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Article(id=1322608015207936009, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322608013010120712, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202504223, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744473600000, receivedDateStr=2025-04-13, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793263923693, onlineDateStr=2026-10-29, pubDate=1762704000000, pubDateStr=2025-11-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793263923693, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793263923693, creator=13701087609, updateTime=1793263923693, updator=13701087609, issue=Issue{id=1322608013010120712, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='21', pageStart='3841', pageEnd='4032', issueExtLink='null', onlineDate='null', pubDate='1762704000000', pubDateStr='2025-11-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793263923169, creator='13701087609', updateTime=1793264266929, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322609454932149086, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322608013010120712, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322609454932149087, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322608013010120712, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3841, endPage=3846, ext={EN=ArticleExt(id=1322608015417651210, articleId=1322608015207936009, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Causal association between osteoporosis and cognitive impairment in community adults, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

The aim of this study is to comprehensively analyze the causal relationship between osteoporosis and cognitive impairment in China community using the combination of observational study and Mendelian randomization (MR).

Methods

Baseline data from the West China Health and Aging Cohort Study were used to explore phenotypic associations between L1-L4 bone mineral density (BMD), total hip BMD, femoral neck BMD, osteoporosis, osteopenia and cognitive function. A two-sample MR design was used to evaluate the causal association between BMD and Alzheimer’s disease (AD) using summary statistics from published genome-wide association studies, with single nucleotide polymorphisms (SNPs) significantly associated with BMD as instrumental variables and AD as the outcome variable.

Results

The cross-sectional study showed that after adjusting for a series of confounding factors, osteoporosis was found to be negatively associated with Mini-Mental State Examination (MMSE) score compared with normal bone mass group (β=-0.84, 95% CI: -1.39~-0.29, P=0.003), indicating that the patients with osteoporosis had lower MMSE scores and worse cognitive function than those with normal bone mass, and the results were robust. The relationship between BMD at different sites and cognitive scores was different.Specifically, total hip BMD (β=1.58, 95% CI: 0.20-2.95, P=0.024) and L1-L4 BMD (β=1.17, 95% CI: 0.13-2.21, P=0.028) were positively correlated with MMSE score, while femoral neck BMD was not significantly correlated with MMSE score (β=1.34, 95% CI: -0.11-2.79, P=0.070). Two-sample MR analysis using 86 SNPs associated with heel BMD-T values in a Chinese population as instrumental variables showed no causal association between heel BMD-T and AD risk (IVW: OR=0.95, 95% CI: 0.84-1.07, P=0.417). The results of multiple sensitivity analyses were robust.

Conclusion

The evidence from this study suggests a positive association between osteoporosis and cognitive impairment in Chinese, but further studies are needed to confirm the causal relationship between the two traits. Based on the common risk factors and pathogenesis, it is beneficial in practice to strengthen the management of osteoporosis patients, especially those with low total hip BMD and L1-L4 BMD, to prevent the occurrence of cognitive impairment.

, authors=Ying KANG1, Ting LIU2, Jin-yu ZHOU2, Xuan WU2, Xia JIANG2, Jia-yuan LI2, authorsList=Ying KANG, Ting LIU, Jin-yu ZHOU, Xuan WU, Xia JIANG, Jia-yuan LI, authorCompany=null, correspAuthors=Xuan WU, 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=1322608019804893204, articleId=1322608015207936009, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=社区成年人骨质疏松与认知功能损害的因果关联研究, 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

本研究旨在使用观察性研究、孟德尔随机化（Mendelian randomization，MR）研究相结合的方法，综合分析中国社区骨质疏松与认知功能损害的因果关联。

方法

利用华西老年人群健康队列的基线数据探索L1～L4骨密度（bone mineral density）、全髋骨密度、股骨颈骨密度、骨质疏松、骨量减少与认知功能之间的表型关联。进一步利用公开发表的全基因组关联研究汇总数据，选择与骨密度显著相关的单核苷酸多态性（single nucleotide polymorphisms，SNPs）位点作为工具变量，阿尔茨海默病（Alzheimer disease，AD）为结局变量，采用两样本MR的设计来评估骨密度流失与AD之间的因果关联。

结果

横断面研究结果显示，在调整一系列混杂因素后，发现与骨量正常组相比骨质疏松与简易精神状态检查量表（mini-mental state examination，MMSE）得分之间存在负向关联（β=-0.84，95% CI：-1.39～-0.29，P=0.003），即骨质疏松组相较骨量正常组MMSE得分更低、认知功能更差，结果十分稳健。不同部位骨密度与认知评分的关系不同，具体而言，全髋骨密度（β=1.58，95% CI：0.20～2.95，P=0.024）和L1～L4骨密度（β=1.17，95% CI：0.13～2.21，P=0.028）与MMSE得分之间存在正向关联，而股骨颈骨密度与MMSE得分关联无统计学意义（β=1.34，95% CI：-0.11～2.79，P= 0.070）。使用86个中国地区足跟骨密度-T值相关SNPs为工具变量进行两样本MR分析，结果显示，足跟骨密度-T值（IVW：OR=0.95，95% CI：0.84～1.07，P=0.417）与AD发病风险之间不存在因果关联。多种敏感性分析结果稳健。

结论

本研究的证据表明，中国地区骨质疏松与认知功能损害存在正相关关系，但二者之间的因果关系需要进一步研究来确证。基于共同的危险因素和发病机制，在实践中加强对骨质疏松患者的管理，尤其是全髋骨密度及L1～L4骨密度较低的骨质疏松患者，对预防认知功能损害的发生是有利的。

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康颖（1981—），男，硕士，工程师，研究方向：卫生信息管理

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武璇，E-mail：
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注：NO.IVs为工具变量数目。

, figureFileSmall=KD21Rk1QIrI/ofnxubpLQA==, figureFileBig=gzHaN+rRza8Cjw/uao+M6Q==, tableContent=null), ArticleFig(id=1322608034845667397, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608015207936009, language=EN, label=Table 1, caption=

Baseline characteristics of participants in the West China Health and Aging Cohort［（），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
因素合计（n=2 171）骨量正常（n=662）骨量减少（n=1 040）骨质疏松（n=469）F/χ2值P值
年龄67.50±5.0267.02±4.8967.28±4.9768.66±5.1616.70<0.001
性别456.36<0.001
男1 034 (47.6)508 (76.7)466 (44.8)60 (12.8)
女1 137 (52.4)154 (23.3)574 (55.2)409 (87.2)
教育76.97<0.001
文盲600 (27.6)128 (19.3)283 (27.2)189 (40.3)
小学444 (20.5)129 (19.5)208 (20.0)107 (22.8)
初中及以上1 127 (51.9)405 (61.2)549 (52.8)173 (36.9)
个人可支配年收入a（万元）37.19<0.001
<1.2496 (23.5)120 (18.8)241 (23.7)135 (29.7)
1.2～1.99375 (17.7)105 (16.4)187 (18.4)83 (18.2)
2.0～5.99825 (39.0)251 (39.2)395 (38.8)179 (39.3)
≥6.0417 (19.7)164 (25.6)195 (19.2)58 (12.7)
婚姻状况40.83<0.001
已婚1 865 (85.9)609 (92.0)887 (85.3)369 (78.7)
其他306 (14.1)53 (8.0)153 (14.7)100 (21.3)
吸烟a120.88<0.001
不吸烟1 610 (74.4)410 (62.1)776 (74.8)424 (91.0)
已戒烟257 (11.9)122 (18.5)117 (11.3)18 (3.9)
吸烟297 (13.7)128 (19.4)145 (14.0)24 (5.2)
饮酒a135.60<0.001
从不或几乎不饮酒1 332 (61.6)313 (47.4)645 (62.1)374 (80.3)
特殊场合偶尔饮酒439 (20.3)175 (26.5)208 (20.0)56 (12.0)
经常饮酒（每周不到一次）76 (3.5)25 (3.8)47 (4.5)4 (0.9)
总是饮酒（每周一次）317 (14.6)147 (22.3)138 (13.3)32 (6.9)
睡眠a7.290.026
6～9 h1 501 (71.1)472 (73.3)730 (72.0)299 (66.2)
其他609 (28.9)172 (26.7)284 (28.0)153 (33.8)
体力活动1.470.480
充足2 046 (94.2)628 (94.9)981 (94.3)437 (93.2)
缺乏125 (5.8)34 (5.1)59 (5.7)32 (6.8)
BMIa117.46<0.001
低体重48 (2.2)3 (0.5)21 (2.0)24 (5.2)
正常850 (39.8)189 (28.9)415 (40.4)246 (53.7)
超重950 (44.5)335 (51.3)467 (45.5)148 (32.3)
肥胖289 (13.5)126 (19.3)123 (12.0)40 (8.7)
高血压5.760.056
否1 326 (61.1)382 (57.7)641 (61.6)303 (64.6)
是845 (38.9)280 (42.3)399 (38.4)166 (35.4)
糖尿病27.44<0.001
否1 809 (83.3)511 (77.2)886 (85.2)412 (87.8)
是362 (16.7)151 (22.8)154 (14.8)57 (12.2)
高脂血症1.490.474
否1 921 (88.5)578 (87.3)923 (88.8)420 (89.6)
是250 (11.5)84 (12.7)117 (11.2)49 (10.4)
卒中0.810.668
否2 076 (95.6)635 (95.9)996 (95.8)445 (94.9)
是95 (4.4)27 (4.1)44 (4.2)24 (5.1)
MMSE（分）24.01±4.6024.87±3.7724.14±4.5822.50±5.3138.43<0.001
认知功能受损35.48<0.001
否1 811 (83.4)579 (87.5)882 (84.8)350 (74.6)
是360 (16.6)83 (12.5)158 (15.2)119 (25.4)
), ArticleFig(id=1322608034925359174, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608015207936009, language=CN, label=表1, caption=

华西老年队列研究对象基本特征［（），n（%）］

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因素合计（n=2 171）骨量正常（n=662）骨量减少（n=1 040）骨质疏松（n=469）F/χ2值P值
年龄67.50±5.0267.02±4.8967.28±4.9768.66±5.1616.70<0.001
性别456.36<0.001
男1 034 (47.6)508 (76.7)466 (44.8)60 (12.8)
女1 137 (52.4)154 (23.3)574 (55.2)409 (87.2)
教育76.97<0.001
文盲600 (27.6)128 (19.3)283 (27.2)189 (40.3)
小学444 (20.5)129 (19.5)208 (20.0)107 (22.8)
初中及以上1 127 (51.9)405 (61.2)549 (52.8)173 (36.9)
个人可支配年收入a（万元）37.19<0.001
<1.2496 (23.5)120 (18.8)241 (23.7)135 (29.7)
1.2～1.99375 (17.7)105 (16.4)187 (18.4)83 (18.2)
2.0～5.99825 (39.0)251 (39.2)395 (38.8)179 (39.3)
≥6.0417 (19.7)164 (25.6)195 (19.2)58 (12.7)
婚姻状况40.83<0.001
已婚1 865 (85.9)609 (92.0)887 (85.3)369 (78.7)
其他306 (14.1)53 (8.0)153 (14.7)100 (21.3)
吸烟a120.88<0.001
不吸烟1 610 (74.4)410 (62.1)776 (74.8)424 (91.0)
已戒烟257 (11.9)122 (18.5)117 (11.3)18 (3.9)
吸烟297 (13.7)128 (19.4)145 (14.0)24 (5.2)
饮酒a135.60<0.001
从不或几乎不饮酒1 332 (61.6)313 (47.4)645 (62.1)374 (80.3)
特殊场合偶尔饮酒439 (20.3)175 (26.5)208 (20.0)56 (12.0)
经常饮酒（每周不到一次）76 (3.5)25 (3.8)47 (4.5)4 (0.9)
总是饮酒（每周一次）317 (14.6)147 (22.3)138 (13.3)32 (6.9)
睡眠a7.290.026
6～9 h1 501 (71.1)472 (73.3)730 (72.0)299 (66.2)
其他609 (28.9)172 (26.7)284 (28.0)153 (33.8)
体力活动1.470.480
充足2 046 (94.2)628 (94.9)981 (94.3)437 (93.2)
缺乏125 (5.8)34 (5.1)59 (5.7)32 (6.8)
BMIa117.46<0.001
低体重48 (2.2)3 (0.5)21 (2.0)24 (5.2)
正常850 (39.8)189 (28.9)415 (40.4)246 (53.7)
超重950 (44.5)335 (51.3)467 (45.5)148 (32.3)
肥胖289 (13.5)126 (19.3)123 (12.0)40 (8.7)
高血压5.760.056
否1 326 (61.1)382 (57.7)641 (61.6)303 (64.6)
是845 (38.9)280 (42.3)399 (38.4)166 (35.4)
糖尿病27.44<0.001
否1 809 (83.3)511 (77.2)886 (85.2)412 (87.8)
是362 (16.7)151 (22.8)154 (14.8)57 (12.2)
高脂血症1.490.474
否1 921 (88.5)578 (87.3)923 (88.8)420 (89.6)
是250 (11.5)84 (12.7)117 (11.2)49 (10.4)
卒中0.810.668
否2 076 (95.6)635 (95.9)996 (95.8)445 (94.9)
是95 (4.4)27 (4.1)44 (4.2)24 (5.1)
MMSE（分）24.01±4.6024.87±3.7724.14±4.5822.50±5.3138.43<0.001
认知功能受损35.48<0.001
否1 811 (83.4)579 (87.5)882 (84.8)350 (74.6)
是360 (16.6)83 (12.5)158 (15.2)119 (25.4)
), ArticleFig(id=1322608035005050951, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608015207936009, language=EN, label=Table 2, caption=

Association between different bone mass status categories and cognitive scores

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模型正常骨量减少骨质疏松
β (95% CI)P值β (95% CI)P值
模型1a0 (ref)-0.28 (-0.74 ～ 0.17)0.222-1.28 (-1.88 ～ -0.68)<0.001
模型2b0 (ref)0.01 (-0.39 ～ 0.41)0.950-0.76 (-1.31 ～ -0.21)0.006
模型3c0 (ref)-0.04 (-0.44 ～ 0.36)0.849-0.84 (-1.39 ～ -0.29)0.003
), ArticleFig(id=1322608035126685768, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608015207936009, language=CN, label=表2, caption=

不同骨量状态与认知评分的关联

, figureFileSmall=null, figureFileBig=null, tableContent=
模型正常骨量减少骨质疏松
β (95% CI)P值β (95% CI)P值
模型1a0 (ref)-0.28 (-0.74 ～ 0.17)0.222-1.28 (-1.88 ～ -0.68)<0.001
模型2b0 (ref)0.01 (-0.39 ～ 0.41)0.950-0.76 (-1.31 ～ -0.21)0.006
模型3c0 (ref)-0.04 (-0.44 ～ 0.36)0.849-0.84 (-1.39 ～ -0.29)0.003
), ArticleFig(id=1322608035218960457, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608015207936009, language=EN, label=Table 3, caption=

Associations between BMD at different sites and cognitive scores

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模型股骨颈骨密度全髋骨密度L1～L4骨密度
β (95% CI)P值β (95% CI)P值β (95% CI)P值
模型1a1.74 (0.16 ～ 3.32)0.0311.89 (0.44 ～ 3.34)0.0112.64 (1.53 ～ 3.75)<0.001
模型2b1.09 (-0.35 ～ 2.53)0.1391.31 (-0.05 ～ 2.67)0.0601.03 (0.00 ～ 2.06)0.050
模型3c1.34 (-0.11 ～ 2.79)0.0701.58 (0.20 ～ 2.95)0.0241.17 (0.13 ～ 2.21)0.028
), ArticleFig(id=1322608035307040842, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608015207936009, language=CN, label=表3, caption=

不同部位骨密度值与认知评分的关联

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模型股骨颈骨密度全髋骨密度L1～L4骨密度
β (95% CI)P值β (95% CI)P值β (95% CI)P值
模型1a1.74 (0.16 ～ 3.32)0.0311.89 (0.44 ～ 3.34)0.0112.64 (1.53 ～ 3.75)<0.001
模型2b1.09 (-0.35 ～ 2.53)0.1391.31 (-0.05 ～ 2.67)0.0601.03 (0.00 ～ 2.06)0.050
模型3c1.34 (-0.11 ～ 2.79)0.0701.58 (0.20 ～ 2.95)0.0241.17 (0.13 ～ 2.21)0.028
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社区成年人骨质疏松与认知功能损害的因果关联研究
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康颖 1 , 刘婷 2 , 周进宇 2 , 武璇 2 , 姜侠 2 , 李佳圆 2
现代预防医学 | 流行病与统计方法 2025,52(21): 3841-3846
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现代预防医学 |流行病与统计方法 2025 , 52 (21) : 3841 -3846
社区成年人骨质疏松与认知功能损害的因果关联研究
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康颖（1981—），男，硕士，工程师，研究方向：卫生信息管理

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康颖1, 刘婷2, 周进宇2, 武璇2 , 姜侠2, 李佳圆2
作者信息
  • 1四川省卫生健康信息中心，四川　成都　610044
  • 2四川大学华西公共卫生学院/华西第四医院，四川　成都　610041
通讯作者:
武璇，E-mail：
作者简介:

康颖（1981—），男，硕士，工程师，研究方向：卫生信息管理

Causal association between osteoporosis and cognitive impairment in community adults
Ying KANG1, Ting LIU2, Jin-yu ZHOU2, Xuan WU2 , Xia JIANG2, Jia-yuan LI2
Affiliations
  • Sichuan Provincial Health Information Center, Chengdu, Sichuan 610044, China
出版时间: 2025-11-10 doi: 10.20043/j.cnki.MPM.202504223
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摘要
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目的

本研究旨在使用观察性研究、孟德尔随机化（Mendelian randomization，MR）研究相结合的方法，综合分析中国社区骨质疏松与认知功能损害的因果关联。

方法

利用华西老年人群健康队列的基线数据探索L1～L4骨密度（bone mineral density）、全髋骨密度、股骨颈骨密度、骨质疏松、骨量减少与认知功能之间的表型关联。进一步利用公开发表的全基因组关联研究汇总数据，选择与骨密度显著相关的单核苷酸多态性（single nucleotide polymorphisms，SNPs）位点作为工具变量，阿尔茨海默病（Alzheimer disease，AD）为结局变量，采用两样本MR的设计来评估骨密度流失与AD之间的因果关联。

结果

横断面研究结果显示，在调整一系列混杂因素后，发现与骨量正常组相比骨质疏松与简易精神状态检查量表（mini-mental state examination，MMSE）得分之间存在负向关联（β=-0.84，95% CI：-1.39～-0.29，P=0.003），即骨质疏松组相较骨量正常组MMSE得分更低、认知功能更差，结果十分稳健。不同部位骨密度与认知评分的关系不同，具体而言，全髋骨密度（β=1.58，95% CI：0.20～2.95，P=0.024）和L1～L4骨密度（β=1.17，95% CI：0.13～2.21，P=0.028）与MMSE得分之间存在正向关联，而股骨颈骨密度与MMSE得分关联无统计学意义（β=1.34，95% CI：-0.11～2.79，P= 0.070）。使用86个中国地区足跟骨密度-T值相关SNPs为工具变量进行两样本MR分析，结果显示，足跟骨密度-T值（IVW：OR=0.95，95% CI：0.84～1.07，P=0.417）与AD发病风险之间不存在因果关联。多种敏感性分析结果稳健。

结论

本研究的证据表明，中国地区骨质疏松与认知功能损害存在正相关关系，但二者之间的因果关系需要进一步研究来确证。基于共同的危险因素和发病机制，在实践中加强对骨质疏松患者的管理，尤其是全髋骨密度及L1～L4骨密度较低的骨质疏松患者，对预防认知功能损害的发生是有利的。

骨质疏松  /  认知  /  表型关联  /  因果关联
Objective

The aim of this study is to comprehensively analyze the causal relationship between osteoporosis and cognitive impairment in China community using the combination of observational study and Mendelian randomization (MR).

Methods

Baseline data from the West China Health and Aging Cohort Study were used to explore phenotypic associations between L1-L4 bone mineral density (BMD), total hip BMD, femoral neck BMD, osteoporosis, osteopenia and cognitive function. A two-sample MR design was used to evaluate the causal association between BMD and Alzheimer’s disease (AD) using summary statistics from published genome-wide association studies, with single nucleotide polymorphisms (SNPs) significantly associated with BMD as instrumental variables and AD as the outcome variable.

Results

The cross-sectional study showed that after adjusting for a series of confounding factors, osteoporosis was found to be negatively associated with Mini-Mental State Examination (MMSE) score compared with normal bone mass group (β=-0.84, 95% CI: -1.39~-0.29, P=0.003), indicating that the patients with osteoporosis had lower MMSE scores and worse cognitive function than those with normal bone mass, and the results were robust. The relationship between BMD at different sites and cognitive scores was different.Specifically, total hip BMD (β=1.58, 95% CI: 0.20-2.95, P=0.024) and L1-L4 BMD (β=1.17, 95% CI: 0.13-2.21, P=0.028) were positively correlated with MMSE score, while femoral neck BMD was not significantly correlated with MMSE score (β=1.34, 95% CI: -0.11-2.79, P=0.070). Two-sample MR analysis using 86 SNPs associated with heel BMD-T values in a Chinese population as instrumental variables showed no causal association between heel BMD-T and AD risk (IVW: OR=0.95, 95% CI: 0.84-1.07, P=0.417). The results of multiple sensitivity analyses were robust.

Conclusion

The evidence from this study suggests a positive association between osteoporosis and cognitive impairment in Chinese, but further studies are needed to confirm the causal relationship between the two traits. Based on the common risk factors and pathogenesis, it is beneficial in practice to strengthen the management of osteoporosis patients, especially those with low total hip BMD and L1-L4 BMD, to prevent the occurrence of cognitive impairment.

Osteoporosis  /  Cognitive function  /  Phenotypic association  /  Causal association
康颖, 刘婷, 周进宇, 武璇, 姜侠, 李佳圆. 社区成年人骨质疏松与认知功能损害的因果关联研究. 现代预防医学, 2025 , 52 (21) : 3841 -3846 . DOI: 10.20043/j.cnki.MPM.202504223
Ying KANG, Ting LIU, Jin-yu ZHOU, Xuan WU, Xia JIANG, Jia-yuan LI. Causal association between osteoporosis and cognitive impairment in community adults[J]. Modern Preventive Medicine, 2025 , 52 (21) : 3841 -3846 . DOI: 10.20043/j.cnki.MPM.202504223
正文
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痴呆是一组由多种病因引起的临床综合征，轻度认知障碍（mild cognitive impairment，MCI）是痴呆连续疾病谱的早期阶段，约33%的MCI患者在五年内发展为痴呆[1]。阿尔茨海默病（Alzheimer disease，AD）是老年人认知障碍的主要原因，占所有认知障碍患者的60%～80%[2]。我国一项2020年流行病学调查[3]指出，中国60岁或以上的老年人中，经年龄和性别校正后，MCI总体患病率为15.5%，痴呆为6.0%，AD为3.9%。
作为一个复杂的疾病，AD经常与许多疾病共存，近年来，越来越多的研究发现AD与骨质疏松[4-5]在流行病学和发病机制上密切相关。骨组织和脑组织可以通过骨脑轴以多种方式相互调节[6]。一项韩国的纵向研究[4]发现AD患者合并骨质疏松的比例明显高于健康对照者（RR=1.08，95% CI:1.04～1.12）。同时，荷兰一项随访了11.1年的研究[7]也显示低骨密度（bone mineral density）组相对于高骨密度组有更高的AD发病风险（RR=1.32，95% CI:1.05～1.66）。然而，现有证据主要来源于国外研究，针对中国老年人群的研究较为有限，因此，本研究利用华西老年人群健康队列的基线数据探索骨质疏松及不同部位骨密度与认知功能之间的关联，同时使用公开发表的全基因组关联硏究（genome-wide association study,GWAS）数据，选择与骨密度显著相关的SNPs位点作为工具变量，AD这种占所有认知障碍患者的60%～80%[2]的疾病为结局变量，采用两样本孟德尔随机化（Mendelian randomization，MR）的方法来评估骨密度流失与AD之间的因果关联。从传统流行病学和遗传流行病学两个角度，深入了解骨质疏松症对认知损伤的影响，以期寻找认知损伤的病因和高危人群，为其防治提供理论支撑和实践指导。
1　对象与方法
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1.1　观察性研究
1.1.1　研究对象
本研究人群来源于华西老年人群健康队列，伦理审批号HXSY-EC-2022034。华西老年人群健康队列（n=10 626）是一项由四川大学华西公共卫生学院/华西第四医院组织开展的，重点关注中国60岁及以上老年人重大慢病发病及演进规律的流行病学前瞻性队列。有关华西老年人群健康队列的详细信息已在之前的文献中进行报道[8]。
本研究使用华西老年人群健康队列的基线调查数据进行分析。纳排情况：（1）参与者完成了简易精神状态检查量表（mini-mental state examination,MMSE）问卷，排除未完成问卷的2 932人；（2）教育信息，缺失无法划分认知障碍，排除55人；（3）由专业医生使用双能X线吸收仪（dual-energy X-ray absorptiometry，DXA）测量了参与者腰椎（L1～L4）、股骨颈、全髋三个部位的骨密度，排除未完成检查的5 449人。另外，本研究排除了骨密度数据填写有误的参与者19人（骨密度值>10 g/cm2；T值>100），最终纳入2 171名研究对象进行后续分析。
1.1.2　T值计算和骨质疏松判定
根据骨质疏松的DXA诊断标准与本研究人群年龄分布情况，计算L1～L4、股骨颈、全髋三个部位的T值，公式：T=（测量者的骨密度-同性别中国正常年轻人峰值骨密度值）/同性别中国正常年轻人峰值骨密度的标准差（SD），以三个区域中最低的T值判断骨量状态（≥-1.0：正常骨密度，-2.49～-1.11：骨量减少，≤-2.5：骨质疏松）。
1.1.3　认知功能评估
使用MMSE问卷得分评估整体认知功能，该问卷由训练有素的调查员亲自进行。MMSE是一种广泛用于评估认知功能的筛查工具，尤其适用于老年人群体的认知功能损害（如AD、血管性痴呆等）评估。MMSE在筛查认知障碍和痴呆方面表现出良好的敏感性（71%～92%）和特异性（56%～96%）[9]。本研究使用MMSE总分作为整体认知功能的主要结局指标，得分越高认知功能越好。
同时，使用教育年限和MMSE得分区分研究人群是否发生认知损害，文盲≤17分、小学及以下≤20分、初中及以上≤24分则认为存在认知功能损害，计算认知功能受损率。
1.1.4　协变量
协变量均是由专职研究人员通过身份证识别、问卷、诊断或体格检查获得，包括年龄、性别、收入、婚姻、吸烟、饮酒、体力活动、睡眠、身体质量指数（body mass index,BMI）、教育、高血压、高脂血症、糖尿病、卒中。
1.1.5　统计学分析
连续变量采用（）、分类变量采用构成比的方式描述不同骨量状态分组下研究对象的基线分布特征。连续型变量采用方差分析，分类变量采用χ2检验进行研究对象基本特征的组间比较，P<0.05则差异具有统计学意义。
采用多因素线性回归分析不同骨量状态（骨质疏松、骨量减少）与认知功能之间的关系，以骨量正常组为参照组。同时，采用多因素线性回归分析L1～L4 骨密度、全髋骨密度、股骨颈骨密度与认知功能之间的关联，计算β及其95%置信区间（confidence interval,CI）。
所有分析均建立三个模型。模型1：调整年龄、性别；模型2：调整模型1+收入、婚姻、吸烟、饮酒、体力活动、睡眠、BMI、教育；模型3：调整模型2+高血压、高脂血症、糖尿病、卒中。
使用R 4.4.2软件进行统计学分析，采用双侧检验，检验水准α=0.05。
1.2　MR分析
1.2.1　数据来源
MR研究基于设计原理，满足因果推断的时序性要求，具有更高的因果论证效力。在分析时，本研究尽量使用中国、东亚地区的GWAS数据，然而中国、东亚地区公开发表的认知功能相关GWAS较少，样本量较小，因此基于AD占所有认知障碍患者的60%～80%[3]、数据限制等原因，结局变量使用欧洲人群AD的GWAS。综合考虑GWAS的样本量大小、发表时间、数据可及性后，本研究选择迄今为止最新、最大的中国人群骨密度-T值GWAS。该GWAS[10]研究人群来源于台湾生物样本库，样本量为92 615，性别比为64%女性（33 692名男性和92 615名女性），该研究通过超声测量足跟骨密度，然后使用亚洲年轻成人参考转换为T值（骨密度-T）。同时，使用欧洲人群骨密度的工具变量做敏感性分析，该研究[11]通过足跟定量超声评估了426 824位来自英国生物银行（UK Biobank,UKB）的参与者骨密度的遗传因素。AD的遗传关联数据来自2019年Kunkle等人[12]发表的GWAS，该GWAS总人群样本量为21 982个病例和41 944个对照，性别比为59%女性（26 210名男性和37 716名女性）。暴露与结局之间没有发现样本重叠。
1.2.2　工具变量的选取
骨密度的工具变量通过PLINK软件的clump函数获取，即：P<5×10-8，连锁不平衡参数r2<0.001，遗传距离10 000kb。排除F统计量小于10的位点后，将剩余位点初步确定为各性状的工具变量，用于下游的MR分析。
1.2.3　两样本MR统计学分析
本研究采用逆方差加权法（inverse-variance weighted，IVW）、MR-Egger回归（MR-Egger regression，MR-Egger）和加权中位数估计法（weighted median estimator，WME）作为主分析。当IVW方法的因果估计达到统计学显著性（P<0.05），且MR-Egger与WME的效应估计方向与IVW结果一致，则认为骨密度与AD之间存在潜在的因果关联。
本研究采用 Cochran Q 统计量对逆方差加权模型的异质性进行检验，检验阈值P<0.05，则表明SNPs之间存在显著的异质性，分析时需采用IVW随机效应模型进行后续分析。本研究采用MR-Egger回归方法进行多效性检测[13]，若截距项的假设检验结果P<0.05，则提示存在显著的水平多效性。
为确保研究结果的稳健性，本研究还进行了几项敏感性分析：（1）去除回文结构的SNPs；（2）去除有多效性的SNPs：根据GWAS catalog（https：//www.ebi.ac.uk/gwas/），去除与具有多效性的SNPs；（3）MR多效性残差和异常值检验（MR pleiotropy residual sum and outlier，MR-PRESSO）；（4）留一法（leave-one-out）；（5）欧洲人群骨密度工具变量的验证分析。
使用R4.4.2软件进行统计分析，双侧检验，检验水准α=0.05。
2　结果
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2.1　观察性研究
2.1.1　研究对象一般特征
本研究共纳入2 171名研究对象，研究人群平均年龄为（67.50±5.02）岁，平均MMSE得分为（24.01±4.60）分。骨量正常组662人，占比30.5%，MMSE得分（24.87±3.77）分；骨量减少患病率47.9%，共1 040人，MMSE得分为（24.14±4.58）分；骨质疏松患病率21.6%，该组共469人，MMSE得分（22.50±5.31）分，差异有统计学意义（P<0.001）。研究对象一般特征见表1。
2.1.2　骨量状态与认知功能受损的关联分析
研究人群认知功能损害的患病率为16.6%，骨量正常组认知功能损害的患病率为12.5%，骨量减少组认知功能损害的患病率为15.2%，骨质疏松组认知功能损害的患病率为25.4%，差异有统计学意义（P<0.001）。
以MMSE得分为因变量，骨量正常组为对照组，进一步调整协变量后发现骨质疏松与MMSE得分之间存在负向关联（β=-0.84，95% CI：-1.39～-0.29，P=0.003），即骨质疏松组相较骨量正常组MMSE得分更低、认知功能更差，在三个模型中均为负相关，结果十分稳健。同时，三个模型中均未发现骨量减少与MMSE得分之间存在关联（β=-0.04，95% CI：-0.44～-0.36，P=0.849）。骨量状态与认知功能受损的多因素线性回归结果见表2。
2.1.3　骨密度与认知功能受损的关联分析
分别以股骨颈骨密度、全髋骨密度、L1～L4骨密度为自变量，MMSE认知得分为因变量进行多因素线性回归，股骨颈骨密度在进一步调整更多协变量后，关联无统计学意义（β=1.34，95% CI：-0.11～2.79，P=0.070），全髋骨密度（β=1.58，95% CI：0.20～2.95，P=0.024）与L1～L4骨密度（β=1.17，95% CI：0.13～2.21，P=0.028）与认知评分之间呈现正向关联。结果见表3。
2.2　MR结果
2.2.1　工具变量
筛选出86个骨密度-T相关SNPs，工具变量的F统计量大小范围为29.93～645.40，均大于10，存在弱工具变量偏倚的可能性较小。
2.2.2　骨密度与AD之间潜在的因果关联
未发现遗传预测的骨密度-T值与AD之间存在因果效应（IVW：OR=0.95，95% CI：0.84～1.07，P=0.417），MR-Egger回归（OR=0.94，95% CI：0.62～1.42，P=0.761）和WME（OR=0.97，95% CI：0.83～1.13，P=0.701）法均得到与IVW一致的结果，提示在不同假设条件下，均未发现低骨密度-T值与AD之间存在因果关联，见图1。MR-Egger回归多效性检验结果显示不存在水平多效性（PEgger_intercept=0.944）。
2.2.3　敏感性分析
Cochran Q检验提示骨密度-T与AD（Q=138.24，P=2.33×10-4）存在异质性，使用IVW方法进行MR分析时选择随机效应模型。
多种敏感性分析结果与主分析结果保持一致，见图1。留一法剔除任意工具变量后结果未发生改变。暴露和结局均为全欧人群的分析结果仍不支持骨密度与AD之间存在因果关联（IVW：OR=1.03，95% CI：0.98～1.09，P=0.252）。该研究结果具有稳健性。
3　讨论
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本研究通过观察性研究、MR研究阐明骨质疏松与认知功能之间的关联，本研究的证据表明，骨质疏松与认知功能受损存在正相关关系，骨密度值越高，认知功能得分越高，尤其是全髋骨密度及L1～L4骨密度与认知功能之间呈正相关，但骨质疏松与AD之间的因果关系需要进一步研究来确证。
观察性研究结果显示骨密度流失与认知功能受损有关。本研究利用华西老年人群健康队列基线数据进行横断面研究结果显示，在调整多种混杂因素后，与骨量正常组相比骨质疏松与MMSE得分之间存在负向关联（β=-0.84，95% CI：-1.39～-0.29，P=0.003），即骨质疏松组相较骨量正常组MMSE得分更低、认知功能更差，结果十分稳健，这与既往多个观察性流行病学研究证据一致[4-5,14-15]。Song Hwangbo等人[4]利用韩国的国民健康数据进行回顾性队列研究，结果发现骨质疏松不仅会增加全因痴呆的风险（RR=1.10，95% CI：1.07～1.13），也会增加AD相关痴呆的风险（RR=1.06，95% CI：1.02～1.10）。更进一步，不同部位骨密度与认知评分的关系也不同，全髋骨密度与L1～L4骨密度与MMSE得分之间存在正向关联，较高的全髋骨密度、L1～L4骨密度与更好的认知功能有关，而股骨颈骨密度与MMSE得分关联无统计学意义。
观察性研究不可避免的残余混杂和反向因果问题可能在一定程度上会影响结果的准确性，因此MR开展因果推断受到青睐。本研究采用两样本MR方法，探讨骨密度与AD之间的因果关联。结果显示，中国人群中足跟骨密度-T值（IVW：OR=0.95，95% CI：0.84～1.07，P=0.417）与AD发病风险之间不存在因果关联。多种敏感性分析结果都十分稳健，这与既往研究一致[16]。
基于MR的研究结果，我们认为，观察性研究展现的关联可能是由共同的发病机制或代谢相互作用驱动的。AD和骨质疏松作为常见的退行性疾病，可能有一些相似的危险因素，包括衰老、女性、遗传因素和生活方式异常等。这些因素可能对AD和骨质疏松症的发生发展有明显的影响。已有研究发现骨质疏松与AD之间可能通过Wnt信号通路异常[17]、中枢和外周炎症[18-19]、钙失调[20]、骨分泌蛋白[21-23]、胞外囊泡[24]等多种途径相互调节。药物的保护作用也可能是骨质疏松与AD呈现这种关联的机制之一[14,25-26]
本研究存在一定的局限性：（1）本研究采用的是横断面研究设计，因果推断效力较低；（2）本研究的部分变量是通过研究对象回顾性自报获得，可能存在回忆偏倚；（3）尽管本研究在多因素线性回归模型中针对多个重要的混杂因素进行了调整，但不能排除其他未测量或因未精准测量的因素而产生的剩余混杂对研究结果的影响；（4）MMSE得分是一种广泛用于评估认知功能的筛查工具，在未进行更进一步检查的情况下，我们无法确定研究对象认知受损的原因；（5）中国乃至东亚人群认知功能的全基因组关联研究较少，AD也尚无公开数据，所以本研究使用欧洲人群数据替代，虽然骨密度-T暴露数据来自中国人群，但仍可能影响中国人群中骨密度与AD发病风险因果关联的推断，期待之后更多东亚地区的GWAS汇总数据。
基于共同的危险因素和发病机制，对骨质疏松患者，尤其是全髋骨密度及L1～L4骨密度较低的患者，常规预防AD是合理的，但骨质疏松与AD之间的因果关系需要进一步研究来确证。
基金
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  • 国家重点研发计划(2022YFC3600604)
  • 四川省科技计划重点研发项目(2022YFS0055)
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2025年第52卷第21期
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doi: 10.20043/j.cnki.MPM.202504223
  • 接收时间：2025-04-13
  • 首发时间：2026-10-29
  • 出版时间：2025-11-10
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  • 收稿日期：2025-04-13
基金
国家重点研发计划(2022YFC3600604)
四川省科技计划重点研发项目(2022YFS0055)
作者信息
    1四川省卫生健康信息中心，四川　成都　610044
    2四川大学华西公共卫生学院/华西第四医院，四川　成都　610041

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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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