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Article(id=1241025207859737413, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241025201983508979, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202411026, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1730476800000, receivedDateStr=2024-11-02, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773813066687, onlineDateStr=2026-03-18, pubDate=1744214400000, pubDateStr=2025-04-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773813066687, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773813066687, creator=13701087609, updateTime=1773813066687, 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=1182, endPage=1187, ext={EN=ArticleExt(id=1241025208161727330, articleId=1241025207859737413, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Research on risk prediction of new cardiovascular events based on the eight essential elements of life indicators, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore whether the newly proposed cardiovascular health indicator LE8 provides greater predictive value for adverse cardiovascular events (ACE) than LS7 in the Chinese population.

Methods

Based on the China Health and Nutrition Survey (CHNS), 5 772 participants who had not experienced ACE prior to the 2009 survey were included. The cardiovascular health indicators LE8 and LS7 were calculated, and the Kaplan-Meier method and Cox proportional hazards model were used to estimate the 5-year risk of ACE occurrence. The differences in predictive value for ACE occurrence between the two indicators were compared using C statistics.

Results

For LE8, the participants in the highest percentile had a 5-year ACE risk of 2.3%,whereas those in the lowest percentile had a risk approximately six times higher. The results of the Cox proportional hazards model indicated that the C statistic for LS7 was 0.728 (95%CI: 0.706-0.75), while that for LE8 was 0.726 (95%CI: 0.703-0.748), with no statistically significant difference (P=0.51).

Conclusion

In the Chinese population, both LE8 and LS7 are associated with new ACE. However, LE8 incurs higher collection costs and has not demonstrated superior predictive value; therefore, LS7, which is easier to implement in clinical practice, is recommended as the risk prediction indicator for new cardiovascular events.

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目的

在中国人群中探究新提出的心血管健康指标LE8是否比LS7能提供更多的不良心血管事件（ACE）预测价值。

方法

基于中国健康与营养调查（CHNS），纳入2009年调查前未发生ACE的5 772名参与者，分别计算其心血管健康指标LE8和LS7，采用Kaplan-Meier方法和Cox比例风险模型估计5年ACE发生风险，并通过C统计量比较不同指标预测ACE发生风险的差异。

结果

以LE8为例，处于最高百分位数的参与者，其5年ACE风险为2.3%；而处于最低百分位数的参与者，其风险则高出约6倍。Cox比例风险模型结果显示，LS7的C统计量为0.728（95%CI：0.706～0.75），LE8的C统计量为0.726（95%CI：0.703～0.748），差异无统计学意义（P=0.51）。

结论

在中国人群中，LE8和LS7都与新发ACE相关。但LE8收集成本更高，且暂未发现有更好的预测价值，因此推荐采用临床实践中更易实施的LS7作为新发心血管事件风险预测指标。

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郭冰，E-mail:
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=jMWb/ZKyhVi9rO2mJ0VutA==, magXml=M9d63BvjLngzjKXM8GZBuA==, pdfUrl=null, pdf=p7zWQ23+FIB1z868P3AQJw==, pdfFileSize=992546, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=WvY5sQVCLcSWdgStb8fMmg==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=reJYQmItc6HTtp+sh18qVA==, mapNumber=null, authorCompany=null, fund=null, authors=

丁志静（1999—），女，硕士在读，研究方向：卫生统计方法与应用

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丁志静（1999—），女，硕士在读，研究方向：卫生统计方法与应用

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International Journal of Environmental Research and Public Health, 2023, 20(4):3084., articleTitle=Predictive value of cardiovascular health score for health outcomes in patients with PCI: comparison between Life's Simple 7 and Life's Essential 8, refAbstract=null)], funds=[Fund(id=1241025216193818974, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207859737413, awardId=82103943, language=CN, fundingSource=国自然青年科学基金项目(82103943), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241025211022242797, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207859737413, xref=null, ext=[AuthorCompanyExt(id=1241025211026437103, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207859737413, companyId=1241025211022242797, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Epidemiology and Health Statistics, West China School of Public Health / West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China), AuthorCompanyExt(id=1241025211030631407, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207859737413, companyId=1241025211022242797, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=四川大学华西公共卫生学院/四川大学华西第四医院流行病与卫生统计学系，四川 成都 610041)])], figs=[ArticleFig(id=1241025214717423851, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207859737413, language=EN, label=Figure 1, caption=Flow chart of inclusion and exclusion of research subjects, figureFileSmall=DtuApF70S00JAH6eZX0knw==, figureFileBig=WvY5sQVCLcSWdgStb8fMmg==, tableContent=null), ArticleFig(id=1241025214839058673, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207859737413, language=CN, label=图1, caption=纳排流程图, figureFileSmall=DtuApF70S00JAH6eZX0knw==, figureFileBig=WvY5sQVCLcSWdgStb8fMmg==, tableContent=null), ArticleFig(id=1241025215044579588, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207859737413, language=EN, label=Figure 2, caption=Distribution of LS7 and LE8, figureFileSmall=3MjVwQq/xZAha8t58bQUXA==, figureFileBig=vVIE+dJMd9k3aMYTfJXhzA==, tableContent=null), ArticleFig(id=1241025215153631505, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207859737413, language=CN, label=图2, caption=LS7和LE8评分的分布, figureFileSmall=3MjVwQq/xZAha8t58bQUXA==, figureFileBig=vVIE+dJMd9k3aMYTfJXhzA==, tableContent=null), ArticleFig(id=1241025215287849240, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207859737413, language=EN, label=Figure 3, caption=The distribution of LE8 at each level of LS7, figureFileSmall=6Myuzj/5fGs2wFEgxiK0PQ==, figureFileBig=OV7koiaNgwef0ER25+dkkA==, tableContent=null), ArticleFig(id=1241025215392706850, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207859737413, language=CN, label=图3, caption=LS7得分水平所对应的LE8分布, figureFileSmall=6Myuzj/5fGs2wFEgxiK0PQ==, figureFileBig=OV7koiaNgwef0ER25+dkkA==, tableContent=null), ArticleFig(id=1241025215484981541, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207859737413, language=EN, label=Figure 4, caption=Estimated 5-year risk for ACE by categorical (A) and continuous (B) LS7 and the LE8, figureFileSmall=i4BUOp1QYm5tDJmgHaVjhw==, figureFileBig=AqUP8TJNx2Ku0KrMu9sqjg==, tableContent=null), ArticleFig(id=1241025215598227760, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207859737413, language=CN, label=图4, caption=基于分类（A）和连续（B）LS7和LE8的5年ACE发生风险估计, figureFileSmall=i4BUOp1QYm5tDJmgHaVjhw==, figureFileBig=AqUP8TJNx2Ku0KrMu9sqjg==, tableContent=null), ArticleFig(id=1241025215740834106, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207859737413, language=EN, label=Table 1, caption=

　The definition of LS7 and LE8

, figureFileSmall=null, figureFileBig=null, tableContent=
项目类别测量方法具体得分
健康行为膳食DASH得分LE8：≥75th，90分；50th～74th，50分；25th～49th，25分；1st～24th，0分
LS7：水果和蔬菜，≥4.5杯/d；鱼肉，≥7盎司/周；全谷物，≥3盎司/d；钠，<1 500 mg/d；含糖饮料≤36盎司/周；假设水果和蔬菜每日用量用份数来代替杯数[17]，考虑到食物中水分含量，一份蔬菜当作100 g，一份水果相当作150 g；满足条目数量，0～1，0分；2～3，1分；4～5，2分
体力活动中高强度体力活动代谢当量（min/周）LE8：≥S6，100分；S5～S6，90分；S4～S5，80分；S3～S4，60分；S2～S3，40分；S1～S2，20分；<S1，0分；其中S代表的是7分位数。
LS7：<T1，0分；T1～T2，1分；≥T2，2分；其中T代表的是3分位数
吸烟自报吸烟情况LE8：从不吸烟，100分；戒烟≥5年，75分；戒烟1～5年，50分；戒烟<1年或现在吸烟<1支/d，25分；现在吸烟≥1支/d，0分
LS7：现在吸烟≥1支/d，0分；现在吸烟<1支/d或戒烟≤1年，1分；戒烟>1年或不吸，2分
睡眠自报平均每晚睡眠时间(h/d)LE8：7～<9，100分；9～<10，90分；6～<7，70分；5～<6或≥10，40分；4～<5，20分；<4，0分
健康因素BMI(kg/m2)体重(kg)/身高的二次方(cm)2LE8：<23，100分；23.0～24.9，75分；25.0～29.9，50分；30.0～34.9，25分；≥35.0，0分
LS7：≥30.0，0分；25.0～29.9，1分；<25，2分
血脂总胆固醇减去高密度脂蛋白胆固醇的差值(LE8)；总胆固醇(LS7)(mg/dl)LE8：<130，100分；130～159，60分；160～189，40分；190～219，20分；≥220，0分；如是治疗后水平，本项减20分
LS7：≥240，0分；200～240/<200(治疗后)，1分；<200(未经治疗)，2分；因未收集降脂药治疗信息，用患代谢性疾病表示经过治疗
血糖FBG(mg/dl)或HbA1C(%) (LE8)；FBG (mg/dl)(LS7)LE8：无糖尿病且FBG<100/HbA1c<5.7，100分；无糖尿病且FBG 100～125/HbA1c5.7～6.4，60分；有糖尿病且HbA1c<7.0，40分；有糖尿病HbA1c7.0～7.9，30分；有糖尿病且HbA1c 8.0～8.9，20分；有糖尿病且HbA1c 9.0～9.9，10分；有糖尿病且HbA1c≥10.0，0分；对同时满足FBG和HbA1C条件的，以较低分数为准；若在糖尿病前期服降糖药至血糖达理想水平，本项减20分
LS7：≥126，0分；100～125/<100(治疗后)，1分；<100(未经治疗)，2分
血压收缩压(SBP)和舒张压(DBP) (mm Hg)LE8：<120/<80，100分；120～129/<80，60分；130～139/80～89，40分；140～159/90～99，20分；≥160/≥100，0分；对同时满足SBP和DBP条件的，以较低分数为准
LS7：≥140/≥90，0分；120～139/80～89或<120/<80 (治疗后)，1分；<120/<80(未经治疗)，2分；如是治疗后水平，本项减20分
), ArticleFig(id=1241025215925383489, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025207859737413, language=CN, label=表1, caption=

心血管健康指标LE8和LS7的定义

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项目类别测量方法具体得分
健康行为膳食DASH得分LE8：≥75th，90分；50th～74th，50分；25th～49th，25分；1st～24th，0分
LS7：水果和蔬菜，≥4.5杯/d；鱼肉，≥7盎司/周；全谷物，≥3盎司/d；钠，<1 500 mg/d；含糖饮料≤36盎司/周；假设水果和蔬菜每日用量用份数来代替杯数[17]，考虑到食物中水分含量，一份蔬菜当作100 g，一份水果相当作150 g；满足条目数量，0～1，0分；2～3，1分；4～5，2分
体力活动中高强度体力活动代谢当量（min/周）LE8：≥S6，100分；S5～S6，90分；S4～S5，80分；S3～S4，60分；S2～S3，40分；S1～S2，20分；<S1，0分；其中S代表的是7分位数。
LS7：<T1，0分；T1～T2，1分；≥T2，2分；其中T代表的是3分位数
吸烟自报吸烟情况LE8：从不吸烟，100分；戒烟≥5年，75分；戒烟1～5年，50分；戒烟<1年或现在吸烟<1支/d，25分；现在吸烟≥1支/d，0分
LS7：现在吸烟≥1支/d，0分；现在吸烟<1支/d或戒烟≤1年，1分；戒烟>1年或不吸，2分
睡眠自报平均每晚睡眠时间(h/d)LE8：7～<9，100分；9～<10，90分；6～<7，70分；5～<6或≥10，40分；4～<5，20分；<4，0分
健康因素BMI(kg/m2)体重(kg)/身高的二次方(cm)2LE8：<23，100分；23.0～24.9，75分；25.0～29.9，50分；30.0～34.9，25分；≥35.0，0分
LS7：≥30.0，0分；25.0～29.9，1分；<25，2分
血脂总胆固醇减去高密度脂蛋白胆固醇的差值(LE8)；总胆固醇(LS7)(mg/dl)LE8：<130，100分；130～159，60分；160～189，40分；190～219，20分；≥220，0分；如是治疗后水平，本项减20分
LS7：≥240，0分；200～240/<200(治疗后)，1分；<200(未经治疗)，2分；因未收集降脂药治疗信息，用患代谢性疾病表示经过治疗
血糖FBG(mg/dl)或HbA1C(%) (LE8)；FBG (mg/dl)(LS7)LE8：无糖尿病且FBG<100/HbA1c<5.7，100分；无糖尿病且FBG 100～125/HbA1c5.7～6.4，60分；有糖尿病且HbA1c<7.0，40分；有糖尿病HbA1c7.0～7.9，30分；有糖尿病且HbA1c 8.0～8.9，20分；有糖尿病且HbA1c 9.0～9.9，10分；有糖尿病且HbA1c≥10.0，0分；对同时满足FBG和HbA1C条件的，以较低分数为准；若在糖尿病前期服降糖药至血糖达理想水平，本项减20分
LS7：≥126，0分；100～125/<100(治疗后)，1分；<100(未经治疗)，2分
血压收缩压(SBP)和舒张压(DBP) (mm Hg)LE8：<120/<80，100分；120～129/<80，60分；130～139/80～89，40分；140～159/90～99，20分；≥160/≥100，0分；对同时满足SBP和DBP条件的，以较低分数为准
LS7：≥140/≥90，0分；120～139/80～89或<120/<80 (治疗后)，1分；<120/<80(未经治疗)，2分；如是治疗后水平，本项减20分
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　Baseline characteristics

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基本特征总人数ACEP值
未发生新发
人口学特征
年龄（岁）48.63±14.4647.96±14.4158.42±11.47<0.001
性别0.849
男性2 621 (45.4)2 453 (45.4)168 (46.0)
女性3 151 (54.6)2 954 (54.6)197 (54.0)
LE8
收缩压（mm Hg）120.17±15.40119.38±14.87131.80±18.13<0.001
舒张压（mm Hg）78.27±9.8377.89±9.6083.88±11.33<0.001
血糖（mg/dl）93.00±14.5392.69±14.1797.64±18.47<0.001
糖化血红蛋白（%）5.48±0.555.46±0.545.65±0.64<0.001
总胆固醇184.74±35.77183.98±35.54195.95±37.31<0.001
BMI（kg/m2）22.96±3.3022.90±3.2823.87±3.48<0.001
DASH得分22.57±2.4922.56±2.4922.71±2.500.273
体力活动当量（min/周）56.27±35.1556.71±35.0049.84±36.88<0.001
睡眠时长（h）6.95±1.156.95±1.146.88±1.260.281
吸烟0.018
从不吸烟4 017 (69.6)3 775 (69.8)242 (66.3)
正在吸烟1 611 (27.9)1 505 (27.8)106 (29.0)
已经戒烟144 (2.5)127 (2.3)17 (4.7)
连续型LE869.14±12.9569.58±12.8662.66±12.64<0.001
LS7
连续型LS79.81±2.029.88±2.008.78±2.06<0.001
血糖<0.001
较差197 (3.4)171 (3.2)26 (7.1)
中等1 112 (19.3)1 024 (18.9)88 (24.1)
理想4 463 (77.3)4 212 (77.9)251 (68.8)
血脂<0.001
较差421 (7.3)384 (7.1)37 (10.1)
中等2 046 (35.4)1 881 (34.8)165 (45.2)
理想3 305 (57.3)3 142 (58.1)163 (44.7)
血压<0.001
较差999 (17.3)848 (15.7)151 (41.4)
中等2 613 (45.3)2 455 (45.4)158 (43.3)
理想2 160 (37.4)2 104 (38.9)56 (15.3)
BMI<0.001
较差159 (2.8)146 (2.7)13 (3.6)
中等1 329 (23.0)1 208 (22.3)121 (33.2)
理想4 284 (74.2)4 053 (75.0)231 (63.3)
吸烟0.778
较差1 598 (27.7)1 493 (27.6)105 (28.8)
中等64 (1.1)59 (1.1)5 (1.4)
理想4 110 (71.2)3 855 (71.3)255 (69.9)
体力活动<0.001
较差2 288 (39.6)2 105 (38.9)183 (50.1)
中等1 444 (25.0)1 374 (25.4)70 (19.2)
理想2 040 (35.3)1 928 (35.7)112 (30.7)
膳食0.083
较差212 (3.7)192 (3.6)20 (5.5)
中等3 833 (66.4)3 605 (66.7)228 (62.5)
理想1 727 (29.9)1 610 (29.8)117 (32.1)
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基线特征

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基本特征总人数ACEP值
未发生新发
人口学特征
年龄（岁）48.63±14.4647.96±14.4158.42±11.47<0.001
性别0.849
男性2 621 (45.4)2 453 (45.4)168 (46.0)
女性3 151 (54.6)2 954 (54.6)197 (54.0)
LE8
收缩压（mm Hg）120.17±15.40119.38±14.87131.80±18.13<0.001
舒张压（mm Hg）78.27±9.8377.89±9.6083.88±11.33<0.001
血糖（mg/dl）93.00±14.5392.69±14.1797.64±18.47<0.001
糖化血红蛋白（%）5.48±0.555.46±0.545.65±0.64<0.001
总胆固醇184.74±35.77183.98±35.54195.95±37.31<0.001
BMI（kg/m2）22.96±3.3022.90±3.2823.87±3.48<0.001
DASH得分22.57±2.4922.56±2.4922.71±2.500.273
体力活动当量（min/周）56.27±35.1556.71±35.0049.84±36.88<0.001
睡眠时长（h）6.95±1.156.95±1.146.88±1.260.281
吸烟0.018
从不吸烟4 017 (69.6)3 775 (69.8)242 (66.3)
正在吸烟1 611 (27.9)1 505 (27.8)106 (29.0)
已经戒烟144 (2.5)127 (2.3)17 (4.7)
连续型LE869.14±12.9569.58±12.8662.66±12.64<0.001
LS7
连续型LS79.81±2.029.88±2.008.78±2.06<0.001
血糖<0.001
较差197 (3.4)171 (3.2)26 (7.1)
中等1 112 (19.3)1 024 (18.9)88 (24.1)
理想4 463 (77.3)4 212 (77.9)251 (68.8)
血脂<0.001
较差421 (7.3)384 (7.1)37 (10.1)
中等2 046 (35.4)1 881 (34.8)165 (45.2)
理想3 305 (57.3)3 142 (58.1)163 (44.7)
血压<0.001
较差999 (17.3)848 (15.7)151 (41.4)
中等2 613 (45.3)2 455 (45.4)158 (43.3)
理想2 160 (37.4)2 104 (38.9)56 (15.3)
BMI<0.001
较差159 (2.8)146 (2.7)13 (3.6)
中等1 329 (23.0)1 208 (22.3)121 (33.2)
理想4 284 (74.2)4 053 (75.0)231 (63.3)
吸烟0.778
较差1 598 (27.7)1 493 (27.6)105 (28.8)
中等64 (1.1)59 (1.1)5 (1.4)
理想4 110 (71.2)3 855 (71.3)255 (69.9)
体力活动<0.001
较差2 288 (39.6)2 105 (38.9)183 (50.1)
中等1 444 (25.0)1 374 (25.4)70 (19.2)
理想2 040 (35.3)1 928 (35.7)112 (30.7)
膳食0.083
较差212 (3.7)192 (3.6)20 (5.5)
中等3 833 (66.4)3 605 (66.7)228 (62.5)
理想1 727 (29.9)1 610 (29.8)117 (32.1)
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丁志静 , 肖心怡 , 陈红宇 , 郭冰
现代预防医学 | 流行病与统计方法 2025,52(7): 1182-1187
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现代预防医学 | 流行病与统计方法 2025, 52(7): 1182-1187
基于生命八要素指标的新发心血管事件风险预测研究
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丁志静, 肖心怡, 陈红宇, 郭冰
作者信息
  • 四川大学华西公共卫生学院/四川大学华西第四医院流行病与卫生统计学系，四川 成都 610041

    • 丁志静（1999—），女，硕士在读，研究方向：卫生统计方法与应用

通讯作者:

郭冰，E-mail:
Research on risk prediction of new cardiovascular events based on the eight essential elements of life indicators
Zhi-jing DING, Xin-yi XIAO, Hong-yu CHEN, Bing GUO
Affiliations
  • Department of Epidemiology and Health Statistics, West China School of Public Health / West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
出版时间: 2025-04-10 doi: 10.20043/j.cnki.MPM.202411026
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摘要
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目的

在中国人群中探究新提出的心血管健康指标LE8是否比LS7能提供更多的不良心血管事件（ACE）预测价值。

方法

基于中国健康与营养调查（CHNS），纳入2009年调查前未发生ACE的5 772名参与者，分别计算其心血管健康指标LE8和LS7，采用Kaplan-Meier方法和Cox比例风险模型估计5年ACE发生风险，并通过C统计量比较不同指标预测ACE发生风险的差异。

结果

以LE8为例，处于最高百分位数的参与者，其5年ACE风险为2.3%；而处于最低百分位数的参与者，其风险则高出约6倍。Cox比例风险模型结果显示，LS7的C统计量为0.728（95%CI：0.706～0.75），LE8的C统计量为0.726（95%CI：0.703～0.748），差异无统计学意义（P=0.51）。

结论

在中国人群中，LE8和LS7都与新发ACE相关。但LE8收集成本更高，且暂未发现有更好的预测价值，因此推荐采用临床实践中更易实施的LS7作为新发心血管事件风险预测指标。

生命八要素  /  心血管健康  /  不良心血管事件  /  CHNS
Objective

To explore whether the newly proposed cardiovascular health indicator LE8 provides greater predictive value for adverse cardiovascular events (ACE) than LS7 in the Chinese population.

Methods

Based on the China Health and Nutrition Survey (CHNS), 5 772 participants who had not experienced ACE prior to the 2009 survey were included. The cardiovascular health indicators LE8 and LS7 were calculated, and the Kaplan-Meier method and Cox proportional hazards model were used to estimate the 5-year risk of ACE occurrence. The differences in predictive value for ACE occurrence between the two indicators were compared using C statistics.

Results

For LE8, the participants in the highest percentile had a 5-year ACE risk of 2.3%,whereas those in the lowest percentile had a risk approximately six times higher. The results of the Cox proportional hazards model indicated that the C statistic for LS7 was 0.728 (95%CI: 0.706-0.75), while that for LE8 was 0.726 (95%CI: 0.703-0.748), with no statistically significant difference (P=0.51).

Conclusion

In the Chinese population, both LE8 and LS7 are associated with new ACE. However, LE8 incurs higher collection costs and has not demonstrated superior predictive value; therefore, LS7, which is easier to implement in clinical practice, is recommended as the risk prediction indicator for new cardiovascular events.

Eight essential elements of life  /  Cardiovascular health  /  Adverse cardiovascular events  /  CHNS
丁志静, 肖心怡, 陈红宇, 郭冰. 基于生命八要素指标的新发心血管事件风险预测研究. 现代预防医学, 2025 , 52 (7) : 1182 -1187 . DOI: 10.20043/j.cnki.MPM.202411026
Zhi-jing DING, Xin-yi XIAO, Hong-yu CHEN, Bing GUO. Research on risk prediction of new cardiovascular events based on the eight essential elements of life indicators[J]. Modern Preventive Medicine, 2025 , 52 (7) : 1182 -1187 . DOI: 10.20043/j.cnki.MPM.202411026
正文
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美国心脏协会（AHA）于2010年提出生命七要素（Life’s Simple 7，LS7）作为评估心血管健康的重要量化指标[1-2]。该指标对饮食、吸烟、血压、身体质量指数（BMI）、体育锻炼、空腹血糖和总胆固醇共7个条目进行综合评估。每个条目分为三个等级，取值为0、1、2，对所有条目求和后，最终得出取值为0～14的总分，得分越高表示心血管越健康[3]。LS7曾广泛应用于中国人群的研究，并被证实与多种健康结局相关，如肾病、认知障碍、卒中等[4-7]。2022年，AHA发布了新的指标生命八要素（Life’s Essential 8，LE8），进一步细化了LS7中的简单分级，并增加了对睡眠情况的评估。具体而言，LE8采用更精细的半定量方式对每个条目进行评估，并更改为百分制计分，所有条目的平均分为最终总分。因此，相较于LS7，LE8在上述精细化测量方面大有改进。自提出以来，LE8已逐步应用于心血管疾病和代谢性疾病等多个领域[8-10]。然而，LE8对采集信息量提出了更高的要求。例如，LE8中采取“阻止高血压饮食法”（DASH）评分的百分位数来量化饮食；在血糖方面，新增对糖尿病患者的血红蛋白（HbA1c）测量等信息。一项基于欧美人群的研究表明，LS7和LE8在预测心血管事件风险方面的区分能力无显著差异[11]。然而，尚不清楚在中国人群中LE8和LS7间的预测性能差异。鉴于此，本研究旨在评估LE8在中国人群中，相较于LS7是否更能准确识别出不良心血管事件(adverse cardiovascular events，ACE)风险较高的人群。
1　资料与方法
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1.1　资料来源
研究对象来自中国健康与营养调查（CHNS）。该调查在1989—2015年间采用多阶段随机整群抽样方法，在中国12个省份和3个特大城市内共实施了多轮调查。调查过程中，由专业工作人员采用结构化问卷收集数据，内容涵盖社会人口学特征、个体生活方式、疾病史等信息[12]。由于CHNS仅在2009年的调查中采集了血液样本并提供血生化检测结果，因此本研究选定CHNS的2009年调查数据作为基线资料，追踪2011和2015年调查中的新发ACE信息。
纳入标准：（1）进行过血生化指标评估；（2）年龄在18岁以上。排除标准：（1）血生化异常至超出限值；（2）基线时妊娠：孕期的心血管状况波动明显[13]；（3）未在2009年参与问卷调查；（4）在2009年前患ACE；（5）衡量LE8和LS7所需的指标缺失；（6）年龄和性别缺失。CHNS经北卡罗莱纳大学伦理委员会和中国疾病预防控制中心委员会审查和批准，参与者在调查前均已签署知情同意书。本研究最终纳入5 772名研究对象。见图1。
1.2　心血管健康指标定义
根据AHA对心血管健康得分（CVH）的定义，LS7是对7个风险因素得分（理想=2分，中等=1分，较差=0分）相加得出的综合评分。这7个因素包括3项健康行为（膳食、体力活动、吸烟）和4项健康因素（血糖、血脂、血压和BMI）。LE8进一步考虑睡眠因素，并对8个风险因素计算未加权平均值。无论LS7或LE8，当其分数越大，表明个体在心血管方面的健康程度越高。
根据上述定义，本研究计算心血管健康指标的过程见表1。值得注意的是，由于在DASH膳食评分高于第95百分位中的参与者较少，因此本研究将其与高于第75百分位的参与者合并，赋90分；体力活动部分，采用代谢当量（min/周）衡量工作、通勤、家务和闲暇时的中高强度体力活动[14]，并按分位数对不同体力活动强度赋分；吸烟部分参考中国人群研究[15]调整，结合吸烟状态、戒烟年限和每天吸烟支数赋分；睡眠部分，考虑到中国人群特有的午睡习惯，因此本研究将午休和夜晚睡眠的总时长减去1 h，以适当减少误差；BMI部分，参照中国人群标准[16]调整，并对超重个体，如男性腰臀比低于0.9或女性低于0.85赋100分，对体重偏轻且BMI低于23 kg/m2的个体，如患癌症赋75分[1]。
1.3　ACE定义
CHNS对健康结局的记录主要来自2011和2015年的问卷调查。对于“医生给您下过高血压的诊断吗？”“医生给您下过中风的诊断吗？”“医生给您下过心肌梗死的诊断吗？”“在过去四周中你因病或伤住院时，医生下的诊断是心脏病吗？”四个问题，其中任一回答“是”，则判定为发生ACE。由于CHNS并未提供调查的具体日期信息，本研究对随访人年的计算方式为：首次诊断年龄、死亡年龄或最后一次参加调查的年龄（以先发生者为准）减去2009年调查时的年龄。
1.4　统计学分析
按照是否发生ACE分组，描述两组人群基线特征并进行组间比较。计量资料用均数和标准差展示分布特征，用t检验进行组间比较；计数资料则采用频数展示构成情况，用χ2检验作差异性分析。鉴于不同尺度的LE8和LS7需保持可比性，本研究采取百分位数排序法，考察处于LE8和LS7的相同相对位置的个体者间ACE发生风险的差异。具体而言，对于LE8，通过四舍五入法将得分调整至最近的5的倍数，以便于统计各分数层的病例数；对于LS7，由于其各小项直接以0、1和2赋值，病例会自然分布在不同分数。当分数层病例太少而无法估计结局发生风险，将其合并，此时得分呈分类属性。
本研究采用Kaplan-Meier方法构建单因素模型（零模型），估计参与者的LE8和LS7在不同百分位数时的5年ACE发生风险；使用Cox比例风险模型估计5年内ACE发生风险，并调整年龄和性别（模型1）；为增强结果稳健性，在模型1的基础上，将分类形式的LE8和LS7调整成初始的连续型得分，将其作为自变量纳入模型（模型2）。进一步地，本研究使用C统计量来量化百分位数下的LE8和LS7模型对ACE的区分能力。C统计量常用于表示预测模型能区分出结局事件的可能性，值为0.5表示预测和随机的结果无差别，值为1则表示模型能准确预测事件发生。采用500次bootstrap抽样分别计算LE8和LS7模型的C统计量差值及其标准差，并用Wald检验对差值进行显著性评估。本研究的统计分析均通过R 4.3.0软件完成并采用双侧检验，检验水准α=0.05。
2　结果
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2.1　研究对象基线特征
本研究共纳入5 772名研究对象，其平均年龄为（48.63±14.46）岁，男性占比为45.4%。在6年随访期内，共有365例（6.3%）新发ACE，其中女性新发ACE占比为54%，男性新发ACE占比为46%。相较于未发生ACE组，新发ACE组在LS7涵盖的7个危险因素中（吸烟和膳食除外），处于“较差”水平的比重更高。在LE8涵盖的8个组成部分中，睡眠和DASH得分在ACE未发生和新发的组间无显著差异。见表2。
2.2　心血管健康指标在研究人群中的分布
根据两个指标的频数分布图，LS7在整个分数段上分布较为集中，而LE8分布范围更广。AHA将LE8得分在50～80间定义为心血管健康呈“中等”水平，本研究中处于“中等”水平的参与者在总人群占比约80%，见图2。随着LS7增加，LE8中位数上升，见图3。两者呈正相关，相关系数为0.86。
2.3　心血管健康指标对预测ACE发生风险的区分能力比较
在评估5年内ACE发生风险时，LE8和LS7均表现出一定的区分能力，这一结论在零模型和模型1中得到验证。零模型中，当参与者处于最高百分位数（LE8=90）时，其5年ACE风险为2.3%；而处于最低百分位数（LE8=40）的参与者，其风险则高出约6倍（14.1%）。同样地，当参与者的LS7得分在最低百分位数上时，其风险高出7.5倍（2.6%～19.5%）。
在LE8和LS7评分中，第10百分位数是一个关键节点，标志着风险变化速度的转折点。从这一点开始，风险的下降速度由快转慢，呈现出明显的非线性特征。以LS7为例，零模型中5年内ACE风险从第2.17百分位（得分5）的19.5%急剧下降至第13.77百分位（得分7）的10.1%，首个十分位数内实现了风险的大幅度减半。然而，在随后的九个十分位数区间内，随着LS7继续增加至最高分13，5年内ACE风险仅缓慢降低至2.6%，这一幅度相比首个十分位数区间减少了一半。在模型1中，LS7和LE8各分数层与5年ACE风险间的负相关关系及其程度与零模型相似，故本研究主要展示模型1结果，并在图下方直观地展示了LS7和LE8各分数层上按百分位数对应的人数分布，以便更清晰地理解风险变化与人群分布的关系，见图4A。
当将LE8和LS7视为连续型变量进行分析时（即模型2），风险下降趋势依然存在，但在低于第10百分位的区间内，风险降幅较分类处理时有所减小。具体而言，从LS7的第1.08百分位（5分）下降至第9.88百分位（7分），风险下降约为30%，这表明连续型LS7对于5年ACE风险变化的捕捉能力略逊于分类型LS7（取整至5的倍数）时的表现，见图4B。
为量化模型间区分能力，本研究计算了C统计量。在模型1中，LS7对应的C统计量为0.729（95%CI：0.707～0.751），LE8的C统计量为0.727（95%CI：0.705～0.749），差异无统计学意义（P=0.79）。类似地，模型2中LS7的C统计量为0.728（95%CI：0.706～0.75），LE8的C统计量0.726（95%CI：0.703～0.748），差异无统计学意义（P=0.51）。这表明，无论将LS7与LE8作为连续或分类变量，它们在区分ACE风险方面的效能相近。
3　讨论
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基于中国CHNS队列，本研究探索了融入睡眠因素后的心血管健康评价指标——LE8，相较于LS7，在预测ACE风险方面的差异。研究发现，当LS7得分高于第10百分位时，其关联的ACE风险下降速度趋于平缓；LE8也呈现出得分增加伴随风险降低的趋势，但风险下降的波动幅度相对较大。C统计量显示，LS7略高于LE8，差异不显著。这表明，在本研究涉及的中国人群中，LE8在预测ACE风险方面并未展现出超越LS7的额外价值。
LE8在LS7基础上，强调了睡眠在心血管疾病上的作用，这一观点已得到研究支持[18]。此外，LE8还进行了包括吸烟评估、膳食模式衡量[19]、代谢不健康状态评估[1，20]，以及糖尿病前期患者预防性用药等多项临床导向的改进。然而，本研究证据提示，这些改进并未显著提升LE8的预测能力，反而在一定程度上增加了评估的复杂性和成本。鉴于LE8和LS7的预测效能相近，但LS7的成本更低、收集更快，因此LS7可能更受临床医生的青睐。
LE8和LS7在整个得分范围内的风险下降模式提示，对于心血管健康处于“较差”水平的个体，当LE8每提高5分或LS7每提高1分，相较于健康水平“中等”的个体，能带来更为显著的健康收益。本研究的发现与REGARDS队列研究结论相呼应[11]，均指出将睡眠因素纳入考虑并未能显著提升对心血管事件的预测能力。不过，两项研究也存在差异：本研究未将冠心病纳入分析；同时，受限于随访年限，本研究仅聚焦于5年内的风险评估，但在最低百分位数区间内，LE8和LS7所估计的ACE风险仅略低于REGARDS研究的结果。在整体呈现的负相关趋势中，本研究并未观察到得分高于第90百分位（得分约为80）时风险的急剧下降，这意味着从“中等”到“理想”水平的心血管健康状态的转变所带来的额外收益在本研究中可能被忽略了。此外，本研究结果与另一项小样本（339名患者）研究结论不一致，后者聚焦于包括严重心律失常等在内的更全面的不良心血管事件[21]。未来需要深入探讨综合与单一结局对LE8预测效果的影响，以明确其最佳应用场景。
本研究存在一定的局限性。第一，睡眠时间的计算基于全天24 h，未严格遵守夜晚睡眠的原始定义，尽管已统一调整，但仍可能影响暴露评估的准确性。第二，结局依赖自我报告，可能会受到回忆和报告偏倚的影响，但本研究通过确认医生诊断及排除不一致报告来减轻此影响。第三，相比于长期风险预测研究，本研究随访时间较短，尽管在估计风险值上表现尚可，但未来需利用CHNS更长期数据验证这一结论，以充分发挥其作为中国人群全国代表性纵向队列的价值。
总体而言，在中国人群的研究中，LE8和LS7均显示出与新发ACE之间的相关性，且当个体心血管健康处于“较差”水平时，通过实施针对性的干预措施，可以有效降低ACE风险。然而，尽管LE8在评估体系上做了改进，但在区分ACE风险方面，它与LE7相比并未展现出统计学上的显著差异。鉴于LE8的数据收集成本相对较高，且并未带来预测能力的显著提升，因此，在临床实践中，LS7由于其成本效益优势，更有可能被广泛采用。
基金
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  • 国自然青年科学基金项目(82103943)
文献
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参考文献 引证文献
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2025年第52卷第7期
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doi: 10.20043/j.cnki.MPM.202411026
  • 接收时间：2024-11-02
  • 首发时间：2026-03-18
  • 出版时间：2025-04-10
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  • 收稿日期：2024-11-02
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国自然青年科学基金项目(82103943)
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    四川大学华西公共卫生学院/四川大学华西第四医院流行病与卫生统计学系，四川 成都 610041

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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
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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