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Article(id=1240375117461639647, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240375105386238038, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202309439, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1695398400000, receivedDateStr=2023-09-23, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773658073057, onlineDateStr=2026-03-16, pubDate=1711296000000, pubDateStr=2024-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773658073057, onlineIssueDateStr=2026-03-16, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773658073057, creator=13701087609, updateTime=1773658073057, updator=13701087609, issue=Issue{id=1240375105386238038, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='6', pageStart='961', pageEnd='1152', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773658070179, creator=13701087609, updateTime=1773658539618, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240377074414833974, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240375105386238038, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240377074414833975, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240375105386238038, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1135, endPage=1140, ext={EN=ArticleExt(id=1240375117897847309, articleId=1240375117461639647, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Causal relationship between type 2 diabetes mellitus and lung cancer subtypes: a Mendelian randomization study, columnId=1228016569138213037, journalTitle=Modern Preventive Medicine, columnName=Clinical Medicine and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the causal relationship between type 2 diabetes mellitus (T2DM) and the risk of lung cancer (LC) (including three subtypes: lung adenocarcinoma (LAC), lung squamous cell carcinoma (LSCC), and small cell lung carcinoma (SCLC)) using a two-sample Mendelian randomization approach (MR).

Methods

The analysis was performed using summary datasets of genome-wide association studies (GWAS) conducted in European ancestry. Using 380 genome-wide significant single nucleotide polymorphic loci (SNP) associated with type 2 diabetes mellitus as instrumental variables (IV), the primary analysis used inverse variance weighted (IVW) method, MR-Egger regression and weighted median estimator method to investigate the causal association between type 2 diabetes mellitus and lung cancer and its subtypes. Sensitivity analyses were performed including MR-Egger regression intercept test, removing SNPs associated with potential confounding traits, excluding palindromic SNPs, and “leave one out” method.

Results

IVW method showed that genetically predicted type 2 diabetes mellitus was not associated with the risk of lung cancer and its subtypes (LC OR: 1.003, 95% CI: 0.974-1.034; LAC OR: 0.981, 95% CI: 0.943-1.021; LSCC OR: 1.043, 95% CI: 0.995-1.093; SCLC OR: 0.988, 95% CI: 0.921-1.061, respectively). The results remained consistent by using MR-Egger method and weighted median estimator method. The robustness of results was collaborated by several sensitivity analyses. Conclusions This study dosen’t provide evidence supporting the causal relationship between type 2 diabetes mellitus and the risk of overall lung cancer and its subtypes.

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目的

采用两样本孟德尔随机化方法分析二型糖尿病与肺癌及其三种亚型（肺腺癌、肺鳞状细胞癌、小细胞癌）发生风险间的因果关联。

方法

收集欧洲人群大样本全基因组关联研究汇总数据，使用380个二型糖尿病高度相关的单核苷酸多态性位点作为工具变量，采用逆方差加权法、MR-Egger回归法和加权中位数法等方法作为主分析，估计二型糖尿病与肺癌及其亚型发生的因果关联。敏感性分析采用MR-Egger多效性检验、排除回文结构及多效性工具变量检验和“leave-one-out”法。

结果

逆方差加权法结果显示遗传预测的二型糖尿病与肺癌及其亚型发生风险无关（肺癌: OR=1.003, 95% CI=0.974~1.034; 肺腺癌: OR=0.981, 95% CI=0.943~1.021; 肺鳞状细胞癌: OR=1.043, 95% CI: 0.995~1.093；小细胞癌: OR=0.988，95% CI=0.921~1.061），MR-Egger法和加权中位数法与逆方差加权法的结果一致。多种敏感性分析结果表明主分析具有稳健性。

结论

尚未发现二型糖尿病与肺癌及其亚型发生之间存在因果关联。

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张本，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=gaX6bZpipLyzGT+bennSXQ==, magXml=Wom8QUuDtgwNhFtqYAEkyQ==, pdfUrl=null, pdf=r7qPnFvYnXhKZdEYGZmWRg==, pdfFileSize=1969788, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=YoXPf/Vc32hifXLyd0va/A==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=E1PkMEzXB9doZfSncR5H2g==, mapNumber=null, authorCompany=null, fund=null, authors=

朱经纬（1997—），男，硕士在读，研究方向：流行病与卫生统计学

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朱经纬（1997—），男，硕士在读，研究方向：流行病与卫生统计学

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Computational Brain & Behavior, 2019, 2(1): 1-11., articleTitle=Limitations ofBayesian leave-one-out cross-validation for model selection, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1240746300841448138, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375117461639647, xref=null, ext=[AuthorCompanyExt(id=1240746300845642443, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375117461639647, companyId=1240746300841448138, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China), AuthorCompanyExt(id=1240746300858225357, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375117461639647, companyId=1240746300841448138, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=四川大学华西公共卫生学院/华西第四医院，四川 成都 610041)])], figs=[ArticleFig(id=1240746303794238297, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375117461639647, language=EN, label=Fig.1, caption=Framework for the two-sample Mendelian randomization study design, figureFileSmall=lyVKtEG8hVpoMj+RGgd02Q==, figureFileBig=2QWkdV19N0WLR6ZveyMGAw==, tableContent=null), ArticleFig(id=1240746303907484508, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375117461639647, language=CN, label=图1, caption=两样本MR设计框架, figureFileSmall=lyVKtEG8hVpoMj+RGgd02Q==, figureFileBig=2QWkdV19N0WLR6ZveyMGAw==, tableContent=null), ArticleFig(id=1240746304054285156, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375117461639647, language=EN, label=Fig.2, caption=Forest plot for the casual relationship between genetically predicted T2DM and A) lung cancer; B) LAC; C) LSCC; D) SCLC, figureFileSmall=oPfKaR4j/oYrYJES8kUkhA==, figureFileBig=sE3FPybqafqcMClou8iqJQ==, tableContent=null), ArticleFig(id=1240746304159142765, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375117461639647, language=CN, label=图2, caption=A)遗传预测的T2DM与肺癌的因果关联森林图；B)遗传预测的T2DM与肺腺癌的因果关联森林图；C)遗传预测的T2DM与肺鳞状细胞癌的因果关联森林图；D)遗传预测的T2DM与小细胞肺癌的因果关联森林图, figureFileSmall=oPfKaR4j/oYrYJES8kUkhA==, figureFileBig=sE3FPybqafqcMClou8iqJQ==, tableContent=null), ArticleFig(id=1240746304247223153, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375117461639647, language=EN, label=Fig.3, caption=Leave-one-out analysis results between T2DM and A) lung cancer; B) LAC; C) LSCC; D) SCLC, figureFileSmall=mEopln3zlRq5lT5KADT/jQ==, figureFileBig=C13++wO5dkii4Oq0Q6AAiw==, tableContent=null), ArticleFig(id=1240746304356275060, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375117461639647, language=CN, label=图3, caption=leave-one-out法分析结果A)T2DM-肺癌 B)T2DM-肺腺癌C)T2DM-肺鳞状细胞癌 D)T2DM-肺小细胞癌, figureFileSmall=mEopln3zlRq5lT5KADT/jQ==, figureFileBig=C13++wO5dkii4Oq0Q6AAiw==, tableContent=null), ArticleFig(id=1240746304461132667, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375117461639647, language=EN, label=Table 1, caption=

GWAS summary data of T2DM and lung cancer subtypes

, figureFileSmall=null, figureFileBig=null, tableContent=
变量样本量（病例/对照）性别人种发表年份PMID
T2DM898 130(74 124/824 006)男女混合欧洲201830 297 969
LC374 687(2 671/372 016)男女混合欧洲2021-
LAC66 756(11 273/55 483)男女混合欧洲201728 604 730
LSCC63 053(7 426/55 627)男女混合欧洲201728 604 730
SClC24 108(2 664/21 444)男女混合欧洲201728 604 730
), ArticleFig(id=1240746304586961792, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375117461639647, language=CN, label=表1, caption=

二型糖尿病与肺癌及其亚型GWAS汇总数据简要信息

, figureFileSmall=null, figureFileBig=null, tableContent=
变量样本量（病例/对照）性别人种发表年份PMID
T2DM898 130(74 124/824 006)男女混合欧洲201830 297 969
LC374 687(2 671/372 016)男女混合欧洲2021-
LAC66 756(11 273/55 483)男女混合欧洲201728 604 730
LSCC63 053(7 426/55 627)男女混合欧洲201728 604 730
SClC24 108(2 664/21 444)男女混合欧洲201728 604 730
), ArticleFig(id=1240746304691819399, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375117461639647, language=EN, label=Table 2, caption=

Basic information of SNPs associated with type 2 diabetes mellitus

, figureFileSmall=null, figureFileBig=null, tableContent=
SNPCHRBPA1A2FREQβSEP
rs11272151117 532 790TC0.42-0.0470.006 42.30E-13
rs14936941120 526 982TC0.110.0840.010 02.10E-16
rs37720712161 135 544TC0.710.0480.007 01.60E-11
rs101952522165 513 091TC0.590.0600.006 41.60E-20
rs1155056145102 422 968TC0.050.1700.015 01.70E-29
… …
rs228014110124 193 181TG0.520.0470.006 32.00E-13
rs1770451071 321 279AG0.68-0.0500.006 91.00E-12
rs231361112 691 500AG0.260.0640.007 46.00E-18
rs2283220112 755 548AG0.690.0430.007 22.70E-09
rs60630482045 598 564AG0.28-0.0470.007 15.80E-11
), ArticleFig(id=1240746304817648524, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240375117461639647, language=CN, label=表2, caption=

二型糖尿病相关的SNPs基本信息

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SNPCHRBPA1A2FREQβSEP
rs11272151117 532 790TC0.42-0.0470.006 42.30E-13
rs14936941120 526 982TC0.110.0840.010 02.10E-16
rs37720712161 135 544TC0.710.0480.007 01.60E-11
rs101952522165 513 091TC0.590.0600.006 41.60E-20
rs1155056145102 422 968TC0.050.1700.015 01.70E-29
… …
rs228014110124 193 181TG0.520.0470.006 32.00E-13
rs1770451071 321 279AG0.68-0.0500.006 91.00E-12
rs231361112 691 500AG0.260.0640.007 46.00E-18
rs2283220112 755 548AG0.690.0430.007 22.70E-09
rs60630482045 598 564AG0.28-0.0470.007 15.80E-11
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二型糖尿病与肺癌及亚型的因果关联：孟德尔随机化研究
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朱经纬 , 肖昌凤 , 覃陈佳蕊 , 章文强 , 姜侠 , 张本
现代预防医学 | 临床与预防 2024,51(6): 1135-1140
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现代预防医学 | 临床与预防 2024, 51(6): 1135-1140
二型糖尿病与肺癌及亚型的因果关联：孟德尔随机化研究
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朱经纬, 肖昌凤, 覃陈佳蕊, 章文强, 姜侠, 张本
作者信息
  • 四川大学华西公共卫生学院/华西第四医院，四川 成都 610041

    • 朱经纬（1997—），男，硕士在读，研究方向：流行病与卫生统计学

通讯作者:

张本，E-mail：
Causal relationship between type 2 diabetes mellitus and lung cancer subtypes: a Mendelian randomization study
Jing-wei ZHU, Chang-feng XIAO, QIN CHEN Jia-rui, Wen-qiang ZHANG, Xia JIANG, Ben ZHANG
Affiliations
  • West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
出版时间: 2024-03-25 doi: 10.20043/j.cnki.MPM.202309439
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摘要
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目的

采用两样本孟德尔随机化方法分析二型糖尿病与肺癌及其三种亚型（肺腺癌、肺鳞状细胞癌、小细胞癌）发生风险间的因果关联。

方法

收集欧洲人群大样本全基因组关联研究汇总数据，使用380个二型糖尿病高度相关的单核苷酸多态性位点作为工具变量，采用逆方差加权法、MR-Egger回归法和加权中位数法等方法作为主分析，估计二型糖尿病与肺癌及其亚型发生的因果关联。敏感性分析采用MR-Egger多效性检验、排除回文结构及多效性工具变量检验和“leave-one-out”法。

结果

逆方差加权法结果显示遗传预测的二型糖尿病与肺癌及其亚型发生风险无关（肺癌: OR=1.003, 95% CI=0.974~1.034; 肺腺癌: OR=0.981, 95% CI=0.943~1.021; 肺鳞状细胞癌: OR=1.043, 95% CI: 0.995~1.093；小细胞癌: OR=0.988，95% CI=0.921~1.061），MR-Egger法和加权中位数法与逆方差加权法的结果一致。多种敏感性分析结果表明主分析具有稳健性。

结论

尚未发现二型糖尿病与肺癌及其亚型发生之间存在因果关联。

孟德尔随机化  /  因果关联  /  二型糖尿病  /  肺癌亚型
Objective

To explore the causal relationship between type 2 diabetes mellitus (T2DM) and the risk of lung cancer (LC) (including three subtypes: lung adenocarcinoma (LAC), lung squamous cell carcinoma (LSCC), and small cell lung carcinoma (SCLC)) using a two-sample Mendelian randomization approach (MR).

Methods

The analysis was performed using summary datasets of genome-wide association studies (GWAS) conducted in European ancestry. Using 380 genome-wide significant single nucleotide polymorphic loci (SNP) associated with type 2 diabetes mellitus as instrumental variables (IV), the primary analysis used inverse variance weighted (IVW) method, MR-Egger regression and weighted median estimator method to investigate the causal association between type 2 diabetes mellitus and lung cancer and its subtypes. Sensitivity analyses were performed including MR-Egger regression intercept test, removing SNPs associated with potential confounding traits, excluding palindromic SNPs, and “leave one out” method.

Results

IVW method showed that genetically predicted type 2 diabetes mellitus was not associated with the risk of lung cancer and its subtypes (LC OR: 1.003, 95% CI: 0.974-1.034; LAC OR: 0.981, 95% CI: 0.943-1.021; LSCC OR: 1.043, 95% CI: 0.995-1.093; SCLC OR: 0.988, 95% CI: 0.921-1.061, respectively). The results remained consistent by using MR-Egger method and weighted median estimator method. The robustness of results was collaborated by several sensitivity analyses. Conclusions This study dosen’t provide evidence supporting the causal relationship between type 2 diabetes mellitus and the risk of overall lung cancer and its subtypes.

Mendelian randomization  /  Causal relationship  /  Type 2 diabetes mellitus  /  Lung cancer subtypes
朱经纬, 肖昌凤, 覃陈佳蕊, 章文强, 姜侠, 张本. 二型糖尿病与肺癌及亚型的因果关联：孟德尔随机化研究. 现代预防医学, 2024 , 51 (6) : 1135 -1140 . DOI: 10.20043/j.cnki.MPM.202309439
Jing-wei ZHU, Chang-feng XIAO, QIN CHEN Jia-rui, Wen-qiang ZHANG, Xia JIANG, Ben ZHANG. Causal relationship between type 2 diabetes mellitus and lung cancer subtypes: a Mendelian randomization study[J]. Modern Preventive Medicine, 2024 , 51 (6) : 1135 -1140 . DOI: 10.20043/j.cnki.MPM.202309439
正文
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根据最新的全球癌症地图提供的数据显示，肺癌（LC）是危害全球各国人民健康最严重的恶性肿瘤之一（发病率第二、死亡率最高），2020年全球共有超过224万新发肺癌病例和180万人死于肺癌[1]。肺癌根据病理特征主要分为非小细胞肺癌（non-small cell lung carcinoma，NSCLC）和小细胞肺癌（small cell lung carcinoma，SCLC），非小细胞肺癌又分为肺腺癌（lung adenocarcinoma，LAC）、肺鳞状细胞癌（lung squamous cell carcinoma，LSCC）和大细胞肺癌（large cell lung carcinomas，LCLC）[2]。肺癌的发生受遗传和环境因素共同调控，其中遗传变异仅能解释18%[3]。尽管肺癌最常见的环境危险因素为吸烟，但仍有约25%的肺癌患者属于从未吸烟者[4]。因此，探索肺癌发病的危险因素对促进肺癌的预防具有重要意义。
二型糖尿病（T2DM）已逐步成为严重危害人类生命健康及全球社会经济发展的慢性疾病。国际糖尿病联合协会发布的第10th糖尿病地图显示，截至2021年，全球大约有5.366亿20~79岁的成年人罹患糖尿病（患病率为10.5%），而世界范围内糖尿病患者数量预计在2045年将增至7.832亿（患病率为12.2%）[5]。先前的研究显示，二型糖尿病与多种肿瘤发病之间存在因果关联，如胰腺癌[6-7]、子宫颈癌、肝癌、子宫癌[7]等。同时越来越多的观察性研究开始关注糖尿病与肺癌发病之间的关联，但结果并不一致[8-13]，可能是由于传统的流行病学研究无法完全克服混杂偏倚并且会受到反向因果的干扰。
孟德尔随机化（mendelian randomization，MR）利用与暴露因素强相关的单核苷酸多态性位点（SNP）作为工具变量（IV）评估暴露因素和结局之间的因果关联，是目前因果推断较为常用的方法。由于等位基因在子代配子形成时是随机分配的（即服从孟德尔第二定律），且后天的疾病过程不会改变基因分型[14]，因此MR可以很好地克服传统流行病学研究中不可避免的混杂偏倚和反向因果的干扰。迄今为止，仅有1篇关于二型糖尿病与肺癌的两样本MR研究（OR=1.02, 95% CI：=0.96~1.08），但未对肺癌进行分亚型分析[7]。因此本研究运用两样本MR方法，同时使用更大的分亚型的肺癌全基因组关联研究汇总数据来探索二型糖尿病与肺癌及其亚型之间是否存在因果关联。
1 资料与研究方法
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1.1 数据来源
本研究使用的GWAS数据均为可获得的公开数据结果。T2DM的数据来源于目前公开可获得的样本量最大的一项全基因组关联研究的汇总数据[15]。该研究整合了先前32项欧洲人群T2DM 的GWAS数据，包括了74 124例 T2DM患者和824 006例非T2DM对照。
肺癌的GWAS汇总数据来源于IEU OPEN GWAS公开数据库（https://gwas.mrcieu.ac.uk/datasets/ieu-b-4954）, 包括了2 671例肺癌患者和372 016例非肺癌对照。肺腺癌（病例数=11 273, 对照数=55 483）、肺鳞状细胞癌（对照数=7 426，对照数=55 627）、小细胞肺癌（对照数=2 664，对照数=21 444）的GWAS汇总数据均来源于一篇2017年发表于Nature Genetics文章中公开的GWAS数据[16]。
二型糖尿病、肺癌及其亚型的GWAS汇总数据均来自欧洲人群，简要信息见表1。
1.2 方法原理
本研究以T2DM作为暴露因素，选择与T2DM高度相关的SNPs作为IVs，肺癌及其亚型作为结局因素，采用两样本MR的方法探究暴露与结局之间的因果关联。使用MR方法进行因果推断需要满足以下三个基本假设[17]：（1）所选择的工具变量（G）与暴露因素（X）之间有较强的关联，即“相关性”假设；（2）所选择的工具变量（G）与暴露-结局（X-Y）关联间的混杂因素（U）不相关，即“独立性”假设；（3）工具变量（G）对结局变量（Y）产生的影响不能通过其他因素而只能通过暴露因素（X）来介导，即“排他性”假设，见图1。
1.3 工具变量的选择与检验
首先直接从GWAS原文提供的附表中选取所报告的386个SNPs作为T2DM初步的IVs，这些SNPs与T2DM显著相关，且这些SNPs间不存在连锁不平衡（linkage disequilibrium，LD）。
F统计量的大小可以用来评估所选择的IVs的强弱，F > 10说明所选择的IVs不属于弱工具变量，即不存在弱工具变量偏倚[18]。F统计量的计算公式为：
式(1)(2)中，N为T2DM的GWAS样本量；K为纳入的SNPs个数；R2为所选择的SNPs能解释T2DM的比例；MAF为最小等位基因频率；β为SNP对T2DM的遗传效应估计值；SE为遗传效应估计值的标准误差[19]。
1.4 主要分析
本研究采用逆方差加权法（IVW法）作为主要的分析方法[20]。IVW法是在所选择的SNPs都是有效IVs的前提下，以每个IV方差的倒数作为权重，对每个IV-肺癌（及其亚型）间的效应值（β2）与该IV-T2DM之间的效应值（β1）的比值（β2/β1）进行加权合并，以得到的所有IVs效应值的加权平均值来评估遗传预测的T2DM与肺癌及其亚型发生风险之间的关联。
此外，为了验证IVW法的稳健性，本研究同时采用了多种分析方法如MR-Egger法[21]和加权中位数法[22]，来综合评估T2DM与肺癌及其亚型之间的因果关联。IVW法在回归时不考虑截距项，而MR-Egger法在回归时考虑截距项。WME法只要有50%的有效IVs，即可得到较为一致的因果关联。
1.5 敏感性分析
根据MR-Egger回归的截距判断是否存在水平多效性，当P 值小于0.05时，提示水平多效性存在[21]；去除具有回文结构的IVs；根据GWAS catalog (https://www.ebi.ac.uk/gwas/)的提供的数据，去除与潜在混杂表型相关的多效性IVs；leave-one-out法[23]，逐个剔除IV后，再利用剩余所有的IVs进行IVW分析，以判断是否存在对结果影响较大的IVs。
所有统计分析均在R 4.2.1 软件中使用“TwoSampleMR”和“MRInstruments”等软件包进行，检验水准α=0.05。
2 结果
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2.1 工具变量
经过筛选，本研究最终共纳入380个SNPs作为IVs，部分SNPs的详细信息见表2。所有SNPs总的R2为9.80% ，单个SNP 对应的F统计量的值全都显著大于10，表明这380个SNPs均是与T2DM强相关的IVs，表明存在弱工具变量偏倚的可能性很小。
2.2 孟德尔随机化主分析结果
IVW分析结果显示，遗传预测的T2DM与肺癌及其亚型（LAC、LSCC、SCLC）发生之间不存在因果关联（OR=1.003, 95% CI=0.974~1.034；OR=0.981, 95% CI=0.943~1.021; OR=1.043,95% CI=0.995~1.093; OR=0.988, 95% CI=0.921~1.061），WME法和MR-Egger法结果与IVW法一致，详细结果见图2。
2.3 敏感性分析结果
T2DM与肺癌及其亚型（LAC、LSCC、SCLC）之间因果关联的MR-Egger截距多效性检验结果分别为P=0.232、P=0.878、P=0.600、P=0.106，表明不存在水平多效性。去除回文结构SNPs后，利用剩余SNPs进行IVW分析，结果与主分析结果一致；去除多效性的SNPs后，利用剩余SNPs进行IVW分析，其结果与主分析结果也一致，详细结果见图2。Leave-one-out法结果显示逐个剔除各SNP 后，剩余SNPs 的 IVW 分析结果无较大变化，表明不存在对因果关联效应值有较大影响的SNP，详细见图3。多种敏感性分析结果证明了该研究结果的稳健性。
3 讨论
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探索二型糖尿病与肺癌发病风险之间的因果关联对于二型糖尿病患者的管理及肺癌的预防具有重要意义。本研究利用大样本GWAS汇总数据，采用两样本孟德尔随机化的研究设计，探索了二型糖尿病与肺癌及其亚型发病风险之间的因果关系，结果并未发现遗传预测的二型糖尿病与肺癌及其亚型的发病风险之间存在因果关联，同时多种敏感性分析也进一步证实了这一结果。
近年来，国内外学者也越来越关注T2DM与肺癌的发生之间是否存在关联，但这些研究的结果仍然存在争议[8-13]。一项有136 432名中国上海城市居民的队列研究结果显示二型糖尿病患者的肺癌发生风险并未增加（男性：HR：0.87，95% CI：0.62~1.21；女性：HR：0.92, 95% CI：0.69~1.24）[8]。另一项利用韩国300 518名从未吸烟者健康数据的队列研究结果显示，在从未吸烟者中，二型糖尿病与肺癌发生风险无关（HR：0.91，95% CI：0.71~1.17）[10]。而先前的一项利用中国台湾居民保险系统（National Health Insurance，NIH）数据的回顾性队列研究（N=1 790 868）发现糖尿病患者较非糖尿病患者肺癌发生风险降低（OR：0.89，95% CI：0.86~0.92）[13]。而另一项同样使用中国台湾NIH数据的研究（N=739 155）却发现二型糖尿病患者的肺癌发生风险显著增加（OR：1.27，95% CI：1.21~1.38）[9]。另外一项使用美国WHI（Women’s Health Initiative）队列绝经后女性数据的研究结果并未发现糖尿病患者肺癌发生风险增加（HR：1.09, 95% CI：0.89~1.33），但是对于需要使用胰岛素治疗的这部分绝经后女性来说，其肺癌发生风险显著提高（HR：1.71，95% CI：1.15~2.53）[12]。
但由于传统流行病学研究的自然局限性，这些研究无法彻底控制所有潜在的混杂因素，以及会受到反向因果的影响。因此，本研究使用两样本MR的分析方法，结果显示尚未发现遗传预测的T2DM与肺癌及其亚型发生之间存在因果关联。本研究是迄今为止探索T2DM和肺癌发生之间因果关联样本量最大，且是唯一对肺癌进行亚型分析的MR研究。MR分析方法以等位基因在配子形成时的随机分配来模拟临床试验中的随机化过程，且遗传信息在配子形成以后一般不会随外界环境等因素而变化，因此有效地避免了混杂偏倚和反向因果效应；此外本研究选择与T2DM高度相关的遗传变异作为IVs，更加满足了MR研究的3个基本假设；同时，本研究还采用了多种敏感性分析，增加了研究结果的稳健性。但由于本研究GWAS汇总数据全部来源于欧洲人群，无法直接将此研究结果推广到中国人等其他人种，需要进一步获得其他人种的GWAS数据，以进行分析验证与外推结果。
4 结论
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综上所述，本研究采用两样本MR研究方法，尚未发现证据支持遗传预测的T2DM与肺癌及其亚型发生之间存在因果关联。但由于不同种族人群基因差异，可进一步获得不同种族人群的GWAS数据，以进一步验证此因果关联。
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doi: 10.20043/j.cnki.MPM.202309439
  • 接收时间：2023-09-23
  • 首发时间：2026-03-16
  • 出版时间：2024-03-25
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    四川大学华西公共卫生学院/华西第四医院，四川 成都 610041

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