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Article(id=1241036244851945770, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241036242561855785, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202501282, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1736956800000, receivedDateStr=2025-01-16, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773815698110, onlineDateStr=2026-03-18, pubDate=1756051200000, pubDateStr=2025-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773815698110, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773815698110, creator=13701087609, updateTime=1773815698110, updator=13701087609, issue=Issue{id=1241036242561855785, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='16', pageStart='2881', pageEnd='3072', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773815697565, creator=13701087609, updateTime=1773840190562, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241138973712634304, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241036242561855785, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241138973712634305, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241036242561855785, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=3006, endPage=3012, ext={EN=ArticleExt(id=1241036245854384431, articleId=1241036244851945770, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Exploring the causal relationship between intake of multiple types of alcoholic and gout risk: a two-sample mendelian randomization study, columnId=1228016572451718132, journalTitle=Modern Preventive Medicine, columnName=Health and Social Behavior, runingTitle=null, highlight=null, articleAbstract=
Objective

Regarding the effect of alcohol intake on the risk of gout, existing studies have shown a diversity of conclusions. Therefore, we conducted Mendelian Randomization (MR) analysis to assess the causal association between various types of alcohol intake and the risk of gout.

Methods

Instrumental Variables (IVs) were obtained from Genome-Wide Association Study (GWAS) databases. We used the TwoSampleMR package in R to perform MR analysis with the Inverse Variance Weighting (IVW) method. The Cochran Q test was employed to assess heterogeneity, and the intercept and P-value from MR-Egger regression were used to test and adjust for horizontal pleiotropy. Sensitivity analysis was conducted using the Leave-One-Out (LOO) method.

Results

The MR analysis revealed a significant positive causal association between Beer/Cider Intake and Gout (logOR=1.187, 95%CI: 0.033 - 2.340, P=0.044). A significant negative causal association was found between White Wine Intake and Gout (logOR=-0.878,95%CI: -1.626--0.131, P=0.021). Similarly, Red Wine Consumption showed a significant negative causal association with gout (logOR=-6.514, 95%CI: -10.678--2.350, P=0.002). Fortified Wine Intake demonstrated a significant positive causal association with Gout (logOR=2.045,95%CI:0.097-3.994, P=0.040). When all types of alcohol were considered as a whole, a significant negative causal association with gout was observed (logOR=-1.462, 95%CI:-2.801--0.123, P=0.032). Spirits showed pleiotropy in relation to gout (P<0.05) and were therefore not included in the analysis.

Conclusion

The MR analysis results indicate that Beer/Cider Intake and Fortified Wine Intake may be a risk factor for the development of gout; Whereas White Wine Intake and Red Wine Consumption may reduce the incidence of gout. When all types of alcohol were considered as a whole, their consumption may reduce the risk of gout.

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目的

关于酒摄入对痛风风险的影响，现有的研究结论呈现出多样性。因此，我们进行了孟德尔随机化（Mendelian Randomization，MR）分析来评估多类酒摄入与痛风发病风险的因果关联。

方法

工具变量（Instrumental Variables，IVs）从全基因组关联研究（Genome-Wide Association Study，GWAS）数据库中获得。采用逆方差加权（Inverse Variance Weighting，IVW）方法进行MR分析，采用Cochran Q检验来判断异质性，利用MR-Egger回归的截距及P值检验测试和校准水平多效性，采用留一法（Leave-One-Out，LOO）进行敏感性检验。

结果

MR分析结果发现啤酒/苹果酒与痛风之间存在显著的正向因果关联（logOR=1.187，95%CI：0.033～2.340，P=0.044）；白葡萄酒与痛风之间存在显著的负因果关联（logOR=-0.878，95%CI：-1.626～-0.131，P=0.021）；红葡萄酒与痛风之间存在显著的负因果关联（logOR=-6.514，95%CI：-10.678～-2.350，P=0.002）；强化葡萄糖酒与痛风之间存在显著的正因果关联（logOR=2.045，95%CI：0.097～3.994，P=0.040）；所有酒精饮品视为一个整体时，与痛风之间存在显著的负因果关联（logOR=-1.462，95%CI：-2.801～-0.123，P=0.032）。烈酒与痛风之间存在多效性（P<0.05），未纳入分析。

结论

MR分析结果显示，啤酒/苹果酒的摄入量可能是痛风发生的一个风险因素；白葡萄酒、红葡萄酒的摄入量可能降低痛风的发病，而强化葡萄酒的摄入量可能是痛风发生的一个风险因素。将所有酒精饮品视为一个整体时，其摄入量可能降低痛风风险。

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伍玉娟，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=BtewZm36KbYKfPza0YvTVg==, magXml=HuenMs0Ik9BvUbcSmfRc+A==, pdfUrl=null, pdf=k7fi+grwROGJa4RjmrZ0+w==, pdfFileSize=1483176, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=NMLDvYoLg+KN7W6+NK8zSQ==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=xtun1yvlJPhD1/3YuZE//g==, mapNumber=null, authorCompany=null, fund=null, authors=

王小勇（1985—），男，本科，副主任医师，研究方向：内分泌代谢

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王小勇（1985—），男，本科，副主任医师，研究方向：内分泌代谢

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王小勇（1985—），男，本科，副主任医师，研究方向：内分泌代谢

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figureFileBig=Li5jhU7rUL0XHPQ/4DCh8g==, tableContent=null), ArticleFig(id=1241057512041009424, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036244851945770, language=CN, label=图1, caption=啤酒/苹果酒与痛风之间关联的森林图, figureFileSmall=vnHrTIVbFmZDkFSdTvdIvw==, figureFileBig=Li5jhU7rUL0XHPQ/4DCh8g==, tableContent=null), ArticleFig(id=1241057512200392986, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036244851945770, language=EN, label=Fig.2, caption=Forest plot illustrating the association between white wine intake and gout, figureFileSmall=3LODyBPZJht0L7rSjQVZpg==, figureFileBig=53kKgM8ScPSEAK4Oye4KmA==, tableContent=null), ArticleFig(id=1241057512305250592, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036244851945770, language=CN, label=图2, caption=白葡萄酒与痛风之间关联的森林图, figureFileSmall=3LODyBPZJht0L7rSjQVZpg==, figureFileBig=53kKgM8ScPSEAK4Oye4KmA==, tableContent=null), ArticleFig(id=1241057512384942373, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036244851945770, language=EN, label=Fig.3, caption=Forest plot illustrating the association between red wine consumption and gout, figureFileSmall=qe3h12gJqMU7gRseFW7SFw==, figureFileBig=v9vpPToy3YrfuIOuS0x0zg==, tableContent=null), ArticleFig(id=1241057512493994280, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036244851945770, language=CN, label=图3, caption=红葡萄酒与痛风之间关联的森林图, figureFileSmall=qe3h12gJqMU7gRseFW7SFw==, figureFileBig=v9vpPToy3YrfuIOuS0x0zg==, tableContent=null), ArticleFig(id=1241057512632406320, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036244851945770, language=EN, label=Fig.4, caption=Forest plot illustrating the association between fortified wine intake and gout, figureFileSmall=rbfHHi5f4WkDX3L3mfNa2A==, figureFileBig=w8y41Nfu7S1l45AKqKxe3w==, tableContent=null), ArticleFig(id=1241057512766624051, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036244851945770, language=CN, label=图4, caption=强化葡萄酒与痛风之间关联的森林图, figureFileSmall=rbfHHi5f4WkDX3L3mfNa2A==, figureFileBig=w8y41Nfu7S1l45AKqKxe3w==, tableContent=null), ArticleFig(id=1241057512900841785, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036244851945770, language=EN, label=Fig.5, caption=Forest plot illustrating the association between alcohol consumption and gout, figureFileSmall=xmaik0f10EolEFPj7NSx/A==, figureFileBig=Z09xNSibv5D3rAFpKnMF/w==, tableContent=null), ArticleFig(id=1241057513035059518, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036244851945770, language=CN, label=图5, caption=酒精饮品与痛风之间关联的森林图, figureFileSmall=xmaik0f10EolEFPj7NSx/A==, figureFileBig=Z09xNSibv5D3rAFpKnMF/w==, tableContent=null), ArticleFig(id=1241057513223803205, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036244851945770, language=EN, label=Fig.6, caption=MR scatter plot showing the association between intake of multiple types of alcoholic and gout, figureFileSmall=qdKu+EWVEys+8eIHwTLV8w==, figureFileBig=8nJXjSi/DF9H1NQThRaF5w==, tableContent=null), ArticleFig(id=1241057513366409547, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036244851945770, language=CN, label=图6, caption=各类酒摄入与痛风之间关联的MR散点图

注：图A为啤酒/苹果酒与痛风；图B为白葡萄酒与痛风；图C为红葡萄酒与痛风；图D为强化葡萄酒与痛风；图E为酒精饮品与痛风。

, figureFileSmall=qdKu+EWVEys+8eIHwTLV8w==, figureFileBig=8nJXjSi/DF9H1NQThRaF5w==, tableContent=null), ArticleFig(id=1241057513475461456, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036244851945770, language=EN, label=Fig.7, caption=Forest plot of LOO sensitivity test illustrating the association between intake of multiple types of alcoholic and gout, figureFileSmall=Qc06IYl5AdpTdU4qUbiz5A==, figureFileBig=3ouGYMjIQqCCvsNMBfxkQQ==, tableContent=null), ArticleFig(id=1241057513597096277, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036244851945770, language=CN, label=图7, caption=各类酒摄入与痛风之间关联的LOO敏感性检验森林图

注：图A为啤酒/苹果酒与痛风；图B为白葡萄酒与痛风；图C为红葡萄酒与痛风；图D为强化葡萄酒与痛风；图E为酒精饮品与痛风。

, figureFileSmall=Qc06IYl5AdpTdU4qUbiz5A==, figureFileBig=3ouGYMjIQqCCvsNMBfxkQQ==, tableContent=null), ArticleFig(id=1241057513701953883, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036244851945770, language=EN, label=Table 1, caption=

Information of five pairs of mendelian randomization samples

, figureFileSmall=null, figureFileBig=null, tableContent=
组别暴露结局GWASID年份（年）样本量SNP数量人种
啤酒/苹果酒与痛风组暴露ukb-b-51742018327 6349 851 867欧洲
结局finn-b-GOUT_IDIO2021216 03516 380 465欧洲
白葡萄糖与痛风组暴露ukb-b-311201864 9499 851 867欧洲
结局finn-b-GOUT_NOS2021217 23516 380 464欧洲
红葡萄酒与痛风组暴露ebi-a-GCST900969222022211 6285 733 790欧洲
结局finn-b-GOUT_IDIO2021216 03516 380 465欧洲
强化葡萄酒与痛风组暴露ukb-b-9524201864 9429 851 867欧洲
结局finn-b-GOUT_STRICT2021217 93816 380 466欧洲
酒精饮品与痛风组暴露ieu-a-12832017112 11712 935 395欧洲
结局finn-b-GOUT_IDIO2021216 03516 380 465欧洲
), ArticleFig(id=1241057513819394400, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036244851945770, language=CN, label=表1, caption=

孟德尔随机化5组双样本信息表

, figureFileSmall=null, figureFileBig=null, tableContent=
组别暴露结局GWASID年份（年）样本量SNP数量人种
啤酒/苹果酒与痛风组暴露ukb-b-51742018327 6349 851 867欧洲
结局finn-b-GOUT_IDIO2021216 03516 380 465欧洲
白葡萄糖与痛风组暴露ukb-b-311201864 9499 851 867欧洲
结局finn-b-GOUT_NOS2021217 23516 380 464欧洲
红葡萄酒与痛风组暴露ebi-a-GCST900969222022211 6285 733 790欧洲
结局finn-b-GOUT_IDIO2021216 03516 380 465欧洲
强化葡萄酒与痛风组暴露ukb-b-9524201864 9429 851 867欧洲
结局finn-b-GOUT_STRICT2021217 93816 380 466欧洲
酒精饮品与痛风组暴露ieu-a-12832017112 11712 935 395欧洲
结局finn-b-GOUT_IDIO2021216 03516 380 465欧洲
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基于孟德尔随机化分析方法探究多类酒摄入与痛风发病风险的因果关联
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王小勇 1 , 马宁宁 2 , 陈远谨 2 , 陈珏莹 2 , 蔡斯悦 2 , 伍玉娟 2
现代预防医学 | 健康与社会行为 2025,52(16): 3006-3012
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现代预防医学 | 健康与社会行为 2025, 52(16): 3006-3012
基于孟德尔随机化分析方法探究多类酒摄入与痛风发病风险的因果关联
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王小勇1, 马宁宁2, 陈远谨2, 陈珏莹2, 蔡斯悦2, 伍玉娟2
作者信息
  • 1.南宁市中医医院， 广西 南宁 530001
  • 2.广西中医药大学第一附属医院

    • 王小勇（1985—），男，本科，副主任医师，研究方向：内分泌代谢

通讯作者:

伍玉娟，E-mail：
Exploring the causal relationship between intake of multiple types of alcoholic and gout risk: a two-sample mendelian randomization study
Xiao-yong WANG1, Ning-ning MA2, Yuan-jin CHEN2, Jue-ying CHEN2, Si-yue CAI2, Yu-juan WU2
Affiliations
  • Nanning Hospital of Traditional Chinese Medicine, Nanning, Guangxi 530001, China
出版时间: 2025-08-25 doi: 10.20043/j.cnki.MPM.202501282
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摘要
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目的

关于酒摄入对痛风风险的影响，现有的研究结论呈现出多样性。因此，我们进行了孟德尔随机化（Mendelian Randomization，MR）分析来评估多类酒摄入与痛风发病风险的因果关联。

方法

工具变量（Instrumental Variables，IVs）从全基因组关联研究（Genome-Wide Association Study，GWAS）数据库中获得。采用逆方差加权（Inverse Variance Weighting，IVW）方法进行MR分析，采用Cochran Q检验来判断异质性，利用MR-Egger回归的截距及P值检验测试和校准水平多效性，采用留一法（Leave-One-Out，LOO）进行敏感性检验。

结果

MR分析结果发现啤酒/苹果酒与痛风之间存在显著的正向因果关联（logOR=1.187，95%CI：0.033～2.340，P=0.044）；白葡萄酒与痛风之间存在显著的负因果关联（logOR=-0.878，95%CI：-1.626～-0.131，P=0.021）；红葡萄酒与痛风之间存在显著的负因果关联（logOR=-6.514，95%CI：-10.678～-2.350，P=0.002）；强化葡萄糖酒与痛风之间存在显著的正因果关联（logOR=2.045，95%CI：0.097～3.994，P=0.040）；所有酒精饮品视为一个整体时，与痛风之间存在显著的负因果关联（logOR=-1.462，95%CI：-2.801～-0.123，P=0.032）。烈酒与痛风之间存在多效性（P<0.05），未纳入分析。

结论

MR分析结果显示，啤酒/苹果酒的摄入量可能是痛风发生的一个风险因素；白葡萄酒、红葡萄酒的摄入量可能降低痛风的发病，而强化葡萄酒的摄入量可能是痛风发生的一个风险因素。将所有酒精饮品视为一个整体时，其摄入量可能降低痛风风险。

酒的摄入  /  痛风  /  孟德尔随机化  /  因果关联
Objective

Regarding the effect of alcohol intake on the risk of gout, existing studies have shown a diversity of conclusions. Therefore, we conducted Mendelian Randomization (MR) analysis to assess the causal association between various types of alcohol intake and the risk of gout.

Methods

Instrumental Variables (IVs) were obtained from Genome-Wide Association Study (GWAS) databases. We used the TwoSampleMR package in R to perform MR analysis with the Inverse Variance Weighting (IVW) method. The Cochran Q test was employed to assess heterogeneity, and the intercept and P-value from MR-Egger regression were used to test and adjust for horizontal pleiotropy. Sensitivity analysis was conducted using the Leave-One-Out (LOO) method.

Results

The MR analysis revealed a significant positive causal association between Beer/Cider Intake and Gout (logOR=1.187, 95%CI: 0.033 - 2.340, P=0.044). A significant negative causal association was found between White Wine Intake and Gout (logOR=-0.878,95%CI: -1.626--0.131, P=0.021). Similarly, Red Wine Consumption showed a significant negative causal association with gout (logOR=-6.514, 95%CI: -10.678--2.350, P=0.002). Fortified Wine Intake demonstrated a significant positive causal association with Gout (logOR=2.045,95%CI:0.097-3.994, P=0.040). When all types of alcohol were considered as a whole, a significant negative causal association with gout was observed (logOR=-1.462, 95%CI:-2.801--0.123, P=0.032). Spirits showed pleiotropy in relation to gout (P<0.05) and were therefore not included in the analysis.

Conclusion

The MR analysis results indicate that Beer/Cider Intake and Fortified Wine Intake may be a risk factor for the development of gout; Whereas White Wine Intake and Red Wine Consumption may reduce the incidence of gout. When all types of alcohol were considered as a whole, their consumption may reduce the risk of gout.

Alcohol consumption  /  Gout  /  Mendelian randomization  /  Causal relationship
王小勇, 马宁宁, 陈远谨, 陈珏莹, 蔡斯悦, 伍玉娟. 基于孟德尔随机化分析方法探究多类酒摄入与痛风发病风险的因果关联. 现代预防医学, 2025 , 52 (16) : 3006 -3012 . DOI: 10.20043/j.cnki.MPM.202501282
Xiao-yong WANG, Ning-ning MA, Yuan-jin CHEN, Jue-ying CHEN, Si-yue CAI, Yu-juan WU. Exploring the causal relationship between intake of multiple types of alcoholic and gout risk: a two-sample mendelian randomization study[J]. Modern Preventive Medicine, 2025 , 52 (16) : 3006 -3012 . DOI: 10.20043/j.cnki.MPM.202501282
正文
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痛风是一种常见的代谢性疾病，其特征是高尿酸血症和反复发作的急性关节炎[1]。近年来，痛风的发病率在全球范围内呈显著上升趋势。流行病学数据显示，我国痛风患病率呈上升趋势，每年增长0.2%～0.4%[2]。痛风不仅影响患者的生活质量，还与多种慢性疾病（如心血管疾病、肾脏疾病和代谢综合征）存在密切关联[3]。因此，深入探讨痛风的风险因素，对于预防和治疗痛风具有重要意义。
饮酒与痛风之间的关联一直是医学界关注的焦点，然而，现有的研究结论呈现出多样性[4]。一些研究指出，酒精，特别是啤酒及其他富含嘌呤物质的酒类，可能会增加痛风发作的风险[3，5]；另外一些研究指出某些类型的酒精（如红酒）可能因含有抗氧化成分而具有保护作用[6]，提出了相反的观点。一项于2022年发表的孟德尔随机化研究则指出增加饮酒量并非痛风发病的直接因果因素[7]。这种观点的多样性使得饮酒与痛风之间的关联仍待进一步阐明。
鉴于上述问题，本研究采用双样本孟德尔随机化（Mendelian Randomization，MR）分析方法[8]，探讨多类酒精摄入（包括啤酒/苹果酒、葡萄酒、烈酒、酒精饮品等）与痛风发病风险之间的因果关联。MR利用遗传变异作为自然发生的工具变量，这些变异在人群中通常是随机分配的，因此受混杂因素的干扰较少，从而能够模拟随机对照试验中的随机化过程。此外，双样本MR可利用现有的大规模基因组关联研究（Genome-Wide Association Study，GWAS）数据，降低研究成本，加速分析过程。
1　方法和数据
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1.1　研究设计
本研究的暴露因素分别为啤酒/苹果酒、葡萄酒、烈酒、酒精饮品，结局变量为痛风。MR分析基于以下三大核心假设设计：（1）关联性假设：即遗传变异（单核苷酸多态性，Single Nucleotide Polymorphisms，SNP）必须与所研究的暴露因素之间有强大的关联性，以确保所选的SNP能够作为暴露因素的有效代理变量[9]。为了筛选与暴露因素强相关的SNP，本研究采用了一个相对严格的显著性阈值P<5×10-6，避免遗漏潜在的重要遗传位点。计算F统计数据以估计样本重叠效应和弱工具偏差，要求F>10[10]，其计算公式为：
其中，N为暴露因素的样本量，K为所纳入工具变量个数，R2为工具变量解释的暴露因素变异比例，EAF是效应等位基因频率，β是效应值，是β值的标准误差[11]。（2）独立性假设：即工具变量（instrumental variables，IVs），也为SNP，必须独立于任何已知或未知的混杂因素，以避免混杂因素对因果关联的干扰，确保推断的因果关联是准确的[12]。在数据筛选阶段，暴露和结局数据需来自独立样本，以避免样本重叠偏差；在数据预处理阶段，移除具有回文结构的SNP。此外，设置连锁不平衡阈值r2=0.001，以确保工具变量之间的独立性[13]。（3）排他性假设：确保SNP仅通过暴露因素对结局产生影响，而不会通过其他直接的因果途径影响结局。通过水平多效性检测、异质性评估和敏感性分析，验证排他性假设的有效性，从而确保因果推断的准确性和结果的稳健性[8]。
1.2　统计分析方法
该双样本MR分析使用R软件（版本4.4.2）和TwoSampleMR（版本0.6.8）包。在本研究中，选用逆方差加权（Inverse Variance Weighting，IVW）方法作为MR分析的主要的方法。此外，其他几种MR分析方法被用于辅助估计因果效应，包括加权中位数（Weighted median，WMed）、MR-Egger回归（MER）、简单模型（Simple model，SM）、加权模型（Weighted model，WM）等。
为确保分析的统计效力，仅筛选出包含超过10个合格SNP的双样本，并计算对数比值比（LogOR）及其95%置信区间（CI），以评估暴露因素与结局之间的因果关联。同时，计算与LogOR对应的P值，用于评估因果关联的显著性。P<0.05则被认为具有统计学显著性，表明暴露因素与结局之间可能存在因果关联。LogOR及95%CI大小表示关联的强度，其中logOR的正值表示暴露因素与结局的正相关，负值表示负相关。使用Cochran Q检验来判断是否存在异质性，Q统计量对应的P> 0.05则表明不存在显著的异质性[14]。同时，利用MR-Egger回归的截距及P值检验测试和校准水平多效性，P>0.05提示不存在显著的水平多效性。对于存在显著异质性或水平多效性的双样本，均予以排除，以减少偏倚、提高结果的稳健性，并满足MR的基本假设。此外，使用留一法（Leave-One-Out，LOO）进行敏感性分析，以评估结果的稳定性。
1.3　数据
本研究的数据来源于IEU OpenGWAS项目（https://gwas.mrcieu.ac.uk/），基于GWAS数据库的特征项，搜索与痛风及啤酒/苹果酒、葡萄酒、烈酒、酒精饮品摄入量相关的GWAS数据集，共纳入253组MR双样本进行分析。基于上述研究设计及统计学方法，最终选择了以下5组MR双样本的暴露和结局数据集进行分析（详见表1）。值得注意的是，在初筛中发现烈酒与痛风之间存在显著关联的两组孟德尔双样本，但由于这两对双样本均存在水平多效性（P值分别为0.022和0.021），因此它们并未被纳入最终的分析范畴。由于数据来源于公开可用的GWAS数据库，因此无需额外的伦理批准。
2　结果
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2.1　啤酒/苹果酒与痛风的因果关联分析
啤酒/苹果酒与痛风组的双样本MR分析中，采用暴露因素的GWASID为ukb-b-5174，该数据集包含啤酒/苹果酒的平均周摄入量。结局因素的GWASID为finn-b-GOUT_IDIO，旨在研究痛风的遗传基础及其与其他健康因素的关联。
暴露和结局识别出具有强相关的SNP共102个，移除2个具有回文结构的SNP（rs60063924和rs9969466），最终纳入100个SNP进行MR分析，SNP的F统计量为20.88～78.44。IVW分析方法发现啤酒/苹果酒与痛风之间存在显著的正向因果关联（logOR=1.187，95% CI：0.033～2.340，P=0.044）（见图1）。Q统计量判断没有证据表明存在显著的异质性（Q=101.63，P=0.436），在MR-Egger回归分析（截距=0.012，，P=0.596）中未发现多效性，MR散点图提示方向一致（见图6-A）。然而，在进行LOO敏感性检验时，剔除部分单个SNP后，置信区间包含0，结果欠稳健（见图7-A）。
2.2　以葡萄酒为主题的双样本孟德尔随机化分析共3个亚组
2.2.1　白葡萄酒与痛风的因果关联分析
在白葡萄酒与痛风亚组的MR分析中，暴露因素的GWASID为ukb-b-311，该数据集记录了白葡萄酒的摄入量。结局因素的GWASID为finn-b-GOUT_NOS，旨在研究痛风的遗传基础及其与其他健康因素的关联。
暴露和结局识别出具有强相关的SNP共15个。在后续分析中，移除了1个具有回文结构的SNP（rs941473），最终纳入14个SNP进行MR分析，SNP的F统计量为21.05 ～ 32.46。IVW分析方法发现白葡萄酒摄入量与痛风之间存在显著的负因果关联（logOR=-0.878，95% CI：-1.626～ -0.131，P=0.021）（见图2）。Q统计量判断没有证据表明存在显著的异质性（Q=14.72，P=0.257）。MR-Egger回归分析（截距=0.016，se=0.391，P=0.698）中未发现多效性，MR散点图提示方向一致（见图6-B）。LOO敏感性检验证实了结果的稳健性（见图7-B）。
2.2.2　红葡萄酒与痛风的因果关联分析
在红葡萄酒与痛风亚组的MR分析中，暴露因素的GWASID为ebi-a-GCST90096922，该数据集记录了红葡萄酒的摄入量，旨在研究饮酒行为与健康结果之间的关联。结局因素的GWASID为finn-b-GOUT_IDIO。
在暴露和结局数据集中，共识别出27个具有强关联的SNP。未发现具有回文结构的SNP。SNP的F统计量为20.86 ～ 57.31。IVW分析方法发现红葡萄酒消耗量与痛风之间存在显著的负因果关联（logOR=-6.514，95% CI：-10.678 ～ -2.350，P=0.002），通过MRE方法分析（logOR=-18.380，95% CI：-33.429～ -3.330，P=0.025），WMed方法分析（logOR=-6.967，95% CI：-12.993～ -0.942，P=0.023）同样存在显著的负因果关联（见图3）。Q统计量判断没有证据表明存在显著的异质性（Q=23.51，P=0.548），在MR-Egger回归分析（截距=0.072，se=0.043，P=0.110）中未发现多效性，MR散点图提示方向一致（见图6-C）。LOO敏感性检验证实了结果的稳健性（见图7-C）。
2.2.3　强化葡萄酒与痛风的因果关联分析
在强化葡萄酒与痛风亚组的双样本MR分析中，暴露因素的GWASID为ukb-b-9524，该数据集记录了强化葡萄酒的摄入量，旨在研究饮酒行为与健康结果之间的关联。结局因素GWASID为finn-b-GOUT_STRICT，该数据集包含痛风病例的相关信息，旨在研究痛风的遗传基础及其与其他健康因素的关联。
暴露和结局识别出具有强相关的SNP共20个。未发现具有回文结构的SNP，因此纳入20个SNP进行MR分析，SNP的F统计量为20.87 ～ 31.42。IVW分析方法发现强化葡萄酒摄入量与痛风之间存在显著的正因果关联（logOR=2.045，95% CI：0.097～ 3.994，P=0.040）（详见图4）。Q统计量判断没有证据表明存在显著的异质性（Q=21.55，P=0.252），在MR-Egger回归分析（截距=0.003，se=0.030，P=0.083）中未发现多效性，MR散点图提示方向一致（详见图6-D）。但在进行LOO敏感性检验时，剔除个别SNP后，置信区间包含0，结果欠稳健（详见图7-D）。
2.3　酒精饮品与痛风的因果关联分析
在酒精饮品与痛风组的双样本MR分析中，暴露因素的GWASID为ieu-a-1283，该数据集记录了酒精饮品的摄入量，旨在研究饮酒量与健康结果之间的关联。结局因素的GWASID为finn-b-GOUT_IDIO。
在暴露和结局数据集中，共识别出45个具有强关联的SNP。移除1个具有回文结构的SNP（rs2122692），最终纳入44个SNP进行MR分析，SNP的F统计量为20.91 ～ 132.61。IVW分析方法发现酒精饮品与痛风之间存在显著的负因果关联（logOR=-1.462，95% CI：-2.801～ -0.123，P=0.032），同样WMed分析也存在显著的负因果关联（logOR=-2.430，95% CI：-4.284～ -0.577，P=0.010）（见图5）。Q统计量判断没有证据表明存在显著的异质性（Q=49.47，P=0.200），MR-Egger回归分析（截距=0.034，，P=0.494）中未发现多效性，MR散点图提示方向一致（见图6-E）。但在进行LOO敏感性检验时，剔除rs1260326或rs4734100后，置信区间包含0，结果欠稳健（详见图7-E）。
3　讨论
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3.1　啤酒/果酒与痛风风险正向关联
基于IVW方法的MR分析结果显示，啤酒/苹果酒的摄入与痛风之间存在显著的正向因果关联。啤酒和果酒中富含嘌呤，这些成分在体内代谢后会产生尿酸，从而增加痛风风险。这与Gaffo、Choi、Curhan、Yu等研究者描述的啤酒摄入量增加痛风风险的结果一致[15-17]。然而，值得注意的是，尽管IVW方法发现了这一显著关联，但置信区间相对较宽，包含了较小的正值和较大的正值，这可能增加了结果解释的不确定性[18]。此外，LOO敏感性检验中结果欠稳健。这可能表明存在其他未观察到的混杂因素或异质性影响了结果。
3.2　不同类型葡萄酒与痛风风险的关联
在以葡萄酒为主题的MR分析中，分别分析了白葡萄酒、红葡萄酒和强化葡萄酒摄入与痛风风险之间的关联。IVW分析方法的结果显示，白葡萄酒及红葡萄糖酒摄入与痛风之间存在显著的负因果关联。值得注意的是，在红葡萄酒与痛风组中，MRE和WMed方法也发现了显著的负因果关联。这一结果与先前的一些研究相一致，进一步支持了白葡萄酒、红葡萄酒中某些成分对痛风的保护作用[2，6]。可能由于白葡萄酒和红葡萄酒中含有丰富的抗氧化成分（如白藜芦醇、花青素等），这些成分可能通过抗氧化作用减少炎症反应，从而降低痛风风险[4]。
而强化葡萄酒摄入与痛风之间存在显著的正因果关联。这一结果提示，强化葡萄酒的摄入可能是痛风发生的一个风险因素，因为强化葡萄酒通常具有较高的糖分，同时强化葡萄酒的酒精含量通常在15%至22%之间，这一范围比红/白葡萄酒的酒精度（8%至15%）要高。Choi 和Curhan等研究者指出，白酒摄入量对尿酸有显著的影响[17]，同时高糖饮食摄入可促进尿酸的产生也得到业界的一致公认[19]。因此我们相信强化葡萄酒相对于红/白葡萄酒，含有较高的酒精度及糖分，增加尿酸的生成，并且酒精能干扰肾脏对尿酸的排泄，进一步增加血尿酸水平。
3.3　酒精饮品与痛风风险的负向关联
在另一项以酒精饮品为主题的分析中，IVW分析方法同样发现了酒精饮品摄入与痛风之间存在显著的负因果关联。这一结果与先前的一些研究相矛盾，这些研究通常认为酒精摄入与痛风风险呈正相关[20-21]。然而，值得注意的是，WMed分析方法也支持了这一负向关联的存在，这可能表明在某些特定条件下或特定类型的酒品中，存在对痛风具有保护作用的成分或机制。
然而，尽管IVW方法发现了这些显著关联，但我们的研究有一些局限。首先，某些结果的P值虽然达到了统计显著性水平，但置信区间较宽。其次，LOO敏感性检验中，啤酒/苹果酒组、强化葡萄酒组、酒精饮品组剔除部分个别SNP后，置信区间包含0，表明剔除的单个SNP对结果的显著性有较大影响。MR分析虽然能够避免传统观察性研究中的一些混杂因素，但仍可能受到其他未考虑的遗传或环境因素的影响。暴露和结局之间的潜在中介变量也可能影响结果的解释[22]。此外，不同数据集之间的异质性也可能影响结果的可靠性[23]。
基于上述的不足，可能存在其他未观察到的混杂因素或个体差异影响了结果。因此，未来研究应进一步验证这些关联，并深入探讨不同类型酒对痛风风险的具体影响及其潜在机制。同时，还应考虑血统差异、饮酒习惯、饮食模式、疾病、性别、年龄、BMI等因素对结果的影响，以更全面地理解各类酒的摄入量与痛风风险之间的关联。
4　结论
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MR分析发现白葡萄酒和红葡萄酒的摄入与痛风发病呈负向因果关联，提示其可能对痛风具有一定的保护作用；啤酒/苹果酒、强化葡萄酒的摄入与痛风发病呈正向因果关联，提示其可能增加痛风的风险。此外，当将所有酒精饮品作为一个整体进行研究时，其摄入与痛风发病呈负向因果关联，提示整体上可能对痛风发病风险具有降低作用。
然而，需要明确的是，这些因果关联的发现是基于MR分析的结果，并不等同于对危险因素的确定性判断。其结果需要通过更多的队列研究和随机对照试验来进一步验证。因此，我们建议谨慎解释和应用本研究的结果，避免过度解读。
基金
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  • 广西壮族自治区科技厅广西重点研发计划(桂科AB25069016)
文献
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2025年第52卷第16期
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doi: 10.20043/j.cnki.MPM.202501282
  • 接收时间：2025-01-16
  • 首发时间：2026-03-18
  • 出版时间：2025-08-25
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  • 收稿日期：2025-01-16
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广西壮族自治区科技厅广西重点研发计划(桂科AB25069016)
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    1.南宁市中医医院， 广西 南宁 530001
    2.广西中医药大学第一附属医院

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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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