Article(id=1323941391776227845, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1323941237702668764, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202507208, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1752336000000, receivedDateStr=2025-07-13, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793581825441, onlineDateStr=2026-11-02, pubDate=1765296000000, pubDateStr=2025-12-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793581825441, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793581825441, creator=13701087609, updateTime=1793581825441, updator=13701087609, issue=Issue{id=1323941237702668764, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='23', pageStart='4225', pageEnd='4416', issueExtLink='null', onlineDate='null', pubDate='1765296000000', pubDateStr='2025-12-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793581788707, creator='13701087609', updateTime=1793581887795, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1323941653374959780, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1323941237702668764, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1323941653374959781, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1323941237702668764, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=4403, endPage=4408, ext={EN=ArticleExt(id=1323941391973360134, articleId=1323941391776227845, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Association between the uric acid to albumin ratio and hypertension: a cross-sectional study based on NHANES, columnId=1228016569138213037, journalTitle=Modern Preventive Medicine, columnName=Clinical Medicine and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the association between the serum uric acid to albumin ratio (UAR) and hypertension.

Methods

This study included data from 23 552 U.S. adults from the NHANES database(2007—2016). Multivariable logistic regression models and smooth curve fitting were used to examine the linear and nonlinear relationships between UAR and hypertension. Threshold effect analysis was applied to identify inflection points, and subgroup analyses were conducted to assess the association across different populations. Finally, the diagnostic performance of UAR for hypertension was evaluated using receiver operating characteristic(ROC) curves.

Results

After adjusting for all confounding factors, multivariable logistic regression analysis showed that higher UAR was significantly associated with an increased prevalence of hypertension (OR=1.11, 95% CI: 1.09-1.13). Compared with the low UAR group, the middle (OR=1.14, 95% CI: 1.05-1.24) and high (OR=1.46, 95% CI: 1.33-1.60) UAR groups had significantly increased risks of hypertension(P<0.05). Smooth curve fitting revealed a J-shaped nonlinear relationship between UAR and hypertension. Threshold effect analysis indicated that below the threshold of 4.66, UAR was negatively correlated with hypertension (OR=0.75, 95% CI: 0.61-0.92), whereas above 4.66, a positive correlation was observed (OR=1.12, 95% CI: 1.10-1.14). Consistent results were obtained in subgroup analyses, and interaction effects were observed across subgroups stratified by sex, age, alcohol consumption, BMI, and diabetes (P<0.05). The ROC curve demonstrated that UAR had moderate predictive ability for hypertension, with an area under the curve (AUC) of 0.639.

Conclusion

UAR is closely associated with hypertension and may serve as a promising biomarker for assessing early hypertension risk.

, authors=Gui-yuan YANG, Yi-ran LI, Yu-lin CHEN, Hong ZHANG, Kai-xuan LIN, authorsList=Gui-yuan YANG, Yi-ran LI, Yu-lin CHEN, Hong ZHANG, Kai-xuan LIN, authorCompany=null, correspAuthors=Kai-xuan LIN, 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=1323941394859041299, articleId=1323941391776227845, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=血清尿酸与白蛋白比值与高血压患病的关联性分析——基于NHANES的横断面研究, columnId=1228016570119680182, journalTitle=现代预防医学, columnName=临床与预防, runingTitle=null, highlight=null, articleAbstract=
目的

探讨血清尿酸与白蛋白比值(uric acid to albumin ratio, UAR)与高血压患病的关联。

方法

基于2007—2016年美国国家营养健康调查(National Health and Nutrition Examination Survey,NHANES) 数据库中23 552名美国成年人数据,采用多因素logistic回归模型与平滑曲线拟合探究UAR与高血压患病的线性与非线性关系,通过阈值效应分析识别拐点,并进行亚组分析观察不同人群中两者的相关性。最后,使用受试者工作特征(receiver operating characteristic,ROC)曲线评估UAR对高血压患病的诊断性能。

结果

多因素logistic回归分析显示,在校正所有混杂因素后,UAR的增加与高血压患病率升高存在显著关联(OR=1.11,95% CI:1.09~1.13),与低值组相比,中值组(OR=1.14,95% CI:1.05~1.24)和高值组(OR= 1.46,95% CI:1.33~1.60)患高血压的几率增加(P<0.05)。平滑曲线揭示两者之间呈J型的非线性关联。阈值效应分析显示,在阈值4.66以下,UAR与高血压患病之间呈负相关(OR=0.75,95% CI:0.61~0.92),而阈值高于4.66,两者呈正相关(OR=1.12,95% CI:1.10~1.14)。亚组分析获得了一致的结果,且在性别、年龄、饮酒情况、BMI及糖尿病亚组上存在交互作用(P<0.05)。ROC曲线表明,UAR在预测高血压患病方面具有中等能力,其曲线下面积为0.639。

结论

UAR与高血压患病存在密切关联,可能是评估早期高血压危险的重要生物标志物。

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杨贵元(1999—),男,硕士在读;研究方向:心病研究

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林凯旋,E-mail:
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Journal of Translational Medicine, 2020, 18 (1): 409., articleTitle=Association of hypertension and hypertriglyceridemia on incident hyperuricemia: an 8-year prospective cohort study, refAbstract=null), Reference(id=1323941402467508841, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, doi=null, pmid=null, pmcid=null, year=2021, volume=35, issue=6, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[34], rfOrder=33, authorNames=Kawamoto R, Kikuchi A, Akase TC, journalName=Journal of Clinical Laboratory Analysis, refType=null, unstructuredReference=Kawamoto R, Kikuchi A, Akase TC, et al. Alcohol consumption and serum uric acid are synergistically associated with renal dysfunction among community-dwelling persons [J]. 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QJM: Monthly Journal of the Association of Physicians, 2022, 115 (5): 271-278., articleTitle=Relation of BMI and waist circumference with the risk of new-onset hyperuricemia in hypertensive patients, refAbstract=null)], funds=[Fund(id=1323941399581827652, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, awardId=GZYZS2024XKG03, language=CN, fundingSource=国家中医药传承发展示范试点项目-院校共建(GZYZS2024XKG03), fundOrder=null, country=null), Fund(id=1323941399661519429, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, awardId=2202779, language=CN, fundingSource=国家中医药传承发展示范试点项目-中山市中医院曾安教授柔性引才专家团队-2024年中医药部门中央补助资金(2202779), fundOrder=null, country=null), Fund(id=1323941399737016902, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, awardId=JCTJ-2024-002-04, language=CN, fundingSource=国家中医药管理局监测统计中心委托办事经费任务(JCTJ-2024-002-04), fundOrder=null, country=null), Fund(id=1323941399820902983, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, awardId=2025JCTJE51, language=CN, fundingSource=国家中医药管理局监测统计中心(2025JCTJE51), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1323941395093922324, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, xref=null, ext=[AuthorCompanyExt(id=1323941395106505237, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, companyId=1323941395093922324, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Internal Medicine I, Zhongshan Hospital of Chinese Medicine Affiliated to Guangzhou University of Chinese Medicine, Zhongshan, Guangdong 528400, China), AuthorCompanyExt(id=1323941395114893846, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, companyId=1323941395093922324, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=广州中医药大学附属中山中医院内一科,广东 中山 528400)])], figs=[ArticleFig(id=1323941398407422518, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, language=EN, label=Figure 1, caption=Participant selection flowchart for NHANES 2007—2016, figureFileSmall=ezVzQuG4LDB+wno7mWjlIw==, figureFileBig=D0KbkxBKIIbclLLKKJ7qjA==, tableContent=null), ArticleFig(id=1323941398474531383, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, language=CN, label=图1, caption=NHANES 2007—2016年参与者选择流程图, figureFileSmall=ezVzQuG4LDB+wno7mWjlIw==, figureFileBig=D0KbkxBKIIbclLLKKJ7qjA==, tableContent=null), ArticleFig(id=1323941398638109240, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, language=EN, label=Figure 2, caption=Smooth curve fitting between UAR and the probability of hypertension incidence, figureFileSmall=Jq8hqls5+E4FSeoj+BNkMg==, figureFileBig=F9BlwM6JulG2+Jc6bVL5KQ==, tableContent=null), ArticleFig(id=1323941398701023801, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, language=CN, label=图2, caption=UAR与高血压患病率的平滑曲线拟合

注:中间浅实线表示变量之间的拟合,而两端深实线带表示 95% CI。

, figureFileSmall=Jq8hqls5+E4FSeoj+BNkMg==, figureFileBig=F9BlwM6JulG2+Jc6bVL5KQ==, tableContent=null), ArticleFig(id=1323941398776521274, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, language=EN, label=Figure 3, caption=ROC curves comparing UAR with serum uric acid and serum albumin alone, figureFileSmall=oU7UUVRAbbIsm+8IWxZOjg==, figureFileBig=cQpWWSDzKbv010WAWr3qbA==, tableContent=null), ArticleFig(id=1323941398847824443, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, language=CN, label=图3, caption=UAR与单独使用血清尿酸和血清白蛋白的ROC曲线比较, figureFileSmall=oU7UUVRAbbIsm+8IWxZOjg==, figureFileBig=cQpWWSDzKbv010WAWr3qbA==, tableContent=null), ArticleFig(id=1323941398927516220, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, language=EN, label=Table 1, caption=

Basic characteristics of the research population [(), n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总数 (n=23 552)非高血压组 (n=13 810)高血压组(n=9 742)P值
年龄(岁)49.29±17.6142.06±15.6759.55±14.91<0.001
性别0.010
男性11 655 (49.49)6 737 (48.78)4 918 (50.48)
女性11 897 (50.51)7 073 (51.22)4 824 (49.52)
种族<0.001
非西班牙裔白种人10 034 (42.6)5 760 (41.71)4 274 (43.87)
非西班牙裔黑种人4 709 (19.99)2 300 (16.65)2 409 (24.73)
墨西哥美国人2 565 (10.89)1 589 (11.51)976 (10.02)
其他种族6 244 (26.51)4 161 (30.13)2 083 (21.38)
受教育程度<0.001
高中以下5 963 (25.32)3 117 (22.57)2 846 (29.21)
高中或同等教育5 290 (22.46)2 939 (21.28)2 351 (24.13)
高中以上12 299 (52.22)7 754 (56.15)4 545 (46.65)
婚姻状况<0.001
已婚或与伴侣生活在一起14 115 (59.93)8 414 (60.93)5 701 (58.52)
独居9 437 (40.07)5 396 (39.07)4 041 (41.48)
PIR<0.001
≤1.307 567 (32.14)4 342 (31.44)3 225 (33.10)
1.31~3.508 959 (38.04)5 173 (37.46)3 786 (38.86)
>3.507 026 (29.83)4 295 (31.10)2 731 (28.03)
饮酒情况<0.001
不饮酒6 369 (27.04)3 430 (24.84)2 939 (30.17)
少量或中度饮酒8 269 (35.11)4 631 (33.53)3 638 (37.34)
重度饮酒8 914 (37.85)5 749 (41.63)3 165 (32.49)
吸烟状况<0.001
从不吸烟13 018 (55.27)8 121 (58.81)4 897 (50.27)
既往吸烟4 878 (20.71)3 019 (21.86)1 859 (19.08)
现在吸烟5 656 (24.01)2 670 (19.33)2 986 (30.65)
BMI(kg/m2)28.98±6.7227.85±6.2730.59±7.01<0.001
钠摄入量(mg)3 455.52± 1 821.963 592.85±1 874.623 293.68±1 742.47<0.001
久坐时间(min)355.44± 203.06348.98±203.29364.59±202.38<0.001
糖尿病<0.001
是5 739 (24.37)192 (1.39)741 (7.61)
否17 813 (75.63)13 618 (98.61)9 001 (92.39)
总胆固醇(mmol/L)4.99± 1.074.97±1.045.01±1.120.007
甘油三酯(mmol/L)1.45±1.151.38±1.071.53±1.25<0.001
HDL-C(mmol/L)1.37±0.421.38±0.411.35±0.43<0.001
LDL-C(mmol/L)2.95±0.972.96±0.962.94±0.980.109
血清肌酐(umol/L)79.38±37.9373.87±20.6987.21±52.61<0.001
血红蛋白(g/dl)14.03±1.5414.12±1.5113.91±1.57<0.001
UAR53.32±7.327.22±1.848.27±2.30<0.001
), ArticleFig(id=1323941399028179517, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, language=CN, label=表1, caption=

研究人群的基本特征 [(),n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总数 (n=23 552)非高血压组 (n=13 810)高血压组(n=9 742)P值
年龄(岁)49.29±17.6142.06±15.6759.55±14.91<0.001
性别0.010
男性11 655 (49.49)6 737 (48.78)4 918 (50.48)
女性11 897 (50.51)7 073 (51.22)4 824 (49.52)
种族<0.001
非西班牙裔白种人10 034 (42.6)5 760 (41.71)4 274 (43.87)
非西班牙裔黑种人4 709 (19.99)2 300 (16.65)2 409 (24.73)
墨西哥美国人2 565 (10.89)1 589 (11.51)976 (10.02)
其他种族6 244 (26.51)4 161 (30.13)2 083 (21.38)
受教育程度<0.001
高中以下5 963 (25.32)3 117 (22.57)2 846 (29.21)
高中或同等教育5 290 (22.46)2 939 (21.28)2 351 (24.13)
高中以上12 299 (52.22)7 754 (56.15)4 545 (46.65)
婚姻状况<0.001
已婚或与伴侣生活在一起14 115 (59.93)8 414 (60.93)5 701 (58.52)
独居9 437 (40.07)5 396 (39.07)4 041 (41.48)
PIR<0.001
≤1.307 567 (32.14)4 342 (31.44)3 225 (33.10)
1.31~3.508 959 (38.04)5 173 (37.46)3 786 (38.86)
>3.507 026 (29.83)4 295 (31.10)2 731 (28.03)
饮酒情况<0.001
不饮酒6 369 (27.04)3 430 (24.84)2 939 (30.17)
少量或中度饮酒8 269 (35.11)4 631 (33.53)3 638 (37.34)
重度饮酒8 914 (37.85)5 749 (41.63)3 165 (32.49)
吸烟状况<0.001
从不吸烟13 018 (55.27)8 121 (58.81)4 897 (50.27)
既往吸烟4 878 (20.71)3 019 (21.86)1 859 (19.08)
现在吸烟5 656 (24.01)2 670 (19.33)2 986 (30.65)
BMI(kg/m2)28.98±6.7227.85±6.2730.59±7.01<0.001
钠摄入量(mg)3 455.52± 1 821.963 592.85±1 874.623 293.68±1 742.47<0.001
久坐时间(min)355.44± 203.06348.98±203.29364.59±202.38<0.001
糖尿病<0.001
是5 739 (24.37)192 (1.39)741 (7.61)
否17 813 (75.63)13 618 (98.61)9 001 (92.39)
总胆固醇(mmol/L)4.99± 1.074.97±1.045.01±1.120.007
甘油三酯(mmol/L)1.45±1.151.38±1.071.53±1.25<0.001
HDL-C(mmol/L)1.37±0.421.38±0.411.35±0.43<0.001
LDL-C(mmol/L)2.95±0.972.96±0.962.94±0.980.109
血清肌酐(umol/L)79.38±37.9373.87±20.6987.21±52.61<0.001
血红蛋白(g/dl)14.03±1.5414.12±1.5113.91±1.57<0.001
UAR53.32±7.327.22±1.848.27±2.30<0.001
), ArticleFig(id=1323941399095288382, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, language=EN, label=Table 2, caption=

Multivariable logistic regression analysis of UAR for hypertension

, figureFileSmall=null, figureFileBig=null, tableContent=
变量模型1模型2模型3
OR (95% CI)P值OR (95% CI)P值OR (95% CI)P值
UAR比值1.29(1.27~1.31)<0.0011.23 (1.21~1.25)<0.0011.11 (1.09~1.13)<0.001
UAR(分类)
低值组 (0.54~6.63)1. 00( Reference)1. 00( Reference)1. 00( Reference)
中值组(6.64~8.33)1.55 (1.45~1.66)<0.0011.41 (1.30~1.52)<0.0011.14 (1.05~1.24)0.001
高值组(8.34~27.67)2.95 (2.76~3.15)<0.0012.34 (2.16~2.54)<0.0011.46 (1.33~1.60)<0.001
), ArticleFig(id=1323941399174980159, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, language=CN, label=表2, caption=

UAR与高血压之间的多因素logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量模型1模型2模型3
OR (95% CI)P值OR (95% CI)P值OR (95% CI)P值
UAR比值1.29(1.27~1.31)<0.0011.23 (1.21~1.25)<0.0011.11 (1.09~1.13)<0.001
UAR(分类)
低值组 (0.54~6.63)1. 00( Reference)1. 00( Reference)1. 00( Reference)
中值组(6.64~8.33)1.55 (1.45~1.66)<0.0011.41 (1.30~1.52)<0.0011.14 (1.05~1.24)0.001
高值组(8.34~27.67)2.95 (2.76~3.15)<0.0012.34 (2.16~2.54)<0.0011.46 (1.33~1.60)<0.001
), ArticleFig(id=1323941399246283328, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, language=EN, label=Table 3, caption=

Analysis of the threshold effect between UAR and hypertension

, figureFileSmall=null, figureFileBig=null, tableContent=
结果OR (95% CI)P值
模型 1:通过标准线性模型拟合1.11 (1.09~1.13)<0.001
模型2:通过分段线性回归拟合模型拐点4.66
<4.660.75 (0.61~0.92)0.004
>4.661.12 (1.10~1.14)<0.001
对数似然比检验的P值<0.001
), ArticleFig(id=1323941399325975105, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, language=CN, label=表3, caption=

UAR与高血压之间的阈值效应分析

, figureFileSmall=null, figureFileBig=null, tableContent=
结果OR (95% CI)P值
模型 1:通过标准线性模型拟合1.11 (1.09~1.13)<0.001
模型2:通过分段线性回归拟合模型拐点4.66
<4.660.75 (0.61~0.92)0.004
>4.661.12 (1.10~1.14)<0.001
对数似然比检验的P值<0.001
), ArticleFig(id=1323941399393083970, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, language=EN, label=Table 4, caption=

Subgroup analysis of the association between UAR and hypertension

, figureFileSmall=null, figureFileBig=null, tableContent=
变量OR (95% CI)P值交互作用P值
性别<0.001
男性1.07 (1.05~1.10)<0.001
女性1.15 (1.12~1.18)<0.001
年龄(岁)0.001
≤451.09 (1.05~1.14)<0.001
46~651.16 (1.12~1.19)<0.001
>651.08 (1.05~1.11)<0.001
饮酒情况0.034
不饮酒1.08 (1.04~1.11)<0.001
少量或中度饮酒1.11 (1.08~1.14)<0.001
重度饮酒1.14 (1.10~1.17)<0.001
吸烟状况0.453
从不吸烟1.12 (1.09~1.15)<0.001
以前吸烟1.11 (1.07~1.15)<0.001
现在吸烟1.09 (1.06~1.13)<0.001
BMI(kg/m2)0.041
≤251.16 (1.11~1.20)<0.001
26~301.09 (1.06~1.13)<0.001
>301.14 (1.11~1.18)<0.001
糖尿病0.012
是1.07 (1.04~1.11)<0.001
否1.13 (1.10~1.15)<0.001
), ArticleFig(id=1323941399464387139, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941391776227845, language=CN, label=表4, caption=

UAR与高血压患病关联的亚组分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量OR (95% CI)P值交互作用P值
性别<0.001
男性1.07 (1.05~1.10)<0.001
女性1.15 (1.12~1.18)<0.001
年龄(岁)0.001
≤451.09 (1.05~1.14)<0.001
46~651.16 (1.12~1.19)<0.001
>651.08 (1.05~1.11)<0.001
饮酒情况0.034
不饮酒1.08 (1.04~1.11)<0.001
少量或中度饮酒1.11 (1.08~1.14)<0.001
重度饮酒1.14 (1.10~1.17)<0.001
吸烟状况0.453
从不吸烟1.12 (1.09~1.15)<0.001
以前吸烟1.11 (1.07~1.15)<0.001
现在吸烟1.09 (1.06~1.13)<0.001
BMI(kg/m2)0.041
≤251.16 (1.11~1.20)<0.001
26~301.09 (1.06~1.13)<0.001
>301.14 (1.11~1.18)<0.001
糖尿病0.012
是1.07 (1.04~1.11)<0.001
否1.13 (1.10~1.15)<0.001
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血清尿酸与白蛋白比值与高血压患病的关联性分析——基于NHANES的横断面研究
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杨贵元 , 李怡然 , 陈瑜琳 , 张泓 , 林凯旋
现代预防医学 | 临床与预防 2025,52(23): 4403-4408
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现代预防医学 |临床与预防 2025 , 52 (23) : 4403 -4408
血清尿酸与白蛋白比值与高血压患病的关联性分析——基于NHANES的横断面研究
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杨贵元, 李怡然, 陈瑜琳, 张泓, 林凯旋
作者信息
  • 广州中医药大学附属中山中医院内一科,广东 中山 528400
通讯作者:
林凯旋,E-mail:
作者简介:

杨贵元(1999—),男,硕士在读;研究方向:心病研究

Association between the uric acid to albumin ratio and hypertension: a cross-sectional study based on NHANES
Gui-yuan YANG, Yi-ran LI, Yu-lin CHEN, Hong ZHANG, Kai-xuan LIN
Affiliations
  • Department of Internal Medicine I, Zhongshan Hospital of Chinese Medicine Affiliated to Guangzhou University of Chinese Medicine, Zhongshan, Guangdong 528400, China
出版时间: 2025-12-10 doi: 10.20043/j.cnki.MPM.202507208
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目的

探讨血清尿酸与白蛋白比值(uric acid to albumin ratio, UAR)与高血压患病的关联。

方法

基于2007—2016年美国国家营养健康调查(National Health and Nutrition Examination Survey,NHANES) 数据库中23 552名美国成年人数据,采用多因素logistic回归模型与平滑曲线拟合探究UAR与高血压患病的线性与非线性关系,通过阈值效应分析识别拐点,并进行亚组分析观察不同人群中两者的相关性。最后,使用受试者工作特征(receiver operating characteristic,ROC)曲线评估UAR对高血压患病的诊断性能。

结果

多因素logistic回归分析显示,在校正所有混杂因素后,UAR的增加与高血压患病率升高存在显著关联(OR=1.11,95% CI:1.09~1.13),与低值组相比,中值组(OR=1.14,95% CI:1.05~1.24)和高值组(OR= 1.46,95% CI:1.33~1.60)患高血压的几率增加(P<0.05)。平滑曲线揭示两者之间呈J型的非线性关联。阈值效应分析显示,在阈值4.66以下,UAR与高血压患病之间呈负相关(OR=0.75,95% CI:0.61~0.92),而阈值高于4.66,两者呈正相关(OR=1.12,95% CI:1.10~1.14)。亚组分析获得了一致的结果,且在性别、年龄、饮酒情况、BMI及糖尿病亚组上存在交互作用(P<0.05)。ROC曲线表明,UAR在预测高血压患病方面具有中等能力,其曲线下面积为0.639。

结论

UAR与高血压患病存在密切关联,可能是评估早期高血压危险的重要生物标志物。

高血压  /  尿酸与白蛋白比值  /  NHANES数据库
Objective

To investigate the association between the serum uric acid to albumin ratio (UAR) and hypertension.

Methods

This study included data from 23 552 U.S. adults from the NHANES database(2007—2016). Multivariable logistic regression models and smooth curve fitting were used to examine the linear and nonlinear relationships between UAR and hypertension. Threshold effect analysis was applied to identify inflection points, and subgroup analyses were conducted to assess the association across different populations. Finally, the diagnostic performance of UAR for hypertension was evaluated using receiver operating characteristic(ROC) curves.

Results

After adjusting for all confounding factors, multivariable logistic regression analysis showed that higher UAR was significantly associated with an increased prevalence of hypertension (OR=1.11, 95% CI: 1.09-1.13). Compared with the low UAR group, the middle (OR=1.14, 95% CI: 1.05-1.24) and high (OR=1.46, 95% CI: 1.33-1.60) UAR groups had significantly increased risks of hypertension(P<0.05). Smooth curve fitting revealed a J-shaped nonlinear relationship between UAR and hypertension. Threshold effect analysis indicated that below the threshold of 4.66, UAR was negatively correlated with hypertension (OR=0.75, 95% CI: 0.61-0.92), whereas above 4.66, a positive correlation was observed (OR=1.12, 95% CI: 1.10-1.14). Consistent results were obtained in subgroup analyses, and interaction effects were observed across subgroups stratified by sex, age, alcohol consumption, BMI, and diabetes (P<0.05). The ROC curve demonstrated that UAR had moderate predictive ability for hypertension, with an area under the curve (AUC) of 0.639.

Conclusion

UAR is closely associated with hypertension and may serve as a promising biomarker for assessing early hypertension risk.

Hypertension  /  Uric acid to albumin ratio  /  NHANES database
杨贵元, 李怡然, 陈瑜琳, 张泓, 林凯旋. 血清尿酸与白蛋白比值与高血压患病的关联性分析——基于NHANES的横断面研究. 现代预防医学, 2025 , 52 (23) : 4403 -4408 . DOI: 10.20043/j.cnki.MPM.202507208
Gui-yuan YANG, Yi-ran LI, Yu-lin CHEN, Hong ZHANG, Kai-xuan LIN. Association between the uric acid to albumin ratio and hypertension: a cross-sectional study based on NHANES[J]. Modern Preventive Medicine, 2025 , 52 (23) : 4403 -4408 . DOI: 10.20043/j.cnki.MPM.202507208
高血压是一种以体循环动脉血压升高为主要特征的常见心血管疾病,影响全球超过10亿人,造成巨大的公共卫生负担[1]。尽管在治疗方面已取得了显著成效[2],但其患病率仍居高不下,其中美国成人高血压患病率已达30%以上[3],中国18岁以上人群患病率高达27.9%[4]。该疾病的发病机制复杂,涉及炎症、氧化应激以及肾素-血管紧张素-醛固酮系统激活等多种病理生理机制[5-7],而生物标志物可通过反映这些病理过程,为疾病风险评估提供客观依据。因此,探索新的生物标志物,对优化高血压的风险评估、促进早期识别与探索其病理机制具有至关重要的意义。
尿酸是人体内嘌呤核苷酸代谢的最终产物[8]。研究表明,尿酸升高是高血压的独立危险因素,其可能通过诱发炎症反应、加剧机体氧化应激,参与高血压的发生与发展[9-10]。白蛋白作为肝脏主要合成的蛋白质,具有抗炎、维持血管内皮完整性等作用[11],研究显示[12-13],低白蛋白水平与血压升高及高血压患病率增加相关。然而,单一尿酸或白蛋白指标在评估高血压风险时存在一定局限性,如血清尿酸水平易受肾功能[14]、饮食[15]及药物[16]等多种因素干扰;血清白蛋白易受营养状态[17]与肝合成功能[18]的影响,二者均难以全面反映与高血压相关的复杂病理生理机制。血清尿酸与白蛋白比值(uric acid to albumin ratio,UAR)作为一种新兴的综合指标[19],不仅能够体现炎症反应与氧化应激的双重信息,还有助于减少单一指标的潜在混淆,提高风险评估的特异性[20],被证实对多种心血管疾病具有独立预测价值[21-22]。然而,目前关于UAR与高血压患病的关联性研究仍较有限。
因此,本研究利用美国国家健康与营养调查(National Health and Nutrition Examination Survey,NHANES) 数据库中2007—2016年的大样本数据,探讨UAR与高血压患病的关联。研究结果将为高血压的风险评估与早期识别提供新的生物标志物依据。
本研究数据来源于美国国家卫生统计中心(National Center for Health Statistics,NCHS)开展的一项具有全国代表性的横断面研究。该研究方案获得了NCHS伦理审查委员会批准,所有参与者在报名时均签署书面知情同意书。本研究纳入2007—2016年NHANES数据库中50 558名参与者,排除年龄<20岁的21 387名参与者,排除4 216名高血压诊断数据缺失参与者以及排除血清白蛋白和血清尿酸数据缺失的1 433名参与者,最终纳入23 552名符合条件的参与者。参与者筛选的流程图见图1。
高血压诊断数据严格遵循 NHANES标准化流程获取。高血压的定义为[23]:(1)收缩压≥140 mm Hg;(2)舒张压≥90 mm Hg;(3)自我报告使用抗高血压药物;(4)医生先前诊断为高血压,满足其中之一可诊断为高血压。
血清尿酸和血清白蛋白浓度使用Beckman Coulter MAXM全自动生化分析仪进行测定。UAR的计算公式等于血清尿酸除以血清白蛋白。此外,根据这一指数,本研究将参与者根据三分位数被分为三组,包括低值组(UAR≤6.63)、中值组(6.63<UAR≤8.33)和高值组(8.33<UAR≤27.67)。
为了尽量减少偏倚,本研究纳入了18个与UAR和高血压相关的混杂因素。人口统计学变量包括年龄、性别、种族、婚姻状况、受教育程度、家庭贫困收入比(the family poverty income ratio,PIR)及身体质量指数(body mass index,BMI,kg/m2)。生活方式因素包括吸烟状况、饮酒情况、久坐时间及钠摄入量。健康风险因素包括糖尿病。实验室指标包括总胆固醇、甘油三酯、高密度脂蛋白胆固醇(high-density lipoprotein cholesterol,HDL-C)、低密度脂蛋白胆固醇(low-density lipoprotein cholesterol,LDL-C)、血清肌酐、血红蛋白。所有这些协变量都可以从NHANES网站上的各种数据模块中获得。
所有统计分析均按照美国疾病预防控制中心制定的指南进行。协变量中的缺失值使用多重插补来解决。在基线特征表中,连续变量属于正态分布数据以()表示,属于非正态分布数据以[M(P25,P75)]表示,采用独立样本t检验或秩和检验进行组间比较。分类数据则用数字和百分比表示,采用χ2检验进行组间比较。
采用多因素logistic回归分析UAR与高血压患病的关联,用95%置信区间(confidence interval,CI)和比值比(odds ratio,OR)表示。为了校正混杂因素,建立了3个logistic回归模型,模型1是未调整的模型;模型2对人口统计学变量(年龄、性别、种族、婚姻状况、受教育程度、PIR)进行了调整;模型3是在模型2的基础上,进一步调整的其他变量,包括糖尿病、吸烟状况、饮酒情况、BMI、钠摄入量、久坐时间、总胆固醇、甘油三酯、HDL-C、LDL-C、血清肌酐、血红蛋白。同时,UAR值也被划分为三等分以探索不同水平之间的关联。
采用平滑曲线拟合对UAR与高血压患病的非线性关系进行分析。当平滑曲线拟合提示非线性关系时,使用分段线性回归模型确定阈值,通过对数似然比检验确定显著性。利用受试者工作特征(receiver operating characteristic,ROC)曲线和曲线下面积(area underthe curve,AUC)评估UAR对高血压的诊断能力。亚组分析以评估UAR与高血压患病的关联是否在不同的人口统计学和临床亚组中有所不同,包括性别、年龄、BMI、吸烟状态、饮酒情况和糖尿病史。同时,加入交互作用检验亚组之间关联的异质性。所有统计分析均使用R软件和EmpowerStats统计软件进行。所有检验均采用双侧检验,检验水准α=0.05。
纳入分析的23 552名美国成年人中,被诊断为高血压9 742名,非高血压13 810人。与非高血压人群相比,高血压人群平均年龄、BMI、久坐时间、总胆固醇、甘油三酯、血清肌酐、UAR水平较高;钠摄入量、HDL-C水平较低,差异有统计学意义(P<0.05)。同时,两组在性别、种族、教育程度、婚姻状况、PIR、饮酒情况、吸烟状况以及糖尿病患病率方面有显著差异(P<0.05)。见表1。
以是否发生高血压为因变量(赋值:是=1,否=0),以UAR(实测值)和UAR三分位数(包括UAR低值组、中值组及高值组)为自变量进行多因素logistic回归分析。结果表明,在模型1中,未调整混杂因素,发现UAR与高血压患病呈正相关,UAR每增加1个单位,高血压患病几率会上升29%(OR=1.29,95% CI:1.27~1.31)。同时,在对模型2与模型3中的协变量进行调整后,这种正相关依然显著。按UAR三分位数进行分析的结果表明,在模型3中,高值组参与者高血压患病几率比中值组增加14%(OR=1.14,95% CI:1.05~1.24)、比低值组增加46%(OR=1.46,95% CI:1.33~1.60)。见表2。
在完全校正混杂因素后,平滑曲线拟合显示UAR与高血压患病之间存在J型的非线性关系,见图2。经过阈值效应分析发现,在UAR=4.66时存在关键拐点。低于该阈值,UAR每升高1个单位,患高血压几率就会减少25%(OR=0.75,95% CI:0.61~0.92)。相反,高于该阈值,UAR每升高1个单位,患高血压几率就会增加12%(OR= 0.75,95% CI:1.10~1.14),对数似然比检验P<0.001。见表3。
通过ROC曲线分析显示,UAR的AUC为0.639,表明其在区分是否患有高血压患病方面有中等能力,见图3。相比之下,血清尿酸和血清白蛋白的AUC略低,分别为0.617和0.585,表明UAR比单独使用血清尿酸或血清白蛋白能更好预测高血压的发生。
亚组分析结果显示,不同年龄、性别、吸烟状况、饮酒情况、BMI和糖尿病分层的亚组中,UAR与高血压之间均显著正相关(P<0.05)。并且在年龄、性别、饮酒情况、BMI和糖尿病分层的亚组中观察到显著的交互作用(P<0.05)。见表4。
本研究利用NHANES数据分析了UAR与高血压患病的关联。在调整了所有混杂因素后,发现UAR升高与高血压的患病率增加显著相关。同时,通过平滑曲线拟合揭示了两者之间存在非线性J型关联,且确认拐点为4.66。ROC曲线证明UAR相对于血清尿酸与血清白蛋白更能预测高血压的风险。亚组分析表明,不同性别、年龄、饮酒情况、BMI及糖尿病亚组中UAR与高血压之间的关联均存在统计学意义。这些研究结果提示UAR可能是评估早期高血压危险的重要生物标志物。
UAR作为一种新型生物标志物,整合了血清尿酸和血清白蛋白成分,与单独使用这些标志物相比,能更全面评估的高血压风险。与主要反映炎症反应的单独血清尿酸或指示营养状况的白蛋白不同,UAR反应了炎症与氧化应激相互作用[24]。这种双组分性质增强了其识别高血压风险的敏感性[25]。此外,这个指标也被证明在高血压及心血管疾病的预测方面优于其他炎症指标,如在非ST段抬高型心肌梗死患者中,UAR比C反应蛋白与白蛋白比值更能预测冠心病的严重程度[26]。UAR被证明与非杓型高血压的存在强关联性,且其诊断效能显著优于其他炎症标志物[25,27]。本研究结果显示,UAR是高血压风险的一个强有力的独立预测因子,其预测高血压风险的能力优于单一的血清尿酸和白蛋白指标。
本研究揭示了UAR和高血压之间的J 型非线性关系,且阈值效应分析表明,当UAR<4.66时,其与高血压之间呈负相关;而当UAR>4.66时,则转为正相关。这种双相关联提示,UAR与高血压之间的关系可能存在不同的病理生理机制路径,可能由尿酸和白蛋白在炎症反应和氧化应激中的双重作用介导。当UAR<4.66时,高血压风险降低可能源于高白蛋白水平和较低尿酸水平的协同作用。一方面,白蛋白相对偏高可增强抗炎缓冲能力,通过结合促炎介质等减少血管炎症损伤[28],而尿酸适度降低虽发挥炎症刺激作用[9],但并未打破抗炎平衡。另一方面,白蛋白具有抗氧化和捕获自由基的特性[29],与尿酸的抗氧化作用形成协同作用,共同减轻氧化应激对血管内皮的损伤。这种抗炎与抗氧化的协同效应,能维持血管舒缩平衡、抑制血管重构,从而降低高血压风险[29]。当UAR >4.66时,过高的尿酸可沉积于血管壁,诱导炎症细胞浸润和氧化应激反应,导致血管平滑肌细胞增殖、血管弹性降低,外周阻力增加,血压升高[30]。同时,高UAR水平可能提示白蛋白水平相对降低,反映机体营养状况或肝功能受损,白蛋白的血管保护作用减弱,进一步加剧血管损伤[31]。这些因素相互作用,共同促进高血压的发生发展。此外,该阈值结果可作为临床实践中风险预警的潜在工具。当UAR高于该阈值,可将其视为高血压发生风险升高的重要信号,有助于早期识别高血压的高危人群,进而加强生活方式干预、定期监测血压或启动早期预防性管理。
亚组分析显示,在年龄、性别、饮酒情况、BMI和糖尿病等亚组中有显著的交互作用,表明UAR与高血压的强度关联受这些因素的修饰,其背后可能涉及多层次的病理生理机制。具体而言,在女性人群中,UAR表现出更强的关联性,可能与绝经后雌激素水平下降有关。雌激素对心血管系统具有保护作用,可通过改善血管内皮功能、抑制肾素-血管紧张素系统活性等机制减轻尿酸介导的血管损伤[30,32]。在45~65岁人群中,UAR与高血压患病的关联更为显著,提示该年龄段人群因长期暴露于多种心血管危险因素,更易出现代谢紊乱状态,从而加剧尿酸代谢异常与白蛋白合成障碍[12,33]。酒精摄入,尤其是重度饮酒,可通过促进嘌呤代谢、增加尿酸生成并抑制肾脏尿酸排泄,进一步放大UAR的病理效应[34]。而在BMI较低和无糖尿病史的个体中,UAR仍显示出显著关联,可能原因是由于肥胖和胰岛素抵抗等强混杂因素的影响较弱,UAR所代表的炎症与氧化应激通路可能更为突出,从而更纯粹地反映其与高血压的病理联系[23,35]。
本研究的优势在于采用具有全国代表性的NHANES数据,其大样本数据增强了研究结果的稳健性。然而也存在着一定的局限,NHANES数据库是一个横断面调查,只能分析UAR与高血压之间的相关性,难以确定因果关系。因此,需要更多的前瞻性研究来验证这些因果关系;此外,尽管努力包含尽可能多的协变量,但仍无法完全消除其他潜在混杂因素的干扰。
综上,本研究证实UAR与高血压患病呈显著正相关,且两者间存在独特的 J 型非线性关联。鉴于UAR 检测具有获取简便、成本低廉的优势,其在高血压早期风险筛查及精准干预中具有重要应用潜力。
  • 国家中医药传承发展示范试点项目-院校共建(GZYZS2024XKG03)
  • 国家中医药传承发展示范试点项目-中山市中医院曾安教授柔性引才专家团队-2024年中医药部门中央补助资金(2202779)
  • 国家中医药管理局监测统计中心委托办事经费任务(JCTJ-2024-002-04)
  • 国家中医药管理局监测统计中心(2025JCTJE51)
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2025年第52卷第23期
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doi: 10.20043/j.cnki.MPM.202507208
  • 接收时间:2025-07-13
  • 首发时间:2026-11-02
  • 出版时间:2025-12-10
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  • 收稿日期:2025-07-13
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国家中医药传承发展示范试点项目-院校共建(GZYZS2024XKG03)
国家中医药传承发展示范试点项目-中山市中医院曾安教授柔性引才专家团队-2024年中医药部门中央补助资金(2202779)
国家中医药管理局监测统计中心委托办事经费任务(JCTJ-2024-002-04)
国家中医药管理局监测统计中心(2025JCTJE51)
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    广州中医药大学附属中山中医院内一科,广东 中山 528400

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2种不同金属材料的力学参数
科
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属数
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genus
种数
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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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