Article(id=1324078106063024548, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324078037817504735, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202509189, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1757692800000, receivedDateStr=2025-09-13, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793614420666, onlineDateStr=2026-11-02, pubDate=1770652800000, pubDateStr=2026-02-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793614420666, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793614420666, creator=13701087609, updateTime=1793614420666, updator=13701087609, issue=Issue{id=1324078037817504735, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='3', pageStart='385', pageEnd='576', issueExtLink='null', onlineDate='null', pubDate='1770652800000', pubDateStr='2026-02-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793614404396, creator='13701087609', updateTime=1793614474144, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324078330420527919, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324078037817504735, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324078330420527920, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324078037817504735, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=557, endPage=563, ext={EN=ArticleExt(id=1324078106474066341, articleId=1324078106063024548, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Correlation between monocyte/albumin ratio and all-cause mortality as well as cardiovascular disease mortality in diabetic patients: a cohort study based on the NHANES database, columnId=1228016569138213037, journalTitle=Modern Preventive Medicine, columnName=Clinical Medicine and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

Based on the National Health and Nutrition Examination Survey (NHANES) database, this study investigates the association between the monocyte-to-albumin ratio (MAR) and all-cause and cardiovascular disease (CVD)mortality among individuals with diabetes mellitus.

Methods

Using NHANES 1999—2018 data linked to mortality outcomes from the National Death Index (NDI), we conducted a study among adults with diabetes mellitus. Weighted Cox proportional-hazards regression, Kaplan-Meier survival curves, restricted cubic spline regression, subgroup analyses, and interaction tests were employed to examine the associations of the monocyte-to-albumin ratio (MAR) with all-cause and CVD mortality among individuals with diabetes mellitus.

Results

A total of 4 558 patients with diabetes mellitus were enrolled in this study. During follow-up, 1 250 all-cause deaths and 429 CVD-specific deaths were documented. MAR was categorized into tertiles. After adjusting for multiple confounding factors, compared with the lowest tertile group (Q1: MAR<0.011), the highest tertile group(Q3: MAR≥0.015) exhibited significantly increased risks of both all-cause mortality (HR=1.44, 95% CI: 1.21-1.71) and CVD mortality (HR=1.44; 95% CI: 1.01-2.05). Kaplan-Meier survival analysis demonstrated that the survival probability in the highest MAR tertile was markedly lower than that in the lowest tertile, with a statistically significant difference (P<0.001).

Conclusion

MAR is positively associated with both all-cause and CVD mortality in patients with diabetes and represents an independent risk factor for these outcomes.

, authors=Yi WANG1, Wen-Jun ZHANG1, Qiong HU1, Wei LI2, Qing WANG2, Xing-Hai ZHAO3, Yi-Mei LI3, Li-Yan LIU3, authorsList=Yi WANG, Wen-Jun ZHANG, Qiong HU, Wei LI, Qing WANG, Xing-Hai ZHAO, Yi-Mei LI, Li-Yan LIU, authorCompany=null, correspAuthors=Li-Yan LIU, 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=1324078110391546295, articleId=1324078106063024548, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=单核细胞/白蛋白比值与糖尿病患者全因死亡率及心血管疾病死亡率的相关性——基于NHANES数据库的队列研究, columnId=1228016570119680182, journalTitle=现代预防医学, columnName=临床与预防, runingTitle=null, highlight=null, articleAbstract=
目的

基于美国国家健康与营养调查 (National Health and Nutrition Examination Survey,NHANES)数据库,探讨单核细胞/白蛋白比值(monocyte-to-albumin ratio,MAR)与糖尿病患者全因死亡率及心血管疾病(cardiovascular disease,CVD)死亡率之间的关系。

方法

本研究基于NHANES数据库1999—2018年的糖尿病患者资料及美国国家死亡索引(National Death Index, NDI)数据库的死亡结果进行分析,采用加权Cox比例风险回归模型、Kaplan-Meier生存曲线、限制性立方样条回归分析、亚组分析和交互作用等研究方法探讨了MAR与糖尿病患者全因及CVD死亡率之间的关系。

结果

本研究共纳入4 558例糖尿病患者,随访过程中,全因死亡患者共1 250例,CVD死亡共429例。将MAR按三分位数进行分组,在调整多项混杂因素后,与最低三分位组(Q1:MAR<0.011)相比,MAR最高三分位组(Q3:MAR≥0.015)全因死亡率(HR=1.44,95% CI:1.21~1.71)和CVD死亡率(HR=1.44,95% CI:1.01~2.05)均显著升高。Kaplan-Meier生存分析显示,MAR最高三分位组的生存率明显低于最低三分位组,差异有统计学意义(P<0.001)。

结论

MAR与糖尿病患者全因死亡率及CVD死亡率之间存在正相关关系,是糖尿病患者全因死亡及CVD死亡的独立危险因素。

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王怡(1982—),女,硕士,副主任医师,研究方向:糖尿病

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刘丽燕,E-mail:
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Brain and Behavior, 2024, 14(10): e70059., articleTitle=Monocyte-to-Albumin ratio is associated with hematoma expansion in spontaneous intracerebral hemorrhage, refAbstract=null), Reference(id=1324078120151691800, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, doi=null, pmid=null, pmcid=null, year=2024, volume=62, issue=5, pageStart=308, pageEnd=313, url=null, language=null, rfNumber=[23], rfOrder=25, authorNames=Sahin M, Karakose CC, Melikoglu MA, journalName=Reumatologia, refType=null, unstructuredReference=Sahin M, Karakose CC, Melikoglu MA. Potential biomarkers in Behcet's disease: monocyte, neutrophil, platelet, and C-reactive protein to albumin ratios[J]. Reumatologia, 2024, 62(5): 308-313., articleTitle=Potential biomarkers in Behcet's disease: monocyte, neutrophil, platelet, and C-reactive protein to albumin ratios, refAbstract=null), Reference(id=1324078120231383577, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, doi=null, pmid=null, pmcid=null, year=2025, volume=30, issue=1, pageStart=806, pageEnd=null, url=null, language=null, rfNumber=[24], rfOrder=26, authorNames=Lv YH, Li QX, Wang XP, journalName=European Journal of Medical Research, refType=null, unstructuredReference=Lv YH, Li QX, Wang XP, et al. Monocyte-to-albumin ratio outperforms triglyceride-glucose index in associating with stroke prevalence among diabetics: an NHANES cross-sectional study[J].European Journal of Medical Research, 2025, 30(1): 806., articleTitle=Monocyte-to-albumin ratio outperforms triglyceride-glucose index in associating with stroke prevalence among diabetics: an NHANES cross-sectional study, refAbstract=null)], funds=[Fund(id=1324078117840630269, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, awardId=SZFCXK202115, language=CN, fundingSource=苏州市医学重点扶持学科(SZFCXK202115), fundOrder=null, country=null), Fund(id=1324078117924516350, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, awardId=昆卫[2024]9号, language=CN, fundingSource=昆山市高层次卫生人才项目(昆卫[2024]9号), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1324078110597067192, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, xref=1., ext=[AuthorCompanyExt(id=1324078110609650105, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, companyId=1324078110597067192, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Endocrinology Department, Kunshan Sixth People’s Hospital, Kunshan, Jiangsu 215300, China), AuthorCompanyExt(id=1324078110618038714, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, companyId=1324078110597067192, 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内分泌科,江苏 昆山 215300)]), AuthorCompany(id=1324078110693536187, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, xref=2., ext=[AuthorCompanyExt(id=1324078110701924796, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, companyId=1324078110693536187, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.昆山市中医医院中心实验室)]), AuthorCompany(id=1324078110802588093, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, xref=3., ext=[AuthorCompanyExt(id=1324078110810976702, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, companyId=1324078110802588093, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.昆山市中医医院老年内分泌科,江苏 昆山 215300)])], figs=[ArticleFig(id=1324078115315659247, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, language=EN, label=Figure 1, caption=Screening process for subjects, figureFileSmall=igf7Uobt5c60Tju7sjf8Cg==, figureFileBig=JLn43vjB28MwcwI3PuOw7g==, tableContent=null), ArticleFig(id=1324078115424711152, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, language=CN, label=图1, caption=受试者筛选流程图, figureFileSmall=igf7Uobt5c60Tju7sjf8Cg==, figureFileBig=JLn43vjB28MwcwI3PuOw7g==, tableContent=null), ArticleFig(id=1324078115638620657, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, language=EN, label=Figure 2, caption=Analysis of survival rates in patients with diabetes stratified by CDAI Levels, figureFileSmall=ebVLU6VwXCTcJmNcp6P75A==, figureFileBig=NeqVf1osl52m5c7Dmcxluw==, tableContent=null), ArticleFig(id=1324078115705729522, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, language=CN, label=图2, caption=不同MAR水平糖尿病患者生存率分析

注:A为全因死亡;B为CVD死亡。

, figureFileSmall=ebVLU6VwXCTcJmNcp6P75A==, figureFileBig=NeqVf1osl52m5c7Dmcxluw==, tableContent=null), ArticleFig(id=1324078115777032691, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, language=EN, label=Figure 3, caption=Subgroup and interaction analyses of the association between MAR and all-cause mortality in patients with diabetes mellitus, figureFileSmall=B2kYDwpeDjqZC9gHbb43tA==, figureFileBig=MfBIW7xrbnV9Mde2hlgzPQ==, tableContent=null), ArticleFig(id=1324078115860918772, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, language=CN, label=图3, caption=MAR与糖尿病全因死亡关系的亚组分析和交互作用分析, figureFileSmall=B2kYDwpeDjqZC9gHbb43tA==, figureFileBig=MfBIW7xrbnV9Mde2hlgzPQ==, tableContent=null), ArticleFig(id=1324078115936416245, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, language=EN, label=Figure 4, caption=Subgroup and interaction analyses of the association between the MAR and CVD mortality in patients with diabetes mellitus, figureFileSmall=IOv3prrMQdpq5mpVp7cAJQ==, figureFileBig=Zduv6xAoEEqM7xAtTmCM8Q==, tableContent=null), ArticleFig(id=1324078116045468150, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, language=CN, label=图4, caption=MAR与糖尿病CVD死亡关系的亚组分析和交互作用分析, figureFileSmall=IOv3prrMQdpq5mpVp7cAJQ==, figureFileBig=Zduv6xAoEEqM7xAtTmCM8Q==, tableContent=null), ArticleFig(id=1324078116137742839, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, language=EN, label=Figure 5, caption=Dose-response relationship between MAR and all-cause and CVD mortality in patients with diabetes, figureFileSmall=mADQaKN/pMY5aD+yq7mFQQ==, figureFileBig=pCRlPYtJEII08+F3yWDQpQ==, tableContent=null), ArticleFig(id=1324078116225823224, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, language=CN, label=图5, caption=MAR与糖尿病患者全因死亡及CVD死亡之间剂量-反应关系

注:A为全因死亡;B为CVD死亡。

, figureFileSmall=mADQaKN/pMY5aD+yq7mFQQ==, figureFileBig=pCRlPYtJEII08+F3yWDQpQ==, tableContent=null), ArticleFig(id=1324078116313903609, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, language=EN, label=Table 1, caption=

General demographic information of the study population [M(P25,P75),(),n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总数(n=4 558)Q1 (n=1 593)Q2 (n=1 528)Q3 (n=1 437)χ2/F/H值P值
ALT(U/L)23.00 (17.00, 31.00)23.00 (17.00, 30.00)23.00 (18.00, 32.00)23.00 (18.00, 32.00)2.950.236
AST(U/L)24.00 (20.00, 29.00)23.00 (20.00, 29.00)24.00 (20.00, 29.00)24.00 (20.00, 30.00)3.120.217
血糖(mmol/L)7.05 (5.88, 9.49)7.11 (6.00, 10.05)6.99 (5.83, 9.27)6.94 (5.77, 9.16)6.390.046
血清肌酐(μmol/L)79.56 (65.42, 95.47)76.02 (63.65, 90.17)79.56 (65.42, 94.59)79.56 (68.07, 99.89)31.29<0.001
HbA1c(%)6.60 (6.00, 7.60)6.60 (5.90, 7.80)6.60 (6.00, 7.40)6.70 (6.10, 7.70)10.680.007
血红蛋白(g/L)14.05±0.0414.01±0.0514.11±0.0714.03±0.060.04*0.841
总胆固醇(mmol/L)4.85±0.024.93±0.044.89±0.044.75±0.057.13*0.009
年龄(岁)60.00 (50.00, 70.00)59.00 (49.00, 69.00)61.00 (50.00, 71.00)61.00 (51.00, 71.00)18.04#<0.001
MAR0.01 (0.01, 0.02)0.01 (0.01, 0.01)0.01 (0.01, 0.01)0.02 (0.02, 0.02)18 735.14<0.001
BMI(kg/m2)63.51<0.001
<25653 (12.90)274 (17.03)204 (12.09)175 (9.66)
25~<301 353 (27.18)504 (28.34)457 (29.59)392 (23.66)
≥302 552 (59.92)815 (54.63)867 (58.32)870 (66.68)
性别16.040.023
男2 344 (50.86)749 (46.64)805 (52.69)790 (53.17)
女2 214 (49.14)844 (53.36)723 (47.31)647 (46.83)
种族77.57<0.001
墨西哥裔美国人831 (9.56)318 (11.20)307 (10.41)206 (7.10)
其他西班牙裔443 (5.48)153 (5.34)160 (6.26)130 (4.85)
非西班牙裔白人1 762 (63.24)480 (56.00)593 (63.96)689 (69.61)
非西班牙裔黑人1 152 (14.52)484 (18.22)360 (13.70)308 (11.71)
其他种族370 (7.20)158 (9.24)108 (5.68)104 (6.73)
PIR7.830.254
≤11 079 (16.77)380 (17.05)339 (14.83)360 (18.41)
>1~<42 647 (55.58)920 (54.83)903 (57.57)824 (54.35)
≥4832 (27.65)293 (28.13)286 (27.59)253 (27.24)
吸烟状态35.30<0.001
从来不吸2 249 (49.09)858 (53.09)768 (49.60)623 (44.66)
以前吸烟1 536 (34.24)503 (31.82)528 (35.98)505 (34.88)
现在吸烟773 (16.67)232 (15.09)232 (14.41)309 (20.45)
教育水平11.610.166
高中以下1 686 (25.84)573 (24.23)592 (26.57)521 (26.70)
高中1 080 (25.79)388 (27.23)345 (23.07)347 (27.09)
高中以上1 792 (48.37)632 (48.54)591 (50.37)569 (46.21)
饮酒7.460.121
不饮酒1 692 (33.64)627 (36.35)564 (32.59)501 (32.04)
饮酒2 866 (66.36)966 (63.65)964 (67.41)936 (67.96)
运动10.280.053
中强度运动2 513 (52.43)835 (49.85)820 (51.82)858 (55.57)
高强度运动2 045 (47.57)758 (50.15)708 (48.18)579 (44.43)
高血压19.960.002
无1 322 (31.07)509 (34.54)450 (31.64)363 (27.10)
有3 227 (68.93)1 081 (65.46)1 074 (68.36)1 072 (72.90)
高脂血症11.500.063
无549 (10.88)212 (12.91)173 (9.08)164 (10.67)
有4 009 (89.12)1 381 (87.09)1 355 (90.92)1 273 (89.33)
全因死亡76.75<0.001
否3 308 (75.47)1 258 (81.33)1 132 (77.22)918 (67.99)
是1 250 (24.53)335 (18.67)396 (22.78)519 (32.01)
CVD死亡26.14<0.001
否4 129 (91.76)1 477 (93.77)1 387 (92.65)1 265 (88.90)
是429 (8.24)116 (6.23)141 (7.35)172 (11.10)
), ArticleFig(id=1324078116393595386, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, language=CN, label=表1, caption=

研究人群的一般资料[M(P25,P75),(),n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总数(n=4 558)Q1 (n=1 593)Q2 (n=1 528)Q3 (n=1 437)χ2/F/H值P值
ALT(U/L)23.00 (17.00, 31.00)23.00 (17.00, 30.00)23.00 (18.00, 32.00)23.00 (18.00, 32.00)2.950.236
AST(U/L)24.00 (20.00, 29.00)23.00 (20.00, 29.00)24.00 (20.00, 29.00)24.00 (20.00, 30.00)3.120.217
血糖(mmol/L)7.05 (5.88, 9.49)7.11 (6.00, 10.05)6.99 (5.83, 9.27)6.94 (5.77, 9.16)6.390.046
血清肌酐(μmol/L)79.56 (65.42, 95.47)76.02 (63.65, 90.17)79.56 (65.42, 94.59)79.56 (68.07, 99.89)31.29<0.001
HbA1c(%)6.60 (6.00, 7.60)6.60 (5.90, 7.80)6.60 (6.00, 7.40)6.70 (6.10, 7.70)10.680.007
血红蛋白(g/L)14.05±0.0414.01±0.0514.11±0.0714.03±0.060.04*0.841
总胆固醇(mmol/L)4.85±0.024.93±0.044.89±0.044.75±0.057.13*0.009
年龄(岁)60.00 (50.00, 70.00)59.00 (49.00, 69.00)61.00 (50.00, 71.00)61.00 (51.00, 71.00)18.04#<0.001
MAR0.01 (0.01, 0.02)0.01 (0.01, 0.01)0.01 (0.01, 0.01)0.02 (0.02, 0.02)18 735.14<0.001
BMI(kg/m2)63.51<0.001
<25653 (12.90)274 (17.03)204 (12.09)175 (9.66)
25~<301 353 (27.18)504 (28.34)457 (29.59)392 (23.66)
≥302 552 (59.92)815 (54.63)867 (58.32)870 (66.68)
性别16.040.023
男2 344 (50.86)749 (46.64)805 (52.69)790 (53.17)
女2 214 (49.14)844 (53.36)723 (47.31)647 (46.83)
种族77.57<0.001
墨西哥裔美国人831 (9.56)318 (11.20)307 (10.41)206 (7.10)
其他西班牙裔443 (5.48)153 (5.34)160 (6.26)130 (4.85)
非西班牙裔白人1 762 (63.24)480 (56.00)593 (63.96)689 (69.61)
非西班牙裔黑人1 152 (14.52)484 (18.22)360 (13.70)308 (11.71)
其他种族370 (7.20)158 (9.24)108 (5.68)104 (6.73)
PIR7.830.254
≤11 079 (16.77)380 (17.05)339 (14.83)360 (18.41)
>1~<42 647 (55.58)920 (54.83)903 (57.57)824 (54.35)
≥4832 (27.65)293 (28.13)286 (27.59)253 (27.24)
吸烟状态35.30<0.001
从来不吸2 249 (49.09)858 (53.09)768 (49.60)623 (44.66)
以前吸烟1 536 (34.24)503 (31.82)528 (35.98)505 (34.88)
现在吸烟773 (16.67)232 (15.09)232 (14.41)309 (20.45)
教育水平11.610.166
高中以下1 686 (25.84)573 (24.23)592 (26.57)521 (26.70)
高中1 080 (25.79)388 (27.23)345 (23.07)347 (27.09)
高中以上1 792 (48.37)632 (48.54)591 (50.37)569 (46.21)
饮酒7.460.121
不饮酒1 692 (33.64)627 (36.35)564 (32.59)501 (32.04)
饮酒2 866 (66.36)966 (63.65)964 (67.41)936 (67.96)
运动10.280.053
中强度运动2 513 (52.43)835 (49.85)820 (51.82)858 (55.57)
高强度运动2 045 (47.57)758 (50.15)708 (48.18)579 (44.43)
高血压19.960.002
无1 322 (31.07)509 (34.54)450 (31.64)363 (27.10)
有3 227 (68.93)1 081 (65.46)1 074 (68.36)1 072 (72.90)
高脂血症11.500.063
无549 (10.88)212 (12.91)173 (9.08)164 (10.67)
有4 009 (89.12)1 381 (87.09)1 355 (90.92)1 273 (89.33)
全因死亡76.75<0.001
否3 308 (75.47)1 258 (81.33)1 132 (77.22)918 (67.99)
是1 250 (24.53)335 (18.67)396 (22.78)519 (32.01)
CVD死亡26.14<0.001
否4 129 (91.76)1 477 (93.77)1 387 (92.65)1 265 (88.90)
是429 (8.24)116 (6.23)141 (7.35)172 (11.10)
), ArticleFig(id=1324078117660275195, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, language=EN, label=Table 2, caption=

Multivariable Cox proportional hazards regression models were employed to analyze the association between MAR and all-cause as well as CVD mortality in individuals with diabetes

, figureFileSmall=null, figureFileBig=null, tableContent=
变量Model 1Model 2Model 3
HR (95%CI)P值HR (95%CI)P值HR (95%CI)P值
全因死亡
MAR1.65(1.45~1.89)<0.0011.56(1.39~1.77)<0.0011.36(1.19~1.57)<0.001
MAR分组
Q11.00 (Reference)1.00 (Reference)1.00 (Reference)
Q21.26 (1.08~1.47)0.0041.09 (0.91~1.29)0.3571.05 (0.89~1.25)0.570
Q31.97 (1.69~2.29)<0.0011.67 (1.41~1.98)<0.0011.44 (1.21~1.71)<0.001
Per SD-increase1.42(1.31~1.53)<0.0011.31(1.20~1.44)<0.0011.21(1.11~1.33)<0.001
CVD死亡
MAR1.76 (1.44~2.14)<0.0011.65 (1.36~2.01)<0.0011.43(1.15~1.78)<0.001
MAR分组
Q11.00 (Reference)1.00 (Reference)1.00 (Reference)
Q21.22 (0.88~1.69)0.2441.03 (0.73~1.44)0.8740.98 (0.70~1.39)0.925
Q32.04 (1.49~2.78)<0.0011.67 (1.18~2.36)0.0031.44 (1.01~2.05)0.044
Per SD-increase1.45 (1.23~1.70)<0.0011.32(1.11~1.58)0.0021.22(1.02~1.47)0.030
), ArticleFig(id=1324078117735772668, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078106063024548, language=CN, label=表2, caption=

多因素Cox比例风险回归模型分析MAR与糖尿病全因死亡及CVD死亡的关联

, figureFileSmall=null, figureFileBig=null, tableContent=
变量Model 1Model 2Model 3
HR (95%CI)P值HR (95%CI)P值HR (95%CI)P值
全因死亡
MAR1.65(1.45~1.89)<0.0011.56(1.39~1.77)<0.0011.36(1.19~1.57)<0.001
MAR分组
Q11.00 (Reference)1.00 (Reference)1.00 (Reference)
Q21.26 (1.08~1.47)0.0041.09 (0.91~1.29)0.3571.05 (0.89~1.25)0.570
Q31.97 (1.69~2.29)<0.0011.67 (1.41~1.98)<0.0011.44 (1.21~1.71)<0.001
Per SD-increase1.42(1.31~1.53)<0.0011.31(1.20~1.44)<0.0011.21(1.11~1.33)<0.001
CVD死亡
MAR1.76 (1.44~2.14)<0.0011.65 (1.36~2.01)<0.0011.43(1.15~1.78)<0.001
MAR分组
Q11.00 (Reference)1.00 (Reference)1.00 (Reference)
Q21.22 (0.88~1.69)0.2441.03 (0.73~1.44)0.8740.98 (0.70~1.39)0.925
Q32.04 (1.49~2.78)<0.0011.67 (1.18~2.36)0.0031.44 (1.01~2.05)0.044
Per SD-increase1.45 (1.23~1.70)<0.0011.32(1.11~1.58)0.0021.22(1.02~1.47)0.030
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单核细胞/白蛋白比值与糖尿病患者全因死亡率及心血管疾病死亡率的相关性——基于NHANES数据库的队列研究
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王怡 1 , 张文俊 1 , 胡琼 1 , 李威 2 , 汪青 2 , 赵星海 3 , 李怡梅 3 , 刘丽燕 3
现代预防医学 | 临床与预防 2026,53(3): 557-563
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现代预防医学 |临床与预防 2026 , 53 (3) : 557 -563
单核细胞/白蛋白比值与糖尿病患者全因死亡率及心血管疾病死亡率的相关性——基于NHANES数据库的队列研究
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王怡1, 张文俊1, 胡琼1, 李威2, 汪青2, 赵星海3, 李怡梅3, 刘丽燕3
作者信息
  • 1.昆山市第六人民医院内分泌科,江苏 昆山 215300
  • 2.昆山市中医医院中心实验室
  • 3.昆山市中医医院老年内分泌科,江苏 昆山 215300
通讯作者:
刘丽燕,E-mail:
作者简介:

王怡(1982—),女,硕士,副主任医师,研究方向:糖尿病

Correlation between monocyte/albumin ratio and all-cause mortality as well as cardiovascular disease mortality in diabetic patients: a cohort study based on the NHANES database
Yi WANG1, Wen-Jun ZHANG1, Qiong HU1, Wei LI2, Qing WANG2, Xing-Hai ZHAO3, Yi-Mei LI3, Li-Yan LIU3
Affiliations
  • Endocrinology Department, Kunshan Sixth People’s Hospital, Kunshan, Jiangsu 215300, China
出版时间: 2026-02-10 doi: 10.20043/j.cnki.MPM.202509189
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目的

基于美国国家健康与营养调查 (National Health and Nutrition Examination Survey,NHANES)数据库,探讨单核细胞/白蛋白比值(monocyte-to-albumin ratio,MAR)与糖尿病患者全因死亡率及心血管疾病(cardiovascular disease,CVD)死亡率之间的关系。

方法

本研究基于NHANES数据库1999—2018年的糖尿病患者资料及美国国家死亡索引(National Death Index, NDI)数据库的死亡结果进行分析,采用加权Cox比例风险回归模型、Kaplan-Meier生存曲线、限制性立方样条回归分析、亚组分析和交互作用等研究方法探讨了MAR与糖尿病患者全因及CVD死亡率之间的关系。

结果

本研究共纳入4 558例糖尿病患者,随访过程中,全因死亡患者共1 250例,CVD死亡共429例。将MAR按三分位数进行分组,在调整多项混杂因素后,与最低三分位组(Q1:MAR<0.011)相比,MAR最高三分位组(Q3:MAR≥0.015)全因死亡率(HR=1.44,95% CI:1.21~1.71)和CVD死亡率(HR=1.44,95% CI:1.01~2.05)均显著升高。Kaplan-Meier生存分析显示,MAR最高三分位组的生存率明显低于最低三分位组,差异有统计学意义(P<0.001)。

结论

MAR与糖尿病患者全因死亡率及CVD死亡率之间存在正相关关系,是糖尿病患者全因死亡及CVD死亡的独立危险因素。

单核细胞/白蛋白比值  /  糖尿病  /  全因死亡率  /  心血管疾病死亡率
Objective

Based on the National Health and Nutrition Examination Survey (NHANES) database, this study investigates the association between the monocyte-to-albumin ratio (MAR) and all-cause and cardiovascular disease (CVD)mortality among individuals with diabetes mellitus.

Methods

Using NHANES 1999—2018 data linked to mortality outcomes from the National Death Index (NDI), we conducted a study among adults with diabetes mellitus. Weighted Cox proportional-hazards regression, Kaplan-Meier survival curves, restricted cubic spline regression, subgroup analyses, and interaction tests were employed to examine the associations of the monocyte-to-albumin ratio (MAR) with all-cause and CVD mortality among individuals with diabetes mellitus.

Results

A total of 4 558 patients with diabetes mellitus were enrolled in this study. During follow-up, 1 250 all-cause deaths and 429 CVD-specific deaths were documented. MAR was categorized into tertiles. After adjusting for multiple confounding factors, compared with the lowest tertile group (Q1: MAR<0.011), the highest tertile group(Q3: MAR≥0.015) exhibited significantly increased risks of both all-cause mortality (HR=1.44, 95% CI: 1.21-1.71) and CVD mortality (HR=1.44; 95% CI: 1.01-2.05). Kaplan-Meier survival analysis demonstrated that the survival probability in the highest MAR tertile was markedly lower than that in the lowest tertile, with a statistically significant difference (P<0.001).

Conclusion

MAR is positively associated with both all-cause and CVD mortality in patients with diabetes and represents an independent risk factor for these outcomes.

Monocyte-to-albumin ratio  /  Diabetes mellitus  /  All-cause mortality  /  Cardiovascular disease mortality
王怡, 张文俊, 胡琼, 李威, 汪青, 赵星海, 李怡梅, 刘丽燕. 单核细胞/白蛋白比值与糖尿病患者全因死亡率及心血管疾病死亡率的相关性——基于NHANES数据库的队列研究. 现代预防医学, 2026 , 53 (3) : 557 -563 . DOI: 10.20043/j.cnki.MPM.202509189
Yi WANG, Wen-Jun ZHANG, Qiong HU, Wei LI, Qing WANG, Xing-Hai ZHAO, Yi-Mei LI, Li-Yan LIU. Correlation between monocyte/albumin ratio and all-cause mortality as well as cardiovascular disease mortality in diabetic patients: a cohort study based on the NHANES database[J]. Modern Preventive Medicine, 2026 , 53 (3) : 557 -563 . DOI: 10.20043/j.cnki.MPM.202509189
糖尿病是导致全因死亡及心血管疾病(cardiovascular disease,CVD)死亡的重要危险因素。糖尿病患者的全因死亡率显著高于非糖尿病人群[1],其中约50%的死亡可归因于CVD[2]。尽管传统的危险分层模型(如基于糖化血红蛋白、血脂谱及肾功能指标)已广泛应用于临床,但糖尿病患者的死亡风险仍存在显著个体差异,需进一步探索能反映病理生理机制的新型生物学标志物,以实现更精准的风险评估与干预。
近年来,慢性低度炎症与免疫失调[3-5]在糖尿病进展中的作用逐渐被重视。长期高血糖可通过晚期糖基化终产物(advanced glycation end products,AGEs)积累、线粒体功能障碍等机制激活单核-巨噬细胞系统,诱发全身性炎症级联反应[6]。单核细胞是先天性免疫的核心组分[7],其数量或比例升高可促进促炎因子(如肿瘤坏死因子-α、白细胞介素-1β等)释放,加速内皮损伤和动脉粥样硬化斑块形成。与此同时,血清白蛋白不仅是营养状态的重要指标,也是关键的抗氧化与抗炎分子[8],低白蛋白血症常伴随氧化应激增强及内皮屏障功能受损,可能进一步放大糖尿病微血管[9]与大血管并发症风险。
近年来,单核细胞/白蛋白比值(monocyte-to-albumin ratio, MAR)作为一种新型炎症免疫标志物,受到了广泛关注,MAR可同时反映机体免疫炎症的激活与机体内环境稳态的失衡。目前,MAR已用于心脑血管疾病、自身免疫性疾病预后的风险评估,但其对糖尿病预后的预测价值尚不明确。本研究的目的是基于美国国家健康与营养调查 (National Health and Nutrition Examination Survey,NHANES)数据库,探索MAR与糖尿病患者全因死亡率及CVD死亡率之间的关联,从而为糖尿病患者的预后评估和及时干预提供新的方向。
研究资料均来自美国NHANES数据库(https://www.cdc.gov/nchs/nhanes/index.htm)。NHANES是一项由美国疾病预防控制中心与国家卫生统计中心联合发起的具有代表性的横断面调查,内容包括标准化的问卷、体格检查和实验室检测,以评估居民的健康状况。该调查严格遵循伦理规范,并获得美国国家卫生统计中心伦理审查委员会的批准。本研究纳入了NHANES数据库1999—2018年的糖尿病患者。纳入标准:该期间参与调查的糖尿病患者。糖尿病的诊断标准为:(1)自我报告有糖尿病病史;(2)使用胰岛素、降糖药等糖尿病药物;(3)糖化血红蛋白(glycated hemoglobin,HbA1c)≥6.5%、空腹血糖≥126 mg/dl或餐后2 h血糖≥200 mg/dl,满足以上任意一条[10]。排除标准:(1)年龄<18周岁;(2)单核细胞和/或白蛋白数据缺失;(3)死亡随访数据缺失。最终,4 558名糖尿病患者被纳入本研究。见图1。
本研究的暴露因素为MAR,其计算公式为:MAR=单核细胞计数/白蛋白浓度。将MAR按照三分位数进行分组,分为Q1(MAR < 0.011)、Q2(0.011≤ MAR<0.015)及Q3(MAR ≥0.015)三组。结局变量包括全因死亡率及CVD死亡率,死亡结果来自美国国家死亡索引(National Death Index, NDI)数据库,NDI数据库由美国国家健康统计中心(National Center for Health Statistics,NCHS)维护,它记录了所有参与者的生存状态数据,包括死亡信息,并与NHANES数据库中的信息相匹配。
协变量包括年龄、性别、种族(墨西哥裔美国人、其他西班牙裔、非西班牙裔白人、非西班牙裔黑人及其他种族)、身体质量指数(body mass index,BMI)、教育水平(高中以下、高中、高中以上)、婚姻状况(已婚并与配偶生活、其他)、贫困收入比率(poverty-income ratio,PIR)、饮酒状态(饮酒或不饮酒)、吸烟状态(从不吸烟、现在吸烟或以前吸烟)、慢性病病史(高血压及高血脂)及实验室检查[血红蛋白、血肌酐、血糖、HbA1c、总胆固醇、丙氨酸氨基转移酶(alanine aminotransferase,ALT)及天门冬氨酸氨基转移酶(aspartate aminotransferase,AST)]。
本研究中,满足以下任意一条即诊断为高血压:自我报告有高血压病史;使用降压药物;收缩压≥140 mm Hg;舒张压≥90 mm Hg。满足以下任意一条即诊断为高血脂:总胆固醇≥2 000 mg/L;甘油三酯≥1 500 mg/L;低密度脂蛋白≥1 300 mg/L;男性高密度脂蛋白≤400 mg/L,女性高密度脂蛋白≤500 mg/L;服用降脂药。BMI=体重/身高的二次方(kg/m2),并分为<25 kg/m2,25~29.9 kg/m2,≥30 kg/m2三组。
本研究采用SPSS 25.0及R(4.4.2)统计软件进行数据分析,所有分析都进行了加权处理。符合正态分布的连续性变量使用() 表示,组间比较采用方差分析;不符合正态分布的连续性变量以[M(P25,P75)]表示,组间比较采用Kruskal-waills test。分类变量采用百分比(%)表示,组间比较采用χ2检验。
本研究使用加权Cox比例风险回归模型分析MAR与糖尿病全因死亡率及CVD死亡率的相关性,绘制Kaplan-Meier生存曲线,Log-rank检验比较各组随访过程中生存率的差异。使用限制性立方样条(restricted cubic spline,RCS)拟合MAR与糖尿病患者全因及CVD死亡率之间的线性关系。检验水准α=0.05。
排除基线非糖尿病患者、年龄<18周岁、单核细胞计数和/或白蛋白数据缺失、死亡随访数据缺失的个体,最终,4 558名糖尿病患者被纳入研究进行分析。其中,男性2 344例(51.43%),女性2 214例(48.57%),平均年龄为59.21岁。随访中位人年为8.43年,中位随访时间为8.5年。与MAR水平较低的Q1组相比,Q3组年龄更大,非西班牙裔白人占比更大,BMI、血清肌酐、HbA1c水平更高,吸烟和患有高血压的比例更大(P<0.05)。见表1。
随访期间,全因死亡共1 250例(27.42%),CVD死亡共429例(9.41%)。与Q1组相比,Q3组全因死亡和CVD死亡的占比更高(P<0.05)。见表1。
采用加权的Cox比例风险回归模型和Kaplan-Meier生存曲线进行分析。结果显示,在调整了性别、年龄、BMI、种族等一系列协变量的Model 3中,与MAR最低三分位组Q1组相比,Q3组全因死亡和CVD死亡的发生率增加了44%(P均<0.05),见表2。Kaplan-Meier生存曲线显示,高水平MAR的Q3组的生存率明显低于低水平的Q1组,差异有统计学意义(P<0.001),见图2。
亚组分析和交互作用分析结果显示,MAR与糖尿病患者全因死亡率和CVD死亡率之间的正相关关系在各个亚组依然存在,具有稳定性。种族是糖尿病人群MAR与全因死亡发生之间的交互因素(P-interaction<0.05)。见图3、4。
RCS结果显示,MAR与糖尿病患者全因死亡及CVD死亡之间呈正相关关系(P均<0.001),但不存在非线性关系。见图5。
本研究探讨了MAR与糖尿病患者全因死亡及CVD死亡率的关系,结果显示,在调整了混杂因素后,MAR最高三分位组的全因死亡率和CVD死亡率是MAR最低三分位组的1.44倍。
糖尿病是全身性代谢紊乱疾病,可以累及心脏、肾脏、神经、血管等脏器,引发各种合并症[11]。糖尿病合并症是影响患者生存状态的重要因素,既往流行病学显示,糖尿病患者的全因死亡率比非糖尿病患者高2倍,CVD的发生风险是非糖尿病患者的1.81倍[12]。因此,探索反映糖尿病预后的生物学标志物对于糖尿病的风险评估和早期干预具有重要意义。
有研究显示,单核细胞能够预测颈总动脉粥样硬化,单核细胞百分比与心血管事件风险有关[13]。一项前瞻性研究发现,单核细胞与2型糖尿病患者全因死亡风险增加独立相关[14]。一项横断面研究发现,在2型糖尿病患者中,单核细胞计数与颈总动脉内膜中层厚度 (common carotid artery intima-media thickness,CCA-IMT) 呈正相关[15],说明单核细胞可以预测大血管并发症发生。因此,在2 型糖尿病患者中,单核细胞水平升高,提示CVD的发生风险更高。有研究发现,低白蛋白血症与动脉粥样硬化的发生和急性冠脉综合征 (acute coronary syndrome,ACS)患者院内死亡率增加有关,白蛋白水平降低可以作为ACS患者院内死亡率的独立预测因子[16]。在相关研究中,白蛋白作为预测冠脉疾病和心衰患者的全因死亡的重要指标[17-18],具有重要的临床应用价值。
MAR是反映机体炎症状态与营养状况的新型生物学标志物,主要由单核细胞计数和白蛋白浓度比值组成。单核细胞作为免疫调节的关键细胞因子,在炎症介质的诱导下进入血液循环,并在分化过程中导致细胞因子水平上升[19]。单核细胞还分泌血管内皮生长因子(vascular endothelial growth factor,VEGF),该因子与糖尿病大血管并发症的发生密切相关[20]。白蛋白反应机体的营养状态,并有抗炎、抗氧化和维持血管内皮功能的作用,低白蛋白状态下机体对炎症因子的中和能力减弱。MAR与以往用于病情评估的炎症生物标志物如CRP、IL-6、肿瘤坏死因子等相比,具有低价高效的优势。多项研究显示MAR能够预测多种疾病的病情和预后,有研究发现,MAR与冠心病、脑出血疾病的预后有关[21-22],MAR可以预测白塞病活动水平[23]。另外,MAR与糖尿病患者中风患病率相关[24],在另一篇关于MAR与ACS的研究中发现,MAR与血糖水平正相关、与冠脉Gensini评分正相关。
本研究也存在一些局限性,首先,健康人群MAR通常为0.2~0.6(109/L·g/L-1),但老年人可能白蛋白水平偏低,导致MAR值偏高。其次,特殊情况可能会导致MAR假性升高,需在临床分析中校正:如感染或急性炎症时,单核细胞升高,白蛋白短暂降低;营养支持时,肠内/肠外营养治疗后白蛋白水平回升可能滞后于单核细胞下降,需结合临床进行综合评判。最后,影响糖尿病患者预后的其他因素,如膳食、睡眠等并未全部纳入。
综上,高MAR值可能反映了更活跃的炎症状态和较差的营养状况,这两者都可能促进糖尿病并发症的发展,从而增加死亡风险。MAR可作为预测糖尿病患者全因死亡和CVD死亡风险的临床应用指标。MAR检验成本低、获取容易,可用于糖尿病患者的预后风险筛查和早期干预,具有重要的临床应用价值,尤其适用于资源有限的地区。
  • 苏州市医学重点扶持学科(SZFCXK202115)
  • 昆山市高层次卫生人才项目(昆卫[2024]9号)
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2026年第53卷第3期
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doi: 10.20043/j.cnki.MPM.202509189
  • 接收时间:2025-09-13
  • 首发时间:2026-11-02
  • 出版时间:2026-02-10
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  • 收稿日期:2025-09-13
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苏州市医学重点扶持学科(SZFCXK202115)
昆山市高层次卫生人才项目(昆卫[2024]9号)
作者信息
    1.昆山市第六人民医院内分泌科,江苏 昆山 215300
    2.昆山市中医医院中心实验室
    3.昆山市中医医院老年内分泌科,江苏 昆山 215300

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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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