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Article(id=1241025207444501287, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241025201983508979, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202409545, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1727539200000, receivedDateStr=2024-09-29, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773813066588, onlineDateStr=2026-03-18, pubDate=1744214400000, pubDateStr=2025-04-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773813066588, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773813066588, creator=13701087609, updateTime=1773813066588, updator=13701087609, issue=Issue{id=1241025201983508979, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='7', pageStart='1153', pageEnd='1344', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773813065285, creator=13701087609, updateTime=1773815493878, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241035388320543403, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241025201983508979, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241035388320543404, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241025201983508979, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1296, endPage=1299, ext={EN=ArticleExt(id=1241025207754879804, articleId=1241025207444501287, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Study on the relationship between different trajectories of depression symptoms and stroke risk in community-dwelling elderly, columnId=1228016572451718132, journalTitle=Modern Preventive Medicine, columnName=Health and Social Behavior, runingTitle=null, highlight=null, articleAbstract=
Objective

To assess the association between the trajectories of depression symptoms and the risk of stroke in the elderly.

Methods

Data from four waves of the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2018 were utilized.Group-based trajectory modeling was employed to identify the trajectories of depression symptoms, and logistic regression models were used to evaluate the relationship between these trajectories and stroke.

Results

Four trajectories of depression symptoms were identified:low stable (n=2 392, 60.7%), moderate decreasing (n=762, 20.2%), moderate increasing (n=416, 12.3%), and high stable (n=250, 6.8%).During the period from 2015 to 2018, a total of 305 respondents experienced a stroke. Compared to participants with the low stable depression symptom trajectory, those following the moderate decreasing (OR=1.48, 95%CI: 1.09-2.02), moderate increasing (OR=2.22,95%CI: 1.58-3.11), and high stable depression symptom trajectories (OR=1.78, 95%CI: 1.12-2.83) had a higher risk of stroke.

Conclusion

Over time, elderly individuals following moderate decreasing, moderate increasing, and high stable trajectories of depression symptoms exhibited an increased risk of stroke. Long-term depression symptoms may serve as a strong predictor for stroke.

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目的

评估老年人抑郁症状发展轨迹与脑卒中风险之间的关联。

方法

使用中国健康与退休纵向研究(CHARLS)2011—2018年四个周期数据，利用组基轨迹模型识别抑郁症状轨迹，运用logistic回归模型评估抑郁症状轨迹与脑卒中的关系。

结果

确定了四种抑郁症状轨迹，分别为低稳定(n=2 392， 60.7%)、中度下降(n=762， 20.2%)、中度增加(n=416， 12.3%)、高稳定(n=250， 6.8%)。2015—2018年，共有305名受访者患有脑卒中。与低稳定抑郁症状轨迹的参与者相比，那些遵循中度下降(OR=1.48， 95%CI: 1.09～2.02)、中度增加(OR=2.22， 95%CI: 1.58～3.11）和高稳定抑郁症状轨迹(OR= 1.78， 95%CI: 1.12～2.83)的参与者患脑卒中的风险更高。

结论

随着时间的推移，遵循中度下降、中度增加和高稳定抑郁症状轨迹老年人的脑卒中风险增加。长期抑郁症状可能是一个强有力的脑卒中预测因素。

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徐德民，E-mail:
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张洁（1997—），女，硕士在读，研究方向：流行病与卫生统计

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张洁（1997—），女，硕士在读，研究方向：流行病与卫生统计

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张洁（1997—），女，硕士在读，研究方向：流行病与卫生统计

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General Hospital Psychiatry,2008, 30(2): 127-137., articleTitle=The association of depression and anxiety with obesity and unhealthy behaviors among community-dwelling US adults, refAbstract=null), Reference(id=1241025219234689488, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, doi=null, pmid=null, pmcid=null, year=2023, volume=6, issue=8, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[16], rfOrder=17, authorNames=Brimavandi M, Abbasi P, KhalediPaveh B, journalName=Health Sci Rep, refType=null, unstructuredReference=Brimavandi M, Abbasi P, KhalediPaveh B, et al. Examining the relationship between depression and medication adherence among elderlies suffering from cardiovascular disease referring to the clinics affiliated with Kermanshah University of Medical Sciences: A cross-sectional study[J]. Health Sci Rep, 2023, 6(8): e1503.(In Chinese), articleTitle=Examining the relationship between depression and medication adherence among elderlies suffering from cardiovascular disease referring to the clinics affiliated with Kermanshah University of Medical Sciences: A cross-sectional study, refAbstract=null)], funds=[Fund(id=1241025215765999930, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, awardId=3502Z20234018, language=CN, fundingSource=厦门市医疗卫生重点项目(3502Z20234018), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241025210447623088, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, xref=1., ext=[AuthorCompanyExt(id=1241025210451817393, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, companyId=1241025210447623088, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Public Health, Xiamen University, Xiamen, Fujian 361000, China), AuthorCompanyExt(id=1241025210456011698, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, companyId=1241025210447623088, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.厦门大学公共卫生学院，福建 厦门 361000)]), AuthorCompany(id=1241025210548286393, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, xref=2., ext=[AuthorCompanyExt(id=1241025210556675002, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, companyId=1241025210548286393, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.复旦大学附属中山医院厦门医院医务部，福建 厦门 361000)]), AuthorCompany(id=1241025210695087045, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, xref=3., ext=[AuthorCompanyExt(id=1241025210699281350, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, companyId=1241025210695087045, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.复旦大学附属中山医院心脏外科，上海 200000)])], figs=[ArticleFig(id=1241025214524485849, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, language=EN, label=Figure 1, caption=Trajectories of depressive symptoms among Chinese older adults, figureFileSmall=UjPrOylA3YDv+xzg8Dfmdg==, figureFileBig=52+gKiABhpGIaA5Y2bAJSA==, tableContent=null), ArticleFig(id=1241025214608371938, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, language=CN, label=图1, caption=中国老年人抑郁症状轨迹, figureFileSmall=UjPrOylA3YDv+xzg8Dfmdg==, figureFileBig=52+gKiABhpGIaA5Y2bAJSA==, tableContent=null), ArticleFig(id=1241025214843252979, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, language=EN, label=Table 1, caption=

Fit statistics of depression symptoms trajectory

, figureFileSmall=null, figureFileBig=null, tableContent=
分组组别AICBICEntropyAvePP
274.71/25.29-39 985.1-40 007.60.790.95/0.89
360.29/32.31/7.39-39 755.3-39 788.60.730.90/0.82/0.86
460.67/20.23/12.26/6.84-39 645.1-39 689.90.750.91/0.71/0.73/0.85
530.53/42.46/11.26/10.67/5.08-39 592.4-39 648.50.640.65/0.82/0.72/0.74/0.85
67.94/51.32/16.21/12.98/7.29/4.26-39 573.3-39 640.70.660.67/0.81/0.65/0.72/0.71/0.83
), ArticleFig(id=1241025214952304892, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, language=CN, label=表1, caption=

抑郁症状轨迹模型拟合指标

, figureFileSmall=null, figureFileBig=null, tableContent=
分组组别AICBICEntropyAvePP
274.71/25.29-39 985.1-40 007.60.790.95/0.89
360.29/32.31/7.39-39 755.3-39 788.60.730.90/0.82/0.86
460.67/20.23/12.26/6.84-39 645.1-39 689.90.750.91/0.71/0.73/0.85
530.53/42.46/11.26/10.67/5.08-39 592.4-39 648.50.640.65/0.82/0.72/0.74/0.85
67.94/51.32/16.21/12.98/7.29/4.26-39 573.3-39 640.70.660.67/0.81/0.65/0.72/0.71/0.83
), ArticleFig(id=1241025215048773894, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, language=EN, label=Table 2, caption=

Baseline characteristics comparison according to depression symptoms trajectories

, figureFileSmall=null, figureFileBig=null, tableContent=
变量低稳定组中度下降组中度增加组高稳定组F(χ2 )值P值
样本量2 392762416250
年龄(岁)66.51±5.6366.51±5.5266.90±5.1465.58±4.8611.760.028
女性1 047 (43.8)460 (60.4)269 (64.7)175 (70.0)143.97<0.001
教育水平56.23<0.001
文盲913 (38.2)235 (30.8)116 (27.9)76 (30.4)
小学562 (23.5)185 (24.3)112 (26.9)55 (22.0)
初中561 (23.5)202 (26.5)113 (27.2)49 (19.6)
高中及以上356 (14.9)140 (18.4)75 (18.0)70 (28.0)
居住在农村1 452 (60.7)564 (74.0)300 (72.1)202 (80.8)83.04<0.001
独居382 (16.0)175 (23.0)81 (19.5)67 (26.8)31.97<0.001
BMI(kg/m2)124.67<0.001
≤18.4177 (7.4)107 (14.0)51 (12.3)21 (8.4)
18.5～23.91 061 (44.4)340 (44.6)173 (41.6)136 (54.4)
24～27.9487 (20.4)132 (17.3)75 (18.0)40 (16.0)
≥28667 (27.9)183 (24.0)117 (28.1)53 (21.2)
饮酒1 018 (42.6)276 (36.2)150 (36.1)85 (34.0)24.930.001
吸烟1 068 (44.6)278 (36.5)140 (33.7)78 (31.2)110.54<0.001
高血压1 116 (46.7)348 (45.7)216 (51.9)112 (44.8)2.170.159
糖尿病300 (12.5)101 (13.3)61 (14.7)30 (12.0)1.180.639
关节炎699 (29.2)365 (47.9)204 (49.0)156 (62.4)30.67<0.001
肺病72 (3.0)48 (6.3)20 (4.8)14 (5.6)27.61<0.001
肾病95 (4.0)53 (7.0)28 (6.7)33 (13.2)41.29<0.001
肝病62 (2.6)34 (4.5)15 (3.6)9 (3.6)9.240.066
消化系统疾病418 (17.5)236 (31.0)123 (29.6)111 (44.4)70.73<0.001
), ArticleFig(id=1241025215149437197, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, language=CN, label=表2, caption=

基于抑郁轨迹的基线特征比较

, figureFileSmall=null, figureFileBig=null, tableContent=
变量低稳定组中度下降组中度增加组高稳定组F(χ2 )值P值
样本量2 392762416250
年龄(岁)66.51±5.6366.51±5.5266.90±5.1465.58±4.8611.760.028
女性1 047 (43.8)460 (60.4)269 (64.7)175 (70.0)143.97<0.001
教育水平56.23<0.001
文盲913 (38.2)235 (30.8)116 (27.9)76 (30.4)
小学562 (23.5)185 (24.3)112 (26.9)55 (22.0)
初中561 (23.5)202 (26.5)113 (27.2)49 (19.6)
高中及以上356 (14.9)140 (18.4)75 (18.0)70 (28.0)
居住在农村1 452 (60.7)564 (74.0)300 (72.1)202 (80.8)83.04<0.001
独居382 (16.0)175 (23.0)81 (19.5)67 (26.8)31.97<0.001
BMI(kg/m2)124.67<0.001
≤18.4177 (7.4)107 (14.0)51 (12.3)21 (8.4)
18.5～23.91 061 (44.4)340 (44.6)173 (41.6)136 (54.4)
24～27.9487 (20.4)132 (17.3)75 (18.0)40 (16.0)
≥28667 (27.9)183 (24.0)117 (28.1)53 (21.2)
饮酒1 018 (42.6)276 (36.2)150 (36.1)85 (34.0)24.930.001
吸烟1 068 (44.6)278 (36.5)140 (33.7)78 (31.2)110.54<0.001
高血压1 116 (46.7)348 (45.7)216 (51.9)112 (44.8)2.170.159
糖尿病300 (12.5)101 (13.3)61 (14.7)30 (12.0)1.180.639
关节炎699 (29.2)365 (47.9)204 (49.0)156 (62.4)30.67<0.001
肺病72 (3.0)48 (6.3)20 (4.8)14 (5.6)27.61<0.001
肾病95 (4.0)53 (7.0)28 (6.7)33 (13.2)41.29<0.001
肝病62 (2.6)34 (4.5)15 (3.6)9 (3.6)9.240.066
消化系统疾病418 (17.5)236 (31.0)123 (29.6)111 (44.4)70.73<0.001
), ArticleFig(id=1241025215279460632, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, language=EN, label=Table 3, caption=

Logistics regression between depressive symptoms trajectories and risk of stroke [OR (95%CI)]

, figureFileSmall=null, figureFileBig=null, tableContent=
抑郁症状轨迹分组低稳定组中度下降组中度增加组高稳定组P值
未经调整1.00 (Ref)1.45(1.08～1.96)2.32(1.68～3.21)1.70(1.10～2.63)<0.001
年龄和性别调整1.00 (Ref)1.43(1.06～1.93)2.25(1.62～3.13)1.66(1.07～2.60)<0.001
多变量调整1.00 (Ref)1.48(1.09～2.02)2.22(1.58～3.11)1.78(1.12～2.83)<0.001
), ArticleFig(id=1241025215388512544, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, language=CN, label=表3, caption=

抑郁症状轨迹与脑卒中风险logistics回归的OR (95%CI)

, figureFileSmall=null, figureFileBig=null, tableContent=
抑郁症状轨迹分组低稳定组中度下降组中度增加组高稳定组P值
未经调整1.00 (Ref)1.45(1.08～1.96)2.32(1.68～3.21)1.70(1.10～2.63)<0.001
年龄和性别调整1.00 (Ref)1.43(1.06～1.93)2.25(1.62～3.13)1.66(1.07～2.60)<0.001
多变量调整1.00 (Ref)1.48(1.09～2.02)2.22(1.58～3.11)1.78(1.12～2.83)<0.001
), ArticleFig(id=1241025215489175847, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, language=EN, label=Table 4, caption=

Subgroup analysis of OR (95%CI) of depressive symptoms trajectories for stroke

, figureFileSmall=null, figureFileBig=null, tableContent=
低稳定组中度下降组中度增加组高稳定组P交互作用
性别0.655
男性1.00 (Ref)1.30(0.81～2.11)2.64(1.58～4.41) ***2.16(1.04～4.48)***
女性1.00 (Ref)1.44(0.83～2.50)2.06(1.35～3.15) ***1.48(1.01～2.18) *
受教育水平0.471
文盲1.00 (Ref)1.47(0.88～2.45)1.79(0.95～3.38)1.44(0.63～3.28)
小学1.00 (Ref)1.24(0.70～2.20)2.20(1.22～3.97) **2.87(1.39～5.95) ***
初中1.00 (Ref)1.34(0.73～2.44)2.95(1.62～5.36) ***2.03(0.81～5.10)
高中及以上1.00 (Ref)0.84(0.18～3.85)2.95(1.12～7.77) **2.69(1.18～6.14) **
同居情况0.899
同居1.00 (Ref)1.42(1.01～2.00) *2.38(1.66～3.41) ***1.53(0.90～2.60)
独居1.00 (Ref)1.51(0.81～2.82)2.07(0.98～4.36)2.04(0.91～4.56)
居住地0.076
城市1.00 (Ref)1.71(1.03～2.85) *1.73(0.92～3.24)3.60(1.72～7.56) ***
农村1.00 (Ref)1.39(0.96～2.01)2.63(1.79～3.87) ***1.32(0.76～2.29)
高血压0.491
否1.00 (Ref)1.64(1.04～2.59) *1.70(0.95～3.07)1.82(0.93～3.55)
是1.00 (Ref)1.36(0.91～2.01)2.59(1.74～3.86) ***1.66(0.93～2.98)
吸烟0.249
否1.00 (Ref)1.57(1.08～2.26) *1.93(1.26～2.95) **1.46(0.83～2.55)
是1.00 (Ref)1.24(0.75～2.07)3.14(1.90～5.21) ***2.23(1.10～4.54) **
饮酒0.512
否1.00 (Ref)1.42(0.97～2.07)2.09(1.38～3.18) ***1.32(0.73～2.38)
是1.00 (Ref)1.51(0.93～2.45)2.75(1.65～4.58) ***2.49(1.29～4.83) ***
), ArticleFig(id=1241025215602422064, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207444501287, language=CN, label=表4, caption=

脑卒中抑郁症状轨迹的OR(95%CI)亚组分析

, figureFileSmall=null, figureFileBig=null, tableContent=
低稳定组中度下降组中度增加组高稳定组P交互作用
性别0.655
男性1.00 (Ref)1.30(0.81～2.11)2.64(1.58～4.41) ***2.16(1.04～4.48)***
女性1.00 (Ref)1.44(0.83～2.50)2.06(1.35～3.15) ***1.48(1.01～2.18) *
受教育水平0.471
文盲1.00 (Ref)1.47(0.88～2.45)1.79(0.95～3.38)1.44(0.63～3.28)
小学1.00 (Ref)1.24(0.70～2.20)2.20(1.22～3.97) **2.87(1.39～5.95) ***
初中1.00 (Ref)1.34(0.73～2.44)2.95(1.62～5.36) ***2.03(0.81～5.10)
高中及以上1.00 (Ref)0.84(0.18～3.85)2.95(1.12～7.77) **2.69(1.18～6.14) **
同居情况0.899
同居1.00 (Ref)1.42(1.01～2.00) *2.38(1.66～3.41) ***1.53(0.90～2.60)
独居1.00 (Ref)1.51(0.81～2.82)2.07(0.98～4.36)2.04(0.91～4.56)
居住地0.076
城市1.00 (Ref)1.71(1.03～2.85) *1.73(0.92～3.24)3.60(1.72～7.56) ***
农村1.00 (Ref)1.39(0.96～2.01)2.63(1.79～3.87) ***1.32(0.76～2.29)
高血压0.491
否1.00 (Ref)1.64(1.04～2.59) *1.70(0.95～3.07)1.82(0.93～3.55)
是1.00 (Ref)1.36(0.91～2.01)2.59(1.74～3.86) ***1.66(0.93～2.98)
吸烟0.249
否1.00 (Ref)1.57(1.08～2.26) *1.93(1.26～2.95) **1.46(0.83～2.55)
是1.00 (Ref)1.24(0.75～2.07)3.14(1.90～5.21) ***2.23(1.10～4.54) **
饮酒0.512
否1.00 (Ref)1.42(0.97～2.07)2.09(1.38～3.18) ***1.32(0.73～2.38)
是1.00 (Ref)1.51(0.93～2.45)2.75(1.65～4.58) ***2.49(1.29～4.83) ***
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不同抑郁症状轨迹与社区老年人脑卒中风险的研究
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张洁 1 , 李佳男 1 , 黄妤洁 2 , 徐德民 2, 3
现代预防医学 | 健康与社会行为 2025,52(7): 1296-1299
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现代预防医学 | 健康与社会行为 2025, 52(7): 1296-1299
不同抑郁症状轨迹与社区老年人脑卒中风险的研究
全屏
张洁1, 李佳男1, 黄妤洁2, 徐德民2, 3
作者信息
  • 1.厦门大学公共卫生学院，福建 厦门 361000
  • 2.复旦大学附属中山医院厦门医院医务部，福建 厦门 361000
  • 3.复旦大学附属中山医院心脏外科，上海 200000

    • 张洁（1997—），女，硕士在读，研究方向：流行病与卫生统计

通讯作者:

徐德民，E-mail:
Study on the relationship between different trajectories of depression symptoms and stroke risk in community-dwelling elderly
Jie ZHANG1, Jia-nan LI1, Yu-jie HUANG2, De-min XU2, 3
Affiliations
  • School of Public Health, Xiamen University, Xiamen, Fujian 361000, China
出版时间: 2025-04-10 doi: 10.20043/j.cnki.MPM.202409545
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摘要
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目的

评估老年人抑郁症状发展轨迹与脑卒中风险之间的关联。

方法

使用中国健康与退休纵向研究(CHARLS)2011—2018年四个周期数据，利用组基轨迹模型识别抑郁症状轨迹，运用logistic回归模型评估抑郁症状轨迹与脑卒中的关系。

结果

确定了四种抑郁症状轨迹，分别为低稳定(n=2 392， 60.7%)、中度下降(n=762， 20.2%)、中度增加(n=416， 12.3%)、高稳定(n=250， 6.8%)。2015—2018年，共有305名受访者患有脑卒中。与低稳定抑郁症状轨迹的参与者相比，那些遵循中度下降(OR=1.48， 95%CI: 1.09～2.02)、中度增加(OR=2.22， 95%CI: 1.58～3.11）和高稳定抑郁症状轨迹(OR= 1.78， 95%CI: 1.12～2.83)的参与者患脑卒中的风险更高。

结论

随着时间的推移，遵循中度下降、中度增加和高稳定抑郁症状轨迹老年人的脑卒中风险增加。长期抑郁症状可能是一个强有力的脑卒中预测因素。

抑郁症状  /  社区老年人  /  中风  /  轨迹  /  CHARLS
Objective

To assess the association between the trajectories of depression symptoms and the risk of stroke in the elderly.

Methods

Data from four waves of the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2018 were utilized.Group-based trajectory modeling was employed to identify the trajectories of depression symptoms, and logistic regression models were used to evaluate the relationship between these trajectories and stroke.

Results

Four trajectories of depression symptoms were identified:low stable (n=2 392, 60.7%), moderate decreasing (n=762, 20.2%), moderate increasing (n=416, 12.3%), and high stable (n=250, 6.8%).During the period from 2015 to 2018, a total of 305 respondents experienced a stroke. Compared to participants with the low stable depression symptom trajectory, those following the moderate decreasing (OR=1.48, 95%CI: 1.09-2.02), moderate increasing (OR=2.22,95%CI: 1.58-3.11), and high stable depression symptom trajectories (OR=1.78, 95%CI: 1.12-2.83) had a higher risk of stroke.

Conclusion

Over time, elderly individuals following moderate decreasing, moderate increasing, and high stable trajectories of depression symptoms exhibited an increased risk of stroke. Long-term depression symptoms may serve as a strong predictor for stroke.

Depression symptoms  /  Community-dwelling elderly  /  Stroke  /  Trajectory  /  CHARLS
张洁, 李佳男, 黄妤洁, 徐德民. 不同抑郁症状轨迹与社区老年人脑卒中风险的研究. 现代预防医学, 2025 , 52 (7) : 1296 -1299 . DOI: 10.20043/j.cnki.MPM.202409545
Jie ZHANG, Jia-nan LI, Yu-jie HUANG, De-min XU. Study on the relationship between different trajectories of depression symptoms and stroke risk in community-dwelling elderly[J]. Modern Preventive Medicine, 2025 , 52 (7) : 1296 -1299 . DOI: 10.20043/j.cnki.MPM.202409545
正文
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脑卒中是全球第二大死亡原因，也是所有残疾调整生命年的第三大原因[1]。据报告，由缺血性卒中引起的伤残调整寿命年（ DALY）在60 ～ 64岁的成人中达到峰值[2]。因此，确定老年人卒中的危险因素具有重要意义。抑郁症是一种常见的精神障碍，在中国，老年人抑郁症发病率持续上升[3]。老年人的抑郁症往往由于认识和治疗的不足，造成巨大负担[4]。研究表明抑郁症状显著增加了中风风险[5]。一项包含共57 761例患者的荟萃分析表明晚年抑郁或抑郁症状是老年人中风的重要危险因素[6]。抑郁症是一种慢性疾病，症状可能会随着时间的推移而缓解，出现或加剧[7]。不同的人群中抑郁症状稳定性或严重程度及发展轨迹不同[8]。因此识别抑郁症状的不同轨迹有助于理解抑郁症状与脑卒中风险之间的复杂关联。本研究的目的是使用CHARLS的数据描述社区老年人抑郁症状发展轨迹，分析抑郁症状轨迹与脑卒中风险之间的关联。
1　对象与方法
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1.1　资料来源
CHARLS是一项在中国中老年人群中进行的具有全国代表性的前瞻性、纵向人群研究。在基线时，共纳入了来自中国28个省份的17 708名参与者。随后在2013—2014年(第2波）、2015—2016年(第3波）和2017—2018年(第4波）对幸存者进行了三次随访。北京大学生物医学伦理审查委员会(IRB 00001052 -11015）批准了CHARLS收集人类受试者数据的伦理申请，所有CHARLS参与者均提供了书面知情同意书。CHARLS数据的细节可在其网站(http://charls.pku.edu.cn/en）上查阅。在基线的17 708例受试者中，纳入了符合以下所有标准的个体：（1）年龄≥60岁;（2）包含3次完整的流行病学研究中心抑郁量表(CES-D)评估测量结果;（3）前3波未报告脑卒中事件史。最终，研究共纳入3 820人。
1.2　抑郁症状的测量
本研究使用CES-D量表测量抑郁症状， CES-D简表由10个项目组成，每个项目的评分为0～3分。总评分范围为0～30分(阳性项目反向编码），评分越高表示抑郁症状的严重程度越高。
1.3　脑卒中事件评估
本研究的主要结果是脑卒中，参与者被询问“医生或其他健康专业人员是否曾告诉过您患有脑卒中？”若回答为是，则诊断为脑卒中事件。
1.4　协变量评估
协变量的选择基于既往研究，包括年龄、性别、居住地（农村、城市）、吸烟状况（吸烟、不吸烟）、教育水平（文盲、小学、初中、高中或以上）、饮酒状况（饮酒、不饮酒）、身体质量指数(body mass index， BMI)、慢性疾病（高血压、糖尿病、肺病、肾病、肝病、关节炎、消化系统疾病）。
1.5　抑郁症状轨迹
基于组基轨迹模型(GBTM)通过STATA “traj”包中的删失正态模型来识别抑郁症状轨迹。GBTM具有将个体分类为具有相似潜在轨迹不同组的优点。在这种方法中，最大似然法用于通过等式所表示的算法来估计GBTM建模。通过迭代预定数量的组以获得更大的似然值来建立轨迹。首先，我们假设抑郁症状轨迹的数量范围为2～6，并测试了包含这些分配和各种形式(例如，截距、线性或二次斜率）以捕获潜在模式。模型选择标准包括贝叶斯信息标准(BIC)、赤池信息准则(AIC）和熵(Entropy)。同时，各组参与者占比大于5%，平均后验概率(average of the posterior probability， Avepp)高于0.7的分组被认为是可接受的[9]。
1.6　统计分析
协变量缺失值使用链式方程进行了多重插补。使用方差分析检验和χ2检验在四个轨迹组之间比较基线特征。采用多因素logistic回归模型估计抑郁症状轨迹与新发脑卒中之间的相关性。模型1未调整，模型2调整了年龄、性别，模型3进一步调整了教育水平、居住地、同居情况、吸烟、饮酒、BMI和慢性疾病，报告比值比(odds ratios， OR)和95%置信区间(confidence intervals， CI）。为了检验结果的稳健性，我们根据性别、受教育水平、高血压史、居住地、同居情况、吸烟、饮酒的亚组进行亚组分析。统计分析使用R 4.3.3完成，检验水准α=0.05。
2　结果
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2.1　研究人群抑郁症状轨迹
根据表1的轨迹模型拟合指标，我们确定了四种抑郁症状轨迹，分别为：整个随访期间保持较低的CES-D评分(低稳定组，n=2 392）；开始CES-D评分较高，但随后评分下降(中度下降组，n=762）；中等起始CES-D评分，然后评分增加(中度增加组，n=416）；在整个随访期间保持较高的CES-D评分(高稳定组，n=250）。见图1。
2.2　研究人群特征
本研究最终纳入3 820名参与者。如表2所示，轨迹间年龄、性别、教育水平、居住地、同居情况、吸烟、饮酒、BMI等存在显著差异。低稳定组的参与者大多为男性、更有可能非独居、生活在城市、超重、吸烟、饮酒。相比之下，具有高稳定组的人更有可能是女性、独居、生活在农村、受教育程度高且患慢性病概率较高。
2.3　抑郁症状轨迹与脑卒中的相关性
2015—2018年，共有305名受访者新发脑卒中。表3总结了单变量和多变量逻辑回归分析的结果。与低稳定组的参与者相比，中度下降组、中度增加组和高稳定组的个体脑卒中风险的OR (95%CI)分别为1.45(1.08～1.96)、2.32(1.68～3.21)和1.70(1.10～2.63)。在进一步调整协变量后，中度下降组(OR=1.48， 95%CI: 1.09～2.02)、中度增加组(OR=2.22， 95%CI: 1.58～3.11)和高稳定组(OR=1.78， 95%CI: 1.12～2.83)与脑卒中的高风险仍显著相关。
如表4所示，在亚组分析中，不同抑郁症状轨迹组与脑卒中之间的关系不因分组而异，交互作用不存在。但在几乎所有亚组中均观察到中度增加组和高稳定组与脑卒中风险的显著相关。
3　讨论
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在这项社区老年人的全国前瞻性队列研究中，我们确定了四种不同的抑郁症状轨迹，分别为低稳定组、中度下降组、中度增加组、高稳定组。与低稳定组相比，中度下降组、中度上升组、高稳定组与发生脑卒中的风险较高相关，且与性别、受教育程度和其他协变量无关。这表明长期存在的抑郁症状会增加脑卒中的风险。
先前的研究已经观察到在不同人群中存在多种抑郁轨迹，但是轨迹的数量和形状不同[10-11]。这些差异可能归因于研究人群的特征、观察时间和持续时间、评估抑郁症状的量表和描述轨迹的时间尺度的差异。与先前研究不同的是，本研究的中度下降抑郁症状轨迹比例达到了20.2%，并且观察到抑郁症状中度下降轨迹与脑卒中间的关系(OR=1.48， 95%CI:1.09～2.02)。与之前的研究类似，高抑郁症状组的个体更可能是女性，农村、独居、社会经济地位较低，慢性病负担较重[8，12]。
本研究结果表明，即使在调整社会人口统计学协变量后，抑郁症状组与脑中风事件有关，即使在抑郁症状改善的患者中也是如此（即中等下降组）。与低稳定组的个体相比，抑郁症状升高的个体在随访期间患脑卒中的风险最大。过往研究通常使用单个时间点评估的抑郁症状探索其与脑卒中事件之间的关联，但本研究表明，随着时间的推移，抑郁症状的发展变化在增加脑卒中风险中发挥了关键作用。抑郁症状的升高与脑卒中的发生密切相关，可以通过生理过程和生活方式行为的积累来解释。首先，抑郁可能促进白细胞介素-1、白细胞介素-6和C-反应蛋白的表达导致炎症反应增加，通过神经内分泌效应导致中风，如激活交感神经系统、下丘脑-垂体-肾上腺皮质轴紊乱和血小板聚集功能障碍增加中风的风险[13-14]。其次，有证据表明，抑郁症状的加重可能会通过减少体力活动、不良饮食和体重增加导致脑卒中风险上升[15]。最后，抑郁与慢性疾病有关（例如，糖尿病、高血压和心脏病），并且患有抑郁的患者可能倾向于对上述疾病具有较差的药物依从性，这两者都可以增加中风的风险[5，16]。本研究也存在局限性。首先，根据CES-D评估抑郁症状，基于医生诊断的自我报告脑卒中，可能会导致信息偏倚。其次，CHARLS研究仅针对中国人群，我们的研究结果可能无法推广到其他人群。最后，本研究不是预先规定的分析，可能受到潜在偏倚和混杂因素的影响。
综上所述，在这项大规模的基于人群的前瞻性研究中，我们确定了抑郁症状的四种轨迹。与低稳定组的个体相比，中度增加组和高稳定组与发生脑卒中的风险较高相关。未来的研究有必要揭示这些关联的生物学机制，并将抑郁症状轨迹（而不是单一评估）作为识别脑卒中个体的筛选方法。
基金
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  • 厦门市医疗卫生重点项目(3502Z20234018)
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doi: 10.20043/j.cnki.MPM.202409545
  • 接收时间：2024-09-29
  • 首发时间：2026-03-18
  • 出版时间：2025-04-10
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  • 收稿日期：2024-09-29
基金
厦门市医疗卫生重点项目(3502Z20234018)
作者信息
    1.厦门大学公共卫生学院，福建 厦门 361000
    2.复旦大学附属中山医院厦门医院医务部，福建 厦门 361000
    3.复旦大学附属中山医院心脏外科，上海 200000

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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
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