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Article(id=1241676525863498142, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241676522256388920, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202404529, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1714320000000, receivedDateStr=2024-04-29, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773968352999, onlineDateStr=2026-03-20, pubDate=1731168000000, pubDateStr=2024-11-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773968352999, onlineIssueDateStr=2026-03-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773968352999, creator=13701087609, updateTime=1773968352999, 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=3973, endPage=3980, ext={EN=ArticleExt(id=1241676526601695662, articleId=1241676525863498142, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=The relationship between mood swings and the risk of pancreatic cancer: a two-sample Mendelian randomization study and mediation analysis, columnId=1228016572451718132, journalTitle=Modern Preventive Medicine, columnName=Health and Social Behavior, runingTitle=null, highlight=null, articleAbstract=
Objective

To assess the association between mood swings and pancreatic cancer using two-sample Mendelian randomization (TSMR) and to identify potential mediators between mood swings and pancreatic cancer through multivariable Mendelian randomization (MVMR).

Methods

Summary statistics for mood swings were obtained from genome-wide association studies (GWAS). Summary data for pancreatic cancer were derived from a cross-population atlas of 220 human phenotypic genetic associations. Single nucleotide polymorphism (SNP) associated with body mass index (BMI) and several common risk factors for pancreatic cancer, including smoking, alcohol consumption, triglycerides, and total cholesterol, were sourced from the UK Biobank and the IEU database. Causal relationships between mood swings and pancreatic cancer were explored using inverse variance weighting (IVW), MR-Egger, weighted median, simple mode, and weighted mode methods. A series of sensitivity analyses were conducted to evaluate the stability of the results.

Results

Genetically predicted mood swings were positively associated with pancreatic cancer risk, with the IVW method indicating that individuals with genetically determined mood swings had an increased risk of developing pancreatic cancer (OR=2.05, 95%CI: 1.21-3.48). After adjusting for BMI using MVMR, the association between mood swings and pancreatic cancer was attenuated (OR=1.45, 95%CI: 0.94-2.23). Mediation analysis estimated that BMI partially mediated the causal relationship between mood swings and pancreatic cancer risk, with a mediation proportion of 48.2%.

Conclusion

The findings of this study indicate a positive correlation between mood swings and the risk of pancreatic cancer, potentially mediated by effects on BMI.

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目的

运用两样本孟德尔随机化(TSMR)评估情绪波动(mood swings)与胰腺癌(pancreatic cancer)之间的关联并且通过多变量孟德尔随机化(MVMR)确定情绪波动与胰腺癌之间的潜在中介。

方法

情绪波动的汇总统计数据来自全基因组关联研究GWAS。使用了来自220种人类表型遗传关联的跨群体图谱作为胰腺癌的汇总数据。身体质量指数(BMI)与几种常见的胰腺癌危险因素包括吸烟、饮酒、甘油三酯、总胆固醇相关的单核苷酸多态性(SNP)数据分别来自英国生物银行及IEU数据库。采用逆方加权法、MR-Egger、加权中位数、简单众数法、加权众数法来研究情绪波动与胰腺癌之间的因果关系。通过一系列敏感性分析来评估结果的稳定性。

结果

基因预测的情绪波动对胰腺癌存在正向因果关系，以逆方差加权（IVW）为例，遗传决定的情绪波动患者发生胰腺癌风险增加(OR=2.05，95%CI：1.21～3.48)。在用MVMR校正BMI后，情绪波动与胰腺癌的关联被削弱(OR=1.45，95%CI：0.94～2.23)。中介分析估计BMI在情绪波动与胰腺癌风险的因果关系中起到部分中介作用，中介比例为48.2%。

结论

本研究结果表明，情绪波动与胰腺癌的发生风险正相关，可能通过影响BMI介导。

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蒋新卫，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=Orli31ZqFl6kQOWKRxVsOw==, magXml=L1Uu6zR58a83l2c9gc6tNA==, pdfUrl=null, pdf=1V7S+GKFibUfedXospxFJA==, pdfFileSize=1039243, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=x2Kinhth9C0uCKzYYB8lVQ==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=ltFAw9MQQTGVSFYLy7gLTA==, mapNumber=null, authorCompany=null, fund=null, authors=

杨光（1995—），男，硕士在读，研究方向：肝胆外科

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杨光（1995—），男，硕士在读，研究方向：肝胆外科

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杨光（1995—），男，硕士在读，研究方向：肝胆外科

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Gastroenterology, 2021, 161(1): 171-184.e10., articleTitle=Bariatric surgery reduces cancer risk in adults with nonalcoholic fatty liver disease and severe obesity, refAbstract=null)], funds=[Fund(id=1241821866340451039, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, awardId=Szlcyxzxj202107, language=CN, fundingSource=苏州市肝胆外科临床医学中心项目(Szlcyxzxj202107), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241821855020024133, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, xref=1., ext=[AuthorCompanyExt(id=1241821855028412742, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, companyId=1241821855020024133, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Hepatobiliary Surgery, Suzhou Municipal Hospital, Jiangsu, Suzhou 215000, China), AuthorCompanyExt(id=1241821855032607047, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, companyId=1241821855020024133, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.南京医科大学姑苏学院，江苏 苏州 215000)]), AuthorCompany(id=1241821855179407699, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, xref=2., ext=[AuthorCompanyExt(id=1241821855187796308, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, companyId=1241821855179407699, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.苏州市立医院肝胆外科，江苏 苏州 215000)])], figs=[ArticleFig(id=1241821859457598023, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, language=EN, label=Figure 1, caption=Study aims and assumptions, figureFileSmall=NFysrD4zmnuJHnTzc0jfRQ==, figureFileBig=SD00ucAHnCZlZsWn2VbgGA==, tableContent=null), ArticleFig(id=1241821859621175896, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, language=CN, label=图1, caption=研究目的和假设, figureFileSmall=NFysrD4zmnuJHnTzc0jfRQ==, figureFileBig=SD00ucAHnCZlZsWn2VbgGA==, tableContent=null), ArticleFig(id=1241821859793142368, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, language=EN, label=Figure 2, caption=Two-sample Mendelian randomization results using Inverse variance weighted, MR-Egger, weighted median, simple mode method, and weighted mode method, figureFileSmall=KtFWXh7tvNhAr+VE+uMdIA==, figureFileBig=3fXI2GgiE4wdb0ifuUG6og==, tableContent=null), ArticleFig(id=1241821859956720237, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, language=CN, label=图2, caption=采用逆方加权法、MR-Egger法、加权中位数、简单众数法、加权众数法的TSMR结果, figureFileSmall=KtFWXh7tvNhAr+VE+uMdIA==, figureFileBig=3fXI2GgiE4wdb0ifuUG6og==, tableContent=null), ArticleFig(id=1241821863433798262, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, language=EN, label=Figure 3, caption=Multivariable MR analyses showing the associations between mood swings and the risk of pancreatic cancer after adjusting for common risk factors of pancreatic cancer, figureFileSmall=Vy0VBgFOVqbxmHW90mK3QA==, figureFileBig=6Cv7UE0SuO9PxhTFsxmlWA==, tableContent=null), ArticleFig(id=1241821863588987521, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, language=CN, label=图3, caption=MVMR分析显示在调整胰腺癌的常见风险因素后情绪波动与胰腺癌发生风险的因果关系, figureFileSmall=Vy0VBgFOVqbxmHW90mK3QA==, figureFileBig=6Cv7UE0SuO9PxhTFsxmlWA==, tableContent=null), ArticleFig(id=1241821863723205254, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, language=EN, label=Table 1, caption=

Summary of GWAS included in this study

, figureFileSmall=null, figureFileBig=null, tableContent=
GWAS数据名称来源样本量人群SNPs数据下载地址
情绪波动(mood swings)GWAS荟萃分析PMID:34017140407 746欧洲人11 039 206http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90013001-GCST90014000/GCST90013873/
身体质量指数(BMI)英国生物银行(UKBiobank)461 460欧洲人9 851 867https://gwas.mrcieu.ac.uk/datasets/ukb-b-19953/
胰腺癌(pancreatic cancer)GWAS荟萃分析PMID:34594039476 245欧洲人24 195 229http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90018001-GCST90019000/GCST90018893/
每日吸烟(cigarettes smoked per day)IEU数据库(IEU database)249 752欧洲人12 003 613https://gwas.mrcieu.ac.uk/datasets/ieu-b-142/
每周饮酒(alcoholic drinks per week)IEU数据库(IEU database)335 394欧洲人11 887 865https://gwas.mrcieu.ac.uk/datasets/ieu-b-73/
甘油三酯(triglycerides)IEU数据库(IEU database)441 016欧洲人12 321 875https://gwas.mrcieu.ac.uk/datasets/ieu-b-111/
总胆固醇(total cholesterol levels)IEU数据库(IEU database)115 082欧洲人11 590 399https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST90092985/
), ArticleFig(id=1241821863861617298, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, language=CN, label=表1, caption=

本研究中GWAS数据汇总信息

, figureFileSmall=null, figureFileBig=null, tableContent=
GWAS数据名称来源样本量人群SNPs数据下载地址
情绪波动(mood swings)GWAS荟萃分析PMID:34017140407 746欧洲人11 039 206http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90013001-GCST90014000/GCST90013873/
身体质量指数(BMI)英国生物银行(UKBiobank)461 460欧洲人9 851 867https://gwas.mrcieu.ac.uk/datasets/ukb-b-19953/
胰腺癌(pancreatic cancer)GWAS荟萃分析PMID:34594039476 245欧洲人24 195 229http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90018001-GCST90019000/GCST90018893/
每日吸烟(cigarettes smoked per day)IEU数据库(IEU database)249 752欧洲人12 003 613https://gwas.mrcieu.ac.uk/datasets/ieu-b-142/
每周饮酒(alcoholic drinks per week)IEU数据库(IEU database)335 394欧洲人11 887 865https://gwas.mrcieu.ac.uk/datasets/ieu-b-73/
甘油三酯(triglycerides)IEU数据库(IEU database)441 016欧洲人12 321 875https://gwas.mrcieu.ac.uk/datasets/ieu-b-111/
总胆固醇(total cholesterol levels)IEU数据库(IEU database)115 082欧洲人11 590 399https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST90092985/
), ArticleFig(id=1241821863991640734, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, language=EN, label=Table 2, caption=

Screening of final instrumental variables

, figureFileSmall=null, figureFileBig=null, tableContent=
工具变量GWAS回文SNPs不兼容等位基因SNPs混杂相关SNPs最终SNPs
情绪波动-胰腺癌465—1526
情绪波动-BMI444—733
BMI-胰腺癌45821220359
), ArticleFig(id=1241821864138441383, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, language=CN, label=表2, caption=

最终工具变量的筛选

, figureFileSmall=null, figureFileBig=null, tableContent=
工具变量GWAS回文SNPs不兼容等位基因SNPs混杂相关SNPs最终SNPs
情绪波动-胰腺癌465—1526
情绪波动-BMI444—733
BMI-胰腺癌45821220359
), ArticleFig(id=1241821864272659121, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, language=EN, label=Table 3, caption=

Mediation effect of mood swings on pancreatic cancer via BMI

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露中介结局总效应直接效应中介效应中介比例
β值(95%CI)β值(95%CI)β值(95%CI)P值(%)(95%CI)
情绪波动BMI胰腺癌0.718(0.189～1.247)0.372(0.060～0.684)0.346(0.133～0.559)0.04448.2 (15.2～94.0)
), ArticleFig(id=1241821864390099645, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, language=CN, label=表3, caption=

情绪波动通过BMI对胰腺癌的中介效应

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露中介结局总效应直接效应中介效应中介比例
β值(95%CI)β值(95%CI)β值(95%CI)P值(%)(95%CI)
情绪波动BMI胰腺癌0.718(0.189～1.247)0.372(0.060～0.684)0.346(0.133～0.559)0.04448.2 (15.2～94.0)
), ArticleFig(id=1241821865921020612, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, language=EN, label=Table 4, caption=

The results of the heterogeneity and horizontal pleiotropy

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因果途径SNPsCochran Q异质性检验P值(IVW)MR Egger水平多效性P值离群值(是/否)校正前P值校正后P值
情绪波动-胰腺癌260.6780.492否——
情绪波动-BMI337.71×10-110.745是4.178×10-71.206×10-8
BMI-胰腺癌3590.6420.199否——
), ArticleFig(id=1241821866084598477, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241676525863498142, language=CN, label=表4, caption=

异质性和水平多效性的结果

, figureFileSmall=null, figureFileBig=null, tableContent=
因果途径SNPsCochran Q异质性检验P值(IVW)MR Egger水平多效性P值离群值(是/否)校正前P值校正后P值
情绪波动-胰腺癌260.6780.492否——
情绪波动-BMI337.71×10-110.745是4.178×10-71.206×10-8
BMI-胰腺癌3590.6420.199否——
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情绪波动与胰腺癌发生风险的关系——两样本孟德尔随机化研究与中介分析
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杨光 1 , 蒋新卫 2
现代预防医学 | 健康与社会行为 2024,51(21): 3973-3980
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现代预防医学 | 健康与社会行为 2024, 51(21): 3973-3980
情绪波动与胰腺癌发生风险的关系——两样本孟德尔随机化研究与中介分析
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杨光1, 蒋新卫2
作者信息
  • 1.南京医科大学姑苏学院，江苏 苏州 215000
  • 2.苏州市立医院肝胆外科，江苏 苏州 215000

    • 杨光（1995—），男，硕士在读，研究方向：肝胆外科

通讯作者:

蒋新卫，E-mail:
The relationship between mood swings and the risk of pancreatic cancer: a two-sample Mendelian randomization study and mediation analysis
Guang YANG1, Xin-wei JIANG2
Affiliations
  • Department of Hepatobiliary Surgery, Suzhou Municipal Hospital, Jiangsu, Suzhou 215000, China
出版时间: 2024-11-10 doi: 10.20043/j.cnki.MPM.202404529
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摘要
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目的

运用两样本孟德尔随机化(TSMR)评估情绪波动(mood swings)与胰腺癌(pancreatic cancer)之间的关联并且通过多变量孟德尔随机化(MVMR)确定情绪波动与胰腺癌之间的潜在中介。

方法

情绪波动的汇总统计数据来自全基因组关联研究GWAS。使用了来自220种人类表型遗传关联的跨群体图谱作为胰腺癌的汇总数据。身体质量指数(BMI)与几种常见的胰腺癌危险因素包括吸烟、饮酒、甘油三酯、总胆固醇相关的单核苷酸多态性(SNP)数据分别来自英国生物银行及IEU数据库。采用逆方加权法、MR-Egger、加权中位数、简单众数法、加权众数法来研究情绪波动与胰腺癌之间的因果关系。通过一系列敏感性分析来评估结果的稳定性。

结果

基因预测的情绪波动对胰腺癌存在正向因果关系，以逆方差加权（IVW）为例，遗传决定的情绪波动患者发生胰腺癌风险增加(OR=2.05，95%CI：1.21～3.48)。在用MVMR校正BMI后，情绪波动与胰腺癌的关联被削弱(OR=1.45，95%CI：0.94～2.23)。中介分析估计BMI在情绪波动与胰腺癌风险的因果关系中起到部分中介作用，中介比例为48.2%。

结论

本研究结果表明，情绪波动与胰腺癌的发生风险正相关，可能通过影响BMI介导。

情绪波动  /  体重指数  /  胰腺癌  /  孟德尔随机化  /  遗传学
Objective

To assess the association between mood swings and pancreatic cancer using two-sample Mendelian randomization (TSMR) and to identify potential mediators between mood swings and pancreatic cancer through multivariable Mendelian randomization (MVMR).

Methods

Summary statistics for mood swings were obtained from genome-wide association studies (GWAS). Summary data for pancreatic cancer were derived from a cross-population atlas of 220 human phenotypic genetic associations. Single nucleotide polymorphism (SNP) associated with body mass index (BMI) and several common risk factors for pancreatic cancer, including smoking, alcohol consumption, triglycerides, and total cholesterol, were sourced from the UK Biobank and the IEU database. Causal relationships between mood swings and pancreatic cancer were explored using inverse variance weighting (IVW), MR-Egger, weighted median, simple mode, and weighted mode methods. A series of sensitivity analyses were conducted to evaluate the stability of the results.

Results

Genetically predicted mood swings were positively associated with pancreatic cancer risk, with the IVW method indicating that individuals with genetically determined mood swings had an increased risk of developing pancreatic cancer (OR=2.05, 95%CI: 1.21-3.48). After adjusting for BMI using MVMR, the association between mood swings and pancreatic cancer was attenuated (OR=1.45, 95%CI: 0.94-2.23). Mediation analysis estimated that BMI partially mediated the causal relationship between mood swings and pancreatic cancer risk, with a mediation proportion of 48.2%.

Conclusion

The findings of this study indicate a positive correlation between mood swings and the risk of pancreatic cancer, potentially mediated by effects on BMI.

Mood swings  /  Body mass index  /  Pancreatic cancer  /  Mendelian randomization  /  Genetics
杨光, 蒋新卫. 情绪波动与胰腺癌发生风险的关系——两样本孟德尔随机化研究与中介分析. 现代预防医学, 2024 , 51 (21) : 3973 -3980 . DOI: 10.20043/j.cnki.MPM.202404529
Guang YANG, Xin-wei JIANG. The relationship between mood swings and the risk of pancreatic cancer: a two-sample Mendelian randomization study and mediation analysis[J]. Modern Preventive Medicine, 2024 , 51 (21) : 3973 -3980 . DOI: 10.20043/j.cnki.MPM.202404529
正文
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胰腺癌是全球和中国癌症死亡的主要原因之一[1]。胰十二指肠切除术被认为是治疗胰腺癌的基石，虽然其技术发展迅速，但总体预后和患者生存率并不乐观[2-3]。由于缺乏准确的筛查生物标志物，基于人群的胰腺癌筛查目前尚不可行[4]。因此，诊断和治疗的关键是尽早发现胰腺癌高危人群[5-6]。历史上，已有研究探索情绪波动作为情绪障碍的一种与癌症之间的关系，并将情绪波动视为癌症或癌症进展的危险因素[7]。此外，有研究表明情绪波动会加剧炎症因子例如白介素-6(interleukin-6, IL-6)、C反应蛋白(c reaction protein, CRP)的释放，导致肿瘤微环境发生改变，从而促进肿瘤的生长[8]。然而炎症过程已被证实与胰腺癌的进展密切相关[9]。情绪波动患者，可能存在不健康生活方式，例如吸烟、饮酒、暴饮暴食造成的肥胖、血脂及血胆固醇升高等，从而加剧胰腺癌的进展。另外，由于摄入富含碳水化合物和脂肪的食物促进大脑血清素合成，可以缓解烦躁不安，情绪波动患者通过上述途径容易导致肥胖[10]。一项关于胰腺癌的最新流行病学证据认为，全球范围内日益增长的肥胖人群与包括胰腺癌在内的常见癌症的发生风险增加有关[11]。总之，这些研究表明，肥胖可能是情绪波动和胰腺癌之间关系的潜在中介，而肥胖的常见标志包括BMI。然而，由于传统研究方法的限制，如潜在的混杂因素和反向因果关系，这种相关的机制及因果关系尚不清楚。因此，本研究旨在探究情绪波动是否通过影响肥胖的发生，从而对胰腺癌风险产生影响。孟德尔随机化(Mendelian randomization，MR)是一种采用遗传变异为工具变量的研究方法，可以有效降低因果关系值的估计值偏移，加强因果推断[12]。基于此，本研究采用两样本孟德尔随机化(two-sample Mendelian randomization，TSMR)模型分析，探讨情绪波动是否会导致胰腺癌的发生风险增加。采用多变量孟德尔随机化(multivariate Mendelian randomization，MVMR)模型分析，探讨肥胖是否介导情绪波动与胰腺癌的发生风险，并计算肥胖在这一机制中的比例。
1　资料与方法
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1.1　数据来源与研究设计
本研究基于大规模全基因组关联研究(genome-wide association study，GWAS)汇总数据集。情绪波动数据来源于全基因组关联研究GWAS：情绪波动的量化，例如情绪波动的发生频率和严重程度，其中包含407 726个样本[13]。胰腺癌的GWAS数据来自220种人类表型遗传关联的跨群体图谱[14]，其中共476 245个样本(nCase=1 196,nControl=475 049). BMI数据来自GWAS汇总数据获得的UK Biobank表型，其中共461 460个样本。吸烟、饮酒、甘油三酯、总胆固醇数据均来自IEU数据库，样本量分别为249 752、335 394、441 016、115 082。见表1。
本研究总共分为三个步骤，见图1。Ⅰ：进行TSMR，将暴露变量确定为情绪波动，将结果变量确定为胰腺癌。Ⅱ：采用TSMR确定暴露变量为情绪波动，结果变量为体重指数。Ⅲ：TSMR确定暴露变量为BMI，结果变量为胰腺癌。以上三个步骤确定了BMI可能是情绪波动与胰腺癌之间的中介因子。Ⅳ、Ⅴ：MVMR研究旨在计算BMI的中介比例以及对几种常见的胰腺癌危险因素进行校正。
1.2　工具变量(instrumental variables，IVs)的选择
选择与暴露具有全基因组显著性(P<5×10-8)的遗传变异SNPs位点进行汇集，并且设置连锁不平衡参数r2=0.001，kb=10 000以选择SNPs。从GWAS提取结局的信息，合并暴露和结局数据集，该合并数据集包含以上工具变量同时与暴露、结局的关系，并删除回文SNPs以及不兼容的等位基因。通过人类基因型-表型关联数据库[15]检索剩余的SNPs相关的表型，排除其对应的表型与结局以及吸烟、饮酒、甘油三酯、总胆固醇等相关混杂因素具有相关意义的SNPs，最终剩余SNPs即为暴露的最终工具变量。在情绪波动与胰腺癌的反向因果关系中，为了确保足够的工具变量，选择P<5×10-6的SNPs位点进行分析，其余值保持不变。最后，为了确保工具变量与暴露之间的稳定联系，排除弱工具变量的干扰，利用计算公式：F=R2/(R2-1)×[(N-K-1)/K]，其中，N为暴露的总样本量，K为SNP个数，R2=2×(1-MAF)(MAF)×(β/SD)2代表工具变量指代了暴露的百分之几。当F>10时，即为强工具变量，F<10为弱工具变量。
1.3　MR分析
本研究主要采用逆方差加权(IVW)作为此次分析的主要方法[16]。IVW法具有较强的因果检测能力，提高了分析精度和测试能力。其他方法包括MR-Egger法、加权中位数（weighted median，WME）、简单众数法(simple mode，SM)、加权众数法(weighted mode，WM). MR-Egger可以识别存在的多效性，而WME可以在多达50%的分析权重来自无效工具变量时提供一致的因果估计[17-18]。此外，可视化以上五种方法的OR值和95%CI。
1.4　敏感性分析
采用Cochran Q检验评估个体遗传变异之间的异质性。若Cochran Q检验的P<0.05[19]，结果存在异质性，则采用随机效应模型，否则采用固定效应模型。使用MR-Egger截距法和MRPRESSO检验检查是否存在水平多效性而违反MR假设，其中Egger-intercept法的截距值估计了遗传变异是否显著通过暴露以外的途径影响结局。最后，使用Radio包来识别离群值。如果存在离群值，则采用MRPRESSO方法评估异常值对结果的影响[20]。
1.5　中介效应的计算
采用TSMR法获得从情绪波动到胰腺癌的的β1值。使用MVMR法来确定在调整BMI后，情绪波动与胰腺癌之间的关系。BMI调整后情绪波动对胰腺癌的β值为β2。采用“中介比例= β 1-β2/β1”计算BMI的中介比例[21]，其中β1-β2代表在机制中的中介效应，β2代表在机制中的直接效应，β1表示在机制中的总效应。以上方法及可视化图形均采用R version 4.3.2和GraphPad Prism 9.0软件获得。
2　结果
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2.1　参与TSMR的最终工具变量
利用R软件从GAWS中筛选出46个与情绪波动相关的遗传变异SNPs，将其与胰腺癌数据集合并，并删除回文SNPs(rs10818399、 rs113976587、 rs2868996、 rs4772079、rs539711)。从人类基因型-表型关联数据库中检索同时符合假设1、2、3的情绪波动相关遗传变异SNPs，删除与BMI(rs10120798、rs13204162、rs34290943、 rs56116032、 rs613872、 rs7202252、rs7818437)、吸烟(rs10210512、 rs11596214、rs11665070、rs1687999、rs297343、rs6889822)、饮酒(rs2678897、rs4309187)相关的SNPs。26个SNPs被认为是情绪波动的最终工具变量。同样，通过情绪波动和BMI数据集合并，删除回文SNPs (rs10818399、rs113976587、rs4772079、rs539711)及与BMI相关的SNPs (rs10120798、rs13204162、rs34290942、rs56116032、rs613872、rs7202252、 rs7818437)，33个SNPs确定为最终工具变量。最后，从GAWS中筛选出458个与BMI相关的SNPs，将BMI和胰腺癌数据集合并，并删除回文SNPs (rs10887578、rs11250094、 rs11634851、rs12507026、 rs1454687、 rs1860750、 rs2396625、rs347551、rs355777、rs396755、rs4419475、 rs4737188、rs79086897、 rs6597975、 rs6713781、 rs67739903、rs7568228、rs765874、rs7704382、rs9388446、rs961498)和不兼容的等位基因(rs7928320、rs9674487)。同时，删除与吸烟(rs10182416、rs13107325、rs6265、rs6567160、rs6744646、rs75499503、rs9876664)、饮酒(rs1078141、rs12089815、 rs1229984、 rs1788808、 rs7124681、rs7498665、rs9843653)、甘油三酯(rs11122450、rs12273545)、总胆固醇(rs12921986、rs2307111、rs429358、rs7707394)相关的SNPs。最后剩下的359个SNPs为BMI的最终工具变量，见表2。最后，利用F值的计算公式，我们计算出从情绪波动到胰腺癌的F值、情绪波动到BMI的F值、BMI到胰腺癌的F值均大于10，证明本次研究所选择的工具变量为强工具变量。
2.2　MR结果分析
情绪波动与胰腺癌之间存在正向因果关系，IVW作为统计效能最高的方法，见图2。相反，在我们的反向MR分析中，胰腺癌不会导致情绪波动的发生(n=15 SNPs，OR=1.01，95%CI：0.99～1.02，P>0.05)。此外，IVW法结果显示情绪波动和BMI之间也存在正向因果关系。如前所述，IVW是用作BMI和胰腺癌的最主要分析方法，代表BMI与胰腺癌之间存在正向因果关系。尽管以上三个途径的MR-Egger、WME、SM、WM结果并不显著，但5种方法的结果方向一致(OR值均>1)，证实该因果具有一定的稳定性。在调整了BMI后，情绪波动和胰腺癌之间无因果关系(n=374 SNPs，OR=1.45，95%CI：0.94～2.23，P>0.05)，见图3。然而在调整情绪波动后，BMI与胰腺癌之间仍存在正向因果关系(n=374 SNPs，OR=1.28，95%CI：1.01～1.63，P< 0.05)。值得注意的是，在调整了吸烟、饮酒、甘油三酯和总胆固醇几种常见的胰腺癌危险因素后，情绪波动对胰腺癌仍存在正向因果关系。上诉结果说明BMI在情绪波动与胰腺癌正向因果中起重要作用，因此我们进行了MVMR以估计情绪波动对通过BMI介导的胰腺癌的影响比例。BMI在情绪波动与胰腺癌正向因果通路中的中介效应为0.346 (95%CI：0.133～0.559，P= 0.044)，占总效应的48.2% (95%CI: 15.2%～94.0%)，见表3。
2.3　质量控制分析
以IVW方法计算了情绪波动与BMI之间MR结果的异质性(P=7.71×10-11)，显示出明显的异质性，由于使用IVW随机效应模型，异质性可以接受。同理，以IVW方法计算了情绪波动与胰腺癌以及BMI与胰腺癌之间MR结果的异质性，P值均大于0.05，表明未显出明显的异质性。经过MR Egger的截距项和Egger-intercept与0进行统计检验，所有P值均大于0.05，这代表没有发现水平多效性的存在。使用Radio识别异常值，情绪波动作为暴露变量，BMI作为结局变量，那么在筛选的33个工具变量中有两个异常值(rs10210512、rs201296523)。初始P值为4.178×10-7用MRPRESSO方法校正后，P值为1.206×10-8，表明异常值对结果的影响很小，见表4。
3　讨论
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本研究首次通过MR分析方法，全面探究了情绪波动与胰腺癌风险之间的因果关系，并揭示了肥胖在其中的潜在中介作用。结果显示，情绪波动导致患胰腺癌的风险显著增加。进一步的中介分析表明，在情绪波动与胰腺癌风险的因果关联中肥胖起到了部分中介作用。回顾大量文献后，发现抑郁等情绪障碍是癌症的重要危险因素[22]。然而，很少有研究人员关注情绪波动作为情绪障碍等精神疾病的重要特征对胰腺癌的影响。最近的一项研究表明，炎症易诱发包括胰腺癌在内癌症的发展，并促进肿瘤发生的所有阶段[23]。癌细胞以及周围的基质细胞和炎性细胞相互作用，形成炎性肿瘤微环境，炎性肿瘤微环境内的细胞具有高度可塑性，不断改变其表型和功能特征，驱动肿瘤的发生、生长、进展和转移[23]。情绪波动患者反复观察到炎症通路活化，表现为促炎细胞因子增加、急性期蛋白增加以及趋化因子和粘附分子表达增加[8]。最常观察到IL-6以及CRP的浓度升高，其中IL-6可促进胰腺上皮内瘤变[9]，CRP也可介导胰腺癌的发生[24]。因此，理论上情绪波动导致的炎症反应，可能对胰腺癌的发展起到促进作用。此外，肥胖对人类疾病谱系产生重大影响，血脂、肥胖和癌症之间的联系受到了相当大的关注。有趣的是，有学者通过MR分析证实了肥胖与胰腺癌之间的正向因果关系，这与本研究结果一致[24]。并且，情绪波动和情绪化进食可能导致食物选择的改变和食物摄入量的增加，从而导致暴饮暴食和肥胖[25]。基于以上信息，本研究旨在验证情绪波动与胰腺癌关系中的中介作用，建立更详细的机制。从孤立和片面的角度来看，情绪波动、肥胖和胰腺癌之间的联系似乎得到了证实。遗传变量被用来避免混淆和反向因果关系，敏感性分析证明任何因果路径的水平多效性检验都不显著，这使本研究能够为这一机制的展示提供足够的可信度。
本研究证实了情绪波动通过肥胖中介促进胰腺癌发生的联动机制。采用情绪波动及BMI同时作为暴露对胰腺癌作为结局的MVMR分析方法，相当于排除BMI的干扰探究情绪波动与胰腺癌之间的因果关系。结果显示，在调整了BMI后，情绪波动对胰腺癌的影响没有统计学意义，即肥胖在情绪波动与胰腺癌因果关系中起重要作用。然而，在调整了吸烟、饮酒、甘油三酯、总胆固醇后，情绪波动仍与胰腺癌发生风险显著相关。以上研究结果表明，肥胖可能是情绪波动导致胰腺癌的正向因果通路中的主要中介。此外，由于有研究表明癌症的诊断和治疗过程可导致情绪波动的发生[7]，本研究也进行了情绪波动与胰腺癌之间的反向因果关系，结果表明胰腺癌并不会导致情绪波动的发生，这归因于情绪波动可能作为胰腺癌的并发症，但不一定是由胰腺癌引起的。
对情绪障碍神经生物学基础的认识不断进步，揭示了共同的生物行为机制可能会导致癌症进展，包括以下途径：（1）炎症和氧化/亚硝基应激；（2）免疫监视功能下降；（3）自主神经系统和下丘脑-垂体-肾上腺轴（hypothalamic-pituitary-adrenal axis，HPA）的功能失调[26]，其中炎症反应释放的炎性因子通过血脑屏障进入大脑，将细胞因子信号传递到特定的脑核团，然后作为中继站传递到其他脑核团，进而对神经递质和促肾上腺皮质激素释放激素（corticotropin releasing hormone，CRH）功能以及行为产生深远影响[26]。动物研究提供了令人信服的证据，即情绪波动通过上述途径诱导疾病行为综合征，包括快感缺乏、饱食症和运动活动减少[8]。除了对神经递质代谢的影响外，炎性细胞因子对下丘脑的HPA轴激素以及CRH具有深刻的刺激作用[26]。这些作用在很大程度上由HPA轴组织内丰富的细胞因子及其受体网络介导，促进下游细胞因子信号转导途径，包括丝裂原活化蛋白激酶（mitogen-activated protein kinase，MAPK）和核因子-kB（nuclear factor-kB，NF-kB）。其中MAPK信号通路与胰腺癌的关系密切[27]，NF-kB的异常激活有助于胰腺癌细胞的显著增殖和迁移[9]。由于这一因果途径及中介机制的阐述，可以通过干预其中任何一个环节，降低胰腺癌的发病率。例如，越来越多的研究表明，碳水化合物密集、加工肉类、高脂肪饮食等肥胖饮食与胰腺癌呈正相关[28]。根据本研究的MR结果，证明了肥胖在胰腺癌发生中的重要性，并证明肥胖饮食可能对胰腺癌的发生有重要的促进作用。长期肥胖饮食的人群存在慢性炎症，如慢性胰腺炎，是胰腺癌的危险因素[29-31]。本研究为通过抗肥胖饮食预防胰腺癌提供了依据，也为通过体育活动、情绪稳定的生活方式，甚至减肥手术来预防胰腺癌的研究提供了依据[32-34]。肥胖的人应该致力于改善他们的饮食习惯，在日常生活中包括减肥和体育锻炼。在临床上，医生应该更加关注肥胖个体的情绪状态。
然而，本研究仍有一些局限性。首先，由于可用的SNPs数量有限，对这些性状的分析能力相对较低。其次，由于GWAS数据主要基于欧洲人群，因此结论的普遍性受到限制，有必要在其他人群中验证这些结论。再次，使用遗传工具变量表明，暴露对结局的影响是终生的，这可能与实际情况有所不同。最后，研究发现情绪波动和胰腺癌之间的联系部分是通过BMI介导的。然而，需要进一步研究的其他潜在介质。尽管如此，本研究在一定程度上证明了情绪波动、BMI和胰腺癌之间的因果关系，并证明了BMI在介导的情绪波动和胰腺癌正向因果关系中的重要性，对指导临床工作决策及人群的饮食和生活方式具有重要意义。
基金
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  • 苏州市肝胆外科临床医学中心项目(Szlcyxzxj202107)
文献
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参考文献 引证文献
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2024年第51卷第21期
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doi: 10.20043/j.cnki.MPM.202404529
  • 接收时间：2024-04-29
  • 首发时间：2026-03-20
  • 出版时间：2024-11-10
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  • 收稿日期：2024-04-29
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苏州市肝胆外科临床医学中心项目(Szlcyxzxj202107)
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    1.南京医科大学姑苏学院，江苏 苏州 215000
    2.苏州市立医院肝胆外科，江苏 苏州 215000

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蒋新卫，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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