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Article(id=1241102815200866417, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241102813938380911, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202403464, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1711382400000, receivedDateStr=2024-03-26, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773831569718, onlineDateStr=2026-03-18, pubDate=1724515200000, pubDateStr=2024-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773831569718, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773831569718, creator=13701087609, updateTime=1773831569718, updator=13701087609, issue=Issue{id=1241102813938380911, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='16', pageStart='2881', pageEnd='3072', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773831569418, creator=13701087609, updateTime=1773831807198, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241103811318706322, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241102813938380911, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241103811318706323, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241102813938380911, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2911, endPage=2917, ext={EN=ArticleExt(id=1241102815511244917, articleId=1241102815200866417, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Association between insulin resistance metabolic score and risk of cardiometabolic multimorbidity in middle-aged and elderly Chinese in a cohort study, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the association between metabolic score of insulin resistance (METS-IR) and the risk of Cardiometabolic multimorbidity (CMM) in middle-aged and elderly Chinese.

Methods

A population-based cohort study was conducted using data from the China Longitudinal Study of Health and Aging (CHARLS) among Chinese residents aged 45 years or older from 2011 to 2018. Cox proportional hazards regression models were used to calculate Hazard Ratio (HR) and 95% confidence intervals (CI) for the association between METS-IR and new CMM, and stratified analyses were performed in different populations to observe the interaction between covariates and METS-IR. The dose-response relationship between METS-IR and new CMM in different body weight population was observed with restricted cubic spline. Receiver operating characteristic (ROC) curve was constructed to analyze the value of METS-IR in predicting the risk of CMM.

Results

A total of 5 349 subjects were included in this study, and the incidence of CMM was 2.08%. After adjusting for all included covariates, METS-IR as a continuous variable was positively correlated with the risk of CMM (HR: 1.07, 95%CI: 1.05-1.09, P<0.001). As a categorical variable, the highest METS-IR group was still positively associated with the risk of CMM (HR: 3.47, 95%CI: 2.06-5.84, P<0.001). In total participants (P for overall <0.001), normal weight (P for overall = 0.022) and overweight and obese (P for overall = 0.008) populations still had a linear relationship between MET-IR and CMM risk. METS-IR was predictive of CMM risk (AUC: 0.651, 95%CI: 0.598-0.704).

Conclusion

The study finds that METS-IR is positively associated with an increased risk of new CMM in middle-aged and older adults and should be of continued concern in both normal weight and overweight obese people to reduce the risk of CMM.

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目的

本研究探索中国中老年人胰岛素抵抗代谢评分（METS-IR）与心血管代谢性共病发病（Cardiometabolic multimorbidity，CMM）风险的关联。

方法

基于人群的队列研究，本研究使用中国健康与养老纵向研究（CHARLS）的数据，在2011—2018年对中国45岁或以上居民进行了调查。Cox比例风险模型被用于计算METS-IR与新发CMM的关联的风险比（HR）和95%可信区间（CI），并在不同人群中进行分层分析，观察协变量与METS-IR的交互作用，应用限制性立方样条观察在不同体重人群中METS-IR与新发CMM的剂量反应关系。构建受试者工作特征（ROC）曲线，分析METS-IR对CMM发病风险的预测价值。

结果

本研究共纳入5 349名研究对象，CMM发病率为2.08%，在调整所有纳入的协变量后，METS-IR作为连续变量与CMM发病风险呈正相关（HR：1.07，95%CI：1.05~1.09，P<0.001）。作为分类变量，METS-IR最高组与CMM发病风险仍呈正相关（HR：3.47，95%CI：2.06~5.84，P<0.001）。在全参与者（P for overall<0.001）、正常体重（P for overall=0.022）和超重肥胖（P for overall=0.008）人群中METS-IR与CMM发病风险仍存在线性关系。METS-IR对CMM发病风险具有一定的预测能力（AUC：0.651，95%CI：0.598~0.704）。

结论

METS-IR与中老年人群新发CMM风险增加存在正向关联，在正常体重或超重肥胖人群中都应该持续关注，从而减少CMM的风险。

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钱燕萍，E-mail：
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钱燕萍（1982—），女，本科，主管护师，研究方向：慢病防控与健康教育

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注：图A为全人群、B为BMI<24 kg/m2人群、C为BMI≥24 kg/m2人群，模型调整研究对象性别，年龄，居住地，婚姻状况，教育程度，吸烟，饮酒；缩写：CMM，心脏代谢性共病；METS-IR，胰岛素抵抗代谢评分。

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注：CMM，心脏代谢性共病；METS-IR，胰岛素抵抗代谢评分。

, figureFileSmall=drP50sFC+bbmh/dVH731WQ==, figureFileBig=hRAJ4H5ERyU8wofuCVIWVw==, tableContent=null), ArticleFig(id=1241102823438479634, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102815200866417, language=EN, label=Table 1, caption=

Assignment of each variable

, figureFileSmall=null, figureFileBig=null, tableContent=
变量赋值情况
性别1=男，2=女
居住地1=农村，2=城市
婚姻状况1=已婚，2=其他
教育程度1=文盲，2=小学，3=初中，4=高中及以上
吸烟1=现在吸烟，2=曾经吸烟，3=不吸烟
饮酒1=是，2=否
心脏代谢性共病1=是，0=否
), ArticleFig(id=1241102823551725843, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102815200866417, language=CN, label=表1, caption=

各变量赋值情况

, figureFileSmall=null, figureFileBig=null, tableContent=
变量赋值情况
性别1=男，2=女
居住地1=农村，2=城市
婚姻状况1=已婚，2=其他
教育程度1=文盲，2=小学，3=初中，4=高中及以上
吸烟1=现在吸烟，2=曾经吸烟，3=不吸烟
饮酒1=是，2=否
心脏代谢性共病1=是，0=否
), ArticleFig(id=1241102823669166359, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102815200866417, language=EN, label=Table 2, caption=

Basic characteristics of the study participants

, figureFileSmall=null, figureFileBig=null, tableContent=
变量CMMP
否是
n5 238(97.92)111(2.08)
年龄（岁）58.09±8.6861.98±8.06
性别0.204
男2 442(98.19)45(1.81)
女2 796(97.69)66(2.31)
居住地0.331
农村3 578(98.05)71(1.95)
城市1 660(97.65)40(2.35)
婚姻状况0.319
已婚4 710(97.86)103(2.14)
其他528(98.51)8(1.49)
教育程度0.119
文盲2 495(97.61)61(2.39)
小学1 173(97.91)25(2.09)
初中1 088(98.82)13(1.18)
高中及以上482(97.57)12(2.43)
吸烟0.232
现在吸烟1 673(98.12)32(1.88)
曾经吸烟355(96.73)12(3.27)
不吸烟3 210(97.96)67(2.04)
饮酒0.779
是3 425(97.89)74(2.11)
否1 813(98.00)37(2.00)
BMI(kg/m2)23.02±2.9924.44±3.24<0.001
TG(mg/dL)126.02±100.76145.86±87.360.040
FBG(mg/dL)104.57±22.45118.52±42.68<0.001
HDL-C(mg/dL)52.44±15.2348.10±14.390.003
METS-IR34.31±6.6938.10±7.67<0.001
), ArticleFig(id=1241102823790801180, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102815200866417, language=CN, label=表2, caption=

研究人群基本特征

, figureFileSmall=null, figureFileBig=null, tableContent=
变量CMMP
否是
n5 238(97.92)111(2.08)
年龄（岁）58.09±8.6861.98±8.06
性别0.204
男2 442(98.19)45(1.81)
女2 796(97.69)66(2.31)
居住地0.331
农村3 578(98.05)71(1.95)
城市1 660(97.65)40(2.35)
婚姻状况0.319
已婚4 710(97.86)103(2.14)
其他528(98.51)8(1.49)
教育程度0.119
文盲2 495(97.61)61(2.39)
小学1 173(97.91)25(2.09)
初中1 088(98.82)13(1.18)
高中及以上482(97.57)12(2.43)
吸烟0.232
现在吸烟1 673(98.12)32(1.88)
曾经吸烟355(96.73)12(3.27)
不吸烟3 210(97.96)67(2.04)
饮酒0.779
是3 425(97.89)74(2.11)
否1 813(98.00)37(2.00)
BMI(kg/m2)23.02±2.9924.44±3.24<0.001
TG(mg/dL)126.02±100.76145.86±87.360.040
FBG(mg/dL)104.57±22.45118.52±42.68<0.001
HDL-C(mg/dL)52.44±15.2348.10±14.390.003
METS-IR34.31±6.6938.10±7.67<0.001
), ArticleFig(id=1241102823862104351, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102815200866417, language=EN, label=Table 3, caption=

Associationbetween METS-IR and the risk of CMM

, figureFileSmall=null, figureFileBig=null, tableContent=
METS-IR模型 1模型 2
HR （95%CI）P值HR（95%CI）P值
METS-IR1.07 (1.04~1.09)<0.0011.07 (1.05~1.09)<0.001
METS-IR分类
Tertile 11.001.00
Tertile 21.45 (0.82~2.56)0.2011.61 (0.91~2.86)0.103
Tertile 33.10 (1.87~5.13)<0.0013.47 (2.06~5.84)<0.001
), ArticleFig(id=1241102823950184737, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102815200866417, language=CN, label=表3, caption=

METS-IR与新发CMM的关联关系

, figureFileSmall=null, figureFileBig=null, tableContent=
METS-IR模型 1模型 2
HR （95%CI）P值HR（95%CI）P值
METS-IR1.07 (1.04~1.09)<0.0011.07 (1.05~1.09)<0.001
METS-IR分类
Tertile 11.001.00
Tertile 21.45 (0.82~2.56)0.2011.61 (0.91~2.86)0.103
Tertile 33.10 (1.87~5.13)<0.0013.47 (2.06~5.84)<0.001
), ArticleFig(id=1241102824021487908, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102815200866417, language=EN, label=Table 4, caption=

Subgroup analysis of the association between METS-IR and the risk of CMM

, figureFileSmall=null, figureFileBig=null, tableContent=
变量METS-IRP for interaction
HR （95%CI）P值
年龄（岁）<0.001
<651.07(1.04~1.10)<0.001
≥651.06(1.02~1.10)0.002
性别<0.001
男1.08(1.05~1.12)<0.001
女1.06(1.03~1.09)<0.001
居住地0.049
农村1.07(1.04~1.10)<0.001
城市1.07(1.03~1.11)<0.001
婚姻状况0.017
已婚1.07(1.05~1.10)<0.001
其他1.01(0.90~1.14)0.865
教育程度0.029
文盲1.08(1.04~1.11)<0.001
小学1.06(1.02~1.11)0.003
初中1.06(0.99~1.14)0.115
高中及以上1.08(0.99~1.16)0.055
吸烟<0.001
现在吸烟1.06(1.03~1.09)<0.001
曾经吸烟1.10(1.00~1.20)0.049
不吸烟1.08(1.04~1.13)<0.001
饮酒0.143
是1.07(1.04~1.10)<0.001
否1.07(1.04~1.11)<0.001
), ArticleFig(id=1241102824101179689, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102815200866417, language=CN, label=表4, caption=

METS-IR与新发CMM的关联关系的亚组分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量METS-IRP for interaction
HR （95%CI）P值
年龄（岁）<0.001
<651.07(1.04~1.10)<0.001
≥651.06(1.02~1.10)0.002
性别<0.001
男1.08(1.05~1.12)<0.001
女1.06(1.03~1.09)<0.001
居住地0.049
农村1.07(1.04~1.10)<0.001
城市1.07(1.03~1.11)<0.001
婚姻状况0.017
已婚1.07(1.05~1.10)<0.001
其他1.01(0.90~1.14)0.865
教育程度0.029
文盲1.08(1.04~1.11)<0.001
小学1.06(1.02~1.11)0.003
初中1.06(0.99~1.14)0.115
高中及以上1.08(0.99~1.16)0.055
吸烟<0.001
现在吸烟1.06(1.03~1.09)<0.001
曾经吸烟1.10(1.00~1.20)0.049
不吸烟1.08(1.04~1.13)<0.001
饮酒0.143
是1.07(1.04~1.10)<0.001
否1.07(1.04~1.11)<0.001
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中国中老年人胰岛素抵抗代谢评分与心血管代谢性共病发病风险的队列研究
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钱燕萍 1 , 楼雪勇 2 , 潘轶斌 3 , 厉玲玲 1 , 郑丹娟 1 , 史萌萌 1
现代预防医学 | 流行病与统计方法 2024,51(16): 2911-2917
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现代预防医学 | 流行病与统计方法 2024, 51(16): 2911-2917
中国中老年人胰岛素抵抗代谢评分与心血管代谢性共病发病风险的队列研究
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钱燕萍1 , 楼雪勇2, 潘轶斌3, 厉玲玲1, 郑丹娟1, 史萌萌1
作者信息
  • 1.金华市中心医院，金华市糖尿病、心血管疾病防治办公室，浙江 金华 321000
  • 2.金华市中心医院，内分泌代谢科
  • 3.金华市中心医院，心血管内科

    • 钱燕萍（1982—），女，本科，主管护师，研究方向：慢病防控与健康教育

通讯作者:

钱燕萍，E-mail：
Association between insulin resistance metabolic score and risk of cardiometabolic multimorbidity in middle-aged and elderly Chinese in a cohort study
Yan-ping QIAN1 , Xue-yong LOU2, Yi-bin PAN3, Ling-ling LI1, Dan-juan ZHENG1, Meng-meng SHI1
Affiliations
  • Jinhua Diabetes, cardiovascular disease prevention office, Jinhua Central Hospital, Jinhua, Zhejiang 321000, China
出版时间: 2024-08-25 doi: 10.20043/j.cnki.MPM.202403464
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摘要
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目的

本研究探索中国中老年人胰岛素抵抗代谢评分（METS-IR）与心血管代谢性共病发病（Cardiometabolic multimorbidity，CMM）风险的关联。

方法

基于人群的队列研究，本研究使用中国健康与养老纵向研究（CHARLS）的数据，在2011—2018年对中国45岁或以上居民进行了调查。Cox比例风险模型被用于计算METS-IR与新发CMM的关联的风险比（HR）和95%可信区间（CI），并在不同人群中进行分层分析，观察协变量与METS-IR的交互作用，应用限制性立方样条观察在不同体重人群中METS-IR与新发CMM的剂量反应关系。构建受试者工作特征（ROC）曲线，分析METS-IR对CMM发病风险的预测价值。

结果

本研究共纳入5 349名研究对象，CMM发病率为2.08%，在调整所有纳入的协变量后，METS-IR作为连续变量与CMM发病风险呈正相关（HR：1.07，95%CI：1.05~1.09，P<0.001）。作为分类变量，METS-IR最高组与CMM发病风险仍呈正相关（HR：3.47，95%CI：2.06~5.84，P<0.001）。在全参与者（P for overall<0.001）、正常体重（P for overall=0.022）和超重肥胖（P for overall=0.008）人群中METS-IR与CMM发病风险仍存在线性关系。METS-IR对CMM发病风险具有一定的预测能力（AUC：0.651，95%CI：0.598~0.704）。

结论

METS-IR与中老年人群新发CMM风险增加存在正向关联，在正常体重或超重肥胖人群中都应该持续关注，从而减少CMM的风险。

胰岛素抵抗代谢评分  /  心血管代谢性共病  /  糖尿病  /  心血管疾病  /  脑卒中
Objective

To explore the association between metabolic score of insulin resistance (METS-IR) and the risk of Cardiometabolic multimorbidity (CMM) in middle-aged and elderly Chinese.

Methods

A population-based cohort study was conducted using data from the China Longitudinal Study of Health and Aging (CHARLS) among Chinese residents aged 45 years or older from 2011 to 2018. Cox proportional hazards regression models were used to calculate Hazard Ratio (HR) and 95% confidence intervals (CI) for the association between METS-IR and new CMM, and stratified analyses were performed in different populations to observe the interaction between covariates and METS-IR. The dose-response relationship between METS-IR and new CMM in different body weight population was observed with restricted cubic spline. Receiver operating characteristic (ROC) curve was constructed to analyze the value of METS-IR in predicting the risk of CMM.

Results

A total of 5 349 subjects were included in this study, and the incidence of CMM was 2.08%. After adjusting for all included covariates, METS-IR as a continuous variable was positively correlated with the risk of CMM (HR: 1.07, 95%CI: 1.05-1.09, P<0.001). As a categorical variable, the highest METS-IR group was still positively associated with the risk of CMM (HR: 3.47, 95%CI: 2.06-5.84, P<0.001). In total participants (P for overall <0.001), normal weight (P for overall = 0.022) and overweight and obese (P for overall = 0.008) populations still had a linear relationship between MET-IR and CMM risk. METS-IR was predictive of CMM risk (AUC: 0.651, 95%CI: 0.598-0.704).

Conclusion

The study finds that METS-IR is positively associated with an increased risk of new CMM in middle-aged and older adults and should be of continued concern in both normal weight and overweight obese people to reduce the risk of CMM.

Insulin resistance metabolic score  /  Cardiometabolic multimorbidity  /  Diabetes  /  Cardiovascular diseases  /  Stroke
钱燕萍, 楼雪勇, 潘轶斌, 厉玲玲, 郑丹娟, 史萌萌. 中国中老年人胰岛素抵抗代谢评分与心血管代谢性共病发病风险的队列研究. 现代预防医学, 2024 , 51 (16) : 2911 -2917 . DOI: 10.20043/j.cnki.MPM.202403464
Yan-ping QIAN, Xue-yong LOU, Yi-bin PAN, Ling-ling LI, Dan-juan ZHENG, Meng-meng SHI. Association between insulin resistance metabolic score and risk of cardiometabolic multimorbidity in middle-aged and elderly Chinese in a cohort study[J]. Modern Preventive Medicine, 2024 , 51 (16) : 2911 -2917 . DOI: 10.20043/j.cnki.MPM.202403464
正文
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心脏代谢性共病（Cardiometabolic multimorbidity，CMM）指至少两种心脏代谢性疾病（cardiometabolic diseases，CMD）同时发生，通常包括糖尿病、心脏病和脑卒中[1]。随着人口老龄化，在中国中老年人群中CMM的患病率正在上升，据Meta分析统计，2010年前CMM患病率为9.2%，2014年后上升到17.9%[2]。与发生单独的CMD相比，CMM患病率的升高将造成患者死亡风险成倍增加，并且缩短预期寿命[3-4]。鉴于上述情况，CMM将严重阻碍中国健康老龄化目标的发展。因此，识别CMM致病风险的危险因素，是降低发病率的重要途径。胰岛素抵抗（Insulin resistance，IR）与心血管及代谢性疾病的患病风险有很大的关系，例如脑卒中、心肌梗死、高血压、糖尿病等疾病[5-7]。虽然高胰岛素-正常血糖钳夹被视为IR测定的黄金标准，其侵入性、复杂性和高费用阻碍了临床应用[8]。因此，先前的研究已经开发了间接指标，即胰岛素抵抗代谢评分（metabolic score for insulin resistance，METS-IR），作为IR替代指标预测疾病的发生[9-10]。此外，已有多项研究报告了METS-IR与单独CMD患病有关[11-13]。然而，目前仍然缺乏关于中国中老年人METS-IR与CMM发病风险的关联研究。因此，本研究将探讨在中国中老年人群中METS-IR与CMM发病风险的关联，为预防CMM提供理论基础。
1　对象与方法
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1.1　研究对象
基于人群的队列研究，本研究使用中国健康与养老纵向研究（CHARLS）的数据，在2011—2018年对中国45岁或以上居民进行了调查，基线时共纳入17 705名参与者。本研究纳入人群包括年龄在45岁或以上、基线时不患CMM受访者，排除了缺失甘油三酯（TG）、空腹血糖（FBG）、高密度脂蛋白胆固醇（HDL-C）、体质指数（BMI）和协变量的人群，并排除失访人群和随访时CMM信息缺失人群，最后5349名参与者纳入分析，见图1。
1.2　研究方法
1.2.1　研究数据
训练有素的调查者根据标准程序收集了大量的问卷信息以及测量其生化指标，包括年龄、性别、居住地、婚姻状况、教育程度、吸烟、饮酒、身高、体重、TG、FBG、HDL、BMI等。
1.2.2　CMM定义
CMM，至少同时发生两种CMD，包括糖尿病，心脏病和脑卒中[14]。CMD通过以下问题进行评估：“你是否被医生诊断出患有糖尿病或高血糖？” 、“你是否被医生告知你被诊断出患有心脏病、心绞痛、冠心病、心力衰竭或其他心脏问题？” 、或“你是否被医生告知被诊断患脑卒中？
1.2.3　暴露定义
METS-IR=ln[(2*FBG (mg/dL)+TG (mg/dL)]*BMI(kg/m)/ ln[HDL (mg/dL)]，且研究将METS-IR进行三等分：Tertile 1组：≤30.83；Tertile2组：>30.83~≤36.37；Tertile 3组：> 36.37。
1.2.4　协变量定义
本研究控制的协变量主要包括：年龄、性别、居住地、婚姻状况、教育程度、吸烟、饮酒，以上协变量均通过问卷获得。见表1。
性别分为：男或女；
居住地分为：农村或城市；
婚姻状况分为：已婚或其他；
教育程度分为：文盲、小学、初中或高中及以上；
吸烟分为：现在吸烟、曾经吸烟或不吸烟；
饮酒分为：是或否。
1.3　统计学分析
连续变量描述的形式为均值和标准差。用例数和百分比来描述分类变量。连续变量组间比较采用独立样本t检验或单因素ANOVA，分类变量组间比较采用卡方检验。Cox比例风险模型被用于计算METS-IR与新发CMM的关联的风险比（HR）和95%可信区间（CI）。研究利用限制性立方样条评估在全人群、正常体重人群（BMI<24 kg/m2）、超重肥胖人群（BMI≥24 kg/m2）中METS-IR与新发CMM的非线性关系。此外，研究还进行了分层分析观察METS-IR在不同人群中与新发CMM的关系，并观察其交互作用，计算P交互。构建受试者工作特征（ROC）曲线，分析METS-IR对CMM发病风险的预测能力。P<0.05均认为有统计学意义。研究采用SPSS 21.0和R 4.1.2进行分析。
2　结果
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截至2018年，中位随访时间为7年，在5 349名研究对象中，总体CMM发病率为2.08%，男性（1.81%）CMM发病率低于女性（2.31%）。患病与不患病人群中在BMI、TG、FBG、HDL-C和METS-IR之间都存在差异（P<0.05），见表2。
模型2结果显示，在调整所有纳入的协变量后，METS-IR作为连续变量与CMM发病风险呈正相关（HR：1.07，95%CI：1.05~1.09）。作为分类变量，METS-IR最高组与CMM发病风险仍呈正相关（HR：3.47，95%CI：2.06~5.84）。见表3。
限制性立方样条结果表明，在调整协变量后，全人群中METS-IR与CMM发病风险存在线性关系（P for overall<0.001），METS-IR水平越高，CMM发病风险越高。在正常体重（P for overall=0.022）和超重肥胖（P for overall=0.008）人群中METS-IR与CMM发病风险仍存在线性关系。结果见图2。
在分层分析中，研究观察到，无论在男女人群中，METS-IR与CMM发病风险仍呈正相关，并且年龄和性别与METS-IR在CMM发病风险中存在交互作用（P for interaction<0.001）。此外，居住地（P for interaction=0.044）、婚姻状况（P for interaction=0.017）、教育程度（P for interaction=0.023）、吸烟（P for interaction<0.001）均与METS-IR在CMM发病风险中存在交互作用。结果见表4。
图3显示了METS-IR对CMM发病风险具有一定的预测能力，ROC曲线下面积为AUC：0.651，95%CI为0.598~0.704。
3　讨论
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据我们所知，本研究是首次使用中国具有代表性的中老年人队列探索胰岛素抵抗代谢评分与心血管代谢性共病发病风险的关联。我们的研究结果显示，METS-IR与中老年人群新发CMM风险增加存在正向关联。并且无论在全人群中、正常体重和超重肥胖人群中，METS-IR与中老年人群新发CMM风险存在剂量反应关系。此外，METS-IR具有一定的CMM发病风险预测能力。因此，本研究为在预防CMM发生过程中监测中国中老年人METS-IR提供理论依据。
多项研究发现IR在各种心血管和代谢紊乱发作中起到关键作用，以往的中老年人研究发现METS-IR与心血管疾病、卒中、心脏问题的风险增加有关[15-18]。中国成年人的一项队列研究也发现METS-IR与糖尿病前期风险独立正相关，在女性中的关联强于男性[19]。广东一项类似的研究也发现，METS-IR与糖尿病前期2型糖尿病显著相关[20]。以往研究中，探索了多项IR指标与冠状动脉疾病的关联，发现METS-IR预测冠状动脉疾病发生风险的能力最高[7,21-22]。开滦队列研究也发现心血管疾病的发病风险随着METS-IR水平的升高而增加，并且随时间推移而上升，提示了METS-IR水平在整个生命周期中的重要性[23]。以上研究均支持本研究的结果，但对比以往研究，我们提供了METS-IR与CMM发病风险的最新数据，通过识别CMM的风险因素实现早期预防，从而完成恰当干预以减轻CMD发病负担。
与正常体重的人相比，超重肥胖会增加患CMD的风险[24]。但本研究发现，无论在正常体重人群中，或超重肥胖人群中METS-IR与CMM发病风险均存在正相关关系。既往研究发现与体重正常但无代谢异常的个体相比，体重正常、代谢异常的个体更容易患代谢紊乱和心血管疾病[25-26]。然而，体重正常的人可能不会定期监测他们的健康指标或采取针对CMD的预防措施。目前关于预防中国正常体重人群CMD发病的证据十分有限。一项在中国正常体重人群中的研究发现三酰甘油-葡萄糖指数的长期轨迹可能有助于预测成年人的心血管疾病[27]。结合以往的发现，本研究为正常体重人群和超重肥胖人群中METS-IR对于CMM发病风险的关联又增添了证据。这一研究仍有一些局限性。第一，CMM的诊断来自自我报告，容易引起结局的错分低估效应，并且可能存在一定的回忆偏倚；其次，研究随访时间不足，发病人群数不多，未来的研究仍需要继续随访；并且，可能存在一定的残余混杂因素并未调整，例如体力活动和饮食情况等；最后，观察CMM发病风险缺乏IR金标准和CMM关联的验证，阻碍了对IR和CMM之间联系的全面理解。
综上所述，心血管代谢性共病在我国中老年人群中值得关注，在今后的多病共存预防控制工作中，无论对于正常体重或超重肥胖中老年人群，都应该持续关注监测胰岛素抵抗指标。
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2024年第51卷第16期
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doi: 10.20043/j.cnki.MPM.202403464
  • 接收时间：2024-03-26
  • 首发时间：2026-03-18
  • 出版时间：2024-08-25
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    1.金华市中心医院，金华市糖尿病、心血管疾病防治办公室，浙江 金华 321000
    2.金华市中心医院，内分泌代谢科
    3.金华市中心医院，心血管内科

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2种不同金属材料的力学参数
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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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