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Article(id=1324050772962451699, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324050669933572771, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202507317, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1753200000000, receivedDateStr=2025-07-23, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793607903948, onlineDateStr=2026-11-02, pubDate=1778342400000, pubDateStr=2026-05-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793607903948, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793607903948, creator=13701087609, updateTime=1793607903948, updator=13701087609, issue=Issue{id=1324050669933572771, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='9', pageStart='1537', pageEnd='1728', issueExtLink='null', onlineDate='null', pubDate='1778342400000', pubDateStr='2026-05-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793607879384, creator='13701087609', updateTime=1793670100562, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324311644528050975, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324050669933572771, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324311644528050976, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324050669933572771, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1663, endPage=1669, ext={EN=ArticleExt(id=1324050773281218804, articleId=1324050772962451699, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=The impact of combined trajectories of sleep duration and sleep quality on diabetes risk among middle-aged and older adults, columnId=1228016572451718132, journalTitle=Modern Preventive Medicine, columnName=Health and Social Behavior, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the impact of combined trajectories of sleep duration and sleep quality on diabetes risk among middle-aged and older adults, providing evidence for precision prevention and control of diabetes.

Methods

Data of the China Health and Retirement Longitudinal Survey from 2011 to 2015 were analyzed to identify the combined trajectories of sleep duration and sleep quality, employing group-based multi-trajectory modeling (GBMTM). The prevalence of diabetes among middle-aged and older adults was tracked using data from 2015 to 2020. The effect of different combined trajectories of sleep duration and sleep quality on diabetes risk was examined using Cox proportional hazards regression models. Meanwhile, subgroup analyses were conducted.

Results

A total of 8 735 middle-aged and older adults were included. Four combined trajectories of sleep duration and sleep quality were identified: increasing sleep duration-limited improvement in sleep quality trajectory (n=1 200, 13.74%, trajectory group 1), stable short sleep duration-stable poor sleep quality trajectory (n=828, 9.48%, trajectory group 2), stable normal sleep duration-stable good sleep quality trajectory (n=5 505, 63.02%, trajectory group 3), and decreasing sleep duration-deteriorating sleep quality trajectory (n=1 202, 13.76%, trajectory group 4). Cox proportional hazards regression modeling showed that, compared with the trajectory group 3, trajectory group 1 (HR=1.29, 95% CI: 1.03-1.63), trajectory group 2 (HR=1.54, 95% CI: 1.19-1.98), trajectory group 4 (HR=1.42, 95% CI: 1.14-1.78) had a higher risk of diabetes. In the subgroup analyses, there was an interaction between sleep duration and sleep quality trajectories and gender (P=0.047).

Conclusion

Among middle-aged and older adults, individuals in trajectory group 1, trajectory group 2, and trajectory group 4 had an increased risk of diabetes.

, authors=Rui-qin ZHANG1, Qiong LI1, Li NIE2, Cui-yong SUN2, authorsList=Rui-qin ZHANG, Qiong LI, Li NIE, Cui-yong SUN, authorCompany=null, correspAuthors=Cui-yong SUN, 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=1324050775562920185, articleId=1324050772962451699, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=睡眠时长和睡眠质量联合轨迹对中老年人糖尿病患病风险的影响研究, columnId=1228016572640461823, journalTitle=现代预防医学, columnName=健康与社会行为, runingTitle=null, highlight=null, articleAbstract=
目的

探讨睡眠时长和睡眠质量联合轨迹对中老年人糖尿病患病风险的影响，为糖尿病精准防治提供参考依据。

方法

运用中国健康与养老追踪调查2011—2015年数据，基于群组多轨迹模型识别睡眠时长和睡眠质量联合轨迹，并追踪2015—2020年中老年人糖尿病患病情况。使用 Cox比例风险模型分析不同睡眠时长和睡眠质量联合轨迹对糖尿病患病风险影响，并进行亚组分析。

结果

共纳入8 735名中老年人。识别出四种轨迹，分别为睡眠时长增加-睡眠质量有限改善组（n=1 200，13.74%，轨迹组1）、睡眠时长持续短-睡眠质量持续差组（n=828，9.48%，轨迹组2）、睡眠时长持续正常-睡眠质量持续好组（n=5 505，63.02%，轨迹组3）、睡氓时长减少-睡眠质量恶化组（n=1 202，13.76%，轨迹组4）。Cox比例风险回归结果显示：与轨迹组3相比，轨迹组1（HR=1.29，95% CI：1.03～1.63）、轨迹组2（HR=1.54，95% CI：1.19～1.98）、轨迹组4（HR=1.42，95% CI：1.14～1.78）糖尿病患病风险更高。亚组分析中，睡眠时长和睡眠质量联合轨迹与性别有交互作用（P= 0.047）。

结论

在中老年人群中，属于轨迹组1、轨迹组2、轨迹组4的个体可以增加糖尿病患病风险。

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张瑞芹（1981—），硕士，副教授，研究方向：公共卫生护理

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孙翠勇，E-mail：
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注aP<0.05;bP<0.01。

, figureFileSmall=BTr0aP3JTEPOiFQ9sKMS2w==, figureFileBig=Ck+dPPiouspTB99EExq2JA==, tableContent=null), ArticleFig(id=1324050780206014753, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050772962451699, language=EN, label=Table 1, caption=

Fit statistics of combined trajectories of sleep duration and sleep quality

, figureFileSmall=null, figureFileBig=null, tableContent=
组数阶数BIC熵AvePP组成员比例（%）
2睡眠时长（2 2）
睡眠质量（2 2）		-92 215.930.8680.927;0.97224.25;75.75
3睡眠时长（2 2 2）
睡眠质量（2 2 2）		-91 395.760.7620.911;0.982;0.85519.18;70.92;9.90
4睡眠时长（2 2 2 2）
睡眠质量（2 2 2 2）		-90 416.860.8920.913;0.898;0.965;0.8889.39;17.19;63.36;10.06
4a睡眠时长（1 1 2 1）
睡眠质量（1 2 2 1）		-90 566.810.8440.839;0.897;0.950;0.83213.74;9.48;63.02;13.76
5睡眠时长（1 1 1 1 1）
睡眠质量（1 1 1 1 1）		-90 343.140.7140.795;0.893;0.684;0.836;0.80712.17;9.84;23.16;42.25;12.58
4b睡眠时长（1 1 2 1）
睡眠质量（1 2 2 1）		-45 367.380.8410.833;0.885;0.949;0.83414.37;8.51;63.07;14.05
), ArticleFig(id=1324050780302483746, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050772962451699, language=CN, label=表1, caption=

睡眠时长和睡眠质量联合轨迹模型拟合指标

, figureFileSmall=null, figureFileBig=null, tableContent=
组数阶数BIC熵AvePP组成员比例（%）
2睡眠时长（2 2）
睡眠质量（2 2）		-92 215.930.8680.927;0.97224.25;75.75
3睡眠时长（2 2 2）
睡眠质量（2 2 2）		-91 395.760.7620.911;0.982;0.85519.18;70.92;9.90
4睡眠时长（2 2 2 2）
睡眠质量（2 2 2 2）		-90 416.860.8920.913;0.898;0.965;0.8889.39;17.19;63.36;10.06
4a睡眠时长（1 1 2 1）
睡眠质量（1 2 2 1）		-90 566.810.8440.839;0.897;0.950;0.83213.74;9.48;63.02;13.76
5睡眠时长（1 1 1 1 1）
睡眠质量（1 1 1 1 1）		-90 343.140.7140.795;0.893;0.684;0.836;0.80712.17;9.84;23.16;42.25;12.58
4b睡眠时长（1 1 2 1）
睡眠质量（1 2 2 1）		-45 367.380.8410.833;0.885;0.949;0.83414.37;8.51;63.07;14.05
), ArticleFig(id=1324050780398952739, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050772962451699, language=EN, label=Table 2, caption=

Baseline characteristics comparison according to the combined trajectories of sleep duration and sleep quality ［n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总计轨迹组1轨迹组2轨迹组3轨迹组4χ2值P值
样本量8 7351 2008285 5051 202
年龄（岁）24.704<0.001
<605 186689（57.42）432（52.17）3 350（60.85）715（59.48）
≥603 549511（42.58）396（47.83）2 155（39.15）487（40.52）
性别244.920<0.001
男4 238541（45.08）253（30.56）2 292（54.35）452（37.60）
女4 497659（54.92）575（69.44）2 513（45.65）750（62.40）
文化程度93.221<0.001
小学及以下6 045881（73.42）660（79.71）3 627（65.89）877（72.96）
初中1 757224（18.66）113（13.65）1 209（21.96）211（17.56）
高中及以上93395（7.92）55（6.64）669（12.15）114（9.48）
居住地
城市2 965409（34.08）251（30.31）1 932（35.10）373（31.03）12.6780.005
农村5 770791（65.92）577（69.69）3 573（64.90）829（68.79）
BMI（kg/m2）26.817<0.001
<244 489635（52.92）470（56.76）2 726（49.52）658（54.74）
24～27.91 794248（20.66）150（18.12）1 184（21.51）212（17.64）
≥282 452317（26.42）208（25.12）1 595（28.97）332（27.62）
高血压0.8080.847
否5 864816（68.00）548（66.18）3 690（67.03）810（67.39）
是2 871384（32.00）280（33.82）1 815（32.97）392（32.61）
心脏病56.843<0.001
否7 7381 041（86.75）676（81.64）4 962（90.14）1 059（88.10）
是997159（13.25）152（18.36）543（9.86）143（11.90）
脑卒中6.1050.107
否8 5711 170（97.50）807（97.46）5 410（98.27）1 184（98.50）
是16430（2.50）21（5.54）95（1.73）18（1.50）
高血脂9.3990.024
否3 819550（45.83）347（41.91）2 360（42.87）562（46.76）
是4 916650（54.17）481（58.09）3 145（57.13）640（53.24）
吸烟49.795<0.001
否6 069832（69.33）641（77.42）3 704（67.28）892（74.21）
是2 666368（30.67）187（22.58）1 801（32.72）310（25.79）
饮酒68.305<0.001
否5 722803（66.92）618（74.64）3 447（62.62）854（71.05）
是3 013397（33.08）210（25.36）2 058（37.38）348（29.95）
社会参与24.024<0.001
否4 487616（51.33）460（55.56）2 734（49.66）677（56.32）
是4 248584（48.67）368（44.44）2 771（50.34）525（43.68）
), ArticleFig(id=1324050780516393252, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050772962451699, language=CN, label=表2, caption=

睡眠时长和睡眠质量联合轨迹基线特征比较［n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总计轨迹组1轨迹组2轨迹组3轨迹组4χ2值P值
样本量8 7351 2008285 5051 202
年龄（岁）24.704<0.001
<605 186689（57.42）432（52.17）3 350（60.85）715（59.48）
≥603 549511（42.58）396（47.83）2 155（39.15）487（40.52）
性别244.920<0.001
男4 238541（45.08）253（30.56）2 292（54.35）452（37.60）
女4 497659（54.92）575（69.44）2 513（45.65）750（62.40）
文化程度93.221<0.001
小学及以下6 045881（73.42）660（79.71）3 627（65.89）877（72.96）
初中1 757224（18.66）113（13.65）1 209（21.96）211（17.56）
高中及以上93395（7.92）55（6.64）669（12.15）114（9.48）
居住地
城市2 965409（34.08）251（30.31）1 932（35.10）373（31.03）12.6780.005
农村5 770791（65.92）577（69.69）3 573（64.90）829（68.79）
BMI（kg/m2）26.817<0.001
<244 489635（52.92）470（56.76）2 726（49.52）658（54.74）
24～27.91 794248（20.66）150（18.12）1 184（21.51）212（17.64）
≥282 452317（26.42）208（25.12）1 595（28.97）332（27.62）
高血压0.8080.847
否5 864816（68.00）548（66.18）3 690（67.03）810（67.39）
是2 871384（32.00）280（33.82）1 815（32.97）392（32.61）
心脏病56.843<0.001
否7 7381 041（86.75）676（81.64）4 962（90.14）1 059（88.10）
是997159（13.25）152（18.36）543（9.86）143（11.90）
脑卒中6.1050.107
否8 5711 170（97.50）807（97.46）5 410（98.27）1 184（98.50）
是16430（2.50）21（5.54）95（1.73）18（1.50）
高血脂9.3990.024
否3 819550（45.83）347（41.91）2 360（42.87）562（46.76）
是4 916650（54.17）481（58.09）3 145（57.13）640（53.24）
吸烟49.795<0.001
否6 069832（69.33）641（77.42）3 704（67.28）892（74.21）
是2 666368（30.67）187（22.58）1 801（32.72）310（25.79）
饮酒68.305<0.001
否5 722803（66.92）618（74.64）3 447（62.62）854（71.05）
是3 013397（33.08）210（25.36）2 058（37.38）348（29.95）
社会参与24.024<0.001
否4 487616（51.33）460（55.56）2 734（49.66）677（56.32）
是4 248584（48.67）368（44.44）2 771（50.34）525（43.68）
), ArticleFig(id=1324050780596085029, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050772962451699, language=EN, label=Table 3, caption=

Cox regression analysis of the association between combined trajectories of sleep duration and sleep quality and the risk of diabetes

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项目模型1模型2模型3模型4
HR值（95% CI）P值HR值（95% CI）P值HR值（95% CI）P值HR值（95% CI）P值
轨迹组31.00（Ref）1.00（Ref）1.00（Ref）1.00（Ref）
轨迹组11.26（1.01～159）0.0451.29（1.03～1.63）0.0281.32（1.04～1.68）0.0231.30（1.04～1.63）0.022
轨迹组21.54（1.21～1.98）<0.0011.54（1.19～1.98）0.0011.47（1.12～1.93）0.0061.60（1.22～2.10）0.001
轨迹组41.39（1.12～1.73）0.0031.42（1.14～1.78）0.0021.36（1.07～1.73）0.0131.40（1.19～1.75）0.003
), ArticleFig(id=1324050780700942630, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324050772962451699, language=CN, label=表3, caption=

睡眠时长和睡眠质量联合轨迹与糖尿病患病风险的Cox回归分析

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项目模型1模型2模型3模型4
HR值（95% CI）P值HR值（95% CI）P值HR值（95% CI）P值HR值（95% CI）P值
轨迹组31.00（Ref）1.00（Ref）1.00（Ref）1.00（Ref）
轨迹组11.26（1.01～159）0.0451.29（1.03～1.63）0.0281.32（1.04～1.68）0.0231.30（1.04～1.63）0.022
轨迹组21.54（1.21～1.98）<0.0011.54（1.19～1.98）0.0011.47（1.12～1.93）0.0061.60（1.22～2.10）0.001
轨迹组41.39（1.12～1.73）0.0031.42（1.14～1.78）0.0021.36（1.07～1.73）0.0131.40（1.19～1.75）0.003
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睡眠时长和睡眠质量联合轨迹对中老年人糖尿病患病风险的影响研究
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张瑞芹 1 , 李琼 1 , 聂丽 2 , 孙翠勇 2
现代预防医学 | 健康与社会行为 2026,53(9): 1663-1669
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现代预防医学 |健康与社会行为 2026 , 53 (9) : 1663 -1669
睡眠时长和睡眠质量联合轨迹对中老年人糖尿病患病风险的影响研究
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张瑞芹1, 李琼1, 聂丽2, 孙翠勇2
作者信息
  • 1.河南医药大学护理学院，河南 新乡 453003
  • 2.河南医药大学卫生健康管理学院，河南 新乡 453003
通讯作者:
孙翠勇，E-mail：
作者简介:

张瑞芹（1981—），硕士，副教授，研究方向：公共卫生护理

The impact of combined trajectories of sleep duration and sleep quality on diabetes risk among middle-aged and older adults
Rui-qin ZHANG1, Qiong LI1, Li NIE2, Cui-yong SUN2
Affiliations
  • School of Nursing, School of Health Management, Henan Medical University, Xinxiang, Henan 453003, China
出版时间: 2026-05-10 doi: 10.20043/j.cnki.MPM.202507317
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摘要
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目的

探讨睡眠时长和睡眠质量联合轨迹对中老年人糖尿病患病风险的影响，为糖尿病精准防治提供参考依据。

方法

运用中国健康与养老追踪调查2011—2015年数据，基于群组多轨迹模型识别睡眠时长和睡眠质量联合轨迹，并追踪2015—2020年中老年人糖尿病患病情况。使用 Cox比例风险模型分析不同睡眠时长和睡眠质量联合轨迹对糖尿病患病风险影响，并进行亚组分析。

结果

共纳入8 735名中老年人。识别出四种轨迹，分别为睡眠时长增加-睡眠质量有限改善组（n=1 200，13.74%，轨迹组1）、睡眠时长持续短-睡眠质量持续差组（n=828，9.48%，轨迹组2）、睡眠时长持续正常-睡眠质量持续好组（n=5 505，63.02%，轨迹组3）、睡氓时长减少-睡眠质量恶化组（n=1 202，13.76%，轨迹组4）。Cox比例风险回归结果显示：与轨迹组3相比，轨迹组1（HR=1.29，95% CI：1.03～1.63）、轨迹组2（HR=1.54，95% CI：1.19～1.98）、轨迹组4（HR=1.42，95% CI：1.14～1.78）糖尿病患病风险更高。亚组分析中，睡眠时长和睡眠质量联合轨迹与性别有交互作用（P= 0.047）。

结论

在中老年人群中，属于轨迹组1、轨迹组2、轨迹组4的个体可以增加糖尿病患病风险。

睡眠时长  /  睡眠质量  /  轨迹  /  糖尿病  /  群组多轨迹模型
Objective

To explore the impact of combined trajectories of sleep duration and sleep quality on diabetes risk among middle-aged and older adults, providing evidence for precision prevention and control of diabetes.

Methods

Data of the China Health and Retirement Longitudinal Survey from 2011 to 2015 were analyzed to identify the combined trajectories of sleep duration and sleep quality, employing group-based multi-trajectory modeling (GBMTM). The prevalence of diabetes among middle-aged and older adults was tracked using data from 2015 to 2020. The effect of different combined trajectories of sleep duration and sleep quality on diabetes risk was examined using Cox proportional hazards regression models. Meanwhile, subgroup analyses were conducted.

Results

A total of 8 735 middle-aged and older adults were included. Four combined trajectories of sleep duration and sleep quality were identified: increasing sleep duration-limited improvement in sleep quality trajectory (n=1 200, 13.74%, trajectory group 1), stable short sleep duration-stable poor sleep quality trajectory (n=828, 9.48%, trajectory group 2), stable normal sleep duration-stable good sleep quality trajectory (n=5 505, 63.02%, trajectory group 3), and decreasing sleep duration-deteriorating sleep quality trajectory (n=1 202, 13.76%, trajectory group 4). Cox proportional hazards regression modeling showed that, compared with the trajectory group 3, trajectory group 1 (HR=1.29, 95% CI: 1.03-1.63), trajectory group 2 (HR=1.54, 95% CI: 1.19-1.98), trajectory group 4 (HR=1.42, 95% CI: 1.14-1.78) had a higher risk of diabetes. In the subgroup analyses, there was an interaction between sleep duration and sleep quality trajectories and gender (P=0.047).

Conclusion

Among middle-aged and older adults, individuals in trajectory group 1, trajectory group 2, and trajectory group 4 had an increased risk of diabetes.

Sleep duration  /  Sleep quality  /  Trajectory  /  Diabetes  /  Group-based multi-trajectory modeling
张瑞芹, 李琼, 聂丽, 孙翠勇. 睡眠时长和睡眠质量联合轨迹对中老年人糖尿病患病风险的影响研究. 现代预防医学, 2026 , 53 (9) : 1663 -1669 . DOI: 10.20043/j.cnki.MPM.202507317
Rui-qin ZHANG, Qiong LI, Li NIE, Cui-yong SUN. The impact of combined trajectories of sleep duration and sleep quality on diabetes risk among middle-aged and older adults[J]. Modern Preventive Medicine, 2026 , 53 (9) : 1663 -1669 . DOI: 10.20043/j.cnki.MPM.202507317
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糖尿病已经成为威胁人类健康主要公共卫生问题之一。到2021年我国糖尿病人数达已到1.41亿[1],给家庭和社会带来沉重的医疗负担[2]。睡眠时间和睡眠质量作为代谢调节的重要环节,已成为糖尿病患病风险的重要影响因素[3]。美国睡眠基金委推荐中老年人每晚睡眠时长为7～9 h, <6 h为短睡眠时长, >9 h为长睡眠时长[4]。研究表明成人每晚睡眠时长<5 h[5]或入睡时间晚、碎片化睡眠等低睡眠质量[6-7],会影响血糖的变异性,是糖尿病的危险因素。目前睡眠时长和睡眠质量对糖尿病的影响主要以横断面或短期随访为主,如来自美国国家健康与营养调查[8]（National Health and Nutrition Examination Survey，NHANES）和我国云南老年人[9]的横断面调查研究发现每晚短睡眠时长和睡眠质量差发生睡眠障碍者,糖尿病患病风险明显增加。然而人的睡眠特征在多种因素影响下会发生改变,这种单一时间点的研究,难以捕捉个体睡眠特征随年龄增长的纵向动态变化,同时关于睡眠时长和睡眠质量共同变化轨迹对糖尿病患病风险协同作用的研究较少。本研究采用基于群组多轨迹模型[10]（group-based multi-trajectory modeling, GBMTM）方法,识别中国中老年人群睡眠时长和睡眠质量的联合发展轨迹,探讨不同睡眠轨迹对糖尿病患病风险的协同作用,为中老年人群糖尿病综合防治的精准干预提供重要参考依据。
1　对象与方法
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1.1　对象
采用中国健康与养老追踪调查（China Health and Retirement Longitudinal Study, CHARLS）基线2011年及随访2013、2015、2018和2020年数据。本研究以2011年45岁及以上中老年人群为研究对象,纳入标准:（1）2011、2013和2015年均有完整的睡眠信息;（2）2011—2015年均未自我报告或均未诊断为糖尿病者;（3）2018或2020年有完整的糖尿病或死亡信息者;（4）基线协变量无缺失者,最终共纳入8 735名参与者。CHARLS调查已获得北京大学生物医学伦理委员会批准（IRB00001052-11015）,所有参与者均已签订书面知情同意书。
1.2　睡眠时长评估
通过询问研究对象“过去一个月内平均每天晚上真正睡着的时间大约是几小时？”获取每晚夜间睡眠时长数据。
1.3　睡眠质量测定
通过询问研究对象最近一周内“我的睡眠不好”发生情况,回答“很少或者根本没有（1 d）”为1分,“不太多（1～2 d）”为2分,“有时或者说有一半的时间（3～4 d）”为3分,“大多数的时间（5～7 d）”为4分,分数越高表示睡眠质量差。
1.4　糖尿病评估
诊断标准：2011—2015年为空腹血糖≥7.0 mmol/L,和（或）糖化血红蛋白≥6.5%[11],和（或）自我报告有糖尿病,和（或）自我报告使用降血糖药物。2018和2020年为自我报告有糖尿病,和（或）自我报告使用降血糖药物。
1.5　随访时间
随访时间为从2015年调查起,至首次出现糖尿病、死亡或随访结束（2020年）等终点事件,以最早发生者为准。
1.6　2011年基线协变量
根据以往参考文献[8,12]，纳入研究对象的人口学信息（性别、年龄、居住地、受教育程度）、健康状况［身体质量指数（body mass index,BMI）、高血压、心脏病、脑卒中、高血脂］、生活行为方式［现在是否饮酒、吸烟、社会参与（近一个月内参与任意一项社会活动为有社会参与,任意一项都没有为无社会参与）等］。采用标准方法测量研究对象的血压、身高、体重。BMI计算公式为:体重/身高的二次方（kg/m2）。
1.7　统计方法
用Stata18.0和R 4.4.1软件进行统计分析。
GBMTM模型允许同时分析两个及以上变量的联合轨迹，识别潜在的个体集群进行联合分析[10]。运用Stata软件中“traj”程序的“cnorm”删失模型识别2011—2015年睡眠时长和睡眠质量的异质性轨迹。分别拟合2～5组轨迹,每组从高阶参数至低阶参数（3～1）进行不断迭代拟合,通过最大似然估计进行筛选。模型拟合指标包括:（1）贝叶斯信息准则（Bayesian information criteria, BIC）绝对值越小表示模型拟合越好;（2）平均后验概率（average posterior probability, AvePP）反映根据轨迹分组后的亚组内成员与该轨迹的符合程度，当AvePP≥0.7表示模型轨迹组分类的可靠性高;（3）每个轨迹组成员比例≥5%,以避免样本不足导致的不稳定性;（4）熵接近1表示分类效果较好。稳健性检验,为每个研究对象分配唯一标识符，随机抽取50%的个体，用GBMTM方法分析睡眠时长和睡眠质量共同发展轨迹，用来检验模型的稳健性。
计数资料用频数和百分比表示,组间比较采用χ2检验。采用Cox比例风险模型分析睡眠时间和睡眠质量联合轨迹与糖尿病患病风险的关联。仅预设年龄、性别、居住地、BMI、高血压、高血脂等关键变量进行亚组和交互作用分析。两项敏感性分析检验结果的稳健性, 一是排除2015年随访一年内报告患有糖尿病的个体;二是进行逆概率加权回归分析。双侧检验，检验水准 α=0.05。
2　结果
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2.1　中老年人睡眠时长和睡眠质量联合轨迹
根据GBMTM拟合指标参数,睡眠时长和睡眠质量联合轨迹4组为最优模型，见表1。轨迹组1为睡眠时长增加-睡眠质量有限改善组（n=1 200, 13.74%）,轨迹组2为睡眠时长持续短-睡眠质量持续差组（n=828,9.48%）,轨迹组3为睡眠时长持续正常-睡眠质量持续好组（n=5 505, 63.02%）,轨迹组4为睡眠时长减少-睡眠质量恶化组（n=1 202, 13.76%）,其中轨迹组3为正常睡眠时长和睡眠质量模式。轨迹见图1。随机抽取50%个体按GBMTM识别睡眠时长和睡眠质量发展轨迹，最佳轨迹图及拟合指标与总体相似，表明轨迹组分类稳健性较好。
2.2　中老年人睡眠时长和睡眠质量联合轨迹基线特征
本研究共纳入中老年人8 735名。参与者的睡眠时长和睡眠质量轨迹基线特征见表2。在年龄、性别、文化程度、居住地、BMI、心脏病、高血脂、吸烟、饮酒、社会参与等方面有显著差异。与轨迹组3相比,老年人、女性、文化程度低、居住在农村、BMI<24kg/m2、有心脏病、无高血脂、目前不抽烟、不饮酒、没有社会参与的人群属于其他3组轨迹可能性更大。
2.3　睡眠时长和睡眠质量联合轨迹与糖尿病患病风险的Cox回归分析
2015—2020年,中老年人共死亡545名,报告患有糖尿病620名。与轨迹组3相比,单变量Cox回归中（模型1）,轨迹组1、轨迹组2、轨迹组4的HR（95% CI）分别为1.26（1.01～1.59）、1.54（1.21～1.98）、1.39（1.12～1.73）,均能增加糖尿病患病风险（P均<0.05）；调整协变量后,轨迹组1、轨迹组2、轨迹组4的HR（95% CI）分别为1.29（1.03～1.63）、1.54（1.19～1.98）、1.42（1.14～1.78）,差异仍有统计学意义, P均<0.05（模型2）。其中轨迹组1和轨迹组4患病风险增加,轨迹组2患病风险没有变化,不受协变量影响,仍为患病风险最高轨迹组。两项敏感性分析（模型3、模型4）结果与模型2相似。见表3。
2.4　睡眠时长和睡眠质量联合轨迹对糖尿病患病风险的亚组分析
对年龄、性别、居住地、BMI、高血压、高血脂等关键变量进行亚组和交互分析。调整协变量后,不同睡眠时长和睡眠质量联合轨迹组对糖尿病患病风险除在性别上有微弱交互作用（P=0.047）外,与年龄（P=0.688）、居住地（P=0.908）、BMI（P=0.433）、高血压（P=0.427）、高血脂（P=0.863）没有显著交互作用,结果见图2。
3　讨论
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本研究利用GBMTM模型识别出CHARLS数据库8 735名中老年人存在四种睡眠时长和睡眠质量联合轨迹,分别为睡眠时长增加-睡眠质量有限改善组（轨迹组1）、睡眠时长持续短-睡眠质量持续差组（轨迹组2）、睡眠时长持续正常-睡眠质量持续好组（轨迹组3）、睡眠时长减少-睡眠质量恶化组（轨迹组4）。本研究结果显示,与轨迹组3相比,其它3个轨迹组均可增加糖尿病患病风险。因此,轨迹组3的个体应继续保持现在良好的生活方式和睡眠习惯。
本研究观察到呈现轨迹组1的个体糖尿病患病风险最低。尽管该轨迹组睡眠时长有所增加,睡眠质量亦改善,但由于基线睡眠状况严重不良,且睡眠时长也未增加至正常水平,仍有增加糖尿病患病的风险。结果表明睡眠时间和睡眠质量的有限优化无法完全逆转既往睡眠障碍所造成的远期不良代谢影响,这与Fang等[13]睡眠时长轨迹对代谢性疾病结果相似,且本研究是睡眠时长和睡眠质量的联合轨迹,更能捕捉睡眠特征的协同效应。此轨迹组是最有可能从进一步干预（如持续的健康行为指导、睡眠障碍的筛查与治疗）中获益的群体,因此应继续强化现有干预措施,将睡眠时间和睡眠质量改善至正常水平,从而将糖尿病风险降至最低。
本研究轨迹组2糖尿病患病风险最高。持续短睡眠通过长期代谢压力负荷触发机体失代偿机制,对β细胞功能和胰岛素敏感性产生渐进性损害,引起血糖升高,这与Li等[14]基于遗传机制和神经生物学途径提出的持续睡眠不足会对健康造成不良结局的结论相互印证。另有学者Shen等[15]利用GNHS队列通过Huber稳健回归发现持续短睡眠轨迹组能显著引起血糖升高变异,与本研究结果一致。睡眠质量持续差不利于糖尿病血糖控制[16-17],可以显著增加糖尿病相关并发症[18]（如心血管疾病风险、周围神经病变、全因死亡率上升等）。这一发现与慢性应激的累积损伤假说高度契合[19],即机体长期承受的应激性损伤效应不断累积,最终导致糖代谢异常,同时不良的睡眠时长和睡眠质量会产生协同消极效应。对该轨迹组的人群应进行睡眠障碍评估,帮助其建立规律睡眠习惯,协同专业人员进行必要的认知行为疗法和药物治疗等干预措施。
本研究轨迹组4虽然在基线时睡眠行为接近正常水平,但在随访期间睡眠时长缩短且睡眠质量进行性恶化,仍能显著增加糖尿病风险，与一项英国老龄化数据库研究结果一致[20]。研究表明[21]睡眠行为进行性恶化,会加剧炎性因子（如IL-6、CRP）的释放,或致使与糖尿病风险相关的其他生物过程的失调。这提示依赖单一时间点的睡眠行为测量可能掩盖长期睡眠动态变化与糖尿病风险之间的真实关联强度。该轨迹组可能是健康预警信号，需早期阻断并逆转睡眠形态恶化进程。应评估个体近期压力、躯体不适等诱因并进行及时干预,若长时间无改善，可进行专业治疗,防止发展为慢性顽固性睡眠障碍。
值得注意的是,本研究中睡眠时长和睡眠质量联合轨迹与性别有交互作用,在女性中糖尿病患病风险更高,与Yu等[22]研究结果一致。与男性相比,中老年女性由于雌激素水平下降及心理社会因素影响[23],有较多显著的睡眠问题:如睡眠效率较低、睡眠时长减少、碎片化睡眠增多[24-25]等,更易增加糖尿病风险。因此在未来的糖尿病精准预防中,有必要关注那些存在持续不良睡眠模式的女性,及时进行干预。
睡眠时长不足和睡眠质量不佳可能通过多种机制增加糖尿病发生风险。如使细胞胰岛素敏感性及糖耐量受损、交感神经系统活性增加影响葡萄糖反应水平[26]、扰乱昼夜节律干扰肝脏糖异生和胰岛素分泌节律[27]。另外，睡眠时间异常或睡眠质量差与全身性炎症升高和炎症细胞因子的高释放有关[28],进一步损害了胰岛素信号传导和葡萄糖耐量。
本研究克服以往单一时间点或单一睡眠指标轨迹研究的局限性，通过构建睡眠时长与睡眠质量的联合轨迹模型,实现了对这两项核心睡眠特征随时间协同变化的动态捕捉,并系统揭示了其与中老年人糖尿病患病风险之间的关联。该方法扩展了睡眠健康的研究维度,更全面地体现睡眠时长和睡眠质量对糖尿病发生的协同作用,从而更准确地揭示睡眠模式与代谢风险之间的动态关联。未来在糖尿病精准防控中,建议重视睡眠时长和睡眠质量的协同作用,构建睡眠“时长-质量”双维度评估体系,通过动态监测睡眠轨迹,进一步优化睡眠障碍和糖尿病综合防控策略。本研究也存在一定局限性。一是，睡眠信息主要来自于研究对象自我报告,如睡眠质量评估使用了简单的评分方法，未纳入觉醒次数、入睡潜伏期、失眠等核心指标，可能存在因指标局限导致轨迹分类偏差的可能。二是，本研究尽管对已知的潜在影响因素进行了调整,但仍可能存在一些未纳入的混杂因素,如研究对象的饮食习惯、遗传易感性等因素，可能影响糖尿病的发生。此外，尽管CHARLS是一项全国性调查，但本研究中没有讨论研究对象人群分布是否与中国中老年人城乡、区域及疾病分布一致，可能限制结果的普适性。
基金
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  • 国家社会科学基金项目(22BGL213)
  • 河南省科技研发计划联合基金(235101610046)
  • 河南省高校人文社会科学研究一般项目(2026-ZZJH-280)
  • 河南省软科学研究项目(232400410214)
文献
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2026年第53卷第9期
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doi: 10.20043/j.cnki.MPM.202507317
  • 接收时间：2025-07-23
  • 首发时间：2026-11-02
  • 出版时间：2026-05-10
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  • 收稿日期：2025-07-23
基金
国家社会科学基金项目(22BGL213)
河南省科技研发计划联合基金(235101610046)
河南省高校人文社会科学研究一般项目(2026-ZZJH-280)
河南省软科学研究项目(232400410214)
作者信息
    1.河南医药大学护理学院，河南 新乡 453003
    2.河南医药大学卫生健康管理学院，河南 新乡 453003

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孙翠勇，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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