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Article(id=1241036331455927119, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241036327177744706, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202504479, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1745769600000, receivedDateStr=2025-04-28, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773815718758, onlineDateStr=2026-03-18, pubDate=1757433600000, pubDateStr=2025-09-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773815718758, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773815718758, creator=13701087609, updateTime=1773815718758, updator=13701087609, issue=Issue{id=1241036327177744706, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='17', pageStart='3073', pageEnd='3264', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773815717738, creator=13701087609, updateTime=1773840080282, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241138511152206262, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241036327177744706, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241138511152206263, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241036327177744706, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=3105, endPage=3110, ext={EN=ArticleExt(id=1241036332458365795, articleId=1241036331455927119, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis and prediction of the disease burden of ischemic stroke attributable to high LDL-C in China from 1990 to 2021, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To analyze the current status and changing trends of ischemic stroke burden attributable to high LDL-C in China from 1990 to 2021. By integrating the prediction results for the next decade, this research offers actionable insights for designing evidence-based interventions targeting ischemic stroke prevention and management in China.

Methods

Data were obtained from the Global Burden of Disease Study (GBD) 2021 database and screened for Chinese regions, causes of ischemic stroke deaths, and high LDL-C risk factors. The disease burden was measured using indicators such as mortality and disability-adjusted life year (DALY) rates, and systematically analyzed the trends using the Joinpoint regression model. The autoregressive moving average (ARIMA) model was employed to predict the standardized mortality and standardized DALY rates of ischemic stroke attributable to high LDL-C in China from 2022 to 2031.

Results

The overall trend of ischemic stroke rates attributable to high LDL-C in the Chinese population was declining between 1990 and 2021, with mean annual percentage changes (AAPC) of-0.42% (95% CI: -0.66%~-0.18%, P<0.05) and -0.46% (95% CI: -0.63%~-0.29%, P<0.05), with significant age and sex disparities observed. According to the ARIMA model, China’s standardized mortality and DALY rates linked to elevated LDL-C would decline to 15.01 per 100 000 population and 319.00 per 100 000 population, respectively, by 2031.

Conclusion

The disease burden of ischemic stroke attributable to high LDL-C remains substantial in China, and the interventions on LDL-C levels in the priority populations of men and elderly ischemic stroke patients should be reinforced, and prevention and control strategies as well as institutional support should be improved for the purpose of alleviating the disease burden of ischemic stroke more effectively.

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目的

分析1990—2021年中国归因于高低密度脂蛋白胆固醇（low-density lipoprotein cholesterol，LDL-C）的缺血性脑卒中疾病负担现状与变化趋势，结合未来十年预测结果，为制定中国缺血性脑卒中的防治措施提供参考依据。

方法

数据来源于全球疾病负担研究（GBD）2021数据库，筛选中国地区、缺血性脑卒中死因及高LDL-C风险因素等数据，采用死亡率与伤残调整生命年（disability-adjusted life year，DALY）率等指标衡量疾病负担，使用joinpoint回归模型对其变化趋势进行系统分析；运用自回归移动平均（autoregressive intergrated moving average，ARIMA）模型对2022—2031年中国归因于高LDL-C的缺血性脑卒中的标化死亡率和标化DALY率进行预测。

结果

1990—2021年，中国总人群中由高LDL-C引起的缺血性脑卒中的标化死亡率和标化DALY率整体呈下降趋势，平均年度变化百分比（AAPC）分别为-0.42%（95% CI：-0.66% ~ -0.18%，P<0.05）、-0.46%（95% CI：-0.63% ~ -0.29%，P<0.05），且存在明显的性别与年龄差异；ARIMA模型预测结果显示，到2031年，中国归因于高LDL-C的标化死亡率和DALY率将分别降至15.01 /10万和319.00 /10万。

结论

中国归因于高LDL-C的缺血性脑卒中的疾病负担仍较重，需重点干预男性及老年人群的LDL-C水平，完善防控策略和制度保障，以有效缓解缺血性脑卒中的疾病负担。

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王高玲，E-mail:
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丁卫（2002—），女，硕士在读，研究方向：社会医学与卫生事业管理

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Chinese Journal of Neuroimmunology and Neurology, 2024, 31(6): 459-467.(In Chinese), articleTitle=Prediction of the epidemic trend of ischemic stroke in China from 2020 to 2040, refAbstract=null)], funds=[Fund(id=1241139377422783283, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036331455927119, awardId=23GLB010, language=CN, fundingSource=江苏省社会科学基金项目(23GLB010), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241139370279883262, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036331455927119, xref=null, ext=[AuthorCompanyExt(id=1241139370288271870, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036331455927119, companyId=1241139370279883262, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Health Economics and Management, Nanjing University of Traditional Chinese Medicine, Nanjing, Jiangsu 210023, China), AuthorCompanyExt(id=1241139370296660479, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036331455927119, companyId=1241139370279883262, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=南京中医药大学卫生经济管理学院，江苏 南京 210023)])], figs=[ArticleFig(id=1241139374277055151, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036331455927119, language=EN, label=Figure 1, caption=Joinpoint regression results for standardized mortality in ischemic stroke attributable to high LDL-C, figureFileSmall=iTpuEswDqS2yDP1s0wQHFA==, figureFileBig=i4OVBTXpgIvQp8J6k/xLSw==, tableContent=null), ArticleFig(id=1241139374373524153, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036331455927119, language=CN, label=图1, caption=归因于高LDL-C的缺血性脑卒中标化死亡率joinpoint回归结果, figureFileSmall=iTpuEswDqS2yDP1s0wQHFA==, figureFileBig=i4OVBTXpgIvQp8J6k/xLSw==, tableContent=null), ArticleFig(id=1241139374486770369, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036331455927119, language=EN, label=Figure 2, caption=Joinpoint regression results for standardized DALY rates in ischemic stroke attributable to high LDL-C, figureFileSmall=UUWvGyf2TmnHZg2KYSZyBw==, figureFileBig=DweXtkPfZA4ySyRF5rEm2w==, tableContent=null), ArticleFig(id=1241139374595822284, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036331455927119, language=CN, label=图2, caption=归因于高LDL-C的缺血性脑卒中标化DALY率joinpoint回归结果, figureFileSmall=UUWvGyf2TmnHZg2KYSZyBw==, figureFileBig=DweXtkPfZA4ySyRF5rEm2w==, tableContent=null), ArticleFig(id=1241139374713262804, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036331455927119, language=EN, label=Figure 3, caption=Projected disease burden of ischemic stroke attributable to high LDL-C in China from 2022 to 2031, figureFileSmall=/APlR8WbgAyrP8Gg3gUJUA==, figureFileBig=JClHjgX+j85Ox359L06kzw==, tableContent=null), ArticleFig(id=1241139374851674840, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036331455927119, language=CN, label=图3, caption=2022—2031年中国缺血性脑卒中归因于高LDL-C的疾病负担预测

注：图A～C分别为总人群、男性和女性的标化死亡率预测图；图D～F分别为总人群、男性和女性的标化DALY率预测图。

, figureFileSmall=/APlR8WbgAyrP8Gg3gUJUA==, figureFileBig=JClHjgX+j85Ox359L06kzw==, tableContent=null), ArticleFig(id=1241139374977503973, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036331455927119, language=EN, label=Table 1, caption=

Changes in ischemic stroke disease burden attributable to high LDL-C in China from 1990 to 2021

, figureFileSmall=null, figureFileBig=null, tableContent=
指标组别1990年(95% UI)2021年(95% UI)变化率(%)
死亡人数(万)男性5.94 (1.85～10.10)17.05 (5.51～29.30)187.01
女性5.52 (1.76～9.77)12.95 (3.93～23.34)134.60
合计11.46 (3.74～19.74)30.01 (9.25～52.75)161.87
DALYs(万人年)男性160.18 (54.18～237.73)394.75 (137.93～662.30)146.44
女性141.69 (51.05～237.73)290.31 (98.45～492.59)104.89
合计301.87 (106.51～493.89)685.01 (231.32～1141.82)126.92
粗死亡率(/10万)男性9.79 (3.05～16.65)23.42 (7.57～41.06)139.22
女性9.69 (3.10～17.16)18.65 (5.66～33.60)92.47
合计9.74 (3.18～16.78)21.09 (6.50～37.07)116.53
粗DALY率(/10万)男性263.96 (89.28～430.16)542.16 (189.44～909.63)105.39
女性248.74 (89.62～417.35)417.93 (141.73～709.13)68.02
合计256.59 (90.53～419.81)481.50 (162.59～802.54)87.65
标化死亡率(/10万)男性21.09 (6.22～37.09)20.96 (6.72～37.13)-0.62
女性16.25 (5.05～29.45)12.36 (3.73～22.43)-23.94
合计18.23 (5.52～33.43)15.93 (4.83～28.04)-12.62
标化DALY率(/10万)男性431.69 (136.54～723.27)416.30 (143.15～703.34)-3.57
女性349.05 (121.16～593.21)268.95 (91.45～455.85)-22.95
合计385.65 (129.83～647.51)335.59 (112.75～566.25)-12.98
), ArticleFig(id=1241139375069778669, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036331455927119, language=CN, label=表1, caption=

1990—2021年中国归因于高LDL-C的缺血性脑卒中疾病负担变化情况

, figureFileSmall=null, figureFileBig=null, tableContent=
指标组别1990年(95% UI)2021年(95% UI)变化率(%)
死亡人数(万)男性5.94 (1.85～10.10)17.05 (5.51～29.30)187.01
女性5.52 (1.76～9.77)12.95 (3.93～23.34)134.60
合计11.46 (3.74～19.74)30.01 (9.25～52.75)161.87
DALYs(万人年)男性160.18 (54.18～237.73)394.75 (137.93～662.30)146.44
女性141.69 (51.05～237.73)290.31 (98.45～492.59)104.89
合计301.87 (106.51～493.89)685.01 (231.32～1141.82)126.92
粗死亡率(/10万)男性9.79 (3.05～16.65)23.42 (7.57～41.06)139.22
女性9.69 (3.10～17.16)18.65 (5.66～33.60)92.47
合计9.74 (3.18～16.78)21.09 (6.50～37.07)116.53
粗DALY率(/10万)男性263.96 (89.28～430.16)542.16 (189.44～909.63)105.39
女性248.74 (89.62～417.35)417.93 (141.73～709.13)68.02
合计256.59 (90.53～419.81)481.50 (162.59～802.54)87.65
标化死亡率(/10万)男性21.09 (6.22～37.09)20.96 (6.72～37.13)-0.62
女性16.25 (5.05～29.45)12.36 (3.73～22.43)-23.94
合计18.23 (5.52～33.43)15.93 (4.83～28.04)-12.62
标化DALY率(/10万)男性431.69 (136.54～723.27)416.30 (143.15～703.34)-3.57
女性349.05 (121.16～593.21)268.95 (91.45～455.85)-22.95
合计385.65 (129.83～647.51)335.59 (112.75～566.25)-12.98
), ArticleFig(id=1241139375212385014, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036331455927119, language=EN, label=Table 2, caption=

Joinpoint regression results of standardized mortality and standardized DALY rate in ischemic stroke attributable to high LDL-C

, figureFileSmall=null, figureFileBig=null, tableContent=
指标AAPC(%,95% CI)t值P值
标化死亡率
男性-0.05(-0.37～0.26)-0.330.739
女性-0.82*(-1.15～-0.50)-4.95<0.001
总人群-0.42*(-0.66～-0.18)-3.42<0.001
标化DALY率
男性-0.15(-0.39～0.09)-1.260.209
女性-0.82*(-1.04～-0.61)-7.45<0.001
总人群-0.46*(-0.63～-0.29)-5.25<0.001
), ArticleFig(id=1241139375317242619, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036331455927119, language=CN, label=表2, caption=

归因于高LDL-C的缺血性脑卒中标化死亡率与标化DALY率joinpoint回归结果

, figureFileSmall=null, figureFileBig=null, tableContent=
指标AAPC(%,95% CI)t值P值
标化死亡率
男性-0.05(-0.37～0.26)-0.330.739
女性-0.82*(-1.15～-0.50)-4.95<0.001
总人群-0.42*(-0.66～-0.18)-3.42<0.001
标化DALY率
男性-0.15(-0.39～0.09)-1.260.209
女性-0.82*(-1.04～-0.61)-7.45<0.001
总人群-0.46*(-0.63～-0.29)-5.25<0.001
), ArticleFig(id=1241139375434683145, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036331455927119, language=EN, label=Table 3, caption=

Trends in ischemic stroke mortality and DALY rates attributable to high LDL-C in different age groups

, figureFileSmall=null, figureFileBig=null, tableContent=
年龄组(岁)死亡率DALY率
AAPC(%,95% CI)t值P值AAPC(%,95% CI)t值P值
25～29-0.36(-0.69～-0.03)-2.110.035-0.29*(-0.45～-0.13)-3.53<0.001
30～34-0.34(-0.92～0.25)-1.140.255-0.18(-0.47～0.11)-1.220.221
35～39-0.59*(-0.98～-0.20)-2.99<0.001-0.28*(-0.51～-0.05)-2.370.018
40～44-0.85*(-1.36～-0.35)-3.30<0.001-0.46*(-0.62～-0.29)-5.34<0.001
45～49-1.25*(-1.75～-0.75)-4.88<0.001-0.77*(-1.16～-0.39)-3.92<0.001
50～54-1.63*(-1.84～-1.42)14.84<0.001-1.19*(-1.35～-1.03)14.38<0.001
55～59-1.62*(-1.92～-1.31)10.33<0.001-1.14*(-1.38～-0.91)-9.49<0.001
60～64-1.10*(-1.30～-0.91)10.95<0.001-0.79*(-0.95～-0.62)-9.40<0.001
65～69-1.04*(-1.34～-0.74)-6.77<0.001-0.74*(-1.00～-0.48)-5.59<0.001
70～74-0.74*(-0.90～-0.58)-9.11<0.001-0.52*(-0.66～-0.38)-7.21<0.001
75～79-0.48*(-0.71～-0.24)-3.90<0.001-0.28*(-0.51～-0.05)-2.370.018
80～84-0.02(-0.37～0.33)-0.12<0.0010.12(-0.18～0.41)0.780.442
85～890.27(-0.23～0.77)1.050.2950.36(-0.10～0.82)1.540.123
90～940.14(-0.42～0.70)0.480.6310.23(-0.30～0.76)0.850.394
), ArticleFig(id=1241139375531152146, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036331455927119, language=CN, label=表3, caption=

不同年龄段归因于高LDL-C的缺血性脑卒中死亡率与DALY率变化趋势

, figureFileSmall=null, figureFileBig=null, tableContent=
年龄组(岁)死亡率DALY率
AAPC(%,95% CI)t值P值AAPC(%,95% CI)t值P值
25～29-0.36(-0.69～-0.03)-2.110.035-0.29*(-0.45～-0.13)-3.53<0.001
30～34-0.34(-0.92～0.25)-1.140.255-0.18(-0.47～0.11)-1.220.221
35～39-0.59*(-0.98～-0.20)-2.99<0.001-0.28*(-0.51～-0.05)-2.370.018
40～44-0.85*(-1.36～-0.35)-3.30<0.001-0.46*(-0.62～-0.29)-5.34<0.001
45～49-1.25*(-1.75～-0.75)-4.88<0.001-0.77*(-1.16～-0.39)-3.92<0.001
50～54-1.63*(-1.84～-1.42)14.84<0.001-1.19*(-1.35～-1.03)14.38<0.001
55～59-1.62*(-1.92～-1.31)10.33<0.001-1.14*(-1.38～-0.91)-9.49<0.001
60～64-1.10*(-1.30～-0.91)10.95<0.001-0.79*(-0.95～-0.62)-9.40<0.001
65～69-1.04*(-1.34～-0.74)-6.77<0.001-0.74*(-1.00～-0.48)-5.59<0.001
70～74-0.74*(-0.90～-0.58)-9.11<0.001-0.52*(-0.66～-0.38)-7.21<0.001
75～79-0.48*(-0.71～-0.24)-3.90<0.001-0.28*(-0.51～-0.05)-2.370.018
80～84-0.02(-0.37～0.33)-0.12<0.0010.12(-0.18～0.41)0.780.442
85～890.27(-0.23～0.77)1.050.2950.36(-0.10～0.82)1.540.123
90～940.14(-0.42～0.70)0.480.6310.23(-0.30～0.76)0.850.394
), ArticleFig(id=1241139377066267417, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036331455927119, language=EN, label=Table 4, caption=

Predictive model fits for ischemic stroke burden of disease attributable to high LDL-C in China from 2022 to 2031

, figureFileSmall=null, figureFileBig=null, tableContent=
组别RMSEMAEMAPE(%)MASEACF1
标化死亡率
总人群0.348 80.223 71.187 00.728 8-0.037 4
男性0.531 30.305 11.289 50.757 90.057 7
女性0.288 00.190 91.237 30.681 7-0.013 1
标化DALY率
总人群4.920 33.133 40.812 70.649 2-0.008 0
男性7.138 14.384 00.959 90.719 50.079 3
女性4.140 42.898 60.903 50.610 90.006 6
), ArticleFig(id=1241139377175319332, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036331455927119, language=CN, label=表4, caption=

2022—2031年中国归因于高LDL-C的缺血性脑卒中疾病负担预测模型拟合情况

, figureFileSmall=null, figureFileBig=null, tableContent=
组别RMSEMAEMAPE(%)MASEACF1
标化死亡率
总人群0.348 80.223 71.187 00.728 8-0.037 4
男性0.531 30.305 11.289 50.757 90.057 7
女性0.288 00.190 91.237 30.681 7-0.013 1
标化DALY率
总人群4.920 33.133 40.812 70.649 2-0.008 0
男性7.138 14.384 00.959 90.719 50.079 3
女性4.140 42.898 60.903 50.610 90.006 6
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1990—2021年中国归因于高低密度脂蛋白胆固醇的缺血性脑卒中疾病负担分析及预测研究
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丁卫 , 王高玲 , 唐若岩
现代预防医学 | 流行病与统计方法 2025,52(17): 3105-3110
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现代预防医学 | 流行病与统计方法 2025, 52(17): 3105-3110
1990—2021年中国归因于高低密度脂蛋白胆固醇的缺血性脑卒中疾病负担分析及预测研究
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丁卫, 王高玲 , 唐若岩
作者信息
  • 南京中医药大学卫生经济管理学院，江苏 南京 210023

    • 丁卫（2002—），女，硕士在读，研究方向：社会医学与卫生事业管理

通讯作者:

王高玲，E-mail:
Analysis and prediction of the disease burden of ischemic stroke attributable to high LDL-C in China from 1990 to 2021
Wei DING, Gao-ling WANG , Ruo-yan TANG
Affiliations
  • School of Health Economics and Management, Nanjing University of Traditional Chinese Medicine, Nanjing, Jiangsu 210023, China
出版时间: 2025-09-10 doi: 10.20043/j.cnki.MPM.202504479
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摘要
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目的

分析1990—2021年中国归因于高低密度脂蛋白胆固醇（low-density lipoprotein cholesterol，LDL-C）的缺血性脑卒中疾病负担现状与变化趋势，结合未来十年预测结果，为制定中国缺血性脑卒中的防治措施提供参考依据。

方法

数据来源于全球疾病负担研究（GBD）2021数据库，筛选中国地区、缺血性脑卒中死因及高LDL-C风险因素等数据，采用死亡率与伤残调整生命年（disability-adjusted life year，DALY）率等指标衡量疾病负担，使用joinpoint回归模型对其变化趋势进行系统分析；运用自回归移动平均（autoregressive intergrated moving average，ARIMA）模型对2022—2031年中国归因于高LDL-C的缺血性脑卒中的标化死亡率和标化DALY率进行预测。

结果

1990—2021年，中国总人群中由高LDL-C引起的缺血性脑卒中的标化死亡率和标化DALY率整体呈下降趋势，平均年度变化百分比（AAPC）分别为-0.42%（95% CI：-0.66% ~ -0.18%，P<0.05）、-0.46%（95% CI：-0.63% ~ -0.29%，P<0.05），且存在明显的性别与年龄差异；ARIMA模型预测结果显示，到2031年，中国归因于高LDL-C的标化死亡率和DALY率将分别降至15.01 /10万和319.00 /10万。

结论

中国归因于高LDL-C的缺血性脑卒中的疾病负担仍较重，需重点干预男性及老年人群的LDL-C水平，完善防控策略和制度保障，以有效缓解缺血性脑卒中的疾病负担。

缺血性脑卒中  /  高LDL-C  /  Joinpoint回归  /  ARIMA模型  /  预测分析
Objective

To analyze the current status and changing trends of ischemic stroke burden attributable to high LDL-C in China from 1990 to 2021. By integrating the prediction results for the next decade, this research offers actionable insights for designing evidence-based interventions targeting ischemic stroke prevention and management in China.

Methods

Data were obtained from the Global Burden of Disease Study (GBD) 2021 database and screened for Chinese regions, causes of ischemic stroke deaths, and high LDL-C risk factors. The disease burden was measured using indicators such as mortality and disability-adjusted life year (DALY) rates, and systematically analyzed the trends using the Joinpoint regression model. The autoregressive moving average (ARIMA) model was employed to predict the standardized mortality and standardized DALY rates of ischemic stroke attributable to high LDL-C in China from 2022 to 2031.

Results

The overall trend of ischemic stroke rates attributable to high LDL-C in the Chinese population was declining between 1990 and 2021, with mean annual percentage changes (AAPC) of-0.42% (95% CI: -0.66%~-0.18%, P<0.05) and -0.46% (95% CI: -0.63%~-0.29%, P<0.05), with significant age and sex disparities observed. According to the ARIMA model, China’s standardized mortality and DALY rates linked to elevated LDL-C would decline to 15.01 per 100 000 population and 319.00 per 100 000 population, respectively, by 2031.

Conclusion

The disease burden of ischemic stroke attributable to high LDL-C remains substantial in China, and the interventions on LDL-C levels in the priority populations of men and elderly ischemic stroke patients should be reinforced, and prevention and control strategies as well as institutional support should be improved for the purpose of alleviating the disease burden of ischemic stroke more effectively.

Ischemic stroke  /  High LDL-C  /  Joinpoint regression  /  ARIMA model  /  Predictive analysis
丁卫, 王高玲, 唐若岩. 1990—2021年中国归因于高低密度脂蛋白胆固醇的缺血性脑卒中疾病负担分析及预测研究. 现代预防医学, 2025 , 52 (17) : 3105 -3110 . DOI: 10.20043/j.cnki.MPM.202504479
Wei DING, Gao-ling WANG, Ruo-yan TANG. Analysis and prediction of the disease burden of ischemic stroke attributable to high LDL-C in China from 1990 to 2021[J]. Modern Preventive Medicine, 2025 , 52 (17) : 3105 -3110 . DOI: 10.20043/j.cnki.MPM.202504479
正文
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缺血性脑卒中是由脑动脉阻塞引起的，约占所有脑卒中病例的80%，具有高发病率、高致残率、高复发率等特点[1-2]。研究显示，2019年中国新增脑卒中病例约394万例，其中287万例为缺血性脑卒中。1990—2019年，脑卒中及其亚型病例数持续增长，其中缺血性脑卒中的增幅最为显著[3]，给家庭和社会带来沉重的经济和照护负担。
科学评估缺血性脑卒中可归因风险因素导致的疾病负担，有助于推动防控策略的优化与实施。已有研究表明，低密度脂蛋白胆固醇（low-density lipoprotein cholesterol, LDL-C）是心血管疾病的主要危险因素和关键治疗靶点[4]，其良好控制在一定程度上可有效预防缺血性脑卒中的复发[5]。然而，当前针对中国人群中高LDL-C所致缺血性脑卒中疾病负担的长期变化趋势及未来预测的研究仍相对不足[6-7]。为此，本研究基于2021年全球疾病负担研究（GBD 2021）数据，对1990—2021年中国因LDL-C升高所致的缺血性脑卒中疾病负担及其变化趋势进行了系统评估，并在此基础上预测未来十年的发展态势，为中国防治缺血性脑卒中的措施制定提供参考依据。
1　资料与方法
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1.1　资料来源
本研究数据来源于GBD 2021公开数据库，该数据库估计了1990—2021年204个国家和地区及其下属811个区域按年龄、性别、地区和年份分列的288种死因造成的死亡率和寿命损失年[8]，并详细分析了88种风险因素的归因负担情况。本研究筛选研究变量如下：地区选择“China”，死亡原因选择“Ischemic stroke”，风险因素选择“High LDL cholesterol”，指标选择“DALYs”“Deaths”，同时，将25~ 94岁人群按5岁间隔划分为14个年龄组，进行分层分析。
1.2　评价指标
本研究主要采用GBD 2021中1990—2021年中国归因于高LDL-C的缺血性脑卒中的死亡人数、死亡率、伤残调整生命年（disability adjusted life years，DALYs）、DALY率以及95%不确定性区间（uncertainty interval，UI）等指标，综合评估疾病负担情况。其中，DALYs是评估疾病负担的综合性指标，反映个体从发病至死亡所损失的健康寿命总年数[9]，包括伤残损失生命年（years of life lost，YLDs）和早死损失生命年（years lived with disability，YLLs），即DALYs=YLDs+YLLs，其数值越高表示疾病负担越重。GBD 2021将高LDL-C定义为血液中LDL-C的浓度超过理论最低风险暴露水平，即1.3 mmol/L[10]。
1.3　模型与方法
1.3.1　Joinpoint回归模型
Joinpoint回归模型主要用于识别时间序列数据中趋势变化的转折点（joinpoint），通过分段回归分析，将数据划分为若干具有不同斜率的线性区间，以描述变化趋势的显著性和特征，并计算年度变化百分比（annual percent change，APC）、平均年度变化百分比（average annual percent change，AAPC）及相应的95%置信区间（confidence interval，CI）。趋势变化的统计显著性由APC或AAPC的95% CI判断[11]，模型检验一般采用蒙特卡罗置换法[12]。检验水准α=0.05。
1.3.2　自回归移动平均（autoregressive intergrated moving average，ARIMA）模型
ARIMA模型在短期预测中具有较高的准确性，能够对具有一定趋势性、周期性或随机波动特征的时间序列数据进行分析和预测，包含自回归阶数（p）、差分阶数（d）和移动平均阶数（q）三个参数[13-15]。
本研究对原始数据进行差分处理（d=2），手动选择p=0 ~ 3与q=0 ~ 3的组合，共构建14个候选模型，并基于赤池信息准则（Akaike information criterion，AIC）和贝叶斯信息准则（Bayesian information criterion，BIC）指标选优，最终通过Ljung-Box检验与ACF/PACF图判断残差无自相关，确保模型拟合合理。
1.4　统计分析
本研究利用Excel 2021整理1990—2021年中国由高LDL-C导致的缺血性脑卒中疾病负担数据，并按性别和年龄组进行分层，采用死亡率和DALY率等指标进行描述性分析；利用Joinpoint Regression Program 5.2.0软件构建joinpoint回归模型，分析疾病负担的时间变化趋势；采用R 4.4.2构建ARIMA模型，对2022—2031年中国缺血性脑卒中归因于高LDL-C的标化死亡率和标化DALY率进行预测。检验水准α=0.05。
2　结果
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2.1　1990—2021年中国归因于高LDL-C的缺血性脑卒中疾病负担变化情况
2021年，中国因高LDL-C导致的缺血性脑卒中的死亡人数为30.01万，DALYs为685.01万人年。其中男性死亡17.05万，DALYs为394.75万人年；女性死亡12.95万例，DALYs为290.31万人年。总人群的粗死亡率和DALY率分别为21.09/10万和481.50 /10万，较1990年分别上升了116.53%和87.65%；而标化死亡率、标化DALY率分别为15.93 /10万、335.59 /10万，较1990年分别下降12.62%、12.98%。对于不同性别组，男性的粗率增长幅度和死亡风险显著高于女性，在年龄标准化率方面，女性的下降幅度更为明显。见表1。
2.2　1990—2021年中国归因于高LDL-C的缺血性脑卒中疾病负担趋势分析
Joinpoint回归结果显示，1990—2021年，中国因高LDL-C导致的缺血性脑卒中的标化死亡率和DALY率随时间变化整体呈下降趋势。总人群标化死亡率的AAPC值为-0.42%（95%CI：-0.66%～-0.18%，P<0.05），在2004—2007年、2010—2015年和2015—2021年呈下降趋势，APC值分别为-4.58%、-2.18%、-0.95%，但在1998—2004年呈上升趋势，APC值为2.90%。总人群标化DALY率的AAPC值为-0.46%（95% CI：-0.63%～-0.29%，P<0.05），在1990—1998年、2004—2007年、2010—2015年和2015—2021年呈下降趋势，APC值分别为-0.40%、-3.63%、-1.61%、-0.82%，但在1998—2004年呈上升趋势，APC值为2.09%。
性别分层结果显示，1990—2021年中国女性归因于高LDL-C的标化死亡率与DALY率的下降幅度显著高于男性。在标化死亡率方面，女性的AAPC为-0.82%（95% CI：-1.15%～-0.50%，P<0.05），而男性的AAPC为-0.05%（95% CI：-0.37%～0.26%，P>0.05）。在标化DALY率方面，女性的AAPC为-0.82%（95% CI：-1.04%～-0.61%，P<0.05），男性的AAPC为-0.15%（95% CI：-0.39%～0.09%，P>0.05）。见表2，图1、 2。
2.3　不同年龄段归因于高LDL-C的缺血性脑卒中疾病负担趋势分析
年龄分层结果显示，1990—2021年中国归因于高LDL-C的缺血性脑卒中年龄别死亡率和DALY率在多数年龄段呈下降趋势，其中，50 ～54岁年龄段下降最为显著，死亡率和DALY率的AAPC值分别为-1.63%（95% CI：-1.84%～-1.42%，P<0.05）与-1.19%（95% CI：-1.35%～-1.03%，P<0.05），其次为55～59岁年龄段，AAPC值分别为-1.62%（95% CI :-1.92%～-1.31%，P<0.05）与-1.14%（95% CI :-1.38%～-0.91%，P<0.05）。相比之下，85岁及以上年龄段死亡率和DALY率变化不显著，AAPC值略有上升。见表3。
2.4　2022—2031年中国归因于高LDL-C的缺血性脑卒中疾病负担预测
本研究通过ARIMA模型对2022—2031年中国归因于高LDL-C的缺血性脑卒中标化死亡率与DALY率进行了趋势预测，模型拟合情况见表4。预测结果显示，未来十年间，中国全人群的标化死亡率与标化DALY率均呈下降趋势。预计到2031年，中国总人群、男性和女性的标化死亡率分别为15.01 /10万、19.29 /10万、11.04 /10万，较2022年分别下降4.82%、7.13%、9.58%；标化DALY率分别为319.00 /10万、392.46 /10万、246.14 /10万，较2022年分别下降4.49%、5.04%、8.27%。此外，预计未来十年内，男性的标化死亡率与标化DALY率将持续高于女性。见图3。
3　讨论
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相关研究表明，2021年缺血性脑卒中的全球疾病负担依旧严峻，病例数大幅上升，但年龄标准化患病率、发病率、死亡率和DALY率均呈下降趋势[16]。本研究结果显示，1990—2021年，中国归因于高LDL-C的缺血性脑卒中的年龄标化率虽有所改善，但总体疾病负担并未明显降低，这可能与中国已进入深度老龄化社会以及庞大的人口基数密切相关。截至2021年底，中国60岁及以上人口已达2.67亿，占全国总人口的18.9%[17]。老龄人口规模的增长在一定程度上抵消了年龄标化后带来的疾病负担的下降效益，老龄化趋势对疾病负担的影响愈加显著。此外，GBD Compare显示，2021年，高LDL-C是中国缺血性脑卒中的第二大可归因风险因素，仅次于高收缩压[18]。因此，亟需采取有效的防治措施，尽早降低由高LDL-C引起的缺血性脑卒中的疾病负担。
本研究发现，性别和年龄是决定缺血性脑卒中疾病负担的关键因素。在性别层面，女性的标化死亡率和DALY率的下降幅度显著高于男性，提示女性人群在高LDL-C控制方面取得了更明显的成效。这一性别差异可能与男性在高BMI、身体活动缺乏及吸烟行为等风险因素方面的暴露率更高有关[19]，上述因素可通过氧化应激加剧LDL-C的致动脉粥样硬化作用[20]。鉴于LDL-C是可干预的风险因素，通过合理膳食、规律运动、药物干预及行为方式调整等措施，均可有效控制其异常升高的状况[21]。因此，建议针对男性群体加强健康教育和干预措施，提高其健康意识和行为依从性，从而降低高LDL-C所致的疾病负担。
年龄层面上，50～54岁年龄段的缺血性脑卒中死亡率和DALY率下降最为显著。55岁以后，死亡率和DALY率的下降幅度随年龄增长逐渐减小，尤其在85岁及以上年龄组中，降幅接近零并出现上升趋势，这与既往研究结果相似[22]。这一结果表明，对于中年群体，高LDL-C的干预措施效果更为明显。随着医学技术的进步，急性缺血性脑卒中的治疗方法得到广泛应用，有效减轻该年龄组的疾病负担。对于老年群体，尤其是85岁及以上群体，机体功能随年龄增长而减弱。尽管相关疾病负担未出现显著变化，老年群体因共病发生率较高，其罹患缺血性脑卒中的风险相应上升[23]。基于此，未来应加强老年群体的健康管理和疾病预防，特别是在高LDL-C的早期筛查与干预方面。
与既往研究一致[24]，本研究ARIMA模型预测结果显示，未来十年中国因高LDL-C导致的缺血性脑卒中的标化死亡率和DALY率将持续下降，至2031年分别下降至15.01 /10万和319.00 /10万。这一趋势可能与中国在缺血性脑卒中治疗水平上的提升，以及公众健康意识增强有关。然而，尽管整体负担呈下降趋势，相关疾病负担依然偏高，且在性别层面存在显著差异，男性群体负担尤为突出。这提示应注重中国归因于高LDL-C的缺血性脑卒中精准长效防控的制度建设，以实现对高风险人群的精准管理与疾病负担的持续缓解。制定中长期行动规划，推动形成多部门协同、多层级联动的长效防控机制；健全高危人群早筛、动态评估及分级管理机制，推动缺血性脑卒中与高LDL-C相关疾病的前端治理与早期干预；以医保支付制度改革为切入点，健全相关激励政策机制，扩大预防服务的覆盖范围，提升群众的参与度与依从性。此外，强化基层医疗卫生服务体系的责任主体的制度建设，提升其在健康教育、用药指导和行为干预中的专业能力与服务效能。
基金
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  • 江苏省社会科学基金项目(23GLB010)
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doi: 10.20043/j.cnki.MPM.202504479
  • 接收时间：2025-04-28
  • 首发时间：2026-03-18
  • 出版时间：2025-09-10
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  • 收稿日期：2025-04-28
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    南京中医药大学卫生经济管理学院，江苏 南京 210023

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