Article(id=1241759331813752861, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241759317016252418, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202403101, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1709654400000, receivedDateStr=2024-03-06, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773988095477, onlineDateStr=2026-03-20, pubDate=1727193600000, pubDateStr=2024-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773988095477, onlineIssueDateStr=2026-03-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773988095477, creator=13701087609, updateTime=1773988095477, updator=13701087609, issue=Issue{id=1241759317016252418, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='18', pageStart='3265', pageEnd='3456', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773988091898, creator=13701087609, updateTime=1773991617194, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241774103024174010, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241759317016252418, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241774103024174011, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241759317016252418, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=3393, endPage=3397, ext={EN=ArticleExt(id=1241759333441142818, articleId=1241759331813752861, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Cognitive trajectories and risk of all-cause mortality: the mediating role of activities of daily living, columnId=null, journalTitle=Modern Preventive Medicine, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Objective

To analyze the mediating role of activities of daily living ability in older adults and the long-term relationship between cognitive trajectories and the risk of all-cause mortality.

Methods

Based on the CLHLS data from 2002 to 2008, the Group-based Trajectory model (GBTM) was used to identify the heterogeneous trajectories of cognitive function in older adults, using CLHLS data from 2008 to 2018 as the follow-up time for all-cause mortality risk. The connection between the cognitive trajectories and the risk of all-cause mortality was analyzed using Kaplan-Meier survival curves, Log-Rank tests and Cox regression analysis. Marginal structure model was used to analyze the mediating effect of ADL in their relationship, with subgroup analysis and sensitivity analysis conducted.

Results

A total of 4 177 older adults aged 65-105 years who participated in follow-up for three consecutive years were enrolled, and two cognitive trajectory groups were identified: the low-decline group (n=691, 16.61%) and the high-stable group (n=3 186, 83.39%). By Log-Rank test, there was a statistically significant difference in survival rate between the different cognitive trajectory groups (χ2=323.69, P<0.01). After adjusting for other covariates, the risk of all-cause death was still lower in the high-stable group (HR=0.64, 95%CI: 0.57-0.71) compared to the low-declining group. ADL had a mediating effect in the relationship between cognitive trajectories and the risk of all-cause mortality, and the indirect hazard ratio was 0.93 (95%CI: 0.87-0.98). The results of subgroup analysis and sensitivity analysis were consistent with the trend of the main research results.

Conclusion

There are different cognitive trajectories in the elderly, and cognitive trajectories can affect the risk of all-cause mortality, ADL plays a mediating role in this relationship. Therefore, it is necessary to dynamically monitor cognition, detect problems in time, and take targeted measures to reduce the risk of death in the elderly.

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目的

研究老年人认知轨迹与全因死亡风险的纵向关联及日常生活活动能力的中介作用。

方法

依托CLHLS 2002—2008年数据,运用组基轨迹模型(GBTM)识别认知的异质性轨迹;将CLHLS 2008—2018年数据作为全因死亡风险的随访时间,绘制各认知轨迹的Kaplan-Meier生存曲线并进行Log-Rank检验,对认知轨迹组与全因死亡风险的关联进行Cox回归分析;基于边际结构模型分析ADL对二者关系的中介效应并进行亚组分析和敏感性分析。

结果

共纳入了4 177名65~105岁且至少连续三次参与随访的老人,确定了2个认知轨迹组:低-下降组(n=691,16.61%)和高-稳定组(n=3 186,83.39%);经Log-Rank检验,不同认知轨迹组间的生存率差异具有统计学意义(χ2=323.69,P<0.01),对协变量进行调整后,与低-下降组相比,高-稳定组(HR=0.64,95%CI:0.57~0.71)的全因死亡风险更低;ADL在认知轨迹与全因死亡风险关系中存在中介效应,ADL介导的间接风险比为0.93(95%CI: 0.87-0.98),亚组分析和敏感性分析的结果与主要研究结果一致。

结论

老人存在着不同的认知轨迹,且认知轨迹会对全因死亡风险产生影响,ADL在这种关联模式中存在中介作用。因此要对认知进行动态监测,及时发现问题,采取针对性措施降低老年人死亡风险。

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陈娜,E-mail:
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秦慧(1998—),女,硕士在读,研究方向:公共管理

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Trajectory evaluation criteria

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轨迹组别组群样本规模分配到这一组群的样本比例
P(%)
估计的组群身份概率
π(%)
组群平均后验概率
AvePP
低-下降组69116.6116.540.917
高-稳定组3 48683.3983.460.983
), ArticleFig(id=1241759352231625461, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759331813752861, language=CN, label=表1, caption=

轨迹评价标准

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轨迹组别组群样本规模分配到这一组群的样本比例
P(%)
估计的组群身份概率
π(%)
组群平均后验概率
AvePP
低-下降组69116.6116.540.917
高-稳定组3 48683.3983.460.983
), ArticleFig(id=1241759352428757763, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759331813752861, language=EN, label=Table 2, caption=

The characteristics of different cognitive trajectories[n (%)]

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变量分类低-下降组高-稳定组χ2P
年龄<80139(20.12)2 380(68.27)558.70<0.001
≥80552(79.88)1 106(31.73)
性别女性479(69.32)1 759(50.46)82.48<0.001
男性212(30.68)1 727(49.54)
教育水平文盲519(75.11)1 776(50.95)136.00<0.001
非文盲172(24.89)1 710(49.05)
居住地农村422(61.07)2 054(58.92)1.100.293
城镇269(38.93)1 432(41.08)
婚姻无配偶517(74.82)1 547(44.38)213.79<0.001
有配偶174(25.18)1 939(55.62)
慢病情况有慢病466(67.44)2 111(60.56)11.560.001
无慢病225(32.56)1 375(39.44)
喝酒喝酒133(19.25)861(24.70)9.450.002
不喝酒558(80.75)2 625(75.30)
吸烟吸烟112(16.21)872(25.01)24.83<0.001
不吸烟579(83.79)2 614(74.99)
), ArticleFig(id=1241759352667833102, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759331813752861, language=CN, label=表2, caption=

不同认知轨迹的特征[n (%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类低-下降组高-稳定组χ2P
年龄<80139(20.12)2 380(68.27)558.70<0.001
≥80552(79.88)1 106(31.73)
性别女性479(69.32)1 759(50.46)82.48<0.001
男性212(30.68)1 727(49.54)
教育水平文盲519(75.11)1 776(50.95)136.00<0.001
非文盲172(24.89)1 710(49.05)
居住地农村422(61.07)2 054(58.92)1.100.293
城镇269(38.93)1 432(41.08)
婚姻无配偶517(74.82)1 547(44.38)213.79<0.001
有配偶174(25.18)1 939(55.62)
慢病情况有慢病466(67.44)2 111(60.56)11.560.001
无慢病225(32.56)1 375(39.44)
喝酒喝酒133(19.25)861(24.70)9.450.002
不喝酒558(80.75)2 625(75.30)
吸烟吸烟112(16.21)872(25.01)24.83<0.001
不吸烟579(83.79)2 614(74.99)
), ArticleFig(id=1241759352831410969, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759331813752861, language=EN, label=Table 3, caption=

Cox proportional risk model for cognitive trajectories and all-cause mortality

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认知轨迹分组总人数
n
死亡人数
n(%)
模型1模型2模型3
HR(95%CIPHR(95%CIPHR(95%CIP
低-下降组691536(77.57)1.001.001.00
高-稳定组3 4861 840(52.78)0.41(0.37~0.46)<0.0010.61(0.54~0.68)<0.0010.64(0.57~0.71)<0.001
), ArticleFig(id=1241759352982405930, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759331813752861, language=CN, label=表3, caption=

认知轨迹与全因死亡风险的Cox比例风险模型

, figureFileSmall=null, figureFileBig=null, tableContent=
认知轨迹分组总人数
n
死亡人数
n(%)
模型1模型2模型3
HR(95%CIPHR(95%CIPHR(95%CIP
低-下降组691536(77.57)1.001.001.00
高-稳定组3 4861 840(52.78)0.41(0.37~0.46)<0.0010.61(0.54~0.68)<0.0010.64(0.57~0.71)<0.001
), ArticleFig(id=1241759353204704060, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759331813752861, language=EN, label=Table 4, caption=

Results of mediation analysis based on marginal structure model

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认知轨迹(参照组:低-下降组)参数估计HR(95%CIZP
直接效应-0.7970.45(0.42~0.49)-19.13<0.001
间接效应(ADL介导)-0.0770.93(0.87~0.98)2.460.014
年龄0.9202.21(2.3~2.70)24.75<0.001
性别0.3951.48(1.37~1.61)9.28<0.001
教育水平-0.2200.80(0.74~0.87)-5.69<0.001
居住地-0.1100.90(0.84~0.96)-3.290.001
婚姻-0.5440.58(0.54~0.62)-16.22<0.001
慢病情况-0.1160.89(0.84~0.95)-3.59<0.001
喝酒-0.0240.98(0.91~1.05)-0.630.229
吸烟-0.0490.95(0.88~1.03)-1.200.528
), ArticleFig(id=1241759353343116103, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759331813752861, language=CN, label=表4, caption=

基于边际结构模型的中介分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
认知轨迹(参照组:低-下降组)参数估计HR(95%CIZP
直接效应-0.7970.45(0.42~0.49)-19.13<0.001
间接效应(ADL介导)-0.0770.93(0.87~0.98)2.460.014
年龄0.9202.21(2.3~2.70)24.75<0.001
性别0.3951.48(1.37~1.61)9.28<0.001
教育水平-0.2200.80(0.74~0.87)-5.69<0.001
居住地-0.1100.90(0.84~0.96)-3.290.001
婚姻-0.5440.58(0.54~0.62)-16.22<0.001
慢病情况-0.1160.89(0.84~0.95)-3.59<0.001
喝酒-0.0240.98(0.91~1.05)-0.630.229
吸烟-0.0490.95(0.88~1.03)-1.200.528
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认知轨迹与全因死亡风险的关系研究:日常生活活动能力的中介作用
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秦慧 1 , 陈娜 2 , 卓力 2
现代预防医学 | 健康与社会行为 2024,51(18): 3393-3397
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现代预防医学 | 健康与社会行为 2024, 51(18): 3393-3397
认知轨迹与全因死亡风险的关系研究:日常生活活动能力的中介作用
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秦慧1, 陈娜2 , 卓力2
作者信息
  • 1.南京中医药大学卫生经济管理学院,江苏 南京 210023
  • 2.南京中医药大学养老服务与管理学院
  • 秦慧(1998—),女,硕士在读,研究方向:公共管理

通讯作者:

陈娜,E-mail:
Cognitive trajectories and risk of all-cause mortality: the mediating role of activities of daily living
Hui QIN1, Na CHEN2 , Li ZHUO2
Affiliations
  • School of Health Economics and Management, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
出版时间: 2024-09-25 doi: 10.20043/j.cnki.MPM.202403101
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目的

研究老年人认知轨迹与全因死亡风险的纵向关联及日常生活活动能力的中介作用。

方法

依托CLHLS 2002—2008年数据,运用组基轨迹模型(GBTM)识别认知的异质性轨迹;将CLHLS 2008—2018年数据作为全因死亡风险的随访时间,绘制各认知轨迹的Kaplan-Meier生存曲线并进行Log-Rank检验,对认知轨迹组与全因死亡风险的关联进行Cox回归分析;基于边际结构模型分析ADL对二者关系的中介效应并进行亚组分析和敏感性分析。

结果

共纳入了4 177名65~105岁且至少连续三次参与随访的老人,确定了2个认知轨迹组:低-下降组(n=691,16.61%)和高-稳定组(n=3 186,83.39%);经Log-Rank检验,不同认知轨迹组间的生存率差异具有统计学意义(χ2=323.69,P<0.01),对协变量进行调整后,与低-下降组相比,高-稳定组(HR=0.64,95%CI:0.57~0.71)的全因死亡风险更低;ADL在认知轨迹与全因死亡风险关系中存在中介效应,ADL介导的间接风险比为0.93(95%CI: 0.87-0.98),亚组分析和敏感性分析的结果与主要研究结果一致。

结论

老人存在着不同的认知轨迹,且认知轨迹会对全因死亡风险产生影响,ADL在这种关联模式中存在中介作用。因此要对认知进行动态监测,及时发现问题,采取针对性措施降低老年人死亡风险。

认知  /  组基发展轨迹  /  全因死亡风险  /  边际结构模型  /  中介分析
Objective

To analyze the mediating role of activities of daily living ability in older adults and the long-term relationship between cognitive trajectories and the risk of all-cause mortality.

Methods

Based on the CLHLS data from 2002 to 2008, the Group-based Trajectory model (GBTM) was used to identify the heterogeneous trajectories of cognitive function in older adults, using CLHLS data from 2008 to 2018 as the follow-up time for all-cause mortality risk. The connection between the cognitive trajectories and the risk of all-cause mortality was analyzed using Kaplan-Meier survival curves, Log-Rank tests and Cox regression analysis. Marginal structure model was used to analyze the mediating effect of ADL in their relationship, with subgroup analysis and sensitivity analysis conducted.

Results

A total of 4 177 older adults aged 65-105 years who participated in follow-up for three consecutive years were enrolled, and two cognitive trajectory groups were identified: the low-decline group (n=691, 16.61%) and the high-stable group (n=3 186, 83.39%). By Log-Rank test, there was a statistically significant difference in survival rate between the different cognitive trajectory groups (χ2=323.69, P<0.01). After adjusting for other covariates, the risk of all-cause death was still lower in the high-stable group (HR=0.64, 95%CI: 0.57-0.71) compared to the low-declining group. ADL had a mediating effect in the relationship between cognitive trajectories and the risk of all-cause mortality, and the indirect hazard ratio was 0.93 (95%CI: 0.87-0.98). The results of subgroup analysis and sensitivity analysis were consistent with the trend of the main research results.

Conclusion

There are different cognitive trajectories in the elderly, and cognitive trajectories can affect the risk of all-cause mortality, ADL plays a mediating role in this relationship. Therefore, it is necessary to dynamically monitor cognition, detect problems in time, and take targeted measures to reduce the risk of death in the elderly.

Cognition  /  Group-based trajectory model  /  Risk of all-cause mortality  /  Marginal structural model  /  Mediation analysis
秦慧, 陈娜, 卓力. 认知轨迹与全因死亡风险的关系研究:日常生活活动能力的中介作用. 现代预防医学, 2024 , 51 (18) : 3393 -3397 . DOI: 10.20043/j.cnki.MPM.202403101
Hui QIN, Na CHEN, Li ZHUO. Cognitive trajectories and risk of all-cause mortality: the mediating role of activities of daily living[J]. Modern Preventive Medicine, 2024 , 51 (18) : 3393 -3397 . DOI: 10.20043/j.cnki.MPM.202403101
随着人口老龄化程度的加深,认知功能受到越来越多的学者关注。研究显示,认知是预测死亡风险的因素[1],认知下降会增加老年人的死亡风险[2-3],同时认知也会影响老人的日常生活能力,进而影响死亡风险[4]。现有文献多是研究基线认知对死亡的影响[5-6],以及疾病相关死亡风险[7-8],但鲜有研究认知轨迹与全因死亡风险的关系,认知轨迹研究能够更好地识别出总体中的轨迹组群,反映认知随时间变化的长期状态[9]。因此,本研究基于CLHLS多期数据,依托组基轨迹模型(Group-Based Trajectory Modeling, GBTM),分析认知功能的异质性轨迹,并探讨各认知轨迹与全因死亡风险的纵向关联[10]以及日常生活活动能力的中介作用,为相关疾病的防治提供依据。
数据源于CLHLS 2002—2018年的调查数据,CLHLS已获得北京大学伦理委员会批准(伦理审批号:IRB00001052-13074),所有参与人员都签署了知情同意书[11]。本研究将2002—2008年连续三次参与调查的老人作为重复测量观察人群,构建认知发展轨迹;将2011—2018年数据作为全因死亡发生情况的观察人群。本研究对年龄在65岁及以上的老年人进行分析,同时考虑到超高龄老年人提供的信息可能存在偏差,剔除105岁以上的样本[12]。研究对象纳入排除标准见图1,经过筛选,最终纳入4 177名老人进行分析。
本研究的因变量的认知功能,CLHLS使用简易精神状态量表(Mini Mental State Scale, MMSE)衡量[9],共24个问题,取值范围为0~30分,得分越高认知功能越好。将日常生活活动(activities of daily living, ADL)作为中介变量,包括洗澡、穿衣以及室内活动等六项,6项指标中有1项或多项需要别人帮助被定义为ADL能力受损,反之则为ADL能力正常[4]。通过对前人文献的研究[26],本研究的协变量包括年龄、性别、教育、婚姻、居住地、慢病情况、吸烟和喝酒。
参照前人的研究,选取2008—2018年进行跟踪随访,将全因死亡作为本研究的终点事件[13-14];将2008年调查到发生终点事件的时间记作生存时间(以年为单位)。对老年人死亡情况的衡量主要是通过调查其家人、邻居以及居委会进行验证。此外样本中还存在删失数据,这部分样本仅保留失访之前和2018年最后一次调查的数据[15]
采用Stata16.0和R语言(4.1.3)进行数据分析及图形绘制。首先采用多重插补法对协变量进行插补;其次采用GBTM拟合认知轨迹图,组基轨迹建模是基于最大似然估计以及模型拟合规则确定具有不同轨迹类型的亚组[10];再次绘制认知轨迹的Kaplan-Meier生存曲线并进行Log-Rank检验,通过Cox回归分析量化认知轨迹与全因死亡风险的关系[14];最后采用基于边际结构模型的中介分析方法(mediation analysis using marginal structural model, MMSM)分析ADL对认知轨迹与全因死亡风险关系的中介效应,并进行亚组分析和敏感性分析[11]P<0.05为差异有统计学意义。
组基轨迹建模的拟合模型由数据类型决定,本研究选择删截正态分布模型(censored normal distribution, CNORM)[16]。最佳模型的标准为:(1)贝叶斯信息准则(Bayesian information criteria, BIC)绝对值最小;(2)各轨迹组成员不少于5%;(3)平均后验概率(Average Posterior Probability, AvePP)大于0.7[17-18]
组基轨迹模型最终拟合的最佳轨迹类型数为2组(BIC=-35 598.19),且将统计显著的2、1非线性函数作为多项式阶数。如图2所示,具体包括组1:低-下降组(n=691,16.61%);组2:高-稳定组(n=3 186,83.39%)。轨迹评价标准如表1表1轨迹评价所示,在本研究中,2个轨迹组的AvePP均大于0.7,轨迹组群估计的身份概率和分配到这一组群的样本比例相差较小,且轨迹组成员不小于5%[18]。因此,数据的区分性特征在拟合过程中得到展示,拟合效果较好[19]
识别出轨迹组群后,对其特征进行描述,如表2所示,除居住地变量外,其他协变量与认知轨迹的差异均具有统计学意义。与高-稳定组相比,高龄、女性、文盲以及无配偶等因素归属于低-下降组的可能性较大。
在平均7年的随访期内,共计死亡2 376例,总体死亡风险为56.88%。图3显示了不同认知轨迹的Kaplan-Meier生存曲线,可以看出两组认知轨迹的生存分布有差异,并且同一时间段内高-稳定组的生存概率明显更高。经Log-Rank检验,不同认知轨迹组间的生存率差异具有统计学意义(χ2=323.69,P<0.01)。
利用Cox回归分析量化认知轨迹与全因死亡风险的关联,将低-下降组作为对照组,依次调整基线的年龄、性别以及协变量,结果如表3所示。单因素分析结果显示,与低-下降组相比,高-稳定组的风险比及95%置信区间为0.41(0.37~0.46),全因死亡风险降低了59%;在调整了性别和年龄后,其死亡风险降低了39%(HR=0.61,95%CI 0.54~0.68);进一步调整其他协变量后,死亡风险降低了36%(HR=0.64,95%CI 0.57~0.71)。
本研究采用基于边际结构模型的中介分析方法,该方法是基于反事实框架,允许将暴露对结局的总影响分解为自然直接影响和通过中介产生的自然间接影响[20-21]。结果如表4所示,在考虑相关协变量后,高-稳定组的全因死亡风险显著升高。认知轨迹对全因死亡风险的影响可以分解为认知轨迹的直接风险比0.45(95% CI: 0.42~0.49)和ADL介导的间接风险比0.93(95%CI: 0.87~0.98)。
以低-下降组为参照,对ADL的中介效应进行亚组分析,在调整所有的协变量后,亚组分析结果与主要分析的结果基本一致。同时,通过逐一剔除随访2年内发生终点事件者以及患有高血压、糖尿病、癌症、肿瘤、帕金森病、痴呆和精神疾病的方式进行敏感性分析,结果显示,各项敏感性分析结果与主要分析结果基本保持一致,表明研究结果具有稳健性。因篇幅限制,文中对此结果不展示。
本研究利用GBTM,构建认知轨迹,比以往仅考虑单一时点的研究更能全面准确地反映长期认知水平变化情况[12]。本研究根据认知的动态变化确定了两个不同的认知轨迹组:低-下降组和高-稳定组,这表明老人的认知轨迹存在异质性[10]。国内外目前关于认知轨迹的研究较分散,不同数据库以及不同的变量衡量方式可能会使轨迹的数量和形状产生差异,但是拟合的轨迹都证明了认知轨迹存在异质性[1719]
近年来,学者普遍认为认知低下与全因死亡风险增加有关[22-24]。但是目前关于认知轨迹与全因死亡的研究相对较少,本研究发现不同认知轨迹组间的生存率差异具有统计学意义(χ2=428.47, P<0.01),且认知轨迹归属于高-稳定组可能与全因死亡风险减少有关,这一结果说明了认知的动态变化会对全因死亡风险产生影响;同时,ADL在认知轨迹与全因死亡风险关系中存在中介效应,认知轨迹对全因死亡风险影响的直接风险比为0.45(95%CI: 0.42~0.49),ADL介导的间接风险比为0.93(95%CI: 0.87~0.98),在考虑了社会人口统计学和健康相关变量后,这种关系仍然存在。因此,对认知进行动态监测,及时发现老年人的认知变化情况,关注日常生活活动能力的中介作用,积极实行针对性的干预措施,对降低老年人死亡风险,延长其寿命有重要的公共卫生意义。
本研究利用GBTM拟合出异质性认知轨迹,在此基础上,采用Cox比例风险回归模型量化认知轨迹与全因死亡风险的关系,发现认知轨迹与全因死亡风险的差异有统计学意义,且ADL在这种关联模式中存在中介作用,同时加入亚组分析和敏感性分析使得结果具有稳健性。但本研究仍存在一些不足之处,首先,平均随访时间为7年,可能会低估认知轨迹对全因死亡风险的影响;其次,死亡可能是由于多种原因造成的,考虑到数据缺失因素,本研究未能将影响死亡风险的所有因素都纳入模型,因此后续需要更深度的研究。
综上所述,老年人的认知存在异质性发展轨迹,且认知轨迹会对全因死亡风险产生影响,ADL在这种关联模式中存在中介效应。因此,及时识别认知轨迹并给予早期干预,可能对降低死亡风险以及延长人群寿命有重要意义。另外,应积极创造老龄友好型社会,鼓励并引导老年人进行社会参与,以缓解身体机能下降。
  • 江苏省中医药科技发展计划专题研究项目(ZT202202)
  • 江苏省高等教育教改研究课题重点项目(2021JSJG053)
  • 养老服务与管理学院(南京中医药大学)专项研究项目(2023YLFWYGL013)
  • 江苏高校“青蓝工程”资助
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2024年第51卷第18期
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doi: 10.20043/j.cnki.MPM.202403101
  • 接收时间:2024-03-06
  • 首发时间:2026-03-20
  • 出版时间:2024-09-25
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  • 收稿日期:2024-03-06
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江苏省中医药科技发展计划专题研究项目(ZT202202)
江苏省高等教育教改研究课题重点项目(2021JSJG053)
养老服务与管理学院(南京中医药大学)专项研究项目(2023YLFWYGL013)
江苏高校“青蓝工程”资助
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    1.南京中医药大学卫生经济管理学院,江苏 南京 210023
    2.南京中医药大学养老服务与管理学院

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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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