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Article(id=1241319151533347457, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241319148798669160, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202411446, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1732377600000, receivedDateStr=2024-11-24, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773883148320, onlineDateStr=2026-03-19, pubDate=1750780800000, pubDateStr=2025-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773883148320, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773883148320, creator=13701087609, updateTime=1773883148320, updator=13701087609, issue=Issue{id=1241319148798669160, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='12', pageStart='2113', pageEnd='2304', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773883147667, creator=13701087609, updateTime=1773885555254, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241329247004971040, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241319148798669160, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241329247004971041, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241319148798669160, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2272, endPage=2277, ext={EN=ArticleExt(id=1241319151835337354, articleId=1241319151533347457, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=A study of the effect of frailty trajectories on rheumatoid arthritis, columnId=1228016569138213037, journalTitle=Modern Preventive Medicine, columnName=Clinical Medicine and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the association between frailty status and RA through trajectory analyses, and to provide a scientific basis for targeted measures to reduce the risk of RA.

Methods

Survey data from the 2004-2018 American Health and Retirement Study (HRS) were analyzed for potential categories of frailty using growth mixture models for the three waves of data from 2004-2010. Using 2010 as the baseline, four waves of data from 2010 to 2018 were tracked to analyze the association between different frailty statuses and participants’ RA risk, and Cox proportional risk models were used to describe the associations between different frailty states and the development of RA.

Results

The baseline population totaled 3 677 individuals, including 1 337 (36.4%) males; 2 340 (63.6%) females. Four frailty trajectories were identified through growth mixture modeling, with a high frailty lowering group (10.6%), a persistent high frailty group (20.1%), a persistent low frailty group (34.0%), and a low frailty elevation group (35.3%). Analysis of Cox proportional risk regression modeling showed that, in the fully adjusted model with the low frailty elevation group as the reference, the persistent high frailty group had a 0.255-fold increased risk of developing RA 0.255-fold (HR=1.255, 95% CI: 1.001-1.576), and the risk of developing RA increased 0.422-fold in the high frailty-reduced group (HR=1.422, 95% CI: 1.071-1.888).

Conclusion

Frailty is a risk factor for RA, and the risk of developing RA is increased in the high frailty reduction group and the persistent high frailty group.

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目的

通过轨迹分析探讨衰弱状态与类风湿关节炎（Rheumatoid Arthritis，RA）的关联，为采取针对性措施降低RA患病风险提供科学依据。

方法

来源于HRS的2004—2018年美国健康和退休研究的调查数据，对2004—2010三波数据采用增长混合模型进行衰弱的潜在类别分析，并以2010年为基线数据，追踪2010—2018四波数据，分析不同衰弱状态对参与者患RA风险的关联，使用Cox比例风险模型分析描述不同衰弱状态与RA发病之间的关联。

结果

基线人群共3 677人，其中男性1 337人（36.4%），女性2 340人（63.6%）。通过建立增长混合模型，确定四种衰弱轨迹，高衰弱降低组（10.6%），持续高衰弱组（20.1%），持续低衰弱组（34.0%），低衰弱升高组（35.3%）。Cox比例风险回归模型分析结果显示：在完全调整后的模型中，以低衰弱升高组为参考，持续高衰弱组患RA风险增加0.255倍（HR=1.255，95% CI：1.001~1.576），高衰弱降低组患RA风险增加0.422倍( HR=1.422，95% CI：1.071 ~ 1.888)。

结论

衰弱是RA的危险因素，且高衰弱降低组和持续高衰弱组会增加患RA的风险。

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袁慧，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=ACfI6UMyfUOamfdf7QOXmw==, magXml=7td7OGTwyFmU+5CQst/zzw==, pdfUrl=null, pdf=93Olc8wPtibQu3Pyj88ZjA==, pdfFileSize=682869, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=dyvveIB9dCHoDiYsH4jCWQ==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=GwOcxRGS8ovukc09hs7uDw==, mapNumber=null, authorCompany=null, fund=null, authors=

刘岩（2000—），男，硕士在读，研究方向：公共卫生

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刘岩（2000—），男，硕士在读，研究方向：公共卫生

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刘岩（2000—），男，硕士在读，研究方向：公共卫生

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注：纳入标准：选择2004—2018年的全部人群；排除标准：排除2010年以前衰弱信息缺失和患有类风湿关节炎的人群以及2010—2018年类风湿关节炎信息缺失的人群。

, figureFileSmall=a3/e35OnqEO1KaD+go7vHA==, figureFileBig=dyvveIB9dCHoDiYsH4jCWQ==, tableContent=null), ArticleFig(id=1241319159540274145, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319151533347457, language=EN, label=Fig.2, caption=Frailty state trajectory, figureFileSmall=Dh8JrC+Ut3udNNlB0KcdCA==, figureFileBig=Vq6N3PYMIuamJm4ozvlM0A==, tableContent=null), ArticleFig(id=1241319159670297584, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319151533347457, language=CN, label=图2, caption=衰弱状态轨迹, figureFileSmall=Dh8JrC+Ut3udNNlB0KcdCA==, figureFileBig=Vq6N3PYMIuamJm4ozvlM0A==, tableContent=null), ArticleFig(id=1241319159833875450, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319151533347457, language=EN, label=Table 1, caption=

Demographic characteristics of survey respondents[n（%）]

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分组总数（n，%)患RA(n，% )不患RA
(n，%)		χ2P
年龄（岁）1.1540.562
<751 332（36.2）211（37.0）1 121（36.1）
75~791 139（31.0）183（32.1）956（30.8）
>801 206（32.8）176（30.9）1 030（33.2）
性别4.6400.031
男1 337（36.4）230（40.4）1 107（35.6）
女2 340（63.6）340（59.6）2 000(64.4)
教育程度5.4730.019
高中以下31（0.8）10（1.8）21（0.7）
高中及以上3 646（99.2）560 (98.2)3 086(99.3)
饮酒10.1260.001
是1 617（44.0）216（37.9）1 401（45.1）
否2 060（56.0）354（62.1）1 706(54.9)
吸烟1.9200.166
是1 325（36.0）220（38.6）1 105（35.6）
否2 352（64.0）350（61.4）2 002(64.4)
婚姻状况0.0170.897
是3 398（92.4）526（92.3）2 872（92.4）
否279（7.6）44（7.7）235( 7.6)
BMI（kg/m2）7.5480.023
<24.9468（12.7）68 (12.0)400(12.9)
25~30926（25.2）120（21.1）806 (25.9)
>302 283（62.1）382（67.0）1 901(61.2)
衰弱状态11.5280.009
Class1739(20.1)129(22.6)610(19.6)
Class21 298(35.3)191(33.5)1 107(35.6)
Class3391(10.6)78(13.7)313(10.1)
Class41 249(34.0)172(30.2)1 077(34.7)
), ArticleFig(id=1241319159951314949, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319151533347457, language=CN, label=表1, caption=

调查对象的人口学特征[n（%）]

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变量分组总数（n，%)患RA(n，% )不患RA
(n，%)		χ2P
年龄（岁）1.1540.562
<751 332（36.2）211（37.0）1 121（36.1）
75~791 139（31.0）183（32.1）956（30.8）
>801 206（32.8）176（30.9）1 030（33.2）
性别4.6400.031
男1 337（36.4）230（40.4）1 107（35.6）
女2 340（63.6）340（59.6）2 000(64.4)
教育程度5.4730.019
高中以下31（0.8）10（1.8）21（0.7）
高中及以上3 646（99.2）560 (98.2)3 086(99.3)
饮酒10.1260.001
是1 617（44.0）216（37.9）1 401（45.1）
否2 060（56.0）354（62.1）1 706(54.9)
吸烟1.9200.166
是1 325（36.0）220（38.6）1 105（35.6）
否2 352（64.0）350（61.4）2 002(64.4)
婚姻状况0.0170.897
是3 398（92.4）526（92.3）2 872（92.4）
否279（7.6）44（7.7）235( 7.6)
BMI（kg/m2）7.5480.023
<24.9468（12.7）68 (12.0)400(12.9)
25~30926（25.2）120（21.1）806 (25.9)
>302 283（62.1）382（67.0）1 901(61.2)
衰弱状态11.5280.009
Class1739(20.1)129(22.6)610(19.6)
Class21 298(35.3)191(33.5)1 107(35.6)
Class3391(10.6)78(13.7)313(10.1)
Class41 249(34.0)172(30.2)1 077(34.7)
), ArticleFig(id=1241319160064561167, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319151533347457, language=EN, label=Table 2, caption=

Mixed growth model fit indices

, figureFileSmall=null, figureFileBig=null, tableContent=
分组AICBICaBICP
230 485.39930 553.70730 518.755<0.001
330 269.01230 355.95030 311.465<0.001
426 138.76726 244.33426 190.3170.027
516 148.76316 272.96016 209.4091.000
), ArticleFig(id=1241319160173613085, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319151533347457, language=CN, label=表2, caption=

混合增长模型拟合指数

, figureFileSmall=null, figureFileBig=null, tableContent=
分组AICBICaBICP
230 485.39930 553.70730 518.755<0.001
330 269.01230 355.95030 311.465<0.001
426 138.76726 244.33426 190.3170.027
516 148.76316 272.96016 209.4091.000
), ArticleFig(id=1241319160282664999, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319151533347457, language=EN, label=Table 3, caption=

Results of the analysis of the trajectory model of frailty with the cox regression model of rheumatoid arthritis[HR（95% CI）]

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分组模型1P模型2P模型3P
衰弱状态(class1)1.225(0.9793~1.532)0.0761.278(1.020~1.603)0.0331.255(1.001~1.576)0.049
（class2）1.000(Ref.)-1.000(Ref.)-1.000(Ref.)-
(class3)1.434(1.102~1.867)0.0071.545(1.180~2.022)0.0021.422(1.071~1.888)0.015
（class4）0.942(0.766~1.158)0.5710.965(0.785~1.189)0.7390.958(0.779~1.180)0.689
年龄（岁）<751.000(Ref.)-1.000(Ref.)-
75~791.013(0.831~1.235)0.8991.029(0.843~1.255)0.777
>800.909(0.743~1.111)0.3490.935(0.763~1.146)0.520
性别男1.000(Ref.)-1.000(Ref.)-
女0.801(0.675~1.951)0.0110.834(0.697~0.996)0.045
教育程度高中及以上0.536(0.277~1.040)0.064
高中以下1.000(Ref.)-
是否吸烟是0.937(0.787~1.116)0.458
否1.000(Ref.)-
), ArticleFig(id=1241319160379134004, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319151533347457, language=CN, label=表3, caption=

衰弱的轨迹模型与类风湿关节炎的cox回归模型分析结果 [HR（95% CI）]

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分组模型1P模型2P模型3P
衰弱状态(class1)1.225(0.9793~1.532)0.0761.278(1.020~1.603)0.0331.255(1.001~1.576)0.049
（class2）1.000(Ref.)-1.000(Ref.)-1.000(Ref.)-
(class3)1.434(1.102~1.867)0.0071.545(1.180~2.022)0.0021.422(1.071~1.888)0.015
（class4）0.942(0.766~1.158)0.5710.965(0.785~1.189)0.7390.958(0.779~1.180)0.689
年龄（岁）<751.000(Ref.)-1.000(Ref.)-
75~791.013(0.831~1.235)0.8991.029(0.843~1.255)0.777
>800.909(0.743~1.111)0.3490.935(0.763~1.146)0.520
性别男1.000(Ref.)-1.000(Ref.)-
女0.801(0.675~1.951)0.0110.834(0.697~0.996)0.045
教育程度高中及以上0.536(0.277~1.040)0.064
高中以下1.000(Ref.)-
是否吸烟是0.937(0.787~1.116)0.458
否1.000(Ref.)-
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衰弱轨迹对类风湿关节炎的影响研究
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刘岩 1 , 郭鑫 1 , 陶梦君 2 , 韩玥 1 , 徐亮 2 , 左坚 1 , 袁慧 1
现代预防医学 | 临床与预防 2025,52(12): 2272-2277
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现代预防医学 | 临床与预防 2025, 52(12): 2272-2277
衰弱轨迹对类风湿关节炎的影响研究
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刘岩1, 郭鑫1, 陶梦君2, 韩玥1, 徐亮2, 左坚1, 袁慧1
作者信息
  • 1.皖南医学院，安徽 芜湖 241002
  • 2.皖南医学院第一附属医院弋矶山医院

    • 刘岩（2000—），男，硕士在读，研究方向：公共卫生

通讯作者:

袁慧，E-mail：
A study of the effect of frailty trajectories on rheumatoid arthritis
Yan LIU1, Xin GUO1, Meng-jun TAO2, Yue HAN1, Liang XU2, Jian ZUO1, Hui YUAN1
Affiliations
  • Wannan Medical College, Wuhu, Anhui 241002，China
出版时间: 2025-06-25 doi: 10.20043/j.cnki.MPM.202411446
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摘要
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目的

通过轨迹分析探讨衰弱状态与类风湿关节炎（Rheumatoid Arthritis，RA）的关联，为采取针对性措施降低RA患病风险提供科学依据。

方法

来源于HRS的2004—2018年美国健康和退休研究的调查数据，对2004—2010三波数据采用增长混合模型进行衰弱的潜在类别分析，并以2010年为基线数据，追踪2010—2018四波数据，分析不同衰弱状态对参与者患RA风险的关联，使用Cox比例风险模型分析描述不同衰弱状态与RA发病之间的关联。

结果

基线人群共3 677人，其中男性1 337人（36.4%），女性2 340人（63.6%）。通过建立增长混合模型，确定四种衰弱轨迹，高衰弱降低组（10.6%），持续高衰弱组（20.1%），持续低衰弱组（34.0%），低衰弱升高组（35.3%）。Cox比例风险回归模型分析结果显示：在完全调整后的模型中，以低衰弱升高组为参考，持续高衰弱组患RA风险增加0.255倍（HR=1.255，95% CI：1.001~1.576），高衰弱降低组患RA风险增加0.422倍( HR=1.422，95% CI：1.071 ~ 1.888)。

结论

衰弱是RA的危险因素，且高衰弱降低组和持续高衰弱组会增加患RA的风险。

衰弱  /  类风湿关节炎  /  增长混合模型
Objective

To explore the association between frailty status and RA through trajectory analyses, and to provide a scientific basis for targeted measures to reduce the risk of RA.

Methods

Survey data from the 2004-2018 American Health and Retirement Study (HRS) were analyzed for potential categories of frailty using growth mixture models for the three waves of data from 2004-2010. Using 2010 as the baseline, four waves of data from 2010 to 2018 were tracked to analyze the association between different frailty statuses and participants’ RA risk, and Cox proportional risk models were used to describe the associations between different frailty states and the development of RA.

Results

The baseline population totaled 3 677 individuals, including 1 337 (36.4%) males; 2 340 (63.6%) females. Four frailty trajectories were identified through growth mixture modeling, with a high frailty lowering group (10.6%), a persistent high frailty group (20.1%), a persistent low frailty group (34.0%), and a low frailty elevation group (35.3%). Analysis of Cox proportional risk regression modeling showed that, in the fully adjusted model with the low frailty elevation group as the reference, the persistent high frailty group had a 0.255-fold increased risk of developing RA 0.255-fold (HR=1.255, 95% CI: 1.001-1.576), and the risk of developing RA increased 0.422-fold in the high frailty-reduced group (HR=1.422, 95% CI: 1.071-1.888).

Conclusion

Frailty is a risk factor for RA, and the risk of developing RA is increased in the high frailty reduction group and the persistent high frailty group.

Frailty  /  Rheumatoid arthritis  /  Growth mixture modeling
刘岩, 郭鑫, 陶梦君, 韩玥, 徐亮, 左坚, 袁慧. 衰弱轨迹对类风湿关节炎的影响研究. 现代预防医学, 2025 , 52 (12) : 2272 -2277 . DOI: 10.20043/j.cnki.MPM.202411446
Yan LIU, Xin GUO, Meng-jun TAO, Yue HAN, Liang XU, Jian ZUO, Hui YUAN. A study of the effect of frailty trajectories on rheumatoid arthritis[J]. Modern Preventive Medicine, 2025 , 52 (12) : 2272 -2277 . DOI: 10.20043/j.cnki.MPM.202411446
正文
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类风湿关节炎（Rheumatoid Arthritis，RA）是一种慢性、系统性自身免疫性疾病，主要病理特征为滑膜炎与肿胀、软骨与骨破坏[1]。其相关并发症包括血管疾病、肺间质病变、关节畸形、恶性肿瘤及抑郁症[2]。2019年全球年龄标化发病率为13.0%[3]。衰弱是一种复杂的临床疾病，其特征是多个器官系统的生理能力下降[4]。
近年来，衰弱在RA的发病机制中的潜在作用受到越来越多的关注。研究表明，衰弱可能通过多种机制促使RA的发生。例如，衰弱导致认知功能和情景记忆的下降乃至抑郁症的发生[5-6]，这些精神压力会通过影响下丘脑-垂体-肾上腺（HPA）轴，使体内促炎因子如IL-6和TNF-α水平升高，从而促进炎症反应[7]。在衰弱状态下，个体的应对能力下降，相关心理疾病更易转化为生理反应，进而可能成为RA发生的一个潜在机制[8]。因此，衰弱可能不仅是RA的并发症，还可能作为RA的潜在促发因素，影响疾病的发生和发展[9]。
衰弱轨迹的研究提供了理解这一动态过程的重要视角。衰弱轨迹指的是个体在一段时间内衰弱状态的变化模式[10]。在RA患者中，衰弱轨迹的识别和分析可以揭示衰弱状态与疾病进展之间的复杂关系。研究表明，健康问题[11-12]、心理压力[13]和社会支持[14]等变化通过动态轨迹，能够较好的预测健康状况及潜在的健康风险。因此，关注衰弱和衰弱轨迹在RA患者中的作用，不仅有助于理解其病理机制，也为临床干预提供了新的思路，促进个体化护理和疾病管理。
虽然已有研究关注衰弱可能增加RA患病的风险[15]，但大多数研究未充分考虑不同衰弱状态对RA的关联。因此，本研究旨在利用美国健康与退休研究（Health and Retirement Study，HRS）数据库，通过增长混合模型（Growth Mixture Modeling，GMM）分析人群衰弱状态，探索不同衰弱轨迹对RA的发生风险，为采取个性化的干预策略有效降低患RA的风险提供科学依据。
1　资料与方法
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1.1　一般资料
数据来源于美国HRS数据库（Health and Retirement Study），使用2004—2018其中七波调查数据。HRS是一项调查了美国约 20000 人的代表性样本的纵向研究，旨在收集美国退休居民的全国代表性样本的人口、社会、经济和健康状况数据。研究由训练有素的调查员进行面对面访谈收集数据。所有调查数据均可在网上公开获取。（Data Downloads | Health and Retirement Study）
1.2　评价指标
1.2.1　衰弱
使用Fried衰弱标准来评估参与者的衰弱程度。Fried衰弱标准[16]被验证为评估 HRS衰弱程度的有效工具。根据其身体特征或表型描述，并客观确定为五个组成部分：消瘦（即，个人报告在2年内体重减轻了至少10%）、衰弱（即，由于健康问题，参与者在举起或搬运超过10磅的重物时是否有任何困难，例如一袋沉重的杂货），缓慢（即，由于健康问题，参与者在长时间坐着后是否难以从椅子上站起来）、疲劳（即，参与者是否有以下任何持续或麻烦的问题：严重疲劳或疲惫？）和跌倒（即参与者在过去2年中是否跌倒）。
1.2.2　RA
在自我报告的医生诊断疾病中，参与者被询问“医生是否告知您患有关节炎”，如果参与者回答是，则进一步询问参与者所患关节炎的具体类型“您患有哪种类型的关节炎？”，从而判断参与者是否患类风湿关节炎。
1.2.3　协变量
协变量指标包括年龄（<75岁、75~79岁、>80岁）、性别（男、女）、体重指数（BMI：体重不足、正常、肥胖、超重）、教育程度（高中以下、高中及以上）、婚姻状况（已婚、未婚）、饮酒（有、无）和吸烟（有、无）。
1.3　统计学方法
1.3.1　一般统计分析
采用率和构成比进行统计描述，采用χ2检验进行组间比较
1.3.2　衰弱轨迹分析
采用增长混合模型拟合衰弱轨迹。使用R 4.3.2软件进行数据整理和分析，利用Mplus 8软件实现轨迹拟合。基于对衰弱轨迹及相关变量的研究，初步构建3至5条轨迹，并按衰弱程度从低到高分组。如果模型结果无统计学意义，则继续拟合，直至每条轨迹的参数均达到显著性。模型选择将基于赤池信息准则（AIC）、贝叶斯信息准则（BIC）、调整后的贝叶斯信息准则（aBIC）、熵值以及调整后的Lo-Mendell-Rubin似然比检验（aLMR）来评估模型质量与分类准确性，最终确定最优模型[17]。
1.3.3　Cox回归分析
采用Cox比例风险模型对衰弱与RA的关联分析，在调整相关因素后进一步评估其关联性。为检验关联的稳健性，依次建立三个模型：模型1为单变量模型；模型2调整了人口学特征，包括年龄、性别和婚姻状况等基本变量；模型3在模型2基础上进一步调整了行为方式变量，如吸烟和饮酒等。所有检验均为双侧检验，检验水准为α=0.05。
1.3.4　生存时间
生存时间被定义为从基线随访时间到首次报告患病的时间（单位为年）。对于未报告患病的个体，生存时间记录至最后一次随访时间。
2　结果
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2.1　基线分析
选取2004—2018年中进行调查的31 055名参与者的数据。排除了2004至2010年三波数据中24 264名衰弱信息缺失的参与者和3 114名在2010年之前患有RA的参与者以及2010年之后类风湿关节炎信息缺失的参与者。
表1介绍了参与者的人口统计学特征及各变量对患RA是否有统计学差异，在基线调查的3 677人，男性占比为36.4%，女性占比为63.6%。其中，大部分人有过高中以上的教育程度。有7.6%的人处于未婚状态。有15.5%的参与者表明自己患有RA。在不同衰弱状态中。高衰弱降低组占比为10.6%，持续高衰弱组占比为20.1%，持续低衰弱组占比为34.0%，低衰弱升高组占比为35.3%。在各变量中，性别（P=0.031），受教育程度（P=0.019），饮酒（P=0.001），BMI（P=0.023）,衰弱（P=0.009）等变量对RA具有统计学意义。
2.2　轨迹分析
2.2.1　增长混合模型
通过不同组的AIC和aBIC的比较表明，第4组被选为最佳拟合模型。与第3组相比，第4组的拟合指数较高，包括较低的AIC（26 138.767）、BIC（26 244.334）和aBIC（26 190.317），这些表明整体拟合效果更好。与第5组相比（AIC:16 148.763，BIC:16 272.96，aBIC:16 209.409），它的拟合指数虽然较低，然而第5组与第四组比较的P值为1.000，差异并无统计学意义。aLMR检验结果显著（P< 0.05）；另外，第4组中每一类别的参与者人数均超过总数的5%，这使得第4组更为稳定。因此，第4组为最佳拟合模型，见表2。
2.2.2　衰弱轨迹分析
老年人的衰弱轨迹，Class1为持续高衰弱组，Class2为低衰弱升高组，Class3为高衰弱下降组，Class4为持续低衰弱组，见图2。
2.3　衰弱轨迹与类风湿关节炎之间的关联
表3介绍了三种模型，模型1表示衰弱与RA的Cox回归模型，并无其他混杂因素，模型2表示在模型1的基础上调整年龄，性别；而模型3表示在模型1和模型2的基础上对所有协变量进行调整。在模型1中，与参照组（低衰弱升高组）相比，高衰弱降低组和持续高衰弱组更易导致RA的发生。模型2中，在调整人口学特征后，高衰弱降低组和持续高衰弱组仍增加患RA风险，年龄，性别并未加强衰弱与RA之间的关联。模型3中，研究显示，在调整所有变量之后，并未发现其他协变量加强衰弱与RA之间的关联。
3　讨论
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本研究利用2004—2018年的HRS数据，纵向分析美国退休人群的不同衰弱程度与RA之间的关联，使用增长混合模型发现衰弱的潜在类别。在研究中，我们确定了四条不同的衰弱轨迹类别，高衰弱降低组，持续高衰弱组，持续低衰弱组和低衰弱程度升高组。数据显示，以低衰弱程度升高组人群为对照，高衰弱降低组和持续高衰弱组增加了患RA的风险，而持续低衰弱组并无显著影响。值得注意的是，高衰弱程度降低组和持续高衰弱组的风险显著高于持续低衰弱组，显示高衰弱状态在RA的发病中发挥关键作用。因此，在临床实践中，早期识别和干预衰弱状态，尤其是针对高风险的高衰弱状态降低组、持续性高衰弱程度具有重要意义。尽管结果显示衰弱程度有所改善，但仍处于较高的衰弱状态中，患病的风险仍然存在。研究显示，在调整人口学特征后，各年龄段之间并未加强衰弱与RA之间的关联，且无统计学意义，然而在一项八年的回顾性队列研究中却显示，在进行相应的人口学调整后，衰弱程度会随着年龄的增加而增加，进而增加RA发生的风险[18]。在进一步完全调整后，并未发现相关因素加强衰弱程度与RA的关联。
研究表明，在机制方面，衰弱可能通过与慢性炎症，免疫系统功能失调和代谢异常的联系间接导致RA的发生。一方面衰弱状态通常伴随慢性炎症，表现为促炎性细胞因子的持续升高[19]。这种炎症环境可能促进自身免疫反应的激活，从而诱发RA[20]；另外一方面，衰弱患者的免疫系统通常表现出过度激活或免疫监视能力下降[21]。这种免疫失调状态可能导致身体对自身抗原的异常反应，最终引发RA的发生[22]。衰弱还与氧化应激和线粒体功能障碍有关，这些因素会进一步加剧炎症反应，并可能通过多种信号通路促进RA的发病[23]，此外，衰弱与肌肉减少症的发生密切相关，肌肉质量和力量的下降不仅会导致身体功能的衰退，还可能影响代谢功能[24]。这些机制的发生均进一步增加RA的发生风险。衰弱还可能与内分泌系统的功能失调有关，特别是下丘脑-垂体-肾上腺轴的变化。衰弱状态下，HPA轴功能紊乱，从而导致应激激素（如皮质醇）的异常分泌。长期的皮质醇水平升高可能抑制免疫系统，破坏免疫耐受性，增加自身免疫反应的可能性，从而诱发RA[25]。
虽然该研究数据来源于美国公共数据库HRS，数据具有一定代表性。但该研究还存在一些缺陷，由于来源于美国公共数据库，并不能代表对其他国家的衰弱与RA之间的关联，导致结果外推受限。此外，在对HRS数据进行提取，处理和分析时，我们对调查期间早期失访，或身体衰弱的老年人被排除在外，因此不能排除选择偏倚。另一方面，在处理基线时仅选取体重指数，吸烟史和饮酒史，并未评估了相关心理及环境因素。这些因素会在多大程度上进一步影响老年人衰弱轨迹，还需未来进行进一步研究。
综上所述，在美国退休人群中，存在衰弱的潜在类别，且这些潜在类别对RA均呈现危险因素，即不同的衰弱程度在一定程度会增加RA患病的风险。因此，对于老年人来说，预防及延缓衰弱的发生，维持良好的健康状态以及保持良好的生活方式也是有一定必要的。
基金
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  • 国家自然科学基金面上项目(82274465)
文献
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2025年第52卷第12期
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doi: 10.20043/j.cnki.MPM.202411446
  • 接收时间：2024-11-24
  • 首发时间：2026-03-19
  • 出版时间：2025-06-25
补充材料
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  • 收稿日期：2024-11-24
基金
国家自然科学基金面上项目(82274465)
作者信息
    1.皖南医学院，安徽 芜湖 241002
    2.皖南医学院第一附属医院弋矶山医院

通讯作者:

袁慧，E-mail：
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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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