Article(id=1241025214327345988, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241025201983508979, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202405250, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1715616000000, receivedDateStr=2024-05-14, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773813068228, onlineDateStr=2026-03-18, pubDate=1744214400000, pubDateStr=2025-04-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773813068228, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773813068228, creator=13701087609, updateTime=1773813068228, updator=13701087609, issue=Issue{id=1241025201983508979, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='7', pageStart='1153', pageEnd='1344', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773813065285, creator=13701087609, updateTime=1773815493878, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241035388320543403, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241025201983508979, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241035388320543404, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241025201983508979, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1168, endPage=1174, ext={EN=ArticleExt(id=1241025214759359340, articleId=1241025214327345988, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Influencing factors and path analysis of the risks of stroke, Parkinson’s disease, and death based on the multi-state model, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To use the multi-state model to study the risks of developing Parkinson’s disease and death after stroke and explore their influencing factors, so as to provide a scientific basis for the prognosis of stroke patients and the prevention of Parkinson’s disease.

Methods

This study was based on 345 585 participants registered in the UK Biobank database from 2006 to 2010, with follow-up until November 30, 2021. The multi-state model was used to analyze the risks of developing Parkinson’s disease and death in stroke patients.

Results

Among the six outcome paths, the cumulative risk from stroke to death was the highest, followed by that of Parkinson’s disease and from the baseline to death. The risk probability of developing Parkinson’s disease after stroke was 2.25 times that of the baseline state, and the death probability of stroke patients was 11.36 times that of the baseline population. The results of the influencing factors in the multi-state model showed that advanced age (over 60 years old), male, depression, smoking, alcohol consumption, and childhood obesity were all risk factors for the transition of the baseline population to the three states (stroke, Parkinson’s disease, and death). In the transition path from stroke to Parkinson’s disease, advanced age (over 60 years old) was a risk factor, while alcohol consumption and female gender were protective factors for stroke patients to develop Parkinson’s disease (HR=0.569, 95%CI: 0.357-0.909), (HR=0.521, 95%CI: 0.344-0.788). In the path from stroke to death, advanced age (over 50 years old), depression (HR=1.980, 95%CI: 1.656-2.369), smoking (HR=1.504,95%CI: 1.358-1.647), and a family history of stroke could increase the risk of stroke to death, while alcohol consumption was a protective factor (HR=0.872, 95%CI: 0.774-0.984). Advanced age (over 60 years old), depression (HR=1.783, 95%CI: 1.295-2.451), and smoking (HR=1.781, 95%CI: 1.397-2.295) were risk factors for the death of Parkinson’s patients, and the mortality rate of female patients was lower than that of male patients (HR=0.797, 95%CI: 0.686-0.926).

Conclusion

Advanced age (over 60 years old), male gender, smoking, depression, and childhood obesity are risk factors for stroke, Parkinson’s disease, and death in the baseline population; advanced age, male, smoking, depression, and a family history of stroke are risk factors for stroke patients to develop Parkinson’s disease and die. The multi-state model can be used to demonstrate the influencing factors and extent of disease transitions, revealing the patterns of disease progression.

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目的

使用多状态模型研究脑卒中后发生帕金森和死亡的风险,并探讨其影响因素,为脑卒中患者预后及帕金森发生的预防提供科学依据。

方法

本研究基于UK Biobank数据库中2006—2010年登记的345 585名参与者,随访至2021年11月30日,采用多状态模型分析脑卒中患者发生帕金森和死亡的风险。

结果

在六种转归路径中,脑卒中到死亡的累积风险最高,其次为帕金森、基线到死亡。脑卒中后发生帕金森的风险概率是基线状态发生帕金森的2.25倍,而脑卒中患者死亡概率是基线人群死亡概率的11.36倍。多状态模型影响因素结果显示:高龄(60岁以上)、男性、抑郁、吸烟、饮酒和儿童期体型肥胖均是基线人群向三种状态(脑卒中、帕金森和死亡)转归的危险因素。在脑卒中到帕金森的转归路径中,高龄(60岁以上)是其危险因素,而饮酒和女性是脑卒中患者发生帕金森的保护因素(HR=0.569,95%CI: 0.357~0.909;HR=0.521,95%CI:0.344~0.788)。在脑卒中到死亡的路径中,高龄(50岁以上)、抑郁(HR=1.980,95%CI:1.656~2.369)、吸烟(HR=1.504, 95%CI:1.358~1.647)和有脑卒中家族史可增加脑卒中到死亡的危险性,而饮酒作为其保护因素(HR=0.872, 95%CI:0.774~0.984)。高龄(60岁以上)、抑郁(HR=1.783, 95%CI:1.295~2.451)、吸烟(HR=1.781, 95%CI:1.397~2.295)是帕金森患者死亡的危险因素,而女性患者死亡率低于男性(HR=0.797, 95%CI:0.686~0.926)。

结论

高龄(60岁以上)、男性、吸烟、抑郁症和儿童期体型肥胖是基线人群发生脑卒中、帕金森和死亡的危险因素;高龄、男性、吸烟、抑郁症和脑卒中家族史是脑卒中患者发生帕金森及死亡的危险因素。多状态模型可用于展示疾病转移的影响因素及程度,揭示疾病进程的变化规律。

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石福艳,E-mail:;
王素珍,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=C5iLXsFUpMRF88shP0QJEw==, magXml=XE0xI0Bck022GrHHmD0GFQ==, pdfUrl=null, pdf=Nmi6x4984OoTVIeC9OPAdw==, pdfFileSize=956599, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=ZZ92tbDLYgMjXxeT01rZHg==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=5ZY/XH7s8e8QOKGLEdE1VQ==, mapNumber=null, authorCompany=null, fund=null, authors=

石福艳与王素珍为共同通信作者

宋旺辰(2000—),男,硕士在读,研究方向:流行病与卫生统计学的研究

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Mov Disord Clin Pract, 2024, 11(12): 1646-1647., articleTitle=Improving insight and application: a commentary on the Link between initial depression and anxiety and Long-Term health outcomes in Parkinson's disease patients, refAbstract=null)], funds=[Fund(id=1241025227795255831, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, awardId=81803337; 81872719; 82003560, language=CN, fundingSource=国家自然科学基金项目(81803337; 81872719; 82003560), fundOrder=null, country=null), Fund(id=1241025227874947612, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, awardId=ZR2023MH313, language=CN, fundingSource=山东省自然科学基金项目(ZR2023MH313), fundOrder=null, country=null), Fund(id=1241025227954639392, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, awardId=2019 - 10-156, Lu-Jiao, language=CN, fundingSource=山东省高等学校青创人才引育计划(2019 - 10-156, Lu-Jiao), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241025217041059894, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, xref=null, ext=[AuthorCompanyExt(id=1241025217045254198, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, companyId=1241025217041059894, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Epidemiology and Health Statistics, School of Public Health, Shandong Second Medical University, Weifang,Shandong 261053, China), AuthorCompanyExt(id=1241025217053642807, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, companyId=1241025217041059894, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=山东第二医科大学公共卫生学院流行病与卫生统计学教研室,山东 潍坊 261053)])], figs=[ArticleFig(id=1241025224557253043, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, language=EN, label=Figure 1, caption=State transition diagram of the multi-state model, figureFileSmall=btdtJYmSE1AdXinHJLg2AQ==, figureFileBig=ZZ92tbDLYgMjXxeT01rZHg==, tableContent=null), ArticleFig(id=1241025224674693564, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, language=CN, label=图1, caption=多状态模型状态转移图, figureFileSmall=btdtJYmSE1AdXinHJLg2AQ==, figureFileBig=ZZ92tbDLYgMjXxeT01rZHg==, tableContent=null), ArticleFig(id=1241025224871825866, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, language=EN, label=Figure 2, caption=Cumulative risk curves for all transition paths, figureFileSmall=C8H4JoB5vU0/GJjf5VD8Xw==, figureFileBig=AVQ4CEUtqJ7qiTKfs8pW5Q==, tableContent=null), ArticleFig(id=1241025224968294863, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, language=CN, label=图2, caption=所有转归路径的累计风险曲线, figureFileSmall=C8H4JoB5vU0/GJjf5VD8Xw==, figureFileBig=AVQ4CEUtqJ7qiTKfs8pW5Q==, tableContent=null), ArticleFig(id=1241025225039598034, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, language=EN, label=Figure 3, caption=Stacked transition probability diagram, figureFileSmall=MzVvHd8ZKbXw18Mh//zioQ==, figureFileBig=yFXoy5Ed+BIkuLVjCtoyYA==, tableContent=null), ArticleFig(id=1241025225123484119, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, language=CN, label=图3, caption=堆积转归概率图, figureFileSmall=MzVvHd8ZKbXw18Mh//zioQ==, figureFileBig=yFXoy5Ed+BIkuLVjCtoyYA==, tableContent=null), ArticleFig(id=1241025225198981594, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, language=EN, label=Figure 4, caption=Incidence and mortality of Parkinson’s disease in different age groups, figureFileSmall=z9NPyI8s88Dn0OntDlumgA==, figureFileBig=AiHoTrylgd2gAw24Ql6+1g==, tableContent=null), ArticleFig(id=1241025225282867680, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, language=CN, label=图4, caption=不同年龄段人群帕金森发病及死亡情况

注:图A不同年龄段人群存活概率图;图B不同年龄段人群存活概率图。

, figureFileSmall=z9NPyI8s88Dn0OntDlumgA==, figureFileBig=AiHoTrylgd2gAw24Ql6+1g==, tableContent=null), ArticleFig(id=1241025225358365154, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, language=EN, label=Figure 5, caption=Stacked transition probability plot by age group, figureFileSmall=JT/z8zdnUdGJRMOXHePgwA==, figureFileBig=6aciiejygZaI7mdDjNyxEg==, tableContent=null), ArticleFig(id=1241025225450639851, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, language=CN, label=图5, caption=不同年龄段人群堆积转归概率图

注:图A为50岁以下人群状态概率;图B为50岁~60岁人群状态概率;图C为60岁以下人群状态概率图。

, figureFileSmall=JT/z8zdnUdGJRMOXHePgwA==, figureFileBig=6aciiejygZaI7mdDjNyxEg==, tableContent=null), ArticleFig(id=1241025225551303150, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, language=EN, label=Table 1, caption=

Variable assignment and composition ratio

, figureFileSmall=null, figureFileBig=null, tableContent=
影响因素赋值例数构成比(%)
年龄(岁)>60132 95538.4
50~60128 73137.2
<5083 89824.2
性别190 34944.9
229 48755.1
儿童期体型55 11412.9
115 55433.4
正常174 91650.6
抑郁症282 21081.6
63 37418.4
社会支持情况频繁270 62778.4
很少或没有5 6311.6
正常69 32620.0
吸烟情况吸烟311 26890.1
不吸烟34 3169.9
饮酒情况饮酒318 10592.1
不饮酒27 4797.9
脑卒中史父母双方13 6683.9
父母一方82 12023.7
249 79672.3
帕金森史父母双方3 4620.9
父母一方11 0103.2
331 11295.9
), ArticleFig(id=1241025225630994929, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, language=CN, label=表1, caption=

变量赋值情况及构成比

, figureFileSmall=null, figureFileBig=null, tableContent=
影响因素赋值例数构成比(%)
年龄(岁)>60132 95538.4
50~60128 73137.2
<5083 89824.2
性别190 34944.9
229 48755.1
儿童期体型55 11412.9
115 55433.4
正常174 91650.6
抑郁症282 21081.6
63 37418.4
社会支持情况频繁270 62778.4
很少或没有5 6311.6
正常69 32620.0
吸烟情况吸烟311 26890.1
不吸烟34 3169.9
饮酒情况饮酒318 10592.1
不饮酒27 4797.9
脑卒中史父母双方13 6683.9
父母一方82 12023.7
249 79672.3
帕金森史父母双方3 4620.9
父母一方11 0103.2
331 11295.9
), ArticleFig(id=1241025225740046839, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, language=EN, label=Table 2, caption=

Number of people and their probabilities of transition between six different states

, figureFileSmall=null, figureFileBig=null, tableContent=
转归前状态转归后状态转归人数概率
基线脑卒中5 8230.017
基线帕金森1 4390.004
基线死亡20 9690.061
基线无转变317 3540.918
脑卒中帕金森760.009
脑卒中死亡5 8230.693
脑卒中无转变2 5030.298
帕金森死亡7510.343
帕金森无转变1 4390.657
), ArticleFig(id=1241025225828127226, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, language=CN, label=表2, caption=

六种不同状态之间的转归人数及其概率

, figureFileSmall=null, figureFileBig=null, tableContent=
转归前状态转归后状态转归人数概率
基线脑卒中5 8230.017
基线帕金森1 4390.004
基线死亡20 9690.061
基线无转变317 3540.918
脑卒中帕金森760.009
脑卒中死亡5 8230.693
脑卒中无转变2 5030.298
帕金森死亡7510.343
帕金森无转变1 4390.657
), ArticleFig(id=1241025225924596226, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, language=EN, label=Table 3, caption=

Multi factor analysis results of different baseline transition paths under the multi state model

, figureFileSmall=null, figureFileBig=null, tableContent=
变量基线→脑卒中基线→帕金森基线→死亡
HR(95%CI)PHR(95%CI)PHR(95%CI)P
年龄(岁,ref: <50)
50~602.214(2.031~7.407)<0.0014.678(3.656~5.986)<0.0012.711(2.562~2.868)<0.001
>604.909(4.538~5.310)<0.00114.327(11.305,18.157)<0.0016.443(6.107~6.796)<0.001
性别(ref:男性)
女性0.641(0.601~0.681)<0.0010.539(0.476~0.554)<0.0010.633(0.599~0.628)<0.001
儿童期体型(ref:正常)
1.029(0.963~1.098)0.4071.182(1.091~1.342)0.0101.159(1.102~1.342)<0.001
1.115(1.044~1.182)<0.0011.175(1.093~1.325)0.0091.189(1.097~1.339)<0.001
抑郁症(ref:有)
0.583(0.543~0.626)<0.0010.538(0.466~0.622)<0.0010.336(0.317~0.356)<0.001
社会支持(ref:正常)
很少或没有1.340(1.161~1.536)<0.0011.133(0.979~1.541)0.4281.757(1.621~1.890)<0.001
频繁1.355(1.165~1.563)<0.0011.044(0.880~1.435)0.7911.721(1.575~1.855)<0.001
吸烟情况(ref:不吸烟)
吸烟1.728(1.619~1.831)<0.0011.379(1.207~1.757)<0.0012.298(2.206~2.368)<0.001
饮酒情况(ref:不饮酒)
饮酒0.668(0.623~0.726)<0.0010.592(0.511~0.638)<0.0010.679(0.651~0.710)<0.001
脑卒中史(ref:无)
父母一方1.132(1.055~1.227)0.0361.032(0.996~1.132)0.2591.094(1.014~1.164)<0.001
父母双方1.443(1.388~1.520)<0.0011.179(0.823~1.386)0.4841.494(1.314~1.866)<0.001
帕金森史(ref:无)
父母一方1.033(0.957~1.092)0.3571.324(1.155~1.678)0.0231.041(1.010~1.071)0.397
父母双方1.081(0.935~1.197)0.1801.882(1.768~2.294)<0.0011.214(1.083~1.519)0.017
), ArticleFig(id=1241025226033648132, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, language=CN, label=表3, caption=

多状态模型下基线不同转移路径的多因素分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量基线→脑卒中基线→帕金森基线→死亡
HR(95%CI)PHR(95%CI)PHR(95%CI)P
年龄(岁,ref: <50)
50~602.214(2.031~7.407)<0.0014.678(3.656~5.986)<0.0012.711(2.562~2.868)<0.001
>604.909(4.538~5.310)<0.00114.327(11.305,18.157)<0.0016.443(6.107~6.796)<0.001
性别(ref:男性)
女性0.641(0.601~0.681)<0.0010.539(0.476~0.554)<0.0010.633(0.599~0.628)<0.001
儿童期体型(ref:正常)
1.029(0.963~1.098)0.4071.182(1.091~1.342)0.0101.159(1.102~1.342)<0.001
1.115(1.044~1.182)<0.0011.175(1.093~1.325)0.0091.189(1.097~1.339)<0.001
抑郁症(ref:有)
0.583(0.543~0.626)<0.0010.538(0.466~0.622)<0.0010.336(0.317~0.356)<0.001
社会支持(ref:正常)
很少或没有1.340(1.161~1.536)<0.0011.133(0.979~1.541)0.4281.757(1.621~1.890)<0.001
频繁1.355(1.165~1.563)<0.0011.044(0.880~1.435)0.7911.721(1.575~1.855)<0.001
吸烟情况(ref:不吸烟)
吸烟1.728(1.619~1.831)<0.0011.379(1.207~1.757)<0.0012.298(2.206~2.368)<0.001
饮酒情况(ref:不饮酒)
饮酒0.668(0.623~0.726)<0.0010.592(0.511~0.638)<0.0010.679(0.651~0.710)<0.001
脑卒中史(ref:无)
父母一方1.132(1.055~1.227)0.0361.032(0.996~1.132)0.2591.094(1.014~1.164)<0.001
父母双方1.443(1.388~1.520)<0.0011.179(0.823~1.386)0.4841.494(1.314~1.866)<0.001
帕金森史(ref:无)
父母一方1.033(0.957~1.092)0.3571.324(1.155~1.678)0.0231.041(1.010~1.071)0.397
父母双方1.081(0.935~1.197)0.1801.882(1.768~2.294)<0.0011.214(1.083~1.519)0.017
), ArticleFig(id=1241025226113339914, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, language=EN, label=Table 4, caption=

Multi factor analysis results of pathway outcomes in stroke and Parkinson’s patients under the multi state model

, figureFileSmall=null, figureFileBig=null, tableContent=
变量脑卒中→帕金森脑卒中→死亡帕金森→死亡
HR(95%CI)PHR(95%CI)PHR(95%CI)P
年龄(岁,ref: <50)
50~602.320(0.693~7.764)0.1721.474(1.664~2.378)<0.0011.839(0.957~3.534)0.063
>605.819(1.839~18.404)0.0272.548(2.212~3.059)<0.0014.383(2.332~8.237)<0.001
性别(ref:男性)
女性0.521(0.344~0.788)0.0030.993(0.915~1.077)0.1710.797(0.686~0.926)<0.001
儿童期体型(ref:正常)
1.142(0.694~1.883)0.5971.059(0.892~1.041)0.8071.026(0.829~1.029)0.233
1.269(0.781~2.058)0.3291.016(0.879~1.014)0.8141.092(0.894~1.332)0.389
抑郁症(ref:有)
0.472(0.207~1.075)0.0730.505(0.422~0.604)<0.0010.561(0.408~0.772)<0.001
社会支持(ref:正常)
很少或没有1.144(0.358~3.346)0.8231.049(0923~1.349)0.3661.335(0.863~2.183)0.181
频繁1.139(0.396~4.433)0.6550.923(0.959~1.199)0.1051.274(0.799~2.237)0.296
吸烟情况(ref:不吸烟)
吸烟1.146(0651~2.000)0.4641.504(1.358~1.674)<0.0011.781(1.397~2.295)<0.001
饮酒情况(ref:不饮酒)
饮酒0.569(0.357~0.909)0.0180.872(0.774~0.984)0.0260.928(0.754~1.141)0.478
脑卒中史(ref:无)
父母一方1.055(0.987~1.126)0.2251.125(1.078~1.251)0.0411.015(0.733~1.203)0.347
父母双方1.335(0.943~1.791)0.1031.433(1.128~1.701)<0.0011.072(0.692~1.238)0.385
帕金森史(ref:无)
父母一方1.016(0.445~2.304)0.5741.045(0.948~1.129)0.3420.938(0.857~1.023)0.114
父母双方1.342(0.859~2.096)0.1951.268(0.776~1.478)0.5710.727(0.634~1.256)0.321
), ArticleFig(id=1241025226247557647, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025214327345988, language=CN, label=表4, caption=

多状态模型下脑卒中和帕金森患者路径转归的多因素分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量脑卒中→帕金森脑卒中→死亡帕金森→死亡
HR(95%CI)PHR(95%CI)PHR(95%CI)P
年龄(岁,ref: <50)
50~602.320(0.693~7.764)0.1721.474(1.664~2.378)<0.0011.839(0.957~3.534)0.063
>605.819(1.839~18.404)0.0272.548(2.212~3.059)<0.0014.383(2.332~8.237)<0.001
性别(ref:男性)
女性0.521(0.344~0.788)0.0030.993(0.915~1.077)0.1710.797(0.686~0.926)<0.001
儿童期体型(ref:正常)
1.142(0.694~1.883)0.5971.059(0.892~1.041)0.8071.026(0.829~1.029)0.233
1.269(0.781~2.058)0.3291.016(0.879~1.014)0.8141.092(0.894~1.332)0.389
抑郁症(ref:有)
0.472(0.207~1.075)0.0730.505(0.422~0.604)<0.0010.561(0.408~0.772)<0.001
社会支持(ref:正常)
很少或没有1.144(0.358~3.346)0.8231.049(0923~1.349)0.3661.335(0.863~2.183)0.181
频繁1.139(0.396~4.433)0.6550.923(0.959~1.199)0.1051.274(0.799~2.237)0.296
吸烟情况(ref:不吸烟)
吸烟1.146(0651~2.000)0.4641.504(1.358~1.674)<0.0011.781(1.397~2.295)<0.001
饮酒情况(ref:不饮酒)
饮酒0.569(0.357~0.909)0.0180.872(0.774~0.984)0.0260.928(0.754~1.141)0.478
脑卒中史(ref:无)
父母一方1.055(0.987~1.126)0.2251.125(1.078~1.251)0.0411.015(0.733~1.203)0.347
父母双方1.335(0.943~1.791)0.1031.433(1.128~1.701)<0.0011.072(0.692~1.238)0.385
帕金森史(ref:无)
父母一方1.016(0.445~2.304)0.5741.045(0.948~1.129)0.3420.938(0.857~1.023)0.114
父母双方1.342(0.859~2.096)0.1951.268(0.776~1.478)0.5710.727(0.634~1.256)0.321
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基于多状态模型的脑卒中、帕金森及死亡风险的影响因素和路径分析
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宋旺辰 , 郭贵雅 , 王爱民 , 黄一铭 , 王凤琳 , 张文婧 , 王清华 , 孔雨佳 , 石福艳 , 王素珍
现代预防医学 | 流行病与统计方法 2025,52(7): 1168-1174
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现代预防医学 | 流行病与统计方法 2025, 52(7): 1168-1174
基于多状态模型的脑卒中、帕金森及死亡风险的影响因素和路径分析
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宋旺辰, 郭贵雅, 王爱民, 黄一铭, 王凤琳, 张文婧, 王清华, 孔雨佳, 石福艳 , 王素珍
作者信息
  • 山东第二医科大学公共卫生学院流行病与卫生统计学教研室,山东 潍坊 261053
  • 宋旺辰(2000—),男,硕士在读,研究方向:流行病与卫生统计学的研究

通讯作者:

石福艳,E-mail:;
王素珍,E-mail:
Influencing factors and path analysis of the risks of stroke, Parkinson’s disease, and death based on the multi-state model
Wang-chen SONG, Gui-ya GUO, Ai-min WANG, Yi-ming HUANG, Feng-lin WANG, Wen-jing ZHANG, Qing-hua WANG, Yu-jia KONG, Fu-yan SHI , Su-zhen WANG
Affiliations
  • Department of Epidemiology and Health Statistics, School of Public Health, Shandong Second Medical University, Weifang,Shandong 261053, China
出版时间: 2025-04-10 doi: 10.20043/j.cnki.MPM.202405250
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目的

使用多状态模型研究脑卒中后发生帕金森和死亡的风险,并探讨其影响因素,为脑卒中患者预后及帕金森发生的预防提供科学依据。

方法

本研究基于UK Biobank数据库中2006—2010年登记的345 585名参与者,随访至2021年11月30日,采用多状态模型分析脑卒中患者发生帕金森和死亡的风险。

结果

在六种转归路径中,脑卒中到死亡的累积风险最高,其次为帕金森、基线到死亡。脑卒中后发生帕金森的风险概率是基线状态发生帕金森的2.25倍,而脑卒中患者死亡概率是基线人群死亡概率的11.36倍。多状态模型影响因素结果显示:高龄(60岁以上)、男性、抑郁、吸烟、饮酒和儿童期体型肥胖均是基线人群向三种状态(脑卒中、帕金森和死亡)转归的危险因素。在脑卒中到帕金森的转归路径中,高龄(60岁以上)是其危险因素,而饮酒和女性是脑卒中患者发生帕金森的保护因素(HR=0.569,95%CI: 0.357~0.909;HR=0.521,95%CI:0.344~0.788)。在脑卒中到死亡的路径中,高龄(50岁以上)、抑郁(HR=1.980,95%CI:1.656~2.369)、吸烟(HR=1.504, 95%CI:1.358~1.647)和有脑卒中家族史可增加脑卒中到死亡的危险性,而饮酒作为其保护因素(HR=0.872, 95%CI:0.774~0.984)。高龄(60岁以上)、抑郁(HR=1.783, 95%CI:1.295~2.451)、吸烟(HR=1.781, 95%CI:1.397~2.295)是帕金森患者死亡的危险因素,而女性患者死亡率低于男性(HR=0.797, 95%CI:0.686~0.926)。

结论

高龄(60岁以上)、男性、吸烟、抑郁症和儿童期体型肥胖是基线人群发生脑卒中、帕金森和死亡的危险因素;高龄、男性、吸烟、抑郁症和脑卒中家族史是脑卒中患者发生帕金森及死亡的危险因素。多状态模型可用于展示疾病转移的影响因素及程度,揭示疾病进程的变化规律。

多状态模型  /  脑卒中  /  帕金森  /  危险因素  /  生存分析
Objective

To use the multi-state model to study the risks of developing Parkinson’s disease and death after stroke and explore their influencing factors, so as to provide a scientific basis for the prognosis of stroke patients and the prevention of Parkinson’s disease.

Methods

This study was based on 345 585 participants registered in the UK Biobank database from 2006 to 2010, with follow-up until November 30, 2021. The multi-state model was used to analyze the risks of developing Parkinson’s disease and death in stroke patients.

Results

Among the six outcome paths, the cumulative risk from stroke to death was the highest, followed by that of Parkinson’s disease and from the baseline to death. The risk probability of developing Parkinson’s disease after stroke was 2.25 times that of the baseline state, and the death probability of stroke patients was 11.36 times that of the baseline population. The results of the influencing factors in the multi-state model showed that advanced age (over 60 years old), male, depression, smoking, alcohol consumption, and childhood obesity were all risk factors for the transition of the baseline population to the three states (stroke, Parkinson’s disease, and death). In the transition path from stroke to Parkinson’s disease, advanced age (over 60 years old) was a risk factor, while alcohol consumption and female gender were protective factors for stroke patients to develop Parkinson’s disease (HR=0.569, 95%CI: 0.357-0.909), (HR=0.521, 95%CI: 0.344-0.788). In the path from stroke to death, advanced age (over 50 years old), depression (HR=1.980, 95%CI: 1.656-2.369), smoking (HR=1.504,95%CI: 1.358-1.647), and a family history of stroke could increase the risk of stroke to death, while alcohol consumption was a protective factor (HR=0.872, 95%CI: 0.774-0.984). Advanced age (over 60 years old), depression (HR=1.783, 95%CI: 1.295-2.451), and smoking (HR=1.781, 95%CI: 1.397-2.295) were risk factors for the death of Parkinson’s patients, and the mortality rate of female patients was lower than that of male patients (HR=0.797, 95%CI: 0.686-0.926).

Conclusion

Advanced age (over 60 years old), male gender, smoking, depression, and childhood obesity are risk factors for stroke, Parkinson’s disease, and death in the baseline population; advanced age, male, smoking, depression, and a family history of stroke are risk factors for stroke patients to develop Parkinson’s disease and die. The multi-state model can be used to demonstrate the influencing factors and extent of disease transitions, revealing the patterns of disease progression.

Multi-state model  /  Stroke  /  Parkinson’s disease  /  Risk factors  /  Survival analysis
宋旺辰, 郭贵雅, 王爱民, 黄一铭, 王凤琳, 张文婧, 王清华, 孔雨佳, 石福艳, 王素珍. 基于多状态模型的脑卒中、帕金森及死亡风险的影响因素和路径分析. 现代预防医学, 2025 , 52 (7) : 1168 -1174 . DOI: 10.20043/j.cnki.MPM.202405250
Wang-chen SONG, Gui-ya GUO, Ai-min WANG, Yi-ming HUANG, Feng-lin WANG, Wen-jing ZHANG, Qing-hua WANG, Yu-jia KONG, Fu-yan SHI, Su-zhen WANG. Influencing factors and path analysis of the risks of stroke, Parkinson’s disease, and death based on the multi-state model[J]. Modern Preventive Medicine, 2025 , 52 (7) : 1168 -1174 . DOI: 10.20043/j.cnki.MPM.202405250
脑卒中和帕金森病是常见的神经系统疾病,对中老年人群健康造成重大影响。脑卒中通常由脑血管破裂或阻塞引起,是死亡和残疾的主要原因之一[1];而帕金森病是一种神经退行性疾病,伴随认知障碍、焦虑和抑郁等症状[2-3]。近年来,研究者猜测这两种疾病之间可能存在关联,尤其是脑卒中后发生帕金森病的风险及其与死亡的关系尚不明确[4-6]
脑卒中后帕金森病是指患者在脑卒中后出现帕金森病症状。了解这一多状态转移过程中的风险因素,对于改善脑卒中患者的预后和制定个体化治疗方案至关重要。多状态模型广泛应用于生物医学领域,用于预测和描述多个相关事件的概率[7]。尽管已有相关研究,但针对脑卒中后帕金森病与死亡之间风险的系统性分析仍较少。本研究旨在通过多状态模型评估脑卒中后帕金森病及死亡的风险,并探讨其影响因素,以期为临床提供更加准确的诊疗依据,改善患者的生活质量和预后效果[8]
本研究资料来自于英国生物银行(UK Biobank)数据库。英国生物银行(UK Biobank)数据库是一个包含50万名英国居民的大型生物医学研究数据库,收集了多种类型的数据,如基因组数据、生物样本数据、生活方式数据、图像数据等。
本研究从英国生物银行(UK Biobank)数据库共导出502 412人的相关数据,并按照以下排除标准筛选研究对象:(1)排除脑卒中发病日期早于开始随访时间(2006年1月1日)的研究对象共5 034人;(2)除脑卒中或帕金森发病日期早于开始随访时间(2006年1月1日)的研究对象共1 015人;(3)排除脑卒中发病日期晚于帕金森发病日期的研究对象185人;(4)排除协变量和时间变量进行调查时,失访或拒绝回答者150 593人。经上述标准,最终共纳入345 585名研究对象。本研究受UK Biobank的支持,已获得批准的项目编号为78500。
多状态模型是一种随机过程的模型,该随机过程在任何时候占据离散状态集合S={1,.,k}个,T=[0,τ],τ<∞是一个时间间隔,将参与者i的状态历史表示为集合{Zi(t),t≥0},其中Zi(t)表示在时间t处占据的状态[9]。该模型由状态转移强度定义,若转移强度与时间无关,则为时间齐次马尔可夫模型(THMM)。
在THMM中,状态转移强度仅依赖于当前状态和前一个状态,公式(1)描述了状态历史H(t)={Z(s),0≤s≤t}的转移过程。
然而实际应用中,考虑到时间变化对个体特征与转移强度的影响,参数模型马尔可夫(CMM)通常被采用。CMM模型的状态转移强度与时间相关假设当前状态只与前一个状态相关,并且不依赖于更早的状态,见公式(2)[10-11]
αhj0(t)是状态k和l之间的基线强度函数,它描述了在没有考虑协变量的情况下,从状态k转移到状态l的基线转移强度,β是一个参数向量,它描述了该变量对状态转换强度的影响程度,Z表示协变量[12-13]
因时间齐次模型建模得到的残差并未均匀分布不满足时间齐次假设,故采用参数模型CMM建模。多状态模型由暂态和吸收态组成,其中暂态可转移到其他状态,而吸收态一旦进入后不再转移[14-15]
本研究中根据多状态模型共定义了六种不同状态转归,分别如下:基线-脑卒中、基线-帕金森、基线-死亡、脑卒中-帕金森、脑卒中-死亡、帕金森-死亡。以往研究发现[16-17]年龄、性别、儿童期体型、抑郁症、社会支持情况、吸烟(有吸烟史即定义为吸烟)、饮酒(有饮酒史即定义为饮酒)、脑卒中家族史、帕金森家族史均是脑卒中、帕金森发病的影响因素,因此将其均纳入多状态模型中建模。
本研究使用R 4.3.1软件中的mstate包进行多状态建模与统计分析,采用ggplot2包对结果进行可视化分析。研究中的多状态模型验证采用对数似然值,统计检验均采用双侧检验,检验水准α=0.05。
本研究共纳入了345 585例受试者,平均年龄为55.3岁。其中,229 487例基线人群为男性,115 554例(33.4%)人群在儿童期体型偏瘦,55 114例(12.9%)在儿童期体型偏胖。
基线人群中社会往来频繁者居多270 627例(78.4%),抑郁症患病人群为5 631例(1.6%),具有脑卒中家族史的有95 788例(27.7%),具有帕金森家族史的有14 472例(4.1%),其他变量情况见表1
本研究的理论多状态模型如图1所示。研究路径分别为,路径一:基线-脑卒中;路径二:基线-脑卒中-帕金森;路径三:基线-脑卒中-死亡;路径四:基线-脑卒中-帕金森-死亡。四条路径中存在六种不同状态之间的转归人数及其概率见表2
表2显示,脑卒中发生死亡(stroke-death)的概率为69.3%,脑卒中发展为帕金森(stroke-PD)的概率为0.9%。图23对不同转归路径的风险进行了可视化分析,结果表明,脑卒中-死亡的转归概率最高,其次是帕金森-死亡和脑卒中-帕金森。图3显示随时间推移,基线人群比例下降,死亡概率最大,其次是脑卒中和帕金森的概率逐渐上升。
为探讨年龄对研究人群状态转归的影响,本研究对不同年龄段人群,尤其是是否患脑卒中,进行了帕金森发病与死亡的可视化分析。图4显示,随着年龄段增加,无论是健康人群还是脑卒中患者,帕金森发病率和死亡率显著上升,尤其是60岁以上人群。图5则表明,随着随访时间的延长,基线比例逐渐下降,而患病和死亡比例随年龄增长显著上升。
本研究通过多状态模型分析了基线人群不同转归路径的影响因素,结果见表3。分析显示,基线发展为脑卒中的危险因素包括高龄、儿童期体型较胖、抑郁症、亲友访问频次异常、吸烟及脑卒中家族史。特别是60岁以上人群,脑卒中患病风险显著高于50岁以下人群(HR=4.909, 95%CI: 4.538~5.310),女性脑卒中风险为男性的0.641倍(HR=0.641, 95%CI: 0.601~0.651)。饮酒则为保护因素(HR=0.668, 95%CI: 0.623~0.726)。在基线发展为帕金森的路径中,高龄(特别是60岁以上)、男性、吸烟、儿童期体型异常、抑郁症及帕金森家族史是危险因素,而饮酒与帕金森患病呈负相关(HR=0.592, 95%CI: 0.511~0.638)。
在基线死亡路径中,年龄、男性、儿童期体型异常、抑郁症、社会支持不足、吸烟、饮酒、脑卒中史和脑卒中家族史为死亡风险因素,饮酒可降低死亡风险(HR=0.679,95%CI: 0.651~0.710)。
60岁以上患者发展为帕金森的风险显著增高(HR=5.819, 95%CI: 1.839~18.804)。此外,饮酒为保护因素,饮酒者患帕金森的概率为不饮酒者的0.569倍(HR=0.569, 95%CI: 0.357~0.909),提示适量饮酒可能降低脑卒中后患帕金森的风险。
表4显示,在脑卒中发展为帕金森的路径中,高龄和男性为危险因素。与小于50岁的脑卒中患者相比,在脑卒中患者死亡的分析中,饮酒同样为保护因素。高龄、抑郁、吸烟及脑卒中家族史为死亡的危险因素,其中年龄大于60岁对死亡风险影响最为显著(HR= 2.548, 95%CI: 2.212~3.059)。
表4可知,在帕金森患者的死亡路径中,抑郁、高龄和吸烟均是影响帕金森患者死亡的重要危险因素。而女性帕金森患者死亡的风险明显低于男性(HR=0.797,95%CI: 0.686~0.926)。
本研究基于UK Biobank数据库,采用多状态模型分析协变量对脑卒中患者发生帕金森或死亡的影响。该模型灵活地评估了影响患者状态的因素,尤其对亚健康和脑卒中早期患者有早期诊断和风险降低的潜力[18]
本研究中的基线人群的三种不同转归路径(脑卒中、帕金森和死亡)中,共同受高龄、男性、儿童期体型异常、抑郁、吸烟等危险因素影响,其中基线到脑卒中转归过程中额外受社会支持的影响,社会支持正常(亲友拜访频率)的人群患脑卒中的概率较低,是保护因素,基线人群中社会支持频率正常作为保护因素可以降低基线人群死亡的概率。由此可知,适当的社会支持可以减轻压力和焦虑感,有助于降低脑卒中的发生风险,其原因可能为过多的社会交往可能导致过度劳累,过少的社会拜访可则会导致孤立感和心理健康问题[19]。另外,有脑卒中家族史的基线人群患脑卒中的概率更高;相应的,存在帕金森家族史也会提高帕金森发病概率;二者均是基线人群发生死亡的危险因素,该研究结论与其他研究结论一致[20-21]
在脑卒中到帕金森转归中,女性脑卒中患者的风险转归率明显低于男性居民,可能与男、女性在生理、生化和遗传等方面存在的差异有关,此外还可能因为女性吸烟和酗酒者较少[22]。而年龄增长可以增大脑卒中患者进一步发展为帕金森以及死亡转归概率,该研究结果与Sanchez等人[23]得到的结论一致。本研究发现,饮酒是脑卒中患者转归为帕金森疾病的一个保护因素,这可能是因为饮酒可以增加多巴胺的释放并且抑制氧化应激,从而改善心血管健康,减少心血管疾病的发生,进而减少帕金森发病和死亡的风险[24]
有关脑卒中人群死亡的影响因素分析结果显示,高龄(特别是60岁以上)、抑郁、吸烟、脑卒中家族史是脑卒中人群发展为死亡的危险因素;其中存在脑卒中家族史作为一个重要的影响因素所得结果与Bl' az Michał等人[25]的研究结论一致。因此,对于具有脑卒中家族史的人群更需要积极治疗,通过健康的生活方式控制高血压、糖尿病等其他慢性病的风险因素,以及及时复查,从而减少脑卒中疾病的进一步恶化甚至死亡的风险[26]
在帕金森人群死亡风险影响因素分析中,高龄(60岁以上)、男性、抑郁和吸烟是帕金森患者发生死亡的危险因素。患抑郁症会增加帕金森人群死亡的转移风险,这与Espejo等人[27]的研究结论一致。抑郁作为一种心理疾病可能会导致帕金森患者的生活方式发生改变,如饮食不健康、缺乏运动、社交孤立等,这些因素可进一步增加心血管疾病的风险,进而增加患者死亡的可能性。该研究结果提示,对于伴有抑郁症的帕金森患者鼓励其平时进行适度的身体活动,在生活中提供情感上的支持和理解,减少独处时间对于降低帕金森患者死亡风险具有积极作用[28]
综上可知,本研究结果可为一般人群、脑卒中患者、帕金森高危人群的健康管理提供一定帮助,也可为脑卒中患者预后及生活质量改善提供参考依据。另外,多状态模型可以为未来其他慢性病研究提供更有价值的见解。然而,本研究也存在一定的局限性。例如,本研究中纳入的研究变量主要为生活方式、家族史等传统变量,缺乏生化指标、遗传因素等微观变量,进而限制了对不同转归状况的内在发生机制的深入理解。另外,多状态模型是基于横断面数据开展的研究,虽然该模型可以评估不同事件之间的关系,但在进行因果推断时仍需要注意。因为观察性研究无法确定因果关系,因此需要进一步的随机对照试验或其他类型的研究设计来验证研究结果。最后对某些变量的定义较为笼统,例如饮酒没有具体定义其次数,可能对分析结果会产生一定的偏性。
  • 国家自然科学基金项目(81803337; 81872719; 82003560)
  • 山东省自然科学基金项目(ZR2023MH313)
  • 山东省高等学校青创人才引育计划(2019 - 10-156, Lu-Jiao)
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doi: 10.20043/j.cnki.MPM.202405250
  • 接收时间:2024-05-14
  • 首发时间:2026-03-18
  • 出版时间:2025-04-10
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  • 收稿日期:2024-05-14
基金
国家自然科学基金项目(81803337; 81872719; 82003560)
山东省自然科学基金项目(ZR2023MH313)
山东省高等学校青创人才引育计划(2019 - 10-156, Lu-Jiao)
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    山东第二医科大学公共卫生学院流行病与卫生统计学教研室,山东 潍坊 261053

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石福艳,E-mail:;
王素珍,E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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