Article(id=1322607888317661479, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322607837742747790, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202504202, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744214400000, receivedDateStr=2025-04-10, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793263893441, onlineDateStr=2026-10-29, pubDate=1761321600000, pubDateStr=2025-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793263893441, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793263893441, creator=13701087609, updateTime=1793263893441, updator=13701087609, issue=Issue{id=1322607837742747790, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='20', pageStart='3649', pageEnd='3840', issueExtLink='null', onlineDate='null', pubDate='1761321600000', pubDateStr='2025-10-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793263881383, creator='13701087609', updateTime=1793264238791, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322609336933794638, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322607837742747790, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322609336933794639, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322607837742747790, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3674, endPage=3679, ext={EN=ArticleExt(id=1322607890347704616, articleId=1322607888317661479, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Association between body roundness index and hypertension risk in middle-aged and older adults, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the correlation between body roundness index (BRI) and hypertension in middle-aged and elderly people.

Methods

The research data were from China Health and Retirement Longitudinal Study (CHARLS). The baseline data of participants in 2011 and the follow-up data from 2013 to 2020 were collected, and the body roundness index was calculated. Cox regression was used to analyze the relationship between BRI and hypertension. The linear trend test was used to explore the linear trend of the incidence of hypertension in the four quartiles of BRI. The dose-response relationship between BRI and hypertension was analyzed by restricted cubic spline model. The receiver operating characteristic curve and the area under the curve were used to evaluate BRI and common obesity assessment, so as to evaluate their ability to predict hypertension risk.

Results

Cox regression analysis showed that after adjusting the relevant covariates, BRI was correlated with the incidence of hypertension. The incidence of hypertension in the highest quartile of BRI was 1.29 times higher than that in the lowest quartile. Linear trend test showed that the incidence of hypertension increased with the increase of BRI quartile level (27.27%, 29.78%, 37.69%, 43.70%). RCS analysis showed that the higher the BRI, the greater the risk of hypertension. ROC curve analysis showed that the diagnostic performance of BRI was better than that of BMI, WHtR and WWI.

Conclusion

High BRI is associated with an increased risk of hypertension in middle-aged and elderly people in China.

, authors=Ying LI1, Hu CHEN1, Mei-qi MIAO2, Ling-mei LI3, Wen PAN2, Qing WANG3, authorsList=Ying LI, Hu CHEN, Mei-qi MIAO, Ling-mei LI, Wen PAN, Qing WANG, authorCompany=null, correspAuthors=Qing WANG, 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=1322607899994603846, articleId=1322607888317661479, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=中国中老年人群身体圆度指数与高血压风险的关联研究, columnId=1228016567632462653, journalTitle=现代预防医学, columnName=流行病与统计方法, runingTitle=null, highlight=null, articleAbstract=
目的

探索身体圆度指数(Body Roundness Index, BRI)与中国中老年人群高血压发病的相关性。

方法

研究数据来自中国健康与养老追踪调查(China Health and Retirement Longitudinal Study, CHARLS)。收集参与者2011年的基线资料及2013年—2020年的随访数据,并计算身体圆度指数。使用COX回归分析BRI与高血压发生的关系。线性趋势检验探讨高血压的发病率在BRI四个四分位的线性趋势。应用限制性立方样条模型(Restricted Cubic Spline, RCS)分析BRI与高血压发生的剂量-反应关系。利用受试者工作特征(Receiver Operating Characteristic, ROC)曲线和曲线下面积评价BRI及肥胖评估常用指标,以评估高血压发生风险的能力。

结果

COX回归分析结果表明,在校正了相关协变量后,BRI与高血压的发生具有相关性,BRI最高四分位高血压的发病率是最低四分位的1.29倍;线性趋势检验显示,高血压的发生率在随着BRI四分位水平的升高呈现上升的趋势(27.27%,29.78%,37.69%,43.70%)。RCS分析显示BRI越高,高血压的发生风险越大;并且利用ROC曲线分析表明,相比于体重指数(Body Mass Index, BMI),腰围身高比(Waist-to-Height Ratio, WHtR)及体重调整腰围指数(Weight-adjusted Waist Index, WWI),BRI的诊断性能更好。

结论

中国中老年人群高BRI与高血压的发生风险升高有关。

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李莹(1989—),女,本科,主治医师,研究方向:内科学

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汪青,E-mail:
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Front Cardiovasc Med, 2024, 11: 1380906., articleTitle=Molecular mechanisms and potential therapeutic targets in the pathogenesis of hypertension in visceral adipose tissue induced by a high-fat diet, refAbstract=null)], funds=[Fund(id=1322607916763431296, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, awardId=昆卫[2024]9号, language=CN, fundingSource=昆山高层次卫生人才项目(昆卫[2024]9号), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1322607900439200072, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, xref=1., ext=[AuthorCompanyExt(id=1322607900451782985, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, companyId=1322607900439200072, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Intensive Care Unit, Kunshan Six People’s Hospital, Kunshan, Jiangsu 215300, China), AuthorCompanyExt(id=1322607900460171594, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, companyId=1322607900439200072, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.昆山市第六人民医院重症医学科,江苏 昆山 215300)]), AuthorCompany(id=1322607900527280459, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, xref=2., ext=[AuthorCompanyExt(id=1322607900535669068, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, companyId=1322607900527280459, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.昆山市中医医院心内科)]), AuthorCompany(id=1322607900808298829, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, xref=3., ext=[AuthorCompanyExt(id=1322607900816687438, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, companyId=1322607900808298829, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.昆山市中医医院科研中心实验室)])], figs=[ArticleFig(id=1322607909603754355, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, language=EN, label=Fig.1, caption=Changes in the incidence of hypertension in the BRI quartile, figureFileSmall=aPnnNGGDsMlskh4QXoygcg==, figureFileBig=vVekZUh/ngvbvxwdMiMA2w==, tableContent=null), ArticleFig(id=1322607910023184756, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, language=CN, label=图1, caption=高血压的发生率在BRI四分位的变化, figureFileSmall=aPnnNGGDsMlskh4QXoygcg==, figureFileBig=vVekZUh/ngvbvxwdMiMA2w==, tableContent=null), ArticleFig(id=1322607910945931637, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, language=EN, label=Fig.2, caption=RCS curve analysis BRI and the occurrence of hypertension showed a linear dose-response relationship, figureFileSmall=jzL8A0e2Fb8kWVjHjm5ndw==, figureFileBig=Nz+mxclUKa15x6tgnkX3vA==, tableContent=null), ArticleFig(id=1322607911700906358, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, language=CN, label=图2, caption=RCS曲线分析BRI与高血压的发生呈线性剂量-反应关系

注:A:未校正;B:校正混杂因素。

, figureFileSmall=jzL8A0e2Fb8kWVjHjm5ndw==, figureFileBig=Nz+mxclUKa15x6tgnkX3vA==, tableContent=null), ArticleFig(id=1322607912183251319, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, language=EN, label=Fig.3, caption=ROC curve to evaluate the diagnostic performance of BRI and common obesity indicators for hypertension, figureFileSmall=v3ophURJWjR4Ehe8VFKo6Q==, figureFileBig=46SKrioi+C1DAktK/5I98Q==, tableContent=null), ArticleFig(id=1322607912955003256, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, language=CN, label=图3, caption=ROC曲线评价BRI及常用肥胖指标对于高血压发生的诊断性能, figureFileSmall=v3ophURJWjR4Ehe8VFKo6Q==, figureFileBig=46SKrioi+C1DAktK/5I98Q==, tableContent=null), ArticleFig(id=1322607913428959609, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, language=EN, label=Table 1, caption=

Comparison of general information of participants

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总人群(n=7 743)无高血压 (n=5 063)新发高血压 (n=2 680)t/Z/χ2P
BRI()3.95±1.263.81±1.194.20±1.34t=-12.45<0.001
BMI(kg/m2)()22.84±3.5822.56±3.4423.38±3.77t=-9.38<0.001
WHtR()0.53±0.060.52±0.060.54±0.06t=-12.50<0.001
WWI()17.45±1.3417.37±1.3717.62±1.27t=-7.77<0.001
年龄(岁)()57.56±8.8656.77±8.5759.07±9.20t=-10.69<0.001
夜间睡眠时间(h)()6.40±1.856.45±1.816.31±1.90t=3.090.002
总胆固醇(mg/dL)()191.69±38.28189.71±36.81195.30±40.61t=-5.09<0.001
高密度脂蛋白(mg/dL)()52.35±15.3952.67±15.0951.76±15.90t=2.120.034
低密度脂蛋白(mg/dL)()115.47±34.16114.23±33.16117.75±35.81t=-3.60<0.001
血糖(mg/dL)[M (P25,P75)]101.34(93.60,111.06)100.26(92.88,109.80)103.14(95.22,114.57)Z=-8.20<0.001
甘油三酯(mg/dL)[M (P25,P75)]99.12(71.68,144.26)96.46(69.92,138.06)104.43(74.34,153.99)Z=-6.08<0.001
出生地[n(%)]χ2=8.440.004
农村6 661(92.97)4 264(92.31)2 397(94.15)
城市504(7.03)355(7.69)149(5.85)
教育水平[n(%)]χ2=18.97<0.001
中学以下3 513(45.38)2 206(43.59)1 307(48.77)
中学及中学以上4 228(54.62)2 855(56.41)1 373(51.23)
婚姻分组[n(%)]χ2=5.650.017
已婚与配偶生活6 583(85.02)4 340(85.72)2 243(83.69)
其他1 160(14.98)723(14.28)437(16.31)
性别[n(%)]χ2=0.330.566
男3 753(48.47)2 442(48.23)1 311(48.92)
女3 990(51.53)2 621(51.77)1 369(51.08)
BMI(kg/m2)[n(%)]χ2=88.11<0.001
<18.5601(7.76)415(8.20)186(6.94)
18.5~23.94 590(59.28)3 164(62.49)1 426(53.21)
≥242 552(32.96)1 484(29.31)1 068(39.85)
血脂异常[n(%)]χ2=2.220.136
否7 185(94.91)4 708(95.19)2 477(94.40)
是385(5.09)238(4.81)147(5.60)
糖尿病[n(%)]χ2=15.90<0.001
否7 386(96.45)4 857(97.06)2 529(95.29)
是272(3.55)147(2.94)125(4.71)
心脏病[n(%)]χ2=6.620.010
否7 073(92.11)4 650(92.68)2 423(91.02)
是606(7.89)367(7.32)239(8.98)
中风[n(%)]χ2=3.650.056
否7 631(99.05)4 997(99.21)2 634(98.76)
是73(0.95)40(0.79)33(1.24)
吸烟状态[n(%)]χ2=4.340.037
否2 499(80.46)1 644(81.55)855(78.44)
是607(19.54)372(18.45)235(21.56)
饮酒状态[n(%)]χ2=0.560.456
否5 071(65.66)3 332(65.95)1 739(65.11)
是2 652(34.34)1 720(34.05)932(34.89)
), ArticleFig(id=1322607913865167226, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, language=CN, label=表1, caption=

参与者的一般资料比较

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总人群(n=7 743)无高血压 (n=5 063)新发高血压 (n=2 680)t/Z/χ2P
BRI()3.95±1.263.81±1.194.20±1.34t=-12.45<0.001
BMI(kg/m2)()22.84±3.5822.56±3.4423.38±3.77t=-9.38<0.001
WHtR()0.53±0.060.52±0.060.54±0.06t=-12.50<0.001
WWI()17.45±1.3417.37±1.3717.62±1.27t=-7.77<0.001
年龄(岁)()57.56±8.8656.77±8.5759.07±9.20t=-10.69<0.001
夜间睡眠时间(h)()6.40±1.856.45±1.816.31±1.90t=3.090.002
总胆固醇(mg/dL)()191.69±38.28189.71±36.81195.30±40.61t=-5.09<0.001
高密度脂蛋白(mg/dL)()52.35±15.3952.67±15.0951.76±15.90t=2.120.034
低密度脂蛋白(mg/dL)()115.47±34.16114.23±33.16117.75±35.81t=-3.60<0.001
血糖(mg/dL)[M (P25,P75)]101.34(93.60,111.06)100.26(92.88,109.80)103.14(95.22,114.57)Z=-8.20<0.001
甘油三酯(mg/dL)[M (P25,P75)]99.12(71.68,144.26)96.46(69.92,138.06)104.43(74.34,153.99)Z=-6.08<0.001
出生地[n(%)]χ2=8.440.004
农村6 661(92.97)4 264(92.31)2 397(94.15)
城市504(7.03)355(7.69)149(5.85)
教育水平[n(%)]χ2=18.97<0.001
中学以下3 513(45.38)2 206(43.59)1 307(48.77)
中学及中学以上4 228(54.62)2 855(56.41)1 373(51.23)
婚姻分组[n(%)]χ2=5.650.017
已婚与配偶生活6 583(85.02)4 340(85.72)2 243(83.69)
其他1 160(14.98)723(14.28)437(16.31)
性别[n(%)]χ2=0.330.566
男3 753(48.47)2 442(48.23)1 311(48.92)
女3 990(51.53)2 621(51.77)1 369(51.08)
BMI(kg/m2)[n(%)]χ2=88.11<0.001
<18.5601(7.76)415(8.20)186(6.94)
18.5~23.94 590(59.28)3 164(62.49)1 426(53.21)
≥242 552(32.96)1 484(29.31)1 068(39.85)
血脂异常[n(%)]χ2=2.220.136
否7 185(94.91)4 708(95.19)2 477(94.40)
是385(5.09)238(4.81)147(5.60)
糖尿病[n(%)]χ2=15.90<0.001
否7 386(96.45)4 857(97.06)2 529(95.29)
是272(3.55)147(2.94)125(4.71)
心脏病[n(%)]χ2=6.620.010
否7 073(92.11)4 650(92.68)2 423(91.02)
是606(7.89)367(7.32)239(8.98)
中风[n(%)]χ2=3.650.056
否7 631(99.05)4 997(99.21)2 634(98.76)
是73(0.95)40(0.79)33(1.24)
吸烟状态[n(%)]χ2=4.340.037
否2 499(80.46)1 644(81.55)855(78.44)
是607(19.54)372(18.45)235(21.56)
饮酒状态[n(%)]χ2=0.560.456
否5 071(65.66)3 332(65.95)1 739(65.11)
是2 652(34.34)1 720(34.05)932(34.89)
), ArticleFig(id=1322607914297180539, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, language=EN, label=Table 2, caption=

Influencing factors of hypertension by univariate Cox regression analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
变量β值 Z值P值HR (95% CI)
教育水平(ref:中学以下)-0.160.04-4.10<0.0010.85(0.79~0.92)
婚姻分组(ref:已婚与配偶生活/其他)0.200.053.91<0.0011.23(1.11~1.36)
BMI(kg/m2)(ref:<18.5/≥24)0.190.082.340.0191.20(1.03~1.41)
糖尿病(ref:否)0.360.093.91<0.0011.43(1.20~1.71)
心脏病(ref:否)0.250.073.68<0.0011.28(1.12~1.47)
中风(ref:否)0.390.182.230.0261.48(1.05~2.08)
吸烟状态(ref:否)-0.160.07-2.110.0350.86(0.74~0.99)
年龄0.030.0014.25<0.0011.03(1.03~1.04)
血糖0.010.007.12<0.0011.01(1.01~1.01)
总胆固醇0.010.003.69<0.0011.01(1.01~1.01)
甘油三酯0.010.003.62<0.0011.01(1.01~1.01)
低密度脂蛋白0.010.002.300.0211.01(1.01~1.01)
夜间睡眠时间-0.030.01-2.860.0040.97(0.95~0.99)
), ArticleFig(id=1322607915064738172, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, language=CN, label=表2, caption=

单因素COX回归分析高血压发生的影响因素

, figureFileSmall=null, figureFileBig=null, tableContent=
变量β值 Z值P值HR (95% CI)
教育水平(ref:中学以下)-0.160.04-4.10<0.0010.85(0.79~0.92)
婚姻分组(ref:已婚与配偶生活/其他)0.200.053.91<0.0011.23(1.11~1.36)
BMI(kg/m2)(ref:<18.5/≥24)0.190.082.340.0191.20(1.03~1.41)
糖尿病(ref:否)0.360.093.91<0.0011.43(1.20~1.71)
心脏病(ref:否)0.250.073.68<0.0011.28(1.12~1.47)
中风(ref:否)0.390.182.230.0261.48(1.05~2.08)
吸烟状态(ref:否)-0.160.07-2.110.0350.86(0.74~0.99)
年龄0.030.0014.25<0.0011.03(1.03~1.04)
血糖0.010.007.12<0.0011.01(1.01~1.01)
总胆固醇0.010.003.69<0.0011.01(1.01~1.01)
甘油三酯0.010.003.62<0.0011.01(1.01~1.01)
低密度脂蛋白0.010.002.300.0211.01(1.01~1.01)
夜间睡眠时间-0.030.01-2.860.0040.97(0.95~0.99)
), ArticleFig(id=1322607915203150205, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, language=EN, label=Table 3, caption=

Relationship between BRI and hypertension

, figureFileSmall=null, figureFileBig=null, tableContent=
变量Model1Model2Model3
HR (95% CI)PHR (95% CI)PHR (95% CI)P
BRI(连续变量)1.16(1.12~1.19)<0.0011.13(1.08~1.18)<0.0011.20(1.09~1.32)<0.001
BRI(分组变量)
Q1(BRI<3.033 5)1.00(Reference)1.00(Reference)1.00(Reference)
Q2(3.033 5≤BRI<3.770 7)1.06(0.94~1.19)0.3351.05(0.93~1.18)0.4681.06(0.92~1.23)0.422
Q3(3.770 7≤BRI<4.691 3)1.34(1.20~1.50)<0.0011.26(1.11~1.43)<0.0011.27(1.09~1.48)0.002
Q4(BRI≥4.691 3)1.56(1.40~1.74)<0.0011.31(1.13~1.51)<0.0011.29(1.08~1.53)0.005
Per SD-increase1.17(1.13~1.22)<0.0011.10(1.05~1.16)<0.0011.10 (1.04~1.16)0.001
), ArticleFig(id=1322607915945542015, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607888317661479, language=CN, label=表3, caption=

BRI与高血压发生的关系

, figureFileSmall=null, figureFileBig=null, tableContent=
变量Model1Model2Model3
HR (95% CI)PHR (95% CI)PHR (95% CI)P
BRI(连续变量)1.16(1.12~1.19)<0.0011.13(1.08~1.18)<0.0011.20(1.09~1.32)<0.001
BRI(分组变量)
Q1(BRI<3.033 5)1.00(Reference)1.00(Reference)1.00(Reference)
Q2(3.033 5≤BRI<3.770 7)1.06(0.94~1.19)0.3351.05(0.93~1.18)0.4681.06(0.92~1.23)0.422
Q3(3.770 7≤BRI<4.691 3)1.34(1.20~1.50)<0.0011.26(1.11~1.43)<0.0011.27(1.09~1.48)0.002
Q4(BRI≥4.691 3)1.56(1.40~1.74)<0.0011.31(1.13~1.51)<0.0011.29(1.08~1.53)0.005
Per SD-increase1.17(1.13~1.22)<0.0011.10(1.05~1.16)<0.0011.10 (1.04~1.16)0.001
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中国中老年人群身体圆度指数与高血压风险的关联研究
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李莹 1 , 陈虎 1 , 缪美琪 2 , 李玲美 3 , 潘文 2 , 汪青 3
现代预防医学 | 流行病与统计方法 2025,52(20): 3674-3679
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现代预防医学 |流行病与统计方法 2025 , 52 (20) : 3674 -3679
中国中老年人群身体圆度指数与高血压风险的关联研究
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李莹1, 陈虎1, 缪美琪2, 李玲美3, 潘文2, 汪青3
作者信息
  • 1.昆山市第六人民医院重症医学科,江苏 昆山 215300
  • 2.昆山市中医医院心内科
  • 3.昆山市中医医院科研中心实验室
通讯作者:
汪青,E-mail:
作者简介:

李莹(1989—),女,本科,主治医师,研究方向:内科学

Association between body roundness index and hypertension risk in middle-aged and older adults
Ying LI1, Hu CHEN1, Mei-qi MIAO2, Ling-mei LI3, Wen PAN2, Qing WANG3
Affiliations
  • Intensive Care Unit, Kunshan Six People’s Hospital, Kunshan, Jiangsu 215300, China
出版时间: 2025-10-25 doi: 10.20043/j.cnki.MPM.202504202
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目的

探索身体圆度指数(Body Roundness Index, BRI)与中国中老年人群高血压发病的相关性。

方法

研究数据来自中国健康与养老追踪调查(China Health and Retirement Longitudinal Study, CHARLS)。收集参与者2011年的基线资料及2013年—2020年的随访数据,并计算身体圆度指数。使用COX回归分析BRI与高血压发生的关系。线性趋势检验探讨高血压的发病率在BRI四个四分位的线性趋势。应用限制性立方样条模型(Restricted Cubic Spline, RCS)分析BRI与高血压发生的剂量-反应关系。利用受试者工作特征(Receiver Operating Characteristic, ROC)曲线和曲线下面积评价BRI及肥胖评估常用指标,以评估高血压发生风险的能力。

结果

COX回归分析结果表明,在校正了相关协变量后,BRI与高血压的发生具有相关性,BRI最高四分位高血压的发病率是最低四分位的1.29倍;线性趋势检验显示,高血压的发生率在随着BRI四分位水平的升高呈现上升的趋势(27.27%,29.78%,37.69%,43.70%)。RCS分析显示BRI越高,高血压的发生风险越大;并且利用ROC曲线分析表明,相比于体重指数(Body Mass Index, BMI),腰围身高比(Waist-to-Height Ratio, WHtR)及体重调整腰围指数(Weight-adjusted Waist Index, WWI),BRI的诊断性能更好。

结论

中国中老年人群高BRI与高血压的发生风险升高有关。

身体圆度指数  /  高血压  /  中国健康与养老追踪调查
Objective

To explore the correlation between body roundness index (BRI) and hypertension in middle-aged and elderly people.

Methods

The research data were from China Health and Retirement Longitudinal Study (CHARLS). The baseline data of participants in 2011 and the follow-up data from 2013 to 2020 were collected, and the body roundness index was calculated. Cox regression was used to analyze the relationship between BRI and hypertension. The linear trend test was used to explore the linear trend of the incidence of hypertension in the four quartiles of BRI. The dose-response relationship between BRI and hypertension was analyzed by restricted cubic spline model. The receiver operating characteristic curve and the area under the curve were used to evaluate BRI and common obesity assessment, so as to evaluate their ability to predict hypertension risk.

Results

Cox regression analysis showed that after adjusting the relevant covariates, BRI was correlated with the incidence of hypertension. The incidence of hypertension in the highest quartile of BRI was 1.29 times higher than that in the lowest quartile. Linear trend test showed that the incidence of hypertension increased with the increase of BRI quartile level (27.27%, 29.78%, 37.69%, 43.70%). RCS analysis showed that the higher the BRI, the greater the risk of hypertension. ROC curve analysis showed that the diagnostic performance of BRI was better than that of BMI, WHtR and WWI.

Conclusion

High BRI is associated with an increased risk of hypertension in middle-aged and elderly people in China.

Body roundness index  /  Hypertension  /  China health and retirement longitudinal study
李莹, 陈虎, 缪美琪, 李玲美, 潘文, 汪青. 中国中老年人群身体圆度指数与高血压风险的关联研究. 现代预防医学, 2025 , 52 (20) : 3674 -3679 . DOI: 10.20043/j.cnki.MPM.202504202
Ying LI, Hu CHEN, Mei-qi MIAO, Ling-mei LI, Wen PAN, Qing WANG. Association between body roundness index and hypertension risk in middle-aged and older adults[J]. Modern Preventive Medicine, 2025 , 52 (20) : 3674 -3679 . DOI: 10.20043/j.cnki.MPM.202504202
高血压是心脑血管疾病的重要危险因素,可导致脑卒中、心衰、肾功能损害等诸多并发症[1-3]。近年来,随着人口老龄化,高血压的患病率逐年升高[4],已成为全球性的社会公共问题[5]。高血压发病机制很复杂,引起血容量增加或血管舒张能力降低的因素可导致高血压[6]。近年来大量的研究表明,肥胖,特别是腹型肥胖,与高血压的发生关系密切[7-8]。2013年,Thomas等人提出一个基于腰围和身高的身体测量指标-身体圆度指数(Body Roundness Index, BRI),以评估腹部脂肪和中心性肥胖程度[9]。BRI测量及计算简单,被广泛应用于临床。一些研究发现,BRI可作为脑卒中、糖尿病、代谢综合征的预测因子[10-12]。在中国人群,BRI与高血压的发生是否具有相关性,与传统肥胖指标相比,BRI预测高血压的发生是否具有优势,鲜有相关研究。本研究基于中国的CHARLS数据库,纵向队列研究,探讨BRI与高血压发生的相关性,为高血压的预防和控制提供新的思路。
研究资料来源于中国健康与养老追踪调查(China Health and Retirement Longitudinal Study, CHARLS)数据库2011年至2020年的随访结果。研究基线数据采集于2011年,随访数据(高血压)来源2013年至2020年。CHARLS调查已经过北京大学生物医学伦理审查委员会批准(伦理号:IRB00001052-11015)。
纳入标准:2011年基线无高血压病史及血压测量正常者。排除标准:1)年龄<45周岁或者年龄不详;2)2011年腰围及身高资料不全者;3)高血压病史或血压测量随访数据缺失。
本研究的暴露因素为BRI,BRI=364.2-365.5*{1-[(腰围/2π)/(0.5*身高)]2}0.5[13]。本研究的结局为高血压,高血压的诊断标准为:1)受试者测量血压,收缩压≥140 mmHg或舒张压≥90 mmHg(1 mmHg=0.133 kPa);2)受试者自我报告患有高血压;3)受试者服用降血压药物,具有以上任一条[14]。
人口学特征包括:年龄,性别,婚姻生活状态(已婚并与配偶生活,其他),吸烟情况(吸烟、不吸烟),饮酒情况(饮酒、不饮酒),出生地(农村、城市),教育水平(中学以下、中学及中学以上);一般健康情况包括:夜间睡眠时间、身高、体重、腰围,慢性病病史(糖尿病、血脂异常、心脏病、中风),实验室检查(血脂、血糖)。
本研究采用SPSS 25.0统计软件及R 4.2.2进行数据分析。符合正态分布的连续变量使用()表示,组间使用t检验。不符合正态分布连续变量以中位数M (P25,P75)表示,组间使用秩和检验。分类变量采用百分比(%)表示,组间比较采用χ2检验。使用COX回归分析BRI与高血压发生的关系。线性趋势检验探讨高血压的发病率在BRI四个四分位的线性趋势。应用限制性立方样条模型(RCS)分析BRI与高血压发生的剂量-反应关系。利用受试者工作特征曲线(ROC)和曲线下面积评价BRI及肥胖常用指标,如体重指数(BMI),腰围身高比(WHtR)及体重调整腰围指数(WWI),以评估高血压发生风险的能力。检验水准α=0.05。
研究共纳入总人群7 743人,其中男性3 753例,女性3 990例,平均年龄为:57.56±8.86岁。新发高血压共2 680例,占比34.61%。两组间的年龄、教育水平,婚姻状态,BMI,WHtR,WWI,慢性病(糖尿病、心脏病)、吸烟、血糖、血脂水平(总胆固醇、甘油三酯、低密度脂蛋白、高密度脂蛋白),夜间睡眠时间及BRI具有统计学差异,见表1。
以高血压的发生作为因变量,以性别、年龄、教育水平、婚姻状态、实验室检查、慢性病史等作为自变量,单因素COX回归分析,结果表明,年龄、BMI、夜间睡眠时间、血脂水平(总胆固醇、甘油三酯、低密度脂蛋白)、血糖、教育水平、婚姻状况、糖尿病、心脏病、中风及吸烟状态是高血压的发生的影响因素,见表2。
将BRI按四分位分类为Q1(BRI<3.033 5),Q2(3.033 5≤BRI<3.770 7),Q3(3.770 7≤BRI<4.691 3),Q4(BRI≥4.691 3)四个组,以高血压的发生作为因变量,以BRI四分位作为自变量,以单因素分析P<0.05作为条件纳入协变量,行多因素COX回归分析,结果表明,在校正了年龄、BMI、夜间睡眠时间、血脂水平(总胆固醇、甘油三酯、低密度脂蛋白)、血糖、教育水平、婚姻状况、糖尿病、心脏病、中风及吸烟状态后,BRI与高血压的发生具有相关性,BRI最高四分位高血压的发病率是最低四分位的1.29倍,见表3。线性趋势检验显示,高血压的发生率在随着BRI四分位水平的升高呈现上升的趋势(27.27%,29.78%,37.69%,43.70%),见图1。
应用RCS曲线分析BRI与高血压发生的关系,结果显示,BRI与高血压的发生呈线性剂量-反应关系。BRI越高,高血压的发生风险越大,见图2。
应用受试者工作曲线(ROC)评价BRI及常用肥胖指标对于高血压发生的诊断性能,结果显示,相比于BMI、WHtR及WWI,BRI的诊断性能更好,见图3。
高血压是全球性的公共卫生问题,全球高血压报告显示,高血压患者的数量从1990年的6.5亿增长至2019年的13亿[15]。高血压危险因素包括高钠膳食、超重和肥胖、高血压家族史、年龄增长、缺乏体力活动、吸烟、过量饮酒、心理社会因素等[16]。研究表明,高血压与肥胖密切相关[17]。在临床实践和流行病学研究中,常用的肥胖评价指标包括体重指数(BMI)、腰围(Waist Circumference, WC)、腰围身高比(WHtR)、体重调整腰围指数(WWI)等。这些指标应用广泛,但仍存在一定的缺陷:BMI无法区别体脂分布,不能评估体脂比例;WC不考虑身高和体重,WWI虽考虑到了体重,但忽略了身高,因此在评估过高过矮个体的肥胖程度中不准确[18],而且不能分辨肌肉和脂肪对于体重的影响;另外WHtR不能区分内脏脂肪和皮下脂肪[19]。近年来,有学者提出了新的人体测量学参数-身体圆度指数(BRI)。BRI兼顾了身高和腰围,测量方便,计算简单,能够反应人体体态及内脏脂肪堆积,现被广泛的应用于临床研究。有研究显示,BRI与糖尿病有显著的相关性[20],并且在预测糖尿病前期时, BRI的可靠性超过了传统的肥胖指标如BMI[21]。另有研究发现BRI与高尿酸血症关系密切,与最低四分位数BRI相比,最高四分位数BRI的新发高尿酸血症风险显著增加[22]。一项队列研究发现,BRI的升高与癌症发病率增加相关[23]。此外,BRI与脑卒中的发生相关,是脑卒中风险的重要预测指标[24]。本研究利用CHARLS数据库进行了横断面研究,结果发现,BRI与高血压具有相关性,BRI最高四分位高血压的患病率是最低四分位的1.29倍。线性趋势检验显示,高血压的发生率在随着BRI四分位水平的升高呈现上升的趋势。应用RCS曲线分析BRI与高血压发生的关系,结果显示BRI越高,高血压的发生风险越大。本研究的结果表明,在中国中老年人群,BRI是高血压发生的独立危险因素。并且进一步利用ROC曲线及曲线下面积评价BRI及常用肥胖指标对于高血压发生的诊断性能,结果显示,相比于BMI、WHtR及WWI,BRI的诊断性能更好。
腹型肥胖,又称中心性肥胖,是以脂肪积聚于腹腔内为特征。是中国肥胖人群的主要类型[25]。有研究表明,内脏脂肪过多与代谢紊乱及心脑血管疾病风险升高具有显著相关性[26]。一方面过多的腹部脂肪可能导致肾脏受压,增加肾内压力,减少钠排泄,激活肾素-血管紧张素-醛固酮系统、引起交感神经系统活动的增强[27-28];另一方面,内脏脂肪堆积过多引起游离脂肪酸、氧化应激和脂毒性增加,导致血管动脉粥样硬化和内皮细胞功能障碍,导致高血压发生发展[29];此外,内脏脂肪积聚会导致胰岛素抵抗增加,影响内皮细胞代谢,降低了胰岛素介导的血管舒张功能,导致血压上升[30]。
本研究的优点及不足之处:首先,本研究的数据来自中国CHARLS数据库,样本量大,且具有全国代表性;其次,本研究结合了横断面及纵向队列研究,使得BRI与高血压的相关性及因果关系更加明确。本研究也存在的一定的不足之处,高血压的发病机制复杂,遗传、饮食,运动等相关的影响因素并未全部纳入研究,可能存在一定的偏倚。
综上,本研究的结果表明,中国中老年人群BRI与高血压的发生具有相关性,BRI越大,高血压的发病风险越高。高BRI人群应重视血压的监测。控制BRI是否有利于预防高血压的发生,有待进一步的研究。
  • 昆山高层次卫生人才项目(昆卫[2024]9号)
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2025年第52卷第20期
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doi: 10.20043/j.cnki.MPM.202504202
  • 接收时间:2025-04-10
  • 首发时间:2026-10-29
  • 出版时间:2025-10-25
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  • 收稿日期:2025-04-10
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昆山高层次卫生人才项目(昆卫[2024]9号)
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    1.昆山市第六人民医院重症医学科,江苏 昆山 215300
    2.昆山市中医医院心内科
    3.昆山市中医医院科研中心实验室

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汪青,E-mail:
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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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