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Article(id=1322590036873294378, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202407249, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1720886400000, receivedDateStr=2024-07-14, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793259637325, onlineDateStr=2026-10-29, pubDate=1736438400000, pubDateStr=2025-01-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793259637325, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793259637325, creator=13701087609, updateTime=1793259637325, 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=69, endPage=74, ext={EN=ArticleExt(id=1322590037133341228, articleId=1322590036873294378, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=A retrospective analysis and Mendelian randomization study on gestational diabetes mellitus and genitourinary tract infections during pregnancy, columnId=1322590037062038059, journalTitle=Modern Preventive Medicine, columnName=Child and Adolescent Health, Maternaland Child Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To evaluate the association between gestational diabetes mellitus (GDM) and genitourinary tract infections during pregnancy (GUTIs) through retrospective analysis and Mendelian randomization (MR) analysis.

Methods

A retrospective analysis was conducted on 7 751 pregnant women who underwent prenatal check-ups and delivered between 2020 and 2022, comparing the rates of GUTIs between women with GDM and those without. Additionally, data from genome-wide association studies (GWAS) were selected, and the inverse-variance weighted (IVW) method was employed as the primary analytical approach, supplemented by weighted median (WM), weighted mode (WM), and MR-Egger regression methods. Sensitivity analyses were performed using MR-Egger regression, Cochran’s Q test, and leave-one-out analysis to investigate the causal relationship between GDM and GUTIs.

Results

Both univariate chi-square tests (P=2.2×10-16) and multivariate stepwise logistic regression analyses (P=2×10-16) revealed a significant correlation between GDM and GUTIs. The two-sample MR study further confirmed a significant association between the two (IVW: β=0.211, P=0.013). The MR-Egger regression method did not indicate significant horizontal pleiotropy (P=0.813), and leave-one-out analysis and Cochran’s Q test showed no significant heterogeneity among all SNPs (P=0.876, P=0.938).

Conclusion

The presence of GDM may increase the risk of GUTIs. Effective screening and management of GDM patients could help reduce and prevent GUTIs.

, authors=Xin WEI1, Yan-lin SU1, Yan WANG2, Li CHENG1, Ling YIN1, Peng-min LIU1, Qing-hua XIE3, authorsList=Xin WEI, Yan-lin SU, Yan WANG, Li CHENG, Ling YIN, Peng-min LIU, Qing-hua XIE, authorCompany=null, correspAuthors=Qing-hua XIE, 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=1322590040379732543, articleId=1322590036873294378, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=妊娠糖尿病与妊娠期泌尿生殖感染之间的回顾性分析和孟德尔随机化研究, columnId=1228016570031604578, journalTitle=现代预防医学, columnName=儿少卫生与妇幼保健, runingTitle=null, highlight=null, articleAbstract=
目的

本研究通过回顾性分析和孟德尔随机化分析（Mendelian randomization, MR），评估了妊娠期糖尿病（gestational diabetes mellitus, GDM）与妊娠期泌尿生殖道感染（genitourinary tract infections during pregnancy, GUTIs）之间的关联。

方法

对2020—2022年产检并分娩的7 751名孕妇进行了回顾性分析，比较GDM和非GDM妇女的GUTIs率。同时，选取了全基因组关联研究（genome-wide association study，GWAS）数据，采用逆方差加权法（inverse-variance weighted method，IVW）作为主要分析方法，加权中介方差（MR-Egger）回归、简单模态法（SM）、加权中位数法和加权众数法作为补充分析，通过MR-Egger回归、Cochran Q检验和留一法作为敏感性检验，进行双样本MR分析，来探讨GDM和GUTIs的因果关系。

结果

单因素χ2检验（P=2.2×10-16）与多因素逐步逻辑回归分析（P=2×10-16）均显示GDM与GUTIs间存在显著相关性；双样本MR研究进一步证实了两者之间的显著关联(IVW：β=0.211，P=0.013)，MR-Egger回归法未发现明显的水平多效性（P=0.813），留一法、Cochran Q检验表明所有SNPs不存在明显异质性（P=0.876，P=0.938）。

结论

GDM的存在可能增加GUTIs风险。有效的筛查和管理GDM患者，将有助于降低和预防GUTIs。

, authors=魏馨1, 粟艳林1, 王妍2, 程利1, 殷玲1, 刘鹏敏1, 谢清华3, authorsList=魏馨, 粟艳林, 王妍, 程利, 殷玲, 刘鹏敏, 谢清华, authorCompany=null, correspAuthors=谢清华, authorNote=

魏馨（1985—），女，硕士，研究方向：妇科炎症、妇科生殖内分泌疾病

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谢清华，E-mail:
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魏馨（1985—），女，硕士，研究方向：妇科炎症、妇科生殖内分泌疾病

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注：图A为5种MR分析模型散点图；图B漏斗图；图C “留一法”分析结果森林图；图D单个SNP的因果效应估计森林图。

, figureFileSmall=599nAS/se/R8vZH2K+NYBg==, figureFileBig=5VZS894zuBarYEfePCM4uQ==, tableContent=null), ArticleFig(id=1322590045861687928, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590036873294378, language=EN, label=Table 1, caption=

GWAS data information in the MR study on gestational diabetes and infections of the genitourinary tract in pregnancy

, figureFileSmall=null, figureFileBig=null, tableContent=
变量人群病例组对照组SNPs（个）年份（年）GWAS ID
GDM欧洲人5 687117 89216 379 7842021finn-b-GEST_DIABETES
GUTIs欧洲人1 401110 33016 379 5532021finn-b-O15_PREG_GU_INFECT
), ArticleFig(id=1322590045949768313, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590036873294378, language=CN, label=表1, caption=

GDM与GUTIs MR研究中GWAS数据信息

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变量人群病例组对照组SNPs（个）年份（年）GWAS ID
GDM欧洲人5 687117 89216 379 7842021finn-b-GEST_DIABETES
GUTIs欧洲人1 401110 33016 379 5532021finn-b-O15_PREG_GU_INFECT
), ArticleFig(id=1322590046046237306, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590036873294378, language=EN, label=Table 2, caption=

Univariate analysis results of genitourinary tract infections during pregnancy ［n（%）］

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变量阳性（n=2 067）阴性（n=5 684）χ2值P值
高龄9 (1.91)119 (1.63)5.2470.020
GDM72 (15.29)872 (11.98)2 399.8012.2×10-16
妊娠期肝内胆汁淤积症9 (1.91)126 (1.73)0.0870.769
妊娠合并甲状腺异常54 (11.46)866 (11.90)3.0970.078
妊娠伴肥胖16 (3.40)190 (2.60)1.0990.295
妊娠合并卵巢囊肿11 (2.34)208 (2.86)0.5770.447
妊娠期高血压疾病16 (3.40)350 (4.80)18.8961.38×10-5
地中海贫血16 (3.40)244 (3.35)5.3170.021
低蛋白血症6 (1.27)91 (1.25)9.9210.002
孕期贫血128 (27.18)1863 (25.59)0.0010.979
新冠感染1 (0.21)37 (0.51)0.0190.893
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GUTIs单因素方差分析结果［n（%）］

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变量阳性（n=2 067）阴性（n=5 684）χ2值P值
高龄9 (1.91)119 (1.63)5.2470.020
GDM72 (15.29)872 (11.98)2 399.8012.2×10-16
妊娠期肝内胆汁淤积症9 (1.91)126 (1.73)0.0870.769
妊娠合并甲状腺异常54 (11.46)866 (11.90)3.0970.078
妊娠伴肥胖16 (3.40)190 (2.60)1.0990.295
妊娠合并卵巢囊肿11 (2.34)208 (2.86)0.5770.447
妊娠期高血压疾病16 (3.40)350 (4.80)18.8961.38×10-5
地中海贫血16 (3.40)244 (3.35)5.3170.021
低蛋白血症6 (1.27)91 (1.25)9.9210.002
孕期贫血128 (27.18)1863 (25.59)0.0010.979
新冠感染1 (0.21)37 (0.51)0.0190.893
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Stepwise regression analysis of risk factors for genitourinary tract infections during pregnancy

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危险因素估计值标准误Z值95%CIP值
截距(Intercept)-1.5670.203-47.8771.567(-1.969～-1.166)<2×10-16
GDM4.1030.13031.5874.103 (3.849～4.358)<2×10-16
低蛋白血症0.5290.2552.0720.529 (0.029～1.030)0.038
地中海贫血0.2750.1631.6930.275 (?0.043～0.594)0.091
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GUTIs危险因素逐步回归分析表

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危险因素估计值标准误Z值95%CIP值
截距(Intercept)-1.5670.203-47.8771.567(-1.969～-1.166)<2×10-16
GDM4.1030.13031.5874.103 (3.849～4.358)<2×10-16
低蛋白血症0.5290.2552.0720.529 (0.029～1.030)0.038
地中海贫血0.2750.1631.6930.275 (?0.043～0.594)0.091
), ArticleFig(id=1322590046381781630, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590036873294378, language=EN, label=Table 4, caption=

Information on selected SNPs in the MR study on gestational diabetes and infections of the genitourinary tract in pregnancy

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SNPs IDCHREAOAGDMGUTIs
β P值Fβ P值
rs1083096311GC0.3600.0211.835×10-64288.0390.0710.0400.076
rs707444010AG0.2100.0262.958×10-1666.9600.0410.0490.404
rs734107746TG0.2570.0446.285×10-933.7030.0380.0860.659
rs7800942CT0.1200.0211.392×10-832.0752.00×10-40.0400.995
rs92753736AG0.2110.0317.941×10-1246.7090.0950.0590.107
rs92954386CT-0.2280.0411.846×10-831.620-0.0710.0770.363
), ArticleFig(id=1322590046453084799, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590036873294378, language=CN, label=表4, caption=

GDM与GUTIs MR研究筛选出的SNPs信息

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SNPs IDCHREAOAGDMGUTIs
β P值Fβ P值
rs1083096311GC0.3600.0211.835×10-64288.0390.0710.0400.076
rs707444010AG0.2100.0262.958×10-1666.9600.0410.0490.404
rs734107746TG0.2570.0446.285×10-933.7030.0380.0860.659
rs7800942CT0.1200.0211.392×10-832.0752.00×10-40.0400.995
rs92753736AG0.2110.0317.941×10-1246.7090.0950.0590.107
rs92954386CT-0.2280.0411.846×10-831.620-0.0710.0770.363
), ArticleFig(id=1322590046524387968, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590036873294378, language=EN, label=Table 5, caption=

Pleiotropy test for MR analysis of gestational diabetes and infections of the genitourinary tract in pregnancy

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结局因素暴露因素Egger截距标准误P值
GUTIsGDM-0.0150.0570.813
), ArticleFig(id=1322590046595691137, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590036873294378, language=CN, label=表5, caption=

GDM与GUTIs MR分析多效性检验

, figureFileSmall=null, figureFileBig=null, tableContent=
结局因素暴露因素Egger截距标准误P值
GUTIsGDM-0.0150.0570.813
), ArticleFig(id=1322590046687965826, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590036873294378, language=EN, label=Table 6, caption=

Heterogeneity test of Mendelian randomization analysis for gestational diabetes mellitus and genitourinary tract infections in pregnancy

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露因素结局因素方法Q值P值
GDMGUTIsMR Egger1.2100.876
GDMGUTIsIVW1.2740.938
), ArticleFig(id=1322590046776046211, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590036873294378, language=CN, label=表6, caption=

GDM与GUTIs MR分析异质性检验

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露因素结局因素方法Q值P值
GDMGUTIsMR Egger1.2100.876
GDMGUTIsIVW1.2740.938
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妊娠糖尿病与妊娠期泌尿生殖感染之间的回顾性分析和孟德尔随机化研究
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魏馨 1 , 粟艳林 1 , 王妍 2 , 程利 1 , 殷玲 1 , 刘鹏敏 1 , 谢清华 3
现代预防医学 | 儿少卫生与妇幼保健 2025,52(1): 69-74
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现代预防医学 |儿少卫生与妇幼保健 2025 , 52 (1) : 69 -74
妊娠糖尿病与妊娠期泌尿生殖感染之间的回顾性分析和孟德尔随机化研究
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魏馨（1985—），女，硕士，研究方向：妇科炎症、妇科生殖内分泌疾病

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魏馨1, 粟艳林1, 王妍2, 程利1, 殷玲1, 刘鹏敏1, 谢清华3
作者信息
  • 1.南华大学附属长沙中心医院妇产科，湖南 长沙 410004
  • 2.南华大学附属长沙中心医院病理科
  • 3.南华大学附属长沙中心医院检验科，湖南 长沙 410004
通讯作者:
谢清华，E-mail:
作者简介:

魏馨（1985—），女，硕士，研究方向：妇科炎症、妇科生殖内分泌疾病

A retrospective analysis and Mendelian randomization study on gestational diabetes mellitus and genitourinary tract infections during pregnancy
Xin WEI1, Yan-lin SU1, Yan WANG2, Li CHENG1, Ling YIN1, Peng-min LIU1, Qing-hua XIE3
Affiliations
  • Department of Obstetrics and Gynecology, Changsha Central Hospital Affiliated to Nanhua University, Changsha, Hunan 410004, China
出版时间: 2025-01-10 doi: 10.20043/j.cnki.MPM.202407249
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摘要
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目的

本研究通过回顾性分析和孟德尔随机化分析（Mendelian randomization, MR），评估了妊娠期糖尿病（gestational diabetes mellitus, GDM）与妊娠期泌尿生殖道感染（genitourinary tract infections during pregnancy, GUTIs）之间的关联。

方法

对2020—2022年产检并分娩的7 751名孕妇进行了回顾性分析，比较GDM和非GDM妇女的GUTIs率。同时，选取了全基因组关联研究（genome-wide association study，GWAS）数据，采用逆方差加权法（inverse-variance weighted method，IVW）作为主要分析方法，加权中介方差（MR-Egger）回归、简单模态法（SM）、加权中位数法和加权众数法作为补充分析，通过MR-Egger回归、Cochran Q检验和留一法作为敏感性检验，进行双样本MR分析，来探讨GDM和GUTIs的因果关系。

结果

单因素χ2检验（P=2.2×10-16）与多因素逐步逻辑回归分析（P=2×10-16）均显示GDM与GUTIs间存在显著相关性；双样本MR研究进一步证实了两者之间的显著关联(IVW：β=0.211，P=0.013)，MR-Egger回归法未发现明显的水平多效性（P=0.813），留一法、Cochran Q检验表明所有SNPs不存在明显异质性（P=0.876，P=0.938）。

结论

GDM的存在可能增加GUTIs风险。有效的筛查和管理GDM患者，将有助于降低和预防GUTIs。

妊娠糖尿病  /  妊娠期泌尿生殖道感染  /  多因素逐步逻辑回归分析  /  孟德尔随机化分析
Objective

To evaluate the association between gestational diabetes mellitus (GDM) and genitourinary tract infections during pregnancy (GUTIs) through retrospective analysis and Mendelian randomization (MR) analysis.

Methods

A retrospective analysis was conducted on 7 751 pregnant women who underwent prenatal check-ups and delivered between 2020 and 2022, comparing the rates of GUTIs between women with GDM and those without. Additionally, data from genome-wide association studies (GWAS) were selected, and the inverse-variance weighted (IVW) method was employed as the primary analytical approach, supplemented by weighted median (WM), weighted mode (WM), and MR-Egger regression methods. Sensitivity analyses were performed using MR-Egger regression, Cochran’s Q test, and leave-one-out analysis to investigate the causal relationship between GDM and GUTIs.

Results

Both univariate chi-square tests (P=2.2×10-16) and multivariate stepwise logistic regression analyses (P=2×10-16) revealed a significant correlation between GDM and GUTIs. The two-sample MR study further confirmed a significant association between the two (IVW: β=0.211, P=0.013). The MR-Egger regression method did not indicate significant horizontal pleiotropy (P=0.813), and leave-one-out analysis and Cochran’s Q test showed no significant heterogeneity among all SNPs (P=0.876, P=0.938).

Conclusion

The presence of GDM may increase the risk of GUTIs. Effective screening and management of GDM patients could help reduce and prevent GUTIs.

Gestational diabetes mellitus  /  Genitourinary tract infections during pregnancy  /  Multivariate stepwise logistic regression analysis  /  Mendelian randomization analysis
魏馨, 粟艳林, 王妍, 程利, 殷玲, 刘鹏敏, 谢清华. 妊娠糖尿病与妊娠期泌尿生殖感染之间的回顾性分析和孟德尔随机化研究. 现代预防医学, 2025 , 52 (1) : 69 -74 . DOI: 10.20043/j.cnki.MPM.202407249
Xin WEI, Yan-lin SU, Yan WANG, Li CHENG, Ling YIN, Peng-min LIU, Qing-hua XIE. A retrospective analysis and Mendelian randomization study on gestational diabetes mellitus and genitourinary tract infections during pregnancy[J]. Modern Preventive Medicine, 2025 , 52 (1) : 69 -74 . DOI: 10.20043/j.cnki.MPM.202407249
正文
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妊娠糖尿病（genitourinary diabetes mellitus, GDM）作为常见的妊娠并发症之一，不仅影响母亲的健康，还可能对胎儿和新生儿的健康产生长远影响[1-2]。根据国际糖尿病联盟2021年的报告，全球约2 106万例活产（占16.7%）受到妊娠期高血糖的影响，这表明GDM的普遍性及其对全球公共卫生的重大影响[3]。控制不佳的GDM与多种妊娠合并症有关，其中包括妊娠期泌尿生殖道感染（genitourinary tract infections，GUTIs）的风险显著增加。
已有研究表明，高血糖环境可能削弱机体免疫功能，抑制中性粒细胞的趋化性、吞噬能力和杀菌作用，同时抑制其他免疫细胞的活性，从而增加泌尿生殖道感染的风险[4]。此外，GDM会导致孕妇尿液中葡萄糖浓度升高，这为病原微生物的生长提供了理想的环境。激素水平的变化，特别是胰岛素抵抗与雌激素、孕激素的平衡紊乱，也可能影响泌尿生殖道的正常菌群平衡，促进有害微生物的过度生长[5]。GDM还常伴有低度慢性炎症，这种状态不仅增加感染风险，还可能导致胎盘功能障碍，进一步危机母胎健康[6]。
尽管先前研究揭示了GDM与GUTIs之间的相关性[7-8]，小鼠GDM模型研究也发现GDM小鼠表现出更强的B族链球菌（Group B Streptococcus，GBS）宫内传播和更差的新生儿结局[9]。但这些研究多为观察性研究，难以完全控制潜在的混杂因素，无法明确推断因果关系。为了初步探索两者之间的关联，我们首先进行了回顾性分析，并基于其结果进一步采用孟德尔随机化（Mendelian randomization，MR）分析来验证GDM与GUTIs之间的因果关系。
回顾性分析的发现为MR分析提供了必要的假设和工具变量选择的依据，增强了研究的科学性和可信度。MR分析利用基因变异作为工具变量推断暴露因素与疾病结局之间因果关系。由于基因在受精时随机分配且不受环境因素影响，MR能够有效减少观察性研究中的混杂因素和逆向因果关系的干扰，从而提供更可靠的因果推断[10]。因此，结合回顾性分析与MR分析，不仅可以克服单一观察性研究的局限，还能增加观察结果的因果推断力，为GDM与GUTIs的因果关联提供更为充分的证据。
1　资料与方法
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1.1　研究设计
1.1.1　研究设计一：回顾性分析
本研究基于临床经验和现有文献，筛选出与GUTIs可能关联的变量，通过单因素分析筛选出与结局显著相关的变量，采用多因素逐步逻辑回归方法控制混杂因素（人口学特征、其他疾病史、生活方式等），最终识别出独立的风险因素。这些变量的选择不仅考虑了统计学意义，还结合了临床和生物学合理性，确保研究结果的科学性和临床应用价值。
1.1.2　研究设计二：两样本MR设计
本研究采用了两样本MR设计，以GDM作为暴露因素，GUTIs作为结局因素，以探讨两者间的因果关系，见图1。MR方法利用SNP作为工具变量，评估暴露因素对结局的影响。与传统观察性研究相比，MR方法能减少测量误差、混杂因素和反向因果关系的影响[11-12]。
1.1.3　MR分析的核心假设
（1）相关性假设：遗传变异（工具变量，G）与暴露因素（X）显著相关；（2）独立性假设：遗传变异（G）与潜在的混杂因素（U）无关联。即遗传工具变量（G）与结局（Y）之间的关联不受混杂因素（U）的影响；（3）排他性假设：G对Y的影响通过X间接实现的，不存在其他独立路径[13]。
1.2　数据来源
本研究的临床数据来源于2020年1月1日—2022年12月31日在南华大学附属长沙中心医院进行规律产检并建档的7 751例孕妇。收集了研究对象的孕期病史和临床特征数据，并导出标准化表格用于统计分析。本研究已获得南华大学附属长沙中心医院伦理委员会的批准，伦理编号为：2021-S0094。
1.2.1　纳入标准
（1）孕期建档：2020年1月1日—2022年12月31日在南华大学附属长沙中心医院进行规律产检并建档的孕妇。（2）妊娠周期：孕妇妊娠期达到12周以上，并持续接受产检。（3）病史记录完整：研究对象具有完整的孕期病史和临床特征数据。（4）知情同意：所有研究对象均签署知情同意书，允许使用其临床数据进行科学研究。
1.2.2　排除标准
（1）多胎妊娠：因多胎妊娠可能对研究结果造成混杂影响，故排除多胎妊娠的孕妇。（2）数据缺失：排除缺少关键临床数据的孕妇，以确保数据分析的完整性和结果的可靠性。（3）未完成产检过程：排除未完成整个产检过程或未在本院分娩的孕妇。
本研究的GDM及GUTIs的GWAS数据来自IEU Open GWAS数据库。由于使用的是公开数据库，无需额外的伦理批准。见表1。
1.3　工具变量的选择
工具变量（IV）的筛选必须遵循三个基本假设：
（1）强关联：选定的单核苷酸多态性（SNPs）必须与暴露变量（GDM）有显著的遗传关联，P值<5×10-8。
（2）独立性：为避免连锁不平衡（LD）引起的偏差，选定的SNPs应在遗传上独立。通常选择LD系数（r2）<0.01，kb=10 000的SNPs。
（3）排他性：工具变量与结局变量之间不存在直接关联，任何影响必须通过暴露因素来传递。为减少弱工具变量的偏差，我们使用F统计量评估工具变量的强度，F值需大于10。F统计量计算公式：。
1.4　统计分析
本研究使用R4.3.2软件进行数据处理与分析。正态分布的计量资料以（均数±标准差）表示，非正态分布的计量资料则以中位数和四分位数间距表示。两组间比较采用成组t检验或Mann-Whitney U检验（用于非正态分布的数据）；组间比较使用χ2检验。多组间比较采用单因素方差分析（ANOVA）或Kruskal-Wallis检验（对于非正态分布数据）。多因素逐步logistic回归分析用于识别孕产妇泌尿生殖道感染的独立影响因素。为控制多次比较引发的错误发现率，我们采用Bonferroni法校正显著性水平，显著性水平设定为校正后的P<0.05。
为评估GDM与GUTIs的因果关系，主要采用逆方差加权（IVW）法进行MR分析。此外，还使用了MR-Egger回归、简单模态法（SM）、加权中位数法和加权众数法作为补充分析，以确保结果的稳健性。
异质性检验通过Cochran Q统计量和I2统计量进行评估，I2值>50%提示存在显著异质性。对于多效性检验，使用MR-Egger回归的截距项来评估水平多效性。若MR-Egger回归模型截距项与0的P值>0.05，则无显著水平多效性。为了验证结果的稳定性，我们采用“逐步剔除法”（leave-one-out analysis）逐一剔除每个SNP，并观察其对整体结果的影响。
2　结果
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2.1　GUTIs单因素方差分析结果
本研究于2020—2022年期间共纳入了7 751例孕妇，其中，GUTIs检出率为26.67%（2 067例），GDM检出率为12.18%（944例）。
单因素方差分析显示，GUTIs阳性和阴性孕妇在妊娠期肝内胆汁淤积症、妊娠合并甲状腺异常、肥胖、卵巢囊肿、缺铁性贫血和新冠肺炎的发生率上差异均无统计学意义(P>0.05)；然而，高龄、GDM、妊娠期高血压疾病、地中海贫血和低蛋白血症在GUTIs阳性组中显著增加（P<0.05）。见表2。
2.2　GUTIs多因素逐步logistic回归分析
将孕产妇高龄、GDM、妊娠期高血压疾病、地中海贫血、低蛋白血症当作自变量，以GUTIs阳性当作结果变量行多因素逐步logistic回归分析，结果显示，GDM和孕妇低蛋白血症为GUTIs的独立危险因素，差异具有统计学意义（P<0.05）。见表3。
2.3　GDM对GUTIs的因果影响
本研究通过两样本单变量MR分析，探讨GDM对GUTIs的因果关系。最终筛选出6个符合标准的SNPs作为工具变量，见表4。所有SNP的F统计量均大于10，避免了弱工具变量可能带来的偏差。
分析结果显示，在GDM患者中，GUTIs的发生率显著增加(IVW：β=0.211，=0.085，P=0.013；WME：β=0.197，=0.097，P=0.043)，具有统计学意义，见图2、3。MR-Egger方法显示的截距为-0.015，标准误为0.057，P值为0.813，见表5。表明没有显著的多效性影响，因果推断稳健。异质性检验结果显示，无论是MR Egger方法还是IVW方法，Q检验的P值分别为0.876和0.938，均远大于0.05，表明工具变量之间不存在显著异质性，见表6。因此，这些工具变量的结果是有效且稳健的，支持GDM与GUTIs之间的因果关系。
2.4　GUTIs对GDM的逆向因果影响
为进一步探索GUTIs与GDM之间的因果关系，我们还进行了逆向MR分析。结果未找到符合本研究筛选标准的SNPs作为工具变量，因此，无法得出GUTIs对GDM存在因果影响的结论。
3　讨论
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本研究通过回顾性分析和MR分析，深入探讨了GDM与GUTIs之间的潜在因果关系。回顾性分析的结果显示，GUTIs的检出率为26.67%，GDM的检出率为12.18%。单因素分析表明，高龄、GDM、妊娠期高血压疾病、地中海贫血和低蛋白血症与GUTIs显著相关（P<0.05）。进一步的多因素logistic回归分析显示，GDM和孕妇低蛋白血症是GUTIs的独立危险因素（P<0.05）。这表明GDM患者发生GUTIs的风险显著增加，同时，强调了营养状况对感染风险的影响。
MR分析进一步验证了GDM是GUTIs的危险因素，强调了GDM在感染发生机制中的重要作用。通过遗传工具变量的使用，我们能够更准确地推断因果关系，从而补充了回顾性分析的观察性关联。我们还使用总蛋白水平和白蛋白水平的暴露数据对GUTIs进行了MR分析，五种不同的MR方法的结果都未显示出总蛋白及血清白蛋白水平与GUTIs之间的显著因果关系。
在遗传因素方面，本研究使用的工具变量包括rs10830963（MTNR1B）、 rs7074440（TCF7L2）、rs73410774（TAP2）、rs780094（GCKR），这些位点与GDM风险显著相关[14]。其中，TCF7L2基因中的遗传变异与GDM风险的关联性最为显著，因其编码的转录因子在胰岛素分泌和胰岛素抵抗中起着关键作用。MTNR1B基因则影响胰岛素敏感性[15]。TAP2基因与免疫反应密切相关[16]，GCKR基因则调节葡萄糖代谢[17]。这些遗传变异可能加剧GDM患者的葡萄糖代谢紊乱，影响胰腺β细胞功能和胰岛素的调节，从而增加GDM的风险[18]。胰岛素抵抗和分泌不足导致代谢失衡会引发血糖升高。从而改变泌尿生殖道的微环境，使得细菌和其他病原体更易引发感染。
在探讨生物学机制的关联时，首先，GDM导致的高血糖环境可能削弱机体的免疫功能。持续的高血糖状态可抑制中性粒细胞的功能，包括趋化性、吞噬能力和杀菌作用，使孕妇更易感染泌尿生殖道病原体[4]。其次，GDM会导致尿液中葡萄糖浓度升高，为病原微生物的生长提供了有利条件，增加了尿路感染的风险。研究发现，表达1型菌毛的大肠杆菌更容易黏附于糖尿病女性的尿路上皮细胞[19]。此外，GDM与胰岛素抵抗相关，可能伴随孕妇体内雌激素和孕激素水平的变化。这些激素变化会影响泌尿生殖道的正常菌群平衡，导致有害微生物的过度生长，增加感染风险[7]。最后，GDM患者常伴有低度慢性炎症状态，这种炎症反应不仅增加泌尿生殖道感染的风险，还可能导致胎盘功能障碍，进一步加重母胎健康风险[6]。
本研究的优势在于结合了回顾性分析和MR分析，既提供了观察性关联，又通过遗传工具验证了因果关系。回顾性分析揭示了GDM与GUTIs之间的关联性，而MR分析则强化了这种关联的因果性。然而，本研究也存在一些局限性。一方面，回顾性分析的数据来源于单中心，样本量有限，可能存在选择偏倚，限制了结果的普适性。另一方面，数据来源于特定人群，可能无法完全代表其他种族或地区的情况。此外，尽管我们使用了多种方法进行敏感性分析，但仍有可能存在未被检测到的多效性或其他系统误差。
在临床实践中，这些发现提示我们应高度重视GDM患者的管理。有效的筛查和管理措施，包括血糖控制和定期泌尿生殖道感染筛查，可能有助于降低相关感染的发生率。基于回顾性分析的结果，我们建议在GDM患者的产前检查中，增加泌尿生殖道感染和营养状况（如蛋白质水平）的评估，以便及早发现和干预，改善母婴健康结局。未来的研究应继续探索其他潜在的机制和干预措施，以更好地保护妊娠期妇女的健康。
综上所述，本研究强调了GDM作为GUTIs风险因素的重要性，提示在临床管理中需要加强对这类患者的关注和干预。这不仅有助于改善妊娠结局，也为公共卫生政策的制定提供了重要依据。
基金
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  • 湖南省自然科学基金项目(2021JJ70131; 2023JJ40073)
  • 湖南省卫生健康委项目(202205012844)
文献
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参考文献 引证文献
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文章信息
doi: 10.20043/j.cnki.MPM.202407249
  • 接收时间：2024-07-14
  • 首发时间：2026-10-29
  • 出版时间：2025-01-10
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  • 收稿日期：2024-07-14
基金
湖南省自然科学基金项目(2021JJ70131; 2023JJ40073)
湖南省卫生健康委项目(202205012844)
作者信息
    1.南华大学附属长沙中心医院妇产科，湖南 长沙 410004
    2.南华大学附属长沙中心医院病理科
    3.南华大学附属长沙中心医院检验科，湖南 长沙 410004

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谢清华，E-mail:
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