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Article(id=1324077591350621030, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077560820282023, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202511321, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1763568000000, receivedDateStr=2025-11-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793614297950, onlineDateStr=2026-11-02, pubDate=1774368000000, pubDateStr=2026-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793614297950, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793614297950, creator=13701087609, updateTime=1793614297950, updator=13701087609, issue=Issue{id=1324077560820282023, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='6', pageStart='961', pageEnd='1152', issueExtLink='null', onlineDate='null', pubDate='1774368000000', pubDateStr='2026-03-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793614290671, creator='13701087609', updateTime=1793614391469, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324077983656444429, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077560820282023, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324077983660638734, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077560820282023, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=968, endPage=972, ext={EN=ArticleExt(id=1324077591623250791, articleId=1324077591350621030, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Association study of sleep duration and cardiovascular health in the elderly based on federated causal learning, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Objective

To address regional heterogeneity in multicenter studies, and to explore the association between sleep duration and cardiovascular disease (CVD) risk in the Chinese elderly population using a federated causal learning framework.

Methods

Data was obtained from the 2020 China Health and Retirement Longitudinal Study (CHARLS). A total of 10 638 adults aged 60 and above were included. To simulate multi-center collaboration scenarios with real regional heterogeneity, the original dataset was divided into seven geographically distributed datasets. A federated augmented inverse probability weighting (AIPW) estimator was used to estimate causal effects with comparison to centralized models. Subgroup analyses by gender were conducted to explore effect heterogeneity. The robustness of the method was explored by a sensitivity analysis.

Results

Estimations from the federated causal learning models were consistent with those from the centralized model. Abnormal sleep duration was a significant factor influencing CVD risk. Compared to normal sleepers, short sleepers had a significantly increased risk of CVD (OR=1.029, 95% CI:1.016-1.042). Long sleepers also showed elevated risk (OR=1.019, 95% CI:1.005-1.034). The subgroup analysis revealed significant gender differences, and the sensitivity analysis also validated the robustness of the model.

Conclusion

In multicenter research, the federated causal learning systematically controls for confounders and regional heterogeneity. It provides methodological support for future privacy-preserving cross-center collaborative research.

, authors=Miao-shuang CHEN, Qiao DENG, Zong-qi CHANG, Tao ZHANG, authorsList=Miao-shuang CHEN, Qiao DENG, Zong-qi CHANG, Tao ZHANG, authorCompany=null, correspAuthors=Tao ZHANG, 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=1324077593028342644, articleId=1324077591350621030, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=基于联邦因果学习的老年人睡眠时长与心血管健康的关联性研究, 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

针对多中心研究中存在地区异质性问题，本研究旨在利用联邦因果学习框架探讨中国老年人群睡眠时长与心血管疾病（CVD）风险的关联。

方法

本研究数据来源于2020年中国健康与养老追踪调查，最终纳入10 638名60岁及以上老年人。为模拟多中心协作场景并保留真实地区异质性，将原始数据按地理区域划分为7个分布式数据集，采用联邦增强逆概率加权模型进行效应估计，并与集中式结果进行对比。此外，按性别进行了亚组分析以探索效应异质性，以及通过敏感性分析以探索方法的稳健性。

结果

联邦因果学习与集中式结果保持一致，睡眠时长异常是CVD风险的显著影响因素。与正常睡眠组相比，短睡眠组患CVD的风险显著增加（OR=1.029, 95% CI: 1.016~1.042），长睡眠组同样表现出风险升高（OR=1.019, 95% CI: 1.005~1.034）。亚组分析进一步揭示了显著的性别差异，敏感性也验证了模型的稳健性。

结论

在多中心研究中，联邦因果学习能够系统性地控制混杂因素与地区异质性，为未来开展隐私安全下的跨中心协作提供了方法学支持。

, authors=陈苗双, 邓巧, 常综启, 张韬, authorsList=陈苗双, 邓巧, 常综启, 张韬, authorCompany=null, correspAuthors=张韬, authorNote=

陈苗双（2002—），女，硕士在读，研究方向：卫生统计方法与应用

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张韬，E-mail:
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Nature and Science of Sleep, 2022, 14: 911-926., articleTitle=Association between habitual night sleep duration and predicted 10-Year cardiovascular risk by sex among young and Middle-Aged adults, refAbstract=null)], funds=[Fund(id=1324077599554679710, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077591350621030, awardId=2022YFC3600600, language=CN, fundingSource=国家重点研发计划项目(2022YFC3600600), fundOrder=null, country=null), Fund(id=1324077599634371487, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077591350621030, awardId=2024ZYD0102, language=CN, fundingSource=四川省科技计划项目-中央引导地方科技发展项目(2024ZYD0102), fundOrder=null, country=null), Fund(id=1324077599718257568, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077591350621030, awardId=2025YFHZ0069, language=CN, fundingSource=四川省科技计划项目-省院省校合作项目(2025YFHZ0069), fundOrder=null, country=null), Fund(id=1324077599802143649, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077591350621030, awardId=2024-YF05-01784-SN, language=CN, fundingSource=成都市科技局项目(2024-YF05-01784-SN), fundOrder=null, country=null), Fund(id=1324077599886029730, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077591350621030, awardId=SYYXH202403; SYYXHPT202406, language=CN, fundingSource=四川省预防医学会基层公共卫生专项科研项目(SYYXH202403; SYYXHPT202406), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1324077593284195189, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077591350621030, xref=null, ext=[AuthorCompanyExt(id=1324077593292583798, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077591350621030, companyId=1324077593284195189, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Epidemiology and Health Statistics, West China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China), AuthorCompanyExt(id=1324077593305166711, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077591350621030, companyId=1324077593284195189, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=四川大学华西公共卫生学院/华西第四医院流行病与卫生统计学系，四川 成都 610041)])], figs=[ArticleFig(id=1324077597075846038, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077591350621030, language=EN, label=Table 1, caption=

Baseline characteristics of the study participants with different sleep duration

, figureFileSmall=null, figureFileBig=null, tableContent=
短睡眠（n=4 395）正常睡眠（n=3 792）长睡眠（n=2 451）P值
年龄[mean(SD)]69.38(7.00)68.11(6.39)69.64(7.34)<0.001
性别(%)<0.001
男1 798(40.91)2 001(52.77)1 346(54.92)
女2 597(59.09)1 791(47.23)1 105(45.08)
婚姻状况(%)<0.001
与配偶同居3 194(72.67)3 061(80.72)1 865(76.09)
不与配偶同居1 201(27.33)731(19.28)586(23.91)
是否缺乏体力活动(%)<0.001
否2 494(56.75)2 343(61.79)1 352(55.16)
是1 901(43.25)1 449(38.21)1 099(44.84)
居住地(%)<0.001
城市（或城镇）中心1 002(22.80)1 061(27.98)432(17.63)
城乡（或镇乡）结合区460(10.47)411(10.84)270(11.02)
农村2 927(66.60)2 315(61.05)1 748(71.32)
特殊区域6(0.14)5(0.13)1(0.04)
教育水平(%)0.028
私塾/在家教育及以下4 188(95.29)3 590(94.67)2 352(95.96)
小学和初中143(3.25)119(3.14)67(2.73)
高中及以上64(1.46)83(2.19)32(1.31)
吸烟(%)<0.001
是3 491(79.43)2 767(72.97)1 766(72.05)
否904(20.57)1 025(27.03)685(27.95)
喝酒(%)<0.001
是3 114(70.85)2 426(63.98)1 673(68.26)
否1 281(29.15)1 366(36.02)778(31.74)
是否缺乏社交活动(%)<0.001
是2 500(56.88)2 020(53.27)1 459(59.53)
否1 895(43.12)1 772(46.73)992(40.47)
地区(%)<0.001
南部383(8.71)348(9.18)230(9.38)
北部463(10.53)525(13.84)273(11.14)
东部1 320(30.03)1 288(33.97)849(34.64)
中部724(16.47)583(15.37)334(13.63)
西南地区986(22.43)558(14.72)439(17.91)
西北地区271(6.17)214(5.64)192(7.83)
东北地区248(5.64)276(7.28)134(5.47)
CVD(%)<0.001
是3 940(89.65)3 505(92.43)2 226(90.82)
否455(10.35)287(7.57)225(9.18)
), ArticleFig(id=1324077597218452375, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077591350621030, language=CN, label=表1, caption=

不同睡眠时长组中研究对象的基线特征比较

, figureFileSmall=null, figureFileBig=null, tableContent=
短睡眠（n=4 395）正常睡眠（n=3 792）长睡眠（n=2 451）P值
年龄[mean(SD)]69.38(7.00)68.11(6.39)69.64(7.34)<0.001
性别(%)<0.001
男1 798(40.91)2 001(52.77)1 346(54.92)
女2 597(59.09)1 791(47.23)1 105(45.08)
婚姻状况(%)<0.001
与配偶同居3 194(72.67)3 061(80.72)1 865(76.09)
不与配偶同居1 201(27.33)731(19.28)586(23.91)
是否缺乏体力活动(%)<0.001
否2 494(56.75)2 343(61.79)1 352(55.16)
是1 901(43.25)1 449(38.21)1 099(44.84)
居住地(%)<0.001
城市（或城镇）中心1 002(22.80)1 061(27.98)432(17.63)
城乡（或镇乡）结合区460(10.47)411(10.84)270(11.02)
农村2 927(66.60)2 315(61.05)1 748(71.32)
特殊区域6(0.14)5(0.13)1(0.04)
教育水平(%)0.028
私塾/在家教育及以下4 188(95.29)3 590(94.67)2 352(95.96)
小学和初中143(3.25)119(3.14)67(2.73)
高中及以上64(1.46)83(2.19)32(1.31)
吸烟(%)<0.001
是3 491(79.43)2 767(72.97)1 766(72.05)
否904(20.57)1 025(27.03)685(27.95)
喝酒(%)<0.001
是3 114(70.85)2 426(63.98)1 673(68.26)
否1 281(29.15)1 366(36.02)778(31.74)
是否缺乏社交活动(%)<0.001
是2 500(56.88)2 020(53.27)1 459(59.53)
否1 895(43.12)1 772(46.73)992(40.47)
地区(%)<0.001
南部383(8.71)348(9.18)230(9.38)
北部463(10.53)525(13.84)273(11.14)
东部1 320(30.03)1 288(33.97)849(34.64)
中部724(16.47)583(15.37)334(13.63)
西南地区986(22.43)558(14.72)439(17.91)
西北地区271(6.17)214(5.64)192(7.83)
东北地区248(5.64)276(7.28)134(5.47)
CVD(%)<0.001
是3 940(89.65)3 505(92.43)2 226(90.82)
否455(10.35)287(7.57)225(9.18)
), ArticleFig(id=1324077597319115672, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077591350621030, language=EN, label=Table 2, caption=

Association between sleep duration and CVD risk estimated by federated AIPW

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模型估计值标准误P值OR(95%CI)E值
短睡眠组
AIPW0.0280.006<0.0011.029(1.016~1.042)1.200
长睡眠组
AIPW0.0190.0070.0091.019(1.005~1.034)1.158
), ArticleFig(id=1324077597403001753, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077591350621030, language=CN, label=表2, caption=

基于联邦因果学习模型的关联分析结果

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模型估计值标准误P值OR(95%CI)E值
短睡眠组
AIPW0.0280.006<0.0011.029(1.016~1.042)1.200
长睡眠组
AIPW0.0190.0070.0091.019(1.005~1.034)1.158
), ArticleFig(id=1324077597470110618, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077591350621030, language=EN, label=Table 3, caption=

Subgroup analysis of the association between sleep duration and CVD risk

, figureFileSmall=null, figureFileBig=null, tableContent=
模型估计值标准误P值OR(95%CI)E值
短睡眠组-男性
AIPW0.0310.0090.0011.031(1.013~1.050)1.212
短睡眠组-女性
AIPW0.0250.0090.0081.025(1.007~1.044)1.185
长睡眠组-男性
AIPW0.0270.0100.0081.027(1.007~1.048)1.194
长睡眠组-女性
AIPW0.0140.0110.1971.014(0.993~1.036)1.135
), ArticleFig(id=1324077597587551131, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077591350621030, language=CN, label=表3, caption=

性别分层的亚组分析

, figureFileSmall=null, figureFileBig=null, tableContent=
模型估计值标准误P值OR(95%CI)E值
短睡眠组-男性
AIPW0.0310.0090.0011.031(1.013~1.050)1.212
短睡眠组-女性
AIPW0.0250.0090.0081.025(1.007~1.044)1.185
长睡眠组-男性
AIPW0.0270.0100.0081.027(1.007~1.048)1.194
长睡眠组-女性
AIPW0.0140.0110.1971.014(0.993~1.036)1.135
), ArticleFig(id=1324077597650465692, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077591350621030, language=EN, label=Table 4, caption=

Sensitive analysis of the association between sleep duration and CVD risk

, figureFileSmall=null, figureFileBig=null, tableContent=
模型估计值标准误P值OR(95%CI)E值
短睡眠组
集中式模型0.0240.007<0.0011.024(1.010, 1.038)1.180
AIPW0.0280.007<0.0011.029(1.015, 1.043)1.201
长睡眠组
集中式模型0.0150.0080.0741.015(0.999, 1.031)1.137
AIPW0.0170.0080.0321.017(1.001, 1.034)1.151
), ArticleFig(id=1324077597826626461, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077591350621030, language=CN, label=表4, caption=

敏感性分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
模型估计值标准误P值OR(95%CI)E值
短睡眠组
集中式模型0.0240.007<0.0011.024(1.010, 1.038)1.180
AIPW0.0280.007<0.0011.029(1.015, 1.043)1.201
长睡眠组
集中式模型0.0150.0080.0741.015(0.999, 1.031)1.137
AIPW0.0170.0080.0321.017(1.001, 1.034)1.151
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基于联邦因果学习的老年人睡眠时长与心血管健康的关联性研究
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陈苗双 , 邓巧 , 常综启 , 张韬
现代预防医学 | 流行病与统计方法 2026,53(6): 968-972
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现代预防医学 |流行病与统计方法 2026 , 53 (6) : 968 -972
基于联邦因果学习的老年人睡眠时长与心血管健康的关联性研究
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陈苗双（2002—），女，硕士在读，研究方向：卫生统计方法与应用

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陈苗双, 邓巧, 常综启, 张韬
作者信息
  • 四川大学华西公共卫生学院/华西第四医院流行病与卫生统计学系，四川 成都 610041
通讯作者:
张韬，E-mail:
作者简介:

陈苗双（2002—），女，硕士在读，研究方向：卫生统计方法与应用

Association study of sleep duration and cardiovascular health in the elderly based on federated causal learning
Miao-shuang CHEN, Qiao DENG, Zong-qi CHANG, Tao ZHANG
Affiliations
  • Department of Epidemiology and Health Statistics, West China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
出版时间: 2026-03-25 doi: 10.20043/j.cnki.MPM.202511321
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摘要
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目的

针对多中心研究中存在地区异质性问题，本研究旨在利用联邦因果学习框架探讨中国老年人群睡眠时长与心血管疾病（CVD）风险的关联。

方法

本研究数据来源于2020年中国健康与养老追踪调查，最终纳入10 638名60岁及以上老年人。为模拟多中心协作场景并保留真实地区异质性，将原始数据按地理区域划分为7个分布式数据集，采用联邦增强逆概率加权模型进行效应估计，并与集中式结果进行对比。此外，按性别进行了亚组分析以探索效应异质性，以及通过敏感性分析以探索方法的稳健性。

结果

联邦因果学习与集中式结果保持一致，睡眠时长异常是CVD风险的显著影响因素。与正常睡眠组相比，短睡眠组患CVD的风险显著增加（OR=1.029, 95% CI: 1.016~1.042），长睡眠组同样表现出风险升高（OR=1.019, 95% CI: 1.005~1.034）。亚组分析进一步揭示了显著的性别差异，敏感性也验证了模型的稳健性。

结论

在多中心研究中，联邦因果学习能够系统性地控制混杂因素与地区异质性，为未来开展隐私安全下的跨中心协作提供了方法学支持。

联邦因果学习  /  地区异质性  /  睡眠时长  /  老年人  /  心血管疾病
Objective

To address regional heterogeneity in multicenter studies, and to explore the association between sleep duration and cardiovascular disease (CVD) risk in the Chinese elderly population using a federated causal learning framework.

Methods

Data was obtained from the 2020 China Health and Retirement Longitudinal Study (CHARLS). A total of 10 638 adults aged 60 and above were included. To simulate multi-center collaboration scenarios with real regional heterogeneity, the original dataset was divided into seven geographically distributed datasets. A federated augmented inverse probability weighting (AIPW) estimator was used to estimate causal effects with comparison to centralized models. Subgroup analyses by gender were conducted to explore effect heterogeneity. The robustness of the method was explored by a sensitivity analysis.

Results

Estimations from the federated causal learning models were consistent with those from the centralized model. Abnormal sleep duration was a significant factor influencing CVD risk. Compared to normal sleepers, short sleepers had a significantly increased risk of CVD (OR=1.029, 95% CI:1.016-1.042). Long sleepers also showed elevated risk (OR=1.019, 95% CI:1.005-1.034). The subgroup analysis revealed significant gender differences, and the sensitivity analysis also validated the robustness of the model.

Conclusion

In multicenter research, the federated causal learning systematically controls for confounders and regional heterogeneity. It provides methodological support for future privacy-preserving cross-center collaborative research.

Federated causal learning  /  Regional heterogeneity  /  Sleep duration  /  Elderly people  /  Cardiovascular disease
陈苗双, 邓巧, 常综启, 张韬. 基于联邦因果学习的老年人睡眠时长与心血管健康的关联性研究. 现代预防医学, 2026 , 53 (6) : 968 -972 . DOI: 10.20043/j.cnki.MPM.202511321
Miao-shuang CHEN, Qiao DENG, Zong-qi CHANG, Tao ZHANG. Association study of sleep duration and cardiovascular health in the elderly based on federated causal learning[J]. Modern Preventive Medicine, 2026 , 53 (6) : 968 -972 . DOI: 10.20043/j.cnki.MPM.202511321
正文
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心血管疾病（Cardiovascular diseases，CVD）是中国死亡和过早死亡的主要原因之一，其占比高达40%[1]。CVD给居民和社会带来的经济负担日渐加重，一直是公共卫生研究中的核心议题，而睡眠时间作为可干预的生活方式，在CVD的一级预防中具有重要意义[2-3]。为了获得具有外推性的科学证据，研究需依托大规模、多中心的真实世界数据，以克服单一中心样本代表性不足等局限。
近年来，联邦学习作为一种分布式计算框架，允许数据保留在本地，为跨机构数据协作提供了新的技术路径。尽管关于联邦学习的研究在算法设计、通信优化和性能评估等方面发展迅速[4-7]，但应用于因果推断领域时，单纯的技术优化难以满足流行病学研究的要求。其核心挑战在于多中心数据固有的地区异质性，这广泛存在于人群结构、暴露分布及社会环境等因素中，是因果效应估计偏倚的主要来源之一[8]。若处理不当，即使单中心内因果假设成立，跨中心整合后仍可能出现偏倚[9]。联邦因果学习的优势在于，其方法论框架内在地提供了一种控制地区异质性的有效途径。与传统集中式分析将全部数据合并建模不同，联邦范式要求在各中心独立拟合因果模型，即在局部估计阶段校正地区特异性因素对因果效应的影响。在此基础上，通过参数或中间量的聚合实现跨中心效应的总体估计[10-12]。
鉴于此，本研究拟采用一种面向多中心流行病学数据的联邦因果学习框架，重点解决地区异质性对效应估计的影响，并将其应用于探讨老年人睡眠时长对心血管健康的关联。本研究旨在隐私保护的前提下实现跨中心协作，并系统控制地区变异所带来的偏倚，为多中心人群研究提供一种兼具科学严谨性与数据安全保障的分析范式。
1　资料与方法
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1.1　资料来源与研究对象
本文数据来源于2020年中国健康与养老追踪调查（China Health and Retirement Longitudinal Study, CHARLS）第五轮数据，共招募了19 367人。基于研究目的，将样本限定于基线年龄60岁及以上的老年受访者。为确保关键协变量的数据质量，仅保留在老年人基本信息及健康相关行为等指标上拥有完整、有效回答的个体。最终纳入分析的样本量为10 638。CHARLS调查已获得北京大学生物医学伦理委员会的伦理审查许可（批准号：IRB00001052-11015)，所有研究对象均签署了知情同意书。
CHARLS调查基于多阶段PPS随机抽样方式，共覆盖了全国28个省。考虑到相似的自然环境、经济发展模式、文化习俗等，这些因素已被国际相关领域的前沿研究认为是导致产生区域异质性的重要来源[13]。本研究将28个省份根据地理区域划分为七个主要研究地区，包括南部（广东省、广西壮族自治区）、北部（北京市、天津市、河北省、山西省、内蒙古自治区）、东部（上海市、山东省、江苏省、安徽省、江西省、浙江省、福建省）、中部（湖北省、湖南省、河南省）、西南地区（重庆市、四川省、贵州省、云南省）、西北地区（陕西省、甘肃省、新疆维吾尔自治区、青海省）和东北地区（黑龙江省、吉林省、辽宁省)。每个地区的数据作为一个独立研究单元进行数据存储，模拟构建分布式数据结构，确保在保留原始数据中固有的地区异质性，以便在联邦学习框架下更真实地反映跨地区协作中的数据结构特征。
1.2　暴露因素与结局变量
睡眠持续时间为“在过去一个月中，您晚上的实际睡眠时间（每晚的平均小时数）”。实际睡眠时间分为三组：<6小时、6~8小时和>8小时，代表短、正常和长睡眠组。结局是是否发生CVD事件，包括心脏病和中风。结局变量是通过问卷来获得：“是否被医生告知您被诊断出患有心脏病、心绞痛、冠心病、心力衰竭或其他心脏问题？”患有心脏病或脑卒中的受试者被定义为患有CVD。
1.3　协变量
据先前的知识[14]，考虑了几个潜在的混杂因素来调整模型，包括人口统计学特征、生活方式因素和健康状况。具体而言，人口学特征包括年龄 （≥60岁）、性别 （男性或女性）、婚姻状况 （是否与配偶同居）、教育程度 （小学或以下、初中或以上） 和居住类型（农村或城市）。生活方式因素包括吸烟和饮酒。吸烟定义为“您吸过烟吗？”、“您现在还在吸烟还是已经戒烟了？”，不吸烟指从未吸过烟的人。饮酒定义为“过去1年您喝过酒吗？包括啤酒、葡萄酒等”，回答“是”的参与者被定义为“饮酒”。
1.4　统计分析
为评估睡眠时长与CVD风险之间的关联，本研究采用两种建模策略进行对比分析。集中式模型是基于完整合并的原始数据，将地区变量作为固定效应纳入模型，以控制地区异质性的影响，并将其作为真实效应（金标准）的参照。联邦模型是基于分布式的数据结构灵活控制地区异质性，即在各中心独立拟合本地模型后，通过样本量加权聚合获得整体效应估计。在每个本地数据集上，独立构建增强逆概率加权（Augmented inverse probability weighting , AIPW）估计器来估计平均处理效应（Average treatment effect, ATE）：
其中，是AIPW得分函数：
其中，和是条件处理组和控制组结局模型的估计值。在中心节点对各数据集的估计进行聚合，采用样本量加权方式：
此外，基于联邦AIPW模型，按性别进行亚组分析，评估睡眠时长对CVD的异质性效应。计量资料以均数（标准差）表示，分类变量采用百分比(%)表示，组间比较采用χ2检验。通过计算各地区内各省份在CVD患病率上的变异系数验证地理分组的合理性，并采用卡方检验评估组内同质性。采用E值作为评估观察性研究中未测量混杂变量对结果影响程度的敏感性指标。最后，为了评估地区异质性对模型的影响，本研究在CHARLS 真实数据的基础上，针对地区偏移量进一步设置了敏感性分析，即引入了服从均值为0.2、标准差为0.15的正态分布的地区偏移量以评估模型的稳健性[10,15]。所有分析使用R 4.5.0 统计软件进行数据分析。检验水准α=0.05。
2　结果
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2.1　参与者的一般资料
本研究的研究对象中，男性5 145名（48.36%），女性5 493名（51.64%），平均年龄（68.99±6.90）岁。大多数研究对象居住地为农村，共6 990名 (65.71%)。不同睡眠时长组（短睡眠、正常睡眠、长睡眠）在人口学特征、生活方式及健康状况方面存在显著差异（P<0.05）。此外，7个地区组内各省份的CVD患病率变异系数范围为0.097~0.263，地区间CVD患病率变异系数（0.251）显著高于地区内各省份的平均变异系数（0.195），且地区分布在睡眠时长组间存在显著差异（P<0.001），证实了地区异质性结构的存在。同时，各地区内各省份在CVD患病率上无显著统计差异（χ2检验P值均>0.05），满足了模拟多中心协作中“组间异质、组内同质”的基本假设。研究对象基本特征详见表1。
2.2　联邦因果学习分析
对于短睡眠（<6小时），集中式模型估计的OR值为1.028，联邦AIPW模型估计的OR值为1.029（95% CI:1.016~1.042）。对于长睡眠（>8小时），集中式模型估计的OR值为1.021，联邦AIPW模型估计的OR值为1.019（95% CI: 1.005~1.034）。结果详见表2。在控制混杂因素并考虑地区异质性后，联邦AIPW模型与集中式模型在点估计上保持一致，同时结果表明睡眠时长异常（无论过短或过长）均是CVD风险的潜在影响因素。
2.3　亚组分析
为进一步探讨睡眠时长对CVD风险的异质性影响，本研究按性别开展了亚组分析。分析结果如表3所示。在短睡眠组中，男性和女性的CVD风险均显著升高，男性 OR=1.031 (95% CI: 1.013~1.050, P=0.001)；女性 OR = 1.024(95% CI: 1.007~1.044, P=0.008)。这表明短睡眠是两性共有的风险因素，对男性的危害可能略大。在长睡眠组中，这种关联同样呈现出明显的性别特异性。长睡眠仅与男性的CVD风险显著相关（OR=1.027, 95% CI: 1.007~1.048, P=0.008），而在女性中，虽然点估计值OR=1.014，但关联不显著（95% CI: 0.993~1.036, P=0.197）。
2.4　敏感性分析
本研究为每个中心加入一个不同的地区偏移量，该偏移进一步加剧了多中心研究中因中心间系统性差异导致的地区异质性。表4显示，当模拟加剧地区异质性后，对于短睡眠组，集中式估计（OR=1.024, 95% CI：1.010~1.038）与真实值（OR=1.028）的偏离更大；而联邦方法的估计（OR=1.029, 95% CI：1.015~1.043）更接近真实值。对于长睡眠组，集中式估计（OR=1.015, 95% CI：0.999~1.031）同样与真实值（OR=1.021）的偏离更大；而联邦方法的估计（OR=1.017, 95% CI：1.001~1.034）也同样更接近真实值。这表明在该模拟情境下，联邦方法校正由地区异质性引起的偏差具有一定稳健性。
3　讨论
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本研究将联邦因果学习框架应用于评估中国老年人睡眠时长与CVD的关联，旨在利用该框架对复杂抽样数据结构的固有适应性，来缓解由地区异质性可能引起的估计偏倚。本研究通过构建联邦因果学习框架，旨在探讨在多中心数据结构下，有效控制地区异质性以估计老年人睡眠时长与心血管疾病（CVD）的关联。我们模拟了基于地理区域的分布式数据环境，并将联邦AIPW模型的估计结果与集中式“金标准”进行对比，以评估该方法在存在地区异质性时的性能。
研究结果显示，基于联邦AIPW模型的估计与多数传统集中式分析在关联方向上保持一致，共同证实了睡眠时长异常是CVD的一个重要风险因素。Yin等[16]的荟萃分析结果显示睡眠时间过短（RR=1.14, 95% CI:1.09~1.20, P=0.04）与睡眠时间过长（RR=1.36, 95% CI:1.26~1.48, P<0.01）均与较高的心血管疾病死亡风险相关。罗玉霞等[17]的研究结果同样显示，短睡眠时长会使CVD患病风险增加（HR=1.19, 95% CI: 1.09~1.29）。此外，有研究显示，与每晚睡眠时长7~8小时的人群相比，睡眠时间长者的冠心病和脑卒中发病风险分别会上升33%和23%[18]。本研究关于睡眠时长与CVD发病风险关联的结果与上述既往研究结论相符。
睡眠时间过短可能通过多种途径影响心血管健康，包括扰乱内分泌与免疫功能、影响碳水化合物代谢、引发炎症反应，并进一步导致胰岛素敏感性下降、交感神经活性增强以及代谢异常，从而加速动脉粥样硬化进程[19]。睡眠时间过长本身可能反映潜在的健康问题，如焦虑抑郁倾向、体力活动不足等。这些因素本身即为心血管疾病的危险因素，同时也可能与某些尚未完全阐明的生物学机制共同作用，导致心血管疾病风险增加[20]。
敏感性分析进一步验证了联邦因果学习框架在处理地区异质性方面的稳健性。当在模拟数据中引入更强的地区偏移以加剧中心间差异时，联邦AIPW模型仍表现出较好的稳定性。在面对多中心研究中不可避免的地区异质性问题时，联邦学习方法不仅能在不共享原始数据的前提下实现协作，而且能通过其分布式的建模机制，系统性地平衡各中心的变异，从而提供更稳健的效应估计。
目前关于性别对睡眠时长与心血管疾病关联尚不明确。Chen等[21]研究发现，在男性中睡眠时间与多种心血管事件之间存在更强的相关性；而在女性中，睡眠时间较短仅与心绞痛和心脏病发作风险显著相关。这一发现与我们的研究结果相符。然而，He等[22]研究表明尽管男性中睡眠不足的比例更高，但统计学上仅女性群体的睡眠不足与心血管疾病风险显著相关，提示女性可能对睡眠不足引发的心血管风险更为敏感。这些研究间的差异可能源于样本特征、控制变量及研究设计等方面的异质性。
此外，需要强调的是，本研究结果并非认为联邦学习的估计结果优于集中式模型，而是证实了在存在地区异质性的多中心研究场景下，联邦因果学习可能提供了一个理论上更具适应性、实践中更稳健的分析工具。其优势在于能够在隐私保护的约束下，有效缓解由地区异质性可能引起的估计偏倚。尽管本研究通过引入联邦因果学习框架为多中心数据提供了新颖的视角，但仍存在一定局限性需在阐释结果时审慎考量。本研究睡眠时长的评估完全依赖于受访者的自我报告，这可能引入回忆偏倚。此外，尽管研究调整了多项重要的混杂因素，但仍可能存在未测量的混杂变量同样可能与睡眠时长和CVD风险相关。
基金
收起
  • 国家重点研发计划项目(2022YFC3600600)
  • 四川省科技计划项目-中央引导地方科技发展项目(2024ZYD0102)
  • 四川省科技计划项目-省院省校合作项目(2025YFHZ0069)
  • 成都市科技局项目(2024-YF05-01784-SN)
  • 四川省预防医学会基层公共卫生专项科研项目(SYYXH202403; SYYXHPT202406)
文献
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2026年第53卷第6期
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doi: 10.20043/j.cnki.MPM.202511321
  • 接收时间：2025-11-20
  • 首发时间：2026-11-02
  • 出版时间：2026-03-25
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  • 收稿日期：2025-11-20
基金
国家重点研发计划项目(2022YFC3600600)
四川省科技计划项目-中央引导地方科技发展项目(2024ZYD0102)
四川省科技计划项目-省院省校合作项目(2025YFHZ0069)
成都市科技局项目(2024-YF05-01784-SN)
四川省预防医学会基层公共卫生专项科研项目(SYYXH202403; SYYXHPT202406)
作者信息
    四川大学华西公共卫生学院/华西第四医院流行病与卫生统计学系，四川 成都 610041

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