Article(id=1322608110196339221, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322608013010120712, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202506066, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1749139200000, receivedDateStr=2025-06-06, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793263946340, onlineDateStr=2026-10-29, pubDate=1762704000000, pubDateStr=2025-11-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793263946340, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793263946340, creator=13701087609, updateTime=1793263946340, updator=13701087609, issue=Issue{id=1322608013010120712, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='21', pageStart='3841', pageEnd='4032', issueExtLink='null', onlineDate='null', pubDate='1762704000000', pubDateStr='2025-11-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793263923169, creator='13701087609', updateTime=1793264266929, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322609454932149086, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322608013010120712, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322609454932149087, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322608013010120712, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3872, endPage=3878, ext={EN=ArticleExt(id=1322608110447997462, articleId=1322608110196339221, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Association between relative fat mass and arthritis in a middle-aged and elderly Chinese population, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

The aim of this study was to investigate the association between relative fat mass (RFM) and the risk of arthritis in middle-aged and elderly Chinese adults.

Methods

A cross-sectional analysis was performed using 2011 CHARLS data and included a total of 12 921 participants, including 6 144 males (47.55%) and 6 777 females (52.45%), with a mean age of (59.49 ± 9.60) years. Multivariable logistic regression and restricted cubic splines (RCS) were used to analyze the cross-sectional association between RFM and arthritis. A total of 8 350 middle-aged and elderly people who were not diagnosed with arthritis in the 2011 cross-sectional study were included in the longitudinal study and followed up until 2018. Kaplan-Meier curves, multivariable Cox proportional hazards regression models, and RCS were used to analyze the longitudinal association between different baseline RFM levels and the risk of incident arthritis in 2011. Meanwhile, the consistency of this association in different population subgroups was assessed by subgroup analysis, and sensitivity analysis was used to verify the stability of the model results.

Results

In the cross-sectional study, multivariable logistic regression analysis showed that higher RFM was associated with an increased risk of arthritis after adjusting for confounders (OR=1.02, 95% CI: 1.02-1.03). Middle-aged and older adults in the Q3 (OR=1.30, 95% CI: 1.15-1.48) and Q4 (OR=1.60, 95% CI: 1.40-1.82) groups had a higher risk of arthritis compared with the first quartile (Q1). In the longitudinal study, a total of 916 individuals were first diagnosed with arthritis during the follow-up period. Multivariable Cox regression analysis showed that higher RFM was associated with an increased risk of incident arthritis after adjusting for confounders (HR=1.02, 95% CI: 1.02-1.04). The risk of incident arthritis was significantly higher in the Q4 group(HR=1.56, 95% CI:1.23-1.98) compared with Q1. Subgroup analysis showed no significant interactions between RFM and each subgroup variable. The results of the sensitivity analysis were generally consistent with the conclusions of the primary analysis. ROC curves showed that RFM had a better diagnostic efficacy for arthritis than obesity indicators such as BMI.

Conclusion

Higher levels of RFM are associated with an increased risk of arthritis in the middle-aged and elderly Chinese population, paying attention to RFM levels may help in the early identification of populations at risk for arthritis, providing new insights for disease prevention and intervention.

, authors=Hui-zhi XUE1, Hui-long CHEN1, Nan YAO2, Wei-ju LI1, 2, Yu-tong ZHANG1, Zheng-hui KONG1, Yu-wei LIU1, Zhao-hui LV2, authorsList=Hui-zhi XUE, Hui-long CHEN, Nan YAO, Wei-ju LI, Yu-tong ZHANG, Zheng-hui KONG, Yu-wei LIU, Zhao-hui LV, authorCompany=null, correspAuthors=Zhao-hui LV, 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=1322608114084459050, articleId=1322608110196339221, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=中国中老年人群相对脂肪量与关节炎的关联研究, 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

本研究旨在探讨相对脂肪量(relative fat mass,RFM)与中国中老年人关节炎风险之间的关联。

方法

使用2011年中国健康与养老追踪调查(China Health and Retirement Longitudinal Study,CHARLS)数据进行横断面分析,共纳入12 921名参与者,其中男性6 144名(47.55%),女性6 777名(52.45%),平均年龄(59.49±9.60)岁。采用多因素logistic回归和限制立方样条(restricted cubic splines,RCS)分析RFM与关节炎之间的横断面关联。将2011年横断面研究中8 350名未被诊断为关节炎的中老年人纳入纵向研究,随访至2018年。采用Kaplan-Meier曲线、多因素Cox比例风险回归模型和RCS分析2011年不同基线RFM水平与新发关节炎风险之间的纵向关联。同时,通过亚组分析评估该关联在不同人群亚组中的一致性,并利用敏感性分析验证模型结果的稳定性。

结果

在横断面研究中,多因素logistic回归分析显示,在调整混杂因素后,较高的RFM与关节炎风险增加相关(OR=1.02,95% CI:1.02~1.03),与第一个四分位数(Q1)相比,Q3(OR=1.30,95% CI:1.15~1.48)和Q4(OR=1.60,95% CI:1.40~1.82)组中老年人具有更高的关节炎风险。在纵向研究中,随访期间共916人被首次诊断为关节炎。多因素Cox回归分析显示,在调整混杂因素后,较高的RFM与新发关节炎风险增加相关(HR=1.02,95% CI:1.02~1.04),与Q1相比,Q4(HR=1.56,95% CI:1.23~1.98)组的新发关节炎风险显著高于Q1组。亚组分析显示,RFM与各亚组变量之间均未表现出显著的交互作用。敏感性分析的结果与主要分析结论基本一致。构建受试者工作特征曲线(receiver operating characteristic curve,ROC)显示,相较BMI等肥胖指标,RFM对关节炎有更好的诊断效能。

结论

在中国中老年人群中,较高水平的RFM与关节炎发病风险增加有关,关注RFM水平可能有助于早期识别关节炎风险人群,为疾病的预防和干预提供新的思路。

, authors=薛慧芝1, 陈慧龙1, 姚楠2, 李伟举1, 2, 章雨童1, 孔政辉1, 刘育伟1, 吕朝晖2, authorsList=薛慧芝, 陈慧龙, 姚楠, 李伟举, 章雨童, 孔政辉, 刘育伟, 吕朝晖, authorCompany=null, correspAuthors=吕朝晖, authorNote=

薛慧芝(2000—),女,硕士在读,研究方向:中西医结合防治骨关节炎

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吕朝晖,E-mail:
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薛慧芝(2000—),女,硕士在读,研究方向:中西医结合防治骨关节炎

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Osteoarthritis and Cartilage / OARS, Osteoarthritis Research Society, 2017, 25 (1): 60-66., articleTitle=The association of waist circumference with walking difficulty among adults with or at risk of knee osteoarthritis: the Osteoarthritis Initiative, refAbstract=null), Reference(id=1322608123622306447, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608110196339221, doi=null, pmid=null, pmcid=null, year=2021, volume=2021, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[23], rfOrder=23, authorNames=Zhang FL, Ren JX, Zhang P, journalName=Journal of Diabetes Research, refType=null, unstructuredReference=Zhang FL, Ren JX, Zhang P, et al. Strong association of waist circumference(WC), body mass index(BMI), Waist-to-Height ratio(WHtR), and Waist-to-Hip ratio(WHR)with diabetes: a Population-Based Cross-Sectional study in Jilin province, China [J]. Journal of Diabetes Research, 2021, 2021: 8812431., articleTitle=Strong association of waist circumference(WC), body mass index(BMI), Waist-to-Height ratio(WHtR), and Waist-to-Hip ratio(WHR)with diabetes: a Population-Based Cross-Sectional study in Jilin province, China, refAbstract=null), Reference(id=1322608123701998224, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608110196339221, doi=null, pmid=null, pmcid=null, year=2023, volume=13, issue=8, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[24], rfOrder=24, authorNames=Woolcott OO, Seuring T, journalName=BMJ Open, refType=null, unstructuredReference=Woolcott OO, Seuring T. Temporal trends in obesity defined by the relative fat mass(RFM)index among adults in the United States from 1999 to 2020: a population-based study [J]. BMJ Open, 2023, 13 (8): e071295., articleTitle=Temporal trends in obesity defined by the relative fat mass(RFM)index among adults in the United States from 1999 to 2020: a population-based study, refAbstract=null), Reference(id=1322608123802661521, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608110196339221, doi=null, pmid=null, pmcid=null, year=2018, volume=61, issue=2, pageStart=103, pageEnd=113, url=null, language=null, rfNumber=[25], rfOrder=25, authorNames=Piché ME, Poirier P, Lemieux I, journalName=Progress in Cardiovascular Diseases, refType=null, unstructuredReference=Piché ME, Poirier P, Lemieux I, et al. Overview of epidemiology and contribution of obesity and body fat distribution to cardiovascular disease: an update [J]. Progress in Cardiovascular Diseases, 2018, 61 (2): 103-113., articleTitle=Overview of epidemiology and contribution of obesity and body fat distribution to cardiovascular disease: an update, refAbstract=null), Reference(id=1322608123894936210, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608110196339221, doi=null, pmid=null, pmcid=null, year=2024, volume=26, issue=8, pageStart=3238, pageEnd=3247, url=null, language=null, rfNumber=[26], rfOrder=26, authorNames=Wang D, Chen ZT, Wu YR, journalName=Diabetes Obesity & Metabolism, refType=null, unstructuredReference=Wang D, Chen ZT, Wu YR, et al. Association between two novel anthropometric measures and type 2 diabetes in a Chinese population [J]. Diabetes Obesity & Metabolism, 2024, 26 (8): 3238-3247., articleTitle=Association between two novel anthropometric measures and type 2 diabetes in a Chinese population, refAbstract=null), Reference(id=1322608123991405203, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608110196339221, doi=null, pmid=null, pmcid=null, year=2024, volume=356, issue=null, pageStart=323, pageEnd=328, url=null, language=null, rfNumber=[27], rfOrder=27, authorNames=Zhu XL, Yue Y, Li L, journalName=Journal of Affective Disorders, refType=null, unstructuredReference=Zhu XL, Yue Y, Li L, et al. The relationship between depression and relative fat mass(RFM): A population-based study [J]. 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tableContent=null), ArticleFig(id=1322608119146984042, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608110196339221, language=CN, label=图5, caption=RFM与新发关节炎风险关联的亚组分析, figureFileSmall=PMs/BYUJ1w9ZMPUtLh3DrA==, figureFileBig=2ai2oNl4HYHfu5QuxODpRA==, tableContent=null), ArticleFig(id=1322608119218287211, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608110196339221, language=EN, label=Figure 6, caption=Analysis of the diagnostic efficiency of RFM, BMI,WC, and WHtR for arthritis, figureFileSmall=FD/1FxMn3YqHuFsPYIxSzw==, figureFileBig=KyVY0ywiAmy9H3O1Va6DyA==, tableContent=null), ArticleFig(id=1322608119293784684, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608110196339221, language=CN, label=图6, caption=RFM、BMI、腰围、腰高比对关节炎的诊断效能分析

注:WC为腰围。

, figureFileSmall=FD/1FxMn3YqHuFsPYIxSzw==, figureFileBig=KyVY0ywiAmy9H3O1Va6DyA==, tableContent=null), ArticleFig(id=1322608119356699245, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608110196339221, language=EN, label=Table 1, caption=

Comparison of baseline characteristics between arthritis and non-arthritis populations in 2011[(),n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
组别总体 (n=12 921)无关节炎组 (n=8 350)关节炎组(n=4 571)t/χ2值P值
年龄(岁)59.49±9.6059.10±9.7860.20±9.23-6.34<0.001
RFM32.73±8.6432.11±8.5433.87±8.70-11.15<0.001
BMI(kg/m2)23.46±3.9023.40±3.8823.57±3.94-2.220.026
身高(cm)158.11±8.63158.96±8.60156.56±8.4615.23<0.001
体重(kg)58.84±11.7159.32±11.7557.94±11.586.41<0.001
腰围(cm)85.30±10.1885.24±10.0785.42±10.38-0.960.336
性别109.89<0.001
女性6 777 (52.45)4 095 (49.04)2 682 (58.67)
男性6 144 (47.55)4 255 (50.96)1 889 (41.33)
婚姻情况5.890.015
已婚并与配偶同居11 210 (86.76)7 289 (87.29)3 921 (85.78)
未婚/分居/丧偶1 711 (13.24)1 061 (12.71)650 (14.22)
文化程度153.62<0.001
高中及以上1 385 (10.72)1 047 (12.54)338 (7.39)
中学2 612 (20.22)1 833 (21.95)779 (17.04)
小学5 271 (40.79)3 264 (39.09)2 007 (43.91)
文盲3 653 (28.27)2 206 (26.42)1 447 (31.66)
居住地1.000.317
农村1 485 (11.49)977 (11.70)508 (11.11)
城市11 436 (88.51)7 373 (88.30)4 063 (88.89)
BMI分类5.350.069
正常范围6 839 (52.93)4 482 (53.68)2 357 (51.56)
超重及肥胖5 189 (40.16)3 297 (39.49)1 892 (41.39)
体重过轻893 (6.91)571 (6.84)322 (7.04)
吸烟37.14<0.001
无7 753 (60.00)4 848 (58.06)2 905 (63.55)
有5 168 (40.00)3 502 (41.94)1 666 (36.45)
饮酒10.88<0.001
无9 720 (75.23)6 204 (74.30)3 516 (76.92)
有3 201 (24.77)2 146 (25.70)1 055 (23.08)
糖尿病13.69<0.001
无11 284 (87.33)7 359 (88.13)3 925 (85.87)
有1 637 (12.67)991 (11.87)646 (14.13)
高血压3.810.051
无7 734 (59.86)5 050 (60.48)2 684 (58.72)
有5 187 (40.14)3 300 (39.52)1 887 (41.28)
血脂异常7.340.007
无8 116 (62.81)5 316 (63.66)2 800 (61.26)
有4 805 (37.19)3 034 (36.34)1 771 (38.74)
), ArticleFig(id=1322608119428002414, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608110196339221, language=CN, label=表1, caption=

2011年关节炎人群与非关节炎人群基线特征对比[(),n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
组别总体 (n=12 921)无关节炎组 (n=8 350)关节炎组(n=4 571)t/χ2值P值
年龄(岁)59.49±9.6059.10±9.7860.20±9.23-6.34<0.001
RFM32.73±8.6432.11±8.5433.87±8.70-11.15<0.001
BMI(kg/m2)23.46±3.9023.40±3.8823.57±3.94-2.220.026
身高(cm)158.11±8.63158.96±8.60156.56±8.4615.23<0.001
体重(kg)58.84±11.7159.32±11.7557.94±11.586.41<0.001
腰围(cm)85.30±10.1885.24±10.0785.42±10.38-0.960.336
性别109.89<0.001
女性6 777 (52.45)4 095 (49.04)2 682 (58.67)
男性6 144 (47.55)4 255 (50.96)1 889 (41.33)
婚姻情况5.890.015
已婚并与配偶同居11 210 (86.76)7 289 (87.29)3 921 (85.78)
未婚/分居/丧偶1 711 (13.24)1 061 (12.71)650 (14.22)
文化程度153.62<0.001
高中及以上1 385 (10.72)1 047 (12.54)338 (7.39)
中学2 612 (20.22)1 833 (21.95)779 (17.04)
小学5 271 (40.79)3 264 (39.09)2 007 (43.91)
文盲3 653 (28.27)2 206 (26.42)1 447 (31.66)
居住地1.000.317
农村1 485 (11.49)977 (11.70)508 (11.11)
城市11 436 (88.51)7 373 (88.30)4 063 (88.89)
BMI分类5.350.069
正常范围6 839 (52.93)4 482 (53.68)2 357 (51.56)
超重及肥胖5 189 (40.16)3 297 (39.49)1 892 (41.39)
体重过轻893 (6.91)571 (6.84)322 (7.04)
吸烟37.14<0.001
无7 753 (60.00)4 848 (58.06)2 905 (63.55)
有5 168 (40.00)3 502 (41.94)1 666 (36.45)
饮酒10.88<0.001
无9 720 (75.23)6 204 (74.30)3 516 (76.92)
有3 201 (24.77)2 146 (25.70)1 055 (23.08)
糖尿病13.69<0.001
无11 284 (87.33)7 359 (88.13)3 925 (85.87)
有1 637 (12.67)991 (11.87)646 (14.13)
高血压3.810.051
无7 734 (59.86)5 050 (60.48)2 684 (58.72)
有5 187 (40.14)3 300 (39.52)1 887 (41.28)
血脂异常7.340.007
无8 116 (62.81)5 316 (63.66)2 800 (61.26)
有4 805 (37.19)3 034 (36.34)1 771 (38.74)
), ArticleFig(id=1322608119499305583, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608110196339221, language=EN, label=Table 2, caption=

Association between RFM and arthritis risk based on multivariable logistic regression analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
组别模型1模型2模型3
OR (95% CI)P值OR (95% CI)P值OR (95% CI)P值
RFM1.02 (1.02~1.03)<0.0011.02 (1.02~1.03)<0.0011.02 (1.02~1.03)<0.001
RFM Q
Q1(Reference)(Reference)(Reference)
Q21.02 (0.91~1.13)0.7821.05 (0.94~1.16)0.3941.04 (0.93~1.16)0.512
Q31.29 (1.16~1.43)<0.0011.30 (1.17~1.45)<0.0011.30 (1.15~1.48)<0.001
Q41.67 (1.50~1.85)<0.0011.61 (1.45~1.79)<0.0011.60 (1.40~1.82)<0.001
), ArticleFig(id=1322608119570608752, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608110196339221, language=CN, label=表2, caption=

基于多因素logistic回归模型分析RFM与关节炎风险的关联

, figureFileSmall=null, figureFileBig=null, tableContent=
组别模型1模型2模型3
OR (95% CI)P值OR (95% CI)P值OR (95% CI)P值
RFM1.02 (1.02~1.03)<0.0011.02 (1.02~1.03)<0.0011.02 (1.02~1.03)<0.001
RFM Q
Q1(Reference)(Reference)(Reference)
Q21.02 (0.91~1.13)0.7821.05 (0.94~1.16)0.3941.04 (0.93~1.16)0.512
Q31.29 (1.16~1.43)<0.0011.30 (1.17~1.45)<0.0011.30 (1.15~1.48)<0.001
Q41.67 (1.50~1.85)<0.0011.61 (1.45~1.79)<0.0011.60 (1.40~1.82)<0.001
), ArticleFig(id=1322608119637717617, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608110196339221, language=EN, label=Table 3, caption=

Association between RFM and incident arthritis risk based on multivariable Cox regression analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
组别模型1模型2模型3
OR (95% CI)P值OR (95% CI)P值OR (95% CI)P值
RFM1.03 (1.02~1.03)<0.0011.03 (1.02~1.03)<0.0011.02 (1.01~1.04)<0.001
RFM Q
Q1(Reference)(Reference)(Reference)
Q21.14 (0.94~1.40)0.1851.15 (0.94~1.41)0.1651.09 (0.89~ 1.33)0.428
Q31.28 (1.05~1.55)0.0131.29 (1.06~1.57)0.0131.26 (1.00~1.59)0.052
Q41.70 (1.41~2.04)<0.0011.68 (1.38~2.03)<0.0011.56 (1.23~1.98)<0.001
), ArticleFig(id=1322608119709020786, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608110196339221, language=CN, label=表3, caption=

基于多因素COX回归模型分析RFM对新发关节炎风险的影响

, figureFileSmall=null, figureFileBig=null, tableContent=
组别模型1模型2模型3
OR (95% CI)P值OR (95% CI)P值OR (95% CI)P值
RFM1.03 (1.02~1.03)<0.0011.03 (1.02~1.03)<0.0011.02 (1.01~1.04)<0.001
RFM Q
Q1(Reference)(Reference)(Reference)
Q21.14 (0.94~1.40)0.1851.15 (0.94~1.41)0.1651.09 (0.89~ 1.33)0.428
Q31.28 (1.05~1.55)0.0131.29 (1.06~1.57)0.0131.26 (1.00~1.59)0.052
Q41.70 (1.41~2.04)<0.0011.68 (1.38~2.03)<0.0011.56 (1.23~1.98)<0.001
), ArticleFig(id=1322608119780323955, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608110196339221, language=EN, label=Table 4, caption=

Sensitivity analyses of RFM and incident arthritis risk

, figureFileSmall=null, figureFileBig=null, tableContent=
组别模型1模型2模型3
HR (95% CI)P值HR (95% CI)P值HR (95% CI)P值
RFM1.03(1.02~1.04)<0.0011.03(1.02~1.04)<0.0011.03(1.02~1.04)<0.001
RFM Q
Q1(Reference)(Reference)(Reference)
Q21.19(0.92~1.55)0.1791.19(0.92~1.54)0.1931.09(0.84~1.42)0.507
Q31.35(1.05~1.73)0.0201.45(1.12~1.87)0.0051.45(1.07~1.95)0.015
Q41.88(1.48~2.38)<0.0011.93(1.51~2.47)<0.0011.81(1.33~2.46)<0.001
), ArticleFig(id=1322608119864210036, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608110196339221, language=CN, label=表4, caption=

RFM与新发关节炎风险的敏感性分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
组别模型1模型2模型3
HR (95% CI)P值HR (95% CI)P值HR (95% CI)P值
RFM1.03(1.02~1.04)<0.0011.03(1.02~1.04)<0.0011.03(1.02~1.04)<0.001
RFM Q
Q1(Reference)(Reference)(Reference)
Q21.19(0.92~1.55)0.1791.19(0.92~1.54)0.1931.09(0.84~1.42)0.507
Q31.35(1.05~1.73)0.0201.45(1.12~1.87)0.0051.45(1.07~1.95)0.015
Q41.88(1.48~2.38)<0.0011.93(1.51~2.47)<0.0011.81(1.33~2.46)<0.001
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中国中老年人群相对脂肪量与关节炎的关联研究
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薛慧芝 1 , 陈慧龙 1 , 姚楠 2 , 李伟举 1, 2 , 章雨童 1 , 孔政辉 1 , 刘育伟 1 , 吕朝晖 2
现代预防医学 | 流行病与统计方法 2025,52(21): 3872-3878
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现代预防医学 |流行病与统计方法 2025 , 52 (21) : 3872 -3878
中国中老年人群相对脂肪量与关节炎的关联研究
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薛慧芝1, 陈慧龙1, 姚楠2, 李伟举1, 2, 章雨童1, 孔政辉1, 刘育伟1, 吕朝晖2
作者信息
  • 1. 广州中医药大学第五临床医学院,广东 广州 510405
  • 2.广东省第二中医院,广东 广州 510095
通讯作者:
吕朝晖,E-mail:
作者简介:

薛慧芝(2000—),女,硕士在读,研究方向:中西医结合防治骨关节炎

Association between relative fat mass and arthritis in a middle-aged and elderly Chinese population
Hui-zhi XUE1, Hui-long CHEN1, Nan YAO2, Wei-ju LI1, 2, Yu-tong ZHANG1, Zheng-hui KONG1, Yu-wei LIU1, Zhao-hui LV2
Affiliations
  • The Fifth Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510405, China
出版时间: 2025-11-10 doi: 10.20043/j.cnki.MPM.202506066
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目的

本研究旨在探讨相对脂肪量(relative fat mass,RFM)与中国中老年人关节炎风险之间的关联。

方法

使用2011年中国健康与养老追踪调查(China Health and Retirement Longitudinal Study,CHARLS)数据进行横断面分析,共纳入12 921名参与者,其中男性6 144名(47.55%),女性6 777名(52.45%),平均年龄(59.49±9.60)岁。采用多因素logistic回归和限制立方样条(restricted cubic splines,RCS)分析RFM与关节炎之间的横断面关联。将2011年横断面研究中8 350名未被诊断为关节炎的中老年人纳入纵向研究,随访至2018年。采用Kaplan-Meier曲线、多因素Cox比例风险回归模型和RCS分析2011年不同基线RFM水平与新发关节炎风险之间的纵向关联。同时,通过亚组分析评估该关联在不同人群亚组中的一致性,并利用敏感性分析验证模型结果的稳定性。

结果

在横断面研究中,多因素logistic回归分析显示,在调整混杂因素后,较高的RFM与关节炎风险增加相关(OR=1.02,95% CI:1.02~1.03),与第一个四分位数(Q1)相比,Q3(OR=1.30,95% CI:1.15~1.48)和Q4(OR=1.60,95% CI:1.40~1.82)组中老年人具有更高的关节炎风险。在纵向研究中,随访期间共916人被首次诊断为关节炎。多因素Cox回归分析显示,在调整混杂因素后,较高的RFM与新发关节炎风险增加相关(HR=1.02,95% CI:1.02~1.04),与Q1相比,Q4(HR=1.56,95% CI:1.23~1.98)组的新发关节炎风险显著高于Q1组。亚组分析显示,RFM与各亚组变量之间均未表现出显著的交互作用。敏感性分析的结果与主要分析结论基本一致。构建受试者工作特征曲线(receiver operating characteristic curve,ROC)显示,相较BMI等肥胖指标,RFM对关节炎有更好的诊断效能。

结论

在中国中老年人群中,较高水平的RFM与关节炎发病风险增加有关,关注RFM水平可能有助于早期识别关节炎风险人群,为疾病的预防和干预提供新的思路。

关节炎  /  相对脂肪量  /  中老年  /  中国健康与养老追踪调查(CHARLS)
Objective

The aim of this study was to investigate the association between relative fat mass (RFM) and the risk of arthritis in middle-aged and elderly Chinese adults.

Methods

A cross-sectional analysis was performed using 2011 CHARLS data and included a total of 12 921 participants, including 6 144 males (47.55%) and 6 777 females (52.45%), with a mean age of (59.49 ± 9.60) years. Multivariable logistic regression and restricted cubic splines (RCS) were used to analyze the cross-sectional association between RFM and arthritis. A total of 8 350 middle-aged and elderly people who were not diagnosed with arthritis in the 2011 cross-sectional study were included in the longitudinal study and followed up until 2018. Kaplan-Meier curves, multivariable Cox proportional hazards regression models, and RCS were used to analyze the longitudinal association between different baseline RFM levels and the risk of incident arthritis in 2011. Meanwhile, the consistency of this association in different population subgroups was assessed by subgroup analysis, and sensitivity analysis was used to verify the stability of the model results.

Results

In the cross-sectional study, multivariable logistic regression analysis showed that higher RFM was associated with an increased risk of arthritis after adjusting for confounders (OR=1.02, 95% CI: 1.02-1.03). Middle-aged and older adults in the Q3 (OR=1.30, 95% CI: 1.15-1.48) and Q4 (OR=1.60, 95% CI: 1.40-1.82) groups had a higher risk of arthritis compared with the first quartile (Q1). In the longitudinal study, a total of 916 individuals were first diagnosed with arthritis during the follow-up period. Multivariable Cox regression analysis showed that higher RFM was associated with an increased risk of incident arthritis after adjusting for confounders (HR=1.02, 95% CI: 1.02-1.04). The risk of incident arthritis was significantly higher in the Q4 group(HR=1.56, 95% CI:1.23-1.98) compared with Q1. Subgroup analysis showed no significant interactions between RFM and each subgroup variable. The results of the sensitivity analysis were generally consistent with the conclusions of the primary analysis. ROC curves showed that RFM had a better diagnostic efficacy for arthritis than obesity indicators such as BMI.

Conclusion

Higher levels of RFM are associated with an increased risk of arthritis in the middle-aged and elderly Chinese population, paying attention to RFM levels may help in the early identification of populations at risk for arthritis, providing new insights for disease prevention and intervention.

Arthritis  /  Relative fat mass  /  Middle-aged and elderly  /  China Health and Retirement Longitudinal Study (CHARLS)
薛慧芝, 陈慧龙, 姚楠, 李伟举, 章雨童, 孔政辉, 刘育伟, 吕朝晖. 中国中老年人群相对脂肪量与关节炎的关联研究. 现代预防医学, 2025 , 52 (21) : 3872 -3878 . DOI: 10.20043/j.cnki.MPM.202506066
Hui-zhi XUE, Hui-long CHEN, Nan YAO, Wei-ju LI, Yu-tong ZHANG, Zheng-hui KONG, Yu-wei LIU, Zhao-hui LV. Association between relative fat mass and arthritis in a middle-aged and elderly Chinese population[J]. Modern Preventive Medicine, 2025 , 52 (21) : 3872 -3878 . DOI: 10.20043/j.cnki.MPM.202506066
关节炎(arthritis)是一种以关节疼痛、僵硬和功能障碍为主要表现的慢性自身免疫性疾病,主要累及骨、关节及其周围软组织。该病可导致关节畸形、软骨退化,严重影响患者的活动能力和生活质量[1-2]。随着人口老龄化的日益严重,关节炎已成为全球范围内的公共卫生问题[3]。在我国,关节炎在中老年人群中的患病率高达38%[4],疾病负担突出。因此,深入研究其危险因素并及早识别高风险人群,对于减轻关节炎带来的危害具有重要意义。
多项研究指出,肥胖通过增加机械负荷、诱导软骨炎症等机制显著增加关节炎患病风险[5-6]。
身体质量指数(body mass index,BMI)是评估个体体重状态的常用指标[7],但在临床应用中存在局限性,既无法区分脂肪质量与肌肉质量,也难以反映脂肪分布的性别差异,仅依赖BMI对关节炎进行风险评估可能导致偏倚[8]。相对脂肪量(relative fat mass,RFM)是一种新兴的人体测量指标,通过腰围、身高和性别计算得出,相比BMI更能准确反映全身脂肪比例[9-10]。目前关于中国人群RFM与关节炎风险关联的全国性研究较为有限。
本研究基于中国健康与养老追踪调查(China health and retirement longitudinal study, CHARLS)数据库,探讨中国中老年人群RFM与关节炎风险之间的潜在联系。
本研究的数据来源于CHARLS(https://charls.pku.edu.cn/)。CHARLS是一项针对全国45岁及以上中老年人的追踪研究,收集了参与者的健康、经济等多维度数据。CHARLS基线调查覆盖了全国28个省、150个县/区的450个村居,总体上代表中国中老年人群。该调查已获得北京大学生物医学伦理委员会的批准(伦理号:IRB00001052-11015),所有研究对象均已签署知情同意书。
本研究中使用CHARLS 2011—2018年的数据进行横断面和纵向研究。横断面研究根据以下标准纳入了12 921名中老年人:(1)年龄≥45岁;(2)参与体格检查,用于计算RFM的指标完整;(3)报告2011年诊断情况;(4)重要协变量如高血压等完整。在纵向研究中,进一步排除了CHARLS 2011年调查中已诊断为关节炎的4 571名参与者,最终共纳入8 350名参与者。受试者纳入流程图见图1。
关节炎的诊断根据询问受试对象“您是否被医生诊断为关节炎?”的回答进行判断,对这个问题回答“是”的参与者被认为患有关节炎。以往研究显示,CHARLS项目中受访对象自我陈述的疾病诊断与其医疗记录的吻合程度较高[11]。
本研究根据既往研究中已经验证的公式计算RFM[10]:RFM(男性)=64-(20×身高÷腰围)。RFM(女性)=64-(20×身高÷腰围)+12。其中,身高和腰围以米为单位。
纳入本研究的协变量,包括社会人口学特征(年龄、文化程度、居住地、婚姻状况)、生活方式(吸烟和饮酒状况)、健康状况(糖尿病、高血压、血脂异常)。高血压定义为收缩压≥140 mm Hg或舒张压≥90 mm Hg或自我报告的高血压诊断史或使用任何抗高血压药物。糖尿病定义为空腹血糖≥7.0 mmol/L或随机血糖≥11.1 mmol/L或糖化血红蛋白≥6.5%或自我报告的糖尿病诊断史或使用任何降糖药物。血脂异常定义为总胆固醇≥240 mg/dl或甘油三酯≥150 mg/dl或低密度脂蛋白胆固醇(low-density lipoprotein cholesterol,LDL-C)≥160 mg/dl或高密度脂蛋白胆固醇(high-density lipoprotein cholesterol,HDL-C)<40 mg/dl或自我报告的血脂异常诊断史或正在接受降脂治疗。
采用R 4.2.0(R Foundation for Statistical Computing)和SPSS 26.0软件进行数据统计分析。对横断面研究参与者的基线特征进行描述,根据参与者是否被诊断为关节炎将研究人群分为二组,年龄等符合正态分布的计量资料以()表示,组间比较采用两独立样本 t 检验。不符合正态分布的计量资料以[M(P25,P75)]表示,两组间比较采用秩和检验。性别等计数资料以频数(百分数)表示,组间比较采用χ2检验。等级资料比较采用秩和检验。采用多因素logistic回归和限制立方样条(restricted cubic splines,RCS)曲线分析RFM与关节炎之间的关联。
在纵向研究中,采用Kaplan-Meier曲线展示关节炎的累积发病率;采用多因素Cox比例风险回归模型计算风险比(hazard ratio, HR)和95% CI,评估2011年不同的基线RFM水平与新发关节炎风险之间的纵向关联;采用RCS曲线评估RFM水平与新发关节炎风险之间可能存在的非线性关系;采用亚组分析探讨社会人口学特征和健康情况在RFM与新发关节炎风险中的调节作用并采用敏感性分析验证模型的稳定性。构建受试者工作特征曲线 (receiver characteristic operating curve,ROC) 并计算曲线下面积 (area under the curve,AUC),比较 RFM与BMI、腰围、腰高比对关节炎的诊断效能。本研究制定了三种不同的模型进行多因素logistic回归和多因素Cox比例风险回归分析:模型1没有进行变量调整;模型2调整了年龄、婚姻状况、受教育程度、居住地;模型3在模型2的基础上调整了吸烟情况、饮酒状况、高血压、糖尿病、血脂异常。检验水准α=0.05。
本研究最终纳入12 921名对象,平均年龄(59.49±9.60)岁。其中男性占47.55%,女性占52.45%。其中关节炎组4 571例(35.38%),无关节炎组8 350例(64.62%)。关节炎组RFM明显高于无关节炎组,分别为(33.87±8.70)和(32.11±8.54),差异有统计学意义(P<0.01)。关节炎组与无关节炎组在年龄、BMI、身高、体重、性别、婚姻情况、受教育程度、吸烟状况、饮酒状况、糖尿病、血脂异常等方面差异均具有统计学意义(P均<0.05)。见表1。
调整混杂因素后,较高的RFM与关节炎风险增加相关,模型3(OR=1.02,95% CI:1.02~1.03,P<0.001)表明RFM每增加一个单位,关节炎的风险升高2%。进一步将RFM转换为分类变量进行分析,以RFM最低四分位数(Q1)作为参照,模型3中Q3组关节炎风险升高了30%(OR=1.30,95% CI:1.15~1.48,P<0.001),Q4组关节炎风险升高了60%(OR= 1.60,95% CI:1.40~1.82,P<0.001)。见表2。
采用RCS曲线探究RFM与关节炎风险之间的剂量-反应关系,结果显示,RFM与关节炎发病风险存在显著的非线性关联(P整体<0.001,P非线性=0.016)。见图2。
在随访期间共916名中老年人被首次诊断为关节炎,发生率为10.97%。其中RFM Q1组、Q2组、Q3组、Q4组分别有182、205、233和296例。发生率分别为2.18%、2.46%、2.79%、3.54%。Kaplan-Meier累积发生率曲线分析显示,随着随访时间的延长,4组的新发关节炎累积发生率呈明显上升趋势,且组间差异具有统计学意义(log-rank P<0.05)。见图3。
在调整混杂因素后,较高的RFM与新发关节炎风险增加相关,模型3(HR=1.02,95% CI:1.01~1.04,P<0.001)表明RFM每增加1个单位,关节炎的发病风险升高2%。进一步将RFM转换为分类变量进行分析,以Q1为参照,模型3中Q4组关节炎发病风险升高了56%(HR=1.56,95% CI:1.23~1.98,P<0.001)。见表3。
进一步运用RCS风险曲线探究RFM与新发关节炎风险之间的剂量-反应关系,结果显示,RFM与新发关节炎风险未呈现显著的非线性关联(P非线性=0.688)。见图4。
调整潜在混杂因素后,RFM与新发关节炎风险之间的正相关关系在绝大多数亚组中依然保持稳定(HR>1,P<0.05)。此外,RFM与各亚组变量之间均未表现出显著的交互作用(交互作用均P>0.05)。见图5。
排除了在随访开始前两年内新发关节炎的参与者后评估了变量间的关联。结果与未排除的分析结论基本一致,表明研究结果具有较好的稳健性和可靠性。见表4。
构建ROC曲线分析RFM、BMI、腰围、腰高比对关节炎的诊断效能,结果显示,RFM诊断效能最优(AUC=0.564)。见图6。
肥胖是关节炎的重要危险因素[12],其作用机制主要包括增加机械负荷、诱发全身性炎症反应、引起代谢紊乱以及导致肠道菌群失衡等[13-16],其中,肥胖引发的系统性和局部炎症反应尤为关键[17-18]。脂肪组织尤其是内脏脂肪的过度积累可促进促炎因子如IL-6的释放,激活NF-κB/JNK等信号通路,诱导滑膜炎症和软骨细胞凋亡。同时,血脂异常还可进一步加剧炎症反应和氧化应激[19-20]。肥胖相关的肠道菌群失调可能通过“肠-关节”轴影响免疫稳态,诱发和加重关节炎症。因此,及早识别肥胖人群并采取针对性的预防措施,对于预防和减轻关节炎的危害具有重要意义。
BMI、腰围被广泛应用于评估肥胖及其相关疾病风险[21-23],然而,两者均存在一定局限:BMI仅依据身高与体重计算,无法区分脂肪组织与肌肉组织的质量[24];WC虽能直接反映腹部脂肪的堆积情况,但其测量结果易受测量者手法、测量位置、受试者呼吸状态等因素影响,且难以全面反映全身脂肪分布情况[25]。RFM作为一种新兴的肥胖评估指标,基于腰围、身高和性别等简单易获取的数据计算得出,其通过比值形式综合考量身体比例,不仅弥补了BMI无法区分身体成分的缺陷,还能更准确估算体脂率,尤其在识别中心性肥胖方面更具优势。此外,RFM在计算中对性别进行了调整,能更准确地评估不同性别人群的体脂水平。因此,RFM被认为是BMI、腰围的补充甚至代替工具。研究发现,RFM在预测其他疾病如2型糖尿病、抑郁症方面也具有出色的表现[26],其在公共卫生和临床研究多个领域均具有潜在的应用价值[27]。
本项针对中国中老年人的大型横断面及纵向研究进一步验证了RFM在关节炎风险评估中的价值。研究发现,RFM与关节炎风险之间存在显著的正相关关系,其诊断效能优于BMI等传统指标,提示RFM可作为评估关节炎风险的有效指标。
本研究具有多项优势:首先,研究基于CHARLS这一大型、具有全国代表性的队列数据,探讨了中国中老年人群中RFM与关节炎风险之间的关联。为增强研究结果的普遍适用性,对多个潜在亚组进行了亚组分析,更准确地评估了RFM与关节炎风险之间的关系。此外,本研究还进行了敏感性分析,验证了模型的稳定性。
本研究也存在一定的局限性:一是,本研究的主要终点依赖于参与者自我报告的医生诊断信息,这种方法可能存在回忆偏差。二是,研究缺乏关于关节炎具体亚型的详细信息。此外,尽管在分析中已对多项协变量进行了调整,但仍无法完全排除残余混杂因素的影响。
综上,在中国中老年人群中,较高水平的RFM与关节炎发病风险增加有关,对RFM水平的密切监测以及将其控制在较低范围,可能在关节炎预防中具有重要意义。
  • 广东省基础与应用基础研究基金项目(2025A1515010409)
  • 广东省中医药局科研项目(20251020; 20221023)
  • 广东省第二中医院科研创新基金项目(SEZYY2023A08)
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doi: 10.20043/j.cnki.MPM.202506066
  • 接收时间:2025-06-06
  • 首发时间:2026-10-29
  • 出版时间:2025-11-10
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  • 收稿日期:2025-06-06
基金
广东省基础与应用基础研究基金项目(2025A1515010409)
广东省中医药局科研项目(20251020; 20221023)
广东省第二中医院科研创新基金项目(SEZYY2023A08)
作者信息
    1. 广州中医药大学第五临床医学院,广东 广州 510405
    2.广东省第二中医院,广东 广州 510095

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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
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