在工具中仅影响暴露因素，而不会通过其他途径对结局产生影响。这种变异必须符合MR假设的单基因特性，例如单核苷酸多态性（single nucleotide polymorphisms，SNPs），具体的MR研究流程见图1。
1.2　筛选工具变量
选择与危险因素相关的遗传变异作为MR分析的工具变量。从GWAS数据中筛选与Lp(a)水平在全基因组显著相关( P<5×10-8)的SNPs作为工具变量。为了避免基因遗传变异存在的连锁不平衡给研究结果带来的影响，SNPs的连锁不平衡必须满足参数r2<0.001和kb=10 000[10]。在GWAS数据库中读取结暴露因素Lp(a)的信息筛选出合适的工具变量SNPs，根据染色体和基因的位置，将上述筛选出的SNPs与结果变量的GWAS数据进行匹配，提出关于结局CSVD的数据提取上述的工具变量的SNPs，从中筛选满足上述MR三个基本假设SNPs，并删除回文SNPs。为了进一步评价弱工具变量的偏倚，F统计量常被引用作为工具变量强度的度量，其中SNPs的F>10说明不存在弱工具变量偏移，则不被踢除[11]。最终剩余的SNPs可以作为暴露Lp(a)的工具变量。
1.3　统计学分析方法
MR分析是在R4.2.1软件中运用Two Sample MR包进行计算的。研究采用了五个常见的MR分析技术分别是逆方差加权法（inverse variance weighted，IVW）、加权中位数法（weighted median）、MR-Egger回归法、基于众数的简单估计（simple mode）以及基于众数的加权估计法（weighted mode）。其中主导的方法为IVW，该方法是在所有工具变量有效的前提下，将其方差的倒数作为权重进行加权计算，在没有异质性和水平多效性存在的情况下提供准确的分析结果，其他分析方法作为辅助。然而，SNPs可能无法满足MR假设，并可能通过多效性效应导致偏差。为了更深入地阐释潜在多效性，我们进行一系列的敏感性分析，包括水平多效性检验、异质性检测以及逐个剔除检测[12]。MR- Egger法是一种加权线性回归分析，截距代表所有SNPs的平均多效性的效应值，可以更稳健的评估工具变量之间是否存在水平多效性。Cochran Q 检验评估SNPs是否存在遗传变异的异质性，如果P<0.05表示分析结果存在异质性。通过使用MR多效性残差和离群值测试（MR pleiotropy residual sum and outlier，MR-PRESSO）方法来检测并纠正偏离的SNPs，一旦发现有任何偏离异常的SNPs存在，就会对其进行剔除并在仅包含剩余工具变量的条件下再次进行研究。此外，逐个剔除敏感性检验来评估因果效应是否被单个或多个SNP显著影响，排除具有多效性关联且可能与暴露因素的关系混杂的SNPs[13]。最终，我们的研究筛选出五个SNPs作为有效工具变量。表明该MR分析纳入的工具变量具有足够的强度证实结果的可靠性的前提是纳入的工具变量均满足统计量F>10。我们使用了OR及其95%CI表示Lp(a)与WMH的风险关系的可能性；检验水准α=0.05。
2　结果
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2.1　筛选出的工具变量信息
本研究在排除连锁不平衡、F值筛选及调整等位基因情况后，经过严格的筛选Lp(a)的GWAS荟萃数据，最终共纳入五个与Lp(a)相关的SNPs为有效工具变量，见表2。我们计算了每个工具变量的F>10，提示在我们的研究中筛选的工具变量受到较小的潜在工具偏倚影响，为MR分析结果提供可靠性。
2.2　Lp（a）对脑血管病的影响
2.2.1　两样本MR分析结果
以IVW作为主要的分析方法，结果表明Lp(a)和WMH之间存在正相关的因果关系（OR=1.083，95%CI: 1.035～1.134）。在敏感性分析中，我们采用对包含多效性或无效工具更为稳健的加权中位数法、MR-Egger回归法、基于众数的简单估计以及基于众数的加权估计法进一步分析，加权中位数法、基于众数的加权估计法和基于众数的简单估计三种分析结果与主要分析方法IVW结果一致( P<0.001，P=0.041，P=0.090 )，进一步证明了Lp(a)与WMH之间有正向相关关系。虽然MR-Egger回归分析结果，回归截距为0.015，P=0.137证明其检验水平多效性，但是由于可能存在潜在离群的工具变量SNPs，与上述IVW分析方法结果存在差异，因此Lp(a)与WMH之间的关系还需要更多的研究进行验证。五种常见的MR分析方法结果见表3，图2、3。
2.2.2　异质性和多效性检验
在MR分析中，较多的工具变量会导致分析结果更准确，并为MR提供更好的效能，但也会导致研究水平多效性和异质性[14]。因此，使用多种方法来评估该研究的水平多效性和异质性是否显著。我们利用MR-Egger回归来评估SNPs和结局的水平多效性是否会对MR分析产生影响，对于Lp(a)和WMH的相关MR-Egger结果揭示了这种水平的多效性并不会导致MR分析的结果出现偏差（PWMH=0.137 > 0.05），因此并没有发现任何关于水平多效性的证据，结果见表2。通过Cochran Q检验筛选的工具变量SNPs是否存在异质性，Q=1.334，P=0.721>0.05，可以看出不存在遗传变异异质性。用MR-PRESSO方法未检出异常的SNPs，P>0.05。我们使用留一法来评估每个SNP的影响，以进行敏感性分析，分析结果见图4，所有MR分析结果均不因剔除单个SNP而出现波动。各种方法的散点图见图3，可直观地看到Lp(a)与WMH风险呈正相关，绘制漏斗图，结果显示本研究未见偏倚，见图5。漏斗图呈不对称分布，这表明因果关系受到潜在偏差的影响较弱。上述结果表明，Lp(a)与WMH之间存在因果关系，且两者存在正向相关关系。
3　讨论
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本研究是基于大规模GWAS研究数据的双样本MR分析，评估Lp(a)与CSVD之间的因果关系。本研究从遗传学角度基于MR理论得出结论，基因预测Lp(a)与WMH的病变存在因果关联，血清Lp(a)水平较高的患者发生WMH的风险更高且Lp(a)与WMH的病变体积呈正相关，Lp(a)是发生WMH（OR=1.083，95%CI:1.035～1.134）的危险因素，Lp(a)与脑微出血(OR=0.956，95%CI: 0.818～1.116，P= 0.567)和腔隙性中风(OR=1.022，95%CI: 0.930～1.123，P=0.651 )之间不存在因果关系。提示Lp(a)可能是WMH的潜在致病危险因素，Lp(a)可以增加WMH风险，且两者的因果关系不受混杂因素的影响，对WMH的临床筛查有一定作用。
目前有大量证据表明，血管内皮功能障碍和炎症是CSVD的发病机制的关键，认为内皮细胞功能障碍、炎症细胞激活和白质病变程度之间存在相关性[15]。既往研究发现Lp(a)可能通过多种机制导致内皮功能障碍，包括血管舒张功能受损、促进LDL氧化和内皮细胞活化、上调粘附分子和炎性细胞因子，以及损害血管壁的稳定性，参与CSVD的发生[16]。本研究结果表明升高的Lp(a)和WMH之间存在相关性，推测Lp(a)可能通过神经炎症和内皮损伤增加WMH的风险。可能的机制如下：第一，Lp(a)在炎症过程中，通过刺激机体分泌细胞因子、趋化因子和其他炎症递质调节神经炎症过程，诱导炎症细胞浸润并积聚在血管周围[17]，导致BBB破坏，刺激脑室内皮细胞产生活性氧，抑制内皮型一氧化氮合酶表达，脑血管舒张调节异常脑血流灌注不足，最终发生WMH；另一方面Lp(a)穿过血脑屏障渗透到内皮下间隙，氧化形成氧化磷脂，氧化的Lp(a)有促炎、致动脉粥样硬化作用以及促血栓形成作用，这些共同介导血管内皮损伤及神经细胞炎症，促进动脉粥样硬化、血栓形成及内皮功能障碍，导致脑卒中、心血管事件的发生[18]。第二，Lp(a)可通过炎症级联反应损伤BBB，BBB破坏可能导致Lp(a)等有害物质可以通过功能异常的血脑屏障，调节脑脊液中的胆固醇水平以及淀粉样β蛋白的沉积，减少淋巴通路对淀粉样蛋白的清除，并影响神经变性，促进动脉粥样硬化、血栓和炎症过程增加心脑血管疾病发生的风险[8]。第三，慢性低灌注导致脑血流量不足也可能是Lp(a)与WMH关系的潜在病理机制，在脑白质病变中，与正常脑组织相比，内皮细胞功能和BBB完整性显着降低且白质病变区域的细胞粘附分子-1含量显着增加加剧炎症反应[19]。这些原因可能解释了Lp(a)在WMH的病理机制。
MR是一种可以验证暴露和结局之间是否存在因果关系的方法，因此使用MR评估Lp(a)与CSVD WMH的因果关联不受外界混杂因素的影响。然而，既往MR关于Lp(a)与WMH的研究结果具有差异性。一项MR研究探索了Lp(a)遗传水平与缺血性中风亚型的相关性，发现高Lp(a)与大动脉卒中风险增加有因果关系，提示Lp(a)对脑血管性疾病的特异性作用，却发现Lp(a)与CSVD存在负性相关关系[20]，是CSVD的保护性因素。与既往MR研究相比，我们的研究结果为Lp(a)与WMH之间存在正向相关关系提供了遗传学证据，提示Lp(a)是WMH的危险因素。Lp(a)在多项临床观察研究中已被证实参与常见的心脑血管疾病，高Lp(a)水平可以引起血脂异常，增加冠状动脉疾病的风险[18]。一项近期荟萃分析中，Lp(a)水平升高是导致缺血性中风的独立危险因素，并且因其在脂蛋白代谢中的重要性而得到证实[21]。由于这些观察性研究中暴露与结果之间的关系不可避免地受到各种因素的影响，这可能导致Lp(a)与WMH的相关性出现差异。本研究与以往研究之间差异的原因可能归因于许多原因，包括研究设计，参与者特征和样本量的差异。另外，CSVD的异质性可能是不可忽略的原因，以前的大多数研究都包括CSVD的多种亚型。我们猜测出现这种差异性的原因可能是由于Lp(a)在WMH疾病进展中的病理机制较为复杂。研究表明，Lp(a)在WMH发生发展中一方面发挥促炎作用加重疾病进展，另一方面Lp(a)存在于脑脊液中并且与血清Lp(a)水平相关，Lp(a)参与脑脊液中胆固醇代谢的调节可能有助于大脑中的脂蛋白代谢和神经血管单元的保护。由于目前的研究存在差异性，关于Lp(a)与WMH的关系，需要在未来的大量研究中得到证实。
本研究的主要优势是：一是，通过两样本MR技术评估了Lp(a)与常见脑小血管病之间的因果关联，从遗传学角度确定影响WMH的危险因素更具说服力。由于遗传变异与其他混杂因素（例如生活方式，饮食和行为因素）不相关，从而可能影响观察研究，因此大大消除了混杂变量和反向因果关系的影响，降低潜在偏倚，减少了未观察到的可能会影响研究结果的混杂因素和反向因果关系。此外，通过使用双样本MR方法，我们能够分析Lp(a)基于大规模队列研究（8 411例WMH；232 596例腔隙性中风；473 513例脑微出血）以及不同种族与CSVD的相关性。二是，我们的分析还通过进行因果推断并综合评估Lp(a)与CSVD之间的关系，从而与以往的MR研究有所不同。对CSVD的综合评估可能有助于更好地了解Lp(a)对WMH的临床后果。除了IVW分析，也使用了多种MR辅助方法如加权中位数法、MR-Egger回归法、基于众数的简单估计以及基于众数的加权估计法等来评估Lp(a)和CSVD之间的因果关联。然而，本研究也有一定的限制性。MR分析结果很容易受到多效性的干扰，所谓多效性就是指SNPs的遗传变异可能与许多不同的表型有关联，这可能导致MR分析得出的因果预测结果存在偏差。而在本研究中，筛选出的与CSVD WMH相关的工具变量较少，且存在较大的弱工具变量偏倚，可能导致两样本MR研究中的因果关联被低估，因此我们应该对结果谨慎解释，未来需要使用更大样本的Lp(a)相关GWAS数据深入研究，可能会发现与脑白质高信号相关的更强位点作为工具变量，来评估当前研究所得出结论是否可靠。此外，我们没有研究其他脂蛋白与CSVD之间的因果关系，因为我们主要关注Lp(a)，其致病机制仍存在争议，需要进一步临床研究来探索其机制。此项研究所采用的非欧洲血统样本数量较少，因此在其他种族中得出结论相对困难。由于Lp(a)存在显著的种族差异，可能需要在其他种族进行同样的MR研究以确认该结论。
综上所述，我们的两样本MR研究发现在基因遗传水平上Lp(a)与WMH之间存在正向因果关系。 Lp(a)水平升高有加重WMH的风险，这为白质高信号可能存在的治疗方法提供新思路。然而Lp(a)与WMH之间的临床机制尚不完全明确，仍需进一步研究探索。
基金
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  • 2021年安徽省高校自然科学研究重点项目(KJ2021A0434)
文献
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2024年第51卷第9期
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文章信息
doi: 10.20043/j.cnki.MPM.202311131
  • 接收时间：2023-11-10
  • 首发时间：2026-03-18
  • 出版时间：2024-05-10
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  • 收稿日期：2023-11-10
基金
2021年安徽省高校自然科学研究重点项目(KJ2021A0434)
作者信息
    1.安徽理工大学第一附属医院神经内科，安徽 淮南 232001
    2.安徽理工大学医学院，安徽 淮南 232001

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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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