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Article(id=1240413932859478951, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240413921266429979, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202504283, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744905600000, receivedDateStr=2025-04-18, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773667327370, onlineDateStr=2026-03-16, pubDate=1754755200000, pubDateStr=2025-08-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773667327370, onlineIssueDateStr=2026-03-16, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773667327370, creator=13701087609, updateTime=1773667327370, updator=13701087609, issue=Issue{id=1240413921266429979, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='15', pageStart='2689', pageEnd='2880', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1773667324606, creator=13701087609, updateTime=1773667356299, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240414054267802325, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240413921266429979, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240414054267802326, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240413921266429979, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2872, endPage=2880, ext={EN=ArticleExt(id=1240413933190829006, articleId=1240413932859478951, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=The longitudinal levels of metabolic insulin resistance score are positively correlated with the risk of incident cardiovascular disease, columnId=1228016569138213037, journalTitle=Modern Preventive Medicine, columnName=Clinical Medicine and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the longitudinal association between the metabolic score for insulin resistance (METS-IR) and the risk of cardiovascular disease (CVD) and its subtypes (heart disease and stroke) in middle-aged and older adults.

Methods

This study used data from 4 567 participants in the China Health and Retirement Longitudinal Study (CHARLS) cohort. METS-IR levels measured in 2011 and 2015 were standardized, and K-means cluster analysis was used to classify participants into four categories (Class 1-4). Cumulative METS-IR exposure levels were calculated. Multivariable Cox regression models, adjusted for potential confounders, were used to evaluate the association between METS-IR and incident CVD. Restricted cubic spline analysis assessed the dose-response relationship between cumulative METS-IR exposure and CVD risk. Subgroup analyses explored heterogeneity by gender and age. The predictive performance of METS-IR for CVD was evaluated using time-dependent receiver operating characteristic (ROC) curves.

Results

Over a median follow-up of 3 years, 531 CVD events occurred among 4 567 participants. Cox regression analysis showed that compared to Class 1 (lowest persistent METS-IR levels), Class 4(highest persistent METS-IR levels) was associated with a 77% increased risk of incident CVD (HR=1.77, 95%CI: 1.24-2.52), including a 67% increased risk of heart disease (HR=1.67, 95%CI: 1.10-2.54) and a significant 117% increased risk of stroke (HR=2.17,95%CI: 1.31-3.59). A significant linear dose-response relationship was observed between cumulative METS-IR exposure and CVD risk. ROC curve analysis indicated that cumulative METS-IR had the best predictive performance for CVD, with an area under the curve (AUC) of 0.613.

Conclusion

Persistently elevated longitudinal levels of METS-IR are associated with an increased risk of incident CVD. Dynamic monitoring of METS-IR levels is important for the prevention and treatment of CVD.

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目的

探讨中老年人群胰岛素抵抗代谢评分（metabolic score for insulin resistance，METS-IR）的纵向水平与心血管疾病（cardiovascular disease，CVD）及其亚型（心脏疾病和脑卒中）的关联。

方法

基于中国健康与养老追踪调查（CHARLS）队列研究中4 567名参与者为研究对象。根据2011和2015年两次测量的METS-IR进行数据标准化，通过K-means聚类分析确定四个类别，同时计算累积METS-IR暴露水平。采用多因素Cox回归分析并控制潜在的混杂因素评估METS-IR与新发CVD的关联。限制性三次样立方样条分析评估累积METS-IR暴露与新发CVD风险的剂量-反应关系。亚组分析用于探讨不同性别和年龄分层间的关联异质性。采用时间依赖性受试者特征曲线（ROC）评估METS-IR对CVD的预测性能。

结果

在为期三年的中位随访期中，共记录到4 567名参与者中发生531例CVD。通过Cox回归分析发现，以METS-IR水平持续最低的类别1为基准，METS-IR水平持续最高的类别4与新发CVD风险增加77%相关（HR=1.77, 95%CI：1.24～2.52），其中心脏疾病的风险增加67%（HR=1.67, 95%CI：1.10～2.54），脑卒中的风险则显著增加117%（HR=2.17, 95%CI：1.31～3.59）。此外，累积METS-IR暴露量与CVD风险之间呈现出显著的线性剂量-反应关系。ROC曲线分析表明，累积METS-IR对CVD的预测效能最佳，曲线下面积为0.613。

结论

METS-IR纵向水平持续升高与新发CVD风险增加相关，动态监测METS-IR水平对CVD的防治具有重要意义。

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任宏强，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=kRJ30GIUTbnbJpZGoMHLqA==, magXml=XAjuOT1iMBKeqUKbPQUrsg==, pdfUrl=null, pdf=vanQBbUmPvj90w8iKhayCg==, pdfFileSize=1193361, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=O8fKQho1324lnPvhmLkI3A==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=glWkT4+ym90WqgcjIASt6Q==, mapNumber=null, authorCompany=null, fund=null, authors=

张荷群（1980—），女，本科，主管护师，研究方向：心血管疾病的诊治、流行病学

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张荷群（1980—），女，本科，主管护师，研究方向：心血管疾病的诊治、流行病学

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METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes[J].European Journal of Endocrinology / European Federation of Endocrine Societies, 2018, 178(5): 533-544., articleTitle=METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes, refAbstract=null), Reference(id=1240424361396597694, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, doi=null, pmid=null, pmcid=null, year=2024, volume=15, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[12], rfOrder=11, authorNames=Su XZ, Zhao CL, Zhang XW, journalName=Frontiers in Endocrinology, refType=null, unstructuredReference=Su XZ, Zhao CL, Zhang XW. Association between METS-IR and heart failure: a cross-sectional study[J]. Frontiers in Endocrinology, 2024, 15: 1416462., articleTitle=Association between METS-IR and heart failure: a cross-sectional study, refAbstract=null), Reference(id=1240424361484678081, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, doi=null, pmid=null, pmcid=null, year=2021, volume=131, issue=21, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[13], rfOrder=12, authorNames=Tokarz VL, Delgado-Olguín P, Klip A, journalName=J Clin invest, refType=null, unstructuredReference=Tokarz VL, Delgado-Olguín P, Klip A. Deprogram and reprogram to solve the riddle of insulin resistance[J]. J Clin invest, 2021, 131(21): e154699., articleTitle=Deprogram and reprogram to solve the riddle of insulin resistance, refAbstract=null), Reference(id=1240424361589535684, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, doi=null, pmid=null, pmcid=null, year=2024, volume=25, issue=15, pageStart=8369, pageEnd=null, url=null, language=null, rfNumber=[14], rfOrder=13, authorNames=Caturano A, Galiero R, Vetrano E, journalName=International Journal of Molecular Sciences, refType=null, unstructuredReference=Caturano A, Galiero R, Vetrano E, et al. Insulin-Heart axis:bridging physiology to insulin resistance[J]. 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BMJ Open Diabetes Res Care,2020, 8(1): e001217., articleTitle=Causal associations of insulin resistance with coronary artery disease and ischemic stroke: a Mendelian randomization analysis, refAbstract=null), Reference(id=1240424362021549012, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, doi=null, pmid=null, pmcid=null, year=2002, volume=59, issue=6, pageStart=809, pageEnd=815, url=null, language=null, rfNumber=[18], rfOrder=17, authorNames=Kernan WN, Inzucchi SE, Viscoli CM, journalName=Neurology, refType=null, unstructuredReference=Kernan WN, Inzucchi SE, Viscoli CM, et al. Insulin resistance and risk for stroke[J]. 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Neurology, 2003, 60(9): 1447-1451., articleTitle=Impaired insulin sensitivity among nondiabetic patients with a recent TIA or ischemic stroke, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1240424352756331106, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, xref=null, ext=[AuthorCompanyExt(id=1240424352764719714, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, companyId=1240424352756331106, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Cardiovascular Center, Suining Central Hospital, Suining, Sichuan 629000, China), AuthorCompanyExt(id=1240424352768914018, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, companyId=1240424352756331106, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=遂宁市中心医院心血管中心，四川 遂宁 629000)])], figs=[ArticleFig(id=1240424357252625196, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, language=EN, label=Figure 1, caption=

Note: Class 1-4 represents Category 1-4.

, figureFileSmall=sqRULra/oWVb5UIlqlYDqQ==, figureFileBig=iLr+fC97v9sJ6mz0A0m9Eg==, tableContent=null), ArticleFig(id=1240424357349094193, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, language=CN, label=图1, caption=2011和2015年METS-IR测量值聚类图及METS-IR测量值聚类后密度分布图

注：Class 1～4为类别1～4；图A、B为聚类图；图C、D为密度分布图。

, figureFileSmall=sqRULra/oWVb5UIlqlYDqQ==, figureFileBig=iLr+fC97v9sJ6mz0A0m9Eg==, tableContent=null), ArticleFig(id=1240424357504283450, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, language=EN, label=Figure 2, caption=

Note: The histogram in the figure represents the proportion of samples in each Cumulative METS-IR interval (on the right ordinate).

, figureFileSmall=ESv9cww6dwCf62+Infy/CQ==, figureFileBig=27SlN4KqzMIEp5It2CQEwA==, tableContent=null), ArticleFig(id=1240424357592363841, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, language=CN, label=图2, caption=限制性立方样条分析累积METS-IR与心血管疾病、心脏疾病和脑卒中风险的相关性

注：图中柱状图是各个累积METS-IR区间内样本数的占比（纵坐标右侧）。

, figureFileSmall=ESv9cww6dwCf62+Infy/CQ==, figureFileBig=27SlN4KqzMIEp5It2CQEwA==, tableContent=null), ArticleFig(id=1240424357667861320, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, language=EN, label=Figure 3, caption=

Note: Class 1-4 represents Category 1-4.

, figureFileSmall=UC8AgnRmzhTpgt4OFJKrcA==, figureFileBig=iD+eUfK2ZH9GNiO6Fn7Psg==, tableContent=null), ArticleFig(id=1240424357776913228, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, language=CN, label=图3, caption=METS-IR类别与CVD、心脏疾病和脑卒中风险的亚组分析

注：Class 1～4分别为类别1～4。

, figureFileSmall=UC8AgnRmzhTpgt4OFJKrcA==, figureFileBig=iD+eUfK2ZH9GNiO6Fn7Psg==, tableContent=null), ArticleFig(id=1240424357869187922, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, language=EN, label=Figure 4, caption=Time-dependent ROC curves for predicting CVD risk based on cumulative METS-IR, BMI, TyG, and TyG-BMI exposures, figureFileSmall=lIM18y3xB8d4entlfHFmeg==, figureFileBig=4qm7y/zxNKvBfj6Zniu+1Q==, tableContent=null), ArticleFig(id=1240424357982434136, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, language=CN, label=图4, caption=累积METS-IR、BMI、TyG、TyG-BMI暴露对CVD风险预测的时间依赖性ROC曲线, figureFileSmall=lIM18y3xB8d4entlfHFmeg==, figureFileBig=4qm7y/zxNKvBfj6Zniu+1Q==, tableContent=null), ArticleFig(id=1240424358091486049, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, language=EN, label=Table 1, caption=

Comparison of baseline data for different METS-IR categories［（），n（%），M（P，P）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量METS-IR
总体类别1类别2类别3类别4P值
样本量4 5677761 4641 391936
年龄（年）58.42±8.5461.46±8.9558.64±8.5457.61±8.3356.76±7.82<0.001
男性2 123 (46.49)436 (56.19)724 (49.45)599 (43.06)364 (38.89)<0.001
腰围（cm）85.02±9.1375.74±5.3281.05±5.9887.65±6.5794.99±7.55<0.001
身高（cm）157.86±8.34157.43±8.61157.65±8.35158.08±8.27158.24±8.200.116
体重（kg）57.7 (51.4，65.1)48.3 (43.8，52.9)54.1 (50.1，59)60.7 (55.95，65.8)69.05 (63.3，75)<0.001
BMI（kg/m2）23.42±3.2919.48±1.5321.91±1.5824.43±1.9427.56±2.47<0.001
吸烟情况<0.001
从不或已戒烟2 816 (61.89)391 (50.65)868 (59.57)909 (65.44)648 (69.53)
目前吸烟1 734 (38.11)381 (49.35)589 (40.43)480 (34.56)284 (30.47)
饮酒情况<0.001
从不或很少2 973 (65.34)431 (55.83)927 (63.62)947 (68.18)668 (71.67)
大于1次/月1 577 (34.66)341 (44.17)530 (36.38)442 (31.82)264 (28.33)
教育0.340
高中及以上2 205 (48.28)396 (51.03)696 (47.54)657 (47.23)456 (48.72)
其他2 362 (51.72)380 (48.97)768 (52.46)734 (52.77)480 (51.28)
收缩压（mm Hg）129.89±26.53125.49±23.75127.23±25.55130.74±25.58136.45±30.06<0.001
舒张压（mm Hg）75.19±12.1071.93±11.7473.33±11.7176.16±11.7479.36±12.17<0.001
脉搏（次/分）71.87±10.3070.90±10.4971.20±10.2371.88±9.7673.71±10.79<0.001
高血压病1 639 (35.89)197 (25.39)408 (27.87)543 (39.04)491 (52.46)<0.001
糖尿病665 (14.56)63 (8.12)152 (10.38)203 (14.59)247 (26.39)<0.001
高脂血症326 (7.33)19 (2.50)62 (4.35)103 (7.61)142 (15.69)<0.001
TG（mg/dl）103.55 (74.34，150.45)74.34
(58.19，94.70)		92.04
(69.03，125.01)		113.28 (82.31，160.19)161.96 (115.94，235.63)<0.001
TC（mg/dl）190.98 (167.40，214.95)191.75 (167.40，214.18)188.66 (165.07，211.08)191.754(168.17，216.11)192.91 (169.72，220.07)0.001
LDL-C（mg/dl）114.05 (93.94，136.86)109.02 (89.69，131.44)113.66 (94.62，134.54)117.91 (97.23，141.11)114.82 (91.24，138.79)<0.001
HDL-C（mg/dl）49.87 (40.98，60.31)63.60 (54.90，75.00)53.74 (46.39，62.63)46.39 (39.82，53.74)38.66 (32.86，46.01)<0.001
FBG（mg/dl）102.06 (94.32，112.14)98.28 (90.72，106.20)100.80 (93.42，109.26)102.42 (95.04，112.41)107.28 (99.00，122.99)<0.001
HbA1c（%）5.24±0.755.11±0.555.16±0.625.23±0.685.51±1.05<0.001
UA（mg/dl）4.24 (3.52，5.07)4.20 (3.48，5.01)4.10 (3.44，4.92)4.25 (3.55，5.06)4.49 (3.71，5.33)<0.001
BUN（mg/dl）15.10 (12.58，18.09)15.91 (13.19，19.17)15.11 (12.63，18.12)14.90 (12.35，17.93)14.87 (12.44，17.42)<0.001
SCR（mg/dl）0.746 (0.64，0.87)0.76 (0.65，0.88)0.75 (0.64，0.86)0.75 (0.64，0.87)0.75 (0.64，0.87)0.450
eGFR
［ml/（min·1.73 m2）］		87.03 (75.65，98.09)83.217 (72.11，93.79)87 (75.61，96.93)88.26 (76.80，99.40)89.95 (78.11，101.13)<0.001
TyG8.66±0.638.23±0.438.47±0.508.74±0.569.19±0.65<0.001
TyG-BMI203.30±35.30160.09±13.71185.40±14.92213.16±18.33252.46±21.60<0.001
降压药物使用665 (14.56)54 (6.96)116 (7.92)225 (16.18)270 (28.85)<0.001
降脂药物使用327 (7.16)19 (2.45)64 (4.37)103 (7.40)141 (15.06)<0.001
降糖药物使用121 (2.65)7 (0.90)21 (1.43)34 (2.44)59 (6.30)<0.001
), ArticleFig(id=1240424358200537956, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, language=CN, label=表1, caption=

不同METS-IR类别基线资料比较［（），n（%），M（P25，P75）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量METS-IR
总体类别1类别2类别3类别4P值
样本量4 5677761 4641 391936
年龄（年）58.42±8.5461.46±8.9558.64±8.5457.61±8.3356.76±7.82<0.001
男性2 123 (46.49)436 (56.19)724 (49.45)599 (43.06)364 (38.89)<0.001
腰围（cm）85.02±9.1375.74±5.3281.05±5.9887.65±6.5794.99±7.55<0.001
身高（cm）157.86±8.34157.43±8.61157.65±8.35158.08±8.27158.24±8.200.116
体重（kg）57.7 (51.4，65.1)48.3 (43.8，52.9)54.1 (50.1，59)60.7 (55.95，65.8)69.05 (63.3，75)<0.001
BMI（kg/m2）23.42±3.2919.48±1.5321.91±1.5824.43±1.9427.56±2.47<0.001
吸烟情况<0.001
从不或已戒烟2 816 (61.89)391 (50.65)868 (59.57)909 (65.44)648 (69.53)
目前吸烟1 734 (38.11)381 (49.35)589 (40.43)480 (34.56)284 (30.47)
饮酒情况<0.001
从不或很少2 973 (65.34)431 (55.83)927 (63.62)947 (68.18)668 (71.67)
大于1次/月1 577 (34.66)341 (44.17)530 (36.38)442 (31.82)264 (28.33)
教育0.340
高中及以上2 205 (48.28)396 (51.03)696 (47.54)657 (47.23)456 (48.72)
其他2 362 (51.72)380 (48.97)768 (52.46)734 (52.77)480 (51.28)
收缩压（mm Hg）129.89±26.53125.49±23.75127.23±25.55130.74±25.58136.45±30.06<0.001
舒张压（mm Hg）75.19±12.1071.93±11.7473.33±11.7176.16±11.7479.36±12.17<0.001
脉搏（次/分）71.87±10.3070.90±10.4971.20±10.2371.88±9.7673.71±10.79<0.001
高血压病1 639 (35.89)197 (25.39)408 (27.87)543 (39.04)491 (52.46)<0.001
糖尿病665 (14.56)63 (8.12)152 (10.38)203 (14.59)247 (26.39)<0.001
高脂血症326 (7.33)19 (2.50)62 (4.35)103 (7.61)142 (15.69)<0.001
TG（mg/dl）103.55 (74.34，150.45)74.34
(58.19，94.70)		92.04
(69.03，125.01)		113.28 (82.31，160.19)161.96 (115.94，235.63)<0.001
TC（mg/dl）190.98 (167.40，214.95)191.75 (167.40，214.18)188.66 (165.07，211.08)191.754(168.17，216.11)192.91 (169.72，220.07)0.001
LDL-C（mg/dl）114.05 (93.94，136.86)109.02 (89.69，131.44)113.66 (94.62，134.54)117.91 (97.23，141.11)114.82 (91.24，138.79)<0.001
HDL-C（mg/dl）49.87 (40.98，60.31)63.60 (54.90，75.00)53.74 (46.39，62.63)46.39 (39.82，53.74)38.66 (32.86，46.01)<0.001
FBG（mg/dl）102.06 (94.32，112.14)98.28 (90.72，106.20)100.80 (93.42，109.26)102.42 (95.04，112.41)107.28 (99.00，122.99)<0.001
HbA1c（%）5.24±0.755.11±0.555.16±0.625.23±0.685.51±1.05<0.001
UA（mg/dl）4.24 (3.52，5.07)4.20 (3.48，5.01)4.10 (3.44，4.92)4.25 (3.55，5.06)4.49 (3.71，5.33)<0.001
BUN（mg/dl）15.10 (12.58，18.09)15.91 (13.19，19.17)15.11 (12.63，18.12)14.90 (12.35，17.93)14.87 (12.44，17.42)<0.001
SCR（mg/dl）0.746 (0.64，0.87)0.76 (0.65，0.88)0.75 (0.64，0.86)0.75 (0.64，0.87)0.75 (0.64，0.87)0.450
eGFR
［ml/（min·1.73 m2）］		87.03 (75.65，98.09)83.217 (72.11，93.79)87 (75.61，96.93)88.26 (76.80，99.40)89.95 (78.11，101.13)<0.001
TyG8.66±0.638.23±0.438.47±0.508.74±0.569.19±0.65<0.001
TyG-BMI203.30±35.30160.09±13.71185.40±14.92213.16±18.33252.46±21.60<0.001
降压药物使用665 (14.56)54 (6.96)116 (7.92)225 (16.18)270 (28.85)<0.001
降脂药物使用327 (7.16)19 (2.45)64 (4.37)103 (7.40)141 (15.06)<0.001
降糖药物使用121 (2.65)7 (0.90)21 (1.43)34 (2.44)59 (6.30)<0.001
), ArticleFig(id=1240424358326367082, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, language=EN, label=Table 2, caption=

Cox regression analysis of METS-IR categories and incident CVD

, figureFileSmall=null, figureFileBig=null, tableContent=
变量Case(%)模型1a模型2b模型3c
HR(95%CI)P 值HR(95%CI)P 值HR(95%CI)P 值
CVD
METS-IR类别
类别165(8.38)1.00(Ref)1.00(Ref)1.00(Ref)
类别2156(10.66)1.29(0.96～1.72)0.0871.43(1.07～1.91)0.0171.46(1.06～2.09)0.022
类别3161(11.57)1.41(1.06～1.88)0.0191.61(1.20～2.16)0.0011.44(1.04～2.00)0.030
类别4149(15.92)1.98(1.48～2.65)<0.0012.33(1.73～3.14)<0.0011.77(1.24～2.52)0.002
P for trend<0.001<0.0010.005
心脏疾病
METS-IR类别
类别148(6.19)1.00(Ref)1.00(Ref)1.00(Ref)
类别2101(6.90)1.12(0.79～1.57)0.531.20(0.85～1.70)0.3021.24(0.84～1.83)0.272
类别3105(7.55)1.23(0.87～1.73)0.241.34(0.94～1.89)0.1031.26(0.85～1.86)0.257
类别495(10.15)1.68(1.19～2.38)0.0031.86(1.30～2.65)0.0011.67(1.10～2.54)0.016
P for trend0.001<0.0010.022
脑卒中
METS-IR类别
类别128(3.61)1.00(Ref)1.00(Ref)1.00(Ref)
类别272(4.92)1.38(0.89～2.14)0.1481.70(1.09～2.63)0.0191.52(0.96～2.42)0.075
类别373(5.25)1.48(0.96～2.29)0.0781.99(1.28～3.09)0.0021.64(1.03～2.64)0.039
类别475(8.01)2.27(1.47～3.51)<0.0013.31(2.12～5.16)<0.0012.17(1.31～3.59)0.003
P for trend<0.001<0.0010.004
), ArticleFig(id=1240424358427030383, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, language=CN, label=表2, caption=

METS-IR类别与新发CVD的Cox回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量Case(%)模型1a模型2b模型3c
HR(95%CI)P 值HR(95%CI)P 值HR(95%CI)P 值
CVD
METS-IR类别
类别165(8.38)1.00(Ref)1.00(Ref)1.00(Ref)
类别2156(10.66)1.29(0.96～1.72)0.0871.43(1.07～1.91)0.0171.46(1.06～2.09)0.022
类别3161(11.57)1.41(1.06～1.88)0.0191.61(1.20～2.16)0.0011.44(1.04～2.00)0.030
类别4149(15.92)1.98(1.48～2.65)<0.0012.33(1.73～3.14)<0.0011.77(1.24～2.52)0.002
P for trend<0.001<0.0010.005
心脏疾病
METS-IR类别
类别148(6.19)1.00(Ref)1.00(Ref)1.00(Ref)
类别2101(6.90)1.12(0.79～1.57)0.531.20(0.85～1.70)0.3021.24(0.84～1.83)0.272
类别3105(7.55)1.23(0.87～1.73)0.241.34(0.94～1.89)0.1031.26(0.85～1.86)0.257
类别495(10.15)1.68(1.19～2.38)0.0031.86(1.30～2.65)0.0011.67(1.10～2.54)0.016
P for trend0.001<0.0010.022
脑卒中
METS-IR类别
类别128(3.61)1.00(Ref)1.00(Ref)1.00(Ref)
类别272(4.92)1.38(0.89～2.14)0.1481.70(1.09～2.63)0.0191.52(0.96～2.42)0.075
类别373(5.25)1.48(0.96～2.29)0.0781.99(1.28～3.09)0.0021.64(1.03～2.64)0.039
类别475(8.01)2.27(1.47～3.51)<0.0013.31(2.12～5.16)<0.0012.17(1.31～3.59)0.003
P for trend<0.001<0.0010.004
), ArticleFig(id=1240424358523499381, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, language=EN, label=Table 3, caption=

Cox regression analysis of the changes in cumulative METS-IR and incident CVD

, figureFileSmall=null, figureFileBig=null, tableContent=
结局事件累积METS-IR模型1a模型2b模型3c
HR(95%CI)P值HR(95%CI)P 值HR(95%CI)P值
心血管疾病每1个单位标准差*1.25(1.15～1.36)<0.0011.31(1.20～1.43)<0.0011.17(1.06～1.31)0.003
心脏疾病每1个单位标准差*1.19(1.08～1.33)0.0011.23(1.11～1.37)<0.0011.16(1.02～1.32)0.027
脑卒中每1个单位标准差*1.28(1.13～1.45)<0.0011.43(1.26～1.63)<0.0011.26(1.08～1.47)0.003
), ArticleFig(id=1240424358649328509, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, language=CN, label=表3, caption=

累积METS-IR与新发CVD的Cox回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
结局事件累积METS-IR模型1a模型2b模型3c
HR(95%CI)P值HR(95%CI)P 值HR(95%CI)P值
心血管疾病每1个单位标准差*1.25(1.15～1.36)<0.0011.31(1.20～1.43)<0.0011.17(1.06～1.31)0.003
心脏疾病每1个单位标准差*1.19(1.08～1.33)0.0011.23(1.11～1.37)<0.0011.16(1.02～1.32)0.027
脑卒中每1个单位标准差*1.28(1.13～1.45)<0.0011.43(1.26～1.63)<0.0011.26(1.08～1.47)0.003
), ArticleFig(id=1240424358745797506, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, language=EN, label=Table 4, caption=

NRI and IDI analysis

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变量IDI (95%CI)P值NRI (95%CI)P 值
心血管疾病
累积METS-IR vs. 累积BMI-0.01 (-0.019～-0.004)<0.001-0.126 (-0.173～-0.064)<0.001
累积METS-IR vs. 累积TyG-0.013 (-0.021～-0.007)<0.001-0.166 (-0.211～-0.104)<0.001
累积METS-IR vs. 累积TyG-BMI-0.009 (-0.017～-0.004)<0.001-0.116 (-0.169～-0.067)<0.001
心脏疾病
累积METS-IR vs. 累积BMI-0.002 (-0.008～0)0.040-0.038 (-0.131～-0.005)0.031
累积METS-IR vs. 累积TyG-0.006 (-0.013～-0.003)<0.001-0.136 (-0.192～-0.075)<0.001
累积METS-IR vs. 累积TyG-BMI-0.003 (-0.008～-0.001)0.044-0.089 (-0.149～-0.014)<0.001
脑卒中
累积METS-IR vs. 累积BMI-0.012 (-0.021～-0.006)<0.001-0.211 (-0.271～-0.125)<0.001
累积METS-IR vs. 累积TyG-0.011 (-0.019～-0.005)<0.001-0.184 (-0.26～-0.113)<0.001
累积METS-IR vs. 累积TyG-BMI-0.011 (-0.019～-0.005)<0.001-0.195 (-0.266～-0.096)<0.001
), ArticleFig(id=1240424358850655111, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240413932859478951, language=CN, label=表4, caption=

NRI与IDI分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量IDI (95%CI)P值NRI (95%CI)P 值
心血管疾病
累积METS-IR vs. 累积BMI-0.01 (-0.019～-0.004)<0.001-0.126 (-0.173～-0.064)<0.001
累积METS-IR vs. 累积TyG-0.013 (-0.021～-0.007)<0.001-0.166 (-0.211～-0.104)<0.001
累积METS-IR vs. 累积TyG-BMI-0.009 (-0.017～-0.004)<0.001-0.116 (-0.169～-0.067)<0.001
心脏疾病
累积METS-IR vs. 累积BMI-0.002 (-0.008～0)0.040-0.038 (-0.131～-0.005)0.031
累积METS-IR vs. 累积TyG-0.006 (-0.013～-0.003)<0.001-0.136 (-0.192～-0.075)<0.001
累积METS-IR vs. 累积TyG-BMI-0.003 (-0.008～-0.001)0.044-0.089 (-0.149～-0.014)<0.001
脑卒中
累积METS-IR vs. 累积BMI-0.012 (-0.021～-0.006)<0.001-0.211 (-0.271～-0.125)<0.001
累积METS-IR vs. 累积TyG-0.011 (-0.019～-0.005)<0.001-0.184 (-0.26～-0.113)<0.001
累积METS-IR vs. 累积TyG-BMI-0.011 (-0.019～-0.005)<0.001-0.195 (-0.266～-0.096)<0.001
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胰岛素抵抗代谢评分的纵向水平与新发心血管疾病风险正相关
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张荷群 , 李棋 , 涂宏 , 任宏强
现代预防医学 | 临床与预防 2025,52(15): 2872-2880
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现代预防医学 | 临床与预防 2025, 52(15): 2872-2880
胰岛素抵抗代谢评分的纵向水平与新发心血管疾病风险正相关
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张荷群, 李棋, 涂宏, 任宏强
作者信息
  • 遂宁市中心医院心血管中心，四川 遂宁 629000

    • 张荷群（1980—），女，本科，主管护师，研究方向：心血管疾病的诊治、流行病学

通讯作者:

任宏强，E-mail:
The longitudinal levels of metabolic insulin resistance score are positively correlated with the risk of incident cardiovascular disease
He-qun ZHANG, Qi LI, Hong TU, Hong-qiang REN
Affiliations
  • Department of Cardiovascular Center, Suining Central Hospital, Suining, Sichuan 629000, China
出版时间: 2025-08-10 doi: 10.20043/j.cnki.MPM.202504283
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摘要
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目的

探讨中老年人群胰岛素抵抗代谢评分（metabolic score for insulin resistance，METS-IR）的纵向水平与心血管疾病（cardiovascular disease，CVD）及其亚型（心脏疾病和脑卒中）的关联。

方法

基于中国健康与养老追踪调查（CHARLS）队列研究中4 567名参与者为研究对象。根据2011和2015年两次测量的METS-IR进行数据标准化，通过K-means聚类分析确定四个类别，同时计算累积METS-IR暴露水平。采用多因素Cox回归分析并控制潜在的混杂因素评估METS-IR与新发CVD的关联。限制性三次样立方样条分析评估累积METS-IR暴露与新发CVD风险的剂量-反应关系。亚组分析用于探讨不同性别和年龄分层间的关联异质性。采用时间依赖性受试者特征曲线（ROC）评估METS-IR对CVD的预测性能。

结果

在为期三年的中位随访期中，共记录到4 567名参与者中发生531例CVD。通过Cox回归分析发现，以METS-IR水平持续最低的类别1为基准，METS-IR水平持续最高的类别4与新发CVD风险增加77%相关（HR=1.77, 95%CI：1.24～2.52），其中心脏疾病的风险增加67%（HR=1.67, 95%CI：1.10～2.54），脑卒中的风险则显著增加117%（HR=2.17, 95%CI：1.31～3.59）。此外，累积METS-IR暴露量与CVD风险之间呈现出显著的线性剂量-反应关系。ROC曲线分析表明，累积METS-IR对CVD的预测效能最佳，曲线下面积为0.613。

结论

METS-IR纵向水平持续升高与新发CVD风险增加相关，动态监测METS-IR水平对CVD的防治具有重要意义。

胰岛素抵抗代谢评分  /  胰岛素抵抗  /  心血管疾病  /  CHARLS
Objective

To investigate the longitudinal association between the metabolic score for insulin resistance (METS-IR) and the risk of cardiovascular disease (CVD) and its subtypes (heart disease and stroke) in middle-aged and older adults.

Methods

This study used data from 4 567 participants in the China Health and Retirement Longitudinal Study (CHARLS) cohort. METS-IR levels measured in 2011 and 2015 were standardized, and K-means cluster analysis was used to classify participants into four categories (Class 1-4). Cumulative METS-IR exposure levels were calculated. Multivariable Cox regression models, adjusted for potential confounders, were used to evaluate the association between METS-IR and incident CVD. Restricted cubic spline analysis assessed the dose-response relationship between cumulative METS-IR exposure and CVD risk. Subgroup analyses explored heterogeneity by gender and age. The predictive performance of METS-IR for CVD was evaluated using time-dependent receiver operating characteristic (ROC) curves.

Results

Over a median follow-up of 3 years, 531 CVD events occurred among 4 567 participants. Cox regression analysis showed that compared to Class 1 (lowest persistent METS-IR levels), Class 4(highest persistent METS-IR levels) was associated with a 77% increased risk of incident CVD (HR=1.77, 95%CI: 1.24-2.52), including a 67% increased risk of heart disease (HR=1.67, 95%CI: 1.10-2.54) and a significant 117% increased risk of stroke (HR=2.17,95%CI: 1.31-3.59). A significant linear dose-response relationship was observed between cumulative METS-IR exposure and CVD risk. ROC curve analysis indicated that cumulative METS-IR had the best predictive performance for CVD, with an area under the curve (AUC) of 0.613.

Conclusion

Persistently elevated longitudinal levels of METS-IR are associated with an increased risk of incident CVD. Dynamic monitoring of METS-IR levels is important for the prevention and treatment of CVD.

Metabolic score for insulin resistance  /  Insulin resistance  /  Cardiovascular disease  /  CHARLS
张荷群, 李棋, 涂宏, 任宏强. 胰岛素抵抗代谢评分的纵向水平与新发心血管疾病风险正相关. 现代预防医学, 2025 , 52 (15) : 2872 -2880 . DOI: 10.20043/j.cnki.MPM.202504283
He-qun ZHANG, Qi LI, Hong TU, Hong-qiang REN. The longitudinal levels of metabolic insulin resistance score are positively correlated with the risk of incident cardiovascular disease[J]. Modern Preventive Medicine, 2025 , 52 (15) : 2872 -2880 . DOI: 10.20043/j.cnki.MPM.202504283
正文
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心血管疾病（cardiovascular diseases, CVD）是导致老年患者残疾和死亡的主要因素。随着全球人口老龄化的加剧，CVD的高发病率和致死率使其成为公共卫生领域最严峻的挑战之一[1]。在过去的30年（1990—2019），全球范围总CVD患病人数增加92.3%（2.71亿增至5.23亿），死亡人数增加53.7%（1 210万增至1 860万）[2]。值得注意的是，以缺血性心脏病和缺血性卒中为代表的代谢性CVD导致了占比高达85.1%的CVD死亡[3]。因此，早期识别高危人群，通过制定有效防治策略进一步降低CVD的发病率和死亡率，对缓解CVD公共负担具有重大意义。
胰岛素抵抗（insulin resistance，IR）是指机体组织细胞对胰岛素的敏感性降低，从而导致糖代谢紊乱为特征的病理生理过程，被认为是2型糖尿病和CVD等代谢性疾病的核心发病基础[4-5]。研究表明，由IR导致的高胰岛素血症可以加速脂肪酸的生成，引发血压和血脂代谢异常，加剧动脉粥样硬化[6]。目前，高胰岛素-正葡萄糖钳夹技术仍是评估IR水平的金标准，但由于其操作繁杂以及费用成本高，难以广泛推广[7]。近年来提出的基于身体测量和常规生化参数计算的胰岛素抵抗代谢评分（metabolic score for insulin resistance，METS-IR），被认为是评估IR简单且稳健的指标，在临床和流行病学研究中显示出了潜在应用价值[8-9]。尽管先前有研究探讨了METS-IR与CVD的联系，但多聚焦于基线水平与CVD的横断面关联，缺乏对METS-IR纵向水平的相关研究。这削弱了基于METS-IR早期评估IR策略的有效性，因此需要进一步探索和验证。
基于目前研究现状存在的缺漏，本研究通过分析中国健康与养老追踪调查（China Health and Retirement Longitudinal Study, CHARLS）的数据，旨在研究45岁及以上人群中METS-IR的纵向和累积暴露水平与CVD发病率的关联。
1　资料与方法
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1.1　研究人群
数据来源于CHARLS队列，这是一项具针对中老年人并且具有全国代表性的纵向调查。第一波调查始于2011年，17 708名来自中国150个区县和450村/居委会的参与者被纳入基线调查，后续每二年进行一次调查，分别是第二波（2013年）、第三波（2015年）、第四波（2018年）和第五波（2020年）。CHARLS调查采取面对面的计算机辅助个人访谈模式，通过标准化问卷收集个人信息。该项目获得了北京大学医学伦理审查委员会的伦理批准（IRB00001052-11015），所有参与者均签署了书面知情同意书，详情在CHARLS网站（https://charls.pku.edu.cn/）上获取。研究排除标准：（1）年龄<45岁；（2）第一波（2011年11月）和第三波（2015年11月）调查中缺失身高、体重、空腹血糖（fasting blood glucose，FBG）、甘油三酯（triglyceride，TG）和高密度脂蛋白胆固醇（high - density lipoprotein cholesterol，HDL-C）的参与者；（3）第一至第三波调查结束时出血死亡、失访、患有心脏疾病和脑卒中的参与者；（4）缺失第四和第五波缺失疾病随访信息者。研究最终纳入4 567名参与者进行分析。
1.2　METS-IR的定义
根据既往研究[8]，METS-IR的计算公式为：
其中FBG、TG和HDL-C的计算单位为mg/dL，身体质量指数（body mass index，BMI）为kg/m2。此外，参考既往研究[10]，累积METS-IR暴露定义为第一波与第三波两次调查的METS-IR之和除以2再乘以间隔时间(单位：年)，计算公式为：
1.3　结局事件的定义
第三波调查后的新发CVD为本研究的结局事件，是一个包含心脏疾病或脑卒中的复合终点事件，以先出现者为准。在CHALRS第四和第五波调查中，参与者被询问“医生是否曾告诉您患有任何心脏病（心肌梗塞、冠心病、心绞痛、充血性心力衰竭或其他心脏问题）或中风？”或者“您目前有没有正在采用以下方式（中医/西药/其他/以上均无）来治疗心脏病或中风”。终点事件的具体定义是基于自我报告的医生诊断，经专业调查人员进行评估。
1.4　协变量的定义
本研究收集了以下数据进行分析：（1）人口统计学变量：年龄、性别、教育水平；（2）生活方式信息：吸烟和饮酒状况；（3）体格检查指标：身高、体重、BMI、腰围、血压和脉搏；（4）病史信息：高血压病、糖尿病和血脂异常，以及相关药物服用信息；（5）实验室检查指标：TG、总胆固醇（total cholesterol，TC）、低密度脂蛋白胆固醇（low density lipoprotein cholesterol，LDL-C）、HDL-C、FBG、糖化血红蛋白（glycosylated hemoglobin，HbA1c）、尿酸、血尿素氮（blood urea nitrogen，BUN）、血肌酐和肾小球滤过率（glomerular filtration ratee，GFR）。葡萄糖甘油三酯指数（TyG）和葡萄糖甘油三酯体重指数（TyG-BMI）计算公式为：
其中FBG、TG的计算单位为mg/dL，BMI为kg/m2。
1.5　统计分析
使用R（版本4.3.3）进行统计分析以及可视化呈现。对第一波和第三波两次调查测量计算的METS-IR进行数据标准化，然后进行K-means聚类分组，这是一种无监督机器学习方法，通过“Elbow法”确定了最优聚类类别数。以不同类别进行分组并进行组间比较，分类变量使用率和构成比描述，采用χ2检验进行组间比较。对于符合正态分布的连续性变量用（均数±标准差）表述，组间比较采用方差分析（ANOVA）；若不符合正态分布，则采用中位数（四分位数间距）表述，组间比较采用Kruskal-Wallis H检验。Cox回归模型用于分析不同类别和累积METS-IR与结局事件（CVD以及其亚型）的相关性。通过调整不同的混杂因素，分别建立3个模型以评估关联的稳健性。模型1未调整任何变量；模型2调整调整了年龄、性别、教育水平、吸烟和饮酒状态；模型3在模型2基础上进一步调整调整了糖尿病、高血压病、血脂异常、HbA1c、LDL-C、尿酸和eGFR。限制性三次立方样条分析评估累积METS-IR暴露与结局事件风险的剂量反应关系。进一步根据年龄（60岁）和性别分析不同亚组间关联，根据不同METS-IR类别与亚组变量（二分类）的乘积交互项的显著性评估是否具有异质性。最后，进行时间依赖的受试者操作特征（receiver operating characteristic，ROC）曲线分析，选取中位随访时间为分析截点，比较累积METS-IR暴露与累积BMI、累积TyG和累积TyG-BMI暴露对CVD以及亚型的预测能力，净重新分类指数（net reclassification improvement，NRI）和综合判别改善指数（integrated discrimination improvement，IDI）进一步验证稳健性。检验水准α=0.05。
2　结果
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2.1　METS-IR聚类分组
运用K-means聚类分析方法，基于2011和2015年的METS-IR测量数据，对4 481名参与者进行分类，最终确定了4个不同的METS-IR类别。具体分类如下：类别1为持续低水平组，其指数范围在26.26～26.61；类别2为始终处于第二低水平组，指数范围为31.42～31.82；类别3为始终处于第二高水平组，指数范围是36.99～37.40；类别4为持续高水平组，指数范围在45.20～44.05，见图1A、B。此外，聚类分层后的两次调查中，不同METS-IR类别的密度分布情况分别见图1C、D。
2.2　基线资料比较
4 567名受试者中，其年龄均值为58岁，男性2 123人。与类别1组相比，类别4组受试者的年龄偏小，男性比例更低，静息血压及脉搏水平均呈现上升趋势。此外，类别4组在高血压、糖尿病及高脂血症的患病率方面显著高于类别1组。生化指标方面，类别4组的TG、TC、LDL、FBG、HbA1c、尿酸均相对较高，而HDL-C低于其他组别（P<0.05）。见表1。
2.3　METS-IR类别与新发CVD的相关性
在中位随访时间三年期间，531名参与者出现新发CVD，包括349例心脏疾病和248例脑卒中。在未调整的模型1中，以类别1为参照组，类别3和类别4与新发CVD风险增加相关。进一步调整人口统计学变量后这种关联强度进一步增加，并且类别2也出现了显著性。在完全调整了混杂因素的模型3中，相较于类别1，类别2、类别3和类别4分别与新发CVD风险增加46%（HR=1.46, 95%CI:1.06～2.09）、44%（HR=1.44, 95%CI:1.04～2.00）和77%（HR=1.77, 95%CI:1.24～2.52）。对于CVD的亚型分析，类别4较类别1与心脏疾病的风险增加67%（HR=1.67, 95%CI:1.10～2.54），与脑卒中的风险增加117%（HR= 2.17, 95%CI:1.31～3.59）。趋势性检验显著提示新发CVD、心脏疾病和脑卒中风险与METS-IR水平增加显著相关（P fortrend<0.05），见表2。
2.4　METS-IR累积暴露与新发CVD的相关性
分析中，将累积METS-IR暴露作为连续变量进行处理。在完全调整混杂因素的模型3中，结果显示，累积METS-IR每增加1个标准差单位（19.6），CVD的风险增加17%。其中，心脏疾病的风险增加16%，而脑卒中的风险则增加26%，见表3。此外，通过限制性三次样条分析进一步探讨累积METS-IR与CVD风险之间的关系。完全调整混杂因素（模型3）后的结果显示，累积METS-IR与CVD风险之间存在显著的正向线性剂量-反应关系（P overall=0.013，P nonliner=0.48）。这种线性关系同样存在于心脏疾病（P overall=0.043，P nonliner=0.674）和脑卒中（P overall=0.011，P nonliner=0.235）与累积METS-IR的风险关联。见图2。
2.5　亚组分析
本研究进一步基于年龄（是否达到60岁）和性别进行了亚组分析。完全调整混杂因素（模型3）后的结果显示，在不同亚组中，不同类别的METS-IR与CVD、心脏疾病和脑卒中风险的关联表现出显著的一致性。具体而言，未发现年龄或性别对METS-IR与CVD风险关联的显著交互作用（P interaction均>0.05）。见图3。
2.6　METS-IR的对CVD的预测能力
采用与累积METS-IR相同的计算方法，分别计算了累积BMI、TyG和TyG-BMI的暴露量。以中位随访时间为时间截点，进行了时间依赖性ROC曲线分析，以评估累积METS-IR、BMI、TyG和TyG-BMI对CVD以及亚型风险的预测能力。结果显示，METS-IR在所有疾病中的AUC值均显著高于其他指标（CVD：0.613；心脏病：0.585；脑卒中：0.652）。进一步的NRI和IDI分析表明，与累积BMI、TyG和TyG-BMI相比，累积METS-IR在CVD及其亚型的预测性能方面均表现出统计学意义上的显著改善。见图4、表4。
3　讨论
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在本项基于CHARLS队列的纵向研究中，我们深入分析了METS-IR纵向轨迹与CVD风险之间的关联。研究结果显示，在对相关混杂因素进行调整后，相较于持续处于最低METS-IR水平（类别1）的参与者，其他METS-IR类别的参与者展现出更高的新发CVD风险，其中持续处于最高METS-IR水平（类别4）的个体风险最高。此外，累积METS-IR水平与新发CVD风险呈正相关，表现出明显的线性剂量-反应关系，这表明长期较高的METS-IR暴露可被视为CVD发生发展的独立危险因素。最后，通过对比不同风险指标对CVD的预测效能，累积METS-IR展现出最强的预测能力。
近年来，METS-IR作为一种考虑到血糖、血脂谱和BMI的新型IR替代指数，受到了广泛的关注。有研究表明，METS-IR与高胰岛素-正葡萄糖钳夹技术评估IR的结果具有良好的一致性，并且METS-IR可作为一种成本低、易获取的指数，可预测西方人群中2型糖尿病的发生[11]。此外，一些研究显示了基线METS-IR与CVD风险的联系。一项NHANES横断面研究显示[12]，METS-IR与心力衰竭风险之间呈正相关（OR=2.44, 95%CI:1.38～4.32）。一项纳入1 461名接受经皮冠状动脉介入治疗的冠心病患者的回顾性研究显示[9]，METS-IR每增加一个标准差单位，术后主要不良心血管事件的风险增加27%（OR=1.27,95%CI:1.04～1.56）。此外，有研究显示METS-IR与美国人群心血管死亡率相关，并且其预测能力优于TyG等IR替代指标[8]。而本研究结论也得出了类似的结论，即METS-IR对CVD的预测能力优于TyG等指标。
本研究聚焦于累积METS-IR暴露与CVD的纵向关联，这在一定程度上反应了长期的IR程度与CVD风险的剂量-反应关联。IR是指机体组织对胰岛素生理反应作用降低的情况，其形成机制复杂，与遗传易感性、不良生活方式和肥胖等因素密切相关[13]。IR通过多种机制共同作用导致代谢紊乱和心血管损伤，包括脂毒性、代谢失调、氧化应激、炎症反应和RAAS激活等多种途径[14]。
本研究通过对CVD的亚型分析发现，持续高水平的METS-IR对于脑卒中风险的影响大于心脏疾病，这可能因为脑血管对IR的敏感性可能高于心脏血管[15]。并且IR会导致脑血管内皮功能障碍，增加血管阻力，促进脑血管病变。目前有充分的研究表明，糖代谢紊乱与中风事件存在双向作用：IR可作为心脑血管事件的早期预测指标和独立危险因素，而中风本身会加剧糖代谢紊乱并诱发胰岛素抵抗[16-17]。流行病学研究显示[18-19]，约30%的缺血性中风患者存在糖尿病前期，50%的非糖尿病缺血性中风患者存在IR。另一项研究显示[20]，胰岛素敏感性受损在近期发生TIA或非致残性缺血性中风的非糖尿病患者中极为普遍。这种糖代谢异常状态在应激状态消失后仍持续存在，并可加重神经损伤，导致不良预后。最后，本研究在亚组分析中未发现METS-IR类别与性别和高龄具有交互作用，这表明IR在各个人群中与CVD的风险是明确一致的。
本研究基于CHARLS数据库，揭示了METS-IR持续处于高水平以及过高的累积暴露与新发CVD及其亚型风险增加之间的显著关联。早期监测METS-IR水平并制定有效的预防和干预策略，对于改善CVD风险具有关键价值。作为一种经济高效的指标，METS-IR在个体化医疗背景下为社区健康管理提供了重要支持。然而，本研究存在以下局限性：一是，尽管这是一项全国性调查研究，但研究对象主要集中在中老年人群，而CVD风险正在逐渐青年化，这可能限制了研究结果的普适性。二是，作为一项观察性研究，尽管在分析中控制了部分混杂因素，但潜在的残余混杂因素仍可能对结果产生影响。此外，本研究仅纳入了2011和2015年两次血液检测数据完整的参与者，可能排除了病情不稳定的潜在高风险人群，从而因选择偏倚而低估了METS-IR与高风险人群之间的关联性。未来的研究应进一步扩大样本量并延长随访时间，以获得更加稳健的结论。
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2025年第52卷第15期
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doi: 10.20043/j.cnki.MPM.202504283
  • 接收时间：2025-04-18
  • 首发时间：2026-03-16
  • 出版时间：2025-08-10
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  • 收稿日期：2025-04-18
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    遂宁市中心医院心血管中心，四川 遂宁 629000

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任宏强，E-mail:
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
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