Article(id=1322929628461007038, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322929547464815391, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.14.003, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1758556800000, receivedDateStr=2025-09-23, revisedDate=1781107200000, revisedDateStr=2026-06-11, acceptedDate=null, acceptedDateStr=null, onlineDate=1793340602269, onlineDateStr=2026-10-30, pubDate=1784908800000, pubDateStr=2026-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793340602269, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793340602269, creator=13701087609, updateTime=1793340602269, updator=13701087609, issue=Issue{id=1322929547464815391, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='14', pageStart='2385', pageEnd='2576', issueExtLink='null', onlineDate='null', pubDate='1784908800000', pubDateStr='2026-07-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793340582958, creator='13701087609', updateTime=1793343111003, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322940150912790807, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322929547464815391, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322940150912790808, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322929547464815391, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2404, endPage=2413, ext={EN=ArticleExt(id=1322929628771385535, articleId=1322929628461007038, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Relationship between the classification of the estimated cardiorespiratory fitness change trajectories and the risk of cardiovascular disease among middle-aged and older adults in China, columnId=1220689484526572183, journalTitle=Chinese Nursing Research, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the change trajectories of estimated cardiorespiratory fitness(eCRF) among middle-aged and older adults in China based on data from the China Health and Retirement Longitudinal Study(CHARLS) and to analyze the relationship betweent different classification of the trajectories and the risk of cardiovascular disease.

Methods

Data from 4 172 middle-aged and older adults who completed follow-up in CHARLS from 2011 to 2020 were included.eCRF levels in 2011,2013,and 2015 were calculated using non-exercise equations.Group-based trajectory modeling was employed to identify the classification of the eCRF change trajectories.Logistic regression was used to analyze the relationship between the classification of the eCRF change trajectories and the risk of cardiovascular disease,followed by subgroup analyses.

Results

Among the 4 172 participants,1 150 developed cardiovascular disease,yielding an incidence rate of 27.6%.Three distinct eCRF change trajectories were identified,namely low-increasing then decreasing group(7.5%),moderate-rapidly decreasing group(28.0%),and high-slowly decreasing group(64.5%).After adjusting for multiple variables,Logistic regression showed that compared with the high-slowly decreasing group,both the low-increasing then decreasing group(OR=1.90,95%CI 1.43-2.51) and the moderate-rapidly decreasing group(OR=1.42,95%CI 1.20-1.69) had a higher risk of cardiovascular disease.Subgroup analyses revealed an interaction between the classification of the eCRF change trajectories and hypertension(P=0.008).

Conclusions

Group heterogeneity exists in eCRF change trajectories among middle-aged and older adults in China.Those with higher eCRF levels tend to have a lower risk of cardiovascular disease.Hypertensive status may influence the relative strength of the association between the classification of the eCRF change trajectories and the risk of cardiovascular disease.

, authors=Yanyan LIANG, Yuhong SU, Zhihao WEN, Jianli HUANG, Qijuan CHEN, authorsList=Yanyan LIANG, Yuhong SU, Zhihao WEN, Jianli HUANG, Qijuan CHEN, authorCompany=null, correspAuthors=Qijuan CHEN, 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=1322929629903847622, articleId=1322929628461007038, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=我国中老年人估算心肺适能水平变化轨迹类别与心血管疾病发生风险的关系, columnId=1220689484681761433, journalTitle=护理研究, columnName=科研论著, runingTitle=null, highlight=null, articleAbstract=
目的

基于中国健康与养老追踪调查研究(CHARLS)数据探究我国中老年人估算心肺适能(eCRF)的变化轨迹,并分析不同轨迹类别与心血管疾病发生风险的关系。

方法

纳入2011—2020年CHARLS中完成随访的4 172名中老年人数据,采用非运动方程计算2011年、2013年及2015年的eCRF水平,并利用群组轨迹模型识别eCRF的变化轨迹类别。采用Logistic回归分析探讨eCRF变化轨迹类别与心血管疾病(CVD)发生风险的关系,并进行亚组分析。

结果

4 172名中老年人中有1 150名中老年人发生心血管疾病,中老年人心血管疾病发病率为27.6%。群组轨迹模型识别出3类eCRF变化轨迹,即低上升后下降组(7.5%)、中快速下降组(28.0%)、高缓慢下降组(64.5%)。调整多个变量后Logistic回归分析结果显示,与高缓慢下降组相比,低上升后下降组[OR=1.90,95%CI(1.43,2.51)]和中快速下降组[OR=1.42,95%CI(1.20,1.69)]的心血管疾病发生风险均较高。亚组分析结果显示,eCRF变化轨迹类别与高血压存在交互作用(P=0.008)。

结论

我国中老年人eCRF的变化轨迹存在群体异质性,eCRF水平较高的中老年人心血管疾病发生风险较低。高血压状态可能影响eCRF变化轨迹类别与心血管疾病发生风险间的相对关联强度。

, authors=梁燕燕, 苏宇虹, 温志浩, 黄俭丽, 陈启娟*, *, authorsList=梁燕燕, 苏宇虹, 温志浩, 黄俭丽, 陈启娟, authorCompany=null, correspAuthors=陈启娟, authorNote=

梁燕燕,副主任护师,本科

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陈启娟,E-mail:
CHEN Qijuan, E-mail:
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of metabolic risks,cardiovascular disease,diabetes,and mortality, refAbstract=null), Reference(id=1322929641605955869, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929628461007038, doi=null, pmid=null, pmcid=null, year=2010, volume=24, issue=4_Suppl, pageStart=27, pageEnd=35, url=null, language=null, rfNumber=34, rfOrder=33, authorNames=Lee D, Artero E G, Sui X M, journalName=Journal of Psychopharmacology, refType=null, unstructuredReference=Lee D,Artero E G,Sui X M,et al.Mortality trends in the general population:the importance of cardiorespiratory fitness[J].Journal of Psychopharmacology,2010,24(4_Suppl):27-35., articleTitle=Mortality trends in the general population:the importance of cardiorespiratory fitness, refAbstract=null), Reference(id=1322929641694036254, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929628461007038, doi=null, pmid=null, pmcid=null, year=2024, volume=83, issue=null, pageStart=3, pageEnd=9, url=null, language=null, rfNumber=35, rfOrder=34, authorNames=Kaminsky L A, Myers J, Brubaker P H, journalName=Progress in Cardiovascular Diseases, refType=null, unstructuredReference=Kaminsky L A,Myers J,Brubaker P H,et al.2023 update:the importance of cardiorespiratory fitness in the United States[J].Progress in Cardiovascular Diseases,2024,83:3-9., articleTitle=2023 update:the importance of cardiorespiratory fitness in the United States, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322929630172283079, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929628461007038, xref=1, ext=[AuthorCompanyExt(id=1322929630180671688, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929628461007038, companyId=1322929630172283079, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1The First Affiliated Hospital of Guangxi University of Chinese Medicine, Guangxi 530023 China), AuthorCompanyExt(id=1322929630189060297, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929628461007038, companyId=1322929630172283079, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1广西中医药大学第一附属医院,广西 530023)])], figs=[ArticleFig(id=1322929634043625710, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929628461007038, language=EN, label=Figure 1, caption=Dynamic eCRF change trajectories among middle-aged and older adults, figureFileSmall=yecks7O/2Dn+i6OjGcWkVg==, figureFileBig=8ExW5cLYYsZEht3HFrQvJQ==, tableContent=null), ArticleFig(id=1322929634106540271, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929628461007038, language=CN, label=图1, caption=中老年人eCRF动态变化轨迹图, figureFileSmall=yecks7O/2Dn+i6OjGcWkVg==, figureFileBig=8ExW5cLYYsZEht3HFrQvJQ==, tableContent=null), ArticleFig(id=1322929634366587120, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929628461007038, language=EN, label=Figure 2, caption=Subgroup analysis of the relationship between the classification of the eCRF change trajectories and CVD risk, figureFileSmall=h3/KSQH5RQpq1bO63BwMMQ==, figureFileBig=fGUOSEan1G/bSRoHS0JckQ==, tableContent=null), ArticleFig(id=1322929634446278897, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929628461007038, language=CN, label=图2, caption=eCRF变化轨迹类别与CVD发生风险间关系的亚组分析, figureFileSmall=h3/KSQH5RQpq1bO63BwMMQ==, figureFileBig=fGUOSEan1G/bSRoHS0JckQ==, tableContent=null), ArticleFig(id=1322929634530164978, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929628461007038, language=EN, label=Table 1, caption=

Comparision of general information between middle-aged and older adults with and without CVD

, figureFileSmall=null, figureFileBig=null, tableContent=
项目分类非CVD组(n=3 022)CVD组(n=1 150)统计值P
年龄(岁)58.2±8.660.5±8.3t=-7.785<0.001
SBP(mmHg)129.0±19.6134.0±20.7t=-7.158<0.001
DBP(mmHg)75.1±11.377.6±11.7t=-6.112<0.001
认知功能评分(分)10.00(7.00,13.00)10.00(7.00,13.00)Z=-0.0540.957
CES-D评分(分)7.00(3.00,11.00)8.00(5.00,13.00)Z=-7.157<0.001
ADL评分(分)12.1±2.112.7±3.2t=-5.541<0.001
握力(kg)33.4±9.132.6±9.4t=2.3170.021
2011年eCRF(METs)10.6±2.09.9±1.9t=10.608<0.001
2013年eCRF(METs)10.2±1.99.5±1.9t=10.879<0.001
2015年eCRF(METs)10.2±1.99.5±1.9t=11.512<0.001
BMI(kg/m2)23.2±3.524.3±3.9t=-8.502<0.001
腰围(cm)84.3±9.787.6±10.9t=-9.622<0.001
FPG(mmol/L)5.97(5.34,6.05)6.05(5.43,6.23)Z=-3.530<0.001
TC(mmol/L)5.03(4.54,5.32)5.03(4.59,5.41)Z=-1.4360.151
TG(mmol/L)1.46(0.92,1.49)1.49(1.03,1.69)Z=-5.517<0.001
HDL-C(mmol/L)1.33(1.15,1.48)1.33(1.08,1.41)Z=-4.592<0.001
LDL-C(mmol/L)3.04(2.61,3.33)3.04(2.63,3.38)Z=-1.3380.181
CRP(mg/L)1.45(0.64,2.47)1.66(0.73,2.47)Z=-2.8560.004
HbA1c(%)5.2±0.65.3±0.9t=-4.634<0.001
性别[例(%)]男1 418(46.9)489(42.5)χ2=6.5010.011
女1 604(53.1)661(57.5)
婚姻状况[例(%)]其他305(10.1)145(12.6)χ2=5.4800.019
已婚2 717(89.9)1 005(87.4)
自评健康状况[例(%)]好812(26.9)153(13.3)
一般1 482(49.0)512(44.5)χ2=163.524<0.001
差728(24.1)485(42.2)
高血压[例(%)]否2 499(82.7)696(60.5)χ2=228.336<0.001
是523(17.3)454(39.5)
糖尿病[例(%)]否2 635(87.2)854(74.3)χ2=101.769<0.001
是387(12.8)296(25.7)
饮酒[例(%)]否1 982(65.6)839(73.0)χ2=20.669<0.001
是1 040(34.4)311(27.0)
吸烟[例(%)]否1 893(62.6)726(63.1)χ2=0.0860.770
是1 129(37.4)424(36.9)
疼痛[例(%)]否2 051(67.9)652(56.7)χ2=45.586<0.001
是971(32.1)498(43.3)
受教育程度[例(%)]专科及以上40(1.3)19(1.7)
初中或高中869(28.8)327(28.4)χ2=2.1130.549
小学1 236(40.9)490(42.6)
小学以下877(29.0)314(27.3)
), ArticleFig(id=1322929634635022579, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929628461007038, language=CN, label=表1, caption=

CVD组与非CVD组中老年人一般资料比较

, figureFileSmall=null, figureFileBig=null, tableContent=
项目分类非CVD组(n=3 022)CVD组(n=1 150)统计值P
年龄(岁)58.2±8.660.5±8.3t=-7.785<0.001
SBP(mmHg)129.0±19.6134.0±20.7t=-7.158<0.001
DBP(mmHg)75.1±11.377.6±11.7t=-6.112<0.001
认知功能评分(分)10.00(7.00,13.00)10.00(7.00,13.00)Z=-0.0540.957
CES-D评分(分)7.00(3.00,11.00)8.00(5.00,13.00)Z=-7.157<0.001
ADL评分(分)12.1±2.112.7±3.2t=-5.541<0.001
握力(kg)33.4±9.132.6±9.4t=2.3170.021
2011年eCRF(METs)10.6±2.09.9±1.9t=10.608<0.001
2013年eCRF(METs)10.2±1.99.5±1.9t=10.879<0.001
2015年eCRF(METs)10.2±1.99.5±1.9t=11.512<0.001
BMI(kg/m2)23.2±3.524.3±3.9t=-8.502<0.001
腰围(cm)84.3±9.787.6±10.9t=-9.622<0.001
FPG(mmol/L)5.97(5.34,6.05)6.05(5.43,6.23)Z=-3.530<0.001
TC(mmol/L)5.03(4.54,5.32)5.03(4.59,5.41)Z=-1.4360.151
TG(mmol/L)1.46(0.92,1.49)1.49(1.03,1.69)Z=-5.517<0.001
HDL-C(mmol/L)1.33(1.15,1.48)1.33(1.08,1.41)Z=-4.592<0.001
LDL-C(mmol/L)3.04(2.61,3.33)3.04(2.63,3.38)Z=-1.3380.181
CRP(mg/L)1.45(0.64,2.47)1.66(0.73,2.47)Z=-2.8560.004
HbA1c(%)5.2±0.65.3±0.9t=-4.634<0.001
性别[例(%)]男1 418(46.9)489(42.5)χ2=6.5010.011
女1 604(53.1)661(57.5)
婚姻状况[例(%)]其他305(10.1)145(12.6)χ2=5.4800.019
已婚2 717(89.9)1 005(87.4)
自评健康状况[例(%)]好812(26.9)153(13.3)
一般1 482(49.0)512(44.5)χ2=163.524<0.001
差728(24.1)485(42.2)
高血压[例(%)]否2 499(82.7)696(60.5)χ2=228.336<0.001
是523(17.3)454(39.5)
糖尿病[例(%)]否2 635(87.2)854(74.3)χ2=101.769<0.001
是387(12.8)296(25.7)
饮酒[例(%)]否1 982(65.6)839(73.0)χ2=20.669<0.001
是1 040(34.4)311(27.0)
吸烟[例(%)]否1 893(62.6)726(63.1)χ2=0.0860.770
是1 129(37.4)424(36.9)
疼痛[例(%)]否2 051(67.9)652(56.7)χ2=45.586<0.001
是971(32.1)498(43.3)
受教育程度[例(%)]专科及以上40(1.3)19(1.7)
初中或高中869(28.8)327(28.4)χ2=2.1130.549
小学1 236(40.9)490(42.6)
小学以下877(29.0)314(27.3)
), ArticleFig(id=1322929634744074484, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929628461007038, language=EN, label=Table 2, caption=

Fit iteration of eCRF change trajectories

, figureFileSmall=null, figureFileBig=null, tableContent=
模型AICBICEntropy轨迹类别占比(%)AvePP
150 460.3250 490.060.893100.0
246 752.6646 819.570.87517.0/83.00.995/0.998
342 159.2842 263.370.88628.0/64.5/7.50.996/0.998/0.994
436 426.9936 560.810.88023.0/37.0/36.0/4.00.996/0.995/0.997/0.994
535 415.4235 593.860.85925.1/18.4/34.9/18.7/2.90.997/0.996/0.996/0.994/0.996
), ArticleFig(id=1322929636421796085, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929628461007038, language=CN, label=表2, caption=

eCRF变化轨迹拟合迭代情况

, figureFileSmall=null, figureFileBig=null, tableContent=
模型AICBICEntropy轨迹类别占比(%)AvePP
150 460.3250 490.060.893100.0
246 752.6646 819.570.87517.0/83.00.995/0.998
342 159.2842 263.370.88628.0/64.5/7.50.996/0.998/0.994
436 426.9936 560.810.88023.0/37.0/36.0/4.00.996/0.995/0.997/0.994
535 415.4235 593.860.85925.1/18.4/34.9/18.7/2.90.997/0.996/0.996/0.994/0.996
), ArticleFig(id=1322929636493099254, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929628461007038, language=EN, label=Table 3, caption=

Comparison of baseline characteristics among different classification of the eCRF change trajectory groups

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类低上升后下降组(n=313)中快速下降组(n=1 167)高缓慢下降组(n=2 692)统计值P
年龄(岁)69.9±7.860.2±7.857.0±7.9F=561.595<0.001
BMI(kg/m2)26.2±4.824.9±3.422.5±3.2F=545.457<0.001
腰围(cm)94.1±10.988.6±9.582.7±9.2F=536.178<0.001
SBP(mmHg)140.0±21.1133.0±20.9128.0±19.0F=132.867<0.001
DBP(mmHg)77.4±11.876.9±11.175.1±11.5F=22.983<0.001
认知功能评分(分)7.00(4.00,10.50)9.00(6.00,12.50)10.50(7.50,13.00)H=150.064<0.001
CES-D评分(分)7.00(3.00,11.00)8.00(4.00,13.00)8.00(5.00,15.00)H=41.052<0.001
ADL评分(分)13.4±3.712.4±2.812.1±2.1F=23.051<0.001
握力(kg)26.6±8.228.7±6.535.9±9.2F=713.087<0.001
FPG(mmol/L)5.91(5.29,6.05)6.05(5.48,6.18)6.05(5.63,6.41)H=61.322<0.001
TC(mmol/L)5.03(4.44,5.21)5.03(4.74,5.55)5.03(4.91,5.85)H=102.237<0.001
TG(mmol/L)1.38(0.88,1.49)1.49(1.10,1.72)1.49(1.19,1.81)H=125.194<0.001
HDL-C(mmol/L)1.33(1.15,1.49)1.33(1.09,1.40)1.33(1.08,1.39)H=22.037<0.001
LDL-C(mmol/L)3.04(2.52,3.23)3.04(2.79,3.51)3.04(2.93,3.78)H=94.242<0.001
CRP(mg/L)2.42(0.95,2.63)1.64(0.76,2.47)1.36(0.60,2.47)H=56.389<0.001
HbA1c(%)5.5±1.05.3±0.85.2±0.6F=65.835<0.001
性别[例(%)]男15(4.8)96(8.2)1 796(66.7)χ2=1 350.580<0.001
女298(95.2)1 071(91.8)896(33.3)
婚姻状况[例(%)]其他95(30.4)159(13.6)196(7.3)χ2=168.659<0.001
已婚218(69.6)1 008(86.4)2 496(92.7)
自评健康状况[例(%)]好50(16.0)238(20.4)677(25.1)
一般141(45.0)530(45.4)1 323(49.1)χ2=49.921<0.001
差122(39.0)399(34.2)692(25.7)
高血压[例(%)]否174(55.6)797(68.3)2 224(82.6)χ2=176.228<0.001
是139(44.4)370(31.7)468(17.4)
糖尿病[例(%)]否213(68.1)910(78.0)2 366(87.9)χ2=117.956<0.001
是100(31.9)257(22.0)326(12.1)
抑郁[例(%)]否175(55.9)707(60.6)1 829(67.9)χ2=31.609<0.001
是138(44.1)460(39.4)863(32.1)
饮酒[例(%)]否279(89.1)1 008(86.4)1 534(57.0)χ2=392.750<0.001
是34(10.9)159(13.6)1 158(43.0)
吸烟[例(%)]否269(85.9)1 026(87.9)1 324(49.2)χ2=600.428<0.001
是44(14.1)141(12.1)1 368(50.8)
疼痛[例(%)]否182(58.1)723(62.0)1 798(66.8)χ2=14.888<0.001
是131(41.9)444(38.0)894(33.2)
发生CVD[例(%)]否175(55.9)772(66.2)2 075(77.1)χ2=94.966<0.001
是138(44.1)395(33.8)617(22.9)
受教育程度[例(%)]专科及以上1(0.3)12(1.0)46(1.7)
初中或高中30(9.6)226(19.4)940(34.9)χ2=325.092<0.001
小学104(33.2)466(39.9)1 156(42.9)
小学以下178(56.9)463(39.7)550(20.4)
), ArticleFig(id=1322929636610539767, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929628461007038, language=CN, label=表3, caption=

不同eCRF变化轨迹类别中老年人一般资料比较

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类低上升后下降组(n=313)中快速下降组(n=1 167)高缓慢下降组(n=2 692)统计值P
年龄(岁)69.9±7.860.2±7.857.0±7.9F=561.595<0.001
BMI(kg/m2)26.2±4.824.9±3.422.5±3.2F=545.457<0.001
腰围(cm)94.1±10.988.6±9.582.7±9.2F=536.178<0.001
SBP(mmHg)140.0±21.1133.0±20.9128.0±19.0F=132.867<0.001
DBP(mmHg)77.4±11.876.9±11.175.1±11.5F=22.983<0.001
认知功能评分(分)7.00(4.00,10.50)9.00(6.00,12.50)10.50(7.50,13.00)H=150.064<0.001
CES-D评分(分)7.00(3.00,11.00)8.00(4.00,13.00)8.00(5.00,15.00)H=41.052<0.001
ADL评分(分)13.4±3.712.4±2.812.1±2.1F=23.051<0.001
握力(kg)26.6±8.228.7±6.535.9±9.2F=713.087<0.001
FPG(mmol/L)5.91(5.29,6.05)6.05(5.48,6.18)6.05(5.63,6.41)H=61.322<0.001
TC(mmol/L)5.03(4.44,5.21)5.03(4.74,5.55)5.03(4.91,5.85)H=102.237<0.001
TG(mmol/L)1.38(0.88,1.49)1.49(1.10,1.72)1.49(1.19,1.81)H=125.194<0.001
HDL-C(mmol/L)1.33(1.15,1.49)1.33(1.09,1.40)1.33(1.08,1.39)H=22.037<0.001
LDL-C(mmol/L)3.04(2.52,3.23)3.04(2.79,3.51)3.04(2.93,3.78)H=94.242<0.001
CRP(mg/L)2.42(0.95,2.63)1.64(0.76,2.47)1.36(0.60,2.47)H=56.389<0.001
HbA1c(%)5.5±1.05.3±0.85.2±0.6F=65.835<0.001
性别[例(%)]男15(4.8)96(8.2)1 796(66.7)χ2=1 350.580<0.001
女298(95.2)1 071(91.8)896(33.3)
婚姻状况[例(%)]其他95(30.4)159(13.6)196(7.3)χ2=168.659<0.001
已婚218(69.6)1 008(86.4)2 496(92.7)
自评健康状况[例(%)]好50(16.0)238(20.4)677(25.1)
一般141(45.0)530(45.4)1 323(49.1)χ2=49.921<0.001
差122(39.0)399(34.2)692(25.7)
高血压[例(%)]否174(55.6)797(68.3)2 224(82.6)χ2=176.228<0.001
是139(44.4)370(31.7)468(17.4)
糖尿病[例(%)]否213(68.1)910(78.0)2 366(87.9)χ2=117.956<0.001
是100(31.9)257(22.0)326(12.1)
抑郁[例(%)]否175(55.9)707(60.6)1 829(67.9)χ2=31.609<0.001
是138(44.1)460(39.4)863(32.1)
饮酒[例(%)]否279(89.1)1 008(86.4)1 534(57.0)χ2=392.750<0.001
是34(10.9)159(13.6)1 158(43.0)
吸烟[例(%)]否269(85.9)1 026(87.9)1 324(49.2)χ2=600.428<0.001
是44(14.1)141(12.1)1 368(50.8)
疼痛[例(%)]否182(58.1)723(62.0)1 798(66.8)χ2=14.888<0.001
是131(41.9)444(38.0)894(33.2)
发生CVD[例(%)]否175(55.9)772(66.2)2 075(77.1)χ2=94.966<0.001
是138(44.1)395(33.8)617(22.9)
受教育程度[例(%)]专科及以上1(0.3)12(1.0)46(1.7)
初中或高中30(9.6)226(19.4)940(34.9)χ2=325.092<0.001
小学104(33.2)466(39.9)1 156(42.9)
小学以下178(56.9)463(39.7)550(20.4)
), ArticleFig(id=1322929636698620152, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929628461007038, language=EN, label=Table 4, caption=

Logistic regression analysis of the relationship between the classification of the eCRF change trajectories and CVD risk

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类别模型1模型2
回归系数OR值[95%CI]P回归系数OR值[95%CI]P
低上升后下降组0.982.65[2.08,3.37]<0.0010.641.90[1.43,2.51]<0.001
中快速下降组0.541.72[1.48,2.00]<0.0010.351.42[1.20,1.69]<0.001
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eCRF变化轨迹类别与CVD发生风险的关系的Logistic回归分析

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类别模型1模型2
回归系数OR值[95%CI]P回归系数OR值[95%CI]P
低上升后下降组0.982.65[2.08,3.37]<0.0010.641.90[1.43,2.51]<0.001
中快速下降组0.541.72[1.48,2.00]<0.0010.351.42[1.20,1.69]<0.001
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CVD incidence and ARD among different classification of the eCRF change trajectory groups

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类别例数发生CVD[例(%)]ARD(%)
低上升后下降组313138(44.1)21.17
中快速下降组1 167395(33.8)10.93
高缓慢下降组2 692617(22.9)参照
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不同eCRF变化轨迹类别中老年人的CVD发生率和ARD

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类别例数发生CVD[例(%)]ARD(%)
低上升后下降组313138(44.1)21.17
中快速下降组1 167395(33.8)10.93
高缓慢下降组2 692617(22.9)参照
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我国中老年人估算心肺适能水平变化轨迹类别与心血管疾病发生风险的关系
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梁燕燕 , 苏宇虹 , 温志浩 , 黄俭丽 , 陈启娟 *, *
护理研究 | 科研论著 2026,40(14): 2404-2413
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护理研究 |科研论著 2026 , 40 (14) : 2404 -2413
我国中老年人估算心肺适能水平变化轨迹类别与心血管疾病发生风险的关系
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梁燕燕, 苏宇虹, 温志浩, 黄俭丽, 陈启娟*, *
作者信息
  • 1广西中医药大学第一附属医院,广西 530023
通讯作者:
陈启娟,E-mail:
CHEN Qijuan, E-mail:
作者简介:

梁燕燕,副主任护师,本科

Relationship between the classification of the estimated cardiorespiratory fitness change trajectories and the risk of cardiovascular disease among middle-aged and older adults in China
Yanyan LIANG, Yuhong SU, Zhihao WEN, Jianli HUANG, Qijuan CHEN
Affiliations
  • 1The First Affiliated Hospital of Guangxi University of Chinese Medicine, Guangxi 530023 China
出版时间: 2026-07-25 doi: 10.12102/j.issn.1009-6493.2026.14.003
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目的

基于中国健康与养老追踪调查研究(CHARLS)数据探究我国中老年人估算心肺适能(eCRF)的变化轨迹,并分析不同轨迹类别与心血管疾病发生风险的关系。

方法

纳入2011—2020年CHARLS中完成随访的4 172名中老年人数据,采用非运动方程计算2011年、2013年及2015年的eCRF水平,并利用群组轨迹模型识别eCRF的变化轨迹类别。采用Logistic回归分析探讨eCRF变化轨迹类别与心血管疾病(CVD)发生风险的关系,并进行亚组分析。

结果

4 172名中老年人中有1 150名中老年人发生心血管疾病,中老年人心血管疾病发病率为27.6%。群组轨迹模型识别出3类eCRF变化轨迹,即低上升后下降组(7.5%)、中快速下降组(28.0%)、高缓慢下降组(64.5%)。调整多个变量后Logistic回归分析结果显示,与高缓慢下降组相比,低上升后下降组[OR=1.90,95%CI(1.43,2.51)]和中快速下降组[OR=1.42,95%CI(1.20,1.69)]的心血管疾病发生风险均较高。亚组分析结果显示,eCRF变化轨迹类别与高血压存在交互作用(P=0.008)。

结论

我国中老年人eCRF的变化轨迹存在群体异质性,eCRF水平较高的中老年人心血管疾病发生风险较低。高血压状态可能影响eCRF变化轨迹类别与心血管疾病发生风险间的相对关联强度。

估算心肺适能  /  心血管疾病  /  群组轨迹模型  /  队列研究
Objective

To explore the change trajectories of estimated cardiorespiratory fitness(eCRF) among middle-aged and older adults in China based on data from the China Health and Retirement Longitudinal Study(CHARLS) and to analyze the relationship betweent different classification of the trajectories and the risk of cardiovascular disease.

Methods

Data from 4 172 middle-aged and older adults who completed follow-up in CHARLS from 2011 to 2020 were included.eCRF levels in 2011,2013,and 2015 were calculated using non-exercise equations.Group-based trajectory modeling was employed to identify the classification of the eCRF change trajectories.Logistic regression was used to analyze the relationship between the classification of the eCRF change trajectories and the risk of cardiovascular disease,followed by subgroup analyses.

Results

Among the 4 172 participants,1 150 developed cardiovascular disease,yielding an incidence rate of 27.6%.Three distinct eCRF change trajectories were identified,namely low-increasing then decreasing group(7.5%),moderate-rapidly decreasing group(28.0%),and high-slowly decreasing group(64.5%).After adjusting for multiple variables,Logistic regression showed that compared with the high-slowly decreasing group,both the low-increasing then decreasing group(OR=1.90,95%CI 1.43-2.51) and the moderate-rapidly decreasing group(OR=1.42,95%CI 1.20-1.69) had a higher risk of cardiovascular disease.Subgroup analyses revealed an interaction between the classification of the eCRF change trajectories and hypertension(P=0.008).

Conclusions

Group heterogeneity exists in eCRF change trajectories among middle-aged and older adults in China.Those with higher eCRF levels tend to have a lower risk of cardiovascular disease.Hypertensive status may influence the relative strength of the association between the classification of the eCRF change trajectories and the risk of cardiovascular disease.

estimated cardiorespiratory fitness  /  cardiovascular disease  /  group-based trajectory modeling  /  cohort study
梁燕燕, 苏宇虹, 温志浩, 黄俭丽, 陈启娟. 我国中老年人估算心肺适能水平变化轨迹类别与心血管疾病发生风险的关系. 护理研究, 2026 , 40 (14) : 2404 -2413 . DOI: 10.12102/j.issn.1009-6493.2026.14.003
Yanyan LIANG, Yuhong SU, Zhihao WEN, Jianli HUANG, Qijuan CHEN. Relationship between the classification of the estimated cardiorespiratory fitness change trajectories and the risk of cardiovascular disease among middle-aged and older adults in China[J]. Chinese Nursing Research, 2026 , 40 (14) : 2404 -2413 . DOI: 10.12102/j.issn.1009-6493.2026.14.003
心血管疾病(cardiovascular disease,CVD)是全球范围内致残与致死的主要病因之一,也是非传染性疾病负担的首要来源。近年来,尽管年龄标准化CVD死亡率呈下降趋势,但实际死亡人数仍持续上升,反映出人口增长和老龄化对疾病负担的影响[1-2]。全球疾病负担研究显示,CVD患病人数已从1990年的2.71亿例增至2019年的5.23亿例,同一时期CVD相关死亡人数也从1 210万例上升至1 860万例[3]。随着社会经济发展和生活方式变化,CVD的防控已成为全球公共卫生领域面临的重要挑战。心肺适能(cardiorespiratory fitness,CRF)指机体在参与体力活动时将氧气输送到各组织器官并加以利用,以实现能量有效转换与传递的能力[4],其作为一个综合性指标,能够集中体现体力活动以及遗传等风险因素对机体产生的综合影响[5]。已有研究表明,心肺适能水平在慢性疾病的预测中具有重要意义,尤其是对于CVD病人而言,心肺适能的预防和治疗起着关键作用[6]。估算心肺适能(estimated cardiorespiratory fitness, eCRF)是一种仅需年龄、性别、体质指数(BMI)、腰围、体力活动、吸烟状况和静息心率即可计算的简易指标,具有便捷性及实用性[7-9]。eCRF可以为客观测量心肺适能提供一种实用且可靠的替代方案,以评估不同人群的死亡风险[10]。目前研究表明,eCRF水平升高与全因死亡率和CVD死亡率风险降低相关[11-13],但关于eCRF发展轨迹的研究相对缺乏,且常存在研究结果的人群适用性限制,不利于在我国的推广应用[8,14-15]。本研究基于我国中老年人样本,采用群组轨迹模型(GBTM)探讨社区中老年人群的eCRF动态轨迹趋势,旨在揭示eCRF变化轨迹类别与CVD的关系,并依据轨迹特征制定个性化的干预和治疗策略。
以2011—2020年中国健康与养老追踪调查研究(CHARLS)为数据来源。CHARLS是一项采用多阶段抽样策略的全国性队列调查,旨在收集我国45岁及以上中老年人家庭和个人的高质量微观数据,用以分析我国人口老龄化问题,推动老龄化问题的跨学科研究。基线调查于2011—2012年在我国28个省、150个县的17 708名参与者中进行,收集的数据包括社会状态、生活方式、药物、健康状况和功能评估结果[16],并于2013年、2015年、2018年和2020年进行随访,其研究设计、抽样程序和数据采集的详细信息已在出版物中描述[17]。本研究根据CHARLS的2011年、2013年、2015年体检数据计算eCRF,并纳入2020年随访数据。中老年人纳入标准:年龄≥45岁;完成2011年CHARLS的基线调查;2013年、2015年和2020年均完成随访。排除标准:2020年随访时CVD信息缺失;基线调查中自我报告患CVD(包括心脏疾病、脑卒中等);计算eCRF的关键变量缺失。最终纳入4 172名中老年人。CHARLS方案已获得北京大学伦理审查委员会批准,所有CHARLS参与者均签订书面知情同意书。本研究已通过广西中医药大学第一附属医院医学伦理委员会审查批准(审批号:伦审GXZYA2024-78)。
通过Jackson等[18]构建的性别特异性模型评估参与者的eCRF,该模型已在成年人中得到验证,结果显示eCRF与长期健康风险相关[19-20]。模型参数包括参与者的年龄、BMI、腰围、静息心率、体力活动和吸烟状况,男性eCRF计算公式为:eCRF(METs)=21.287 0+(年龄×0.165 4)-(年龄2×0.002 3)-(BMI×0.231 8)-(腰围×0.033 7)-(静息心率×0.039 0)+(体力活动×0.635 1)-(吸烟状况×0.426 3);女性eCRF计算公式为:eCRF(METs)=14.787 3+(年龄×0.115 9)-(年龄2×0.001 7)-(BMI×0.153 4)-(腰围×0.008 8)-(静息心率×0.364 0)+(体力活动×0.598 7)-(吸烟状况×0.299 4)。其中,体力活动通过自我报告的体力活动强度和频率定义,如果参与者报告每周进行1次以上的剧烈或中度体力活动,则中度至剧烈体力活动为1,否则中度至剧烈的体力活动为0;如果参与者自我报告目前吸烟则吸烟为1,否则为0。
当前研究的主要结局为新发CVD(包括脑卒中或心脏疾病相关事件),其中,脑卒中评估通过以下问题确定:“您是否被医生诊断为脑卒中?”;心脏疾病评估通过以下问题确定:“您是否被医生诊断为心脏病发作、冠心病、心绞痛、充血性心力衰竭或其他心脏问题?”[13]。
本研究基于文献分析结果纳入潜在的混杂协变量基线数据,包括社会人口学变量(年龄、性别、受教育程度、婚姻状况)、生活方式因素(饮酒、吸烟)、体格检查数据[BMI、腰围、收缩压(SBP)、舒张压(DBP)和握力]、既往慢性疾病(高血压、糖尿病)、心理与功能状态[流调中心用抑郁量表(CES-D)评分、日常生活活动(ADL)评分、认知功能评分、自评健康状况和疼痛]以及生化/体检指标[空腹血糖(FPG)、总胆固醇(TC)、糖化血红蛋白(HbA1c)、高密度脂蛋白胆固醇(HDL-C)、低密度脂蛋白胆固醇(LDL-C)、C反应蛋白(CRP)、三酰甘油(TG)等],其中,CES-D包含10个条目,每个条目采用4级评分,“很少或没有时间”计0分,“不太多”计1分,“有时或一半的时间”计2分,“大多数时间”计3分,条目5和条目8为反向计分,总分0~30分,≥10分代表有抑郁症状[21]。认知功能采用CHARLS数据库标准认知评估方案进行测量,包括情景记忆与心理完整性2个维度,情景记忆通过10个词语的即时回忆与延迟回忆测试评估,得分为0~10分;心理完整性基于电话认知状态访谈部分条目评估,包括连续减7、时间定向及图形复制等内容,得分为0~11分,两部分得分相加为总体认知功能评分,总分0~21分,得分越高表示认知功能越好[22]。自评健康状况采用CHARLS数据库中的单项自评指标进行评估[23],通过询问“您觉得自己的健康状况如何?”测量受访者对整体健康状态的主观评价,分为“非常好”“好”“一般”“差”“很差”5个等级,若受访者表示“好”或“很好”则分类为“好”,若表示“一般”则分类为“一般”,其他情况分类为“差”。疼痛通过自我报告进行分类,参与者表示“无疼痛”分类为“否”,其他情况分类为“是”[24]。
采用RStudio软件进行统计分析及轨迹模型构建。正态分布的定量资料以均数±标准差(x±s)表示,组间比较采用t检验或方差分析;非正态分布的定量资料以中位数(四分位数)[M(P25,P75)]表示,组间比较采用Kruskal-Wallis H检验;定性资料以频数、百分比(%)表示,组间比较采用χ2检验。采用RStudio中的gbmt包进行群组轨迹分析,群组轨迹模型是有限混合模型的一种专门形式,该模型假定总体内存在一些遵循相似发展轨迹的成员集群,用不同“组”的分布集合近似总体分布,进而用“组”间差异反映成员特征差异[25]。基于群体的轨迹建模方法需要预先确定潜在轨迹的数量及其函数形式。鉴于心肺适能水平主要随生理衰老过程发生变化,本研究以年龄作为时间尺度以提高不同入组年龄个体之间的可比性,同时也可更准确刻画心肺适能的变化趋势。模型构建过程中,先验设定潜在轨迹类别数上限为5类,依次拟合包含1~5个轨迹类别的模型,并分别评估各轨迹的线性、二次及三次多项式增长形式,以确定能够最优描述 eCRF动态变化的轨迹数量及其多项式函数形式。通过对艾凯克信息准则(AIC)、贝叶斯信息准则(BIC)、通过信息熵(Entropy)、平均验后分组概率(AvePP)及轨迹类别占比进行比较,选择最佳拟合模型[18]。其中,BIC绝对值越低表示模型拟合越好;AvePP反映轨迹分组后的亚组内成员与该轨迹的符合程度,>0.70表明模型的拟合效果良好[19];轨迹类别占比中,每个轨迹类别占5%以上,有利于确保模型后续分析的准确性[20-21]。确定轨迹类别数后,为研究eCRF变化轨迹类别与CVD发生风险之间的关系,以eCRF变化轨迹类别为自变量、CVD发生情况为因变量进行Logistic回归分析,构建2个模型,模型1不调整协变量;考虑到eCRF是根据年龄、BMI、腰围、静息心率、体力活动和吸烟状况计算获得,为避免过度调整,模型2仅调整单因素分析中对CVD发生有影响但不包含eCRF构成成分的协变量。针对eCRF变化轨迹类别计算CVD的累积发病率和绝对风险差(ARD),其中ARD为各轨迹类别与参照组间累积发病率的差异。在多变量调整模型中进一步引入eCRF变化轨迹类别与性别、婚姻状况、高血压、糖尿病、饮酒、疼痛及抑郁状态等变量的乘法交互项以探讨eCRF变化轨迹类别与CVD发生风险间的关联是否在不同人群特征中存在差异。以P<0.05为差异有统计学意义。
4 172名中老年人年龄(58.9±8.0)岁,1 150名中老年人在2020年发生CVD,3 022名中老年人在2020年未发生CVD事件,中老年人CVD发病率为27.6%。CVD组与非CVD组中老年人一般资料比较结果见表1。
以递增方式建立1~5类的轨迹模型,通过比较确定研究期间eCRF动态变化异质性的最佳拟合模型,结果表明,随着类别逐渐增加,BIC值逐渐降低,模型4和模型5有轨迹类别占比<5%。最终确定模型3为最佳模型。见表2。根据图形模式为eCRF变化轨迹命名,类别1共313人(7.5%),命名为“低上升后下降组”;类别2共1 167人(28.0%),命名为“中快速下降组”;类别3共2 692人(64.5%),命名为“高缓慢下降组”。见图1。
以eCRF变化轨迹类别为自变量[高缓慢下降组=(0,0),低上升后下降组=(1,0),中快速下降组=(0,1)],以CVD发生情况为因变量(否=0,是=1)进行Logistic回归分析。模型1未调整协变量,结果显示,与高缓慢下降组相比,低上升后下降组和中快速下降组更易发生CVD;模型2经过多变量调整后结果显示,与高缓慢下降组相比,低上升后下降组和中快速下降组的CVD发生风险分别增加0.90倍和0.42倍。见表4。
在性别、婚姻状态、糖尿病、饮酒、抑郁及疼痛分层中,eCRF变化轨迹类别与CVD发生风险的关联方向总体一致,交互作用检验均无统计学意义(P>0.05)。部分分层效应估计的95%CI相对较宽,提示结果可能受样本量或事件数限制,其解释需结合整体分析结果综合考虑。在高血压分层中,eCRF变化轨迹类别与CVD发生风险间存在基于乘法的交互作用(P=0.008),提示不同血压状态下eCRF变化轨迹类别与CVD发生风险的相对关联强度可能存在差异。但该结果主要体现统计学层面的相对效应差异,其临床意义仍有待进一步评估。因此,本研究将高血压相关的交互作用结果视为探索性发现,用于提示潜在的风险分层线索,而非作为制定明确临床分层干预策略的直接依据,后续研究中仍需进一步验证其稳定性与实践价值。见图2。
本研究基于2011—2020年CHARLS调查数据进行分析,识别出3种eCRF变化轨迹,即低上升后下降组、中快速下降组、高缓慢下降组。研究结果表明,随着时间推移,eCRF变化轨迹在不同群体中有所不同,3种eCRF变化轨迹基线水平存在差异,eCRF水平较高时CVD发生风险较低。
Zhao等[19]通过Cox比例风险模型探讨了我国人群eCRF变化轨迹类别与CVD死亡风险间的关系,结果显示,随着eCRF提高,我国人群死亡风险下降,eCRF的受试者工作特征曲线下面积(AUC)高于腰围、BMI、静息心率,在预测死亡风险方面更具优势。Peterman等[26]比较了4种已建立的eCRF方程在CVD病人全因死亡率方面的预测能力,结果发现,4种eCRF方程都能预测死亡率;峰值METs每增加1个单位,死亡风险降低16%~24%。该结论支持了在无法进行心肺功能直接评估时计算eCRF以针对病人进行风险分层的建议。CHARLS相关研究表明,eCRF变化轨迹类别与CVD发生风险间存在联系[13,27]。Lee等[8,14]分别从广义CVD和脑卒中角度验证了eCRF变化轨迹类别的预测价值,结果表明,eCRF的长期变化可能在心血管健康中发挥关键作用。本研究进行亚组分析发现,在非高血压人群中,eCRF变化轨迹类别是CVD发生的重要影响因素,但在高血压病人中这一关系无统计学意义,原因可能为长期高血压可导致动脉僵硬、微血管稀疏与内皮功能障碍,这些中枢与外周血管损伤与CVD发生的因果联系较强且病人基础风险较高,可能使eCRF的相对风险梯度被削弱[28];此外,在高血压人群中,强化降压可降低主要心血管事件,这种强而直接的干预效应可能在统计上超过eCRF的贡献,导致eCRF变化轨迹类别与CVD发生风险的关联在已治疗或控制良好的高血压亚组内无统计学意义[29-30]。
1)eCRF是血管内皮功能健康的一个强有力指征。规律运动提升的心肺适能可通过增加血流对血管壁的剪切应力,持续刺激内皮细胞释放一氧化氮(NO)等血管舒张物质。NO不仅能有效促进血管舒张、降低血压,还具有抗炎和抗动脉粥样硬化的特性[31]。因此,高eCRF代表的积极生活方式会直接对应更优的血管内皮功能和更强的血管弹性,是心血管保护效应的防线之一。2)eCRF水平较高通常与迷走神经张力增强和交感神经活性降低有关,表现为更高的心率变异性(HRV)和更低的静息心率[32-33]。这种自主神经平衡的转变可减少心脏氧耗,从而降低心律失常和心源性猝死风险。3)eCRF是全身性代谢健康的集成性生物标志物之一。eCRF水平较高与优异的胰岛素敏感性、更有利的脂质谱[如更高的HDL-C、更低的TG以及LDL-C向更大、浮力更好的良性表型转化]以及更低的内脏脂肪含量密切相关[34]。这些代谢益处共同作用,有利于减轻导致动脉粥样硬化的代谢负担。eCRF通过整合相关代谢参数,能够反映个体在糖脂代谢和体成分方面的整体健康状态,从而提示长期的CVD发生风险。4)eCRF所代表的活跃生活模式与系统性的抗炎和抗氧化状态密不可分。慢性低度炎症是CVD发生的病理基础。已有研究结果表明,心肺适能或eCRF水平较高个体的CRP、白细胞介素-6(IL-6)和肿瘤坏死因子-α(TNF-α)等炎症标志物水平较低[27]。同时,规律运动能上调内源性抗氧化酶活性,减轻氧化应激对血管的损伤。因此,eCRF水平较高可被视为低炎症负荷和强抗氧化防御能力的替代指标,有利于稳定动脉斑块,预防急性心血管事件发生。
相关指南指出,心肺适能对心血管事件与死亡的预测价值较高,建议将其作为临床常规评估指标纳入医疗体系[4]。但直接测量心肺适能需进行运动负荷或心肺运动试验,受设备、人员培训与安全性限制,难以规模化开展。而eCRF可通过常规体检测得的变量(年龄、BMI、腰围、静息心率、体力活动与吸烟)计算获得,嵌入电子健康档案或体检系统即可实现自动计算与动态更新,是现实可行的心肺适能测量替代方案。
相较单时间点eCRF,eCRF变化轨迹可将对当前水平的调查升级为对长期趋势的探究,更贴近慢性病管理的随访逻辑。本研究基于CHARLS的2011年、2013年与2015年数据计算获得eCRF并进行轨迹分析,识别出不同动态模式,且研究结果显示,低上升后下降组CVD发生风险较高,提示在社区体检、家庭医生签约随访或慢性病门诊就诊中,可按实际情况重复计算eCRF,并基于轨迹进行人群分层,将变化轨迹不佳的人群优先纳入强化管理与随访。
在具体干预层面,eCRF由体力活动、吸烟、中心性肥胖(腰围)、BMI及静息心率等可干预要素共同决定,因此其变化轨迹不仅可用于风险识别,还可作为过程性反馈指标,如当实施运动处方/体力活动提升、戒烟、体重与腰围管理等措施后,若eCRF变化轨迹由下降转为相对平稳或呈上升趋势,则提示干预可能带来综合心血管获益;反之,若轨迹持续下降,则可触发更密集的生活方式指导、风险因素评估及必要的专科转诊。该“计算—分层—干预—再评估”的闭环模式有利于在基层医疗机构实现低成本、可解释、可持续的心血管一级预防[4]。
值得强调的是,eCRF变化轨迹应定位为风险分层与管理触发工具,而非替代运动试验的诊断手段;未来仍需在不同人群与不同临床场景中进一步明确最佳监测间隔、可操作阈值以及基于本土数据的方程校准,以提升转化应用的准确性与可推广性[35]。
本研究利用CHARLS数据进行分析,该研究是一项大规模、具有全国代表性的队列研究,并进行了反复的随访评估,为研究eCRF变化轨迹类别与CVD发生风险的关系提供了坚实基础。基于群组轨迹建模能够捕获eCRF变化的长期模式,与单时间点测量相比,其能更科学地反映个体心肺适应度的动态演变过程。纵向设计使研究能够评估eCRF变化轨迹类别与CVD发生风险间的时间关联,从而减少反向因果关系的可能性。此外,本研究纳入了较多的社会人口、生活方式和健康相关协变量,有利于控制混杂因素,增强研究结果的稳健性。
1)使用非运动算法而非直接的心肺运动试验评估心肺适能,可能存在测量误差,尤其是对于服用影响静息心率等药物的中老年人而言,出现误差的风险较大。未来研究需借助包含详细药理信息的数据集,开展药物特异性、敏感性分析,并进一步验证eCRF变化轨迹分析结果的稳健性。2)CVD结局主要由中老年人自我报告,可能因回忆偏倚和潜在的漏报导致研究结果准确性受到影响。3)尽管调整了社会人口学、生活方式和健康相关变量,但不能完全排除饮食、空气污染、睡眠等混杂因素。4)eCRF属于由多项基础特征(年龄、BMI、腰围、静息心率、体力活动与吸烟)通过非运动方程合成的综合指标,在以eCRF变化轨迹类别为主要暴露时,若在多变量模型中对其构成变量进行调整,则会对暴露的组成信息进行重复控制,从而低估eCRF与CVD的关联强度;同时,在某些因果关系下还可能引入由共同决定因素或中间通路控制导致的偏倚,使结果解释难度增加。为保持统计效能,这些变量在多变量回归模型中未被额外调整[15]。由于CHARLS队列主要由我国中老年人组成,本研究结果对年轻人或非我国人群的普适性有限。
本研究结果表明,在我国社区中老年人中,eCRF水平较高与CVD发生风险较低有关。由于eCRF相关指标较易获得,因此,在流行病学或人群研究中,特别是在无法通过运动测试直接测量心肺适能时,可作为一种实用且有效的风险评估工具。

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2026年第40卷第14期
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doi: 10.12102/j.issn.1009-6493.2026.14.003
  • 接收时间:2025-09-23
  • 首发时间:2026-10-30
  • 出版时间:2026-07-25
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  • 收稿日期:2025-09-23
  • 修回日期:2026-06-11
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    1广西中医药大学第一附属医院,广西 530023

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陈启娟,E-mail:
CHEN Qijuan, E-mail:
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2种不同金属材料的力学参数
科
Family
属数
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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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