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Article(id=1241035816051462444, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035810628235909, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202406438, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1719417600000, receivedDateStr=2024-06-27, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773815595877, onlineDateStr=2026-03-18, pubDate=1733760000000, pubDateStr=2024-12-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773815595877, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773815595877, creator=13701087609, updateTime=1773815595877, updator=13701087609, issue=Issue{id=1241035810628235909, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='23', pageStart='4225', pageEnd='4416', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773815594584, creator=13701087609, updateTime=1773815743629, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241036435843764756, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035810628235909, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241036435843764757, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035810628235909, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=4411, endPage=4416, ext={EN=ArticleExt(id=1241035816873546069, articleId=1241035816051462444, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Study on the relationship between obesity phenotypes in middle-aged and elderly individuals and stroke risk, and the regulatory role of C-reactive protein, columnId=1228016569138213037, journalTitle=Modern Preventive Medicine, columnName=Clinical Medicine and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate whether C-reactive protein (CRP) can modulate the relationship between obesity phenotypes and stroke risk in middle-aged and elderly individuals through a cohort study.

Methods

Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS), participants from the baseline survey in 2011 and follow-ups in 2013, 2015, and 2018 were selected. Based on the participants’ weight status (overweight/obese) and metabolic abnormalities, they were categorized into four obesity phenotypes: Metabolically Healthy Normal Weight (MHNO), Metabolically Healthy Overweight/Obese (MHO), Metabolically Unhealthy Normal Weight (MANO), and Metabolically Unhealthy Overweight/Obese(MAO). Additionally, participants were grouped into three categories based on CRP quartiles. The Cox proportional hazards regression model was employed to analyze the relationship between obesity phenotypes, CRP, and stroke, incorporating interaction terms between obesity phenotypes and CRP in the regression model. Finally, stratified analyses were conducted to explore whether CRP modulates the relationship between obesity phenotypes and stroke.

Results

Totally 6 868 participants were included in this study, with 421 (6.13%) experiencing a stroke during the 7-year follow-up period. The Cox proportional hazards regression model indicated that, compared to the MHNO group, the risk of stroke increased by 66%, 85%, and 151% for the MHO, MANO, and MAO groups, respectively, with hazard ratios (HR) and 95% confidence intervals (CI) of 1.66 (1.15-2.38), 1.85 (1.39-2.46), and 2.51 (1.92-3.29), respectively. Furthermore, stratified analysis revealed that in the CRP T1 group, the MHO and MANO groups did not show an increased risk of stroke compared to the MHNO group (P > 0.05). In the CRP T2 group, the risk of stroke for the MHO and MANO groups increased by 111% and 127%, with HR and 95%CI of 2.11 (1.11-4.02) and 2.27 (1.29-3.98), respectively. Similarly, in the CRP T3 group, the risks for the MHO and MANO groups increased by 93% and 89%, with HR and 95%CI of 1.93 (1.06-3.50) and 1.89 (1.17-3.05), respectively.

Conclusion

CRP can modulate the relationship between obesity phenotypes and stroke in middle-aged and elderly individuals.

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目的

通过队列研究探讨C反应蛋白(CRP)是否可以调节中老年人肥胖表型与脑卒中的关系。

方法

本研究利用中国健康与养老追踪调查数据，选取了参加2011年基线调查与2013、2015和2018年三次随访的研究对象。根据研究对象是否超重/肥胖以及是否存在代谢异常，将他们分为四种肥胖表型：代谢健康正常体重组（MHNO）、代谢健康超重/肥胖组（MHO）、代谢异常正常体重组（MANO)、代谢异常超重/肥胖组（MAO），同时根据CRP的三分位数将其分为三组。采用Cox比例风险回归模型分析肥胖表型、CRP与脑卒中关系，并在回归模型中加入肥胖表型与CRP的交互项，最后通过分层分析探讨CRP是否可以调节肥胖表型与脑卒中关系。

结果

本研究共纳入6 868名研究对象，7年随访期间，共有421(6.13%)名研究对象发生脑卒中。Cox比例风险回归模型显示：与MHNO组相比，MHO、MANO和MAO组发生脑卒中风险分别上升66%、85%和151%，其HR（95%CI）分别为1.66(1.15～2.38)、1.85(1.39～2.46)和2.51(1.92～3.29)。同时分层分析中发现，在CRP T1组中，与MHNO组相比，MHO和MANO组并未增加脑卒中的发病风险(P>0.05)；而在CRP T2中，MHO和MANO组发生脑卒中的风险分别上升了111%和127%，其HR（95%CI）分别为2.11(1.11～4.02)、2.27(1.29～3.98)；同样在CRP T3中，MHO和MANO组发生脑卒中的风险分别上升了93%和89%，其HR（95%CI）分别为1.93(1.06～3.50)、1.89(1.17～3.05)。

结论

CRP可以调节中老年人肥胖表型与脑卒中的关系。

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王舒，E-mail:
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李胜男（1987—），女，本科，护师，研究方向：疾病康复与护理

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Low-Density lipoprotein cholesterol is not a major determinant of High-Sensitivity C-Reactive protein levels in People without known diabetes mellitus[J].Metabolic Syndrome and Related Disorders, 2023, 21(10): 567-572., articleTitle=Low-Density lipoprotein cholesterol is not a major determinant of High-Sensitivity C-Reactive protein levels in People without known diabetes mellitus, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1241069116975535034, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035816051462444, xref=null, ext=[AuthorCompanyExt(id=1241069116983923643, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035816051462444, companyId=1241069116975535034, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Shengjing Hospital Affiliated to China Medical University, Shenyang, Liaoning 110004, China), AuthorCompanyExt(id=1241069116992312252, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035816051462444, companyId=1241069116975535034, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=中国医科大学附属盛京医院，辽宁 沈阳 110004)])], figs=[ArticleFig(id=1241069119995433071, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035816051462444, language=EN, label=Table 1, caption=

Baseline information of research subjects ，n（%）］

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研究变量全部对象脑卒中F/χ2值P值
否是
年龄(岁)58.2±8.658.0±8.560.8±8.441.063<0.001
睡眠时间(h)6.4±1.96.4±1.86.2±1.93.8820.049
CRP (mg/dl)2.5±6.82.4±6.63.41±0.39.1340.003
性别1.9810.088
男3 101(45.2)2 897(44.9)204(48.5)
女3 767(54.8)3 550(55.1)217(51.5)
教育状况0.8120.668
小学以下3 167(46.1)2 964(46.0)203(48.2)
小学初中2 995(43.6)2 819(43.7)176(41.8)
高中以上706(10.3)664(10.3)42(10.0)
婚姻状况2.0740.088
在婚同居5 889(85.7)5 538(85.9)351(83.4)
其他979(14.3)909(14.1)70(16.6)
居住地0.0790.411
农村5 808(84.6)5 454(84.6)354(84.1)
城市1 060(15.4)993(15.4)67(15.9)
吸烟情况6.5980.037
不吸烟4 297(62.6)4 057(62.9)240(57.0)
戒烟203(3.0)186(2.9)17(4.0)
吸烟2 368(34.5)2 204(34.2)164(39.0)
饮酒情况2.3120.137
不饮酒5 062(73.7)4 765(73.9)297(70.5)
饮酒1 806(26.3)1 682(26.1)124(29.5)
体力活动6.4130.041
低4 384(63.8)4 098(63.6)286(67.9)
中522(7.6)485(7.5)37(8.8)
高1 962(28.6)1 864(28.9)98(23.3)
), ArticleFig(id=1241069120079319159, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035816051462444, language=CN, label=表1, caption=

研究对象基线信息［，n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
研究变量全部对象脑卒中F/χ2值P值
否是
年龄(岁)58.2±8.658.0±8.560.8±8.441.063<0.001
睡眠时间(h)6.4±1.96.4±1.86.2±1.93.8820.049
CRP (mg/dl)2.5±6.82.4±6.63.41±0.39.1340.003
性别1.9810.088
男3 101(45.2)2 897(44.9)204(48.5)
女3 767(54.8)3 550(55.1)217(51.5)
教育状况0.8120.668
小学以下3 167(46.1)2 964(46.0)203(48.2)
小学初中2 995(43.6)2 819(43.7)176(41.8)
高中以上706(10.3)664(10.3)42(10.0)
婚姻状况2.0740.088
在婚同居5 889(85.7)5 538(85.9)351(83.4)
其他979(14.3)909(14.1)70(16.6)
居住地0.0790.411
农村5 808(84.6)5 454(84.6)354(84.1)
城市1 060(15.4)993(15.4)67(15.9)
吸烟情况6.5980.037
不吸烟4 297(62.6)4 057(62.9)240(57.0)
戒烟203(3.0)186(2.9)17(4.0)
吸烟2 368(34.5)2 204(34.2)164(39.0)
饮酒情况2.3120.137
不饮酒5 062(73.7)4 765(73.9)297(70.5)
饮酒1 806(26.3)1 682(26.1)124(29.5)
体力活动6.4130.041
低4 384(63.8)4 098(63.6)286(67.9)
中522(7.6)485(7.5)37(8.8)
高1 962(28.6)1 864(28.9)98(23.3)
), ArticleFig(id=1241069120179982463, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035816051462444, language=EN, label=Table 2, caption=

The relationship between obesity phenotype and stroke

, figureFileSmall=null, figureFileBig=null, tableContent=
肥胖表型新发病例数/总人数模型1模型2
HR (95%CI)P值HR (95%CI)P值
MHNO81/2 1861.0 (ref)1.0 (ref)
MHO48/9241.61(1.13～2.31)0.0091.66(1.15～2.38)0.006
MANO119/1 6791.84(1.39～2.45)<0.0011.85(1.39～2.46)<0.001
MAO173/2 0792.44(1.87～3.18)<0.0012.51(1.92～3.29)<0.001
), ArticleFig(id=1241069120326783113, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035816051462444, language=CN, label=表2, caption=

肥胖表型与脑卒中关系

, figureFileSmall=null, figureFileBig=null, tableContent=
肥胖表型新发病例数/总人数模型1模型2
HR (95%CI)P值HR (95%CI)P值
MHNO81/2 1861.0 (ref)1.0 (ref)
MHO48/9241.61(1.13～2.31)0.0091.66(1.15～2.38)0.006
MANO119/1 6791.84(1.39～2.45)<0.0011.85(1.39～2.46)<0.001
MAO173/2 0792.44(1.87～3.18)<0.0012.51(1.92～3.29)<0.001
), ArticleFig(id=1241069120465195154, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035816051462444, language=EN, label=Table 3, caption=

CRP tertiles and the risk of stroke

, figureFileSmall=null, figureFileBig=null, tableContent=
CRP分组新发病例数/总人数模型 1模型2
HR (95%CI)P值HR (95%CI)P值
T1107/2 3081.0 (ref)1.0 (ref)
T2128/2 2751.16(0.89～1.49)0.2741.16(0.89～1.50)0.286
T3186/2 2851.66(1.30～2.11)<0.0011.65(1.30～2.10)<0.001
), ArticleFig(id=1241069120611995805, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035816051462444, language=CN, label=表3, caption=

CRP三分位数与脑卒中发病风险

, figureFileSmall=null, figureFileBig=null, tableContent=
CRP分组新发病例数/总人数模型 1模型2
HR (95%CI)P值HR (95%CI)P值
T1107/2 3081.0 (ref)1.0 (ref)
T2128/2 2751.16(0.89～1.49)0.2741.16(0.89～1.50)0.286
T3186/2 2851.66(1.30～2.11)<0.0011.65(1.30～2.10)<0.001
), ArticleFig(id=1241069120742019237, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035816051462444, language=EN, label=Table 4, caption=

The modifying effect of CRP on obesity phenotypes

, figureFileSmall=null, figureFileBig=null, tableContent=
CRP分组新发病例数/总人数模型 1模型2
HR (95%CI)P值HR (95%CI)P值
T1
MHNO36/9671.0 (ref)1.0 (ref)
MHO9/3190.93(0.46～2.03)0.9340.99(0.48～2.11)0.987
MANO35/6141.45(0.91～2.31)0.1231.45(0.91～2.32)0.119
MAO27/4082.05(1.24～3.39)0.0052.09(1.26～3.47)0.004
T2
MHNO19/6831.0 (ref)1.0 (ref)
MHO19/3382.12(1.12～4.01)0.0222.11(1.11～4.02)0.023
MANO35/5272.33(1.33～4.09)0.0032.27(1.29-3.98)0.004
MAO55/7272.82(1.67～4.78)<0.0012.79(1.64～4.77)<0.001
T3
MHNO26/5361.0 (ref)1.0 (ref)
MHO20/2671.87(1.03～3.37)0.0391.93(1.06～3.50)0.031
MANO49/5381.85(1.15～2.99)0.0121.89(1.17～3.05)0.010
MAO91/9442.24(1.44～3.49)<0.0012.40(1.53～3.77)<0.001
), ArticleFig(id=1241069120884625584, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035816051462444, language=CN, label=表4, caption=

CRP对肥胖表型的调节效应

, figureFileSmall=null, figureFileBig=null, tableContent=
CRP分组新发病例数/总人数模型 1模型2
HR (95%CI)P值HR (95%CI)P值
T1
MHNO36/9671.0 (ref)1.0 (ref)
MHO9/3190.93(0.46～2.03)0.9340.99(0.48～2.11)0.987
MANO35/6141.45(0.91～2.31)0.1231.45(0.91～2.32)0.119
MAO27/4082.05(1.24～3.39)0.0052.09(1.26～3.47)0.004
T2
MHNO19/6831.0 (ref)1.0 (ref)
MHO19/3382.12(1.12～4.01)0.0222.11(1.11～4.02)0.023
MANO35/5272.33(1.33～4.09)0.0032.27(1.29-3.98)0.004
MAO55/7272.82(1.67～4.78)<0.0012.79(1.64～4.77)<0.001
T3
MHNO26/5361.0 (ref)1.0 (ref)
MHO20/2671.87(1.03～3.37)0.0391.93(1.06～3.50)0.031
MANO49/5381.85(1.15～2.99)0.0121.89(1.17～3.05)0.010
MAO91/9442.24(1.44～3.49)<0.0012.40(1.53～3.77)<0.001
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中老年人肥胖表型与脑卒中发病风险研究及C反应蛋白的调节作用研究
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李胜男 , 王舒
现代预防医学 | 临床与预防 2024,51(23): 4411-4416
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现代预防医学 | 临床与预防 2024, 51(23): 4411-4416
中老年人肥胖表型与脑卒中发病风险研究及C反应蛋白的调节作用研究
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李胜男, 王舒
作者信息
  • 中国医科大学附属盛京医院，辽宁 沈阳 110004

    • 李胜男（1987—），女，本科，护师，研究方向：疾病康复与护理

通讯作者:

王舒，E-mail:
Study on the relationship between obesity phenotypes in middle-aged and elderly individuals and stroke risk, and the regulatory role of C-reactive protein
Sheng-nan LI, Shu WANG
Affiliations
  • Shengjing Hospital Affiliated to China Medical University, Shenyang, Liaoning 110004, China
出版时间: 2024-12-10 doi: 10.20043/j.cnki.MPM.202406438
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摘要
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目的

通过队列研究探讨C反应蛋白(CRP)是否可以调节中老年人肥胖表型与脑卒中的关系。

方法

本研究利用中国健康与养老追踪调查数据，选取了参加2011年基线调查与2013、2015和2018年三次随访的研究对象。根据研究对象是否超重/肥胖以及是否存在代谢异常，将他们分为四种肥胖表型：代谢健康正常体重组（MHNO）、代谢健康超重/肥胖组（MHO）、代谢异常正常体重组（MANO)、代谢异常超重/肥胖组（MAO），同时根据CRP的三分位数将其分为三组。采用Cox比例风险回归模型分析肥胖表型、CRP与脑卒中关系，并在回归模型中加入肥胖表型与CRP的交互项，最后通过分层分析探讨CRP是否可以调节肥胖表型与脑卒中关系。

结果

本研究共纳入6 868名研究对象，7年随访期间，共有421(6.13%)名研究对象发生脑卒中。Cox比例风险回归模型显示：与MHNO组相比，MHO、MANO和MAO组发生脑卒中风险分别上升66%、85%和151%，其HR（95%CI）分别为1.66(1.15～2.38)、1.85(1.39～2.46)和2.51(1.92～3.29)。同时分层分析中发现，在CRP T1组中，与MHNO组相比，MHO和MANO组并未增加脑卒中的发病风险(P>0.05)；而在CRP T2中，MHO和MANO组发生脑卒中的风险分别上升了111%和127%，其HR（95%CI）分别为2.11(1.11～4.02)、2.27(1.29～3.98)；同样在CRP T3中，MHO和MANO组发生脑卒中的风险分别上升了93%和89%，其HR（95%CI）分别为1.93(1.06～3.50)、1.89(1.17～3.05)。

结论

CRP可以调节中老年人肥胖表型与脑卒中的关系。

肥胖表型  /  C反应蛋白  /  脑卒中  /  队列研究  /  调节效应
Objective

To investigate whether C-reactive protein (CRP) can modulate the relationship between obesity phenotypes and stroke risk in middle-aged and elderly individuals through a cohort study.

Methods

Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS), participants from the baseline survey in 2011 and follow-ups in 2013, 2015, and 2018 were selected. Based on the participants’ weight status (overweight/obese) and metabolic abnormalities, they were categorized into four obesity phenotypes: Metabolically Healthy Normal Weight (MHNO), Metabolically Healthy Overweight/Obese (MHO), Metabolically Unhealthy Normal Weight (MANO), and Metabolically Unhealthy Overweight/Obese(MAO). Additionally, participants were grouped into three categories based on CRP quartiles. The Cox proportional hazards regression model was employed to analyze the relationship between obesity phenotypes, CRP, and stroke, incorporating interaction terms between obesity phenotypes and CRP in the regression model. Finally, stratified analyses were conducted to explore whether CRP modulates the relationship between obesity phenotypes and stroke.

Results

Totally 6 868 participants were included in this study, with 421 (6.13%) experiencing a stroke during the 7-year follow-up period. The Cox proportional hazards regression model indicated that, compared to the MHNO group, the risk of stroke increased by 66%, 85%, and 151% for the MHO, MANO, and MAO groups, respectively, with hazard ratios (HR) and 95% confidence intervals (CI) of 1.66 (1.15-2.38), 1.85 (1.39-2.46), and 2.51 (1.92-3.29), respectively. Furthermore, stratified analysis revealed that in the CRP T1 group, the MHO and MANO groups did not show an increased risk of stroke compared to the MHNO group (P > 0.05). In the CRP T2 group, the risk of stroke for the MHO and MANO groups increased by 111% and 127%, with HR and 95%CI of 2.11 (1.11-4.02) and 2.27 (1.29-3.98), respectively. Similarly, in the CRP T3 group, the risks for the MHO and MANO groups increased by 93% and 89%, with HR and 95%CI of 1.93 (1.06-3.50) and 1.89 (1.17-3.05), respectively.

Conclusion

CRP can modulate the relationship between obesity phenotypes and stroke in middle-aged and elderly individuals.

Obesity phenotype  /  C-reactive protein  /  Stroke  /  Cohort study  /  Moderating effect
李胜男, 王舒. 中老年人肥胖表型与脑卒中发病风险研究及C反应蛋白的调节作用研究. 现代预防医学, 2024 , 51 (23) : 4411 -4416 . DOI: 10.20043/j.cnki.MPM.202406438
Sheng-nan LI, Shu WANG. Study on the relationship between obesity phenotypes in middle-aged and elderly individuals and stroke risk, and the regulatory role of C-reactive protein[J]. Modern Preventive Medicine, 2024 , 51 (23) : 4411 -4416 . DOI: 10.20043/j.cnki.MPM.202406438
正文
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脑卒中是一种急性神经系统疾病，其发病机制在于脑部血流的急剧中断，导致局灶性或全身性的神经功能损伤[1]。在全球范围内，每年有超过1 370万人发生脑卒中，更有550万人因此离世[2]。此外，脑卒中极易引起痴呆和残疾[3]，这些后遗症不仅严重影响了患者的生活品质，同时也给家庭和社会带来了沉重的负担。因此，深入研究脑卒中的预防和治疗策略尤为重要。
尽管已有研究揭示了超重/肥胖与代谢紊乱均会增加脑卒中的发病风险[4-5]。然而基于肥胖和代谢特征定义的代谢健康超重肥胖（MHO）表型与脑卒中关系目前仍存在争议[6-7]。此外，C反应蛋白（C-reactive protein，CRP），作为一种重要的炎性标志物，其水平变化与脑卒中风险及预后紧密相关[8-9]。
有研究发现肥胖和代谢紊乱常与炎症反应紧密相关，且不同肥胖表型间CRP水平存在显著差异，这提示我们在探讨肥胖表型与脑卒中关系时，必须综合考虑肥胖表型、CRP及其相互作用的复杂性[10-11]。目前对该部分的研究仍较少，因此，本研究针对中国中老年人进行了深入调查，旨在探究中老年人肥胖表型、CRP对脑卒中风险的影响，并进一步分析CRP在其中可能发挥的调节作用。
1 材料与方法
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1.1 数据来源
中国健康与养老追踪调查(China Health and Retirement Longitudinal Study, CHARLS) 是一项正在进行的，旨在探讨中国中老年人健康与养老状况的纵向追踪调查[12]。本研究选择参加2011年基线调查与2013、2015和2018年三次随访的研究对象，排除标准：（1）2011年(基线)自报脑卒中病史的个体；（2）身高、体重、空腹血糖、血压、高密度脂蛋白、甘油三脂、CRP等关键数据缺失者；（3）身体质量指数(body mass index, BMI）<18.5kg/m2；（4）基线年龄<45岁；（5）体力活动等关键协变量缺失者，最终本研究共纳入6 868名研究对象。
1.2 基线调查
本研究通过标准化问卷收集了研究对象的性别、年龄、教育情况、婚姻状态、居住环境以及高血压和糖尿病史等基本信息。身体测量涵盖了研究对象的身高、体重等身体指标。血生化检测项目较多，主要包涵空腹血糖(FPG)水平、高密度脂蛋白胆固醇(HDL-C)、总胆固醇、糖化血红蛋白水平、低密度脂蛋白胆固醇(LDL-C)、甘油三酯(TG)和CRP等关键指标。
通过询问研究对象每周参加体力活动的类型、频率和时间计算体力活动水平。体力活动的类型分为高强度（如挖地、耕作）、中等强度（如拖地、打太极）、低强度（如家中散步、走路等）的体力活动；频率为每周的运动次数；时间分为0、20、75、180和240 min五个档次。每周体力活动时间为活动频率和时间的乘积。采用代谢当量（metabolic equivalent，MET）计算体力活动的运动量，根据国际体力活动问卷评判标准，将高强度活动的MET定义为8.0，中强度活动的MET定义为4.0，低强度的定义为3.3[13]。故体力活动水平的计算方式为：8.0×高强度活动每周持续时间+4.0×中强度活动每周持续时间+3.3×低强度活动每周持续时间[14]。根据国际体力活动标准将体力活动水平分为低(<600METs/周)、中 (600～3 000 METs/周)、高(>3 000 METs/周)三个水平[13]。
1.3 相关定义及诊断标准
1.3.1 脑卒中
基于问卷调查诊断脑卒中：“您曾经是否被医生诊断为脑卒中？”和“您第一次被诊断出或知道自己患有脑卒中是在哪一年或多少岁？”。
1.3.2 肥胖
BMI常被用于判定研究对象的肥胖状况。BMI = 体重(kg)/身高的二次方(m)2。根据中国成人体重分类标准将研究对象分为两组：正常体重(18.5kg/m2≤BMI<24.0 kg/m2)、超重/肥胖（BMI ≥ 24.0 kg/m2）。
1.3.3 代谢综合征（metabolic syndrome，Mets）
根据NCEP-ATP III（胆固醇教育计划第三版）的准则，研究对象符合以下四个条件中的两个或以上，则可被诊断为代谢综合征[15]：
（1）TG水平≥1.7 mmol/L 或使用降脂药物;
（2）HDL-C水平<1.03 mmol/L (男性)或<1.29 mmol/L (女性);
（3）FPG≥5.6 mmol/L 或药物治疗高血糖；
（4）收缩压(SBP)≥130 mm Hg 或舒张压(DBP)≥85 mm Hg 或使用药物治疗高血压。
1.3.4 肥胖表型
根据研究对象是否超重/肥胖及是否存在代谢异常，将研究对象分为以下四种肥胖表型：代谢健康正常体重组（metabolically healthy non-overweight/obesity，MHNO）、代谢健康超重/肥胖组（metabolically healthy overweight/obesity，MHO）、代谢异常正常体重组（metabolically abnormal non-overweight/obesity，MANO)、代谢异常超重/肥胖组（metabolically abnormal overweight/obesity，MAO）。
1.4 统计学方法
在本研究中，连续性变量采用（均值±标准差）进行描述，分类数据则通过频数和百分比表达。通过单因素方差分析或χ2检验评估组间差异。根据CRP的三分位数将研究对象分为三组，同时利用Cox比例风险回归模型，在调整性别、年龄、教育状况、居住地、睡眠时间等潜在混杂因素后，分析肥胖表型、CRP与脑卒中的关系。在回归模型中加入肥胖表型与CRP的交互项，结果显示交互项存在统计学意义(P<0.05)。基于此发现，我们进行了CRP三分位数的分层分析，以探索不同CRP水平下肥胖表型与脑卒中的关系。数据分析使用SPSS 26.0软件，检验水准为 α=0.05。
2 结果
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2.1 研究对象基线特征
本研究共纳入6 868人，其中男性3 101(45.2%)人，女性3 767(54.8%)人，研究对象平均年龄为（58.2±8.6）岁。在七年随访期间，共有421(6.13%)人发生脑卒中，与没有发生脑卒中的研究对象相比，发生脑卒中者平均年龄更大，CRP浓度更高，睡眠时间更短，吸烟、饮酒者占比更高。见表1。
2.2 不同肥胖表型与脑卒中发病风险
在七年随访期间，共有421名研究对象发生脑卒中。其中MHNO、MHO、MANO和MAO组脑卒中的发生率分别为3.71%、5.19%、7.09%和8.32%，呈逐渐递增趋势。在调整性别、年龄、学历、婚姻状况等混杂因素后(模型2)，以MHNO组作为参照，MHO、MANO和MAO组发生脑卒中风险分别上升66%、85%和151%，其HR（95%CI）分别为1.66(1.15～2.38)、1.85(1.39～2.46)和2.51(1.92～3.29)。见表2。
2.3 CRP与脑卒中发病风险
将CRP按照三分位数分为T1、T2、T3组。随访期间，T1、T2、T3组分别有107(4.64%)、128(5.62%)和186(8.14%)人发生脑卒中。调整性别、年龄、学历、婚姻状况、居住地、教育情况、睡眠时间等混杂因素后(模型2)，T3组发生脑卒中的风险是T1组的1.65倍(95%CI: 1.30～2.10)。见表3。
2.4 CRP对肥胖表型的调节效应
通过在Cox回归模型中引入CRP和肥胖表型的交互项，发现交互项的P<0.05，表明CRP和肥胖表型间存在调节效应。将CRP按照三分位数分层，研究CRP对肥胖表型与脑卒中关系的调节效应。从表4可知，在CRP T1层中，与MHNO组相比，MHO和MANO组并未增加脑卒中的发病风险(P>0.05)；而在CRP T2和T3层中，MHO、MANO组均显著增加了脑卒中的发病风险(P<0.05)。
3 讨论
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本研究依托于一项为期七年的纵向队列研究深入探讨了中老年人肥胖表型与脑卒中的关系，并特别关注了CRP在这一过程中的调节作用。研究发现肥胖表型与脑卒中发病风险相关且具有统计学意义，并且这种关系受到CRP水平的影响。具体而言，当CRP浓度处于较低水平时，MHO、MANO表型并不会增加脑卒中的发病风险，而当CRP浓度处于较高水平时，肥胖表型与脑卒中风险相关且具有统计学意义。
在本研究中，我们观察到与代谢健康正常体重组（MHNO）相比，代谢健康超重/肥胖组（MHO）、代谢异常正常体重组（MANO）以及代谢异常超重/肥胖组（MAO）均会显著增加脑卒中的发病风险。这一发现与先前的研究相一致，如Tang等人[12]的研究指出，不同类型的肥胖表型与脑卒中发病风险相关且具有统计学意义，且这种风险会随着代谢异常指标数量的增多而增加，这一结果不仅凸显了代谢状况在脑卒中发病风险中的独立作用，同时也突出了预防和控制肥胖对于降低脑卒中风险的重要性。然而，也有部分研究认为MHO组并未增加脑卒中的风险，如欧洲白人队列和韩国国家健康保险服务-样本队列的研究结果显示，与MHNO组相比，MHO组并未增加脑卒中的发病风险[6,16]，造成该差异的原因可能与研究人群、方法学及肥胖表型的划分标准有关。
CRP作为炎性因子，是评估体内炎症活动的重要生物标志物。当CRP水平上升时，通常意味着体内正在发生炎症反应，这种生物标志物的增加可以作为炎症状态的预警信号，有助于医生及时识别和干预潜在的健康问题。在本研究中，我们观察到随着CRP水平的升高，脑卒中的发病风险也呈现出上升的趋势，这和Yang等人[17]的研究结果相一致。
本研究发现CRP在肥胖表型与脑卒中风险之间起调节作用。首先本研究通过在回归模型中引入肥胖表型与CRP的交互项，证实了两者之间存在交互作用(P<0.05)；其次通过进一步的分层分析发现，在CRP水平较低的情况下，MHO和MANO表型并不增加脑卒中的发病风险。这一结果突出了CRP在调节脑卒中关系中的重要作用，降低炎症反应可以改善血脂代谢，减少血液中LDL-C等有害物质的水平，进而减轻对脑组织的损伤。值得注意的是，据我们所知，本研究是首次探讨CRP对肥胖表型与脑卒中关系的影响，因此研究结果可能还不够充分，未来仍需更多研究来深入理解它们之间的复杂关系。
CRP在肥胖表型与脑卒中关系中的调节作用可能涉及多个方面。首先，肥胖状态通常与体内慢性低度炎症状态有关，这种炎症状态可能会加剧动脉粥样硬化进程。动脉粥样硬化是血管壁上脂质和其他物质积累的结果，它会导致血管狭窄和硬化，从而增加了脑卒中发病风险[18]。其次，CRP作为一种重要的炎症标志物，其水平升高可能会影响血管内皮功能，造成血管壁的损伤，进而促进血栓形成[19]。最后，CRP还可能通过影响胰岛素抵抗、脂质代谢等途径，调节肥胖、代谢综合征对脑卒中的影响[20]。
尽管本研究提供了肥胖表型与脑卒中关系的新视角，但仍存在一定的局限性。首先，本研究是一项观察性研究，可能无法确立因果关系。其次，尽管本研究已调整多种潜在混杂因素，但仍可能存在未测量(如饮食因素)或未完全调整的混杂因素。再次，由于脑卒中是通过问卷调查获得的，因此无法区分脑卒中的类型。最后，CRP作为单一炎症标志物，可能无法全面反映机体的炎症状态。未来的研究应考虑纳入更多炎症标志物，并采用更为严谨的实验设计，进一步验证肥胖表型、炎症因子与脑卒中的关系。
综上所述，本研究发现CRP可以调节中老年人肥胖表型与脑卒中关系。这一发现不仅为理解和预防中老年人脑卒中提供了新的视角和策略，也为我们进一步研究肥胖表型、CRP与脑卒中之间的关系提供了重要的参考。
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2024年第51卷第23期
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doi: 10.20043/j.cnki.MPM.202406438
  • 接收时间：2024-06-27
  • 首发时间：2026-03-18
  • 出版时间：2024-12-10
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  • 收稿日期：2024-06-27
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    中国医科大学附属盛京医院，辽宁 沈阳 110004

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Number of
species		 占总种数比例
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total species (%)		 属
Genus		 种数
Number of
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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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