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Article(id=1241676526069019040, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241676522256388920, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202405267, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1715961600000, receivedDateStr=2024-05-18, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773968353048, onlineDateStr=2026-03-20, pubDate=1731168000000, pubDateStr=2024-11-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773968353048, onlineIssueDateStr=2026-03-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773968353048, creator=13701087609, updateTime=1773968353048, updator=13701087609, issue=Issue{id=1241676522256388920, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='21', pageStart='3841', pageEnd='4032', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773968352140, creator=13701087609, updateTime=1773968629818, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241677686985249701, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241676522256388920, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241677686985249702, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241676522256388920, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=3867, endPage=3872, ext={EN=ArticleExt(id=1241676526438117798, articleId=1241676526069019040, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Mendelian randomization study of the causal relationship between serum micronutrients and hypertension, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the causal relationship between serum micronutrients and hypertension using Mendelian randomization, thereby promoting the prevention and health management of hypertension.

Methods

Fourteen serum micronutrients were selected as exposure factors, with hypertension as the outcome. Two-sample Mendelian randomization analyses were conducted based on genome-wide association study data from European populations, followed by multivariable Mendelian randomization analyses to identify variables that may have a causal relationship with the outcome, determining the independent effects of each exposure factor on the outcome.

Results

Univariate Mendelian randomization analysis indicated a positive causal effect of elevated copper levels on the incidence of hypertension (P<0.05). Multivariable Mendelian randomization showed that the causal relationship between copper and hypertension remained significant after controlling for potassium and vitamin B6 (OR=1.028, 95%CI: 1.009-1.047, P<0.05).

Conclusion

There is a causal relationship between elevated copper levels and hypertension. However, the findings of this study require validation through randomized controlled trials.

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目的

本研究旨在利用孟德尔随机化分析探究血清微量营养素与高血压之间的因果关系，促进人群高血压的预防和健康管理。

方法

选取14种血清微量营养素作为暴露因素，以高血压为结局指标。采用基于欧洲人群的全基因组关联研究数据进行两样本孟德尔随机化分析，随后采用多变量孟德尔随机化分析，筛选可能与结局存在因果关系的变量，确定每种暴露因素对结局的独立影响。

结果

单变量孟德尔随机化分析显示，铜水平升高与高血压发病存在正向因果效应（P<0.05）。多变量孟德尔随机化显示，在控制钾和维生素B6的情况下，铜与高血压的因果关系仍然显著（OR= 1.028, 95%CI: 1.009～1.047, P<0.05）。

结论

铜水平升高与高血压之间存在因果关系。然而，本研究结果仍需通过随机对照试验进行验证。

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李娅亨，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=T+tS9zZSIziE6OCtEUNplg==, magXml=PESSFbcNBsoAIVKqPEM43Q==, pdfUrl=null, pdf=RZxxgccgdP7g5CBvHPU4mA==, pdfFileSize=1817908, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=2vpXh0zb2ehnUWXqwG3ozQ==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=hn3GDIYofX6dxBd2mASeIg==, mapNumber=null, authorCompany=null, fund=null, authors=

宋文柱（1996—），男，博士在读，研究方向：临床统计建模

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注：热图中颜色深浅代表各MR分析方法的P值大小;颜色越深表示P值越大；颜色越浅表示P值越小。

, figureFileSmall=Un9e3Bx8HsiXothLklgbTg==, figureFileBig=Cln7Qy9z5q7ZyCJHtliVFA==, tableContent=null), ArticleFig(id=1241821863991640733, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676526069019040, language=EN, label=Figure 3, caption=Univariate MR analysis results of variables included in multivariable analysis, figureFileSmall=IGMOpGrPqD214pKyJepPiQ==, figureFileBig=4Je1x8rqNFN5QnipD1YcvQ==, tableContent=null), ArticleFig(id=1241821864130052775, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676526069019040, language=CN, label=图3, caption=纳入多变量分析的各变量的单因素MR分析结果, figureFileSmall=IGMOpGrPqD214pKyJepPiQ==, figureFileBig=4Je1x8rqNFN5QnipD1YcvQ==, tableContent=null), ArticleFig(id=1241821864281047731, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676526069019040, language=EN, label=Figure 4, caption=Results of multivariable IVW-MR analysis, figureFileSmall=M26a9aLbn1tb3nUbduvemQ==, figureFileBig=JVtf3BuRFv1h9Nqs/TKbzQ==, tableContent=null), ArticleFig(id=1241821864444625597, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676526069019040, language=CN, label=图4, caption=多变量IVW-MR分析结果, figureFileSmall=M26a9aLbn1tb3nUbduvemQ==, figureFileBig=JVtf3BuRFv1h9Nqs/TKbzQ==, tableContent=null), ArticleFig(id=1241821865983935176, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676526069019040, language=EN, label=Table 1, caption=

Summary-level statistics of genome-wide association studies for serum micronutrients

, figureFileSmall=null, figureFileBig=null, tableContent=
ID暴露SNPs数量样本量种族
ieu-a-1073铜2 543 6462 603欧洲
ukb-b-8951钙9 851 86764 979欧洲
ieu-a-1079锌2 543 6102 603欧洲
ukb-b-20447铁9 851 86764 979欧洲
ukb-b-7372镁9 851 86764 979欧洲
ukb-b-17881钾9 851 86764 979欧洲
ukb-b-11349叶酸9 851 86764 979欧洲
ukb-b-16202胡萝卜素9 851 86764 979欧洲
ukb-b-9596维生素A9 851 867460 351欧洲
ukb-b-19390维生素C9 851 86764 979欧洲
ukb-b-18593维生素D9 851 86764 979欧洲
ukb-b-6888维生素E9 851 86764 979欧洲
ukb-b-7864维生素 B69 851 86764 979欧洲
ukb-b-19524维生素 B129 851 86764 979欧洲
finngen_R10_N14高血压—406 745欧洲
), ArticleFig(id=1241821866143318736, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676526069019040, language=CN, label=表1, caption=

血清微量营养素全基因组关联分析汇总数据

, figureFileSmall=null, figureFileBig=null, tableContent=
ID暴露SNPs数量样本量种族
ieu-a-1073铜2 543 6462 603欧洲
ukb-b-8951钙9 851 86764 979欧洲
ieu-a-1079锌2 543 6102 603欧洲
ukb-b-20447铁9 851 86764 979欧洲
ukb-b-7372镁9 851 86764 979欧洲
ukb-b-17881钾9 851 86764 979欧洲
ukb-b-11349叶酸9 851 86764 979欧洲
ukb-b-16202胡萝卜素9 851 86764 979欧洲
ukb-b-9596维生素A9 851 867460 351欧洲
ukb-b-19390维生素C9 851 86764 979欧洲
ukb-b-18593维生素D9 851 86764 979欧洲
ukb-b-6888维生素E9 851 86764 979欧洲
ukb-b-7864维生素 B69 851 86764 979欧洲
ukb-b-19524维生素 B129 851 86764 979欧洲
finngen_R10_N14高血压—406 745欧洲
), ArticleFig(id=1241821866298507995, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676526069019040, language=EN, label=Table 2, caption=

Sensitivity analysis of Mendelian randomization for serum micronutrients on hypertension

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露MR-PRESSO检验水平多效性检验异质性检验MR Egger
P值Egger截距P值QP值
铜0.553-0.0050.2652.0260.731
锌0.055-0.0050.74915.0090.020
叶酸0.073-0.0020.75319.8990.030
胡萝卜素0.9580.0070.1954.5030.985
钾0.328-0.0010.90813.9370.236
维生素D0.5130.0040.63011.0270.441
维生素C0.627-0.0100.1363.5920.892
维生素B120.264-0.0100.2547.2330.300
铁0.1280.0060.47613.2830.102
维生素E0.409-0.0050.36311.1670.345
镁0.839-0.0050.2399.4350.854
维生素B60.0630.0070.28124.3910.059
钙0.082-0.0090.37938.1650.001
维生素A0.337-0.0040.67311.1320.267
), ArticleFig(id=1241821866466280164, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676526069019040, language=CN, label=表2, caption=

血清微量营养素对高血压MR的敏感性分析

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露MR-PRESSO检验水平多效性检验异质性检验MR Egger
P值Egger截距P值QP值
铜0.553-0.0050.2652.0260.731
锌0.055-0.0050.74915.0090.020
叶酸0.073-0.0020.75319.8990.030
胡萝卜素0.9580.0070.1954.5030.985
钾0.328-0.0010.90813.9370.236
维生素D0.5130.0040.63011.0270.441
维生素C0.627-0.0100.1363.5920.892
维生素B120.264-0.0100.2547.2330.300
铁0.1280.0060.47613.2830.102
维生素E0.409-0.0050.36311.1670.345
镁0.839-0.0050.2399.4350.854
维生素B60.0630.0070.28124.3910.059
钙0.082-0.0090.37938.1650.001
维生素A0.337-0.0040.67311.1320.267
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血清微量营养素与高血压因果关联的孟德尔随机化研究
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宋文柱 1, 2, 3 , 宋文浩 4 , 梁小玲 4 , 岳振华 5 , 李娅亨 1, 2
现代预防医学 | 流行病与统计方法 2024,51(21): 3867-3872
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现代预防医学 | 流行病与统计方法 2024, 51(21): 3867-3872
血清微量营养素与高血压因果关联的孟德尔随机化研究
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宋文柱1, 2, 3, 宋文浩4, 梁小玲4, 岳振华5, 李娅亨1, 2
作者信息
  • 1.山西省人民医院肾内科，山西 太原030012
  • 2.山西医科大学第五临床医学院（山西省人民医院）肾脏病山西省重点实验室，山西 太原030012
  • 3.浙江大学公共卫生学院，浙江 杭州310058
  • 4.中山市中医院老年病科
  • 5.同济大学附属上海市第四人民医院

    • 宋文柱（1996—），男，博士在读，研究方向：临床统计建模

通讯作者:

李娅亨，E-mail:
Mendelian randomization study of the causal relationship between serum micronutrients and hypertension
Wen-zhu SONG1, 2, 3, Wen-hao SONG4, Xiao-ling LIANG4, Zhen-hua YUE5, Ya-heng LI1, 2
Affiliations
  • Department of Nephrology, Shanxi Provincial People’s Hospital, Taiyuan, Shanxi 030012, China
出版时间: 2024-11-10 doi: 10.20043/j.cnki.MPM.202405267
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摘要
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目的

本研究旨在利用孟德尔随机化分析探究血清微量营养素与高血压之间的因果关系，促进人群高血压的预防和健康管理。

方法

选取14种血清微量营养素作为暴露因素，以高血压为结局指标。采用基于欧洲人群的全基因组关联研究数据进行两样本孟德尔随机化分析，随后采用多变量孟德尔随机化分析，筛选可能与结局存在因果关系的变量，确定每种暴露因素对结局的独立影响。

结果

单变量孟德尔随机化分析显示，铜水平升高与高血压发病存在正向因果效应（P<0.05）。多变量孟德尔随机化显示，在控制钾和维生素B6的情况下，铜与高血压的因果关系仍然显著（OR= 1.028, 95%CI: 1.009～1.047, P<0.05）。

结论

铜水平升高与高血压之间存在因果关系。然而，本研究结果仍需通过随机对照试验进行验证。

微量营养素  /  高血压  /  孟德尔随机化  /  危险因素  /  因果效应
Objective

To explore the causal relationship between serum micronutrients and hypertension using Mendelian randomization, thereby promoting the prevention and health management of hypertension.

Methods

Fourteen serum micronutrients were selected as exposure factors, with hypertension as the outcome. Two-sample Mendelian randomization analyses were conducted based on genome-wide association study data from European populations, followed by multivariable Mendelian randomization analyses to identify variables that may have a causal relationship with the outcome, determining the independent effects of each exposure factor on the outcome.

Results

Univariate Mendelian randomization analysis indicated a positive causal effect of elevated copper levels on the incidence of hypertension (P<0.05). Multivariable Mendelian randomization showed that the causal relationship between copper and hypertension remained significant after controlling for potassium and vitamin B6 (OR=1.028, 95%CI: 1.009-1.047, P<0.05).

Conclusion

There is a causal relationship between elevated copper levels and hypertension. However, the findings of this study require validation through randomized controlled trials.

Micronutrients  /  Hypertension  /  Mendelian randomization  /  Risk factors  /  Causal effect
宋文柱, 宋文浩, 梁小玲, 岳振华, 李娅亨. 血清微量营养素与高血压因果关联的孟德尔随机化研究. 现代预防医学, 2024 , 51 (21) : 3867 -3872 . DOI: 10.20043/j.cnki.MPM.202405267
Wen-zhu SONG, Wen-hao SONG, Xiao-ling LIANG, Zhen-hua YUE, Ya-heng LI. Mendelian randomization study of the causal relationship between serum micronutrients and hypertension[J]. Modern Preventive Medicine, 2024 , 51 (21) : 3867 -3872 . DOI: 10.20043/j.cnki.MPM.202405267
正文
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高血压是导致各种心血管疾病的重要危险因素，同时还可引发肺、肾和脑等器官的功能性或结构性损害。数据显示，2019年高血压致使1 080万人死亡，是全球负担最重的疾病之一[1]。目前，我国高血压管理面临两大挑战，一是高血压患病率持续上升、发病年龄趋于年轻化，中国≥18岁高血压患病人数高达2.45亿；二是高血压控制率亟待提高和改善[2]，中国≥18岁人群高血压知晓率和控制率仅为51.6%和16.8%[3]。因此，寻找可改变的高血压病因并促进疾病预防和健康管理极具实际意义。
传统的流行病学研究表明，高血压的发病诱因极为复杂，可能与遗传、肥胖、饮酒、心理和膳食等因素有关[4]。近年来，血清微量营养素失衡与高血压患病风险之间的关联备受关注。研究显示，血清铜、硒和铬浓度可能与高血压独立相关；锌和镁/锰比值升高也可能会影响血压水平[5]。还有研究表明，较高的血清铜、锌和铜/锌比水平会增加高血压的患病风险，而锌、铜水平本身与高血压无关[6]。然而，传统流行病学研究易受到反向因果和混杂因素的干扰[7]，研究结果的可靠性有待验证。
孟德尔随机化（Mendelian randomization, MR）设计采用遗传变异作为暴露的工具变量，可显著提高疾病因果推断的可信度[8]。鉴于遗传变异在受孕时遵循随机分配的原则，且等位基因在出生时即确定，不会随疾病的发生或进展而改变，故MR可减少混杂因素干扰和反向因果的可能性，使因果推断更具说服力[9]。
本研究旨在利用14种血清微量营养素的单核苷酸多态性（single nucleotide polymorphism, SNP）作为工具变量（instrumental variables, IVs），深入探讨血清微量营养素与高血压之间的因果关联，以为高血压的预防和健康管理提供参考。
1　资料与方法
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1.1　研究设计
本研究是一项基于全基因组关联研究（Genome-Wide Association Studies，GWAS）汇总数据库的两样本MR研究，旨在探究14种血清微量营养素（包括铜、钙、胡萝卜素、叶酸、铁、镁、钾、锌、维生素A、维生素B12、维生素B6、维生素C、维生素D、维生素E）与高血压之间的关系。MR分析要求满足三个前提假设。
假设1：IVs与感兴趣的暴露密切相关。本研究在全基因组水平上选择与暴露显著相关的SNPs，阈值为：P< 5×10-6，以尽可能地获得更多的SNPs。此外，在10 000 Kb的窗口内，工具变量间的连锁不平衡的测量值设置为r2<0.001。最后，统计值应满足F>10以确保IVs与暴露具有强相关。F值的计算公式为：，其中N代表暴露的样本量，K为纳入SNP的数量，R2表示由SNP解释的暴露变异的比例。R2的计算公式为：,其中MAF表示次要等位基因频率，β表示效应等位基因，s表示β值的标准误差。
假设2：IVs不应与潜在影响暴露和结局的因素相关联。
假设3：工具变量应只通过暴露而不是通过其他替代途径影响结局的风险。如果IVs通过其他途径对结局产生影响，则显示相关遗传变异具有水平多效性。
在满足上述三个前提假设的情况下，本研究进一步采用敏感性分析以验证研究结果的可靠性，包括Cochran Q检验、漏斗图、留一法和孟德尔随机化-多效性残差和异常值（Mendelian randomization pleiotropy residual sum and outlier, MR-PRESSO）等，见图1。
假设1：工具变量应与感兴趣的暴露密切相关；假设2：工具变量不应该与潜在的混杂因素相关联；假设3：工具变量应只通过暴露而不是通过其他替代途径影响结果的风险。SNP：单核苷酸多态性；IVW：逆方差加权；MR-PRESSO：孟德尔随机化-多效性残差和异常值。
1.2　数据来源
MR数据源自公开可获取的GWAS数据库。14种血清微量营养素的GWAS数据来自IEU Open Gwas Project（https://gwas.mrcieu.ac.uk/）。该数据存储库包含来自50 044个GWAS汇总数据集的346 553 965 049个遗传关联数据。结局数据来源于FINNGEN联盟（https://www.finngen.fi/en/access_results）。该联盟是一个大规模的基因组学计划，已经分析了超过50万个芬兰生物库样本，旨在将遗传差异与健康数据联系起来，以阐明疾病机制和易感性。该合作计划涉及芬兰的研究机构、生物银行和国际行业合作伙伴[10]。有关相关汇总数据的信息，见表1。
1.3　MR分析
本研究采用了多种MR分析方法，其中以基于随机效应模型的逆方差加权法（inverse variance weighted, IVW）为主要分析方法，并辅以MR-Egger法、加权中位数法（weighted median）、加权众数法（weighted mode）。
IVW方法采用随机效应逆方差方法对SNP特异性Wald比率进行meta分析，考虑了测量中可能存在的异质性，并通过标准误差对每个比率进行加权[11]。相比之下，MR-Egger是一种可以解释SNP对结果影响的方法，即使截距也可用于测试方向多效性[12]。Weighted median通过根据其与结果的关联对其贡献进行加权，从而允许对具有更大影响的SNP进行评估。即使只有一半的SNPs符合所有三个MR假设，该方法仍然是稳健的[13]。Weighted mode是一种用于合并多个MR估计结果的方法。该方法将不同遗传变异对性状的因果效应进行加权，然后取加权后的众数作为最终的因果效应估计[14]。
1.4　敏感性分析
本研究的敏感性分析方法包括：Cochran Q检验、MR-Egger回归法、留一法和MR-PRESSO法。
Cochran Q检验是有效的检测异质性的方法。采用MR-Egger回归方法以检测水平多效性。MR-Egger法作为评估水平多效性是否存在的依据，以截距为参考项。当截距接近零时，所得结果与IVW法相似；若截距与零相距较远，则说明SNPs间可能存在较大的水平多效性。采用留一法逐个剔除单个SNP并计算其他SNPs的合并效应，以判断单个SNP是否对结局产生不成比例的影响。MR-PRESSO方法能检测并校正可能存在的异常值，通过将SNP结果估计数与SNP暴露估计数进行回归，以检验离群的SNPs，并输出修正后的MR估计数[15]。
1.5　统计分析
本本研究采用R软件(4.2.0版)进行分析。在单变量MR分析的基础上，本研究将满足以下三个条件的变量纳入多变量MR分析：在IVW方法中，P<0.1；四种MR分析方法的效应值为同向；不存在水平多效性。研究使用“TwoSampleMR”“MRPRESSO”“MendelianRandomization”等R包进行分析。血清微量营养素风险对血压的影响以效应估计值（odds ratio, OR）和95%置信区间（confidence interval, CI）表示。检验水准α=0.05。
2　结果
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2.1　工具变量筛选
在筛选工具变量时，我们以P <5×10-6，并在10 000 Kb范围内进行聚类，连锁不平衡阈值设定为r2<0.001为标准，最终筛选得到的SNPs数量分别为：钙20个、胡萝卜素16个、铜6个、叶酸15个、铁14个、镁19个、钾16个、维生素A 13个、维生素B12 12个、维生素B6 18个、维生素C 11个、维生素D 14个、维生素E 12个、锌8个。
其中，符合F值>10的各微量营养素工具变量数量为：钙16个、胡萝卜素15个、铜6个、叶酸12个、铁10个、镁17个、钾13个、维生素A 11个、维生素B12 8个、维生素B6 17个、维生素C 10个、维生素D 13个、维生素E 12个、锌8个，表明这些工具变量具有良好的特征。
2.2　单变量MR分析
逆方差加权MR分析方法显示，较高的铜水平与高血压存在正向因果效应(P=0.004, OR=1.023)，而钾水平升高(P=0.085, OR=0.888)和维生素B水平升高(P=0.099, OR=0.897)与高血压存在负向因果效应，但这一关联并不显著。其他血清微量营养素的MR分析结果见图2。敏感性分析结果显示，除钙元素外，其他血清微量营养素的工具变量不存在离群值（P>0.05），也未发现水平多效性、部分血清微量营养素（锌、叶酸、钙）存在异质性，见表2。本研究在去除钙元素的两个工具变量存在两个离群值（rs117456360、rs8109178）后进行MR分析。留一法和漏斗图法也证实了结果的稳健性。综合分析提示，较高的铜水平可能是导致高血压患病风险升高的一个潜在风险因素。
2.3　多变量MR分析
本研究将在单因素IVW分析中满足P<0.1且不存在水平多效性的变量纳入多因素IVW-MR分析，包括铜、钾、维生素B6，见图3。IVW-MR分析显示，铜水平与高血压之间存在显著正向因果效应（OR=1.028, P=0.004）。钾水平升高与高血压患病风险存在正向因果效应（OR=1.114, P=0.781），而维生素B6水平升高则呈现负向效应（P=0.309, OR=0.673），但均不显著，见图4。MR-Egger回归方法显示多变量MR分析结果不存在水平多效性（截距=0.001，P=0.703），且Cochran Q检验结果表明不存在异质性（Q=10.766, P=0.292）。
3　讨论
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本研究运用大规模GWAS数据，通过双样本单变量和多变量MR，运用4种MR分析方法探讨了14种血清微量营养素与高血压的因果关系。研究结果表明，血清铜水平与高血压患病风险之间存在显著正向因果效应，即铜水平升高可能是高血压发生发展的风险因素（OR=1.028, P=0.004）。而血清钾水平升高与高血压存在正向关联（OR=1.114, P=0.781），维生素B6水平升高则呈现负向关联（OR=0.673, P=0.309），但二者效应均不显著。
铜是人体必需的血清微量营养素之一，参与体内多种酶的组成和活化，在造血、能量代谢等过程中起着重要作用。铜还具有抗氧化性，尤其在与低硒抗氧化剂的协同作用下，可以增强动脉粥样硬化的致病作用，从而引起血压升高[16]。同时，铜可能刺激活性氧的生成，引起血管内皮依赖性舒张功能受损，继而增加高血压的发生风险[17]。此外，在同型半胱氨酸存在的情况下，铜与其形成铜-同型半胱氨酸络合物，可加重血管功能障碍，进而影响心血管系统的正常功能[18]。
铜与血压水平的升高有关。Zhang等人[5]的一项涉及6 754人的横断面研究发现血清铜处于最高四分位数组的人群患高血压的风险显著高于最低四分位数组。Chen等人[19]在一项横断面研究中观察到，血浆铜水平升高与儿童高血压风险呈正相关，与本研究结果一致。除血清铜外，尿液中的铜水平也引起了研究人员的关注。Wu等人[20]采用病例对照研究发现，相较于低暴露水平人群，高尿铜暴露水平人群患高血压的风险增加了将近4倍。Zhong等人[21]以尿铜作为暴露评估指标，研究发现铜与新发高血压的显著关联。
钠和钾作为重要的矿物质元素，可通过控制血容量而维持人体的细胞外和细胞内渗透平衡来调节血压。研究表明，高钠和低钾饮食可诱导原发性高血压[22]，在高血压人群中，低钠和高钾摄入量均可降低人群的血压[23]。然而，本研究显示钾水平升高会增加高血压的患病风险，虽然这一关联并不显著（P=0.781, OR= 1.114），但其临床效应与以往研究相违背。未来应采取多中心的研究来进一步验证这一发现。
维生素B6为人类健康所必需，是参与多种代谢和调节过程所必需的微量营养素。大多数维生素B不能由身体合成，其摄取主要依赖于膳食来源[24]。研究表明，各种形式的维生素B6都具有抗氧化特性，摄入含量较高的维生素B6可降低高血压的患病风险[25]。无独有偶，另一研究显示，适度剂量的维生素B6可降低与遗传和非遗传高血压模型相关的高血压[26]。本研究结果虽然显示维生素B6水平与高血压患病风险之间不存在显著因果关联，（P=0.309, OR=0.673），但这一结果符合临床实际。
相较于传统流行病学研究，本研究的优势在于MR方法类似于随机对照研究，通过在配子形成过程中随机分配等位基因给后代配子，因此所得结果具有较高的证据级别；MR方法不受伦理限制，同时能够节约研究成本和时间；研究加深了对血清微量营养素与高血压患病风险潜在因果关联的理解，为未来的研究提供了基础。
然而，本研究也存在一些局限性。研究虽涉及大规模的欧洲人群，在一定程度上可减少人口分层引起的偏差，但无法证明研究结果在其他种族人群中的适用性；研究采用汇总统计量数据进行MR分析，未能深入探讨不同年龄、性别人群之间血清微量营养素与高血压之间的因果效应；在单变量MR分析中，研究发现锌、叶酸、钙元素存在异质性，可能是由于不同人群特征导致的；虽然MR可以帮助减少一些偏差，但无法完全解释未测量的混杂因素或反向因果关系。因此，在解释研究结果时应谨慎。
总之，本研究采用两样本的单变量和多变量MR分析表明，较高的血清铜水平与高血压患病之间存在显著正向因果效应。然而，本研究所得出的结论仍需随机对照研究来验证。
基金
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  • 山西省青年科学研究项目(202203021212078)
文献
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2024年第51卷第21期
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doi: 10.20043/j.cnki.MPM.202405267
  • 接收时间：2024-05-18
  • 首发时间：2026-03-20
  • 出版时间：2024-11-10
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  • 收稿日期：2024-05-18
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山西省青年科学研究项目(202203021212078)
作者信息
    1.山西省人民医院肾内科，山西 太原030012
    2.山西医科大学第五临床医学院（山西省人民医院）肾脏病山西省重点实验室，山西 太原030012
    3.浙江大学公共卫生学院，浙江 杭州310058
    4.中山市中医院老年病科
    5.同济大学附属上海市第四人民医院

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李娅亨，E-mail:
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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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