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Article(id=1324044720325931523, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324044701648695723, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202509431, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1758729600000, receivedDateStr=2025-09-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793606460886, onlineDateStr=2026-11-02, pubDate=1779638400000, pubDateStr=2026-05-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793606460886, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793606460886, creator=13701087609, updateTime=1793606460886, updator=13701087609, issue=Issue{id=1324044701648695723, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='10', pageStart='1729', pageEnd='1920', issueExtLink='null', onlineDate='null', pubDate='1779638400000', pubDateStr='2026-05-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793606456434, creator='13701087609', updateTime=1793670074552, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324311535425815306, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324044701648695723, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324311535425815307, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324044701648695723, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1760, endPage=1765, ext={EN=ArticleExt(id=1324044720573395460, articleId=1324044720325931523, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Heterogeneous health dynamic trajectories and an integrated prediction model for multimorbidity phenotypes in middle-aged and older adults, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To identify multimorbidity phenotypes in middle-aged and older adults, examine their long-term health trajectories, and develop a predictive model for functional dependency.

Methods

This longitudinal analysis used five waves(2011-2020) of the China Health and Retirement Longitudinal Study(CHARLS) and included 298 participants with complete follow-up. Phenotype subgroups were derived by Kmeans clustering based on baseline disease count. Generalized Estimating Equations(GEE) were applied to assess health differences and trajectories across phenotypes. A care-needs prediction model was developed by integrating prior health status(2018) and pre-2018 dynamic slopes.

Results

Three multimorbidity burden phenotypes were identified: high, moderate, and low. A higher baseline disease count was independently associated with increased risks of functional dependency(ADL: OR=2.398; IADL: OR=2.346), depression, and pain(all P<0.001). The high-burden phenotype showed faster rates of decline in cognitive function, pain accumulation, and life satisfaction(all P<0.01). The prediction model, incorporating prior functional status, depressive symptoms, phenotype, and rate of pain progression, demonstrated good discrimination(AUC for ADL dependency=0.807, 95%CI: 0.740-0.874; AUC for IADL dependency=0.765, 95%CI: 0.678-0.851).

Conclusion

Multimorbidity phenotypes are associated with distinct long-term health trajectories. A model combining static phenotypes and dynamic health indicators can help identify individuals at high risk of functional dependency, supporting targeted prevention and care planning.

, authors=Jun-jie GAO1, 2, Yuan XUE1, 2, authorsList=Jun-jie GAO, Yuan XUE, authorCompany=null, correspAuthors=Yuan XUE, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1324044726751605270, articleId=1324044720325931523, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=中老年慢性病共病表型的异质性健康动态轨迹及整合预测模型研究, columnId=1228016567632462653, journalTitle=现代预防医学, columnName=流行病与统计方法, runingTitle=null, highlight=null, articleAbstract=
目的

探索中老年慢性病共病表型的长期健康动态演变轨迹，并构建功能依赖风险预测模型。

方法

基于CHARLS五期纵向数据（2011~2020），纳入298名全程随访者。通过基线共病数量进行聚类，识别不同负担的表型亚组；采用GEE分析健康差异与轨迹，并构建整合前序状态及动态斜率的照护需求预测模型。

结果

识别出高、中、低三类共病负担表型。基线共病数量与更高的功能依赖（ADL：OR=1.398，IADL：OR=1.346）、抑郁及疼痛风险独立相关（均P<0.001）。高负担表型在认知功能、疼痛累积及生活满意度方面呈现更快的衰退速率（均P<0.01）。整合前序功能、抑郁症状、共病表型及疼痛变化率的预测模型具有良好的判别效能（ADL依赖AUC=0.807，95%CI：0.740~0.874；IADL依赖AUC=0.765，95%CI：0.678~0.851）。

结论

共病表型与多维健康衰退轨迹相关。融合静态表型与动态指标的预测工具有助于识别高危个体，为实施分层管理与精准干预提供参考。

, authors=高俊杰1, 2, 薛原1, 2, authorsList=高俊杰, 薛原, authorCompany=null, correspAuthors=薛原, authorNote=

高俊杰（1990—），男，硕士，助理研究员，研究方向：公共管理，思想政治教育

, correspAuthorsNote=
薛原，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=eL6KJqz8fZ1ZUPvtuRE3FA==, magXml=meOp1YlIyBgoCBknGEO/HA==, pdfUrl=null, pdf=jQ8T10RJMWY20xqgFTvZTA==, pdfFileSize=779322, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=yJxCH/b5/Wk+SKCrmONasA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=i1+KBtG5mDI2BGNWq7MtJQ==, mapNumber=null, fund=null)}, authors=[Author(id=1324045005509239082, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1324045005601513773, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, authorId=1324045005509239082, language=EN, stringName=Jun-jie GAO, firstName=Jun-jie, middleName=null, lastName=GAO, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, 2, address=Nanjing University of Chinese Medicine Hanlin College, Taizhou, Jiangsu 225300, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1324045009145700654, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, authorId=1324045005509239082, language=CN, stringName=高俊杰, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, 2, address=1.南京中医药大学翰林学院，江苏 泰州 225300
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高俊杰（1990—），男，硕士，助理研究员，研究方向：公共管理，思想政治教育

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高俊杰（1990—），男，硕士，助理研究员，研究方向：公共管理，思想政治教育

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Asian Journal of Psychiatry, 2024, 102: 104279., articleTitle=Investigating intrinsic and situational predictors of depression among older adults：An analysis of the CHARLS database, refAbstract=null)], funds=[Fund(id=1324045015315521865, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, awardId=苏教师函［2023］27号, language=CN, fundingSource=江苏省“青蓝工程”优秀青年骨干教师资助项目(苏教师函［2023］27号), fundOrder=null, country=null), Fund(id=1324045015391019338, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, awardId=2025SJYB1709, language=CN, fundingSource=2025年度江苏高校哲学社会科学研究一般项目(2025SJYB1709), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1324045005316301092, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, xref=1., ext=[AuthorCompanyExt(id=1324045005328884005, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, companyId=1324045005316301092, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Nanjing University of Chinese Medicine Hanlin College, Taizhou, Jiangsu 225300, China), AuthorCompanyExt(id=1324045005337272614, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, companyId=1324045005316301092, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.南京中医药大学翰林学院，江苏 泰州 225300)]), AuthorCompany(id=1324045005408575783, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, xref=2., ext=[AuthorCompanyExt(id=1324045005416964392, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, companyId=1324045005408575783, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.南京中医药大学泰州校区)])], figs=[ArticleFig(id=1324045013386142011, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, language=EN, label=Fig.1, caption=Dynamic trajectories of multimorbidity count during follow-up by multimorbidity burden phenotype, figureFileSmall=+V0nb+WbkloetbBZMy/FYA==, figureFileBig=yJxCH/b5/Wk+SKCrmONasA==, tableContent=null), ArticleFig(id=1324045013679743292, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, language=CN, label=图1, caption=各共病负担表型随访期内共病数量动态轨迹, figureFileSmall=+V0nb+WbkloetbBZMy/FYA==, figureFileBig=yJxCH/b5/Wk+SKCrmONasA==, tableContent=null), ArticleFig(id=1324045013839126845, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, language=EN, label=Table 1, caption=

Definition and measurement of multi-dimensional health outcome indicators

, figureFileSmall=null, figureFileBig=null, tableContent=
维度具体指标测量方式与临界值变量类型
身体功能ADL依赖穿衣、洗澡、吃饭、上下床、如厕和控制大小便6项基础功能≥1项“需要帮助”或“无法完成”二分变量
IADL依赖家务、做饭、购物、吃药和管理财务等5项复杂功能≥1项“有困难”或“无法完成”二分变量
认知功能综合认知得分MMSE量表（0~21分），分数越高越优连续变量
心理健康抑郁症状CES-D量表（0~30分），分数越高越差连续变量
生活满意度单项5级评分(1~5分)，分数越高越满意有序分类
疼痛负担疼痛部位数头疼、肩膀疼、胳膊疼、手腕疼、手指疼、胸疼、胃疼、背疼、腰疼、臀部疼、腿疼、膝盖疼、脚踝疼、脚趾头疼、脖子疼，共计15部位疼痛数之和计数变量
医疗利用住院风险过去一年是否住院二分变量
), ArticleFig(id=1324045014036259134, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, language=CN, label=表1, caption=

多维度健康结局指标的定义与测量

, figureFileSmall=null, figureFileBig=null, tableContent=
维度具体指标测量方式与临界值变量类型
身体功能ADL依赖穿衣、洗澡、吃饭、上下床、如厕和控制大小便6项基础功能≥1项“需要帮助”或“无法完成”二分变量
IADL依赖家务、做饭、购物、吃药和管理财务等5项复杂功能≥1项“有困难”或“无法完成”二分变量
认知功能综合认知得分MMSE量表（0~21分），分数越高越优连续变量
心理健康抑郁症状CES-D量表（0~30分），分数越高越差连续变量
生活满意度单项5级评分(1~5分)，分数越高越满意有序分类
疼痛负担疼痛部位数头疼、肩膀疼、胳膊疼、手腕疼、手指疼、胸疼、胃疼、背疼、腰疼、臀部疼、腿疼、膝盖疼、脚踝疼、脚趾头疼、脖子疼，共计15部位疼痛数之和计数变量
医疗利用住院风险过去一年是否住院二分变量
), ArticleFig(id=1324045014120145215, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, language=EN, label=Table 2, caption=

Baseline characteristics of study participants (n=298)

, figureFileSmall=null, figureFileBig=null, tableContent=
变量数值
年龄（岁，）56.8±7.6
女性［n(%)］157(52.7)
农村户籍［n(%)］189(63.4)
养老保险覆盖［n(%)］94(31.5)
锻炼［n(%)］70(23.5)
基线共病数（种，）1.38±1.39
基线ADL依赖［n(%)］31(10.4)
基线IADL依赖［n(%)］42(14.1)
), ArticleFig(id=1324045014199836992, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, language=CN, label=表2, caption=

研究对象基线特征（n=298）

, figureFileSmall=null, figureFileBig=null, tableContent=
变量数值
年龄（岁，）56.8±7.6
女性［n(%)］157(52.7)
农村户籍［n(%)］189(63.4)
养老保险覆盖［n(%)］94(31.5)
锻炼［n(%)］70(23.5)
基线共病数（种，）1.38±1.39
基线ADL依赖［n(%)］31(10.4)
基线IADL依赖［n(%)］42(14.1)
), ArticleFig(id=1324045014279528769, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, language=EN, label=Table 3, caption=

Comparison of baseline characteristics between included and excluded groups

, figureFileSmall=null, figureFileBig=null, tableContent=
变量纳入组
(n=298)		脱落组
(n=10 102)		P值
人口学特征
年龄（岁，）56.68±7.6157.76±8.980.017
女性［n(%)］149(50.00)5 497(54.41)0.132
教育水平［n(%)］<0.001
小学以下71(23.83)4 784(47.45)
小学毕业76(25.50)2 143(21.25)
初中毕业105(35.23)2 025(20.08)
高中及以上46(15.44)998(9.90)
在婚［n(%)］281(94.30)9 087(90.01)0.015
城镇户口［n(%)］64(21.48)1 580(15.66)0.007
心理与症状负担
认知能力（分，）13.33±2.7511.82±3.50<0.001
心理健康得分（分，）7.14±5.868.44±6.30<0.001
ADL依赖［n(%)］32(10.74)1 490(14.99)0.042
IADL依赖［n(%)］33(11.07)2 001(19.95)<0.001
慢性病与健康行为
关节炎［n(%)］82(27.52)3 356(33.44)0.032
高血压［n(%)］71(23.83)2 340(23.39)0.861
), ArticleFig(id=1324045014392774978, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, language=CN, label=表3, caption=

纳入组与脱落组基线特征比较

, figureFileSmall=null, figureFileBig=null, tableContent=
变量纳入组
(n=298)		脱落组
(n=10 102)		P值
人口学特征
年龄（岁，）56.68±7.6157.76±8.980.017
女性［n(%)］149(50.00)5 497(54.41)0.132
教育水平［n(%)］<0.001
小学以下71(23.83)4 784(47.45)
小学毕业76(25.50)2 143(21.25)
初中毕业105(35.23)2 025(20.08)
高中及以上46(15.44)998(9.90)
在婚［n(%)］281(94.30)9 087(90.01)0.015
城镇户口［n(%)］64(21.48)1 580(15.66)0.007
心理与症状负担
认知能力（分，）13.33±2.7511.82±3.50<0.001
心理健康得分（分，）7.14±5.868.44±6.30<0.001
ADL依赖［n(%)］32(10.74)1 490(14.99)0.042
IADL依赖［n(%)］33(11.07)2 001(19.95)<0.001
慢性病与健康行为
关节炎［n(%)］82(27.52)3 356(33.44)0.032
高血压［n(%)］71(23.83)2 340(23.39)0.861
), ArticleFig(id=1324045014480855363, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, language=EN, label=Table 4, caption=

Comparison of baseline characteristics across multimorbidity burden phenotype subgroups

, figureFileSmall=null, figureFileBig=null, tableContent=
变量高负担组
(n=30)		中负担组
(n=106)		低负担组
(n=162)		P值
人口学特征
年龄（岁，）58.57±8.1457.59±7.3055.73±7.610.052
女性［n(%)］12(40.0)50(47.2)87(53.7)0.297
共病与功能负担
高血压患病［n(%)］21(70.0)38(35.8)12(7.4)<0.001
糖尿病患病［n(%)］7(23.3)12(11.3)1(0.6)<0.001
关节炎患病［n(%)］18(60.0)44(41.5)20(12.3)<0.001
ADL依赖［n(%)］11(36.7)13(12.3)8(4.9)<0.001
IADL依赖［n(%)］10(33.3)18(17.0)5(3.1)<0.001
心理与症状负担
抑郁得分（分，）9.57±6.218.43±6.605.84±4.90<0.001
), ArticleFig(id=1324045014560547140, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, language=CN, label=表4, caption=

不同共病负担表型亚组的基线特征比较

, figureFileSmall=null, figureFileBig=null, tableContent=
变量高负担组
(n=30)		中负担组
(n=106)		低负担组
(n=162)		P值
人口学特征
年龄（岁，）58.57±8.1457.59±7.3055.73±7.610.052
女性［n(%)］12(40.0)50(47.2)87(53.7)0.297
共病与功能负担
高血压患病［n(%)］21(70.0)38(35.8)12(7.4)<0.001
糖尿病患病［n(%)］7(23.3)12(11.3)1(0.6)<0.001
关节炎患病［n(%)］18(60.0)44(41.5)20(12.3)<0.001
ADL依赖［n(%)］11(36.7)13(12.3)8(4.9)<0.001
IADL依赖［n(%)］10(33.3)18(17.0)5(3.1)<0.001
心理与症状负担
抑郁得分（分，）9.57±6.218.43±6.605.84±4.90<0.001
), ArticleFig(id=1324045014677987653, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, language=EN, label=Table 5, caption=

Core results of GEE analysis on longitudinal associations of baseline multimorbidity count and phenotype with health outcomes

, figureFileSmall=null, figureFileBig=null, tableContent=
健康结局共病数量的效应
(每增加1种)		表型的效应
(相对低负担组)		交互效应：
时间×表型		其他显著协变量
ADL依赖OR=1.398, P<0.001OR=1.174, P=0.436β=-0.052, P=0.174男性 (OR=0.386, P=0.013)
IADL依赖OR=1.346, P<0.001OR=1.309, P=0.163β=-0.030, P=0.443无
认知功能β=0.079, P=0.195β=0.280, P=0.064β=-0.084, P=0.009教育水平高 (β=1.082, P<0.001)；
有医疗保险 (β=1.035, P=0.020)
抑郁症状β=0.594, P<0.001β=0.142, P=0.717β=-0.027, P=0.727男性 (β=-2.781, P<0.001)；
在婚 (β=-1.411, P=0.029)
疼痛部位β=0.203, P<0.001β=-0.154, P=0.179β=0.064, P=0.008男性 (β=-0.694, P=0.002)；
当前吸烟 (β=0.471, P=0.010)
生活满意度β=-0.045, P=0.012β=-0.078, P=0.095β=0.029, P=0.001在婚 (β=0.274, P=0.001)；
社交活动多 (β=0.043, P=0.045)
住院风险OR=1.485, P<0.001OR=0.943, P=0.763β=-0.021, P=0.621男性 (OR=1.903, P=0.021)；
当前吸烟 (OR=0.373, P=0.005)
), ArticleFig(id=1324045014803816774, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, language=CN, label=表5, caption=

基线共病数量、表型与健康结局纵向关联的GEE分析核心结果

, figureFileSmall=null, figureFileBig=null, tableContent=
健康结局共病数量的效应
(每增加1种)		表型的效应
(相对低负担组)		交互效应：
时间×表型		其他显著协变量
ADL依赖OR=1.398, P<0.001OR=1.174, P=0.436β=-0.052, P=0.174男性 (OR=0.386, P=0.013)
IADL依赖OR=1.346, P<0.001OR=1.309, P=0.163β=-0.030, P=0.443无
认知功能β=0.079, P=0.195β=0.280, P=0.064β=-0.084, P=0.009教育水平高 (β=1.082, P<0.001)；
有医疗保险 (β=1.035, P=0.020)
抑郁症状β=0.594, P<0.001β=0.142, P=0.717β=-0.027, P=0.727男性 (β=-2.781, P<0.001)；
在婚 (β=-1.411, P=0.029)
疼痛部位β=0.203, P<0.001β=-0.154, P=0.179β=0.064, P=0.008男性 (β=-0.694, P=0.002)；
当前吸烟 (β=0.471, P=0.010)
生活满意度β=-0.045, P=0.012β=-0.078, P=0.095β=0.029, P=0.001在婚 (β=0.274, P=0.001)；
社交活动多 (β=0.043, P=0.045)
住院风险OR=1.485, P<0.001OR=0.943, P=0.763β=-0.021, P=0.621男性 (OR=1.903, P=0.021)；
当前吸烟 (OR=0.373, P=0.005)
), ArticleFig(id=1324045014959006023, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, language=EN, label=Table 6, caption=

Significant predictors in the care needs prediction model for 2020

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预测目标(2020年)显著预测因子OR(95% CI)P值
ADL依赖前序IADL障碍4.405(1.857~10.447)0.001
抑郁症状1.147(1.066~1.234)<0.001
共病负担表型1.931(1.189~3.137)0.008
疼痛部位年化变化率2.497(1.276~4.885)0.008
IADL依赖抑郁症状1.075(1.010~1.145)0.023
共病负担表型1.856(1.099~3.134)0.021
前序疼痛部位数1.216(1.061~1.394)0.005
疼痛部位年化变化率0.362(0.157~0.839)0.018
), ArticleFig(id=1324045015126778184, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044720325931523, language=CN, label=表6, caption=

2020年照护需求预测模型的显著因子

, figureFileSmall=null, figureFileBig=null, tableContent=
预测目标(2020年)显著预测因子OR(95% CI)P值
ADL依赖前序IADL障碍4.405(1.857~10.447)0.001
抑郁症状1.147(1.066~1.234)<0.001
共病负担表型1.931(1.189~3.137)0.008
疼痛部位年化变化率2.497(1.276~4.885)0.008
IADL依赖抑郁症状1.075(1.010~1.145)0.023
共病负担表型1.856(1.099~3.134)0.021
前序疼痛部位数1.216(1.061~1.394)0.005
疼痛部位年化变化率0.362(0.157~0.839)0.018
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中老年慢性病共病表型的异质性健康动态轨迹及整合预测模型研究
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高俊杰 1, 2 , 薛原 1, 2
现代预防医学 | 流行病与统计方法 2026,53(10): 1760-1765
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现代预防医学 |流行病与统计方法 2026 , 53 (10) : 1760 -1765
中老年慢性病共病表型的异质性健康动态轨迹及整合预测模型研究
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2.南京中医药大学泰州校区, bio={"content":"

高俊杰（1990—），男，硕士，助理研究员，研究方向：公共管理，思想政治教育

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高俊杰（1990—），男，硕士，助理研究员，研究方向：公共管理，思想政治教育

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高俊杰1, 2, 薛原1, 2
作者信息
  • 1.南京中医药大学翰林学院，江苏 泰州 225300
  • 2.南京中医药大学泰州校区
通讯作者:
薛原，E-mail：
作者简介:

高俊杰（1990—），男，硕士，助理研究员，研究方向：公共管理，思想政治教育

Heterogeneous health dynamic trajectories and an integrated prediction model for multimorbidity phenotypes in middle-aged and older adults
Jun-jie GAO1, 2, Yuan XUE1, 2
Affiliations
  • Nanjing University of Chinese Medicine Hanlin College, Taizhou, Jiangsu 225300, China
出版时间: 2026-05-25 doi: 10.20043/j.cnki.MPM.202509431
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摘要
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目的

探索中老年慢性病共病表型的长期健康动态演变轨迹，并构建功能依赖风险预测模型。

方法

基于CHARLS五期纵向数据（2011~2020），纳入298名全程随访者。通过基线共病数量进行聚类，识别不同负担的表型亚组；采用GEE分析健康差异与轨迹，并构建整合前序状态及动态斜率的照护需求预测模型。

结果

识别出高、中、低三类共病负担表型。基线共病数量与更高的功能依赖（ADL：OR=1.398，IADL：OR=1.346）、抑郁及疼痛风险独立相关（均P<0.001）。高负担表型在认知功能、疼痛累积及生活满意度方面呈现更快的衰退速率（均P<0.01）。整合前序功能、抑郁症状、共病表型及疼痛变化率的预测模型具有良好的判别效能（ADL依赖AUC=0.807，95%CI：0.740~0.874；IADL依赖AUC=0.765，95%CI：0.678~0.851）。

结论

共病表型与多维健康衰退轨迹相关。融合静态表型与动态指标的预测工具有助于识别高危个体，为实施分层管理与精准干预提供参考。

慢性病共病  /  健康动态轨迹  /  功能依赖
Objective

To identify multimorbidity phenotypes in middle-aged and older adults, examine their long-term health trajectories, and develop a predictive model for functional dependency.

Methods

This longitudinal analysis used five waves(2011-2020) of the China Health and Retirement Longitudinal Study(CHARLS) and included 298 participants with complete follow-up. Phenotype subgroups were derived by Kmeans clustering based on baseline disease count. Generalized Estimating Equations(GEE) were applied to assess health differences and trajectories across phenotypes. A care-needs prediction model was developed by integrating prior health status(2018) and pre-2018 dynamic slopes.

Results

Three multimorbidity burden phenotypes were identified: high, moderate, and low. A higher baseline disease count was independently associated with increased risks of functional dependency(ADL: OR=2.398; IADL: OR=2.346), depression, and pain(all P<0.001). The high-burden phenotype showed faster rates of decline in cognitive function, pain accumulation, and life satisfaction(all P<0.01). The prediction model, incorporating prior functional status, depressive symptoms, phenotype, and rate of pain progression, demonstrated good discrimination(AUC for ADL dependency=0.807, 95%CI: 0.740-0.874; AUC for IADL dependency=0.765, 95%CI: 0.678-0.851).

Conclusion

Multimorbidity phenotypes are associated with distinct long-term health trajectories. A model combining static phenotypes and dynamic health indicators can help identify individuals at high risk of functional dependency, supporting targeted prevention and care planning.

Chronic comorbidities  /  Health trajectories  /  Functional dependence
高俊杰, 薛原. 中老年慢性病共病表型的异质性健康动态轨迹及整合预测模型研究. 现代预防医学, 2026 , 53 (10) : 1760 -1765 . DOI: 10.20043/j.cnki.MPM.202509431
Jun-jie GAO, Yuan XUE. Heterogeneous health dynamic trajectories and an integrated prediction model for multimorbidity phenotypes in middle-aged and older adults[J]. Modern Preventive Medicine, 2026 , 53 (10) : 1760 -1765 . DOI: 10.20043/j.cnki.MPM.202509431
正文
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1　引言
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人口老龄化背景下，中老年群体慢性病共病已成为常态与核心健康挑战[1]。共病不仅加剧疾病交互，更通过复杂路径导致躯体功能下降、心理健康恶化及医疗负担加重[2]。日常生活能力（ADL）与工具性日常生活能力（IADL）是评估个体失能程度的关键指标。ADL依赖威胁基础生存自主性，而IADL依赖则标志社会参与能力下降，且常早于ADL衰退，是失能进程的早期信号[3]。因此ADL/IADL依赖成为识别高危群体、优化照护资源配置的重要预警指标。
然而，现有研究多局限于静态共病计数，缺乏对疾病长期动态演变轨迹的探讨，亦未能充分阐明共病影响健康结局的路径，且现有预测模型多基于横断面数据，对诸如功能衰退速率等动态指标整合不足，导致风险预警滞后。基于世界卫生组织国际功能、残疾和健康分类（ICF）的生物-心理-社会模型[4]，本研究构建涵盖身体功能、认知、心理健康、疼痛和医疗利用的多维评估体系，旨在通过纵向数据分析，揭示共病与健康结局之间的时序关联与动态演变轨迹。
2　方法
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2.1　数据来源与样本
本研究采用中国健康与养老追踪调查（China Health and Retirement Longitudinal Study，CHARLS）2011，2013，2015，2018，2020年五期数据[5]。为构建平衡面板数据，要求研究对象完整参与五次随访且核心变量无缺失。基线（2011年）共15 517名45岁及以上受访者，其中五次随访均参与的受访者10 400名，全程数据完整的有298名。通过比较纳入者与基线脱落者（n=10 102）的特征以评估选择偏倚。
CHARLS采用多阶段分层抽样。根据CHARLS官方指南，其提供的权重适用于横截面分析，并无适用于多期平衡面板的统一面板权重。本研究基于五期平衡面板数据（N=298）探索纵向关联与预测模型，而非进行横断面总体推断。根据方法学惯例，此类关注模型内部有效性的分析通常不直接使用为横断面设计的复杂抽样权重。为控制潜在抽样设计影响，保障统计推断的可靠性，本研究在构建样本时，要求研究对象完整参与五次随访以形成稳定研究队列，减少样本损耗带来的复杂性，并在统计分析中，对所有核心模型均采用稳健标准误进行估计。
2.2　关键变量定义与测量
2.2.1　共病指标
基于CHARLS问卷中医生临床确诊的慢性疾病清单，纳入高血压、糖尿病、心脏病、脑卒中、慢性肺病、肝肾疾病、消化道疾病、关节炎、精神病等14类疾病。通过计数（0~14分）量化每次调查的共病负担。
2.2.2　多维度健康结局评估
基于ICF框架，结合CHARLS研究数据可及性，从五个维度评估健康结局（表1）。
2.2.3　动态斜率指标
在照护需求预测模型中，为反映长期趋势，计算每个个体ADL、IADL、认知、抑郁、疼痛五项指标在9年随访期内的年化变化率（斜率）。该指标以随访时间为自变量、个体五次测量值为因变量拟合线性回归模型获得。
2.2.4　协变量
包括基线人口学变量：年龄（岁）、性别（男=1/女=0）；社会经济变量：教育水平（小学以下=1，小学毕业=2，初中毕业=3，高中及以上=4）、居住地（农村=1/城镇=0）、医疗保险和养老保险状态（有=1/无=0）；健康行为变量：当前吸烟与饮酒状态（有=1/无=0）、体育锻炼习惯（有=1/无=0）等。
2.3　统计分析
使用SPSS 26.0，显著性水平α=0.05。
2.3.1　基线表型识别与验证
基于基线共病数量采用K-means聚类识别表型亚组。也尝试基于疾病谱的K-prototype聚类，但因部分亚组样本量过小（n<10）而未采用。通过方差分析或卡方检验比较各表型亚组基线特征，验证该表型划分临床意义。
2.3.2　纵向关联与轨迹异质性分析
采用广义估计方程（GEE）分析纵向关联与轨迹异质性。依据结局变量类型选择连接函数与分布，二分类变量（如ADL依赖、是否住院）使用logit连接函数与二项分布，连续变量（如认知得分）使用恒等连接函数与正态分布，设定等相关结构并采用稳健标准误。核心自变量包括基线表型亚组（编码为高负担=3，中负担=2，低负担=1）、连续随访时间及其交互项。交互项用于检验不同表型健康变化速率是否存在统计学显著差异，即轨迹异质性。
需要说明的是，CHARLS采用多阶段分层概率比例抽样设计。本研究聚焦于利用平衡面板数据探索纵向关联的内部机制，而非横断面总体推断。在此类固定样本的纵向分析中直接纳入为横断面设计的复杂抽样信息存在方法学争议，且可能因样本损耗导致结构不匹配。因此，本研究未使用分层、聚类等抽样设计信息，而采用稳健标准误（Huber-White估计量）作为控制抽样设计影响的严谨替代方案，以保障统计推断的可靠性。
2.3.3　照护需求预测模型构建
以2020年ADL/IADL依赖为结局构建预测模型。预测因子库包含基线协变量、前序健康指标及其年化变化率。采用Lasso回归筛选核心预测因子，最终通过二元logistic回归建立模型，并通过ROC曲线下面积评估判别性能（AUC>0.75）。
2.3.4　统计验证与稳健性
通过重复测量方差分析验证健康指标随时间变化的整体趋势。并在预测模型中将“年化变化率”替换为“绝对变化值”进行敏感性分析，验证核心结论稳健性。所有主要模型均计算稳健标准误。
3　结果
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3.1　基线样本分析
最终纳入298名完成五次随访的中老年人。基线特征见表2，参与者平均年龄56.8±7.6岁，女性占52.7%，农村户籍占63.4%。基线共病数为1.38±1.39种，ADL依赖比例为10.4%，IADL依赖比例为14.1%。
为评估选择偏倚，将纳入样本（n=298）与基线脱落样本（n=10 102）比较（表3）。纳入组基线时更年轻、认知功能更好、抑郁症状更轻，ADL与IADL依赖比例更低，且教育水平、在婚比例、城镇户口比例及规律锻炼比例均更高（均P<0.05），提示纳入样本为基线时更年轻、健康且社会经济地位更高的亚群体。
3.2　基线共病表型聚类结果
基于基线共病数量的K-means聚类识别出三类表型（轮廓系数=0.535, P<0.001）：高负担组（10.1%）、中负担组（35.6%）和低负担组（54.4%）。三组在疾病负担、躯体功能及心理健康方面均呈现显著梯度差异（均P<0.001），而在人口学特征上无显著差异（表4）。
从基线到末期，高、中、低负担组的共病数分别从3.93、1.75、0.35种增至5.37、3.20、1.21种。高负担组绝对增量最大（+1.44种），低负担组相对增幅最高（245.7%），但绝对增量最小（+0.86种）。所有随访时点的组间差异均显著（Tukey HSD P<0.05，图1），证实分组稳健性。
3.3　基线共病表型亚组与健康结局的纵向关联及时间效应
为探究不同表型亚组的长期健康轨迹差异，本研究构建了广义估计方程模型。与仅比较组间水平差异的横断面分析不同，特别纳入“时间×表型”的交互项，以直接检验不同表型亚组的健康结局随时间的变化速率（即斜率）是否存在统计学显著差异。所有模型均采用稳健标准误（Huber-White估计量）进行估计，以控制潜在异方差及抽样设计的影响。结果显示（表5），基线共病数量与更高的ADL依赖（OR=1.398）、IADL依赖（OR=1.346）、抑郁症状（β=0.594）、疼痛负担（β=0.203）及住院风险（OR=1.485）独立相关（均P<0.001），并与更低的生活满意度相关（β=-0.045, P=0.012）。
时间与表型的交互效应揭示了轨迹异质性。在认知功能、疼痛负担和生活满意度三个结局上，交互项均具有统计学显著性（均P<0.01）。具体而言，疾病负担越重的表型亚组，其认知功能下降、疼痛部位累积以及生活满意度下降的速率更快。在ADL/IADL依赖、抑郁症状及住院风险方面，不同表型亚组的演变速率无显著差异。
3.4　照护需求预测模型
基于Lasso回归筛选的logit模型显示（详见表6），ADL依赖预测模型的判别效能良好，AUC为0.807（95%CI: 0.740~0.874），整体预测准确率为85.91%。核心预测因子包括前序IADL障碍、抑郁症状、疼痛部位年化变化率及共病负担表型。主模型采用稳健标准误进行估计。IADL依赖预测模型的AUC为0.765（95%CI: 0.678~0.851），整体预测准确率为87.58%。核心预测因子与前者相似，并额外包含了前序疼痛部位数，但前序IADL障碍在本模型中未达统计学显著性（P=0.095）。详见表6。
值得注意的是，疼痛部位的年化变化率是唯一被筛选进入两个最终模型的动态斜率指标，提示其对功能依赖具有特异性预测价值。采用稳健标准误重新拟合上述模型以进行敏感性分析，所有核心预测因子的比值比与统计学显著性均未发生实质性改变，表明模型结果稳健。
3.5　线性趋势假设与模型稳健性验证
重复测量方差分析显示疼痛部位数随时间有显著主效应（F(4,1 188)=35.80, P<0.001），且效应量达到中高水平（偏η2=0.108）。将疼痛部位年化变化率替换为疼痛部位绝对变化值进行敏感性分析，结果依然显示疼痛负担增加是ADL依赖的显著风险因素（OR=1.205, 95%CI: 1.086~1.337, P<0.001），表明结果对于不同的变化测量方式具有稳健性。
4　讨论
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4.1　共病表型的异质性模式与全球研究的印证
基于共病数量识别的表型亚组能够有效区分不同健康风险的群体。高负担组表现为高共病、高功能受限、高心理负担的负荷状态，与现有多项研究共识吻合[6-7]。基线高共病负担已被广泛证实是未来健康恶化的强预测因子之一[8]。既有研究指出，其内在恶性循环涉及疾病交互[9]、治疗复杂性[10]、躯体功能下降及慢性炎症状态等[11]。这回应了方法学上的关切，即本研究的表型划分并非只是简单数量分层，而是捕捉了体现健康损害严重程度的综合临床表型。这为不同表型遵循异质性健康轨迹的现象提供了部分解释。
低负担组虽绝对增量小，但其高相对增幅（245.7%）提示从基本健康（<1种疾病）跨入了共病前期（≥2种疾病）的临界风险。关注绝对与相对变化，有助于制定分层精准管理策略。
4.2　共病、症状与功能结局的时序关联模式
本研究为观察性设计，主要目的在于揭示疾病、症状与功能结局间的纵向时序关联。基线共病数量与后续更重的抑郁、疼痛及更高的功能依赖风险相关，支持慢性病累积负荷假说[12-13]。同时高负担组与认知、疼痛及生活满意度的更快衰退速率相关。数据呈现出一个可被观测的时序关联链条，即更多的疾病数量与更重的抑郁和疼痛症状相关，而这些加重的症状又与后续随访中更高的功能依赖风险相关。这一关联与慢性应激导致多系统功能失调的理论模型相符[14]，为理解其背后的复杂关联提供了线索。
4.3　保护性相关因素的发现与启示
研究发现，在婚状态、更多社交互动与更好的心理健康及生活满意度相关，这与多国大型队列研究高度一致，社会联结如婚姻可能通过提供情感支持、增强社会归属感等途径，与更好的心理健康水平相关联[15]。体育锻炼与较低的 IADL依赖风险存在关联，与针对社区老年人的运动干预研究结果一致[16]。共病负担中等群体可能是干预的关键窗口期[17]，针对此群体实施以运动为代表的健康行为干预，或能产生较显著的收益[18]。此外，女性与更高的抑郁风险症状相关，这与多项大型研究[19]结果一致，提示需要特别关注性别差异。
4.4　创新性与局限性
本研究创新在于构建了整合静态共病表型与动态健康变化斜率的预测模型，并发现疼痛变化率对不同功能依赖的预测作用存在异质性。但仍存在局限性：样本存在选择偏倚（纳入者基线更健康）；且由于缺乏适用于平衡面板的统一权重，未能对复杂抽样设计及纵向样本损耗进行正式调整，尽管已采用稳健标准误进行了校正；总样本量有限，尤其高负担亚组人数较少；预测模型对功能依赖群体的识别率可能偏低。因此结论外推需谨慎，未来需更大样本和更优模型设计加以验证。
5　结论
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本研究通过对中老年的长期追踪，揭示了慢性病共病的两种关联模式：共病数量与功能依赖、抑郁、疼痛等健康结局的恶化独立相关；高共病负担表型在认知功能、疼痛累积及生活满意度方面呈现更快的衰退速率。整合前序功能状态、心理症状、共病表型及疼痛动态变化的预测模型，为识别未来功能依赖风险较高的个体提供了有一定价值的评估工具。研究提示，在应对中老年共病挑战时，临床与公共卫生实践需超越静态的疾病计数，对高共病负担群体实施以症状管理与功能维持为核心的整合照护，并利用多维预测工具实现干预资源的精准配置，助力健康老龄化战略。
基金
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  • 江苏省“青蓝工程”优秀青年骨干教师资助项目(苏教师函［2023］27号)
  • 2025年度江苏高校哲学社会科学研究一般项目(2025SJYB1709)
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2026年第53卷第10期
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doi: 10.20043/j.cnki.MPM.202509431
  • 接收时间：2025-09-25
  • 首发时间：2026-11-02
  • 出版时间：2026-05-25
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  • 收稿日期：2025-09-25
基金
江苏省“青蓝工程”优秀青年骨干教师资助项目(苏教师函［2023］27号)
2025年度江苏高校哲学社会科学研究一般项目(2025SJYB1709)
作者信息
    1.南京中医药大学翰林学院，江苏 泰州 225300
    2.南京中医药大学泰州校区

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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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
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