Article(id=1311349910981672975, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311349802038821144, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202511378, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1763740800000, receivedDateStr=2025-11-22, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790579782481, onlineDateStr=2026-09-28, pubDate=1783612800000, pubDateStr=2026-07-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790579782481, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790579782481, creator=13701087609, updateTime=1790579782481, updator=13701087609, issue=Issue{id=1311349802038821144, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='13', pageStart='2305', pageEnd='2496', issueExtLink='null', onlineDate='null', pubDate='1783612800000', pubDateStr='2026-07-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790579756507, creator='13701087609', updateTime=1790588376143, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311385955492258162, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311349802038821144, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311385955496452467, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311349802038821144, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2451, endPage=2456, ext={EN=ArticleExt(id=1311349911245914128, articleId=1311349910981672975, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Association between different obesity phenotypes and dementia risk in an elderly health examination population, columnId=1228016572451718132, journalTitle=Modern Preventive Medicine, columnName=Health and Social Behavior, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the association between different obesity phenotypes and the risk of dementia in an elderly health examination population.

Methods

Based on the health examination database of the Health Management (Examination) Center of the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine from 2015 to 2025, participants were categorized according to body mass index (BMI) and metabolic health status into four groups: metabolically healthy non-obese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese(MUO). The Fine-Gray competing risk model was used to evaluate the association between different obesity phenotypes and the cumulative risk of dementia, and subdistribution hazard ratios (SHR) with their 95% confidence intervals (CI) were calculated.

Results

A total of 42 915 participants were eventually included, consisting of 17 015 in the MHNO group, 7 520 in the MHO group, 8 480 in the MUNO group, and 9 900 in the MUO group. Fine-Gray competing risk model analysis showed that, using the MHNO group as the reference, the risk of all-cause dementia, Alzheimer’s disease (AD), and vascular dementia(VD)showed an increasing trend with the severity of metabolic abnormalities and obesity. After full adjustment for confounding factors, the SHR(95% CI) for all-cause dementia was 1.102 (1.040-1.226) in the MHO group, 1.392 (1.257-1.542) in the MUNO group, and 1.725 (1.571-1.894) in the MUO group, with similar gradients observed for AD and VD risks. The results of sensitivity analysis were consistent with the main analysis.

Conclusion

A significant gradient association was observed between different obesity phenotypes and dementia risk, demonstrating a sequential increase in dementia risk with the severity of metabolic abnormalities and obesity. These findings highlight the need to prioritize the MUO population in dementia prevention and implement stratified intervention strategies for different phenotypes.

, authors=Jing JIANG, authorsList=Jing JIANG, authorCompany=null, correspAuthors=null, 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=1311349912579702807, articleId=1311349910981672975, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=老年健康体检人群中不同肥胖表型与痴呆风险的关联研究, columnId=1228016572640461823, journalTitle=现代预防医学, columnName=健康与社会行为, runingTitle=null, highlight=null, articleAbstract=
目的

探究老年健康体检人群中不同肥胖表型与痴呆风险的关联。

方法

基于成都中医药大学附属医院健康管理(体检)中心2015—2025年的健康体检数据库,根据身体质量指数(body mass index,BMI)与代谢健康状态将研究对象分为代谢健康非肥胖组(MHNO)、代谢健康肥胖组(MHO)、代谢不健康非肥胖组(MUNO)、代谢不健康肥胖组(MUO)四组,采用Fine-Gray竞争风险模型评估不同肥胖表型与痴呆累积发病风险的关联,计算亚分布风险比(subdistribution hazard ratio,SHR)及其95%置信区间。

结果

最终纳入42 915名参与者,其中MHNO组17 015例,MHO组7 520例,MUNO组8 480例,MUO组9 900例。Fine-Gray竞争风险模型分析显示,以MHNO组为参照,随着代谢异常与肥胖程度加重,全因痴呆、阿尔茨海默及血管性痴呆风险呈递增趋势。在完全调整混杂因素后,MHO、MUNO、MUO组全因痴呆的SHR(95%CI)分别为1.102(1.040~1.226)、1.392(1.257~1.542)、1.725(1.571~1.894),阿尔茨海默和血管性痴呆风险亦呈现相似梯度。敏感性分析结果与主分析一致。

结论

不同肥胖表型与痴呆风险呈现显著的梯度关联,即随着代谢异常与肥胖程度的加重,痴呆风险依次递增,提示在痴呆预防中需重点关注MUO人群,并对不同表型实施分层干预策略。

, authors=姜静, authorsList=姜静, authorCompany=null, correspAuthors=null, authorNote=

姜静(1986-),女,本科,副主任护师,研究方向:护理、健康管理

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姜静(1986-),女,本科,副主任护师,研究方向:护理、健康管理

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姜静(1986-),女,本科,副主任护师,研究方向:护理、健康管理

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Molecular Neurodegeneration, 2025, 20(1): 61., articleTitle=Bridging systemic metabolic dysfunction and Alzheimer’s disease: the liver interface, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1311349912885887000, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349910981672975, xref=null, ext=[AuthorCompanyExt(id=1311349912894275609, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349910981672975, companyId=1311349912885887000, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Physical Examination Center, Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610072, China), AuthorCompanyExt(id=1311349912915247130, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349910981672975, companyId=1311349912885887000, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=成都中医药大学附属医院体检中心,四川 成都 610072)])], figs=[ArticleFig(id=1311349915331166248, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349910981672975, language=EN, label=Figure 1, caption=Forest plot of factor analysis by dementia status, figureFileSmall=r14/nZ+ttYpQitY2pcr6wA==, figureFileBig=gLZdEMFqcJwIcPTrj0wb4A==, tableContent=null), ArticleFig(id=1311349915410858025, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349910981672975, language=CN, label=图1, caption=不同痴呆状态因素分析森林图, figureFileSmall=r14/nZ+ttYpQitY2pcr6wA==, figureFileBig=gLZdEMFqcJwIcPTrj0wb4A==, tableContent=null), ArticleFig(id=1311349915607990314, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349910981672975, language=EN, label=Table 1, caption=

Baseline characteristics by dementia status[(), n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
项目痴呆组(n=2 128)非痴呆组(n=40 787)t/χ2P值
性别0.0260.873
男1 170(54.98)22 353(54.80)
女958(45.02)18 434(45.20)
年龄(岁)68.95±3.8764.83±3.2855.948<0.001
BMI(kg/m2)24.84±3.5224.63±4.941.9360.053
腰围(cm)94.57±11.2889.26±10.6422.375<0.001
收缩压(mm Hg)119.07±10.44125.23±11.3224.564<0.001
空腹血糖(mmol/L)5.45±0.525.24±0.4919.214<0.001
甘油三酯(mmol/L)1.44±0.331.35±0.417.746<0.001
高密度脂蛋白胆固醇(mmol/L)1.48±0.431.52±0.355.076<0.001
APOEε4携带者154.151<0.001
是723(33.98)9 123(22.37)
否1 405(66.02)31 664(77.63)
教育水平(年)10.28±3.2811.75±3.4719.102<0.001
体力活动(MET-分钟/周)2 467.83±1 887.562 992.45±2 078.9411.398<0.001
吸烟0.2770.599
有354(16.64)6 609(16.20)
无1 774(83.36)34 178(83.80)
酗酒0.1000.752
有301(14.14)5 670(13.90)
无1 827(85.86)35 117(86.10)
抑郁症史0.0940.759
有267(12.55)5 026(12.32)
无1 861(87.45)35 761(87.68)
冠心病史1.1520.283
有304(14.29)5 494(13.47)
无1 824(85.71)35 293(86.53)
卒中/TIA病史0.9150.339
有191(8.98)3 420(8.39)
无1 937(91.02)37 367(91.61)
肥胖表型209.877<0.001
MHNO596(28.01)16 419(40.27)
MHO329(15.46)7 191(17.64)
MUNO475(22.32)8 005(19.63)
MUO728(34.21)9 172(22.49)
), ArticleFig(id=1311349915679293483, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349910981672975, language=CN, label=表1, caption=

基于痴呆状态的基线特征 [(),n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
项目痴呆组(n=2 128)非痴呆组(n=40 787)t/χ2P值
性别0.0260.873
男1 170(54.98)22 353(54.80)
女958(45.02)18 434(45.20)
年龄(岁)68.95±3.8764.83±3.2855.948<0.001
BMI(kg/m2)24.84±3.5224.63±4.941.9360.053
腰围(cm)94.57±11.2889.26±10.6422.375<0.001
收缩压(mm Hg)119.07±10.44125.23±11.3224.564<0.001
空腹血糖(mmol/L)5.45±0.525.24±0.4919.214<0.001
甘油三酯(mmol/L)1.44±0.331.35±0.417.746<0.001
高密度脂蛋白胆固醇(mmol/L)1.48±0.431.52±0.355.076<0.001
APOEε4携带者154.151<0.001
是723(33.98)9 123(22.37)
否1 405(66.02)31 664(77.63)
教育水平(年)10.28±3.2811.75±3.4719.102<0.001
体力活动(MET-分钟/周)2 467.83±1 887.562 992.45±2 078.9411.398<0.001
吸烟0.2770.599
有354(16.64)6 609(16.20)
无1 774(83.36)34 178(83.80)
酗酒0.1000.752
有301(14.14)5 670(13.90)
无1 827(85.86)35 117(86.10)
抑郁症史0.0940.759
有267(12.55)5 026(12.32)
无1 861(87.45)35 761(87.68)
冠心病史1.1520.283
有304(14.29)5 494(13.47)
无1 824(85.71)35 293(86.53)
卒中/TIA病史0.9150.339
有191(8.98)3 420(8.39)
无1 937(91.02)37 367(91.61)
肥胖表型209.877<0.001
MHNO596(28.01)16 419(40.27)
MHO329(15.46)7 191(17.64)
MUNO475(22.32)8 005(19.63)
MUO728(34.21)9 172(22.49)
), ArticleFig(id=1311349915754790956, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349910981672975, language=EN, label=Table 2, caption=

Fine-Gray competing risk model analysis of different metabolic obesity phenotypes and dementia subtypes

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模型因素全因痴呆ADVD
SHR(95% CI)P值校正后
P值
SHR(95% CI)P值校正后
P值
SHR(95% CI)P值校正后
P值
模型1MHNO1.000——1.000——1.000——
MHO1.198 (1.082~1.327)<0.001<0.0011.186 (1.058~1.330)<0.001<0.0011.225 (1.058~1.419)0.0020.006
MUNO1.525 (1.386~1.678)<0.001<0.0011.458 (1.305~1.628)<0.001<0.0011.535 (1.332~1.768)<0.001<0.001
MUO1.895 (1.716~2.092)<0.001<0.0011.804 (1.624~2.003)<0.001<0.0011.935 (1.685~2.222)<0.001<0.001
模型2MHNO1.000——1.000——1.000——
MHO1.175 (1.051~1.314)0.0010.0031.162 (1.035~1.305)0.0020.0061.192 (1.029~1.381)0.0050.015
MUNO1.482 (1.342~1.637)<0.001<0.0011.419 (1.270~1.586)<0.001<0.0011.495 (1.298~1.722)<0.001<0.001
MUO1.842 (1.662~2.042)<0.001<0.0011.748 (1.574~1.942)<0.001<0.0011.878 (1.635~2.157)<0.001<0.001
模型3MHNO1.000——1.000——1.000——
MHO1.102 (1.040~1.226)0.0080.0241.090 (1.007~1.229)0.010.031.135 (1.005~1.316)0.0200.046
MUNO1.392 (1.257~1.542)<0.001<0.0011.364 (1.205~1.544)<0.001<0.0011.460 (1.258~1.694)<0.001<0.001
MUO1.725 (1.571~1.894)<0.001<0.0011.684 (1.498~1.893)<0.001<0.0011.823 (1.573~2.113)<0.001<0.001
), ArticleFig(id=1311349915842871341, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349910981672975, language=CN, label=表2, caption=

不同代谢肥胖表型与痴呆亚型的Fine-Gray竞争风险模型分析

, figureFileSmall=null, figureFileBig=null, tableContent=
模型因素全因痴呆ADVD
SHR(95% CI)P值校正后
P值
SHR(95% CI)P值校正后
P值
SHR(95% CI)P值校正后
P值
模型1MHNO1.000——1.000——1.000——
MHO1.198 (1.082~1.327)<0.001<0.0011.186 (1.058~1.330)<0.001<0.0011.225 (1.058~1.419)0.0020.006
MUNO1.525 (1.386~1.678)<0.001<0.0011.458 (1.305~1.628)<0.001<0.0011.535 (1.332~1.768)<0.001<0.001
MUO1.895 (1.716~2.092)<0.001<0.0011.804 (1.624~2.003)<0.001<0.0011.935 (1.685~2.222)<0.001<0.001
模型2MHNO1.000——1.000——1.000——
MHO1.175 (1.051~1.314)0.0010.0031.162 (1.035~1.305)0.0020.0061.192 (1.029~1.381)0.0050.015
MUNO1.482 (1.342~1.637)<0.001<0.0011.419 (1.270~1.586)<0.001<0.0011.495 (1.298~1.722)<0.001<0.001
MUO1.842 (1.662~2.042)<0.001<0.0011.748 (1.574~1.942)<0.001<0.0011.878 (1.635~2.157)<0.001<0.001
模型3MHNO1.000——1.000——1.000——
MHO1.102 (1.040~1.226)0.0080.0241.090 (1.007~1.229)0.010.031.135 (1.005~1.316)0.0200.046
MUNO1.392 (1.257~1.542)<0.001<0.0011.364 (1.205~1.544)<0.001<0.0011.460 (1.258~1.694)<0.001<0.001
MUO1.725 (1.571~1.894)<0.001<0.0011.684 (1.498~1.893)<0.001<0.0011.823 (1.573~2.113)<0.001<0.001
), ArticleFig(id=1311349915918368814, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349910981672975, language=EN, label=Table 3, caption=

Association between different obesity phenotypes and the risk of all-cause dementia

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因素模型1模型2模型3
SHR(95% CI)P值SHR(95% CI)P值SHR(95% CI)P值
MHO1.198 (1.082~1.327)<0.0011.175 (1.051~1.314)0.0011.102 (1.009~1.226)0.008
MUNO1.525 (1.386~1.678)<0.0011.482 (1.342~1.637)<0.0011.392 (1.257~1.542)<0.001
MUO1.895 (1.716~2.092)<0.0011.842 (1.662~2.042)<0.0011.725 (1.571~1.894)<0.001
), ArticleFig(id=1311349915981283375, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349910981672975, language=CN, label=表3, caption=

不同肥胖表型与全因痴呆风险关联分析

, figureFileSmall=null, figureFileBig=null, tableContent=
因素模型1模型2模型3
SHR(95% CI)P值SHR(95% CI)P值SHR(95% CI)P值
MHO1.198 (1.082~1.327)<0.0011.175 (1.051~1.314)0.0011.102 (1.009~1.226)0.008
MUNO1.525 (1.386~1.678)<0.0011.482 (1.342~1.637)<0.0011.392 (1.257~1.542)<0.001
MUO1.895 (1.716~2.092)<0.0011.842 (1.662~2.042)<0.0011.725 (1.571~1.894)<0.001
), ArticleFig(id=1311349916065169456, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349910981672975, language=EN, label=Table 4, caption=

Sensitivity analysis

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因素模型1模型2模型3
SHR(95% CI)P值SHR(95% CI)P值SHR(95% CI)P值
MHO1.183 (1.061~1.319)<0.0011.158 (1.032~1.299)0.0021.089 (1.067~1.226)0.012
MUNO1.509 (1.369~1.663)<0.0011.468 (1.325~1.626)<0.0011.380 (1.247~1.527)<0.001
MUO1.872 (1.691~2.073)<0.0011.820 (1.638~2.023)<0.0011.708 (1.527~1.910)<0.001
), ArticleFig(id=1311349916157444145, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349910981672975, language=CN, label=表4, caption=

敏感性分析

, figureFileSmall=null, figureFileBig=null, tableContent=
因素模型1模型2模型3
SHR(95% CI)P值SHR(95% CI)P值SHR(95% CI)P值
MHO1.183 (1.061~1.319)<0.0011.158 (1.032~1.299)0.0021.089 (1.067~1.226)0.012
MUNO1.509 (1.369~1.663)<0.0011.468 (1.325~1.626)<0.0011.380 (1.247~1.527)<0.001
MUO1.872 (1.691~2.073)<0.0011.820 (1.638~2.023)<0.0011.708 (1.527~1.910)<0.001
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老年健康体检人群中不同肥胖表型与痴呆风险的关联研究
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姜静
现代预防医学 | 健康与社会行为 2026,53(13): 2451-2456
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现代预防医学 |健康与社会行为 2026 , 53 (13) : 2451 -2456
老年健康体检人群中不同肥胖表型与痴呆风险的关联研究
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姜静(1986-),女,本科,副主任护师,研究方向:护理、健康管理

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姜静(1986-),女,本科,副主任护师,研究方向:护理、健康管理

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姜静
作者信息
  • 成都中医药大学附属医院体检中心,四川 成都 610072
作者简介:

姜静(1986-),女,本科,副主任护师,研究方向:护理、健康管理

Association between different obesity phenotypes and dementia risk in an elderly health examination population
Jing JIANG
Affiliations
  • Physical Examination Center, Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610072, China
出版时间: 2026-07-10 doi: 10.20043/j.cnki.MPM.202511378
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目的

探究老年健康体检人群中不同肥胖表型与痴呆风险的关联。

方法

基于成都中医药大学附属医院健康管理(体检)中心2015—2025年的健康体检数据库,根据身体质量指数(body mass index,BMI)与代谢健康状态将研究对象分为代谢健康非肥胖组(MHNO)、代谢健康肥胖组(MHO)、代谢不健康非肥胖组(MUNO)、代谢不健康肥胖组(MUO)四组,采用Fine-Gray竞争风险模型评估不同肥胖表型与痴呆累积发病风险的关联,计算亚分布风险比(subdistribution hazard ratio,SHR)及其95%置信区间。

结果

最终纳入42 915名参与者,其中MHNO组17 015例,MHO组7 520例,MUNO组8 480例,MUO组9 900例。Fine-Gray竞争风险模型分析显示,以MHNO组为参照,随着代谢异常与肥胖程度加重,全因痴呆、阿尔茨海默及血管性痴呆风险呈递增趋势。在完全调整混杂因素后,MHO、MUNO、MUO组全因痴呆的SHR(95%CI)分别为1.102(1.040~1.226)、1.392(1.257~1.542)、1.725(1.571~1.894),阿尔茨海默和血管性痴呆风险亦呈现相似梯度。敏感性分析结果与主分析一致。

结论

不同肥胖表型与痴呆风险呈现显著的梯度关联,即随着代谢异常与肥胖程度的加重,痴呆风险依次递增,提示在痴呆预防中需重点关注MUO人群,并对不同表型实施分层干预策略。

肥胖表型  /  代谢  /  痴呆  /  关联
Objective

To investigate the association between different obesity phenotypes and the risk of dementia in an elderly health examination population.

Methods

Based on the health examination database of the Health Management (Examination) Center of the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine from 2015 to 2025, participants were categorized according to body mass index (BMI) and metabolic health status into four groups: metabolically healthy non-obese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese(MUO). The Fine-Gray competing risk model was used to evaluate the association between different obesity phenotypes and the cumulative risk of dementia, and subdistribution hazard ratios (SHR) with their 95% confidence intervals (CI) were calculated.

Results

A total of 42 915 participants were eventually included, consisting of 17 015 in the MHNO group, 7 520 in the MHO group, 8 480 in the MUNO group, and 9 900 in the MUO group. Fine-Gray competing risk model analysis showed that, using the MHNO group as the reference, the risk of all-cause dementia, Alzheimer’s disease (AD), and vascular dementia(VD)showed an increasing trend with the severity of metabolic abnormalities and obesity. After full adjustment for confounding factors, the SHR(95% CI) for all-cause dementia was 1.102 (1.040-1.226) in the MHO group, 1.392 (1.257-1.542) in the MUNO group, and 1.725 (1.571-1.894) in the MUO group, with similar gradients observed for AD and VD risks. The results of sensitivity analysis were consistent with the main analysis.

Conclusion

A significant gradient association was observed between different obesity phenotypes and dementia risk, demonstrating a sequential increase in dementia risk with the severity of metabolic abnormalities and obesity. These findings highlight the need to prioritize the MUO population in dementia prevention and implement stratified intervention strategies for different phenotypes.

Obesity phenotypes  /  Metabolism  /  Dementia  /  Association
姜静. 老年健康体检人群中不同肥胖表型与痴呆风险的关联研究. 现代预防医学, 2026 , 53 (13) : 2451 -2456 . DOI: 10.20043/j.cnki.MPM.202511378
Jing JIANG. Association between different obesity phenotypes and dementia risk in an elderly health examination population[J]. Modern Preventive Medicine, 2026 , 53 (13) : 2451 -2456 . DOI: 10.20043/j.cnki.MPM.202511378
随着全球人口老龄化加速,痴呆症已成为重大公共卫生问题,其进行性认知功能减退严重影响患者生活质量,并给家庭与社会医疗体系带来沉重负担[1]。在众多可干预的危险因素中,肥胖与代谢异常已被确认为重要的可调控风险因素。大量证据表明,肥胖可通过诱发慢性炎症、促进胰岛素抵抗、导致血管内皮功能障碍及增加氧化应激等多重机制,损害大脑结构与功能,从而增加全因痴呆、阿尔茨海默病及血管性痴呆的发病风险[2]。然而,以往研究多将肥胖视为同质性疾病,忽略了其内在异质性。近年研究发现,肥胖与代谢健康状态存在分离现象,部分肥胖个体代谢指标正常,而部分体重正常者却存在明显代谢紊乱[3]。这一发现提示,肥胖与代谢健康的组合模式可能比单纯体重更能准确反映个体的病理生理状态,进而对认知健康产生不同影响。
目前,关于不同肥胖代谢表型与痴呆风险关联的研究已初步开展,但仍存在关键空白:现有研究多聚焦于中老年混合人群,专门针对高龄老年(≥60岁)这一痴呆最高危群体的证据有限;且多数研究受限于样本量、随访时间或诊断准确性,缺乏来自大规模中国老年队列的长期随访证据,制约了精准化预防策略的制定。基于此,本研究旨在探讨四种肥胖代谢表型与老年人群痴呆发病风险的关联,以期为痴呆的精准防控提供循证依据。
本研究数据来源于成都中医药大学附属医院健康管理(体检)中心2015—2025年的健康体检数据库。研究纳入年龄≥60岁、完成至少一次全面健康体检的老年人群。所有研究对象均具备完整的基础健康评估数据、代谢指标与体格检查记录、完整的社会人口学及生活方式信息,以及随访信息。本研究排除了基线时已确诊痴呆、患有恶性肿瘤、严重肝肾功能不全及关键变量缺失的个体,最终共纳入42 915名老年体检者。随访时间定义为从研究对象首次入组体检日期(索引日期)开始,至以下最早发生的事件日期为止:(1)痴呆确诊日期;(2)死亡日期;(3)最后一次健康体检或电子病历记录日期(即删失日期)。本研究人群的中位随访时间为6.2年。由于数据来源于年度健康体检,随访间隔并非固定,但通过关联的电子健康档案系统可实现对终点事件的持续监测。本研究已通过成都中医药大学附属医院伦理委员会批准(批号:2025KL-037),所有体检数据均经脱敏处理,伦理委员会批准豁免知情同意。
通过标准化信息采集表获取性别、出生日期及教育水平。人体测量由统一培训的医护人员按标准操作执行,研究对象着轻便衣物、脱鞋免冠。身高、体重采用定期校准的电子身高体重仪测量,并计算身体质量指数(body mass index,BMI)。腰围于腋中线肋弓下缘与髂嵴上缘中点水平,在呼气末用软皮尺测量。
采用电子血压计测量静息血压,研究对象静坐至少5 min后测量坐位右上臂血压,连续两次取平均值。所有研究对象禁食至少8 h,于清晨采集空腹外周静脉血,使用全自动生化分析仪检测空腹血糖、甘油三酯及高密度脂蛋白胆固醇,检测遵循实验室标准质控程序。
采集外周血样本提取基因组DNA,采用TaqMan探针法等技术检测载脂蛋白E(apolipoprotein E,APOE)基因rs429358和rs7412位点,以确定APOE ε4等位基因携带状态。
通过标准化问卷收集生活方式信息。体力活动采用国际体力活动问卷长版评估,涵盖职业、交通、家务、休闲四领域,并统一转化为代谢当量分钟/周进行量化。
疾病史信息包括抑郁症、冠心病、卒中/短暂性脑缺血发作(transient ischemic attack,TIA)以及痴呆[阿尔茨海默病(Alzheimer disease,AD)、血管性痴呆(vascular dementia,VD)]的诊断,均通过查询与研究队列关联的电子健康档案系统进行确认。痴呆及其亚型的所有病例均需有神经内科或精神科专科医师基于临床评估作出的明确诊断,且诊断由病案室根据医师结论标注对应的《国际疾病分类第十次修订版(ICD-10)》编码。本研究采用的具体编码为:全因痴呆(F00-F03)、阿尔茨海默病(G30与F00)、血管性痴呆(F01)。所有病例的诊断与编码均由两名研究人员独立核对确认,以确保信息的准确性与可靠性。
本研究依据BMI与代谢健康状态将研究对象划分为四种肥胖代谢表型。BMI≥28.0 kg/m2为肥胖[4]。代谢健康状态则参照改良后的美国国家胆固醇教育计划成人治疗组第三次报告[5]标准进行评估,满足以下五项指标中的至少三项即被判定为“代谢不健康”:(1)中心性肥胖(男性腰围≥90 cm,女性腰围≥85 cm);(2)血压升高(收缩压≥130 mm Hg/舒张压≥85 mm Hg或已接受降压治疗);(3)空腹血糖升高(≥5.6 mmol/L或已确诊糖尿病并接受治疗);(4)空腹甘油三酯升高(≥1.70 mmol/L);(5)高密度脂蛋白胆固醇降低(男性<1.04 mmol/L,女性<1.30 mmol/L)。最终确立以下四组:(1)代谢健康非肥胖组(metabolically healthy non-obese,MHNO):同时满足BMI<28.0 kg/m2及代谢健康状态;(2)代谢健康肥胖组(metabolically healthy obese,MHO):同时满足BMI≥28.0 kg/m2及代谢健康状态;(3)代谢不健康非肥胖组(metabolically unhealthy non-obese,MUNO):满足BMI<28.0 kg/m2但处于代谢不健康状态;(4)代谢不健康肥胖组(metabolically unhealthy obese,MUO):同时满足BMI≥28.0 kg/m2及代谢不健康状态。
本研究采用SAS 9.4与R 4.2.0软件进行所有统计分析。计量数据则以(均值±标准差)表示,组间比较采用单因素方差分析;计数数据采用例(%)进行表示,并通过χ2检验进行统计分析。由于死亡与痴呆的发生存在竞争风险关系,本研究采用Fine-Gray竞争风险模型评估不同肥胖表型与痴呆累积发病风险的关联,计算亚分布风险比(subdistribution hazard ratio, SHR)及其95%置信区间,将全因死亡作为竞争事件。模型构建与主分析一致,分为三个层级逐步调整混杂因素:模型1调整年龄与性别;模型2在模型1基础上增加教育水平与APOE ε4携带状态;模型3在模型2基础上增加体力活动、吸烟状况、饮酒状况、抑郁症史、冠心病史及卒中/TIA病史。采用Bonferroni校正法进行了校正,将显著性水准校正为α’=0.05/3≈0.016 7。为检验结果的稳健性,排除随访开始后2年内确诊的痴呆病例后进行敏感性分析。检验水准α=0.05。
42 915名参与者中痴呆组2 128例,非痴呆组40 787例。两组参与者在年龄、腰围、收缩压、空腹血糖、高密度脂蛋白胆固醇、APOE ε4携带率、教育水平、体力活动、肥胖表型方面存在统计学差异(P<0.05),在性别、BMI、甘油三酯、吸烟、酗酒、冠心病史和卒中/TIA病史方面不存在统计学差异(P>0.05)。见表1。各变量在两组间的分布差异森林图见图1。
采用Fine-Gray竞争风险模型,以全因死亡作为竞争事件,评估不同肥胖代谢表型与痴呆亚型发病风险的关联。以MHNO组为参照,在仅调整年龄与性别的模型1中,MHO、MUNO、MUO组的全因痴呆、AD与VD风险均呈现递增趋势;在进一步调整教育水平、APOE ε4携带状态后的模型2和完全调整了生活方式因素、共病情况的模型3后,各组的SHR虽略有下降,但数据趋势与统计学意义均保持不变。
在最终完全调整的模型3中,以MHNO组为参照,MHO组全因痴呆风险增加(SHR=1.102,95% CI:1.040~1.226),其AD与VD风险均升高(SHR=1.090,95% CI: 1.007~1.229;SHR=1.135,95% CI: 1.005~1.316)。MUNO组全因痴呆风险进一步升高(SHR=1.392,95% CI: 1.257~1.542),AD与VD风险同步增加(SHR=1.364,95% CI: 1.205~1.544;SHR=1.460,95% CI: 1.258~1.694)。MUO组风险最高,全因痴呆SHR为1.725(95% CI: 1.571~1.894),AD与VD风险分别为1.684(95% CI: 1.498~1.893)和1.823(95%CI: 1.573~2.113)。见表2。
在仅调整年龄和性别的模型1中,MHO、MUNO、MUO组的SHR分别为1.198(95% CI:1.082~1.327)、1.525(95% CI:1.386~1.678)、1.895(95% CI:1.716~2.092)。在进一步调整教育水平和APOE ε4携带状态后的模型2中,MHO、MUNO、MUO组的亚分布风险比略有下降,SHR分别为1.175(95% CI:1.051~1.314)、1.482(95% CI:1.342~1.637)、1.842(95% CI:1.662~2.042)。在完全调整了生活方式因素和共病情况的模型3中,MHO、MUNO、MUO组SHR的进一步调整为1.102(95% CI:1.009~1.226)、1.392(95% CI:1.257~1.542)、1.725(95% CI:1.571~1.894)。见表3。
在排除随访早期发生的痴呆病例后,不同肥胖表型与全因痴呆风险关联的各SHR均有所降低。模型1中,MHO、MUNO、MUO组的SHR分别为1.183(95% CI:1.061~1.319)、1.509(95% CI:1.369~1.663)、1.872(95% CI:1.691~2.073)。模型2中,MHO、MUNO、MUO组的SHR分别为1.158(95%CI:1.032~1.299)、1.468(95% CI:1.325~1.626)、1.820(95% CI:1.638~2.023)。模型3中,MHO、MUNO、MUO组的SHR分别为1.089(95% CI:1.067~1.226)、1.380(95% CI:1.247~1.527)、1.708(95% CI:1.527~1.910)。见表4。
本研究纳入的42 915名参与者中,四种肥胖代谢表型的分布呈现明显规律,其中MHNO组17 015例,MHO组7 520例,MUNO组8 480例,MUO组9 900例。四组人群在代谢相关指标方面存在显著差异,从MHNO组到MUO组,BMI、腰围、收缩压、空腹血糖、甘油三酯依次升高,高密度脂蛋白胆固醇依次降低,与Karra等[6]的研究结果一致。MUNO组虽BMI正常,但代谢指标异常程度显著高于MHO组,支持“代谢健康肥胖悖论”的存在[7]。教育水平差异反映了认知储备对健康行为的促进作用,高教育水平个体更易维持代谢健康状态[8]。体力活动递减趋势提示运动不足通过降低胰岛素敏感性、促进异位脂肪沉积参与肥胖及代谢异常发生[9]。冠心病史、卒中/TIA病史的组间差异体现了代谢异常与心血管疾病共享胰岛素抵抗、慢性炎症等共同病理机制[10]。痴呆发生率的梯度上升趋势与代谢紊乱程度高度一致,提示代谢异常可能是连接肥胖与痴呆的重要中介[11]。
在痴呆组与非痴呆组的基线特征比较中,42 915名参与者中痴呆组2 128例,非痴呆组40 787例,两组参与者在年龄、腰围、收缩压、空腹血糖、高密度脂蛋白胆固醇、APOE ε4携带率、教育水平、体力活动、肥胖表型方面存在统计学差异。增龄导致大脑清除异常蛋白能力下降、血管弹性减弱[12]。腰围增大、血压升高、血糖异常及高密度脂蛋白胆固醇降低构成代谢综合征核心特征,通过促进脑血管病变和神经炎症参与痴呆病理[13]。APOE ε4携带率差异表明该基因型在Aβ清除障碍和tau过度磷酸化中起关键作用,这与Lozupone等人[14]研究结论一致。教育水平差异体现认知储备理论,较高教育程度通过增强神经突触可塑性提高大脑病理耐受能力[15]。体力活动差异反映了运动促进脑源性神经营养因子分泌、改善脑血流灌注等保护机制[16]。本研究中,痴呆组中MUO表型占比最高,而MHNO表型占比最低,提示同时存在肥胖和代谢异常可能是痴呆的强风险因素,因此在评估老年人痴呆风险时,应将体重与代谢健康状态结合考量。上述发现从不同层面揭示了痴呆发生机制,为制定综合性预防策略提供了理论依据。
Fine-Gray竞争风险模型分析揭示了不同肥胖代谢表型与痴呆风险之间存在明显关联。与MHNO组相比,MHO、MUNO、MUO组的痴呆风险逐渐增加,且在AD和VD两种亚型中均保持一致。MUNO组的痴呆风险显著高于MHO组,提示代谢异常状态可能比单纯的肥胖对认知功能的影响更为重要。MUO组表现出最高的痴呆风险,支持肥胖与代谢异常在促进神经退行性变过程中可能存在协同作用[17]。通过三个逐步调整的模型,本研究验证了不同肥胖代谢表型与痴呆风险关联的稳健性。在仅调整年龄和性别的模型1中,各表型组的风险比最高,随着教育水平、APOE ε4携带状态(模型2)以及生活方式因素和共病情况(模型3)的逐步调整,风险比呈现轻微但稳定的下降趋势。这种变化模式提示,教育水平和遗传因素可能部分介导了肥胖代谢表型与痴呆风险之间的关联,而生活方式和共病情况则可能作为混杂因素影响这一关系。模型3中关联仍保持统计学显著性,表明不同肥胖代谢表型与痴呆风险之间的关联具有独立性。通过排除随访早期病例的敏感性分析,进一步验证了主要结果的可靠性。与主要分析相比,敏感性分析中各表型组的风险比均出现轻微下降,但保持统计学显著性且置信区间与主要分析基本一致。排除早期发病病例有助于减少反向因果关系的干扰,而本研究的主要发现不受潜在反向因果关系的影响,增强了结论的可靠性。
本研究通过大样本长期随访发现,随代谢异常与肥胖程度加重,全因痴呆及其亚型风险呈显著梯度上升,提示临床应综合考量个体肥胖状态与代谢健康状况。本研究局限性包括:(1)残余混杂因素;(2)单一中心数据限制外推性;(3)痴呆亚型诊断基于临床标准,缺乏神经病理学确认。未来需开展多中心研究,结合神经影像学与生物标志物,进一步探索其生物学机制。

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doi: 10.20043/j.cnki.MPM.202511378
  • 接收时间:2025-11-22
  • 首发时间:2026-09-28
  • 出版时间:2026-07-10
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  • 收稿日期:2025-11-22
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    成都中医药大学附属医院体检中心,四川 成都 610072
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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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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
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