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Article(id=1322608039862055019, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322608013010120712, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202504323, 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=1793263929571, onlineDateStr=2026-10-29, pubDate=1762704000000, pubDateStr=2025-11-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793263929571, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793263929571, creator=13701087609, updateTime=1793263929571, updator=13701087609, issue=Issue{id=1322608013010120712, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='21', pageStart='3841', pageEnd='4032', issueExtLink='null', onlineDate='null', pubDate='1762704000000', pubDateStr='2025-11-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793263923169, creator='13701087609', updateTime=1793264266929, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322609454932149086, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322608013010120712, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322609454932149087, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322608013010120712, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3847, endPage=3853, ext={EN=ArticleExt(id=1322608040050798700, articleId=1322608039862055019, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=A prospective cohort study on the relationship between dynamic changes in cholesterol, high-density lipoprotein, and glucose index and cardiovascular disease, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the relationship between dynamic changes in cholesterol, high-density lipoprotein, and glucose index (CHG) and new-onset cardiovascular disease (CVD) in the middle-aged and elderly Chinese population.

Methods

In this study, the China Health and Retirement Longitudinal Study (CHARLS) data were utilized. Both K-means clustering and cumulative measures were used to characterize the dynamic changes of CHG. Cox proportional hazards regression model was used to assess the association between CHG changes with CVD. The nonlinear relationship between cumulative CHG and CVD was further assessed using restricted cubic spline. Subgroup analyses were conducted to assess whether the association between CHG changes and CVD differed across participant subgroups.

Results

A total of 4 710 participants were included, and a total of 785 (16.7%) developed CVD during the follow-up period. The patterns of changes in CHG over the two follow-up visits were categorized into three classes by K-means clustering: persistently low (class 1), persistently medium (class 2), and persistently high (class 3). The risk of CVD was significantly increased in class 2 (HR=1.38, 95% CI: 1.17-1.64) and class 3 (HR=1.44, 95% CI: 1.11-1.87), compared with class 1. With the first quartile as reference, the third and fourth quartile of cumulative CHG had an increased risk of CVD and stroke. RCS analysis revealed a significant positive linear relationship between cumulative CHG and the risk of CVD (Pnonlinearity=0.372) and stroke (Pnonlinearity=0.200). Subgroup analyses did not identify variables that significantly interacted with changes in CHG.

Conclusion

Dynamic changes in CHG are significantly associated with the risk of CVD. The simplicity and affordability of CHG make it particularly valuable for large-scale screening, providing a new reference index for the risk prediction of CVD in the future.

, authors=Le-mei HAN1, Cheng-yuan SUN2, Jian-xiang TANG1, Ming-jun MOU1, Xing ZHAO1, Bing GUO1, authorsList=Le-mei HAN, Cheng-yuan SUN, Jian-xiang TANG, Ming-jun MOU, Xing ZHAO, Bing GUO, authorCompany=null, correspAuthors=Bing GUO, 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=1322608043313967225, articleId=1322608039862055019, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=胆固醇、高密度脂蛋白和葡萄糖指数的动态变化与心血管疾病关系的前瞻性队列研究, columnId=1228016567632462653, journalTitle=现代预防医学, columnName=流行病与统计方法, runingTitle=null, highlight=null, articleAbstract=
目的

探讨中国中老年人群胆固醇、高密度脂蛋白和葡萄糖指数（cholesterol，high-density lipoprotein，and glucose index，CHG）的动态变化与心血管疾病（cardiovascular disease，CVD）的关系。

方法

本研究利用中国健康与养老追踪调查（China Health and Retirement Longitudinal Study，CHARLS）数据。采用K-means聚类和累积值两种方式表征CHG的动态变化。采用Cox比例风险回归模型评估CHG变化与CVD的关联，限制性立方样条用于评估累积CHG与CVD的非线性关系。进行亚组分析，评估不同特征人群CHG变化与CVD的关联差异。

结果

最终纳入4 710名研究对象，随访期间共计785人（16.7%）发生CVD。K-means聚类将两次随访的CHG变化模式分为三类，分别为：持续低水平（类1）、持续中水平（类2）和持续高水平（类3）。与类1相比，类2（HR=1.38，95% CI：1.171.64）和类3（HR=1.44，95% CI：1.111.87）发生CVD风险显著增加。对累积CHG进行分析，以第一个四分位数为参考，第三、四分位数发生CVD、中风的风险增加。累积CHG与CVD（P非线性=0.372）、中风（P非线性=0.200）呈显著正向线性关系。亚组分析未发现与CHG变化有显著交互作用的变量。

结论

CHG的动态变化与CVD的风险显著相关。CHG具备成本低廉、计算简便等特点，为今后CVD的风险预测提供了新的参考指标。

, authors=韩乐梅1, 孙承媛2, 唐健翔1, 牟明军1, 赵星1, 郭冰1, authorsList=韩乐梅, 孙承媛, 唐健翔, 牟明军, 赵星, 郭冰, authorCompany=null, correspAuthors=郭冰, authorNote=

韩乐梅（2000—），女，硕士在读，研究方向：卫生统计方法与应用

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郭冰，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=nGpJeGFGFykVWZXO8o+S0A==, magXml=EOEjRuoy5T9K8/zYqIEiBg==, pdfUrl=null, pdf=Zcs6YfXpm8NkIgW6RFxzLw==, pdfFileSize=897485, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=4Nq+AsP8OvhMkJfCuyPVtw==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=J9yZSpZ58n1RMYPY1P0c1w==, mapNumber=null, fund=null)}, authors=[Author(id=1322608043762757760, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608039862055019, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322608043842449538, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608039862055019, authorId=1322608043762757760, language=EN, stringName=Le-mei HAN, firstName=Le-mei, middleName=null, lastName=HAN, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=Department of Epidemiology and Health Statistics, West China School of Public Health / West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322608043913752707, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608039862055019, authorId=1322608043762757760, language=CN, stringName=韩乐梅, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1.四川大学华西公共卫生学院/华西第四医院流行病与卫生统计学系，四川　成都　610041, bio={"content":"

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注：CHG_2012为2012年的CHG取值；CHG_2015为2015年的CHG取值。

, figureFileSmall=5W5cx95k2XNhQHRK/v3cWQ==, figureFileBig=4Nq+AsP8OvhMkJfCuyPVtw==, tableContent=null), ArticleFig(id=1322608047822844071, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608039862055019, language=EN, label=Figure 2, caption=Nonlinear associations between cumulative CHG and outcomes, figureFileSmall=Dhol6EA2uLiBzHVZuLGR6w==, figureFileBig=cE4rN/O421ZnEb7H9x+d1A==, tableContent=null), ArticleFig(id=1322608047898341544, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608039862055019, language=CN, label=图2, caption=累积CHG与结局的非线性关联, figureFileSmall=Dhol6EA2uLiBzHVZuLGR6w==, figureFileBig=cE4rN/O421ZnEb7H9x+d1A==, tableContent=null), ArticleFig(id=1322608048007393449, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608039862055019, language=EN, label=Table 1, caption=

Basic characteristics of research objects［（），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总人群CHG变化类别P值
类1类2类3
人数4 7101 9852 236489
年龄（岁）57.86±8.3757.44±8.4558.23±8.3757.88±8.000.009
性别
男2 134 (45.3)940 (47.4)988 (44.2)206 (42.1)0.039
女2 576 (54.7)1 045 (52.6)1 248 (55.8)283 (57.9)
居住地
城市1 520 (32.3)539 (27.2)788 (35.2)193 (39.5)<0.001
农村3 190 (67.7)1 446 (72.8)1 448 (64.8)296 (60.5)
婚姻状况
已婚4 099 (87.0)1 726 (87.0)1 940 (86.8)433 (88.5)0.573
其他611 (13.0)259 (13.0)296 (13.2)56 (11.5)
受教育程度
文盲1 348 (28.6)563 (28.4)656 (29.3)129 (26.4)0.170
小学1 923 (40.8)838 (42.2)894 (40.0)191 (39.1)
中学及以上1 439 (30.6)584 (29.4)686 (30.7)169 (34.6)
BMI（kg/m2）23.72±10.7822.76±15.9224.20±3.8325.40±3.99<0.001
吸烟
否3 285 (69.7)1 330 (67.0)1 598 (71.5)357 (73.0)0.002
是1 425 (30.3)655 (33.0)638 (28.5)132 (27.0)
饮酒
否3 106 (65.9)1 242 (62.6)1 516 (67.8)348 (71.2)<0.001
是1 604 (34.1)743 (37.4)720 (32.2)141 (28.8)
是否患有高血压
否3 711 (79.1)1 692 (85.6)1 700 (76.4)319 (65.6)<0.001
是978 (20.9)285 (14.4)526 (23.6)167 (34.4)
是否患有糖尿病
否4 441 (95.0)1 944 (98.6)2 132 (96.0)365 (75.9)<0.001
是233 (5.0)28 (1.4)89 (4.0)116 (24.1)
是否血脂异常
否4 258 (92.1)1 865 (95.6)2 000 (91.2)393 (81.7)<0.001
是367 (7.9)85 (4.4)194 (8.8)88 (18.3)
是否患有肝脏疾病
否4 562 (97.2)1 911 (96.5)2 177 (97.8)474 (97.5)0.039
是130 (2.8)69 (3.5)49 (2.2)12 (2.5)
累积CHG15.77±1.0914.84±0.4616.12±0.4317.96±0.95<0.001
CHG20125.32±0.444.98±0.225.45±0.226.14±0.50<0.001
CHG20155.19±0.364.92±0.205.30±0.205.84±0.40<0.001
TC2012 (mg/L)1 931.7±81.71 793.9±321.31 996.6±354.02 194.2±496.3<0.001
TC2015 (mg/L)1 853.8±368.81 731.6±308.41 909.3±339.22 095.6±514.9<0.001
HDL2012 (mg/L)512.8±152.1605.2±146.6462.8±109.7366.9±115.3<0.001
HDL2015 (mg/L)517.7±118.1577.0±123.1481.4±89.4442.7±101.9<0.001
FBG2012 (mg/L)1 092.2±345.1975.0±135.61 078.4±190.51 631.2±748.8<0.001
FBG2015 (mg/L)1 034.3±340.1913.7±124.41 022.5±183.21 577.8±737.4<0.001
CVD
否3 925 (83.3)1 722 (86.8)1 822 (81.5)381 (77.9)<0.001
是785 (16.7)263 (13.2)414 (18.5)108 (22.1)
中风
否4 420 (93.8)1 907 (96.1)2 067 (92.4)446 (91.2)<0.001
是290 (6.2)78 (3.9)169 (7.6)43 (8.8)
心脏病
否4 152 (88.2)1 782 (89.8)1 954 (87.4)416 (85.1)0.005
是558 (11.8)203 (10.2)282 (12.6)73 (14.9)
), ArticleFig(id=1322608048116445354, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608039862055019, language=CN, label=表1, caption=

研究对象的基本特征［（），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总人群CHG变化类别P值
类1类2类3
人数4 7101 9852 236489
年龄（岁）57.86±8.3757.44±8.4558.23±8.3757.88±8.000.009
性别
男2 134 (45.3)940 (47.4)988 (44.2)206 (42.1)0.039
女2 576 (54.7)1 045 (52.6)1 248 (55.8)283 (57.9)
居住地
城市1 520 (32.3)539 (27.2)788 (35.2)193 (39.5)<0.001
农村3 190 (67.7)1 446 (72.8)1 448 (64.8)296 (60.5)
婚姻状况
已婚4 099 (87.0)1 726 (87.0)1 940 (86.8)433 (88.5)0.573
其他611 (13.0)259 (13.0)296 (13.2)56 (11.5)
受教育程度
文盲1 348 (28.6)563 (28.4)656 (29.3)129 (26.4)0.170
小学1 923 (40.8)838 (42.2)894 (40.0)191 (39.1)
中学及以上1 439 (30.6)584 (29.4)686 (30.7)169 (34.6)
BMI（kg/m2）23.72±10.7822.76±15.9224.20±3.8325.40±3.99<0.001
吸烟
否3 285 (69.7)1 330 (67.0)1 598 (71.5)357 (73.0)0.002
是1 425 (30.3)655 (33.0)638 (28.5)132 (27.0)
饮酒
否3 106 (65.9)1 242 (62.6)1 516 (67.8)348 (71.2)<0.001
是1 604 (34.1)743 (37.4)720 (32.2)141 (28.8)
是否患有高血压
否3 711 (79.1)1 692 (85.6)1 700 (76.4)319 (65.6)<0.001
是978 (20.9)285 (14.4)526 (23.6)167 (34.4)
是否患有糖尿病
否4 441 (95.0)1 944 (98.6)2 132 (96.0)365 (75.9)<0.001
是233 (5.0)28 (1.4)89 (4.0)116 (24.1)
是否血脂异常
否4 258 (92.1)1 865 (95.6)2 000 (91.2)393 (81.7)<0.001
是367 (7.9)85 (4.4)194 (8.8)88 (18.3)
是否患有肝脏疾病
否4 562 (97.2)1 911 (96.5)2 177 (97.8)474 (97.5)0.039
是130 (2.8)69 (3.5)49 (2.2)12 (2.5)
累积CHG15.77±1.0914.84±0.4616.12±0.4317.96±0.95<0.001
CHG20125.32±0.444.98±0.225.45±0.226.14±0.50<0.001
CHG20155.19±0.364.92±0.205.30±0.205.84±0.40<0.001
TC2012 (mg/L)1 931.7±81.71 793.9±321.31 996.6±354.02 194.2±496.3<0.001
TC2015 (mg/L)1 853.8±368.81 731.6±308.41 909.3±339.22 095.6±514.9<0.001
HDL2012 (mg/L)512.8±152.1605.2±146.6462.8±109.7366.9±115.3<0.001
HDL2015 (mg/L)517.7±118.1577.0±123.1481.4±89.4442.7±101.9<0.001
FBG2012 (mg/L)1 092.2±345.1975.0±135.61 078.4±190.51 631.2±748.8<0.001
FBG2015 (mg/L)1 034.3±340.1913.7±124.41 022.5±183.21 577.8±737.4<0.001
CVD
否3 925 (83.3)1 722 (86.8)1 822 (81.5)381 (77.9)<0.001
是785 (16.7)263 (13.2)414 (18.5)108 (22.1)
中风
否4 420 (93.8)1 907 (96.1)2 067 (92.4)446 (91.2)<0.001
是290 (6.2)78 (3.9)169 (7.6)43 (8.8)
心脏病
否4 152 (88.2)1 782 (89.8)1 954 (87.4)416 (85.1)0.005
是558 (11.8)203 (10.2)282 (12.6)73 (14.9)
), ArticleFig(id=1322608048217108651, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608039862055019, language=EN, label=Table 2, caption=

Analysis of the association between CHG changes and CVD

, figureFileSmall=null, figureFileBig=null, tableContent=
CHG变化模型1模型2模型3
HR (95% CI)P值HR (95% CI)P值HR (95% CI)P值
CHG改变类别
类1
类21.44 (1.23～1.68)<0.0011.43 (1.22～1.67)<0.0011.38 (1.17～1.64)<0.001
类31.75 (1.40～2.19)<0.0011.76 (1.41～2.21)<0.0011.44 (1.11～1.87)0.006
累积CHG
Q1
Q21.04 (0.83～1.29)0.7581.03 (0.83～1.28)0.8050.99 (0.78～1.26)0.957
Q31.43 (1.16～1.75)0.0011.40 (1.14～1.73)0.0011.37 (1.10～1.71)0.005
Q41.62 (1.32～1.97)<0.0011.62 (1.33～1.99)<0.0011.40 (1.12～1.76)0.003
P趋势1.21 (1.14～1.29)<0.0011.22 (1.14～1.30)<0.0011.16 (1.08～1.25)<0.001
), ArticleFig(id=1322608048309383340, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608039862055019, language=CN, label=表2, caption=

CHG变化与CVD的关联分析

, figureFileSmall=null, figureFileBig=null, tableContent=
CHG变化模型1模型2模型3
HR (95% CI)P值HR (95% CI)P值HR (95% CI)P值
CHG改变类别
类1
类21.44 (1.23～1.68)<0.0011.43 (1.22～1.67)<0.0011.38 (1.17～1.64)<0.001
类31.75 (1.40～2.19)<0.0011.76 (1.41～2.21)<0.0011.44 (1.11～1.87)0.006
累积CHG
Q1
Q21.04 (0.83～1.29)0.7581.03 (0.83～1.28)0.8050.99 (0.78～1.26)0.957
Q31.43 (1.16～1.75)0.0011.40 (1.14～1.73)0.0011.37 (1.10～1.71)0.005
Q41.62 (1.32～1.97)<0.0011.62 (1.33～1.99)<0.0011.40 (1.12～1.76)0.003
P趋势1.21 (1.14～1.29)<0.0011.22 (1.14～1.30)<0.0011.16 (1.08～1.25)<0.001
), ArticleFig(id=1322608048389075117, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608039862055019, language=EN, label=Table 3, caption=

Analysis of the association between CHG changes and stroke

, figureFileSmall=null, figureFileBig=null, tableContent=
CHG变化模型1模型2模型3
HR (95% CI)P值HR (95% CI)P值HR (95% CI)P值
CHG改变类别
类1
类21.96 (1.50～2.56)<0.0012.01 (1.53～2.63)<0.0011.97 (1.47～2.64)<0.001
类32.30 (1.58～3.33)<0.0012.45 (1.68～3.57)<0.0012.01 (1.31～3.08)0.001
累积CHG
Q1
Q21.22 (0.83～1.81)0.3101.25 (0.85～1.85)0.2601.21 (0.79～1.85)0.380
Q31.88 (1.31～2.69)0.0011.91 (1.33～2.74)<0.0011.96 (1.33～2.88)0.001
Q42.30 (1.62～3.25)<0.0012.47 (1.74～3.50)<0.0012.11 (1.43～3.13)<0.001
P趋势1.34 (1.21～1.47)<0.0011.37 (1.24～1.51)<0.0011.32 (1.17～1.47)<0.001
), ArticleFig(id=1322608048531681454, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608039862055019, language=CN, label=表3, caption=

CHG变化与中风的关联分析

, figureFileSmall=null, figureFileBig=null, tableContent=
CHG变化模型1模型2模型3
HR (95% CI)P值HR (95% CI)P值HR (95% CI)P值
CHG改变类别
类1
类21.96 (1.50～2.56)<0.0012.01 (1.53～2.63)<0.0011.97 (1.47～2.64)<0.001
类32.30 (1.58～3.33)<0.0012.45 (1.68～3.57)<0.0012.01 (1.31～3.08)0.001
累积CHG
Q1
Q21.22 (0.83～1.81)0.3101.25 (0.85～1.85)0.2601.21 (0.79～1.85)0.380
Q31.88 (1.31～2.69)0.0011.91 (1.33～2.74)<0.0011.96 (1.33～2.88)0.001
Q42.30 (1.62～3.25)<0.0012.47 (1.74～3.50)<0.0012.11 (1.43～3.13)<0.001
P趋势1.34 (1.21～1.47)<0.0011.37 (1.24～1.51)<0.0011.32 (1.17～1.47)<0.001
), ArticleFig(id=1322608048628150447, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608039862055019, language=EN, label=Table 4, caption=

Analysis of the association between CHG changes and heart disease

, figureFileSmall=null, figureFileBig=null, tableContent=
CHG变化模型1模型2模型3
HR (95% CI)P值HR (95% CI)P值HR (95% CI)P值
CHG改变类别
类1
类21.25 (1.04～1.50)0.0161.23 (1.03～1.47)0.0251.17 (0.96～1.42)0.116
类31.50 (1.15～1.96)0.0031.48 (1.13～1.93)0.0041.16 (0.85～1.58)0.362
累积CHG
Q1
Q20.97 (0.76～1.26)0.8440.96 (0.75～1.24)0.7530.93 (0.71～1.22)0.609
Q31.27 (1.00～1.62)0.0471.24 (0.98～1.58)0.0751.16 (0.90～1.50)0.250
Q41.37 (1.08～1.73)0.0081.35 (1.07～1.71)0.0131.15 (0.88～1.49)0.312
P趋势1.14 (1.06～1.24)0.0011.14 (1.05～1.23)0.0011.06 (0.97～1.16)0.190
), ArticleFig(id=1322608048703647920, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608039862055019, language=CN, label=表4, caption=

CHG变化与心脏病的关联分析

, figureFileSmall=null, figureFileBig=null, tableContent=
CHG变化模型1模型2模型3
HR (95% CI)P值HR (95% CI)P值HR (95% CI)P值
CHG改变类别
类1
类21.25 (1.04～1.50)0.0161.23 (1.03～1.47)0.0251.17 (0.96～1.42)0.116
类31.50 (1.15～1.96)0.0031.48 (1.13～1.93)0.0041.16 (0.85～1.58)0.362
累积CHG
Q1
Q20.97 (0.76～1.26)0.8440.96 (0.75～1.24)0.7530.93 (0.71～1.22)0.609
Q31.27 (1.00～1.62)0.0471.24 (0.98～1.58)0.0751.16 (0.90～1.50)0.250
Q41.37 (1.08～1.73)0.0081.35 (1.07～1.71)0.0131.15 (0.88～1.49)0.312
P趋势1.14 (1.06～1.24)0.0011.14 (1.05～1.23)0.0011.06 (0.97～1.16)0.190
), ArticleFig(id=1322608048808505521, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608039862055019, language=EN, label=Table 5, caption=

Subgroup analysis of the association between CHG categories and CVD［HR（95% CI）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量类1类2类3P交互值
年龄（岁）1.000.270
≤601.001.34 (1.07～1.68)1.05 (0.72～1.51)
>601.001.44 (1.11～1.86)1.96 (1.34～2.86)
性别1.000.701
男1.001.28 (0.98～1.66)1.53 (1.01～2.31)
女1.001.36 (1.09～1.71)1.24 (0.88～1.76)
居住地1.000.158
城市1.001.67 (1.18～2.36)2.18 (1.37～3.48)
农村1.001.22 (1.00～1.50)1.09 (0.78～1.52)
婚姻状况1.000.077
已婚1.001.33 (1.10～1.60)1.44 (1.09～1.91)
其他1.001.27 (0.80～2.01)0.67 (0.25～1.78)
受教育水平1.000.766
文盲1.001.24 (0.90～1.69)1.41 (0.87～2.28)
小学1.001.41 (1.09～1.83)1.47 (0.98～2.22)
中学及以上1.001.27 (0.90～1.78)1.14 (0.68～1.91)
是否吸烟1.000.620
否1.001.40 (1.14～1.72)1.36 (0.99～1.85)
是1.001.17 (0.85～1.60)1.28 (0.77～2.14)
是否饮酒1.000.945
否1.001.29 (1.04～1.59)1.30 (0.95～1.79)
是1.001.38 (1.03～1.86)1.56 (0.95～2.56)
BMI（kg/m2）1.000.646
体重过轻1.001.38 (0.64～2.97)1.77 (0.32～9.74)
正常体重1.001.35 (1.07～1.70)1.33 (0.86～2.06)
超重1.001.28 (0.92～1.78)1.44 (0.93～2.21)
肥胖1.000.89 (0.53～1.49)0.79 (0.42～1.52)
是否患有高血压1.000.127
否1.001.42 (1.16～1.74)1.21 (0.85～1.72)
是1.001.09 (0.78～1.51)1.35 (0.88～2.07)
是否患有糖尿病1.000.338
否1.001.32 (1.11～1.57)1.23 (0.92～1.64)
是1.001.09 (0.34～3.46)1.68 (0.56～5.04)
是否血脂异常1.000.218
否1.001.37 (1.14～1.65)1.32 (0.98～1.78)
是1.000.84 (0.51～1.40)1.09 (0.59～2.02)
是否患有肝脏疾病1.000.229
否1.001.27 (1.07～1.52)1.25 (0.96～1.64)
是1.004.05 (1.39～11.83)5.61 (1.47～21.44)
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CHG类别与CVD关联的亚组分析［HR（95% CI）］

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变量类1类2类3P交互值
年龄（岁）1.000.270
≤601.001.34 (1.07～1.68)1.05 (0.72～1.51)
>601.001.44 (1.11～1.86)1.96 (1.34～2.86)
性别1.000.701
男1.001.28 (0.98～1.66)1.53 (1.01～2.31)
女1.001.36 (1.09～1.71)1.24 (0.88～1.76)
居住地1.000.158
城市1.001.67 (1.18～2.36)2.18 (1.37～3.48)
农村1.001.22 (1.00～1.50)1.09 (0.78～1.52)
婚姻状况1.000.077
已婚1.001.33 (1.10～1.60)1.44 (1.09～1.91)
其他1.001.27 (0.80～2.01)0.67 (0.25～1.78)
受教育水平1.000.766
文盲1.001.24 (0.90～1.69)1.41 (0.87～2.28)
小学1.001.41 (1.09～1.83)1.47 (0.98～2.22)
中学及以上1.001.27 (0.90～1.78)1.14 (0.68～1.91)
是否吸烟1.000.620
否1.001.40 (1.14～1.72)1.36 (0.99～1.85)
是1.001.17 (0.85～1.60)1.28 (0.77～2.14)
是否饮酒1.000.945
否1.001.29 (1.04～1.59)1.30 (0.95～1.79)
是1.001.38 (1.03～1.86)1.56 (0.95～2.56)
BMI（kg/m2）1.000.646
体重过轻1.001.38 (0.64～2.97)1.77 (0.32～9.74)
正常体重1.001.35 (1.07～1.70)1.33 (0.86～2.06)
超重1.001.28 (0.92～1.78)1.44 (0.93～2.21)
肥胖1.000.89 (0.53～1.49)0.79 (0.42～1.52)
是否患有高血压1.000.127
否1.001.42 (1.16～1.74)1.21 (0.85～1.72)
是1.001.09 (0.78～1.51)1.35 (0.88～2.07)
是否患有糖尿病1.000.338
否1.001.32 (1.11～1.57)1.23 (0.92～1.64)
是1.001.09 (0.34～3.46)1.68 (0.56～5.04)
是否血脂异常1.000.218
否1.001.37 (1.14～1.65)1.32 (0.98～1.78)
是1.000.84 (0.51～1.40)1.09 (0.59～2.02)
是否患有肝脏疾病1.000.229
否1.001.27 (1.07～1.52)1.25 (0.96～1.64)
是1.004.05 (1.39～11.83)5.61 (1.47～21.44)
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胆固醇、高密度脂蛋白和葡萄糖指数的动态变化与心血管疾病关系的前瞻性队列研究
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韩乐梅 1 , 孙承媛 2 , 唐健翔 1 , 牟明军 1 , 赵星 1 , 郭冰 1
现代预防医学 | 流行病与统计方法 2025,52(21): 3847-3853
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现代预防医学 |流行病与统计方法 2025 , 52 (21) : 3847 -3853
胆固醇、高密度脂蛋白和葡萄糖指数的动态变化与心血管疾病关系的前瞻性队列研究
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韩乐梅1, 孙承媛2, 唐健翔1, 牟明军1, 赵星1, 郭冰1
作者信息
  • 1.四川大学华西公共卫生学院/华西第四医院流行病与卫生统计学系，四川　成都　610041
  • 2.成都市疾病预防控制中心（成都卫生监督所
通讯作者:
郭冰，E-mail：
作者简介:

韩乐梅（2000—），女，硕士在读，研究方向：卫生统计方法与应用

A prospective cohort study on the relationship between dynamic changes in cholesterol, high-density lipoprotein, and glucose index and cardiovascular disease
Le-mei HAN1, Cheng-yuan SUN2, Jian-xiang TANG1, Ming-jun MOU1, Xing ZHAO1, Bing GUO1
Affiliations
  • Department of Epidemiology and Health Statistics, West China School of Public Health / West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
出版时间: 2025-11-10 doi: 10.20043/j.cnki.MPM.202504323
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摘要
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目的

探讨中国中老年人群胆固醇、高密度脂蛋白和葡萄糖指数（cholesterol，high-density lipoprotein，and glucose index，CHG）的动态变化与心血管疾病（cardiovascular disease，CVD）的关系。

方法

本研究利用中国健康与养老追踪调查（China Health and Retirement Longitudinal Study，CHARLS）数据。采用K-means聚类和累积值两种方式表征CHG的动态变化。采用Cox比例风险回归模型评估CHG变化与CVD的关联，限制性立方样条用于评估累积CHG与CVD的非线性关系。进行亚组分析，评估不同特征人群CHG变化与CVD的关联差异。

结果

最终纳入4 710名研究对象，随访期间共计785人（16.7%）发生CVD。K-means聚类将两次随访的CHG变化模式分为三类，分别为：持续低水平（类1）、持续中水平（类2）和持续高水平（类3）。与类1相比，类2（HR=1.38，95% CI：1.171.64）和类3（HR=1.44，95% CI：1.111.87）发生CVD风险显著增加。对累积CHG进行分析，以第一个四分位数为参考，第三、四分位数发生CVD、中风的风险增加。累积CHG与CVD（P非线性=0.372）、中风（P非线性=0.200）呈显著正向线性关系。亚组分析未发现与CHG变化有显著交互作用的变量。

结论

CHG的动态变化与CVD的风险显著相关。CHG具备成本低廉、计算简便等特点，为今后CVD的风险预测提供了新的参考指标。

中国中老年人群  /  胆固醇、高密度脂蛋白和葡萄糖指数  /  动态变化  /  心血管疾病
Objective

To investigate the relationship between dynamic changes in cholesterol, high-density lipoprotein, and glucose index (CHG) and new-onset cardiovascular disease (CVD) in the middle-aged and elderly Chinese population.

Methods

In this study, the China Health and Retirement Longitudinal Study (CHARLS) data were utilized. Both K-means clustering and cumulative measures were used to characterize the dynamic changes of CHG. Cox proportional hazards regression model was used to assess the association between CHG changes with CVD. The nonlinear relationship between cumulative CHG and CVD was further assessed using restricted cubic spline. Subgroup analyses were conducted to assess whether the association between CHG changes and CVD differed across participant subgroups.

Results

A total of 4 710 participants were included, and a total of 785 (16.7%) developed CVD during the follow-up period. The patterns of changes in CHG over the two follow-up visits were categorized into three classes by K-means clustering: persistently low (class 1), persistently medium (class 2), and persistently high (class 3). The risk of CVD was significantly increased in class 2 (HR=1.38, 95% CI: 1.17-1.64) and class 3 (HR=1.44, 95% CI: 1.11-1.87), compared with class 1. With the first quartile as reference, the third and fourth quartile of cumulative CHG had an increased risk of CVD and stroke. RCS analysis revealed a significant positive linear relationship between cumulative CHG and the risk of CVD (Pnonlinearity=0.372) and stroke (Pnonlinearity=0.200). Subgroup analyses did not identify variables that significantly interacted with changes in CHG.

Conclusion

Dynamic changes in CHG are significantly associated with the risk of CVD. The simplicity and affordability of CHG make it particularly valuable for large-scale screening, providing a new reference index for the risk prediction of CVD in the future.

Middle-aged and elderly Chinese population  /  Cholesterol, high-density lipoprotein and glucose index  /  Dynamic change  /  Cardiovascular disease
韩乐梅, 孙承媛, 唐健翔, 牟明军, 赵星, 郭冰. 胆固醇、高密度脂蛋白和葡萄糖指数的动态变化与心血管疾病关系的前瞻性队列研究. 现代预防医学, 2025 , 52 (21) : 3847 -3853 . DOI: 10.20043/j.cnki.MPM.202504323
Le-mei HAN, Cheng-yuan SUN, Jian-xiang TANG, Ming-jun MOU, Xing ZHAO, Bing GUO. A prospective cohort study on the relationship between dynamic changes in cholesterol, high-density lipoprotein, and glucose index and cardiovascular disease[J]. Modern Preventive Medicine, 2025 , 52 (21) : 3847 -3853 . DOI: 10.20043/j.cnki.MPM.202504323
正文
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心血管疾病（cardiovascular disease, CVD）长期位于全球死亡谱前列，其导致的健康损失、经济负担持续加剧公共卫生系统的压力。数据显示，2019年全球约有1 860万人因此死亡，其中绝大多数由心脏病和中风引起，且主要发生在中低收入国家[1]。基于这一重大公共卫生问题，联合国已将减少CVD等非传染性疾病纳入2030年可持续发展目标[2]。目前，中国CVD患病率处于持续上升阶段，并已成为城乡居民死亡的主要原因[3]，因此寻找可靠的CVD风险预测标志物对国内外公共卫生系统均有重要意义。
胰岛素抵抗（insulin resistance, IR）作为代谢异常的关键病理生理机制，已被证实和CVD的发生密切相关。高胰岛素-正血糖钳夹（hyperinsulinemic-euglycemic clamp, HEC）作为测定胰岛素敏感性的金标准[4]，由于操作复杂和费用高昂，在基层医疗中的应用受到限制。因此，开发经济高效、操作简便的IR替代指标已成为当前公共卫生领域的迫切需求，既往研究提出的甘油三酯-葡萄糖指数(triglyceride-glucose index, TyG)、估计葡萄糖处置率(estimated glucose disposal rate, eGDR)和胰岛素抵抗代谢评分（metabolic score for insulin resistance, METS-IR）等指标与CVD的关系均已被证实[5]，但其预测效能仍有提升空间。
为了实现高精度、低成本的预测疾病，近来，有研究者提出了一个新型复合指标，胆固醇、高密度脂蛋白和葡萄糖指数（cholesterol, high-density lipoprotein, and glucose index, CHG），并发现相比于TyG，其在预测2型糖尿病时具有更好的识别性能[6]。该指数由总胆固醇（total cholesterol）、高密度脂蛋白（high-density lipoprotein, HDL）与空腹血糖（fasting blood glucose）组成。其中，FBG反应了个体的糖代谢状态，总胆固醇/HDL比值已被证明与2型糖尿病和IR密切相关[7]，CHG通过整合这三项指标，能从糖代谢和脂代谢两个通路共同反映个体复杂代谢状态。研究发现单次测量的CHG与CVD存在密切关联[8]，但值得注意的是，代谢状态在个体生命周期中呈现动态变化，单次测量指标无法全面反映个体真实的代谢水平，代谢指标的动态变化对识别疾病的高风险个体可能具有更高的敏感性和时效性[9-10]。因此，探索CHG在动态变化维度上与CVD之间的关联，可能有助于更准确地识别CVD高风险人群。
本研究利用中国健康与养老追踪调查（China Health and Retirement Longitudinal Study, CHARLS）的纵向数据，旨在探讨中国中老年人群中CHG的动态变化与CVD之间的关系，期望为CVD的风险分层和精准预防提供新的参考指标。
1　资料与方法
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1.1　数据来源和研究设计
本研究数据来源于CHARLS数据库。CHARLS采用多阶段概率抽样策略，基线调查时从中国28个省的150个县和450个村的10 257户家庭中选择了17 708名参与者，通过每两年一次的面对面的计算机辅助个人访谈收集了社会人口学特征、生活方式等多维度数据[11]，基线调查在2011—2012年进行，随后分别在2013、2015、2018和2020年进行了随访，并在第一轮和第三轮调查采集了血样。本研究利用CHARLS第一轮和第三轮调查数据的血样信息构建CHG，并记录从2015—2020年随访期间发生CVD的对象。参与第一轮和第三轮调查的对象包括14 576人，在此基础上排除年龄小于45岁者463人，第一轮（2011—2012年）调查时CVD患者及2012—2015年发生CVD者3 369人，缺乏两次完整CHG者5 233人，缺少随访CVD结局数据740人，以及缺少完整协变量数据61人，研究最终纳入4 710人。
CHARLS严格遵循伦理规范，所有调查程序均获得北京大学生物医学伦理委员会的审核批准（IRB00001052-11015），受访者均签署了知情同意书。
1.2　研究内容
1.2.1　一般资料收集
研究纳入社会人口因素、生活方式和健康状况等资料。人口学特征包括年龄、性别、受教育程度、婚姻状况和居住地。受教育程度定义为文盲、小学、中学及以上，居住地分为农村和城市。生活方式包含目前是否抽烟、喝酒，均定义为是或否。健康状况包含是否患有高血压、糖尿病、血脂异常、肝脏疾病以及身体质量指数（body mass index, BMI），其中BMI的公式为：BMI=体重（kg）/身高的二次方（m2）。根据成人体重判定标准[12]分类，体重过轻：BMI<18.5 kg/m2，正常体重：18.5 kg/m2≤BMI<24 kg/m2，超重：24 kg/m2≤BMI<28 kg/m2，肥胖：BMI≥28 kg/m2。
1.2.2　暴露变量
通过血检信息构建CHG以及累积CHG。CHG以及累积CHG的计算[6]如下：
其中，TC为总胆固醇，CHG2012，CHG2015分别为第一轮调查及第三轮调查的CHG值。
1.2.3　结局变量
主要结果是新发的CVD，包括心脏病（心脏病发作、冠心病、心绞痛、充血性心力衰竭或其他心脏问题）和中风。次要结果是新发性的心脏病或中风。与先前研究一致[4]，通过CHARLS问卷中的“是否有医生曾经告诉过您有心脏病（如心肌梗塞、冠心病、心绞痛、充血性心力衰竭和其他心脏疾病）？”以及“是否有医生曾经告诉过您有中风（包括脑梗和脑出血）？”的回答作为判断依据。“1”表示发生，“0”表示未发生。研究对象从2015—2020年发生了心脏病或中风事件则视为发生CVD，CVD发生的时间定义为随访时间点与2015年的差值[13]。
1.3　统计学方法
采用R语言（4.4.2）统计学软件进行数据分析。采用K-means聚类对2012和2015年的CHG的动态变化进行分类，并采用肘部法确定最优聚类数。此外，本研究还采用累积CHG来评估CHG的变化，根据四分位数将累积CHG分为四组，以调查其与结局的关联。计量资料采用（均数±标准差）表示，计数资料采用频数和构成比表示，根据数据类型，使用方差分析或χ2检验进行描述性分析。Cox比例风险回归模型用于评估CHG变化类别和累积暴露水平与CVD之间的关系，结果报告为危险比（hazard ratio, HR）和95%置信区间（confidence interval, CI）。进一步构建限制性立方样条（restricted cubic spline,RCS）模型来评估累积CHG和CVD之间潜在的非线性关系。最后根据《中国心血管健康与疾病报告2023》[13]和中国既往关于CVD的研究[14-15]，采用亚组分析来评估社会人口学特征、生活方式和健康状况因素是否调节CHG与CVD发生率之间的相关性，使用交互项和似然比检验来评估交互作用的值。所有分析均采用双侧检验，检验水准α=0.05。
2　结果
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2.1　研究对象的基本特征
本研究最终纳入4 710名研究对象，平均随访时间为4.67年。根据K-means聚类结果将研究对象的CHG变化分为三个类别，图1显示每个类的变化：类1（n=1 985），从2012年的（4.975±0.220）到2015年的（4.917±0.198），代表CHG持续低水平；类2（n=2 236）从2012年的（5.448±0.225）到2015年的（5.300±0.200），代表CHG持续中水平；类3（n=489）从2012年的（6.137±0.496）到2015年的（5.836±0.400），代表CHG水平最高，并且呈缓慢下降趋势。研究对象的平均年龄为57.86岁，大多数为女性，占54.7%，已婚占87.0%，居住在农村地区占67.7%，小学学历占40.8%。基线时的吸烟者和饮酒者分别占30.3%和34.1%，高血压患病率较高，为20.9%。随访期间分别有785（16.7%）、290(6.2%)和558人(11.8%)确诊CVD、中风以及心脏病，2015年的CHG、HDL、总胆固醇、空腹血糖水平相较于基线均有所下降。见表1。
2.2　CHG变化和结局的关联
利用Cox比例风险回归模型评估CHG变化（CHG变化类别和累积CHG）与CVD、中风和心脏病风险之间的关联。在完全调整（模型3）的模型中，与类1相比，类2和类3发生CVD（类2：HR=1.38, 95% CI:1.171.64；类3：HR= 1.44,95% CI:1.111.87）和中风（类2：HR=1.97, 95% CI:1.472.64；类3：HR=2.01, 95% CI:1.313.08）的风险显著升高。在针对心脏病的分析中，类2和类3在模型1和模型2中显示出显著的风险增加，但在模型3中显著性减弱。此外，累积CHG也被用于评估CHG的变化，模型1中，以第一个四分位数作为参考，第三、四分位数发生CVD、中风和心脏病的风险增加，调整潜在的混杂因素后，这些结果仍然相似，进一步证实了CHG与CVD风险之间的关联。见表2、3、4。
2.3　累积CHG和结局的非线性关联
为了评价累积CHG与结果之间的剂量-效应关系，本研究运用了RCS模型。如图2所示，在调整模型3对应的潜在混杂因素后，仍观察到累积CHG与CVD（P总体<0.001；P非线性=0.372）、中风（P总体<0.001；P非线性= 0.200）之间的显著正向线性关联。
2.4　亚组分析
表5展示了CHG动态变化类别与CVD关联的亚组分析结果。在>60岁、城市居民、已婚的人群中，CVD发生的风险较高，但是所有亚组都没有显著的交互作用，意味着CHG动态变化类别与CVD的关联不受年龄、性别、居住地、婚姻状况、受教育程度、吸烟、饮酒、BMI、高血压、糖尿病、血脂异常等因素的影响（P交互<0.05）。
3　讨论
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本研究基于CHARLS数据，首次探讨了CHG动态变化与中老年人群CVD之间的关系，在对多种混杂因素进行调节后，发现CHG变化与CVD之间仍存在显著的线性关联，尤其是在CHG持续高水平的群体中，心血管风险显著增加。这一发现表明，CHG作为一个新提出的代谢综合指标，可能为现有的CVD预防策略提供新的参考依据。
一项研究表明相比于TyG，CHG是一个更显著的CVD风险预测因子[8]，但其仅关注单次检测的CHG与CVD的关联，并未考虑CHG的动态变化与CVD的关联。而近来的多项研究表明，代谢指标的动态变化往往可能比单次检测的指标提供更准确的疾病风险预测，如估计葡萄糖处置率[4]、甘油三酯葡萄糖-体质指数[15]、甘油三酯葡萄糖-腰围指数[16]等胰岛素抵抗指标与CVD、中风的关联。针对CHG这个新提出的指标，目前尚未有研究探索其动态改变与CVD的关联。
为了填补上述研究空白，本研究根据K-means聚类将两轮调查的CHG动态变化分为三类，三类的CHG都呈现轻微的下降趋势，进一步分析发现，这是由于构成CHG的总胆固醇、HDL以及空腹血糖在2015年的水平都低于2012年，与之前的研究一致[16]。与CHG持续低水平的类1相比，类2（持续中水平）、类3（持续高水平）的个体发生CVD的风险显著增加，当根据累积CHG的四分位数对个体进行分类时，累积CHG最高的群体与CVD、中风和心脏病的风险最高。RCS结果显示累积CHG与CVD、中风的风险呈正线性关系。这一发现与之前关于CHG与CVD关系的研究[8]结果一致，该研究将单次检测的CHG划分为四分位数，发现与最低的四分位数Q1相比，Q2（HR=1.22, 95% CI:1.06～1.41, P=0.005），Q3（HR= 1.24,95% CI:1.08～1.44, P=0.003）发生CVD的风险更高。
CHG是由血脂和血糖构成的复合指标，其与CVD的关联的机制尚未得到充分探索，但其构成部分对CVD发生的作用机制已被广泛研究。比如，HDL通过逆向胆固醇转运机制[17]，抑制炎症反应[18]，起到保护血管健康的作用；血糖可以通过氧化应激和炎症反应加速CVD的发生[19]；胆固醇水平的波动会导致产生氧化压力、损伤内皮细胞，并激活炎症反应[20]。在此基础上，本研究推测，CHG的动态变化可能通过脂质代谢和糖代谢引发氧化应激、炎症等病理过程，从而增加CVD发生的风险。相比于传统的TyG和总胆固醇/HDL比值[21-22]，CHG提供了一个更为全面的代谢评估框架，使得它能够捕捉更多潜在的心血管风险因素，具备更优的风险识别能力。
但本研究也存在一些局限性，一是，研究结局基于参与对象的自我报告确定，可能存在回忆偏差，但先前研究表明自报患病数据与医疗系统数据一致性较高[23]。二是，结局事件的具体发生时间无法获得，本研究在分析时采用近似生存时间进行建模，这种处理可能引入生存时间的测量误差。此外，研究主要基于中国中老年人群，在不同文化背景和不同地区的群体以及青年群体中推广性有待考证，未来的研究应进一步探索其他人群中的CHG变化与CVD的关系。
综上，本研究首次探索了CHG的动态变化和CVD风险的关系，并发现两者之间存在显著的线性关联。作为一种新型的代谢复合指标，CHG具备成本低廉、计算简便等特点，适用于经济和医疗资源较为落后地区的大规模人群筛查，为今后CVD的风险预测提供了新的参考。
基金
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  • 国家重点研发计划(2023YFC3605601)
文献
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2025年第52卷第21期
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doi: 10.20043/j.cnki.MPM.202504323
  • 接收时间：2025-04-18
  • 首发时间：2026-10-29
  • 出版时间：2025-11-10
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  • 收稿日期：2025-04-18
基金
国家重点研发计划(2023YFC3605601)
作者信息
    1.四川大学华西公共卫生学院/华西第四医院流行病与卫生统计学系，四川　成都　610041
    2.成都市疾病预防控制中心（成都卫生监督所

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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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