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Article(id=1311349876521272061, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311349802038821144, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202601404, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1769184000000, receivedDateStr=2026-01-24, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790579774265, onlineDateStr=2026-09-28, pubDate=1783612800000, pubDateStr=2026-07-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790579774265, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790579774265, creator=13701087609, updateTime=1790579774265, updator=13701087609, issue=Issue{id=1311349802038821144, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='13', pageStart='2305', pageEnd='2496', issueExtLink='null', onlineDate='null', pubDate='1783612800000', pubDateStr='2026-07-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790579756507, creator='13701087609', updateTime=1790588376143, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311385955492258162, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311349802038821144, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311385955496452467, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311349802038821144, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2417, endPage=2422, ext={EN=ArticleExt(id=1311349876739375870, articleId=1311349876521272061, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=The association between the triglyceride glucose-body mass index and stroke incidence among residents in rural areas of Wujin District,Changzhou, columnId=1228016572892119056, journalTitle=Modern Preventive Medicine, columnName=Primary Health Services, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the correlation and dose-response relationship between the triglyceride glucose-body mass index (TyG-BMI) and the incidence of stroke.

Methods

A prospective cohort study design was employed to compare differences in demographic characteristics, behavioral lifestyles, family history, and blood biochemical results between individuals who developed stroke and those who did not. All subjects were divided into four groups, Q1 to Q4, based on the quartiles of TyG-BMI in ascending order. The Cox proportional hazards regression model was used to analyze the association between TyG-BMI and stroke risk, calculating the hazard ratio (HR) and its 95% confidence interval (CI). A Restricted Cubic Spline (RCS) model was used to analyze the dose-response relationship between TyG-BMI and stroke risk. After the RCS analysis suggested a nonlinear trend, we further utilized the segmented package in R software to perform piecewise Cox regression to identify the threshold effect. The goodness of fit of the models was compared using the Davies test and the likelihood ratio test.

Results

This study included 17 173 subjects with a median follow-up time of 19.5 years. During the follow-up period, 2 374 new stroke cases occurred, with an incidence density of 805 per 100 000 person-years. Significant differences(P<0.05) were observed between the stroke group and the non-stroke group in terms of age, education level, income, physical activity intensity, history of hypertension, family history of stroke, systolic blood pressure (SBP), diastolic blood pressure (DBP),body mass index (BMI), waist circumference (WC), fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), and TyG-BMI. After adjusting for confounding factors, the risk of stroke gradually increased with rising TyG-BMI levels (P<0.001). Compared to the Q1 group, the risk of new-onset stroke increased by 18.1% (HR=1.181, 95% CI: 1.037-1.346), 33.3% (HR=1.333, 95% CI: 1.171-1.517), and 57.0% (HR=1.570, 95% CI: 1.366-1.804) in the Q2, Q3, and Q4 groups, respectively. The RCS analysis showed a positive association between TyG-BMI and stroke risk (P for overall <0.05), and a non-linear dose-response relationship was identified (P for non-linearity <0.05). Piecewise Cox regression identified a threshold value of 214.3,and the likelihood ratio test confirmed that the piecewise model had a significantly better fit than the traditional linear model (P=0.037). On both sides of the threshold, each unit increase in TyG-BMI was significantly associated with an increased risk of stroke (below the threshold: HR=1.151, 95% CI: 1.032-1.325, P<0.001; above the threshold: HR=1.232, 95% CI: 1.085-1.418,P<0.001).

Conclusion

TyG-BMI exhibits a nonlinear dose-response relationship with the risk of stroke, with a significant threshold effect identified at 214.3. The risk of stroke increased more markedly when TyG-BMI exceeded this threshold. TyG-BMI may serve as a simple indicator for stroke risk screening in rural community populations.

, authors=Xiao-ling KONG1, De-ren QIANG1, Yu-piao YAN1, Yi-lin REN1, Jing ZONG1, Jia-cheng YANG1, Su-yi SHI1, Dong HANG2, Yi-shu LIU2, Xi-bing ZHANG1, authorsList=Xiao-ling KONG, De-ren QIANG, Yu-piao YAN, Yi-lin REN, Jing ZONG, Jia-cheng YANG, Su-yi SHI, Dong HANG, Yi-shu LIU, Xi-bing ZHANG, authorCompany=null, correspAuthors=Xi-bing ZHANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1311349879163683589, articleId=1311349876521272061, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=甘油三酯-葡萄糖-体重指数与常州市武进区农村地区居民脑卒中发病风险的关联研究, columnId=1228016573533847611, journalTitle=现代预防医学, columnName=基层卫生服务, runingTitle=null, highlight=null, articleAbstract=
目的

探讨甘油三酯-葡萄糖-身体质量指数（triglyceride glucose-body mass index，TyG-BMI）与脑卒中发病的相关性和剂量-反应关系。

方法

采用前瞻性队列研究设计，比较脑卒中发病与未发病人群之间的人口学特征、行为生活方式、家族史、血生化结果等指标差异。根据TyG-BMI的四分位数，将全部研究对象由低到高分为Q1～Q4四组。采用Cox比例风险回归模型分析TyG-BMI与脑卒中发病风险之间的关联，计算风险比（hazard ratio，HR）及其95%置信区间（confidence interval，CI）。采用限制性立方样条（restricted cubic spline，RCS）模型分析TyG-BMI与脑卒中发病风险的剂量-反应关系。在RCS提示非线性趋势后，进一步利用R软件segmented包进行分段Cox回归识别阈值效应，并通过Davies检验和似然比检验比较模型拟合优度。

结果

本研究共纳入17 173名研究对象，中位随访时间为19.5年，随访期间内新发脑卒中2 374例，发病密度为805/10万人年。脑卒中发病人群与未发病人群的年龄、学历、收入、体力活动强度、高血压史、脑卒中家族史、收缩压、舒张压、身体质量指数（body mass index，BMI）、腰围、空腹血糖、总胆固醇、甘油三酯、TyG-BMI比较，差异具有统计学意义（P<0.05）。调整混杂因素后，随着TyG-BMI水平增加，脑卒中发病风险逐渐增加（P<0.001）。与Q1组相比，Q2、Q3、Q4组新发脑卒中风险分别增加了18.1%（HR=1.181，95%CI：1.037～1.346）、33.3%（HR=1.333，95%CI：1.171～1.517）、57.0%（HR=1.570，95%CI：1.366～1.804）。RCS分析结果显示，TyG-BMI与脑卒中发病风险存在正向关联（P总体<0.05），且存在非线性剂量-反应关系（P非线性<0.05）。分段Cox回归识别出阈值为214.3，似然比检验证实分段模型拟合优度显著优于传统线性模型（P=0.037）。在阈值两侧，TyG-BMI每增加一个单位，脑卒中风险均显著增加（低于阈值：HR=1.151，95%CI：1.032～1.325，P<0.001；高于阈值：HR=1.232，95%CI：1.085～1.418，P<0.001）。

结论

TyG-BMI与脑卒中发病风险呈非线性剂量-反应关系，且存在显著的阈值效应（214.3）。当TyG-BMI超过该阈值后，脑卒中风险上升幅度更为明显。TyG-BMI可作为农村社区人群脑卒中风险筛查的简易指标。

, authors=孔晓玲1, 强德仁1, 闫于飘1, 任怡霖1, 宗菁1, 杨佳成1, 石素逸1, 杭栋2, 刘逸舒2, 张锡炳1, authorsList=孔晓玲, 强德仁, 闫于飘, 任怡霖, 宗菁, 杨佳成, 石素逸, 杭栋, 刘逸舒, 张锡炳, authorCompany=null, correspAuthors=张锡炳, authorNote=

孔晓玲（1994—），女，硕士，主管医师，研究方向：慢性病预防与控制

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张锡炳，E-mail：
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孔晓玲（1994—），女，硕士，主管医师，研究方向：慢性病预防与控制

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Nutrients, 2023, 15(21): 4655., articleTitle=Deficiency of apoA-IV in female 129X1/ SvJ mice leads to Diet-Induced obesity, insulin resistance, and decreased energy expenditure, refAbstract=null), Reference(id=1311349888378569566, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349876521272061, doi=null, pmid=null, pmcid=null, year=2024, volume=43, issue=6, pageStart=860, pageEnd=869, url=null, language=null, rfNumber=[15], rfOrder=18, authorNames=Jiang L, Xu L, Lu Y, journalName=Journal?of?Medical?Biochemistry, refType=null, unstructuredReference=Jiang L, Xu L, Lu Y, et al. The correlation between insulin resistance and blood lipids in children[J]. Journal?of?Medical?Biochemistry, 2024, 43(6): 860-869., articleTitle=The correlation between insulin resistance and blood lipids in children, refAbstract=null), Reference(id=1311349888458261343, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349876521272061, doi=null, pmid=null, pmcid=null, year=2025, volume=24, issue=1, pageStart=274, pageEnd=null, url=null, language=null, rfNumber=[16], rfOrder=19, authorNames=Sun JA, Meng XL, Guo LC, journalName=Cardiovascular Diabetology, refType=null, unstructuredReference=Sun JA, Meng XL, Guo LC, et al. Association between modified triglyceride glucose indices and stroke risk in middle-aged and older Chinese adults: a prospective cohort study[J]. 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Comparison of baseline characteristics between the two groups ［（）， n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
项目未发生脑卒中（n=14 799）新发脑卒中（n=2 374）χ2t值P值
年龄（岁）52.02±11.7858.97±10.19-30.17<0.001
性别3.090.079
女8 575(57.9)1 330(56.0)
男6 224(42.1)1 044(44.0)
学历268.55<0.001
文盲1 842(12.4)489(20.6)
小学5 062(34.2)1 024(43.1)
初中6 316(42.7)706(29.7)
高中1 364(9.2)123(5.2)
大专及以上78(0.5)11(0.5)
未知137(0.9)21(0.9)
家庭收入10.800.013
低1 928(13.0)366(15.4)
中等12 223(82.6)1 906(80.3)
高487(3.3)73(3.1)
未知161(1.1)29(1.2)
吸烟状况2.610.625
从不10 086(68.2)1 596(67.2)
已戒烟586(4.0)92(3.9)
现在吸烟3 994(27.0)664(28.0)
未知133(0.9)22(0.9)
饮酒状况9.190.056
从不8 815(59.6)1 407(59.3)
偶尔2 484(16.8)365(15.4)
经常496(3.4)68(2.9)
每天2 866(19.4)513(21.6)
未知138(0.9)21(0.9)
体力活动强度36.67<0.001
轻6 123(41.4)1 138(47.9)
中6 958(47.0)1 003(42.2)
重1 567(10.6)212(8.9)
未知151(1.0)21(0.9)
高血压433.60<0.001
否11 657(78.8)1 432(60.3)
是2 636(17.8)861(36.3)
未知506(3.4)81(3.4)
脑卒中家族史28.79<0.001
否11 789(79.7)1 770(74.6)
是2 038(13.8)404(17.0)
未知972(6.6)200(8.4)
收缩压（mm Hg）124.33±19.70135.24±21.99-22.73<0.001
舒张压（mm Hg）79.53±10.5383.29±11.56-14.88<0.001
BMI（kg/m2）23.29±3.3323.90±3.43-8.00<0.001
腰围（cm）79.78±9.7482.62±9.98-13.12<0.001
空腹血糖（mmol/L）5.27±1.565.58±1.98-7.33<0.001
总胆固醇（mmol/L）4.39±1.054.62±1.10-9.52<0.001
甘油三酯（mmol/L）1.48±1.311.63±1.37-5.17<0.001
HDL-C（mmol/L）1.52±0.501.54±0.53-1.510.131
TyG-BMI98.44<0.001
Q13 837(25.9)454(19.1)
Q23 744(25.3)539(22.7)
Q33 667(24.8)621(26.2)
Q43 551(24.0)760(32.0)
), ArticleFig(id=1311349884976988999, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349876521272061, language=CN, label=表1, caption=

两组基线资料比较［（），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
项目未发生脑卒中（n=14 799）新发脑卒中（n=2 374）χ2t值P值
年龄（岁）52.02±11.7858.97±10.19-30.17<0.001
性别3.090.079
女8 575(57.9)1 330(56.0)
男6 224(42.1)1 044(44.0)
学历268.55<0.001
文盲1 842(12.4)489(20.6)
小学5 062(34.2)1 024(43.1)
初中6 316(42.7)706(29.7)
高中1 364(9.2)123(5.2)
大专及以上78(0.5)11(0.5)
未知137(0.9)21(0.9)
家庭收入10.800.013
低1 928(13.0)366(15.4)
中等12 223(82.6)1 906(80.3)
高487(3.3)73(3.1)
未知161(1.1)29(1.2)
吸烟状况2.610.625
从不10 086(68.2)1 596(67.2)
已戒烟586(4.0)92(3.9)
现在吸烟3 994(27.0)664(28.0)
未知133(0.9)22(0.9)
饮酒状况9.190.056
从不8 815(59.6)1 407(59.3)
偶尔2 484(16.8)365(15.4)
经常496(3.4)68(2.9)
每天2 866(19.4)513(21.6)
未知138(0.9)21(0.9)
体力活动强度36.67<0.001
轻6 123(41.4)1 138(47.9)
中6 958(47.0)1 003(42.2)
重1 567(10.6)212(8.9)
未知151(1.0)21(0.9)
高血压433.60<0.001
否11 657(78.8)1 432(60.3)
是2 636(17.8)861(36.3)
未知506(3.4)81(3.4)
脑卒中家族史28.79<0.001
否11 789(79.7)1 770(74.6)
是2 038(13.8)404(17.0)
未知972(6.6)200(8.4)
收缩压（mm Hg）124.33±19.70135.24±21.99-22.73<0.001
舒张压（mm Hg）79.53±10.5383.29±11.56-14.88<0.001
BMI（kg/m2）23.29±3.3323.90±3.43-8.00<0.001
腰围（cm）79.78±9.7482.62±9.98-13.12<0.001
空腹血糖（mmol/L）5.27±1.565.58±1.98-7.33<0.001
总胆固醇（mmol/L）4.39±1.054.62±1.10-9.52<0.001
甘油三酯（mmol/L）1.48±1.311.63±1.37-5.17<0.001
HDL-C（mmol/L）1.52±0.501.54±0.53-1.510.131
TyG-BMI98.44<0.001
Q13 837(25.9)454(19.1)
Q23 744(25.3)539(22.7)
Q33 667(24.8)621(26.2)
Q43 551(24.0)760(32.0)
), ArticleFig(id=1311349885081846600, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349876521272061, language=EN, label=Table 2, caption=

Association between TyG-BMI and new-onset stroke

, figureFileSmall=null, figureFileBig=null, tableContent=
变量NCases发病密度（1/10万人年）HR(95%CI)P值aHR(95%CI)P值
全人群
Q14 2914546261.0001.000
Q24 2835397251.150(1.015～1.303)0.0281.181(1.037～1.346)0.012
Q34 2886218341.323(1.172～1.493)<0.0011.333(1.171～1.517)<0.001
Q44 31176010381.656(1.474～1.860)<0.0011.570(1.366～1.804)<0.001
趋势检验P值<0.001<0.001
), ArticleFig(id=1311349885174121289, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349876521272061, language=CN, label=表2, caption=

TyG-BMI与新发脑卒中的关联

, figureFileSmall=null, figureFileBig=null, tableContent=
变量NCases发病密度（1/10万人年）HR(95%CI)P值aHR(95%CI)P值
全人群
Q14 2914546261.0001.000
Q24 2835397251.150(1.015～1.303)0.0281.181(1.037～1.346)0.012
Q34 2886218341.323(1.172～1.493)<0.0011.333(1.171～1.517)<0.001
Q44 31176010381.656(1.474～1.860)<0.0011.570(1.366～1.804)<0.001
趋势检验P值<0.001<0.001
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甘油三酯-葡萄糖-体重指数与常州市武进区农村地区居民脑卒中发病风险的关联研究
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孔晓玲 1 , 强德仁 1 , 闫于飘 1 , 任怡霖 1 , 宗菁 1 , 杨佳成 1 , 石素逸 1 , 杭栋 2 , 刘逸舒 2 , 张锡炳 1
现代预防医学 | 基层卫生服务 2026,53(13): 2417-2422
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现代预防医学 |基层卫生服务 2026 , 53 (13) : 2417 -2422
甘油三酯-葡萄糖-体重指数与常州市武进区农村地区居民脑卒中发病风险的关联研究
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孔晓玲1, 强德仁1, 闫于飘1, 任怡霖1, 宗菁1, 杨佳成1, 石素逸1, 杭栋2, 刘逸舒2, 张锡炳1
作者信息
  • 1.常州市武进区疾病预防控制中心，江苏 常州 213161
  • 2.南京医科大学公共卫生学院
通讯作者:
张锡炳，E-mail：
作者简介:

孔晓玲（1994—），女，硕士，主管医师，研究方向：慢性病预防与控制

The association between the triglyceride glucose-body mass index and stroke incidence among residents in rural areas of Wujin District,Changzhou
Xiao-ling KONG1, De-ren QIANG1, Yu-piao YAN1, Yi-lin REN1, Jing ZONG1, Jia-cheng YANG1, Su-yi SHI1, Dong HANG2, Yi-shu LIU2, Xi-bing ZHANG1
Affiliations
  • Wujin District Center for Disease Control and Prevention, Changzhou, Jiangsu 213161, China
出版时间: 2026-07-10 doi: 10.20043/j.cnki.MPM.202601404
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摘要
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目的

探讨甘油三酯-葡萄糖-身体质量指数（triglyceride glucose-body mass index，TyG-BMI）与脑卒中发病的相关性和剂量-反应关系。

方法

采用前瞻性队列研究设计，比较脑卒中发病与未发病人群之间的人口学特征、行为生活方式、家族史、血生化结果等指标差异。根据TyG-BMI的四分位数，将全部研究对象由低到高分为Q1～Q4四组。采用Cox比例风险回归模型分析TyG-BMI与脑卒中发病风险之间的关联，计算风险比（hazard ratio，HR）及其95%置信区间（confidence interval，CI）。采用限制性立方样条（restricted cubic spline，RCS）模型分析TyG-BMI与脑卒中发病风险的剂量-反应关系。在RCS提示非线性趋势后，进一步利用R软件segmented包进行分段Cox回归识别阈值效应，并通过Davies检验和似然比检验比较模型拟合优度。

结果

本研究共纳入17 173名研究对象，中位随访时间为19.5年，随访期间内新发脑卒中2 374例，发病密度为805/10万人年。脑卒中发病人群与未发病人群的年龄、学历、收入、体力活动强度、高血压史、脑卒中家族史、收缩压、舒张压、身体质量指数（body mass index，BMI）、腰围、空腹血糖、总胆固醇、甘油三酯、TyG-BMI比较，差异具有统计学意义（P<0.05）。调整混杂因素后，随着TyG-BMI水平增加，脑卒中发病风险逐渐增加（P<0.001）。与Q1组相比，Q2、Q3、Q4组新发脑卒中风险分别增加了18.1%（HR=1.181，95%CI：1.037～1.346）、33.3%（HR=1.333，95%CI：1.171～1.517）、57.0%（HR=1.570，95%CI：1.366～1.804）。RCS分析结果显示，TyG-BMI与脑卒中发病风险存在正向关联（P总体<0.05），且存在非线性剂量-反应关系（P非线性<0.05）。分段Cox回归识别出阈值为214.3，似然比检验证实分段模型拟合优度显著优于传统线性模型（P=0.037）。在阈值两侧，TyG-BMI每增加一个单位，脑卒中风险均显著增加（低于阈值：HR=1.151，95%CI：1.032～1.325，P<0.001；高于阈值：HR=1.232，95%CI：1.085～1.418，P<0.001）。

结论

TyG-BMI与脑卒中发病风险呈非线性剂量-反应关系，且存在显著的阈值效应（214.3）。当TyG-BMI超过该阈值后，脑卒中风险上升幅度更为明显。TyG-BMI可作为农村社区人群脑卒中风险筛查的简易指标。

脑卒中  /  甘油三酯-葡萄糖-体重指数  /  队列研究
Objective

To investigate the correlation and dose-response relationship between the triglyceride glucose-body mass index (TyG-BMI) and the incidence of stroke.

Methods

A prospective cohort study design was employed to compare differences in demographic characteristics, behavioral lifestyles, family history, and blood biochemical results between individuals who developed stroke and those who did not. All subjects were divided into four groups, Q1 to Q4, based on the quartiles of TyG-BMI in ascending order. The Cox proportional hazards regression model was used to analyze the association between TyG-BMI and stroke risk, calculating the hazard ratio (HR) and its 95% confidence interval (CI). A Restricted Cubic Spline (RCS) model was used to analyze the dose-response relationship between TyG-BMI and stroke risk. After the RCS analysis suggested a nonlinear trend, we further utilized the segmented package in R software to perform piecewise Cox regression to identify the threshold effect. The goodness of fit of the models was compared using the Davies test and the likelihood ratio test.

Results

This study included 17 173 subjects with a median follow-up time of 19.5 years. During the follow-up period, 2 374 new stroke cases occurred, with an incidence density of 805 per 100 000 person-years. Significant differences(P<0.05) were observed between the stroke group and the non-stroke group in terms of age, education level, income, physical activity intensity, history of hypertension, family history of stroke, systolic blood pressure (SBP), diastolic blood pressure (DBP),body mass index (BMI), waist circumference (WC), fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), and TyG-BMI. After adjusting for confounding factors, the risk of stroke gradually increased with rising TyG-BMI levels (P<0.001). Compared to the Q1 group, the risk of new-onset stroke increased by 18.1% (HR=1.181, 95% CI: 1.037-1.346), 33.3% (HR=1.333, 95% CI: 1.171-1.517), and 57.0% (HR=1.570, 95% CI: 1.366-1.804) in the Q2, Q3, and Q4 groups, respectively. The RCS analysis showed a positive association between TyG-BMI and stroke risk (P for overall <0.05), and a non-linear dose-response relationship was identified (P for non-linearity <0.05). Piecewise Cox regression identified a threshold value of 214.3,and the likelihood ratio test confirmed that the piecewise model had a significantly better fit than the traditional linear model (P=0.037). On both sides of the threshold, each unit increase in TyG-BMI was significantly associated with an increased risk of stroke (below the threshold: HR=1.151, 95% CI: 1.032-1.325, P<0.001; above the threshold: HR=1.232, 95% CI: 1.085-1.418,P<0.001).

Conclusion

TyG-BMI exhibits a nonlinear dose-response relationship with the risk of stroke, with a significant threshold effect identified at 214.3. The risk of stroke increased more markedly when TyG-BMI exceeded this threshold. TyG-BMI may serve as a simple indicator for stroke risk screening in rural community populations.

Stroke  /  Triglyceride glucose-body mass index (TyG-BMI)  /  Cohort study
孔晓玲, 强德仁, 闫于飘, 任怡霖, 宗菁, 杨佳成, 石素逸, 杭栋, 刘逸舒, 张锡炳. 甘油三酯-葡萄糖-体重指数与常州市武进区农村地区居民脑卒中发病风险的关联研究. 现代预防医学, 2026 , 53 (13) : 2417 -2422 . DOI: 10.20043/j.cnki.MPM.202601404
Xiao-ling KONG, De-ren QIANG, Yu-piao YAN, Yi-lin REN, Jing ZONG, Jia-cheng YANG, Su-yi SHI, Dong HANG, Yi-shu LIU, Xi-bing ZHANG. The association between the triglyceride glucose-body mass index and stroke incidence among residents in rural areas of Wujin District,Changzhou[J]. Modern Preventive Medicine, 2026 , 53 (13) : 2417 -2422 . DOI: 10.20043/j.cnki.MPM.202601404
正文
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脑卒中作为全球主要致残和致死病因之一，早期识别其危险因素至关重要。胰岛素抵抗（insulin resistance, IR）是脑卒中的独立危险因素，但其复杂检测方法限制了大规模应用[1-2]。因此，开发简便、经济、可靠的替代指标成为研究重点。
在众多IR替代指标中，甘油三酯-葡萄糖（triglyceride glucose，TyG）指数及其衍生指标因仅需常规生化检测而备受关注。单纯TyG指数未纳入肥胖参数，为此研究者提出了TyG-身体质量指数（TyG-body mass index, TyG-BMI）、TyG-腰围（TyG-waist craumference，TyG-WC）、TyG-腰围身高比（TyG-waist-to- height ratio,TyG-WHtR）等复合指标。本研究优先选择TyG-BMI，主要基于以下考虑：其一，契合基层公共卫生实践。BMI是基本公共卫生服务及“健康体重”专项行动的核心监测指标，数据规范完整，选择TyG-BMI有助于科研成果与慢病管理衔接。其二，保障数据质量。基于区域健康档案系统发现，本区农村人群BMI数据的完整和规范性显著优于腰围指标，选用TyG-BMI可避免因大量缺失值影响结论稳健性。其三，便于社区干预转化。TyG-BMI能直观反映“体重超标与血脂异常协同增加胰岛素抵抗风险”的机制，有利于在基层健康宣教中转化为通俗信息，提高居民对代谢风险的认知。
TyG-BMI指数结合了血脂、血糖和肥胖指标，在评估IR及预测心脑血管事件方面具有潜在优势[3]。一项荟萃分析证实，TyG-BMI升高与脑卒中风险增加显著相关，且在亚洲人群中尤为突出[2]。既往研究已初步验证其关联：一项针对中国中老年人群的10年随访研究表明TyG-BMI最高四分位组缺血性脑卒中风险增加70.5%[4]；基于CHARLS数据库的研究进一步指出，TyG在BMI与脑卒中风险之间起中介作用，中介效应达50%[5]。然而，现有证据多集中于城市或中老年人群[6-8]，针对农村全人群的剂量-反应关系研究有限。农村居民在生活方式、膳食结构、体力活动及医疗资源可及性等方面具有独特性，其代谢特征可能与城市人群存在差异，故TyG-BMI与脑卒中的关联形式尚需独立验证。鉴于脑卒中已成为我国农村居民的主要疾病负担之一[9]，探索简便、经济的风险评估工具对农村一级预防具有重要公共卫生意义。本研究基于武进区农村队列，旨在探讨TyG-BMI与新发脑卒中风险的剂量-反应关系，为农村人群脑卒中防控提供科学依据。
1　对象与方法
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1.1　 研究对象
本研究依托“江苏省常州市武进区农村自然人群队列”，基线调查于2004年6月—2005年9月进行，采用多阶段整群抽样方法，在武进区24个乡镇中抽取6个乡镇的23个行政村/居委会，共建立20 803人的研究队列。纳入标准：（1）基线调查时年龄≥35岁；（2）在当地居住时间≥6个月。排除年龄<35岁3 119例、基线患脑卒中298例、关键社会人口学信息缺失213例。最终共纳入研究对象17 173例，其中161例在随访期间失访，在生存分析中作为删失数据予以利用。因此，实际纳入Cox回归模型的分析样本为17 173例。所有参与者均在调查前签署书面知情同意书。本队列分别于2008—2009、2012—2013和2018—2019年完成了三轮面对面随访，每次随访均采用与基线一致的半结构化问卷收集信息，并进行了系统的体格检查与血液生化检测。本研究关注的结局事件为脑卒中的首次发病。结局信息主要通过以下途径获取：对于2018—2019年之后的发病事件，于2023—2024年通过江苏省死因监测系统、常州市慢性病网络管理信息系统及武进区公共卫生服务管理系统，系统搜集了全部研究对象的脑卒中首次发病信息及死亡结局。本研究通过复旦大学泰州健康科学研究院伦理委员会批准（EC_AF_006），调查对象均自愿参加本研究，并签署知情同意书。
1.2　研究方法
问卷调查：采用南京医科大学课题组统一编制的调查问卷，由经过系统培训并通过考核的调查员进行面对面访谈。问卷内容涵盖人口学特征、社会经济状况、行为与生活方式、饮食习惯、体力活动水平、个人疾病史及家族史等信息。体格检查：由经培训的测量员使用标准化方法进行体格测量。测量指标包括身高、体重、腰围和血压。身高、体重用于计算BMI，BMI=体重（kg）/身高的二次方（m）2。依据标准程序测量血压并检测血液生化指标，血压测量要求研究对象在安静环境下静坐15 min，随后使用经校验的电子血压计连续测量其左上臂血压3次，每次间隔5 min，最终计算平均值用于分析。血液标本于研究对象空腹至少8 h后采集，并应用OLYMPUS AU640全自动生化分析仪测定空腹血糖、总胆固醇、甘油三酯、及高密度脂蛋白胆固醇（high-density lipoprotein cholesterol, HDL-C）浓度。全部检测仪器与操作流程均通过常州市疾病预防控制中心实验室认证。
1.3　变量定义
（1）脑卒中：经二级及以上医疗机构明确诊断为脑卒中且在常州市慢性病网络管理信息系统中有报卡记录；或研究对象自报有脑卒中病史。脑卒中类型包括蛛网膜下腔出血、脑出血、脑梗死以及未分类脑卒中，其对应的国际疾病分类第10次修订本（international classification of diseases，tenthrevision，ICD-10），编码范围为I60至I64。（2）家庭收入：基线调查时所有共同生活的家庭成员税后净收入的总和，家庭月收入分为3个等级，低收入（<1 000元）、中等收入（1 000～2 999元）、高收入（≥3 000元）。（3）吸烟状况：①从不吸烟：一生中从未吸过烟；②已戒烟：曾有吸烟史，但在基线调查时已完全停止吸烟；③现在吸烟：基线调查时，每日至少吸烟1支、且持续至少6个月及以上，并且调查时仍在吸烟。（4）饮酒状况：①从不饮酒：从未饮用过任何类型的酒精饮品；②偶尔饮酒：过去超过6个月的时间里，平均每周饮酒1～2次；③经常饮酒：指在过去超过6个月的时间里，平均每周饮酒达到3次或以上。（5）体力活动强度：依据国际体力活动问卷（International Physical Activity Questionnaire, IPAQ）的标准，对个体的体力活动水平进行评估和分级。IPAQ将体力活动水平划分为高、中、低三个等级，计算个体每周从事某项体力活动水平的公式为该项体力活动对应的代谢当量（metablic equivalent，MET）×每周频率（d/w）×每天时间（min/d）。其中高强度组满足下述二条标准中任何一条：①各类高强度体力活动合计≥3 d，且每周总体力活动水平≥1 500MET-min/w；②3种强度的体力活动合计≥7 d且每周总体力活动水平≥3 000 MET-min/w。中等强度组满足下述三条标准中任何一条：①满足每天至少20 min的各类高强度体力活动，合计≥3d；②满足每天至少30 min的各类中等强度和/或步行类活动，合计≥5 d；③3种强度的体力活动合计≥5 d，且每周总体力活动水平≥600 MET-min/w。低强度组满足下述二条标准中任何一条：①没有报告任何活动；②报告了一些活动，但是尚不满足上述中、高分组标准[10]。（6）脑卒中家族史：指研究对象的一级亲属（父母、子女或兄弟姐妹）中，至少有一人患有脑卒中。（7）高血压：基线时平均收缩压≥140 mm Hg和/或舒张压≥90 mm Hg，或调查前已被二级及以上医疗机构确诊为高血压或近两周服用降压药者。
1.4　 统计学方法
采用SPSS 26.0软件进行所有统计分析。连续变量经正态性检验后，符合正态分布者以（均数±标准）描述，组间比较采用独立样本t检验；不符合正态分布者以中位数M（P25，P75）描述，组间比较采用Mann-Whitney U检验。分类变量以［n（%）］表示，组间比较采用χ2检验。脑卒中的发病密度为随访期间新发病例数与总随访人年数之比。采用Cox比例风险回归模型分析TyG-BMI与脑卒中发病之间的关系，并计算HR和95%CI。其中TyG-BMI按照四分位数分为四组：Q1组（TyG-BMI≤171.087），Q2组（171.088≤TyG-BMI≤194.952），Q3组（194.953≤TyG-BMI≤223.321），Q4组（223.322≤TyG-BMI≤459.953）。为验证结果的稳定性，采用两种模型：模型1未校正协变量；为了控制潜在的混杂偏倚，基于先验知识和既往文献构建了多变量回归模型，模型2中调整的协变量均为根据既往文献确定的、可能与暴露和结局相关的因素。具体包括社会人口学因素（年龄、学历、收入），它们既是心脑血管疾病的危险因素，也与多种生活方式暴露相关；生活方式因素（吸烟、饮酒、身体活动强度），它们是公认的心脑血管疾病风险因素；以及临床指标和遗传背景（是否高血压、脑卒中家族史、腰围、BMI、空腹血糖、收缩压、舒张压、总胆固醇、甘油三酯、HDL-C），其中高血压是心脑血管疾病最强的危险因素之一，而家族史则反映了共同的遗传易感性。采用MSTATA V0.96软件选取4个节点（P5，P35，P65，P95）绘制限制立方样条（restricted cubic splines,RCS）图，探讨TyG-BMI与新发脑卒中的剂量-反应关系。在RCS提示非线性趋势后，进一步采用分段Cox回归模型识别潜在的阈值效应。该模型利用R语言segmented包（版本2.1-4）自动搜索断点位置，并在断点前后分别拟合不同的风险比。断点的选择基于Davies检验和似然比检验，以判断分段模型是否显著优于传统模型。检验水准α=0.05。
2　结果
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2.1　研究对象特征
本研究纳入调查对象17 173例，其中男7 268例（42.32%），女9 905例（57.68%），年龄（52.98±11.82）岁。中位随访时间为19.5年，累积新发脑卒中患者2 374例，发病密度为805/10万人年（男性为857/10万人年，女性为769/10万人年），累积发病率为13.82%（男为14.36%，女为13.43%），标化发病率为12.97%（男为13.30%，女为12.52%）。脑卒中发病人群的收缩压、舒张压、BMI、腰围、空腹血糖、总胆固醇、甘油三酯水平均高于未发病人群，差异有统计学意义（P<0.05）。脑卒中发病人群与未发病人群的年龄、学历、收入、体力活动强度、是否患高血压、脑卒中家族史、TyG-BMI分布差异具有统计学意义（P<0.05）。见表1。
2.2　TyG-BMI与新发脑卒中的剂量反应关系
以连续性TyG-BMI为自变量，是否新发脑卒中为因变量进行RCS评估，在校正混杂因素后，TyG-BMI与脑卒中发病存在非线性剂量反应关系，随着TyC-BMI升高，脑卒中的发病风险呈上升趋势(P总体<0.05，P非线性<0.05)。通过分段Cox回归识别TyG-BMI的阈值为214.3，当TyG-BMI低于214.3时，每增加一个单位与脑卒中风险呈显著正相关（调整后HR= 1.151，95%CI: 1.032～1.325，P<0.001）；当TyG-BMI高于214.3时，其每增加一个单位与脑卒中风险显著正相关（调整后HR=1.232，95%CI: 1.085～1.418，P<0.001）。似然比检验显示分段模型的拟合优度显著优于传统线性模型（P=0.037），证实了阈值效应的存在。见图1。
采用TyG-BMI四分位数分组进行Cox回归分析，结果显示，随着TyG-BMI四分位数的升高，脑卒中发病密度呈递增趋势。校正混杂因素后，多因素Cox回归分析显示，Q2、Q3、Q4组新发脑卒中风险较Q1组分别增加了18.1%（P=0.012）、33.3%（P<0.001）、57.0%（P<0.001），且趋势性检验达到统计学意义（P<0.001）。该结果与RCS分析提示的阈值效应相一致——阈值214.3位于位于Q3组内，进一步支持TyG-BMI升高与脑卒中风险增加存在正向关联。见表2。
3　讨论
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本研究发现，随着TyG-BMI水平升高，脑卒中发病风险逐渐增加。与Q1组相比，Q2、Q3、Q4组的风险分别增加18.1%、33.3%、57.0%。这一结果与近年多项大规模研究一致。基于CHARLS的研究也发现，TyG-BMI每增加10个单位，脑卒中风险增加4.9%[11]。此外，本研究还发现了TyG-BMI与脑卒中风险之间的非线性剂量-反应关系，这与2024年发表的一项研究结果一致[12]。
TyG-BMI作为IR的替代指标，其与脑卒中风险的关联涉及多种机制。IR状态下，胰岛素介导的一氧化氮合成受损，导致血管收缩、炎症及血栓形成[13]；同时IR促进血小板聚集，增加动脉阻塞风险[14]。IR常伴血脂异常，加速动脉粥样硬化[15]。与单纯TyG相比，TyG-BMI增加了肥胖维度，肥胖者IR水平更高[2]；脂肪过度堆积释放促炎因子，加剧慢性炎症与IR[3]，这可能是TyG-BMI预测脑卒中风险更优的重要原因。
本研究采用前瞻性队列设计，中位随访19.5年，样本量大（n=17 173），结果具有较高的统计学效能和可靠性。同时，采用RCS模型分析剂量-反应关系，精确描述了TyG-BMI与脑卒中风险的关联模式，尤其发现了非线性关系。与近期重要研究相比，结果既有一致性也有补充。2025年一项针对中国中老年人的研究表明，三种改良TyG指数（含TyG-BMI）均与脑卒中风险显著相关[16]；2023年的研究发现，与Q1组相比，Q2～Q4组年轻成年人早发中风风险显著增加[17]。本研究进一步证实TyG-BMI是脑卒中风险的独立危险因素，并为非线性关系提供了新证据。人群代表性方面，本研究基于常州市武进区农村居民，丰富了现有证据。此前研究多集中于城市或中老年人群，农村地区证据较少。考虑到城乡在生活方式、医疗资源等方面的差异，本研究为农村地区脑卒中一级预防提供了重要参考。
本研究发现，TyG-BMI与新发脑卒中风险存在非线性剂量-反应关系，首次在常州武进区农村人群中识别出阈值为214.3。分段Cox回归显示，阈值两侧风险均显著增加，超过214.3后风险上升更明显，提示该值可作为脑卒中一级预防的重要参考切点，将其纳入社区健康管理有助于精准识别高危人群、优化资源分配。
尽管本研究基于大样本、长期随访的前瞻性队列设计，但仍存在以下局限性：第一，作为观察性研究，虽校正了多项传统心血管危险因素，但仍可能存在残留混杂。未能获取降脂药、降糖药的具体用药史及依从性信息，可能影响关联强度的真实估计。第二，TyG-BMI基于基线单次测量计算，未反映19.5年随访期内的动态变化；身高、体重为自报数据，可能存在测量误差。第三，研究对象仅来自常州武进区农村地区，结果向其他种族或经济发达地区外推时需谨慎。第四，未区分脑卒中亚型（缺血性与出血性），未来需进一步探讨TyG-BMI与不同亚型的关联。第五，本研究未评价TyG-BMI的预测效能，未来需专门设计队列研究验证其在风险预测中的增量价值。
综上所述，TyG-BMI是脑卒中发病的危险因素，两者之间存在正向非线性剂量-反应关系且存在显著的阈值效应。
基金
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  • 南京医科大学常州公共卫生高等研究院开放课题(CPHN202303)
  • 2024年武进区科技创新发展专项资金
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2026年第53卷第13期
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doi: 10.20043/j.cnki.MPM.202601404
  • 接收时间：2026-01-24
  • 首发时间：2026-09-28
  • 出版时间：2026-07-10
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  • 收稿日期：2026-01-24
基金
南京医科大学常州公共卫生高等研究院开放课题(CPHN202303)
2024年武进区科技创新发展专项资金
作者信息
    1.常州市武进区疾病预防控制中心，江苏 常州 213161
    2.南京医科大学公共卫生学院

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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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