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Article(id=1322590005978055086, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202406005, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1717171200000, receivedDateStr=2024-06-01, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793259629959, onlineDateStr=2026-10-29, pubDate=1736438400000, pubDateStr=2025-01-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793259629959, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793259629959, creator=13701087609, updateTime=1793259629959, updator=13701087609, issue=Issue{id=1322589954702688372, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='1', pageStart='1', pageEnd='192', issueExtLink='null', onlineDate='null', pubDate='1736438400000', pubDateStr='2025-01-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793259617734, creator='13701087609', updateTime=1793260023378, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322591656147276425, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322591656147276426, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1, endPage=7, ext={EN=ArticleExt(id=1322590006233907631, articleId=1322590005978055086, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Exploring the causal relationship between four lipid parameters and uterine leiomyoma based on two-sample Mendelian randomization, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the causal relationship between four lipid parameters—triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C)—and uterine leiomyoma (UL) using a two-sample Mendelian randomization (MR) analysis approach.

Methods

Genome-wide association study (GWAS) data for UL and the four lipid parameters were obtained from the IEU open GWAS database. Single nucleotide polymorphisms (SNPs) strongly associated with the lipid parameters were selected as instrumental variables. Two-sample MR analyses were conducted using methods such as inverse variance weighted (IVW) analysis. Subsequently, reverse MR analysis was performed to assess whether there was a reverse causal relationship between the lipid parameters and UL.

Results

The IVW method indicated a negative correlation between TG and the risk of UL (OR=0.996, 95%CI: 0.994-0.998, P<0.001) and a positive correlation between HDL-C and the risk of UL (OR=1.002, 95%CI: 1.000-1.003, P=0.012). No correlation was found between TC or LDL-C and the incidence of UL (P>0.05). Reverse MR analysis did not reveal any reverse causal relationship between the lipid parameters and UL (P>0.05). No horizontal pleiotropy was detected in the above studies (P>0.05).

Conclusion

The level of TG in the body is negatively correlated with the incidence of UL, while the level of HDL-C is positively correlated with the incidence of UL. There is no reverse causal relationship between the four lipid parameters and UL.

, authors=Zhuo-ting CHEN1, Ling ZHU2, authorsList=Zhuo-ting CHEN, Ling ZHU, authorCompany=null, correspAuthors=Ling ZHU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322590009270583735, articleId=1322590005978055086, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=基于两样本孟德尔随机化探索血脂四项与子宫平滑肌瘤的因果关系, columnId=1228016567632462653, journalTitle=现代预防医学, columnName=流行病与统计方法, runingTitle=null, highlight=null, articleAbstract=
目的

采用两样本孟德尔随机化(Mendelian randomization, MR)分析的方法研究血脂四项［甘油三酯（triglyceride，TG）、总胆固醇（total cholesterol，TC）、高密度脂蛋白胆固醇（high-density lipoprotein cholesterol，HDL-C）和低密度脂蛋白胆固醇（low-density lipoprotein cholesterol，LDL-C）］与子宫平滑肌瘤(uterine leiomyoma，UL）的因果关系。

方法

从IEU open GWAS数据库获取UL及血脂四项的全基因组关联研究（GWAS）数据，筛选出与血脂四项强相关的单核苷酸多态性作为工具变量，使用逆方差加权法（inverse variance weighted，IVW）等进行两样本MR分析。然后对血脂四项与UL进行反向MR分析以评估两者是否存在反向因果关系。

结果

IVW法显示TG与UL发病风险呈负相关（OR=0.996，95%CI：0.994～0.998，P<0.001），HDL-C与UL发病风险呈正相关（OR=1.002，95%CI：1.000～1.003，P=0.012），TC、LDL-C与UL发病无相关性（P>0.05），反向MR分析未见血脂四项与UL存在反向因果关系（P>0.05）。以上的研究均未发现水平多效性（P>0.05）。

结论

人体内TG的水平与UL的发病呈负相关，HDL-C的水平与UL的发病呈正相关，血脂四项与UL不存在反向因果关系。

, authors=陈卓婷1, 朱玲2, authorsList=陈卓婷, 朱玲, authorCompany=null, correspAuthors=朱玲, authorNote=

陈卓婷（1998—），女，硕士在读，研究方向：中西医结合防治妇科疾病

, correspAuthorsNote=
朱玲，E-mail:
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陈卓婷（1998—），女，硕士在读，研究方向：中西医结合防治妇科疾病

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陈卓婷（1998—），女，硕士在读，研究方向：中西医结合防治妇科疾病

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European Heart Journal, 2023, 44(16): 1394-1407., articleTitle=High- density lipoprotein revisited: biological functions and clinical relevance, refAbstract=null), Reference(id=1322590018904900079, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590005978055086, doi=null, pmid=null, pmcid=null, year=2012, volume=11, issue=2, pageStart=5, pageEnd=11, url=null, language=null, rfNumber=[17], rfOrder=16, authorNames=Sersam LW, Shaymaa A, journalName=The Iraqi Postgraduate Medical Journal, refType=null, unstructuredReference=Sersam LW, Shaymaa A. Study of lipid profile in patients with uterine fibroid[J]. The Iraqi Postgraduate Medical Journal, 2012, 11(2): 5-11., articleTitle=Study of lipid profile in patients with uterine fibroid, refAbstract=null), Reference(id=1322590019211084272, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590005978055086, doi=null, pmid=null, pmcid=null, year=2023, volume=15, issue=3, pageStart=715, pageEnd=null, url=null, language=null, rfNumber=[18], rfOrder=17, authorNames=Afrin S, Ramaiyer M, Begum UAM, journalName=Nutrients, refType=null, unstructuredReference=Afrin S, Ramaiyer M, Begum UAM, et al. Adipocyte and adipokines promote a uterine leiomyoma friendly microenvironment[J]. Nutrients, 2023, 15(3): 715., articleTitle=Adipocyte and adipokines promote a uterine leiomyoma friendly microenvironment, refAbstract=null), Reference(id=1322590019320136177, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590005978055086, doi=null, pmid=null, pmcid=null, year=2024, volume=309, issue=5, pageStart=1825, pageEnd=1831, url=null, language=null, rfNumber=[19], rfOrder=18, authorNames=Kiesler ZG, Hunter MI, Balboula AZ, journalName=Archives of Gynecology and Obstetrics, refType=null, unstructuredReference=Kiesler ZG, Hunter MI, Balboula AZ, et al. Periostin's role in uterine leiomyoma development: a mini-review on the potential periostin poses as a pharmacological intervention for uterine leiomyoma[J]. Archives of Gynecology and Obstetrics, 2024, 309(5):1825-1831., articleTitle=Periostin's role in uterine leiomyoma development: a mini-review on the potential periostin poses as a pharmacological intervention for uterine leiomyoma, refAbstract=null), Reference(id=1322590019395633650, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590005978055086, doi=null, pmid=null, pmcid=null, year=2022, volume=56, issue=2, pageStart=163, pageEnd=172, url=null, language=null, rfNumber=[20], rfOrder=19, authorNames=Miyashita-Ishiwata M, El Sabeh M, Reschke LD, journalName=Free Radical Research, refType=null, unstructuredReference=Miyashita-Ishiwata M, El Sabeh M, Reschke LD, et al. Hypoxia induces proliferation via NOX4-Mediated oxidative stress and TGF-β3 signaling in uterine leiomyoma cells[J]. Free Radical Research, 2022, 56(2): 163-172., articleTitle=Hypoxia induces proliferation via NOX4-Mediated oxidative stress and TGF-β3 signaling in uterine leiomyoma cells, refAbstract=null), Reference(id=1322590019458548211, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590005978055086, doi=null, pmid=null, pmcid=null, year=2023, volume=122, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[21], rfOrder=20, authorNames=Shukla R, Arshee MR, Laws MJ, journalName=Reproductive Toxicology, refType=null, unstructuredReference=Shukla R, Arshee MR, Laws MJ, et al. Chronic exposure of mice to phthalates enhances TGF beta signaling and promotes uterine fibrosis[J]. 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注：结局因素为UL；图A暴露变量为TG；图B暴露变量为TC；图C暴露变量为HDL-C；图D暴露变量为LDL-C。

, figureFileSmall=jvIAlRxhJJbkZzrrbf/Qug==, figureFileBig=Ndwv5F99LyTY4dpiTHlM4g==, tableContent=null), ArticleFig(id=1322590012735078865, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590005978055086, language=EN, label=Figure 2, caption=Funnel diagram of MR analysis, figureFileSmall=ARGYaPVlJ6DYs/MKluv12Q==, figureFileBig=/depfnJayaI3bLMnHl5aSQ==, tableContent=null), ArticleFig(id=1322590012818964946, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590005978055086, language=CN, label=图2, caption=MR分析漏斗图

注：结局因素为UL；图A暴露变量为TG；图B暴露变量为TC；图C暴露变量为HDL-C；图D暴露变量为LDL-C。

, figureFileSmall=ARGYaPVlJ6DYs/MKluv12Q==, figureFileBig=/depfnJayaI3bLMnHl5aSQ==, tableContent=null), ArticleFig(id=1322590012915433939, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590005978055086, language=EN, label=Figure 3, caption=Leave-one-out sensitivity analysis results, figureFileSmall=3AC6oFjLEYpX30hE4bWQnw==, figureFileBig=EU+s5s4IBva4B71dQC/saw==, tableContent=null), ArticleFig(id=1322590013007708628, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590005978055086, language=CN, label=图3, caption=留一法敏感性分析

注：结局因素为UL；图A暴露变量为TG；图B暴露变量为TC；图C暴露变量为LDL-C；图D暴露变量为HDL-C；图B、C、D因数据量大，仅截取部分展示。

, figureFileSmall=3AC6oFjLEYpX30hE4bWQnw==, figureFileBig=EU+s5s4IBva4B71dQC/saw==, tableContent=null), ArticleFig(id=1322590013074817493, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590005978055086, language=EN, label=Table 1, caption=

Information on GWAS data for MR analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
变量GWAS ID年份（年）人群样本量（例）SNPs数量（个）
ULukb-a-5222017欧洲人337 19910 894 596
TGieu-b-48502022欧洲人78 7007 892 037
TCmet-d-Total_C2020欧洲人115 07812 321 875
HDL-Cieu-b-1092020欧洲人403 94312 321 875
LDL-Cieu-b-1102020欧洲人440 54612 321 875
), ArticleFig(id=1322590013196452310, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590005978055086, language=CN, label=表1, caption=

MR分析的GWAS数据相关信息

, figureFileSmall=null, figureFileBig=null, tableContent=
变量GWAS ID年份（年）人群样本量（例）SNPs数量（个）
ULukb-a-5222017欧洲人337 19910 894 596
TGieu-b-48502022欧洲人78 7007 892 037
TCmet-d-Total_C2020欧洲人115 07812 321 875
HDL-Cieu-b-1092020欧洲人403 94312 321 875
LDL-Cieu-b-1102020欧洲人440 54612 321 875
), ArticleFig(id=1322590013322281431, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590005978055086, language=EN, label=Table 2, caption=

IVW results of MR analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露因素SNPs数目βsP值OR值（95%CI）
TG35-0.0040.001<0.0010.996（0.994～0.998）
TC55<0.0010.0010.7911.000（0.999～1.002）
HDL-C3100.0020.0010.0121.002（1.000～1.003）
LDL-C157-0.0010.0010.1510.999（0.997～1.000）
), ArticleFig(id=1322590013422944728, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590005978055086, language=CN, label=表2, caption=

MR分析的IVW法结果

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露因素SNPs数目βsP值OR值（95%CI）
TG35-0.0040.001<0.0010.996（0.994～0.998）
TC55<0.0010.0010.7911.000（0.999～1.002）
HDL-C3100.0020.0010.0121.002（1.000～1.003）
LDL-C157-0.0010.0010.1510.999（0.997～1.000）
), ArticleFig(id=1322590013506830809, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590005978055086, language=EN, label=Table 3, caption=

Heterogeneity analysis and horizontal pleiotropy analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露因素Cochran Q检验MR-Egge截距法
检验统计量P值截距P值
TG43.0960.1365.60×10-60.964
TC57.0850.361-1.20×10-40.125
HDL-C276.1300.9111.11×10-50.706
LDL-C184.0150.062-1.44×10-50.751
), ArticleFig(id=1322590013582328282, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590005978055086, language=CN, label=表3, caption=

异质性分析与水平多效性分析

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露因素Cochran Q检验MR-Egge截距法
检验统计量P值截距P值
TG43.0960.1365.60×10-60.964
TC57.0850.361-1.20×10-40.125
HDL-C276.1300.9111.11×10-50.706
LDL-C184.0150.062-1.44×10-50.751
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基于两样本孟德尔随机化探索血脂四项与子宫平滑肌瘤的因果关系
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陈卓婷 1 , 朱玲 2
现代预防医学 | 流行病与统计方法 2025,52(1): 1-7
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现代预防医学 |流行病与统计方法 2025 , 52 (1) : 1 -7
基于两样本孟德尔随机化探索血脂四项与子宫平滑肌瘤的因果关系
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陈卓婷1, 朱玲2
作者信息
  • 1.广州中医药大学，广东 广州 510405
  • 2.广州中医药大学第一附属医院妇儿中心一妇科，广东 广州 510405
通讯作者:
朱玲，E-mail:
作者简介:

陈卓婷（1998—），女，硕士在读，研究方向：中西医结合防治妇科疾病

Exploring the causal relationship between four lipid parameters and uterine leiomyoma based on two-sample Mendelian randomization
Zhuo-ting CHEN1, Ling ZHU2
Affiliations
  • Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510405, China
出版时间: 2025-01-10 doi: 10.20043/j.cnki.MPM.202406005
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摘要
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目的

采用两样本孟德尔随机化(Mendelian randomization, MR)分析的方法研究血脂四项［甘油三酯（triglyceride，TG）、总胆固醇（total cholesterol，TC）、高密度脂蛋白胆固醇（high-density lipoprotein cholesterol，HDL-C）和低密度脂蛋白胆固醇（low-density lipoprotein cholesterol，LDL-C）］与子宫平滑肌瘤(uterine leiomyoma，UL）的因果关系。

方法

从IEU open GWAS数据库获取UL及血脂四项的全基因组关联研究（GWAS）数据，筛选出与血脂四项强相关的单核苷酸多态性作为工具变量，使用逆方差加权法（inverse variance weighted，IVW）等进行两样本MR分析。然后对血脂四项与UL进行反向MR分析以评估两者是否存在反向因果关系。

结果

IVW法显示TG与UL发病风险呈负相关（OR=0.996，95%CI：0.994～0.998，P<0.001），HDL-C与UL发病风险呈正相关（OR=1.002，95%CI：1.000～1.003，P=0.012），TC、LDL-C与UL发病无相关性（P>0.05），反向MR分析未见血脂四项与UL存在反向因果关系（P>0.05）。以上的研究均未发现水平多效性（P>0.05）。

结论

人体内TG的水平与UL的发病呈负相关，HDL-C的水平与UL的发病呈正相关，血脂四项与UL不存在反向因果关系。

孟德尔随机化  /  子宫平滑肌瘤  /  甘油三酯  /  高密度脂蛋白胆固醇
Objective

To investigate the causal relationship between four lipid parameters—triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C)—and uterine leiomyoma (UL) using a two-sample Mendelian randomization (MR) analysis approach.

Methods

Genome-wide association study (GWAS) data for UL and the four lipid parameters were obtained from the IEU open GWAS database. Single nucleotide polymorphisms (SNPs) strongly associated with the lipid parameters were selected as instrumental variables. Two-sample MR analyses were conducted using methods such as inverse variance weighted (IVW) analysis. Subsequently, reverse MR analysis was performed to assess whether there was a reverse causal relationship between the lipid parameters and UL.

Results

The IVW method indicated a negative correlation between TG and the risk of UL (OR=0.996, 95%CI: 0.994-0.998, P<0.001) and a positive correlation between HDL-C and the risk of UL (OR=1.002, 95%CI: 1.000-1.003, P=0.012). No correlation was found between TC or LDL-C and the incidence of UL (P>0.05). Reverse MR analysis did not reveal any reverse causal relationship between the lipid parameters and UL (P>0.05). No horizontal pleiotropy was detected in the above studies (P>0.05).

Conclusion

The level of TG in the body is negatively correlated with the incidence of UL, while the level of HDL-C is positively correlated with the incidence of UL. There is no reverse causal relationship between the four lipid parameters and UL.

Mendelian randomization  /  Uterine leiomyoma  /  Triglycerides  /  High-density lipoprotein cholesterol
陈卓婷, 朱玲. 基于两样本孟德尔随机化探索血脂四项与子宫平滑肌瘤的因果关系. 现代预防医学, 2025 , 52 (1) : 1 -7 . DOI: 10.20043/j.cnki.MPM.202406005
Zhuo-ting CHEN, Ling ZHU. Exploring the causal relationship between four lipid parameters and uterine leiomyoma based on two-sample Mendelian randomization[J]. Modern Preventive Medicine, 2025 , 52 (1) : 1 -7 . DOI: 10.20043/j.cnki.MPM.202406005
正文
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子宫平滑肌瘤（uterine leiomyoma，UL）是最常见的妇科良性肿瘤，育龄期女性的发病率达20%～40%[1]。UL不仅会引起月经过多、经期延长、性交不适、尿频、便秘等症状，还会影响育龄期女性的生育，引发不孕、自然流产等生殖问题，远期还可能导致子宫内膜癌等[1-2]。因UL而行子宫切除术占临床上所有子宫切除术的40%～60%，并同时占18～44岁年轻女性子宫切除术的30%[3]。保留子宫的UL剔除术，术后存在复发的可能[1]。由此可见，UL严重影响了女性的身心健康，因此，积极探索UL的危险因素并针对其进行预防，有望减少UL的发生，减轻个人和社会的负担。
血脂四项包括甘油三酯（triglyceride，TG）、总胆固醇（total cholesterol，TC）、高密度脂蛋白胆固醇（high-density lipoprotein cholesterol，HDL-C）以及低密度脂蛋白胆固醇（low-density lipoprotein cholesterol，LDL-C）。有不少观察性研究发现血脂水平与UL的发病存在相关性。一项采用病例对照的研究发现，UL患者的TC水平比对照组高，且随着LDL-C的升高，UL的发病率也随之升高[4-5]。一项使用高效液相色谱质谱联用法的研究发现，初诊UL、复发性UL和对照组相比，参与TG代谢的脂质、血浆胆固醇酯、血浆TG等物质的水平均存在显著差异[6]。也有病例对照研究发现，HDL-C能促进UL的发病[7]。然而，观察性研究容易受环境等混杂因素及反向因果关系的干扰，进行因果推断的证据等级较差。随机对照试验是流行病学研究中进行因果推断较为准确的方法，但由于研究设计复杂、实施过程困难等原因，难以直接用于病因分析。
孟德尔随机化（Mendelian randomization，MR）分析是利用与暴露因素强相关的单核苷酸多态性（single nucleotide polymorphisms，SNPs）作为工具变量（instrumental variables，IVs），来评价暴露与结局之间因果关系的分析方法[8]。配子的形成遵循孟德尔随机分配定律，等位基因随机分配，与随机对照试验类似，故不会受到后天混杂因素的干扰，还能最大限度减少反向因果关系的存在。在现有的研究中，关于血脂四项与UL的MR分析研究较少，仅Park等人[9]把TG和2 600种疾病性状的表型进行MR分析，结果发现TG是UL的危险因素，但该研究未剔除弱IVs，且未充分考虑到回文SNP对MR结果的影响。此外，目前尚未有研究对血脂四项中的TC、HDL-C、LDL-C与UL的因果关系进行分析。因此，本研究运用两样本MR分析方法，以SNPs作为IVs，研究血脂四项与UL的因果关系，以期为UL的预防与治疗提供新依据。
1　资料与方法
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1.1　研究设计
本研究采用两样本MR分析方法来探讨血脂四项与UL的因果关系。利用血脂四项作为暴露因素，与血脂四项显著相关的SNPs作为IVs，UL为结局变量，剔除离群值后，运用两样本MR分析方法进行因果分析，并进行异质性检验和水平多效性分析等来验证因果关系的可靠性。其中，为了保证MR分析结果的准确性，本研究需要满足3个核心假设[8]：（1）关联性：SNPs与血脂四项强相关；（2）排他性：SNPs仅通过血脂四项对UL产生影响；（3）独立性：SNPs与任何混杂因素之间不存在相关性。即SNPs与混杂因素是相互独立的。
1.2　数据来源
本研究数据均来源于IEU open GWAS数据库（https://gwas.mrcieu.ac.uk/）。其中，UL数据集样本量为337 199人，含10 894 596个SNPs；TG数据集样本量为78 700人，含7 892 037个SNPs；TC数据集样本量为115 078人，含12 321 875个SNPs；HDL-C数据集样本量为403 943人，含12 321 875个SNPs；LDL-C数据集样本量为440 546人，含12 321 875个SNPs。见表1。本项目已获得广州中医药大学第一附属医院伦理委员会批准（批准号：N0.JY2024-133）。
1.3　IVs的筛选
为了确保本研究结论的全面性及准确性，采用以下质量控制步骤来选择最佳IVs。首先，设置相关参数P<5×10-8，以提取与血脂四项强相关的SNPs。其次，去除连锁不平衡的SNPs：通过设置遗传距离为10 000 kb，R2=0.001，来剔除遗传距离在10 000 kb范围内，且与显著SNPs的R2>0.001的SNPs，以保证IVs之间的相互独立性。另外，为了保证研究结果的可靠性，MR分析前需要剔除弱工具变量，它是指与暴露因素不具有强相关性但会影响MR分析结果的准确性的一类IVs[8]，具体使用F检验来评估弱工具变量，剔除F<10的SNPs。通过上述步骤，可以从血脂四项的数据集里提取出最佳SNPs，接着与UL数据集合并，同时剔除具有回文结构的SNP。最后，利用MR-PRESSO (MR pleiotropy residual sum and outlier, MR-PRESSO)进行离群值检验，评估离群值对暴露与结局之间的影响，并剔除离群值。
1.4　MR分析
采用R 4.3.2版本中的Two Sample MR包进行MR分析，用逆方差加权法（inverse variance weighted，IVW）作为评估因果效应的主要方法，采用加权中位数法（weighted median estimator，WME）、MR-Egger回归法（mendelian randomization egger regression，MR-Egger）、简单模式（simple mode）和加权模型（weighted mode）作为辅助方法。IVW法通过汇总基因数据，合并每个SNP的Wald估计值来获得总体因果效应值，若数据存在异质性，则采用随机效应模型，反之则采用固定效应模型。IVW分析中，设定检验水准α=0.05，P<0.05的结果被认为具有统计学显著性，则表明本研究中暴露因素与结局因素存在因果关系。OR值>1时，表明暴露因素与结局因素之间存在正相关关系，反之则存在负相关关系。
1.5　可靠性检验
此外，为了验证结果的可靠性，本研究将进行异质性分析、水平多效性分析以及敏感性分析。采用IVW法的Cochran Q检验来判断是否存在异质性，若Cochran Q检验中P>0.05，则认为数据不存在异质性。漏斗图也可用于检测数据的异质性，如果两边图形中的SNPs大致对称，那么可认为本研究不存在异质性。水平多效性分析采用MR-Egge截距法进行检测，若P>0.05，则认为数据不存在水平多效性。采用留一法进行敏感性分析，对SNPs逐一剔除，观察效应值是否发生较大的改变，以此来检验研究结果是否受单个SNP的影响较大，以推断结果的稳定性。
1.6　反向MR分析
为了使研究结果更可靠、全面，本研究利用相同的方法，对血脂四项与UL进行反向MR分析。将UL作为暴露因素，血脂四项作为结局因素，进行MR分析，以探究血脂四项与UL是否存在反向因果关系。
2　结果
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2.1　IVs的筛选结果
经过设置相关参数P<5×10-8、去除连锁不平衡以及弱工具变量后，从TG数据集、TC数据集、HDL-C数据集和LDL-C数据集各筛选出44、62、353和175个SNPs，经过与UL数据集合并整理及删除回文SNPs后，最终各得到35、55、310和157个SNPs，并且经MR-PRESSO检验均未发现离群值。其中，从TG数据集发现并删除了7个回文SNPs，分别为rs1471251、rs4711750、rs5112、 rs59347135、rs7008241、rs7783857、rs964184；从TC数据集发现并删除1个回文SNP（rs12027110）；从HDL-C数据集发现并删除了9个回文SNPs，分别为rs10119644、rs10901802、 rs1125873、 rs12044156、rs12588117、rs1281959、 rs133015、 rs2588859、rs407133；从LDL-C数据集发现并删除了3个回文SNPs，分别是rs28601761、rs4782568、rs990619。
2.2　MR分析
MR分析中IVW法显示，TG、HDL-C与UL发病风险存在因果关系，见表2。其中，TG与UL发病风险呈负相关（OR=0.996，95%CI：0.994～0.998，P<0.001），HDL-C与UL发病风险呈正相关（OR=1.002，95%CI：1.000～1.003，P=0.012）。且其余四种MR分析的方法与IVW法趋势相同，见图1。而TC、LDL-C与UL的MR分析中IVW法P值均>0.05，不具有统计学意义，表明TC、LDL-C两者与UL发病风险不存在因果关系。
2.3　可靠性检验
异质性分析中的Cochran Q检验、水平多效性分析结果见表3。由表3可知，血脂四项Cochran Q检验P值均>0.05，表示研究数据不存在异质性。而MR-Egger回归截距均<0.001，P值均>0.05，说明SNPs不存在水平多效性，MR分析结果可靠。另外，漏斗图结果显示纳入的SNPs具有对称性，也表明研究不存在异质性，见图2。敏感性分析见图3，留一法结果发现，依次剔除TG、HDL-C以及LDL-C数据中的SNPs，剩余SNPs的效应值均与总的MR分析值相近，且均在无效线一侧，说明MR分析结果稳健。而TC数据中有2个SNPs在无效线的另一侧，但MR-PRESSO分析中并未发现TC数据有离群值，故未予剔除这2个SNPs。
2.4　反向MR分析
将UL作为暴露因素，设置相关性参数P<5×10-8，遗传距离为10 000 kb，R2=0.001以及剔除弱工具变量后发现有效SNPs较少，故设置相关性参数P<5×10-5，余筛选条件不变，可得到38个SNPs，经MR分析后发现结果均无统计学意义，表明血脂四项与UL不存在反向因果关系。在异质性分析中，UL与HDL-C数据的结果存在潜在异质性（P=0.002）。当存在异质性时，IVW法会使用随机效应模型来分析。而水平多效性分析结果的P值均>0.05，表明数据均不存在水平多效性，综上可认为结果可靠。
3　讨论
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本研究利用GWAS数据库中UL与血脂四项的数据，使用MR分析后发现TG是UL发病的保护因素，与UL的发病成负相关；HDL-C是UL的危险因素，与UL的发病成正相关。TC及LDL-C与UL发病无相关性。
TG具有合成细胞膜、储存能量、保温以及激素样信号传导的作用，还参与炎症产生、血栓形成的过程[10-11]。TG降低UL发病风险的具体机制尚未明确。目前已知雌激素水平与UL的发病密切相关，可能与雌激素能促进生长因子、细胞因子和趋化因子等的合成相关[12]。有研究发现，雌激素浓度的下降与TG水平的升高有关[13]，但更多的研究关注雌激素水平的下降会导致TG的升高[14]。有研究发现绝经后女性的TG水平随着年龄的增长而升高，且比同年龄段的男性升高速度更快，这也说明TG水平与雌激素水平相关[15]。MR分析可以在一定程度上确定二者间的因果关系。本研究中我们认为TG可能是UL的保护因素，可能是由于体内TG水平降低，促进了女性患者的雌激素分泌，进而导致了UL的发生。但是目前的研究对脂类影响雌激素的关注较少，结果有待实验验证。已发表的另一项TG与UL的MR分析结果[9]与本研究不同，可能与未剔除弱工具变量及回文SNP有关，且研究选取的样本不同亦可能导致结果的差异。
HDL-C主要由肝脏合成，因其具有降低胆固醇、保护内皮细胞，从而降低动脉粥样硬化发病率的作用而引起广泛的关注。除此之外，HDL-C还参与了机体许多其他的新陈代谢的过程，包括炎症、氧化应激等[16]。一项病例对照研究发现，与无UL对照组对比，患有UL女性的血清HDL-C水平显著升高，且最大肌瘤体积与HDL-C水平呈显着正相关[17]，与本研究结果相似。在具体机制上，UL中PI3K/AKT通路蛋白和靶标的激活比正常子宫肌层更显著，表明了UL的发生与PI3K/AKT信号传导相关[18]。此外，有研究在培养UL和正常子宫肌层的细胞时，发现PI3K是雌激素依赖性细胞生长所必需的；而在体外实验中，AKT抑制剂可限制UL的生长以及促进细胞的死亡[19]。子宫肌层中存在三种不同的TGF-β亚型（TGF-β1、2、3）[19-20]，TGF-β信号的激活会导致平滑肌细胞增殖和细胞外基质沉积[21]，且用TGF-β处理UL细胞后可增加细胞中的纤维连接蛋白并促进细胞增殖[19]。因此，结合上述的研究结果，本研究认为HDL-C可能是通过激活PI3K/AKT通路来诱导TGF-β的表达，从而促进了UL的发病。
一项观察性研究发现，UL组TC及LDL-C水平比对照组高，表明了TC与LDL-C可能与UL发病相关[4-5]，这与本研究的结果不同，可能是因为观察性研究无法避免混杂因素干扰所致的。MR分析是从遗传学的角度评估暴露因素与结局因素之间的因果关系，本研究使用遗传变异作为IVs，通过研究血脂四项与UL之间的双向因果关系，可以更大程度的减少环境、生活方式等混杂因素的干扰。除此之外，本研究采用GWAS数据库中的人群数据，样本量大，并对IVs进行了严格的筛选，还进行了可靠性检验如异质性分析、水平多效性分析及敏感性分析等，能够确保结果的准确性；最后还进行了反向MR分析，证明血脂四项与UL不存在反向因果关系，避免了反向因果的干扰，使结果更具有可靠性。
除此之外，本研究尚存在一定的局限性。因本研究的数据来源是欧洲人群，虽减少了异质性，但结果无法推广到其他人群或种族；另外，本研究数据未对年龄、体重进行分层，无法推断血脂四项与UL的非线性关系。因此，未来需要进行更大的样本量以及更细分层的MR研究或随机对照试验，以获得更准确可靠的结果。
综上，本研究通过两样本MR分析的方法对血脂四项与UL进行了因果关系的探讨，认为TG是UL发病的保护因素，HDL-C是UL发病的危险因素，TC、LDL-C与UL发病无相关性。这可为今后对UL发病因素的进一步研究提供参考依据，有望为疾病的预防和临床治疗提供新思路。
基金
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  • 国家自然科学基金项目(82174418)
  • 广东省基础与应用基础研究基金项目（省企联合基金）(2022A1515220084)
  • 罗颂平全国名老中医药专家传承工作室(国中医药人教函〔2022〕75号)
  • 罗颂平全国名中医工作室(国中医药人教发〔2022〕5号)
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doi: 10.20043/j.cnki.MPM.202406005
  • 接收时间：2024-06-01
  • 首发时间：2026-10-29
  • 出版时间：2025-01-10
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  • 收稿日期：2024-06-01
基金
国家自然科学基金项目(82174418)
广东省基础与应用基础研究基金项目（省企联合基金）(2022A1515220084)
罗颂平全国名老中医药专家传承工作室(国中医药人教函〔2022〕75号)
罗颂平全国名中医工作室(国中医药人教发〔2022〕5号)
作者信息
    1.广州中医药大学，广东 广州 510405
    2.广州中医药大学第一附属医院妇儿中心一妇科，广东 广州 510405

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
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