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Article(id=1241522859193988019, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522846384583426, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202307054, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1688486400000, receivedDateStr=2023-07-05, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773931716011, onlineDateStr=2026-03-19, pubDate=1707494400000, pubDateStr=2024-02-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773931716011, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773931716011, creator=13701087609, updateTime=1773931716011, updator=13701087609, issue=Issue{id=1241522846384583426, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='3', pageStart='385', pageEnd='576', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773931712956, creator=13701087609, updateTime=1773931842201, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241523388544504301, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522846384583426, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241523388544504302, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522846384583426, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=417, endPage=421, ext={EN=ArticleExt(id=1241522861035286543, articleId=1241522859193988019, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis of the relationship between metabolism score for visceral fat and new stroke in middle-aged and elderly population, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the relationship between the metabolism score for visceral fat (METS-VF) and new stroke cases in the middle-aged and elderly.

Methods

Three Logistic regression models were established based on the data of China Health and Retirement Longitudinal Study (CHARLS) in 2011 and 2018 to study the association between METS-VF and new stroke cases, and the restricted cubic spline model was used to investigate the possible nonlinear relationship.

Results

In total 7 007 participants were included, of which 407 had new stroke. The results of regression model showed that compared with the group with the lowest METS-VF index (Q1), the group with the highest METS-VF index (Q4) had the highest risk of new stroke, with the increased risk by 153% (OR=2.53, 95%CI: 1.87-3.47). Restricted cubic spline regression analysis found that there was a dose-response relationship between METS-VF index and the prevalence of new stroke (P <0.001), showing a J-shape relationship (P Nonlinear < 0.001).

Conclusion

METS-VF index is closely related to the prevalence of new stroke, and there is a dose-response relationship between them.

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目的

探讨中老年人内脏脂肪代谢评分(METS-VF)与新发脑卒中之间的关系。

方法

使用2011和2018年中国健康与养老追踪调查(CHARLS)的数据建立三个logistic回归模型以研究METS-VF与新发脑卒中之间的关联，并采用限制立方样条模型检查可能的非线性关系。

结果

纳入了7 007名研究对象，其中有407人出现新发脑卒中。回归模型结果显示，与METS-VF指数最低组Q1相比，METS-VF指数最高组Q4的新发脑卒中风险最高，患病风险增加了153%(OR=2.53，95%CI：1.87～3.47)。通过限制性三次样条回归分析发现，METS-VF指数与新发脑卒中患病率存在剂量反应关系(P<0.001)，并且呈现J型关系(PNonliner<0.001)。

结论

METS-VF指数与新发脑卒中患病率密切相关，并且两者存在剂量-反应关系。

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李国徽,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=dovvCENz9wxU7KuCUIEaOg==, magXml=MSFC4rf8H2t2ijng0/e08A==, pdfUrl=null, pdf=d8dDuRmKLVnsIdumm019Og==, pdfFileSize=615614, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=RGfjRsrTtBY/SwmiU6E1Zg==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=7T2bH+DMDgJC7lYTz0mH4w==, mapNumber=null, authorCompany=null, fund=null, authors=

韩向东(1999—),男,硕士在读，研究方向:中西医结合防治脑血管相关疾病研究

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韩向东(1999—),男,硕士在读，研究方向:中西医结合防治脑血管相关疾病研究

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韩向东(1999—),男,硕士在读，研究方向:中西医结合防治脑血管相关疾病研究

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Basic characteristics of the study population

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变量全部(n=7 007)正常(n=6 600)新发脑卒中(n=407)统计量t/Z/χ2值P值
年龄（岁）58(51,64)58(51,64)61(55,67)7.024<0.001
性别1.6510.217
女性3 831(54.67)3 621(54.86)210(51.6)
男性3 176(45.33)2 979(45.14)197(48.4)
婚姻8.600<0.001
结婚6 247(89.15)5 902(89.42)345(84.77)
独居760(10.85)698(10.58)62(15.23)
教育程度2.8810.237
小学及以下4 896(69.87)4 598(69.67)298(73.22)
初中1 430(20.41)1 360(20.61)70(17.20)
高中681(9.72)642(9.72)39(9.58)
城乡0.8780.406
城镇6 569(93.75)6 183(93.68)386(94.84)
农村438(6.25)417(6.32)21(5.16)
抽烟10.090<0.001
不抽烟4 340(61.94)4 107(62.23)233(57.25)
已戒烟524(7.48)478(7.24)46(11.30)
抽烟2 143(30.58)2 015(30.53)128(31.45)
饮酒0.4150.812
不饮酒4 657(66.46)4 392(66.55)265(65.11)
一个月少于一次566(8.08)533(8.08)33(8.11)
一个月超过一次1 784(25.46)1 675(25.37)109(26.78)
睡眠时间（h）7(5,8)7(5,8)6(5,8)0.9300.353
BMI（kg/m2）23.05(20.85,25.56)23.01(20.8,25.5)23.77(21.61,26.65)4.817<0.001
血糖（mmol/L）101.88(94.14,111.78)101.7(93.96,111.6)103.86(95.76,115.47)3.536<0.001
甘油三酯（mg/dl）103.54(74.34,147.79)103.54(73.46,147.79)112.39(84.96,153.99)4.001<0.001
高密度脂蛋白（mg/dl）49.87(40.98,60.31)49.87(40.98,60.7)47.55(39.82,57.41)3.159<0.001
METS-VF6.66(6.26,7.01)6.64(6.24,6.99)6.89(6.48,7.15)7.741<0.001
), ArticleFig(id=1241680451409072668, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522859193988019, language=CN, label=表1, caption=

研究对象的基本情况

, figureFileSmall=null, figureFileBig=null, tableContent=
变量全部(n=7 007)正常(n=6 600)新发脑卒中(n=407)统计量t/Z/χ2值P值
年龄（岁）58(51,64)58(51,64)61(55,67)7.024<0.001
性别1.6510.217
女性3 831(54.67)3 621(54.86)210(51.6)
男性3 176(45.33)2 979(45.14)197(48.4)
婚姻8.600<0.001
结婚6 247(89.15)5 902(89.42)345(84.77)
独居760(10.85)698(10.58)62(15.23)
教育程度2.8810.237
小学及以下4 896(69.87)4 598(69.67)298(73.22)
初中1 430(20.41)1 360(20.61)70(17.20)
高中681(9.72)642(9.72)39(9.58)
城乡0.8780.406
城镇6 569(93.75)6 183(93.68)386(94.84)
农村438(6.25)417(6.32)21(5.16)
抽烟10.090<0.001
不抽烟4 340(61.94)4 107(62.23)233(57.25)
已戒烟524(7.48)478(7.24)46(11.30)
抽烟2 143(30.58)2 015(30.53)128(31.45)
饮酒0.4150.812
不饮酒4 657(66.46)4 392(66.55)265(65.11)
一个月少于一次566(8.08)533(8.08)33(8.11)
一个月超过一次1 784(25.46)1 675(25.37)109(26.78)
睡眠时间（h）7(5,8)7(5,8)6(5,8)0.9300.353
BMI（kg/m2）23.05(20.85,25.56)23.01(20.8,25.5)23.77(21.61,26.65)4.817<0.001
血糖（mmol/L）101.88(94.14,111.78)101.7(93.96,111.6)103.86(95.76,115.47)3.536<0.001
甘油三酯（mg/dl）103.54(74.34,147.79)103.54(73.46,147.79)112.39(84.96,153.99)4.001<0.001
高密度脂蛋白（mg/dl）49.87(40.98,60.31)49.87(40.98,60.7)47.55(39.82,57.41)3.159<0.001
METS-VF6.66(6.26,7.01)6.64(6.24,6.99)6.89(6.48,7.15)7.741<0.001
), ArticleFig(id=1241680451505541665, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522859193988019, language=EN, label=Table 2, caption=

Results of logistic analysis of the METS-VF index and new stroke cases [OR (95%CI)]

, figureFileSmall=null, figureFileBig=null, tableContent=
变量模型1P值模型2P值模型3P值
METS-VF每上升四分位数1.012(0.98,1.045)0.4831.006(0.975,1.037)0.7221.005(0.975,1.036)0.735
Q1参照参照参照
Q21.26(0.89, 1.78)0.1921.23(0.88, 1.75)0.2301.24(0.88, 1.76)0.216
Q31.80(1.31, 2.50)<0.0011.75(1.27, 2.43)0.0011.76(1.28, 2.45)<0.001
Q42.84(2.11, 3.87)<0.0012.51(1.85, 3.43)<0.0012.53(1.87, 3.47)<0.001
P for trend<0.001<0.001<0.001
), ArticleFig(id=1241680451753005609, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522859193988019, language=CN, label=表2, caption=

METS-VF指数与新发脑卒中的logistic分析结果［OR值（95%CI）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量模型1P值模型2P值模型3P值
METS-VF每上升四分位数1.012(0.98,1.045)0.4831.006(0.975,1.037)0.7221.005(0.975,1.036)0.735
Q1参照参照参照
Q21.26(0.89, 1.78)0.1921.23(0.88, 1.75)0.2301.24(0.88, 1.76)0.216
Q31.80(1.31, 2.50)<0.0011.75(1.27, 2.43)0.0011.76(1.28, 2.45)<0.001
Q42.84(2.11, 3.87)<0.0012.51(1.85, 3.43)<0.0012.53(1.87, 3.47)<0.001
P for trend<0.001<0.001<0.001
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中老年人群内脏脂肪代谢评分与新发脑卒中的相关性分析
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韩向东 1, 2 , 李国徽 2 , 陈佳 2
现代预防医学 | 流行病与统计方法 2024,51(3): 417-421
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现代预防医学 | 流行病与统计方法 2024, 51(3): 417-421
中老年人群内脏脂肪代谢评分与新发脑卒中的相关性分析
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韩向东1, 2, 李国徽2 , 陈佳2
作者信息
  • 1.宁夏医科大学，宁夏 银川 750001
  • 2.银川市中医医院，宁夏 银川 750001

    • 韩向东(1999—),男,硕士在读，研究方向:中西医结合防治脑血管相关疾病研究

通讯作者:

李国徽,E-mail:
Analysis of the relationship between metabolism score for visceral fat and new stroke in middle-aged and elderly population
Xiang-dong HAN1, 2, Guo-hui LI2 , Jia CHEN2
Affiliations
  • Ningxia Medical University, Yinchuan, Ningxia 750001, China
出版时间: 2024-02-10 doi: 10.20043/j.cnki.MPM.202307054
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摘要
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目的

探讨中老年人内脏脂肪代谢评分(METS-VF)与新发脑卒中之间的关系。

方法

使用2011和2018年中国健康与养老追踪调查(CHARLS)的数据建立三个logistic回归模型以研究METS-VF与新发脑卒中之间的关联，并采用限制立方样条模型检查可能的非线性关系。

结果

纳入了7 007名研究对象，其中有407人出现新发脑卒中。回归模型结果显示，与METS-VF指数最低组Q1相比，METS-VF指数最高组Q4的新发脑卒中风险最高，患病风险增加了153%(OR=2.53，95%CI：1.87～3.47)。通过限制性三次样条回归分析发现，METS-VF指数与新发脑卒中患病率存在剂量反应关系(P<0.001)，并且呈现J型关系(PNonliner<0.001)。

结论

METS-VF指数与新发脑卒中患病率密切相关，并且两者存在剂量-反应关系。

中老年人  /  内脏脂肪代谢评分  /  新发脑卒中  /  剂量-反应关系  /  CHARLS
Objective

To investigate the relationship between the metabolism score for visceral fat (METS-VF) and new stroke cases in the middle-aged and elderly.

Methods

Three Logistic regression models were established based on the data of China Health and Retirement Longitudinal Study (CHARLS) in 2011 and 2018 to study the association between METS-VF and new stroke cases, and the restricted cubic spline model was used to investigate the possible nonlinear relationship.

Results

In total 7 007 participants were included, of which 407 had new stroke. The results of regression model showed that compared with the group with the lowest METS-VF index (Q1), the group with the highest METS-VF index (Q4) had the highest risk of new stroke, with the increased risk by 153% (OR=2.53, 95%CI: 1.87-3.47). Restricted cubic spline regression analysis found that there was a dose-response relationship between METS-VF index and the prevalence of new stroke (P <0.001), showing a J-shape relationship (P Nonlinear < 0.001).

Conclusion

METS-VF index is closely related to the prevalence of new stroke, and there is a dose-response relationship between them.

Middle-aged and elderly  /  Metabolism score for visceral fat  /  New stroke cases  /  Dose-response relationship  /  CHARLS
韩向东, 李国徽, 陈佳. 中老年人群内脏脂肪代谢评分与新发脑卒中的相关性分析. 现代预防医学, 2024 , 51 (3) : 417 -421 . DOI: 10.20043/j.cnki.MPM.202307054
Xiang-dong HAN, Guo-hui LI, Jia CHEN. Analysis of the relationship between metabolism score for visceral fat and new stroke in middle-aged and elderly population[J]. Modern Preventive Medicine, 2024 , 51 (3) : 417 -421 . DOI: 10.20043/j.cnki.MPM.202307054
正文
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包括中国在内的全球众多国家中，代谢异常和患有病态肥胖症的个体的患病率正在更显著地增加[1]。在这种趋势下，人们对脂肪组织的生物学和功能逐渐了解，并对其在健康和疾病中的作用的理解发生了重大转变。脂肪组织已被认为是心脑血管健康的关键调节剂。目前已有越来越多的研究表明，基于内脏脂肪组织的代谢紊乱与心脑血管疾病风险相关[2]。这导致了与代谢异常相关的心脑血管疾病的患病率也在逐渐增加。
磁共振成像(MRI)扫描和双能X射线吸收法(DXA)扫描及身体质量指数(BMI)、腰围(WC)和腰高比(WHtR)等指标在评估脂肪的各自缺点不容忽视。为了寻求一种易于访问、简单、准确的内脏脂肪组织测量方法，研究者利用临床上常用的参数，通过数学模型开发了内脏脂肪代谢评分(METS-VF)[3]。METS-VF与内脏脂肪组织相关性更强。同时，对简单且普遍可用的临床参数的使用使其在成本受限的环境中非常有用。
已有部分研究探究METS-VF在糖尿病、慢性肾脏病及高血压等疾病中的预测作用。但对METS-VF与脑卒中的关联的研究少之又少。本研究使用METS-VF评估内脏脂肪组织，用以探究内脏脂肪组织在脑卒中发生中的作用，以更进一步指导脑卒中的预防及临床诊疗。
1　资料与方法
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1.1　研究人群
本研究使用了中国健康与退休纵向研究(CHARLS)2011—2018年的4波数据。CHARLS是对中国45岁及以上人群及其配偶的全国代表性纵向持续性调查，样本覆盖量大、覆盖范围广，涉及众多地区及家庭，更能反映45岁及以上的中国人的经济社会和健康情况。目前CHARLS存在4波数据，即Wave 1(2011)、Wave 2(2013)、Wave 3(2015)和Wave 4(2018)。
所有参与者均在自愿加入CHARLS之前都提供了书面知情同意书。CHARLS中的所有数据均由北京大学国家发展研究院维护。数据的收集和使用已分别获得北京大学生物医学伦理审查委员会的伦理批准(IRB00001052-11015)和纽卡斯尔大学人类研究伦理委员会的伦理批准(H-2015-0290)。CHARLS数据公开，研究人员可登录网站访问并使用相关数据。
通过使用上述数据库为期七年的随访数据并根据排除标准(年龄小于45岁、服用降脂药物史、METS-VF数据不完整、2011和2018波脑卒中事件信息缺失以及基线时脑卒中事件的发生）排除大部分受试者后，在2011年共纳入7 007人以研究METS-VF与新发脑卒中之间的关系。
1.2　METS-VF的评估
血液样品及时运送到当地实验室并在4℃下储存。血液样品离心并储存在-20℃，然后运输到北京的中心实验室，并在-80℃下冷冻，然后分析。所有研究实验室都拥有标准化认证。使用标准酶比色法从储存在冰箱中的血液样本中测试血糖(GLU，mmol/L)、甘油三脂(TG，mg/dl)和高密度脂蛋白(HDL-C，mg/dl)。使用SecaTM213视距仪测量身高(cm)，使用OmronTM HN-286称测量体重(kg)，使用 soft tape测量腰围(cm)。根据身高和体重及身高和腰围分别得出BMI和腰围身高比(WHtR)，METS-VF通过结合非胰岛素基础的METS-IR指数、WHtR、年龄和性别，使用之前发表的算法来估计METS-VF。使用空腹血清甘油三酯、高密度脂蛋白胆固醇和空腹血糖计算METS-IR[3-4]。
1.3　新发脑卒中的评估
新发脑卒中的情况是通过”你是否被医生诊断为中风？”来衡量。其中，每种情况都由训练有素的调查人员分别进行评估。自我报告中Wave 1(2011)并未被记录为脑卒中，但在后续的七年内至少一次中风发作的参与者被记录为有新发脑卒中事件(0=否，1=是)，并且该变量被定义为二进制变量。
1.4　协变量的评估
在这项研究中，社会人口特征及健康行为状况包括年龄、性别、教育水平、婚姻状况、户口状况、吸烟饮酒史、合并症及睡眠时间。性别分为男性和女性。教育水平分为小学及以下、高中或大学及以上。婚姻状况分为已婚或未婚。户口身份分为农村和城市。吸烟状态分为从不吸烟、以前吸烟或现在吸烟。饮酒频率分为从不、每月少于一次或每月多于一次。作为影响内脏脂肪的重要指标，空腹血糖、TG和HDL-C的水平也在我们的考虑范围内。
1.5　统计分析
计量资料符合正态分布采用（），否则采用［M（P25，P75）］进行描述；计数资料采用率和构成比进行描述，采用χ2检验或Fisher确切概率法进行组间比较。建立了三个logistic回归模型来探究METS-VF指数与新发脑卒中的关联，并且将METS-VF指数作为连续(每四分位数增量)或分类(四分位数)进行分析，结果以优势比(OR)和95%置信区间(CI)表示。采用三种模型来探讨METS-VF指数与新发脑卒中的关系，包括未调整的粗模型(模型1)；模型2根据年龄、性别、受教育程度、居住地点和婚姻状况进行调整；模型3进一步调整吸烟状态、饮酒状态和睡眠时间。同时进一步进行了限制性三次样条分析以检查可能的非线性关联，并可视化CAI指数与新发脑卒中之间的剂量-反应关系。所有检验均采用双侧检验，检验水准α=0.05。
2　结果
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2.1　研究对线基本特征
本研究符合条件的研究对象共7 007人(年龄中位数为58岁)，其中男性3 176人(45.33%)，女性3 831人(54.67%)；新发脑卒中407人(5.81%)，正常人群6 600人。基线METS-VF的中位数和四分位间距是6.66(6.26, 7.01)，新发脑卒中人群的METS-VF指数明显高于正常人群(6.89 vs. 6.64, P<0.001)。与正常人相比，新发脑卒中的人的年龄更大、独居人群、已戒烟、血糖偏高、TG偏高、高密度脂蛋白偏低(P<0.001)。见表1。
2.2　METS-VF指数与新发脑卒中的logistic分析
粗模型结果显示，METS-VF指数Q3组和Q4组新发生脑卒中的患病风险是最低组(Q1)的 1.80倍 (OR=1.80，95%CI：1.31～2.50)和2.84倍 (OR=2.84，95% CI：2.11～3.87)；模型2调整了社会人口学变量后，结果仍具有统计学意义，METS-VF指数Q4组新发生脑卒中的患病风险是最低组(Q1)的2.51倍 (OR= 2.51，95%CI：1.85～3.43)。模型3调整了年龄、性别、教育水平、居住地、婚姻状况、吸烟状况、饮酒状况和睡眠时间后发现，与METS-VF指数最低组(Q1)相比，METS-VF指数最高组(Q4)新发生脑卒中的风险最高，新发生脑卒中的患病风险增加153% (OR= 2.53，95%CI：1.87～3.47)，另外，趋势分析结果发现，新发脑卒中风险随着METS-VF指数的分位数增加而逐步增加(P for trend< 0.001) 。见表2。
2.3　METS-VF指数与新发脑卒中的剂量-反应关系
METS-VF指数与新发脑卒中发生风险的剂量-反应关系如图1所示。调整年龄、性别、教育水平、居住地、婚姻状况、吸烟状况、饮酒状况和睡眠时间后进一步使用限制性三次样条回归分析显示METS-VF指数与新发脑卒中发病率之间存在剂量反应关系(P<0.001)且呈J型关系(PNonliner<0.001)。结果发现当METS-VF指数大于6.595时，METS-VF为脑卒中的危险因素且累积高血压发病风险逐渐增加。
3　讨论
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本研究发现METS-VF与新发脑卒中的风险呈正相关的J型剂量反应关系。作为脑卒中的危险因素，当METS-VF指数大于6.595时，脑卒中的发生风险逐渐增加。此次研究与之前的研究发现基本一致。已有研究指出，内脏脂肪组织与代谢综合征因素和心脑血管事件显著相关[5]。研究确定了METS-VF临界点>6.595，不同于之前国外有研究把健康患者中METS-VF临界点确定为>7.180[3]。可能为纳入人群的不同，毕竟国内外人群脂肪分布及生活方式有所不同。但具体数值仍需进一步研究。
内脏肥胖会增加心脑血管疾病的风险。正常生理条件下，脂肪组织通过脂肪因子协调全身营养状态和能量代谢之间的平衡。糖和脂质代谢平衡有助于维持心脑血管的正常功能。而当糖脂代谢紊乱和胰岛素抵抗时，心脑血管疾病的发生风险将增加。在病理状态下，脂肪组织的过度扩张和积聚导致脂肪组织的生物学异常和功能障碍。脂肪组织中衰老脂肪细胞的积累会损伤脂肪祖细胞导致脂肪组织中葡萄糖代谢、脂质代谢、内分泌功能障碍和免疫失调的紊乱[6-7]。这些作用伴随着异位脂肪的积聚，导致器官功能受损[8]。肥胖相关疾病的严重程度与全身脂肪的积累没有直接关系，而是与其分布有关。内脏脂肪组织(而不是皮下脂肪)通过分泌脂肪因子等活性物质，促进TNF-α和C反应蛋白(CRP)产生和氧化应激刺激并改变炎性细胞因子的作用和产生直接或间接地调节炎症级联反应，全身或局部炎症和胰岛素抵抗会逆转其抗动脉粥样硬化和抗炎特性，导致脂肪组织分泌从抗炎和抗动脉粥样硬化转变为促炎和促动脉粥样硬化，从而在动脉粥样硬化的病理生理学中发挥重要作用[9-10]。同时也将诱导高血压，引起心脑血管疾病发生，包括脑卒中。另外，过量的内脏脂肪堆积导致胰腺细胞中胰岛素清除和信号传导途径的损害，它会影响心脑血管的能量供应，导致脑卒中等心脑血管意外。
METS-VF是易于使用的更能反映和评估内脏脂肪组织的指标。评估内脏脂肪组织的金标准是MRI，具有昂贵、可用性有限、有尺寸限制，辐射暴露多并且需要较多的专业知识和时间来解释的缺点。DXA具有较低的辐射暴露，但它再次受到其可用性、成本和缺乏用于比较的种族特定规范数据的限制[11]。BMI并不能识别具有高代谢风险的个体[11]。WC、腰臀比和腰高比等也无法精确估计内脏脂肪组织[12]，且很难精确测量病态肥胖个体的腰围。此外，这些肥胖指标不能在测量部位区分所谓的“良好皮下脂肪”与代谢毒性内脏或异位脂肪的存在。内脏脂肪组织的主要预测因子包括胰岛素抵抗、人体测量的体脂分布-腰围身高比(WHtR)、年龄和性别[5]。METS-VF结合了年龄、性别、WHtR、METS-IR、空腹血糖(FPG)和TG水平，是基于DXA估计内脏脂肪组织的替代指标，但在一定程度上与通过DXA、MRI和BMI测量的内脏脂肪参数相比也具有良好的一致性。因此，METS-VF是反映肥胖人群中不利的脂肪因子分布、评估内脏脂肪的代谢状况、心脑血管的代谢健康及反映人体患病的风险的指标[3]。
该研究中，新发脑卒中的人群特征表现为：年龄更大、独居生活、已戒烟、血糖偏高、TG偏高和HDL-C偏低。研究表明，衰老会导致新陈代谢降低，导致腹内脂肪可能有显著的积累，尤其是在60岁以后的女性，由于雌激素水平下降，血压和脂肪组织重新分布受到影响[13-14]。不良情绪及生活方式的转变也在肥胖发展中占有重要作用，我们推测独居生活和已戒烟可能通过情绪及生活方式的改变引起脂肪障碍及肥胖，从而对新发脑卒中产生负面影响。具体原因需要进一步研究去验证。肥胖个体中的胰岛素清除障碍，以及伴随的组织和器官中的胰岛素抵抗和葡萄糖代谢障碍，促进了病态肥胖[15]。循环中血糖偏高、TG水平的增加、HDL-C清除率的降低以及长期维持较低水平的HDL-C都与肥胖的发生有关[16-18]。
虽然本研究通过对大样本的回顾性队列研究发现了METS-VF指数与新发脑卒中存在相关性，但是本研究也存在一定的局限性。首先，新发脑卒中是基于自我报告的可能存在一定的回忆偏移。其次，尽管校正了多种因素，但数据限制导致无法校正遗传因素、饮食和其他因素。最后，本次调查的参与者均为年龄≥45岁的中国中年人和老年人；因此，研究结果是否适用于来自其他国家或年龄≤45岁的人，仍有待验证。
综上所述，本研究采用建立logistic回归模型的方法探究METS-VF指数与新发脑卒中之间的关系，结果表明高METS-VF指数可能是新发脑卒中风险增加的可靠预测指标，有助于识别脑卒中高风险个体，也可能是在初级保健实践和研究环境中评估心脏代谢风险的有用工具。后续可考虑其成为评估脑卒中风险和协助脑卒中诊断的重要指标。同时可能需要对METS-VF高于6.595，并且存在高龄、独居生活、已戒烟、高血糖、TG水平偏高和低HDL-C水平的人群提高警惕，必要时可能需要接受额外的筛查和预防性干预。
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  • 银川市科技创新重大重点专项(2021-SF-003)
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2024年第51卷第3期
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doi: 10.20043/j.cnki.MPM.202307054
  • 接收时间：2023-07-05
  • 首发时间：2026-03-19
  • 出版时间：2024-02-10
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  • 收稿日期：2023-07-05
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银川市科技创新重大重点专项(2021-SF-003)
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    1.宁夏医科大学，宁夏 银川 750001
    2.银川市中医医院，宁夏 银川 750001

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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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