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Article(id=1240651439492625289, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240651438955754377, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202404059, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1712073600000, receivedDateStr=2024-04-03, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773723953364, onlineDateStr=2026-03-17, pubDate=1719244800000, pubDateStr=2024-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773723953364, onlineIssueDateStr=2026-03-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773723953364, creator=13701087609, updateTime=1773723953364, updator=13701087609, issue=Issue{id=1240651438955754377, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='12', pageStart='2113', pageEnd='2912', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773723953236, creator=13701087609, updateTime=1773723953236, updator=13701087609, preIssue=null, nextIssue=null, ext=null, issueFiles=null}, startPage=2113, endPage=2120, ext={EN=ArticleExt(id=1240651439723312010, articleId=1240651439492625289, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Lifestyle associated with incidence and progression trajectories of cardiometabolic multimorbidity, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the association between lifestyle and the incidence and development trajectory of cardiometabolic multimorbidity.

Methods

Based on the China Health and Retirement Longitudinal Study(CHARLS) from 2011 to 2020, a total of 8 202 individuals aged 45 years and older were included. A Multi-state Markov model (MSM) was applied to analyze the effects of lifestyle on the trajectories of cardiometabolic multimorbidity.

Results

The disease progression trajectories mainly involved transitions from health to first cardiometabolic diseases (FCMD), health to cardiometabolic multimorbidity (CMM) and FCMD to CMM. During the follow-up period, 2 962 participants developed FCMD, 687 participants developed CMM, and 699 participants died. The results indicated that transitioning from health to FCMD, being overweight or obese, having longer nighttime sleep duration, and lacking a habit of daytime naps were positively associated with FCMD, which were 1.40(95%CI: 1.30-1.52) times, 1.16(95%CI: 1.02-1.32) times, and 1.21(95%CI: 1.06-1.39) times higher than those of healthy lifestyle people, respectively. During the transition from health to CMM, being overweight or obese was positively associated with the increased incidence of CMM (HR=1.42,95%CI: 1.06-1.90). Transitioning from FCMD to CMM, only overweight or obesity was observed as a risk factor (HR=1.44,95%CI: 1.17-1.78).

Conclusion

Lifestyle may increase the risk of cardiometabolic diseases and multimorbidity, emphasizing the importance of lifestyle interventions in health management practices.

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目的

本研究旨在探究生活方式与心血管代谢性共病发生发展轨迹的关联。

方法

基于中国健康与养老追踪调查（China Health and Retirement Longitudinal Study, CHARLS）2011—2020年数据库中8 202名≥45岁的中老年人群，应用多状态马尔可夫模型（Multi-state Markov model, MSM模型）分析生活方式（体型、吸烟、饮酒、饮食、体力活动、睡眠）对心血管代谢性共病发生发展轨迹的影响。

结果

疾病进展轨迹主要涉及从健康到首发心血管代谢性疾病（first cardiometabolic disease, FCMD）、健康到心血管代谢性共病（cardiometabolic multimorbidity, CMM）、FCMD到CMM。在随访过程中，2 962人出现FCMD，687人发生CMM，699人死亡。在健康向FCMD过渡中，超重或肥胖（HR=1.40，95% CI：1.30~1.52）、夜间睡眠时间过长（HR=1.16，95%CI：1.02~1.32）、无午睡习惯（HR=1.21，95%CI：1.06~1.39）人群的FCMD发病风险更高。健康向CMM过渡中，超重或肥胖与CMM发病呈正相关（HR=1.42，95%CI：1.06~1.90）。FCMD向CMM的过渡中，超重或肥胖为危险因素（HR=1.44，95%CI：1.17~1.78）。

结论

不良生活方式增加CMM的风险，因此要重视生活方式预防在健康管理中的应用。

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刘相佟，E-mail：
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许珊（2001—），女，本科在读，研究方向：预防医学研究

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许珊（2001—），女，本科在读，研究方向：预防医学研究

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European Journal of Preventive Cardiology, 2022, 29(15): 1982-1991., articleTitle=Self-reported daytime napping, daytime sleepiness, and other sleep phenotypes in the development of cardiometabolic diseases: a Mendelian randomization study, refAbstract=null), Reference(id=1240651453094752829, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, doi=null, pmid=null, pmcid=null, year=2022, volume=65, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[24], rfOrder=30, authorNames=Sun JH, Ma CW, Zhao M, journalName=Sleep Medicine Reviews, refType=null, unstructuredReference=Sun JH, Ma CW, Zhao M, et al. Daytime napping and cardiovascular risk factors, cardiovascular disease, and mortality: A systematic review[J]. Sleep Medicine Reviews, 2022, 65: 101682., articleTitle=Daytime napping and cardiovascular risk factors, cardiovascular disease, and mortality: A systematic review, refAbstract=null), Reference(id=1240651453166056004, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, doi=null, pmid=null, pmcid=null, year=2022, volume=21, issue=1, pageStart=199, pageEnd=null, url=null, language=null, rfNumber=[25], rfOrder=31, authorNames=Xie HJ, Li JC, Zhu XM, journalName=Cardiovascular Diabetology, refType=null, unstructuredReference=Xie HJ, Li JC, Zhu XM, et al. Association between healthy lifestyle and the occurrence of cardiometabolic multimorbidity in hypertensive patients: a prospective cohort study of UK Biobank[J]. Cardiovascular Diabetology, 2022, 21(1): 199., articleTitle=Association between healthy lifestyle and the occurrence of cardiometabolic multimorbidity in hypertensive patients: a prospective cohort study of UK Biobank, refAbstract=null)], funds=[Fund(id=1240651447939952889, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, awardId=82003559, language=CN, fundingSource=国家自然科学基金项目(82003559), fundOrder=null, country=null), Fund(id=1240651448040616194, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, awardId=HZKY20220056, language=CN, fundingSource=教育部“春晖计划”合作科研项目(HZKY20220056), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1240651440977408932, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, xref=1., ext=[AuthorCompanyExt(id=1240651440985797540, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, companyId=1240651440977408932, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Public Health, Capital Medical University, Beijing 100069, China), AuthorCompanyExt(id=1240651440989991845, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, companyId=1240651440977408932, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.首都医科大学公共卫生学院，北京 100069)]), AuthorCompany(id=1240651441057100711, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, xref=2., ext=[AuthorCompanyExt(id=1240651441065489321, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, companyId=1240651441057100711, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.临床流行病学北京市重点实验室)])], figs=[ArticleFig(id=1240651445087826039, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, language=EN, label=Fig.1, caption=Flowchart of research object selection, figureFileSmall=PmsEGPw6I2H0TwxaP7cNDA==, figureFileBig=Vbh5B9Xwewli4R6F2Hiz2Q==, tableContent=null), ArticleFig(id=1240651445167517826, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, language=CN, label=图1, caption=研究对象选择流程图, figureFileSmall=PmsEGPw6I2H0TwxaP7cNDA==, figureFileBig=Vbh5B9Xwewli4R6F2Hiz2Q==, tableContent=null), ArticleFig(id=1240651445398204567, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, language=EN, label=Fig.2, caption=Disease state transition in study subjects, figureFileSmall=b62iGLcqVW2wBYvtfHh5xA==, figureFileBig=AAteXTicl7jhwOgIfIuBuQ==, tableContent=null), ArticleFig(id=1240651445503062182, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, language=CN, label=图2, caption=研究对象疾病状态转移图, figureFileSmall=b62iGLcqVW2wBYvtfHh5xA==, figureFileBig=AAteXTicl7jhwOgIfIuBuQ==, tableContent=null), ArticleFig(id=1240651445649862828, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, language=EN, label=Fig.3, caption=Associations of lifestyle factors with the incidence of FCMD and CMM, figureFileSmall=orGIO6KQm/OU8WChJOX/6w==, figureFileBig=BCgD376yyevGNgOb2h3/DQ==, tableContent=null), ArticleFig(id=1240651445805052087, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, language=CN, label=图3, caption=生活方式与FCMD和CMM发病的关联

注：(a) Cox比例风险模型显示的从健康到FCMD的过渡态中生活方式对过渡态的影响及其95%CI；(b) Cox比例风险模型显示的从健康到CMM的过渡态中生活方式对过渡态的影响及其95%CI；图中三角形表明P<0.05，圆形表明P>0.05。

, figureFileSmall=orGIO6KQm/OU8WChJOX/6w==, figureFileBig=BCgD376yyevGNgOb2h3/DQ==, tableContent=null), ArticleFig(id=1240651445880549563, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, language=EN, label=Fig.4, caption=Association of lifestyle factors with FCMD and CMM incidence, stratified by potential modifiers, figureFileSmall=jsiW3EO6kGCKeEfxJo7i9Q==, figureFileBig=bb8d0j8ipFtihDc0lP1g9A==, tableContent=null), ArticleFig(id=1240651445960241346, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, language=CN, label=图4, caption=按照潜在修正因素分层的生活方式与FCMD和CMM发病的关联

注：（a）从健康到FCMD的过渡态中，生活方式与混杂因素变量交互作用；（b）从健康到CMM的过渡态中，生活方式与混杂因素变量；图中三角形表明P<0.05，正方形表明P>0.05。

, figureFileSmall=jsiW3EO6kGCKeEfxJo7i9Q==, figureFileBig=bb8d0j8ipFtihDc0lP1g9A==, tableContent=null), ArticleFig(id=1240651446065098954, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, language=EN, label=Table 1, caption=

Characteristics of study participants at baseline(n=8 202)

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总体(n=8 202)非CMM (n=7 515)CMM (n=687)χ2/ZP值
年龄（岁），n(%)28.651<0.001a
45~501 942(23.7)1 836(24.4)106(15.4)
>50~<602 835(34.6)2 579(34.3)256(37.3)
≥603 425(41.8)3 100(41.3)325(47.3)
性别，n(%)1.3300.248
男4 023(49.0)3 701(49.2)322(46.9)
女4 179(51.0)3 814(50.8)365(53.1)
职业，n(%)2.3290.118
农民2 352(28.7)5 013(66.7)438(63.8)
非农民2 751(33.5)2 502(33.3)249(36.2)
教育水平，n(%)0.8610.355
小学及以下5 849(71.3)5 348(71.2)501(72.9)
中学及以上2 352(28.7)2 166(28.8)186(27.1)
婚姻状况，n(%)<0.0010.951
已婚7 217(88.0)6 613(88.0)604(87.9)
离婚、丧偶或未婚985(12.0)902(12.0)83(12.1)
生活水平，n(%)1.4170.225
贫困1 011(12.3)916(12.2)95(13.8)
非贫困7 191(87.7)6 599(87.8)592(86.2)
供暖燃料，n(%)3.4270.062
清洁燃料1 586(19.3)1 472(19.6)114(16.6)
非清洁燃料6 616(80.7)6 043(80.4)573(83.4)
烹饪燃料，n(%)2.8710.085
清洁燃料3 154(38.5)2 911(38.7)243(35.4)
非清洁燃料5 048(61.5)4 604(61.3)444(64.6)
PM2.5暴露量（μg/m3），M(IQR)43.3(31.3)43.0(30.5)48.3(33.5)-4.874< 0.001a
高血压疾病72.890<0.001a
无2 913(35.5)2 566(34.1)347(50.5)
有5 289(64.5)4 949(65.9)340(49.5)
血脂异常3.5500.055
无5 938(72.4)5 419(72.1)519(75.5)
有2 264(27.6)2 096(27.9)168(24.5)
体型，n(%)76.734<0.001a
正常4 074(49.7)3 820(50.8)254(37.0)
消瘦618(7.5)587(7.8)31(4.5)
超重或肥胖3 510(42.8)3 108(41.4)402(58.5)
饮酒情况，n(%)0.0010.932
不饮酒5 574(68.0)5 108(68.0)466(67.8)
饮酒2 628(32.0)2 407(32.0)221(32.2)
吸烟情况，n(%)1.4520.220
不吸烟5 471(66.7)4 998(66.5)473(68.9)
吸烟2 731(33.3)2 517(33.5)214(31.1)
夜间睡眠（h），n(%)1.2490.523
<64 207(51.3)3 717(49.5)351(51.1)
6~<83 410(41.6)3 138(41.8)272(39.6)
≥84 068(49.6)660(8.8)64(9.3)
日间睡眠，n(%)0.3800.480
有7 634(93.1)6 999(93.1)635(92.4)
无568(6.9)516(6.9)52(7.6)
体力活动情况，n(%)0.3240.540
有3 206(39.1)2 930(39.0)276(40.2)
无4 996(60.9)4 585(61.0)411(59.8)
饮食状况，n(%)2.7720.088
3餐/日6 929(84.5)6 333(84.3)596(86.8)
非3餐/日1 273(15.5)1 182(15.7)91(13.2)
), ArticleFig(id=1240651446203511002, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, language=CN, label=表1, caption=

研究人群的基线特征(n=8 202)

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总体(n=8 202)非CMM (n=7 515)CMM (n=687)χ2/ZP值
年龄（岁），n(%)28.651<0.001a
45~501 942(23.7)1 836(24.4)106(15.4)
>50~<602 835(34.6)2 579(34.3)256(37.3)
≥603 425(41.8)3 100(41.3)325(47.3)
性别，n(%)1.3300.248
男4 023(49.0)3 701(49.2)322(46.9)
女4 179(51.0)3 814(50.8)365(53.1)
职业，n(%)2.3290.118
农民2 352(28.7)5 013(66.7)438(63.8)
非农民2 751(33.5)2 502(33.3)249(36.2)
教育水平，n(%)0.8610.355
小学及以下5 849(71.3)5 348(71.2)501(72.9)
中学及以上2 352(28.7)2 166(28.8)186(27.1)
婚姻状况，n(%)<0.0010.951
已婚7 217(88.0)6 613(88.0)604(87.9)
离婚、丧偶或未婚985(12.0)902(12.0)83(12.1)
生活水平，n(%)1.4170.225
贫困1 011(12.3)916(12.2)95(13.8)
非贫困7 191(87.7)6 599(87.8)592(86.2)
供暖燃料，n(%)3.4270.062
清洁燃料1 586(19.3)1 472(19.6)114(16.6)
非清洁燃料6 616(80.7)6 043(80.4)573(83.4)
烹饪燃料，n(%)2.8710.085
清洁燃料3 154(38.5)2 911(38.7)243(35.4)
非清洁燃料5 048(61.5)4 604(61.3)444(64.6)
PM2.5暴露量（μg/m3），M(IQR)43.3(31.3)43.0(30.5)48.3(33.5)-4.874< 0.001a
高血压疾病72.890<0.001a
无2 913(35.5)2 566(34.1)347(50.5)
有5 289(64.5)4 949(65.9)340(49.5)
血脂异常3.5500.055
无5 938(72.4)5 419(72.1)519(75.5)
有2 264(27.6)2 096(27.9)168(24.5)
体型，n(%)76.734<0.001a
正常4 074(49.7)3 820(50.8)254(37.0)
消瘦618(7.5)587(7.8)31(4.5)
超重或肥胖3 510(42.8)3 108(41.4)402(58.5)
饮酒情况，n(%)0.0010.932
不饮酒5 574(68.0)5 108(68.0)466(67.8)
饮酒2 628(32.0)2 407(32.0)221(32.2)
吸烟情况，n(%)1.4520.220
不吸烟5 471(66.7)4 998(66.5)473(68.9)
吸烟2 731(33.3)2 517(33.5)214(31.1)
夜间睡眠（h），n(%)1.2490.523
<64 207(51.3)3 717(49.5)351(51.1)
6~<83 410(41.6)3 138(41.8)272(39.6)
≥84 068(49.6)660(8.8)64(9.3)
日间睡眠，n(%)0.3800.480
有7 634(93.1)6 999(93.1)635(92.4)
无568(6.9)516(6.9)52(7.6)
体力活动情况，n(%)0.3240.540
有3 206(39.1)2 930(39.0)276(40.2)
无4 996(60.9)4 585(61.0)411(59.8)
饮食状况，n(%)2.7720.088
3餐/日6 929(84.5)6 333(84.3)596(86.8)
非3餐/日1 273(15.5)1 182(15.7)91(13.2)
), ArticleFig(id=1240651446299980006, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, language=EN, label=Table 2, caption=

Associations between lifestyle factors and different transition stages

, figureFileSmall=null, figureFileBig=null, tableContent=
变量健康→FCMD健康→CMM健康→死亡
HR(95% CI)P值HR(95% CI)P值HR(95% CI)P值
体型
正常1.00(ref.)1.00(ref.)1.00(ref.)
低体重0.90(0.77, 1.06)0.2060.71(0.37, 1.37)0.3101.97(1.58, 2.45)<0.001
超重或肥胖1.40(1.30, 1.52)<0.0011.42(1.06, 1.90)0.0170.80(0.65,0.97)0.027
吸烟情况
否1.00(ref.)1.00(ref.)1.00(ref.)
是1.04(0.94, 1.15)0.4460.92(0.65, 1.32)0.6650.99(0.82, 1.20)0.913
饮酒情况
否1.00(ref.)1.00(ref.)1.00(ref.)
是0.97(0.89, 1.06)0.5340.96(0.69, 1.33)0.8171.17(0.97, 1.41)0.107
饮食情况
正常三餐1.00(ref.)1.00(ref.)1.00(ref.)
非三餐0.91(0.82, 1.01)0.0750.89(0.59, 1.33)0.5641.24(1.01, 1.52)0.037
体力活动情况
有1.00(ref.)1.00(ref.)1.00(ref.)
无1.01(0.94, 1.09)0.8131.07(0.81, 1.40)0.6431.18(0.99, 1.40)0.063
日间睡眠
有1.00(ref.)1.00(ref.)1.00(ref.)
无1.21(1.06, 1.39)0.0050.69(0.38, 1.27)0.2351.42(1.08, 1.87)0.013
夜间睡眠（h）
6~<81.00(ref.)1.00(ref.)1.00(ref.)
<61.05(0.97, 1.13)0.2241.12(0.85, 1.48)0.4190.94(0.79, 1.12)0.493
≥81.16(1.02, 1.32)0.0231.06(0.64, 1.74)0.8311.01(0.76, 1.35)0.930
变量FCMD→CMMFCMD→死亡
HR(95% CI)P值HR(95% CI)P值
体型
正常1.00(ref.)1.00(ref.)
低体重0.94(0.59, 1.48)0.7841.54(0.89, 2.66)0.125
超重或肥胖1.44(1.17, 1.78)0.0010.91(0.60,1.40)0.676
吸烟情况
否1.00(ref.)1.00(ref.)
是1.00(0.78, 1.28)0.9911.39(0.89, 2.19)0.150
饮酒情况
否1.00(ref.)1.00(ref.)
是1.15(0.91, 1.44)0.2431.04(0.68, 1.59)0.870
饮食情况
正常三餐1.00(ref.)1.00(ref.)
非三餐1.10(0.84, 1.45)0.4821.19(0.74, 1.90)0.477
体力活动情况
有1.00(ref.)1.00(ref.)
无0.93(0.77, 1.12)0.4191.35(0.91, 2.00)0.133
日间睡眠
有1.00(ref.)1.00(ref.)
无1.01(0.73, 1.40)0.9600.95(0.52, 1.76)0.877
夜间睡眠（h）
6~<81.00(ref.)1.00(ref.)
<61.04(0.86, 1.27)0.6831.27(0.84, 1.93)0.263
≥80.96(0.69, 1.34)0.8291.58(0.89, 2.79)0.118
), ArticleFig(id=1240651447797346542, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240651439492625289, language=CN, label=表2, caption=

生活方式与多个过渡态的关联

, figureFileSmall=null, figureFileBig=null, tableContent=
变量健康→FCMD健康→CMM健康→死亡
HR(95% CI)P值HR(95% CI)P值HR(95% CI)P值
体型
正常1.00(ref.)1.00(ref.)1.00(ref.)
低体重0.90(0.77, 1.06)0.2060.71(0.37, 1.37)0.3101.97(1.58, 2.45)<0.001
超重或肥胖1.40(1.30, 1.52)<0.0011.42(1.06, 1.90)0.0170.80(0.65,0.97)0.027
吸烟情况
否1.00(ref.)1.00(ref.)1.00(ref.)
是1.04(0.94, 1.15)0.4460.92(0.65, 1.32)0.6650.99(0.82, 1.20)0.913
饮酒情况
否1.00(ref.)1.00(ref.)1.00(ref.)
是0.97(0.89, 1.06)0.5340.96(0.69, 1.33)0.8171.17(0.97, 1.41)0.107
饮食情况
正常三餐1.00(ref.)1.00(ref.)1.00(ref.)
非三餐0.91(0.82, 1.01)0.0750.89(0.59, 1.33)0.5641.24(1.01, 1.52)0.037
体力活动情况
有1.00(ref.)1.00(ref.)1.00(ref.)
无1.01(0.94, 1.09)0.8131.07(0.81, 1.40)0.6431.18(0.99, 1.40)0.063
日间睡眠
有1.00(ref.)1.00(ref.)1.00(ref.)
无1.21(1.06, 1.39)0.0050.69(0.38, 1.27)0.2351.42(1.08, 1.87)0.013
夜间睡眠（h）
6~<81.00(ref.)1.00(ref.)1.00(ref.)
<61.05(0.97, 1.13)0.2241.12(0.85, 1.48)0.4190.94(0.79, 1.12)0.493
≥81.16(1.02, 1.32)0.0231.06(0.64, 1.74)0.8311.01(0.76, 1.35)0.930
变量FCMD→CMMFCMD→死亡
HR(95% CI)P值HR(95% CI)P值
体型
正常1.00(ref.)1.00(ref.)
低体重0.94(0.59, 1.48)0.7841.54(0.89, 2.66)0.125
超重或肥胖1.44(1.17, 1.78)0.0010.91(0.60,1.40)0.676
吸烟情况
否1.00(ref.)1.00(ref.)
是1.00(0.78, 1.28)0.9911.39(0.89, 2.19)0.150
饮酒情况
否1.00(ref.)1.00(ref.)
是1.15(0.91, 1.44)0.2431.04(0.68, 1.59)0.870
饮食情况
正常三餐1.00(ref.)1.00(ref.)
非三餐1.10(0.84, 1.45)0.4821.19(0.74, 1.90)0.477
体力活动情况
有1.00(ref.)1.00(ref.)
无0.93(0.77, 1.12)0.4191.35(0.91, 2.00)0.133
日间睡眠
有1.00(ref.)1.00(ref.)
无1.01(0.73, 1.40)0.9600.95(0.52, 1.76)0.877
夜间睡眠（h）
6~<81.00(ref.)1.00(ref.)
<61.04(0.86, 1.27)0.6831.27(0.84, 1.93)0.263
≥80.96(0.69, 1.34)0.8291.58(0.89, 2.79)0.118
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生活方式与心血管代谢性共病发生发展轨迹的关联研究
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许珊 1 , 张书迎 1 , 谢文涵 1 , 凌可欣 1 , 谭艳芳 1 , 刘相佟 1, 2
现代预防医学 | 流行病与统计方法 2024,51(12): 2113-2120
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现代预防医学 | 流行病与统计方法 2024, 51(12): 2113-2120
生活方式与心血管代谢性共病发生发展轨迹的关联研究
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许珊1, 张书迎1, 谢文涵1, 凌可欣1, 谭艳芳1, 刘相佟1, 2
作者信息
  • 1.首都医科大学公共卫生学院，北京 100069
  • 2.临床流行病学北京市重点实验室

    • 许珊（2001—），女，本科在读，研究方向：预防医学研究

通讯作者:

刘相佟，E-mail：
Lifestyle associated with incidence and progression trajectories of cardiometabolic multimorbidity
Shan XU1, Shu-ying ZHANG1, Wen-han XIE1, Ke-xin LING1, Yan-fang TAN1, Xiang-tong LIU1, 2
Affiliations
  • School of Public Health, Capital Medical University, Beijing 100069, China
出版时间: 2024-06-25 doi: 10.20043/j.cnki.MPM.202404059
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摘要
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目的

本研究旨在探究生活方式与心血管代谢性共病发生发展轨迹的关联。

方法

基于中国健康与养老追踪调查（China Health and Retirement Longitudinal Study, CHARLS）2011—2020年数据库中8 202名≥45岁的中老年人群，应用多状态马尔可夫模型（Multi-state Markov model, MSM模型）分析生活方式（体型、吸烟、饮酒、饮食、体力活动、睡眠）对心血管代谢性共病发生发展轨迹的影响。

结果

疾病进展轨迹主要涉及从健康到首发心血管代谢性疾病（first cardiometabolic disease, FCMD）、健康到心血管代谢性共病（cardiometabolic multimorbidity, CMM）、FCMD到CMM。在随访过程中，2 962人出现FCMD，687人发生CMM，699人死亡。在健康向FCMD过渡中，超重或肥胖（HR=1.40，95% CI：1.30~1.52）、夜间睡眠时间过长（HR=1.16，95%CI：1.02~1.32）、无午睡习惯（HR=1.21，95%CI：1.06~1.39）人群的FCMD发病风险更高。健康向CMM过渡中，超重或肥胖与CMM发病呈正相关（HR=1.42，95%CI：1.06~1.90）。FCMD向CMM的过渡中，超重或肥胖为危险因素（HR=1.44，95%CI：1.17~1.78）。

结论

不良生活方式增加CMM的风险，因此要重视生活方式预防在健康管理中的应用。

中老年人  /  心血管代谢性共病  /  多状态马尔可夫模型  /  生活方式
Objective

To investigate the association between lifestyle and the incidence and development trajectory of cardiometabolic multimorbidity.

Methods

Based on the China Health and Retirement Longitudinal Study(CHARLS) from 2011 to 2020, a total of 8 202 individuals aged 45 years and older were included. A Multi-state Markov model (MSM) was applied to analyze the effects of lifestyle on the trajectories of cardiometabolic multimorbidity.

Results

The disease progression trajectories mainly involved transitions from health to first cardiometabolic diseases (FCMD), health to cardiometabolic multimorbidity (CMM) and FCMD to CMM. During the follow-up period, 2 962 participants developed FCMD, 687 participants developed CMM, and 699 participants died. The results indicated that transitioning from health to FCMD, being overweight or obese, having longer nighttime sleep duration, and lacking a habit of daytime naps were positively associated with FCMD, which were 1.40(95%CI: 1.30-1.52) times, 1.16(95%CI: 1.02-1.32) times, and 1.21(95%CI: 1.06-1.39) times higher than those of healthy lifestyle people, respectively. During the transition from health to CMM, being overweight or obese was positively associated with the increased incidence of CMM (HR=1.42,95%CI: 1.06-1.90). Transitioning from FCMD to CMM, only overweight or obesity was observed as a risk factor (HR=1.44,95%CI: 1.17-1.78).

Conclusion

Lifestyle may increase the risk of cardiometabolic diseases and multimorbidity, emphasizing the importance of lifestyle interventions in health management practices.

Middle-aged and older adults  /  Cardiometabolic multimorbidity  /  Multi-state Markov model  /  Lifestyle
许珊, 张书迎, 谢文涵, 凌可欣, 谭艳芳, 刘相佟. 生活方式与心血管代谢性共病发生发展轨迹的关联研究. 现代预防医学, 2024 , 51 (12) : 2113 -2120 . DOI: 10.20043/j.cnki.MPM.202404059
Shan XU, Shu-ying ZHANG, Wen-han XIE, Ke-xin LING, Yan-fang TAN, Xiang-tong LIU. Lifestyle associated with incidence and progression trajectories of cardiometabolic multimorbidity[J]. Modern Preventive Medicine, 2024 , 51 (12) : 2113 -2120 . DOI: 10.20043/j.cnki.MPM.202404059
正文
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随着人口老龄化增加以及预期寿命延长，共病在老年群体中越来越普遍，已经成为日益增长的公共卫生负担[1-2]。心血管代谢性共病（cardiometabolic multimorbidity, CMM）是最可复制的共病之一，定义为至少两种心血管代谢性疾病共存，通常包括2型糖尿病、缺血性心脏病和卒中[3-4]。CMM比单发一种疾病具有更高的死亡风险[5]，同时造成老年人较高的疾病经济负担，导致更高的药物使用率和医疗支出[6]。既往研究表明，一些可改变的生活方式，如不吸烟、中度饮酒、适当进行体育活动、健康饮食和减少肥胖与降低心血管疾病风险有关[7]。然而以往研究更重视生活方式与单个心血管代谢性疾病的关联，限制了评估生活方式在CMM进展轨迹即从无心血管代谢性疾病到首发心血管代谢性疾病（first cardiometabolic disease, FCMD）、进而到CMM、再到死亡的过渡中的影响作用。
因此，本研究基于中国健康与养老追踪调查（China Health and Retirement Longitudinal Study, CHARLS）数据，应用多状态马尔可夫模型（Multi-state Markov model, MSM模型）分析特定生活方式对于CMM进展轨迹内不同阶段的作用，旨在为CMM预防提供依据。
1　资料与方法
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1.1　研究对象
本研究选取的研究对象来自CHARLS队列基线（2011—2012年）及四次随访（2013年、2015年、2018年、2020年）的调查数据[8]，数据经研究者申请并获得使用许可。研究人群的纳入和排除标准：（1）纳入基线无心血管代谢性疾病的中老年人；（2）排除基线因身高、体重或腰围缺失而无法定义体型的研究对象；（3）排除死亡数据缺失、随访心血管代谢性疾病数据缺失、基线患有癌症的研究对象；（4）排除缺少生活方式的人群；（5）排除其它协变量数据缺失人群。研究对象纳入排除流程如图1所示。本研究已通过北京大学生物医学伦理委员会批准（IRB00001052-11015），所有受试对象均在调查前签署了书面知情同意书。
1.2　相关定义及标准
特定的生活方式基于以往研究选择[9-10]，并通过基线问卷调查和身体测量评估。六种生活方式均以分类变量的形式纳入研究，体型由体质指数（body mass index, BMI）与腰围共同决定，BMI由体重（kg）/ [身高（m）]2获得，结合我国成人体重判定标准[11]，BMI<18.5 kg/m2定义为体型消瘦，BMI≥24 kg/m2或腰围为腹型肥胖者（男性腰围>90 cm，女性腰围>85 cm）定义为超重或肥胖；吸烟、饮酒、体力活动均定义为二分类变量，即是否吸烟、饮酒和进行体力活动，吸烟定义为当前吸烟者或曾经吸烟者，饮酒定义为当前饮酒者或曾经饮酒者，体力活动情况将至少进行10分钟剧烈活动或适度体力活动或步行定义为“有体力活动”。本研究以体型正常、不吸烟、不饮酒、进行体力活动、睡眠6~<8 h、有午睡习惯作为参考组；定义超重或肥胖、吸烟、饮酒、不进行体力活动、夜间睡眠<6 h或≥8 h、无午睡习惯为高危生活方式，并统计高危生活方式个数。
CMM定义为至少两种心血管代谢性疾病共存，包括2型糖尿病、缺血性心脏病、卒中，仅出现一种疾病类型为FCMD，出现两种及以上心血管代谢性疾病为CMM。结合数据特征与相关指南[12]，2型糖尿病定义为基线自我报告或基线时测量的空腹血糖 ≥ 7.0 mmol/L或糖化血红蛋白 ≥ 6.5%或随机血糖 ≥ 11.1 mmol/L或用药；缺血性心脏病和卒中根据自我报告定义。疾病主要进展轨迹涉及从健康到FCMD、健康到CMM、健康到死亡、FCMD到CMM、FCMD到死亡、CMM到死亡6个阶段。本研究根据既往研究以及通过绘制有向无环图（directed acyclic graph, DAG）[13-14]，最终选定了如下的一组变量作为协变量：年龄、性别、职业、婚姻状况、教育程度、生活水平、PM2.5暴露、供暖燃料、烹饪燃料、高血压及血脂异常。其中PM2.5数据来源于中国大气成分近实时追踪数据集（Tracking Air Pollution in China，TAP，http://tapdata.org.cn）。本研究使用数据集中2010年—2020年分辨率为10 km×10 km 的PM2.5数据，与研究对象住址相匹配，并通过对10年数据进行平均得到个人的年均质量浓度。高血压定义为基线自我报告或体检数据中收缩压≥140 mmHg或舒张压≥90 mmHg或有用药史；血脂异常结合相关指南[15]定义为基线自我报告或具有低密度脂蛋白≥4.1 mmol/L、甘油三酯 ≥ 2.3 mmol/L、总胆固醇 ≥ 6.2 mmol/L或（和）高密度脂蛋白<1.0 mmol/L中任意一项或有用药史。
1.3　统计学分析
本研究采用SPSS 26.0、R 4.3.1进行统计分析, 多状态分析由R中mstate包实现。PM2.5平均浓度为偏态分布，由M（IQR）描述。计量资料组间比较采用Mann-Whitney U检验,计数资料组间比较采用卡方检验。采用多状态模型评估生活方式与CMM发生发展轨迹的关联。多状态模型是传统Cox比例风险模型的扩展，通过将多个后续或竞争事件作为过渡状态，在调查风险因素对疾病进展不同阶段的影响方面提供了独特的优势，同时考虑了竞争风险[16]。此外，由于生活方式具有依时性，容易随时间变化或发生疾病而改变，本研究匹配了数据库中2013、2015、2018年的生活方式数据，构建了3个Cox回归模型：（1）模型1：纳入2013年生活方式变量和基线需要纳入的其他协变量，排除2013年发病人群，随访2015、2018和2020年发病情况；（2）模型2：纳入2015年生活方式变量和基线需要纳入的其他协变量，排除2013和2015年发病人群，随访2018和2020年发病情况；（3）模型3：纳入2018年生活方式变量和基线需要纳入的其他协变量，排除2013、2015和2018年发病人群，随访2020年发病情况。为了验证结果的稳健性，本研究进行以下敏感性分析：（1）通过增加居住地、脉搏、PM2.5的五种组分和抑郁情况几个协变量进行敏感性分析；（2）将定义的高危生活方式按照数量分为0~2个高危生活方式组及3~7个高危生活方式组，在年龄、性别、职业、婚姻状况、PM2.5暴露、生活水平方面比较与高危生活方式之间的交互作用，以探究混杂因素的效应修饰作用。其中PM2.5暴露量按照环境空气质量标准（GB3095-2012）中的年平均二级浓度限值[17]分为≤35 μg/m3与>35 μg/m3。分析采用双侧检验，检验水准α=0.05。
2　结果
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2.1　描述性分析
本研究最终纳入8 202名无心血管代谢性疾病的参与者，并随访至2020年，其中有2 962（36.1%）人在随访过程中发生FCMD，687人发生CMM，其中463（15.6%）人由FCMD发展为CMM，随访中共699人死亡，见图2。研究对象基线特征分布如表1所示。研究人群的年龄组主要集中在大于60岁，女性较多，占总人群的51.0%。患有CMM和未患CMM在不同年龄、PM2.5暴露、是否患有高血压疾病、体型之间构成比差异具有统计学意义（P<0.05）。与随访期间没有发生心血管代谢性疾病的幸存者相比，经历过一个或多个心血管代谢性疾病的幸存者多数为年龄更大者、PM2.5年平均暴露水平高者、高血压患者、超重或肥胖者。
2.2　多状态分析
通过使用多状态模型，本研究观察了生活方式在CMM轨迹的每个过渡阶段的不同作用，具体见表2。表2显示了多状态分析的结果，在健康向FCMD过渡阶段，观察到超重或肥胖、无午睡习惯、夜间睡眠> 8h为FCMD发病的危险因素，其中体型为超重或肥胖人群的发病风险为体型正常人群的1.4倍（95%CI：1.30~1.52），无午睡习惯人群发病风险为有午睡习惯人群的1.21倍（95%CI：1.06~1.39），夜间睡眠时间> 8h是夜间睡眠时间6 ~ 8h人群发病风险的1.16倍（95%CI：1.02~1.32）。在FCMD向CMM过渡的阶段，仅观察到超重或肥胖与FCMD患者进一步进展为CMM之间呈显著的相关关系，HR为1.44（95%CI：1.17~1.78）。在FCMD向死亡过渡的阶段，未观察到有统计学意义的变量。
2.3　Cox分析
多因素Cox比例风险模型的结果显示，在2013、2015和2018年三次随访中体型与发生FCMD之间呈显著相关关系，见图3。超重或肥胖导致FCMD发病的风险分别为正常体型发病的1.3倍（95%CI: 1.2~1.4），1.3倍（95%CI：1.0~1.6）和1.4倍（95%CI：1.1~1.7）。纳入2018年随访的生活方式，发现非三餐饮食为FCMD发病的危险因素，但在纳入前两年随访的生活方式时，未观察到此有意义变量。在纳入2013年随访的生活方式时，观察到夜间睡眠<6 h人群患CMM风险为睡眠6~8小时人群的1.3倍（95%CI：1.0~1.6）。除此之外，未观察到其他与FCMD和CMM发病有关联的有统计学意义变量。
2.4　敏感性分析
敏感性分析证实了研究结果的稳健性，见图4。
第一，增加居住面积、脉搏、PM2.5的五种组分、抑郁情况几个协变量后，研究结果与多状态分析结果一致性较强，仅在从健康向死亡的过渡中缺少了午睡和体力活动两个有统计学意义的变量，在其它过渡态中的结果均保持一致。
第二，将定义的高危生活方式按照数量分为0~2个高危生活方式组及3~7个高危生活方式组，在年龄、性别、职业、婚姻状况、教育程度、PM2.5暴露、生活水平、基线患有高血压或血脂异常几个方面比较与高危生活方式之间的交互作用。结果发现，在健康向FCMD过渡的过程中，年龄大于60岁及基线患有高血压对生活方式有效应修饰作用，年龄大于60岁及基线患有高血压增大了高危生活方式对从基线到FCMD的转变的影响。在健康向CMM过渡的过程中，未观察到与高危生活方式有统计学意义的交互作用（P≥ 0.05）。
3　讨论
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本研究纳入8 202名中老年人，使用多状态模型研究生活方式包括吸烟、饮酒、体力活动、饮食、睡眠及体型对心血管代谢性疾病发病及轨迹的影响。研究结果显示，不同的生活方式对从健康到FCMD、健康到CMM以及FCMD到CMM有不同的影响。其中，超重或肥胖、无午睡习惯、夜间睡眠时间过长（≥ 8 h）与健康人向FCMD过渡呈正相关，超重或肥胖与FCMD患者进展为CMM存在正相关。在Cox模型中进一步观察到超重或肥胖在三个模型中与健康向FCMD过渡有较稳定的正相关关系。亚组分析显示在健康向FCMD过渡中，年龄>60岁及基线患有高血压对高危生活方式有效应修饰作用，在健康向CMM过渡过程中未出现有统计学意义的亚组，敏感性分析进一步验证了主要分析的稳健性。
在几种生活方式中，体型与发生FCMD及进展为CMM的相关性最强。这一发现与以往研究一致。在一项对开滦队列的分析中，与健康的个体相比，一般和中心肥胖指数均增加了从基线FCMD和从FCMD到CMM过渡的风险[18]。以往研究表明，过量的皮下脂肪、内脏脂肪是超重和肥胖使CMM风险增高的重要原因[19]，晚期糖基化终末产物及其受体通路在人类肥胖和心血管代谢疾病中具有潜在作用[20]。除此之外，睡眠无论是夜间睡眠还是日间睡眠都与FCMD发病相关。本研究观察到夜间睡眠时间>8 h是FCMD发病的危险因素，这与以往研究相一致。既往研究表明，长时间睡眠可以增加心血管疾病发病风险，与中风的风险增加有关[21]，导致发生2型糖尿病的风险较大[22]。同时，无午睡习惯亦会增加FCMD发病风险。虽然以往研究表明午睡对心血管代谢性疾病的发展有因果作用[23]，但其他相关研究发现短时间的日间午睡与心血管疾病发生风险无显著性关系[24]。
本研究中观察到在健康向FCMD过渡阶段，年龄、基线患有高血压与生活方式之间具有效应修饰作用。以往一项队列研究探究健康生活方式对高血压患者患CMM风险的影响，发现与非常不健康的群体相比，非常健康的群体在高血压患者中患CMM的风险降低了41%，与本研究结果类似但本研究中的效应修饰作用仅存在于健康向FCMD过渡，并未在健康向CMM过渡中观察到[25]。
本研究具有一定的优势，首先，多状态模型可以区分生活方式对健康、FCMD、CMM和死亡的转化之间影响的差异，可以观察到基于特定过渡态的危险因素。第二，本研究拓宽了生活方式的选取，相比以往研究采用的五种生活方式，增加了睡眠对于CMM风险的效应评估，并取得了有意义的结果。第三，本研究考虑了生活方式的依时性，纳入几次随访的生活方式观察效应，减弱了生活方式改变所导致的错误分类影响。第四，本研究采用CHARLS数据库，数据规模大，数据所包含的范围广，具有良好的代表性。同时，本研究也有一定的限制性。第一，本研究的结局均基于CHARLS数据库中疾病的自我报告、用药及实验室检查数据，因此存在一定的回忆偏倚和一定的未检出病例被遗漏的情况，这可能会导致错误分类而使风险被低估。第二，由于数据库中自我报告的心脏性疾病未做具体区分分型或分类，因此可能产生错误分类，对研究结果造成一定影响，对于结果解释应谨慎。第三，本研究排除了身高、体重及腰围缺失无法定义体型的人群，因此可能影响对结局的效应评估。第四，本研究由于样本量限制，缺少CMM向死亡的过渡阶段，无法观察该阶段的效应值。
基金
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  • 国家自然科学基金项目(82003559)
  • 教育部“春晖计划”合作科研项目(HZKY20220056)
文献
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2024年第51卷第12期
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文章信息
doi: 10.20043/j.cnki.MPM.202404059
  • 接收时间：2024-04-03
  • 首发时间：2026-03-17
  • 出版时间：2024-06-25
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  • 收稿日期：2024-04-03
基金
国家自然科学基金项目(82003559)
教育部“春晖计划”合作科研项目(HZKY20220056)
作者信息
    1.首都医科大学公共卫生学院，北京 100069
    2.临床流行病学北京市重点实验室

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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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