Article(id=1324077771152039960, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077560820282023, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202511324, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1763568000000, receivedDateStr=2025-11-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793614340817, onlineDateStr=2026-11-02, pubDate=1774368000000, pubDateStr=2026-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793614340817, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793614340817, creator=13701087609, updateTime=1793614340817, updator=13701087609, issue=Issue{id=1324077560820282023, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='6', pageStart='961', pageEnd='1152', issueExtLink='null', onlineDate='null', pubDate='1774368000000', pubDateStr='2026-03-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793614290671, creator='13701087609', updateTime=1793614391469, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324077983656444429, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077560820282023, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324077983660638734, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077560820282023, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1147, endPage=1152, ext={EN=ArticleExt(id=1324077771407892505, articleId=1324077771152039960, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Impact of body mass index on diminished ovarian reserve among women in southwest China, columnId=1228016569138213037, journalTitle=Modern Preventive Medicine, columnName=Clinical Medicine and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the relationship between body mass index (BMI) and diminished ovarian reserve (DOR) in women from southwestern China, analyzing the correlation between BMI and ovarian reserve markers: anti-Müllerian hormone (AMH) and antral follicle count (AFC).

Methods

A 1:1 age-matched case-control study was conducted, enrolling 3 751 women with DOR and 3 751 women with normal ovarian reserve who sought assisted reproductive services at a tertiary hospital in Sichuan Province between January 2022 and August 2024. Conditional logistic regression analysis and restricted cubic spline models were employed to examine the association and dose-response relationship between BMI and DOR. Spearman correlation and multivariable linear regression analyses were applied to assess the relationships between BMI and AMH and AFC.

Results

Conditional logistic regression analysis revealed a significant association between obesity (OR=1.394, 95% CI: 1.112-1.747) and DOR. After adjusting for ethnicity, occupation, marital status, education level, smoking, and alcohol consumption, obesity (OR=1.357, 95% CI: 1.065-1.730) remained a risk factor for DOR. No non-linear relationship was observed between BMI and DOR. Spearman correlation analysis showed that both AMH and AFC were negatively correlated with BMI (P<0.001). In multivariable linear regression analyses adjusting for confounders, BMI demonstrated a negative association with AMH (P<0.001), whereas no significant association was observed between BMI and AFC (P>0.05).

Conclusion

Obesity is a risk factor for diminished ovarian reserve. BMI is negatively associated with AMH but not significantly correlated with AFC.

, authors=Fan ZHAO1, Peng-hao LI2, Chong-bi HUANG1, Tony Cheung3, 4, Leesa lin3, 4, 5, Jing LI1, authorsList=Fan ZHAO, Peng-hao LI, Chong-bi HUANG, Tony Cheung, Leesa lin, Jing LI, authorCompany=null, correspAuthors=Jing LI, 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=1324077771982512156, articleId=1324077771152039960, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=中国西南地区女性体质指数对卵巢储备功能减退的影响, columnId=1228016570119680182, journalTitle=现代预防医学, columnName=临床与预防, runingTitle=null, highlight=null, articleAbstract=
目的

探讨中国西南地区女性体质指数(body mass index,BMI)与卵巢储备功能减退(diminished ovarian reserve, DOR)之间的关系,分析BMI与卵巢储备功能标志物抗苗勒管激素(anti-Müllerian hormone, AMH)及窦卵泡数(antral follicle count, AFC)的相关性。

方法

采用年龄1:1精确匹配的病例-对照研究设计,纳入2022年1月至2024年8月期间在四川省某三甲医院寻求辅助生殖医疗服务的DOR和卵巢储备功能正常的女性患者,各3 751例。采用条件logistic回归模型评估BMI与DOR发生风险之间的关联,并利用限制性立方样条模型分析其剂量-反应关系;同时采用Spearman相关分析及多元线性回归模型探讨BMI与AMH、AFC之间的相关性。

结果

条件logistic回归分析结果显示,肥胖(OR=1.394,95% CI:1.112~1.747)与DOR患病存在统计学关联。调整潜在混杂因素后,肥胖(OR=1.357,95% CI:1.065~1.730)是DOR的风险因素,且BMI与DOR发生风险之间未观察到显著的非线性剂量-反应关系。Spearman相关性分析结果显示, AMH和AFC与BMI均呈负相关(P<0.001)。进一步调整混杂因素后,多元线性回归分析结果显示,BMI与AMH呈现负向效应(P<0.001),而AFC与BMI之间无显著相关性(P>0.05)。

结论

肥胖与卵巢储备功能减退风险增加相关,BMI与AMH水平呈负相关,而与AFC无显著相关性。

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赵帆(1999—),女,硕士在读,研究方向:肿瘤的人群防治与女性生殖健康促进研究

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李静,E-mail:
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Human Reproduction, 2013, 28(6): 1661-1669., articleTitle=Leptin suppresses anti-Mullerian hormone gene expression through the JAK2/STAT3 pathway in luteinized granulosa cells of women undergoing IVF, refAbstract=null)], funds=[Fund(id=1324077779674865755, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, awardId=null, language=CN, fundingSource=AIR@InnoHK航空科技研究计划资助项目, fundOrder=null, country=null)], companyList=[AuthorCompany(id=1324077772309667869, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, xref=1., ext=[AuthorCompanyExt(id=1324077772318056478, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, companyId=1324077772309667869, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=West China School of Public Health, Sichuan University / West China Fourth Hospital, Chengdu, Sichuan 610041, China), AuthorCompanyExt(id=1324077772330639391, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, companyId=1324077772309667869, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.四川大学华西公共卫生学院/华西第四医院,四川 成都 610041)]), AuthorCompany(id=1324077772414525472, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, xref=2., ext=[AuthorCompanyExt(id=1324077772422914081, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, companyId=1324077772414525472, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.四川锦欣西囡妇女儿童医院)]), AuthorCompany(id=1324077772485828642, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, xref=3., ext=[AuthorCompanyExt(id=1324077772494217251, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, companyId=1324077772485828642, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.医卫大数据深析实验室(中国香港)]), AuthorCompany(id=1324077772561326116, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, xref=4., ext=[AuthorCompanyExt(id=1324077772569714725, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, companyId=1324077772561326116, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=4.香港大学,李嘉诚医学院公共卫生学院(中国香港)]), AuthorCompany(id=1324077772649406502, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, xref=5., ext=[AuthorCompanyExt(id=1324077772657795111, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, companyId=1324077772649406502, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=5.伦敦卫生与热带医学院,流行病与人口健康系(英国伦敦)])], figs=[ArticleFig(id=1324077776881459283, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, language=EN, label=Fig.1, caption=Analysis of the relationship between BMI and DOR based on the RCS model, figureFileSmall=RZ2xFg8Fe2ek2/SOqJeSKA==, figureFileBig=OOt5dCKolyS0b58y+FLG0w==, tableContent=null), ArticleFig(id=1324077776961151060, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, language=CN, label=图1, caption=基于限制性立方样条模型分析BMI与卵巢储备功能减退的关系, figureFileSmall=RZ2xFg8Fe2ek2/SOqJeSKA==, figureFileBig=OOt5dCKolyS0b58y+FLG0w==, tableContent=null), ArticleFig(id=1324077778798256213, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, language=EN, label=Table 1, caption=

Basic characteristics of the study participants

, figureFileSmall=null, figureFileBig=null, tableContent=
变量DOR(n=3 751)对照(n=3 751)统计量P值
年龄[岁,M(P25, P75)]36(32~39)--
20~351 852(49.4)1 852(49.4)
36~471 899(50.6)1 899(50.6)
常居地[n(%)]2.0910.150
西南3 620(96.5)3 642(97.1)
非西南131(3.5)109(2.9)
民族[n(%)]14.725<0.001
汉族3 160(84.2)3 277(87.4)
其他591(15.8)474(12.6)
职业[n(%)]8.1900.017
体力劳动类420(11.2)352(9.4)
脑力劳动类1 618(43.1)1 702(45.4)
其他1 712(45.7)1 696(45.2)
婚姻状态[n(%)]2.5570.278
在婚3 377(97)3 336(97.7)
未婚87(2.5)68(2.0)
离婚16(0.5)12(0.3)
教育水平[n(%)]0.7520.687
高中及以下2 399(0.64)2 415(64.4)
本科1 242(33.1)1 238(33.0)
硕士及以上110(2.9)98(2.6)
BMI [kg/m2,M(P25, P75)]22.22(20.34~24.44)22.06(20.31~24.41)10.3820.016
体重过低[n(%)]243(6.5)275(7.3)
正常[n(%)]2 384(63.6)2 397(63.9)
超重[n(%)]929(24.8)937(25.0)
肥胖[n(%)]195(5.2)142(3.8)
吸烟[n(%)]0.2140.644
否3 197(94.7)3 493(95.0)
是179(5.3)185(5.0)
饮酒[n(%)]1.0710.300
否3 738(99.7)3 745(99.9)
是10(0.3)5(0.1)
AMH [ng/ml,M(P25, P75)]0.68(0.38~0.94)2.86(1.94~4.35)<0.001
FSH [mIU/ml,M(P25, P75)]9.14(7.00~12.44)7.22(6.08~8.50)<0.001
E2 [pg/ml,M(P25, P75)]33(23~46)25.9~41.89<0.001
P [ng/ml,M(P25, P75)]0.46(0.29~0.71)0.46(0.31~0.70)0.662
PRL(uIU/ml)236.34(158.87~335.84)251.17(176.26~346.97)<0.001
LH [mIU/ml,M(P25, P75)]3.77(2.68~5.29)3.74(2.77~4.93)0.002
T [ng/dl,M(P25, P75)]26.48(10.51~40.63)32.92(18.77~46.09)<0.001
AFC [个,M(P25, P75)]5(3~8)15(10~20)<0.001
VitD [ng/ml,M(P25, P75)]21.18(16.98~27.3)21.06(17.05~27.68)0.144
), ArticleFig(id=1324077778932473942, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, language=CN, label=表1, caption=

研究对象基本特征

, figureFileSmall=null, figureFileBig=null, tableContent=
变量DOR(n=3 751)对照(n=3 751)统计量P值
年龄[岁,M(P25, P75)]36(32~39)--
20~351 852(49.4)1 852(49.4)
36~471 899(50.6)1 899(50.6)
常居地[n(%)]2.0910.150
西南3 620(96.5)3 642(97.1)
非西南131(3.5)109(2.9)
民族[n(%)]14.725<0.001
汉族3 160(84.2)3 277(87.4)
其他591(15.8)474(12.6)
职业[n(%)]8.1900.017
体力劳动类420(11.2)352(9.4)
脑力劳动类1 618(43.1)1 702(45.4)
其他1 712(45.7)1 696(45.2)
婚姻状态[n(%)]2.5570.278
在婚3 377(97)3 336(97.7)
未婚87(2.5)68(2.0)
离婚16(0.5)12(0.3)
教育水平[n(%)]0.7520.687
高中及以下2 399(0.64)2 415(64.4)
本科1 242(33.1)1 238(33.0)
硕士及以上110(2.9)98(2.6)
BMI [kg/m2,M(P25, P75)]22.22(20.34~24.44)22.06(20.31~24.41)10.3820.016
体重过低[n(%)]243(6.5)275(7.3)
正常[n(%)]2 384(63.6)2 397(63.9)
超重[n(%)]929(24.8)937(25.0)
肥胖[n(%)]195(5.2)142(3.8)
吸烟[n(%)]0.2140.644
否3 197(94.7)3 493(95.0)
是179(5.3)185(5.0)
饮酒[n(%)]1.0710.300
否3 738(99.7)3 745(99.9)
是10(0.3)5(0.1)
AMH [ng/ml,M(P25, P75)]0.68(0.38~0.94)2.86(1.94~4.35)<0.001
FSH [mIU/ml,M(P25, P75)]9.14(7.00~12.44)7.22(6.08~8.50)<0.001
E2 [pg/ml,M(P25, P75)]33(23~46)25.9~41.89<0.001
P [ng/ml,M(P25, P75)]0.46(0.29~0.71)0.46(0.31~0.70)0.662
PRL(uIU/ml)236.34(158.87~335.84)251.17(176.26~346.97)<0.001
LH [mIU/ml,M(P25, P75)]3.77(2.68~5.29)3.74(2.77~4.93)0.002
T [ng/dl,M(P25, P75)]26.48(10.51~40.63)32.92(18.77~46.09)<0.001
AFC [个,M(P25, P75)]5(3~8)15(10~20)<0.001
VitD [ng/ml,M(P25, P75)]21.18(16.98~27.3)21.06(17.05~27.68)0.144
), ArticleFig(id=1324077779108634711, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, language=EN, label=Table 2, caption=

Conditional logistic regression analysis of the association between BMI and DOR

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模型β值SE值ZP值OR(95%CI)
模型1a
体重过低-0.1240.093-1.3320.1830.884(0.737~1.060)
正常参考
超重-0.0050.054-0.0860.9310.995(0.895~1.107)
肥胖0.3320.1152.8820.0041.394(1.112~1.747)
模型2b
体重过低-0.1090.093-1.1670.2430.897(0.747~1.077)
正常参考
超重-0.0260.055-0.4690.6390.975(0.875~1.085)
肥胖0.3010.1162.5960.0091.351(1.077~1.696)
模型3c
体重过低-0.0670.935-0.6760.4990.935(0.769~1.137)
正常参考
超重-0.0160.059-0.2680.7890.984(0.876~1.105)
肥胖0.3050.1242.4680.0141.357(1.065~1.730)
), ArticleFig(id=1324077779213492312, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, language=CN, label=表2, caption=

BMI与DOR的条件logistic分析

, figureFileSmall=null, figureFileBig=null, tableContent=
模型β值SE值ZP值OR(95%CI)
模型1a
体重过低-0.1240.093-1.3320.1830.884(0.737~1.060)
正常参考
超重-0.0050.054-0.0860.9310.995(0.895~1.107)
肥胖0.3320.1152.8820.0041.394(1.112~1.747)
模型2b
体重过低-0.1090.093-1.1670.2430.897(0.747~1.077)
正常参考
超重-0.0260.055-0.4690.6390.975(0.875~1.085)
肥胖0.3010.1162.5960.0091.351(1.077~1.696)
模型3c
体重过低-0.0670.935-0.6760.4990.935(0.769~1.137)
正常参考
超重-0.0160.059-0.2680.7890.984(0.876~1.105)
肥胖0.3050.1242.4680.0141.357(1.065~1.730)
), ArticleFig(id=1324077779452567641, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, language=EN, label=Table 3, caption=

Analysis of the association between BMI and ovarian reserve markers(AMH and AFC)

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变量rP1β(95%CI)P2
AMH
BMI-0.054<0.001-0.007(-0.011~-0.003)<0.001
BMI×组别--0.006(0.0005~0.011)0.032
AFC
BMI-0.044<0.0010.002(-0.003~0.008)0.372
BMI×组别---0.003(-0.010~0.004)0.392
), ArticleFig(id=1324077779553230938, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077771152039960, language=CN, label=表3, caption=

BMI与卵巢储备标志物(AMH、AFC)的关联性分析

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变量rP1β(95%CI)P2
AMH
BMI-0.054<0.001-0.007(-0.011~-0.003)<0.001
BMI×组别--0.006(0.0005~0.011)0.032
AFC
BMI-0.044<0.0010.002(-0.003~0.008)0.372
BMI×组别---0.003(-0.010~0.004)0.392
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中国西南地区女性体质指数对卵巢储备功能减退的影响
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赵帆 1 , 李鹏昊 2 , 黄崇弼 1 , Tony Cheung 3, 4 , Leesa lin 3, 4, 5 , 李静 1
现代预防医学 | 临床与预防 2026,53(6): 1147-1152
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现代预防医学 |临床与预防 2026 , 53 (6) : 1147 -1152
中国西南地区女性体质指数对卵巢储备功能减退的影响
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赵帆1, 李鹏昊2, 黄崇弼1, Tony Cheung3, 4, Leesa lin3, 4, 5, 李静1
作者信息
  • 1.四川大学华西公共卫生学院/华西第四医院,四川 成都 610041
  • 2.四川锦欣西囡妇女儿童医院
  • 3.医卫大数据深析实验室(中国香港
  • 4.香港大学,李嘉诚医学院公共卫生学院(中国香港
  • 5.伦敦卫生与热带医学院,流行病与人口健康系(英国伦敦
通讯作者:
李静,E-mail:
作者简介:

赵帆(1999—),女,硕士在读,研究方向:肿瘤的人群防治与女性生殖健康促进研究

Impact of body mass index on diminished ovarian reserve among women in southwest China
Fan ZHAO1, Peng-hao LI2, Chong-bi HUANG1, Tony Cheung3, 4, Leesa lin3, 4, 5, Jing LI1
Affiliations
  • West China School of Public Health, Sichuan University / West China Fourth Hospital, Chengdu, Sichuan 610041, China
出版时间: 2026-03-25 doi: 10.20043/j.cnki.MPM.202511324
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目的

探讨中国西南地区女性体质指数(body mass index,BMI)与卵巢储备功能减退(diminished ovarian reserve, DOR)之间的关系,分析BMI与卵巢储备功能标志物抗苗勒管激素(anti-Müllerian hormone, AMH)及窦卵泡数(antral follicle count, AFC)的相关性。

方法

采用年龄1:1精确匹配的病例-对照研究设计,纳入2022年1月至2024年8月期间在四川省某三甲医院寻求辅助生殖医疗服务的DOR和卵巢储备功能正常的女性患者,各3 751例。采用条件logistic回归模型评估BMI与DOR发生风险之间的关联,并利用限制性立方样条模型分析其剂量-反应关系;同时采用Spearman相关分析及多元线性回归模型探讨BMI与AMH、AFC之间的相关性。

结果

条件logistic回归分析结果显示,肥胖(OR=1.394,95% CI:1.112~1.747)与DOR患病存在统计学关联。调整潜在混杂因素后,肥胖(OR=1.357,95% CI:1.065~1.730)是DOR的风险因素,且BMI与DOR发生风险之间未观察到显著的非线性剂量-反应关系。Spearman相关性分析结果显示, AMH和AFC与BMI均呈负相关(P<0.001)。进一步调整混杂因素后,多元线性回归分析结果显示,BMI与AMH呈现负向效应(P<0.001),而AFC与BMI之间无显著相关性(P>0.05)。

结论

肥胖与卵巢储备功能减退风险增加相关,BMI与AMH水平呈负相关,而与AFC无显著相关性。

卵巢储备功能减退  /  体质指数  /  肥胖  /  抗苗勒管激素  /  窦卵泡数
Objective

To investigate the relationship between body mass index (BMI) and diminished ovarian reserve (DOR) in women from southwestern China, analyzing the correlation between BMI and ovarian reserve markers: anti-Müllerian hormone (AMH) and antral follicle count (AFC).

Methods

A 1:1 age-matched case-control study was conducted, enrolling 3 751 women with DOR and 3 751 women with normal ovarian reserve who sought assisted reproductive services at a tertiary hospital in Sichuan Province between January 2022 and August 2024. Conditional logistic regression analysis and restricted cubic spline models were employed to examine the association and dose-response relationship between BMI and DOR. Spearman correlation and multivariable linear regression analyses were applied to assess the relationships between BMI and AMH and AFC.

Results

Conditional logistic regression analysis revealed a significant association between obesity (OR=1.394, 95% CI: 1.112-1.747) and DOR. After adjusting for ethnicity, occupation, marital status, education level, smoking, and alcohol consumption, obesity (OR=1.357, 95% CI: 1.065-1.730) remained a risk factor for DOR. No non-linear relationship was observed between BMI and DOR. Spearman correlation analysis showed that both AMH and AFC were negatively correlated with BMI (P<0.001). In multivariable linear regression analyses adjusting for confounders, BMI demonstrated a negative association with AMH (P<0.001), whereas no significant association was observed between BMI and AFC (P>0.05).

Conclusion

Obesity is a risk factor for diminished ovarian reserve. BMI is negatively associated with AMH but not significantly correlated with AFC.

Diminished ovarian reserve  /  Body mass index  /  Obesity  /  Anti-Müllerian hormone  /  Antral follicle count
赵帆, 李鹏昊, 黄崇弼, Tony Cheung, Leesa lin, 李静. 中国西南地区女性体质指数对卵巢储备功能减退的影响. 现代预防医学, 2026 , 53 (6) : 1147 -1152 . DOI: 10.20043/j.cnki.MPM.202511324
Fan ZHAO, Peng-hao LI, Chong-bi HUANG, Tony Cheung, Leesa lin, Jing LI. Impact of body mass index on diminished ovarian reserve among women in southwest China[J]. Modern Preventive Medicine, 2026 , 53 (6) : 1147 -1152 . DOI: 10.20043/j.cnki.MPM.202511324
卵巢储备取决于剩余的卵母细胞的数量和质量,两者都会随着年龄的增长而下降。卵巢储备功能减退(diminished ovarian reserve, DOR)是指与同龄女性相比,月经规律的育龄女性的生育能力降低或对卵巢刺激的反应降低[1-2]。通常使用多种测试进行临床评估,包括血液生物标志物和超声检查[1,3]。在所有评估卵巢储备功能的研究中,血清抗苗勒管激素(anti-müllerian hormone, AMH)和窦卵泡数(antral follicle count, AFC)被认为是评估卵巢功能的最佳指标[1,3]。目前DOR的病因尚未完全明确,一般认为可能与遗传因素、自身免疫性疾病、医源性原因和环境因素在内的多种因素有关[4]。近年来,DOR检出率逐渐升高,呈现低龄化特征,人群中诊出率为10%~35%[5]。DOR不仅会引起月经周期紊乱、反复流产、不孕等生殖功能障碍,还可能引起绝经提前,从而对女性的身心健康及生活质量产生不利影响。
有研究表明,肥胖与着床率、妊娠率和活产率较低,以及流产率和不良围产期结局较高相关[6-7]。体质指数(body mass index, BMI)增加时更容易发生排卵异常、月经失调、生育延迟和不孕。然而,BMI与卵巢储备功能之间的关系尚存在争议。一些研究表明AMH和BMI之间存在负相关性[8-9],但也有荟萃分析得出矛盾的结果[10-11]。研究结果之间的差异可能归因于样本量有限或研究设计差异。此外,多数研究多关注卵巢储备标志物与BMI的关系,而以DOR作为结局变量进行分析的研究相对较少。
随着女性结婚和生育年龄的普遍推迟,识别与卵巢储备功能相关的可干预因素具有重要的公共卫生意义。近年来,我国超重肥胖问题日益突出,生育率面临持续下降的压力,国家相继出台体重管理和生育支持相关政策。在此背景下,了解BMI与卵巢储备功能减退的风险关系以及如何影响卵巢储备功能对于提高女性生活质量和落实体重管理政策、优化生育支持措施具有重要的实践价值。因此,本研究采用年龄1:1精确匹配的病例-对照研究设计,在大样本量人群中系统评估BMI与DOR发生风险之间的关系,并进一步探讨BMI与卵巢储备标志物(AMH和AFC)之间的相关性,以期为DOR的早期预防和风险评估提供依据。
回顾性收集2022年1月至2024年8月期间在四川省某三级甲等医院接受辅助生殖医疗服务的女性患者。根据年龄进行1:1精确匹配,最终共纳入DOR和对照各3 751例。DOR的诊断基于对卵巢储备功能的评价,根据《卵巢储备功能减退临床诊治专家共识》[2]进行评估。满足以下任意两项即判定为DOR:①AMH<1.1 ng/ml;②两侧卵巢AFC总数<5;③ 卵泡刺激素(FSH)≥10 IU/L。本研究经四川大学华西第四医院/华西公共卫生学院伦理委员会批准(伦理批号:Gwll2024143),并获准免除知情同意。
病例组的纳排标准。纳入标准:①符合DOR诊断标准;②年龄20~47周岁。排除标准:①近3个月月内使用过口服避孕药或其他外源性激素类药物;② 存在染色体核型异常;③合并代谢或内分泌系统疾病的患者,如糖尿病、甲状腺功能异常、高泌乳素血症等;④既往卵巢手术或子宫内膜异位症等。
对照组的纳排标准。纳入标准:①同期因输卵管或男性伴侣原因就诊的卵巢储备功能正常的女性;②年龄20~47周岁。排除标准:①AMH<1.1 ng/ml;②AFC<5枚的女性;③其余排除标准同病例组。
通过医院电子病例系统提取研究对象的一般资料,包括年龄、民族、职业、婚姻状态、教育水平、吸烟史、饮酒史、身高、体量以及既往手术史、遗传史和家族史等信息。临床检测指标方面,于研究对象月经周期第2~4天采集静脉血样,采用免疫化学发光法测定基础性激素水平,包括AMH、FSH、雌二醇(E2)、孕酮(P)、泌乳素(PRL)、黄体生成素(LH)和睾酮(T)以及维生素D(VitD)。经阴道超声测定双侧卵巢AFC。
BMI的计算方式为体重除以身高的平方(kg/m2)。根据《肥胖症中国诊疗指南(2024年版)》[12],将BMI (kg/m2) 分为四组:体重过低(BMI<18.5);正常(18.5≤BMI≤23.9);超重(24≤BMI≤27.9)和肥胖(BMI>28)。
采用描述性分析对研究对象的一般特征进行描述,计数资料以率或构成比表示,组间比较采用χ2检验。计量资料使用Kolmogorov-Smirnov方法进行正态性检验,资料均不符合正态性,以中位数M(P25, P75)表示,组间比较采用配对Wilcoxon符号秩检验。基于组间分布差异与既往研究证据[3,13],将民族、职业、婚姻状态、教育水平、吸烟和饮酒作为协变量。采用条件logistic回归模型分析BMI与DOR发生风险之间的关联。构建限制性立方样条模型分析BMI与卵巢储备功能减退风险的剂量反应关系,设定节点数为4(P5,P25,P75,P95)[14]。为全面分析BMI与卵巢储备功能减退的关系,使用Spearman相关性分析初步探索BMI与卵巢储备标志物AMH、AFC之间的相关性,以评估二者之间的单变量相关趋势。对AMH和AFC进行对数转换,分别构建多元线性回归模型,以调整潜在混杂因素后进一步量化BMI与卵巢储备标志物之间的独立关联性。在R 4.4.2软件中进行统计分析,所有检验均采取双侧检验,检验水准α=0.05。
病例组和对照组在民族和职业分布存在差异,病例组的汉族比例低于对照组(P<0.001),且病例组较对照组更多从事体力劳动类职业(P<0.05)。两组在婚姻状态、教育水平、吸烟和饮酒分布不存在统计学差异(P>0.05)。病例组的BMI中位数高于对照组,且肥胖比例高于对照组(P<0.05)。在临床生化指标方面,病例组的卵巢储备标志物AMH水平显著低于对照组(P<0.001),AFC显著低于对照组(P<0.001)。其他基础性激素水平中,FSH、E2、LH、PRL和T激素水平在两组间的分布均具有统计学差异,病例组FSH、E2和LH激素水平高于对照组,PRL和T激素水平低于对照组(P<0.001)。P及维生素D水平在两组间差异均无统计学意义(均P > 0.05)。见表1。
表2显示,在未调整混杂因素的模型1中,与正常体重女性相比,肥胖与DOR发生风险存在统计学关联(OR=1.394,95% CI:1.112~1.747)。进一步调整民族和职业调整之后,该关联仍具有统计学意义(模型2)(OR=1.351,95% CI:1.077~1.696)。考虑婚姻状态、教育水平、吸烟和饮酒之后(模型3),肥胖与DOR发生风险增加仍显著相关(OR=1.357,95% CI:1.065~1.730)。肥胖女性发生DOR的风险是正常体重女性的1.357倍。
在校正民族、职业、婚姻状态、教育水平、吸烟及饮酒后,采用限制性立方样条模型分析BMI与DOR发生风险之间的剂量-反应关系,结果未观察到显著的非线性关联(非线性检验,P=0.646)。见图1。
Spearman相关性分析结果显示,卵巢储备标志物AMH和AFC与BMI均呈负相关(P<0.001)。进一步调整混在因素后,多元线性回归分析结果显示,BMI对AMH影响显著,呈现负向效应(P<0.001),且影响在DOR组和对照组之间存在差异(P<0.05);而AFC与BMI之间无显著相关性(P>0.05)。见表3。
卵巢储备功能减退不仅影响女性的生殖结局,还与心血管代谢性疾病、恶性肿瘤、神经系统疾病及全因死亡风险增加相关[15]。一些研究已经研究了BMI或肥胖与AMH的关系,但多数研究样本量有限,结论尚不一致,而且目前缺少关于中国西南地区女性卵巢储备功能减退与BMI关系的研究。本研究以四川省某三甲医院就诊女性为研究对象,采用按年龄进行精确匹配的大样本病例-对照研究设计,系统评估BMI与DOR之间的关联,发现肥胖(BMI>28 kg/m2)与DOR发生风险增加显著相关,且BMI与卵巢储备标志物AMH之间为显著的负相关,与AFC不存在明显的相关性。为BMI与卵巢储备功能减退之间的关联提供了来自中国西南地区女性人群的流行病学证据。
目前,DOR的发病机制尚不明确,既往研究提示可能与遗传因素、感染、环境暴露、社会心理因素以及既往生殖系统手术或放化疗等医源性因素相关[2]。肥胖会影响生殖健康,但是否对卵巢储备存在影响,目前仍存在争议,可能与研究人群特征(如种族构成)及BMI分类标准差异有关。与亚洲和西班牙女性相比,白种人女性的AMH水平相对较高,且在白种人和非洲裔女性中,BMI升高与AMH水平降低相关,而在亚洲和西班牙女性中该关联并不显著[8-9,16]。我们研究显示,汉族女性的DOR患病率低于其他民族。此外,BMI作为衡量肥胖的常用指标,其适用阈值在不同种族人群中可能存在差异。相较于欧洲人群,亚洲人群在相同BMI水平下往往具有更高的体脂百分比。因此,目前世界卫生组织的肥胖和超重标准可能无法准确反映亚洲人代谢紊乱的风险。Hazarika S等在印度不孕女性中未发现肥胖(BMI>30 kg/m2)与DOR之间的关联[17]。而美国的一项研究表明与非肥胖的DOR女性相比,肥胖(BMI>30 kg/m2)女性的血清AMH水平较低[18]。在我国,一项同济医院不孕女性中进行的研究发现BMI与AMH呈负相关,并且肥胖(BMI>30 kg/m2)会影响胚胎结局,包括取出的卵母细胞、成熟卵母细胞和受精卵母细胞的数量以及会增加不良围产期结局的风险[19]。本研究按照中国成人超重和肥胖的BMI划分标准,将BMI>28 kg/m2划分为肥胖,并且将BMI分别视为分类变量和连续变量评估与DOR以及卵巢储备标志物的关系,结论与Moslehi N等开展的一项系统综述与荟萃分析的相似[20],肥胖女性的AMH和FSH显着低于非肥胖女性,且BMI与AMH呈负相关,与AFC无显著相关。
AMH由卵巢内正在生长的小卵泡的颗粒细胞分泌,并释放在卵泡腔内,其水平准确地反映了窦卵泡池的大小,代表剩余原始卵泡的数量。AFC是在早卵泡期经阴道超声测量的直径2~10 mm卵泡数量之和,反映与获卵数相关的卵泡簇,可用于预测卵巢的反应性。本研究中BMI和AFC之间的未显著关联,可能由于经阴道超声检测期间无法区分健康卵泡和闭锁卵泡,高估了AFC的数量,结果准确性受到影响。也需要考虑AFC在周期内和周期间的差异较大,其在肥胖女性中的预测准确性可能受到了限制,因此,AMH因较稳定和方便检查方便等特点被认为是卵巢储备功能更可靠的预测指标[21]。肥胖影响卵巢功能的机制尚不清楚。一种可能是脂肪组织中芳香酶活性升高,促进外周雄激素向雌激素的转化,导致下丘脑-垂体-卵巢轴的负反馈,从而抑制卵巢卵泡生成[22]。另一种可能是,肥胖通过脂联素和瘦素等脂肪因子与颗粒细胞的相互作用调节卵巢功能,诱导颗粒细胞凋亡作用,减少AMH产生,这种细胞凋亡作用还有可能引起卵巢卵泡闭锁,最终导致卵巢储备功能下降[23]。
样本量足够大以及按年龄精确匹配病例和对照以减少混杂变量的影响是我们研究的优势。但仍然存在一些局限性。首先,BMI通常用于评估肥胖,但它可能无法准确反映体脂分布和体脂含量,且BMI数据来源于入院时测量,由于研究设计的时间顺序限制,无法明确BMI与DOR之间的因果关系。其次,尽管在统计分析时对已知因素进行了调整,但由于医院电子病历系统的局限性,遗传易感性、感染史、居住环境、社会心理因素及其他环境暴露等重要变量未能纳入分析,残余混杂因素仍不可避免。此外,目前临床上尚缺乏统一的卵巢储备功能减退诊断标准,不同医疗机构及临床医生在诊断依据和阈值选择上可能存在差异,亦可能对研究结果造成一定偏倚。综上所述,本研究发现BMI与卵巢储备标志物AMH呈负相关关系,肥胖是卵巢储备功能减退的危险因素。未来仍需进行纵向的临床研究以及有效的肥胖和脂肪分布测量方法,以了解肥胖与卵巢储备功能之间复杂的关系,并评估体重干预在多大程度上可以逆转BMI升高所导致的AMH下降。
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2026年第53卷第6期
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doi: 10.20043/j.cnki.MPM.202511324
  • 接收时间:2025-11-20
  • 首发时间:2026-11-02
  • 出版时间:2026-03-25
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  • 收稿日期:2025-11-20
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AIR@InnoHK航空科技研究计划资助项目
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    1.四川大学华西公共卫生学院/华西第四医院,四川 成都 610041
    2.四川锦欣西囡妇女儿童医院
    3.医卫大数据深析实验室(中国香港
    4.香港大学,李嘉诚医学院公共卫生学院(中国香港
    5.伦敦卫生与热带医学院,流行病与人口健康系(英国伦敦

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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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