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Article(id=1324078400146637059, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324078333767594037, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202508178, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1754928000000, receivedDateStr=2025-08-12, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793614490782, onlineDateStr=2026-11-02, pubDate=1767974400000, pubDateStr=2026-01-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793614490782, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793614490782, creator=13701087609, updateTime=1793614490782, updator=13701087609, issue=Issue{id=1324078333767594037, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='1', pageStart='1', pageEnd='192', issueExtLink='null', onlineDate='null', pubDate='1767974400000', pubDateStr='2026-01-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793614474956, creator='13701087609', updateTime=1793614571752, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324078739914621270, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324078333767594037, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324078739914621271, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324078333767594037, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=46, endPage=52, ext={EN=ArticleExt(id=1324078400364740868, articleId=1324078400146637059, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=A combined cross-sectional and mendelian randomization analysis of the association between abdominal obesity and bone mineral density in different age groups, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the correlation and potential causal association between abdominal obesity and bone mineral density(BMD) in people of different age groups.

Methods

This study combined a cross-sectional study with Mendelian randomization (MR) analysis.Cross-sectional study data were derived from the U.S. National Health and Nutrition Examination Survey (NHANES) 2011—2018. A total of 7 452 participants aged 20-60 years were included. Weighted linear regression analysis was employed to investigate the association between a body shape index (ABSI) and total body less head bone mineral density (TB-BMD). The MR study utilized a two-sample MR analysis to explore the causal relationship between genetically predicted waist circumference-adjusted for body mass index(WCadjBMI), an index of abdominal obesity, and TB-BMD.

Results

Cross-sectional analysis revealed a significant negative correlation between ABSI and TB-BMD(β=-3.53, 95% CI: -4.146~-2.915, P<0.001), which was more pronounced in males and individuals aged 45-60 years. In the 45-60 age group, an “inverted U-shaped” non-linear relationship was observed between ABSI and pelvic BMD (inflection point=0.076). MR analysis indicated a negative causal relationship between WCadjBMI and TB-BMD (β=-0.16, 95% CI: -0.26~-0.07,P<0.01), which was significant across age groups of <15 years, 30-45 years, and >60 years.

Conclusion

Abdominal obesity is clearly associated with bone loss, and this negative effect varies among different age and sex groups. Maintaining abdominal fat at a low level before significant bone loss occurs is crucial for preserving skeletal health.

, authors=Camilo Alberto Pinzon Galvis1, Yu-hong JIANG2, Xian-hao HUANG1, Ning LI1, Ling-yun LU1, authorsList=Camilo Alberto Pinzon Galvis, Yu-hong JIANG, Xian-hao HUANG, Ning LI, Ling-yun LU, authorCompany=null, correspAuthors=Ling-yun LU, 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=1324078403162341647, articleId=1324078400146637059, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=腹型肥胖与不同年龄阶段骨密度的横断面与孟德尔随机化联合分析, 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

探究腹型肥胖与不同年龄阶段人群骨密度的相关性及潜在因果关联。

方法

本研究联合了横断面研究与孟德尔随机化（Mendelian randomization，MR）分析。横断面研究数据来源于美国国家健康与营养调查2011—2018年数据。共纳入7 452名20～60岁参与者，采用加权线性回归分析探究体型指数（a body shape index,ABSI）与全身去头骨密度（total body less head bone mineral density，TB-BMD）的关联。MR研究采用双样本MR分析，探究遗传预测的腹型肥胖指数（waist circumference-adjusted for body mass index,WCadjBMI）与TB-BMD的因果关系。

结果

横断面分析显示，ABSI与TB-BMD呈负相关关系（β=-3.53, 95% CI: -4.146～-2.915, P<0.001），并且在男性及45～60岁人群中此特征更为突出。在45～60岁人群中，ABSI与骨盆骨密度存在“倒U型”非线性关系（拐点=0.076）。MR分析结果显示，WCadjBMI与TB-BMD存在负向因果关系（β=-0.16, 95% CI: -0.26～-0.07, P<0.01），且在<15岁、30～45岁及>60岁年龄组中关系显著。

结论

腹型肥胖对骨密度下降具有明确的关联性，且在不同年龄和性别的人群中存在差异。在骨量发生显著流失之前将腹部脂肪控制在较低水平，对于维持骨骼健康至关重要。

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Camilo Alberto Pinzon Galvis（1990—），男，硕士在读，研究方向：中西医结合临床

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鲁凌云，E-mail:
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注：A为ABSI与TB-BMD；B为ABSI与PE-BMD；C为ABSI与LS-BMD。

, figureFileSmall=93ZB8bawyF4bJom7TDsxrQ==, figureFileBig=+kXSPm1flQU3umKkuehz4A==, tableContent=null), ArticleFig(id=1324078407855767868, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078400146637059, language=EN, label=Figure 3, caption=Forest plot of the causal association between WCadjBMI and TB-BMD, figureFileSmall=4itRNrr4kcx+hWEj0W7s9w==, figureFileBig=rkJhXXEjFb1/owBfM0uKmg==, tableContent=null), ArticleFig(id=1324078408023540029, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078400146637059, language=CN, label=图3, caption=MR森林图（WCadjBMI与TB-BMD的因果关联）

注：TB为全年龄段TB-BMD；TB_15_or_less为<15岁人群TB-BMD；TB_3045为30～45岁人群TB-BMD；TB_60_or_more为>60岁人群TB-BMD。

, figureFileSmall=4itRNrr4kcx+hWEj0W7s9w==, figureFileBig=rkJhXXEjFb1/owBfM0uKmg==, tableContent=null), ArticleFig(id=1324078408124203326, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078400146637059, language=EN, label=Table 1, caption=

Baseline characteristics of the population by ABSI tertiles [M（P25，P75），n（%）]

, figureFileSmall=null, figureFileBig=null, tableContent=
特征总（n=7 452）
（0.058～0.099）		低（n=2 484）
（0.058～0.078）		中（n=2 484）
（0.078～0.082）		高（n=2 489）
（0.082～0.099）		P值
年龄（岁）35.59 (35.34,35.84)31.38 (31.02,31.75)35.69 (35.28,36.11)40.24 (39.79,40.68)<0.001
20～292 257 (30.29)1 139 (45.85)707 (28.46)411 (16.55)
30～443 029 (40.65)947 (38.12)1 101 (44.32)981 (39.49)
45～592 166 (29.07)398 (16.02)676 (27.21)1 092 (43.96)
种族<0.001
墨西哥裔美国人1 210 (16.24)296 (11.92)445 (17.91)469 (18.88)
其他西班牙裔775 (10.40)248 (9.98)262 (10.55)265 (10.67)
非西班牙裔白人2 348 (31.51)661 (26.61)811 (32.65)876 (35.27)
非西班牙裔黑人1 597 (21.43)812 (32.69)434 (17.47)351 (14.13)
其他种族1 522 (20.42)467 (18.80)532 (21.42)523 (21.05)
FIPR1.88(1.84,1.93)1.85(1.78,1.92)1.96 (1.89,2.04)1.84(1.77,1.91)0.046
教育水平<0.001
高中以下1 365 (18.32)360 (14.50)463 (18.64)542 (21.82)
高中毕业1 652 (22.17)519 (20.91)549 (22.10)584 (23.51)
大学及以上4 433 (59.50)1 603 (64.59)1 472 (59.26)1 358 (54.67)
吸烟<0.001
是2 661 (35.73)756 (30.43)890 (35.86)1 015 (40.89)
否4 787 (64.27)1 728 (69.57)1 592 (64.14)1 467 (59.11)
饮酒<0.001
是820 (13.58)221 (10.77)274 (13.47)325 (16.64)
否5 219 (86.42)1 831 (89.23)1 760 (86.53)1 628 (83.36)
中等体力活动0.585
是2 987 (40.08)1 011 (40.70)1 000 (40.26)976 (39.29)
否4 465 (59.92)1 473 (59.30)1 484 (59.74)1 508 (60.71)
糖尿病<0.001
是385 (5.25)58 (2.36)103 (4.21%)224 (9.23)
否6 954 (94.75)2 404 (97.64)2 346 (95.79)2 204 (90.77)
ALP (U/L)63.75 (63.30,64.20)60.25 (59.55,60.96)63.22 (62.47,63.97)68.00 (67.15,68.85)<0.001
ALT (U/L)22.17 (21.91,22.44)20.04 (19.65,20.44)22.79 (22.33,23.27)23.85 (23.35,24.36)<0.001
AST (U/L)22.94 (22.75,23.12)22.40 (22.10,22.71)23.00 (22.69,23.30)23.41 (23.06,23.78)<0.001
肌酐 (mg/dl)0.82(0.81,0.82)0.84(0.84,0.85)0.82 (0.81,0.82)0.80 (0.79,0.80)<0.001
总胆固醇 (mg/dl)185.25 (184.36,186.14)177.11 (175.67,178.56)186.56 (185.06,188.07)192.35 (190.75,193.96)<0.001
HDL-C (mg/dl)50.11 (49.78,50.45)53.25 (52.66,53.85)49.48 (48.92,50.05)47.78 (47.24,48.33)<0.001
磷 (mg/dl)3.67 (3.66,3.68)3.69 (3.67,3.72)3.66 (3.64,3.68)3.66 (3.63,3.68)0.038
钙 (mg/dl)9.36 (9.35,9.37)9.38 (9.37,9.40)9.35 (9.34,9.37)9.35 (9.34,9.36)0.002
25(OH)D(nmol/L)53.97 (53.45,54.50)52.92 (51.99,53.87)54.24 (53.36,55.14)54.77 (53.87,55.69)0.118
BMI (kg/m2)27.66 (27.52,27.79)26.98 (26.75,27.22)27.69 (27.46,27.92)28.31 (28.07,28.55)<0.001
身高 (cm)167.66 (167.44,167.88)167.37 (167.00,167.74)167.82 (167.44,168.20)167.79 (167.41,168.18)0.079
腰围 (cm)94.47 (94.13,94.82)87.51 (86.99,88.03)94.80 (94.26,95.34)101.64 (101.04,102.24)<0.001
TB-BMD(g/cm2)0.97(0.97,0.98)1.00(0.99,1.00)0.97(0.97,0.98)0.95(0.95,0.96)<0.001
PE-BMD (g/cm2)1.24(1.24,1.24)1.26(1.25,1.26)1.24(1.24,1.25)1.22(1.22,1.23)<0.001
LS-BMD(g/cm2)1.02(1.02,1.03)1.07(1.07,1.08)1.02(1.02,1.03)0.98(0.98,0.99)<0.001
), ArticleFig(id=1324078408266809663, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078400146637059, language=CN, label=表1, caption=

基于ABSI三分位分组的人群基线特征[M（P25，P75），n（%）]

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特征总（n=7 452）
（0.058～0.099）		低（n=2 484）
（0.058～0.078）		中（n=2 484）
（0.078～0.082）		高（n=2 489）
（0.082～0.099）		P值
年龄（岁）35.59 (35.34,35.84)31.38 (31.02,31.75)35.69 (35.28,36.11)40.24 (39.79,40.68)<0.001
20～292 257 (30.29)1 139 (45.85)707 (28.46)411 (16.55)
30～443 029 (40.65)947 (38.12)1 101 (44.32)981 (39.49)
45～592 166 (29.07)398 (16.02)676 (27.21)1 092 (43.96)
种族<0.001
墨西哥裔美国人1 210 (16.24)296 (11.92)445 (17.91)469 (18.88)
其他西班牙裔775 (10.40)248 (9.98)262 (10.55)265 (10.67)
非西班牙裔白人2 348 (31.51)661 (26.61)811 (32.65)876 (35.27)
非西班牙裔黑人1 597 (21.43)812 (32.69)434 (17.47)351 (14.13)
其他种族1 522 (20.42)467 (18.80)532 (21.42)523 (21.05)
FIPR1.88(1.84,1.93)1.85(1.78,1.92)1.96 (1.89,2.04)1.84(1.77,1.91)0.046
教育水平<0.001
高中以下1 365 (18.32)360 (14.50)463 (18.64)542 (21.82)
高中毕业1 652 (22.17)519 (20.91)549 (22.10)584 (23.51)
大学及以上4 433 (59.50)1 603 (64.59)1 472 (59.26)1 358 (54.67)
吸烟<0.001
是2 661 (35.73)756 (30.43)890 (35.86)1 015 (40.89)
否4 787 (64.27)1 728 (69.57)1 592 (64.14)1 467 (59.11)
饮酒<0.001
是820 (13.58)221 (10.77)274 (13.47)325 (16.64)
否5 219 (86.42)1 831 (89.23)1 760 (86.53)1 628 (83.36)
中等体力活动0.585
是2 987 (40.08)1 011 (40.70)1 000 (40.26)976 (39.29)
否4 465 (59.92)1 473 (59.30)1 484 (59.74)1 508 (60.71)
糖尿病<0.001
是385 (5.25)58 (2.36)103 (4.21%)224 (9.23)
否6 954 (94.75)2 404 (97.64)2 346 (95.79)2 204 (90.77)
ALP (U/L)63.75 (63.30,64.20)60.25 (59.55,60.96)63.22 (62.47,63.97)68.00 (67.15,68.85)<0.001
ALT (U/L)22.17 (21.91,22.44)20.04 (19.65,20.44)22.79 (22.33,23.27)23.85 (23.35,24.36)<0.001
AST (U/L)22.94 (22.75,23.12)22.40 (22.10,22.71)23.00 (22.69,23.30)23.41 (23.06,23.78)<0.001
肌酐 (mg/dl)0.82(0.81,0.82)0.84(0.84,0.85)0.82 (0.81,0.82)0.80 (0.79,0.80)<0.001
总胆固醇 (mg/dl)185.25 (184.36,186.14)177.11 (175.67,178.56)186.56 (185.06,188.07)192.35 (190.75,193.96)<0.001
HDL-C (mg/dl)50.11 (49.78,50.45)53.25 (52.66,53.85)49.48 (48.92,50.05)47.78 (47.24,48.33)<0.001
磷 (mg/dl)3.67 (3.66,3.68)3.69 (3.67,3.72)3.66 (3.64,3.68)3.66 (3.63,3.68)0.038
钙 (mg/dl)9.36 (9.35,9.37)9.38 (9.37,9.40)9.35 (9.34,9.37)9.35 (9.34,9.36)0.002
25(OH)D(nmol/L)53.97 (53.45,54.50)52.92 (51.99,53.87)54.24 (53.36,55.14)54.77 (53.87,55.69)0.118
BMI (kg/m2)27.66 (27.52,27.79)26.98 (26.75,27.22)27.69 (27.46,27.92)28.31 (28.07,28.55)<0.001
身高 (cm)167.66 (167.44,167.88)167.37 (167.00,167.74)167.82 (167.44,168.20)167.79 (167.41,168.18)0.079
腰围 (cm)94.47 (94.13,94.82)87.51 (86.99,88.03)94.80 (94.26,95.34)101.64 (101.04,102.24)<0.001
TB-BMD(g/cm2)0.97(0.97,0.98)1.00(0.99,1.00)0.97(0.97,0.98)0.95(0.95,0.96)<0.001
PE-BMD (g/cm2)1.24(1.24,1.24)1.26(1.25,1.26)1.24(1.24,1.25)1.22(1.22,1.23)<0.001
LS-BMD(g/cm2)1.02(1.02,1.03)1.07(1.07,1.08)1.02(1.02,1.03)0.98(0.98,0.99)<0.001
), ArticleFig(id=1324078408359084352, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078400146637059, language=EN, label=Table 2, caption=

Linear regression analysis of ABSI and TB-BMD

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暴露模型1a
β（95%CI）		P值模型2b
β（95%CI）		P值模型3c
β（95%CI）		P值
ABSI (连续)-4.345 (-4.897～-3.792)<0.001-3.923 (-4.42～-3.421)<0.001-3.531 (-4.146～-2.915)<0.001
ABSI (三分位)
低RefRefRef
-0.018-0.017-0.017
中<0.001<0.001<0.001
(-0.023～-0.012)(-0.022～-0.012)(-0.023～-0.011)
-0.038-0.035-0.032
高<0.001<0.001<0.001
(-0.044～-0.033)(-0.040～-0.030)(-0.038～-0.026)
P for trend<0.001<0.001<0.001
), ArticleFig(id=1324078408430387521, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078400146637059, language=CN, label=表2, caption=

ABSI与TB-BMD的线性回归分析

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暴露模型1a
β（95%CI）		P值模型2b
β（95%CI）		P值模型3c
β（95%CI）		P值
ABSI (连续)-4.345 (-4.897～-3.792)<0.001-3.923 (-4.42～-3.421)<0.001-3.531 (-4.146～-2.915)<0.001
ABSI (三分位)
低RefRefRef
-0.018-0.017-0.017
中<0.001<0.001<0.001
(-0.023～-0.012)(-0.022～-0.012)(-0.023～-0.011)
-0.038-0.035-0.032
高<0.001<0.001<0.001
(-0.044～-0.033)(-0.040～-0.030)(-0.038～-0.026)
P for trend<0.001<0.001<0.001
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腹型肥胖与不同年龄阶段骨密度的横断面与孟德尔随机化联合分析
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Camilo Alberto Pinzon Galvis 1 , 江宇虹 2 , 黄显皓 1 , 李宁 1 , 鲁凌云 1
现代预防医学 | 流行病与统计方法 2026,53(1): 46-52
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现代预防医学 |流行病与统计方法 2026 , 53 (1) : 46 -52
腹型肥胖与不同年龄阶段骨密度的横断面与孟德尔随机化联合分析
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Camilo Alberto Pinzon Galvis1, 江宇虹2, 黄显皓1, 李宁1, 鲁凌云1
作者信息
  • 1.四川大学华西医院中西医结合中心，中西医结合内科，四川 成都 610041
  • 2.四川大学华西医院中西医结合中心，胰腺炎中心
通讯作者:
鲁凌云，E-mail:
作者简介:

Camilo Alberto Pinzon Galvis（1990—），男，硕士在读，研究方向：中西医结合临床

A combined cross-sectional and mendelian randomization analysis of the association between abdominal obesity and bone mineral density in different age groups
Camilo Alberto Pinzon Galvis1, Yu-hong JIANG2, Xian-hao HUANG1, Ning LI1, Ling-yun LU1
Affiliations
  • Division of Internal Medicine, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
出版时间: 2026-01-10 doi: 10.20043/j.cnki.MPM.202508178
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摘要
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目的

探究腹型肥胖与不同年龄阶段人群骨密度的相关性及潜在因果关联。

方法

本研究联合了横断面研究与孟德尔随机化（Mendelian randomization，MR）分析。横断面研究数据来源于美国国家健康与营养调查2011—2018年数据。共纳入7 452名20～60岁参与者，采用加权线性回归分析探究体型指数（a body shape index,ABSI）与全身去头骨密度（total body less head bone mineral density，TB-BMD）的关联。MR研究采用双样本MR分析，探究遗传预测的腹型肥胖指数（waist circumference-adjusted for body mass index,WCadjBMI）与TB-BMD的因果关系。

结果

横断面分析显示，ABSI与TB-BMD呈负相关关系（β=-3.53, 95% CI: -4.146～-2.915, P<0.001），并且在男性及45～60岁人群中此特征更为突出。在45～60岁人群中，ABSI与骨盆骨密度存在“倒U型”非线性关系（拐点=0.076）。MR分析结果显示，WCadjBMI与TB-BMD存在负向因果关系（β=-0.16, 95% CI: -0.26～-0.07, P<0.01），且在<15岁、30～45岁及>60岁年龄组中关系显著。

结论

腹型肥胖对骨密度下降具有明确的关联性，且在不同年龄和性别的人群中存在差异。在骨量发生显著流失之前将腹部脂肪控制在较低水平，对于维持骨骼健康至关重要。

腹型肥胖  /  骨密度  /  体型指数  /  孟德尔随机化  /  NHANES
Objective

To investigate the correlation and potential causal association between abdominal obesity and bone mineral density(BMD) in people of different age groups.

Methods

This study combined a cross-sectional study with Mendelian randomization (MR) analysis.Cross-sectional study data were derived from the U.S. National Health and Nutrition Examination Survey (NHANES) 2011—2018. A total of 7 452 participants aged 20-60 years were included. Weighted linear regression analysis was employed to investigate the association between a body shape index (ABSI) and total body less head bone mineral density (TB-BMD). The MR study utilized a two-sample MR analysis to explore the causal relationship between genetically predicted waist circumference-adjusted for body mass index(WCadjBMI), an index of abdominal obesity, and TB-BMD.

Results

Cross-sectional analysis revealed a significant negative correlation between ABSI and TB-BMD(β=-3.53, 95% CI: -4.146~-2.915, P<0.001), which was more pronounced in males and individuals aged 45-60 years. In the 45-60 age group, an “inverted U-shaped” non-linear relationship was observed between ABSI and pelvic BMD (inflection point=0.076). MR analysis indicated a negative causal relationship between WCadjBMI and TB-BMD (β=-0.16, 95% CI: -0.26~-0.07,P<0.01), which was significant across age groups of <15 years, 30-45 years, and >60 years.

Conclusion

Abdominal obesity is clearly associated with bone loss, and this negative effect varies among different age and sex groups. Maintaining abdominal fat at a low level before significant bone loss occurs is crucial for preserving skeletal health.

Abdominal obesity  /  Bone mineral density  /  A body shape index  /  Mendelian randomization  /  NHANES
Camilo Alberto Pinzon Galvis, 江宇虹, 黄显皓, 李宁, 鲁凌云. 腹型肥胖与不同年龄阶段骨密度的横断面与孟德尔随机化联合分析. 现代预防医学, 2026 , 53 (1) : 46 -52 . DOI: 10.20043/j.cnki.MPM.202508178
Camilo Alberto Pinzon Galvis, Yu-hong JIANG, Xian-hao HUANG, Ning LI, Ling-yun LU. A combined cross-sectional and mendelian randomization analysis of the association between abdominal obesity and bone mineral density in different age groups[J]. Modern Preventive Medicine, 2026 , 53 (1) : 46 -52 . DOI: 10.20043/j.cnki.MPM.202508178
正文
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骨质疏松症是一种以骨密度降低和骨微结构退化为特征的骨骼疾病，目前已成为了重大的公共卫生问题[1]。与此同时，肥胖的发生率亦日益攀升[2]。然而肥胖与骨密度的关系仍存争议。传统观点认为，一定程度的肥胖由于机械负荷的增加对骨骼具有保护作用[3]，但越来越多的研究提示，肥胖——尤其是伴随的代谢异常的肥胖状态可能对骨骼健康产生不利影响[4]。
这些相互矛盾的研究结果，一定程度上归因于单纯依赖身体质量指数（body mass index, BMI）这一单一指标进行肥胖评估[5-6]。BMI无法有效区分脂肪与瘦组织，反应脂肪分布，可能掩盖某些类型的肥胖（比如腹型肥胖）产生的潜在病理影响[7]。因此，越来越多的研究开始探究更能反映脂肪分布的肥胖指标,如腰围，以及对腰围进行BMI调整以排除BMI干扰的衍生指标：经BMI调整的腰围（waist circumference-adjusted for BMI，WCadjBMI）、体型指数（a body shape index, ABSI）等。这些指标被证明能更好地评估与内脏脂肪相关的病理性肥胖所带来的健康风险[8]。
尽管已有研究探究了腹型肥胖与骨骼健康的关系，但对于腹型肥胖在骨骼发育的不同阶段的作用仍然缺乏系统的了解。人体骨骼在儿童青少年期快速生长、成年后达到平台期，老年后出现加速流失，呈现截然不同的代谢特征。因此腹型肥胖对骨骼影响很可能在不同年龄阶段存在差异[9-10]。为填补这一空白，本研究首先利用了美国国家健康与营养调查（National Health and Nutrition Examination Survey,NHANES, 2011—2018）的大样本数据，并采用ABSI作为腹型肥胖指标，旨在全面探究ABSI与不同年龄阶段人群骨密度的相关性模式。同时，为了避免横断面研究的潜在混杂因素和反向因果的干扰，我们采用了孟德尔随机化（Mendelian randomization, MR）分析，利用与WCadjBMI相关的遗传变异作为工具变量，进一步探究了遗传预测的腹型肥胖与骨密度下降之间是否存在明确的因果关系，以及这样的关系在不同年龄阶段是否存在差异。旨在为制定分阶段、精准化的骨质疏松症防治策略，以及不同年龄段人群的肥胖管理，提供有力科学依据。
1　材料与方法
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1.1　研究数据来源
横断面研究数据来源于NHANES 2011—2018。该项目经美国国家健康统计中心伦理审查委员会批准，所有参与者均签署知情同意书。根据预设纳入排除标准（主要排除年龄<20岁、缺乏暴露与结局相关数据、患有影响骨密度的慢性或重大疾病的人群），最终纳入7 452名成年受试者进行分析。MR分析流程见图1。
1.2　变量评估
横断面研究选择常用于反映腹部脂肪分布的肥胖指标—ABSI作为暴露指标[8]，具体计算公式如下。其中，WC为腰围（m），BMI单位为kg/m2，Height为身高（m）。采用双能X线吸收法评估结局指标骨密度：包括全身去头骨密度（total body less head bone mineral density，TB-BMD）、骨盆骨密度（pelvic bone mineral density，PE-BMD）和腰椎骨密度（lumbar spiner bone mineral density,LS-BMD）[11]。MR研究采用遗传流行病学中常用于反映腹部脂肪分布的肥胖指标—WCadjBMI作为暴露指标，其GWAS汇总数据来自GIANT联盟，该研究纳入了 142 762名混合种族的受试者[12]。结局指标为TB-BMD，其GWAS汇总数据来自GEFOS联盟，共纳入 56 284名混合种族受试者[13]。
1.3　统计分析
横断面研究所有分析均采用推荐的样本权重。分类变量用频率（%）表示，组间比较采用加权χ2检验；连续变量以（）或M（IQR）表示，组间比较采用加权线性回归或Kruskal-Wallis检验。考虑到骨骼发育的年龄特异性，我们在亚组分析中将人群划分为三个年龄段：20～30岁（骨量累积期）、30～45岁（骨量平台期）和45～60岁（骨量快速流失期），以系统探究腹型肥胖在不同骨骼发育阶段的影响模式。采用多重加权线性回归模型评估ABSI与TB-BMD的关联，结果以β系数和95% CI表示，同时将ABSI分为三等分以进行趋势检验（P for trend< 0.05）。基于既往文献和临床经验，我们确定了潜在的混杂因素[14]，并在回归模型中进行控制。模型3控制了所有混杂因素，包括性别、年龄、种族、家庭收入贫困比、教育水平、吸烟、饮酒、中等强度活动、糖尿病、碱性磷酸酶、丙氨酸氨基转移酶、天门冬氨酸氨基转移酶、肌酐、总胆固醇、高密度脂蛋白胆固醇、磷、钙及维生素D。采用平滑曲线拟合和阈值效应分析评估潜在的非线性关联，这包括使用递归算法识别拐点，并在拐点两侧应用分段线性回归模型。进一步采用分层线性回归模型进行亚组分析，同时纳入预先设定的潜在效应修饰因子。通过在模型中包含交互项并进行似然比检验来评估亚组间的异质性，并识别这些效应修饰因子（P for interaction<0.05）。
MR研究基于逆方差加权法（inverse-variance weighted, IVW）构建主分析模型，并辅以MR-Egger、加权中位数法等多种模型进行稳健性检验。选取的SNPs满足以下条件：显著性阈值（P<5×10-8）、校正后显著性[错误发现率（false discovery rate，FDR）<0.05]、F统计量（>10）和连锁不平衡（linkage disequilibrium,LD）（r2<0.1），以减少工具变量的偏倚。为保证结果的可靠性，我们进一步进行了一系列敏感性分析，具体如下：Cochran Q检验评估异质性；MR-Egger截距项和MR-PRESSO检验水平多效性；“留一法”分析评估单个SNP的影响；Steiger检验验证因果方向；计算F统计量以排除弱工具变量偏倚（F>10）；采用Benjamini-Hochberg法控制FDR；采用交换暴露与结局变量构建反向MR模型排除反向因果干扰。
所有统计分析均使用R 4.3.1，并辅以EmpowerStats、TwoSampleMR等软件包进行分析。对于所有分析，检验水准α=0.05。
2　结果
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2.1　横断面研究回归分析
7 452名受试者统计分析结果显示，人群基线特征随ABSI三分位分组呈现一定差异，见表1。处于高ABSI区间的人群倾向于具有较低的TB-BMD、PE-BMD和LS-BMD水平。多重加权线性回归分析结果进一步表明，在调整了潜在混杂因素后，ABSI与TB-BMD呈显著负相关。具体而言，ABSI每增加1个单位，TB-BMD将降低3.531个单位（β=-3.531，95% CI: -4.146～-2.915，P<0.001）。此外，我们将ABSI分为三分位以分析其潜在的趋势效应。结果显示，处于高分位组中的ABSI与TB-BMD的负相关性较中、低ABSI组更强。趋势性检验具有统计学意义（P for trend<0.05）。见表2。
2.2　平滑曲线拟合与阈值效应分析
考虑到骨骼在不同生长发育阶段呈现不同的骨代谢模式，我们将不同年龄人群进一步分为三个组：20～30岁（峰值前骨量组）、30～45岁（峰值骨量组）和45～60岁（峰值后骨量组），并采用平滑曲线拟合和阈值效应分析，评估腹型肥胖与骨密度在不同年龄阶段的相关性模式差异。结果显示，在不同年龄分层中，ABSI与骨密度的关系存在差异，并具有统计学意义，见图2。特别是在45～60岁年龄组中，ABSI与PE-BMD之间并非持续的负相关关系，而呈现类似“倒U型”曲线，见图2B。进一步使用加权两段线性回归模型和递归算法进行的阈值效应分析显示，拐点（K值）为0.076。即ABSI在达到0.076之前与PE-BMD呈正相关（β=9.202, 95% CI：0.185～18.219，P=0.046），而在此之后呈负相关（β=-3.221, 95% CI：-5.419～-1.023, P=0.004）。然而，在20～30岁和30～45岁年龄组中，0.076并非拐点，而仅作为转折点，ABSI与骨密度呈现出类似L形曲线的关系。相反的是，在30～45岁年龄组中，曲线显示ABSI与TB-BMD和PE-BMD在超过0.086的转折点后呈正相关，但这种关系并无统计学显著性（P>0.05）。见图2A、B。
2.3　亚组分析
为了评估ABSI与TB-BMD之间的负相关性结果的敏感性，我们进一步进行了亚组分析。结果显示，在不同性别、年龄、种族等分层中，ABSI与TB-BMD之间均为负相关，结果具有良好的稳定性。然而值得注意的是，该负相关效应的程度在不同性别、年龄和活动水平中存在具有统计学意义的差异（P for interaction<0.05）。ABSI对TB-BMD的负面影响在45～60岁年龄组（峰值后骨量）中最为显著（β=-7.530, 95% CI：-9.246～-5.814）。同时值得注意的是，ABSI的增加对TB-BMD的负面影响在男性群体中似乎更为显著（β=-5.601, 95% CI：-6.589～-4.614），而在女性群体中的影响相对较小（β=-1.624,95% CI: -2.375～-0.873）。高ABSI的男性TB-BMD丢失量约为女性的3.46倍。
2.4　MR分析
为了避免横断面研究的潜在混杂因素和反向因果的干扰，我们进一步采用了MR分析，以明确腹型肥胖增加与骨密度下降之间的潜在因果关联。IVW结果表明，WCadjBMI与TB-BMD之间存在负向因果关联（β=-0.16；95% CI：-0.26～-0.07），且这种负向关联在特定年龄段中尤为显著：<15岁（β=-0.26；95% CI：-0.39～-0.12），30～45岁（β=-0.19；95% CI：-0.35～-0.03），>60岁（β=-0.22；95% CI：-0.35～-0.10），见图3。即WCadjBMI的增加可能导致这些年龄组TB-BMD的减少。加权中位数法进一步验证了上述结果的稳健性（P<0.05）。
2.5　MR敏感性分析
在敏感性分析中，我们首先通过留一法评估了各年龄组WCadjBMI与TB-BMD因果关联的稳健性。结果显示，剔除任一SNP后，汇总效应量均未发生显著偏移，表明结果对单个遗传工具变量的依赖性较低。进一步通过漏斗图检验潜在偏倚，发现数据点呈对称分布，未检测到明显的水平多效性证据。Steiger方向性检验进一步证实了WCadjBMI对TB-BMD因果关系的方向性。同时，为全面验证结论的可靠性，我们进行了反向MR分析，结果显示不同年龄组的TB-BMD对WCadjBMI均无显著因果效应。进一步验证了WCadjBMI与TB-BMD的负向因果关系方向明确，并且具有年龄特异性。
3　讨论
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本研究结合了大规模人群横断面研究与MR分析，旨在系统阐明腹型肥胖与不同骨骼发育阶段骨密度的复杂关系。结果显示，腹型肥胖与骨密度存在明确负相关，且相关性模式在不同年龄阶段呈现异质性。
在横断面研究中，我们发现ABSI与TB-BMD的负相关关系，这一结果与部分既往研究的结论一致[14,9]。人体骨量在儿童期稳步增加，在青春期加速增长，并在25～30岁之间达到峰值并保持稳定[15]，直到在45～60岁后出现明显下降[16]。因此，为系统探究腹型肥胖在骨骼发育的不同阶段的作用是否存在差异，我们进一步将年龄分为三个组：20～30岁（峰值前骨量组）、30～45岁（峰值骨量组）和45～60岁（峰值后骨量组），并首次揭示了腹型肥胖在不同年龄阶段对骨密度的影响模式。
在45～60岁的中老年人群中，ABSI与TB-BMD的负向关联表现得尤为显著，提示腹型肥胖可能加剧了老年性骨丢失的进程。同时，在该年龄层中，我们发现了ABSI与PE-BMD并不只是呈简单的随着ABSI升高PE-BMD下降的负相关关系，而是存在一个“倒U型”的非线性关系，拐点位于ABSI=0.076。即当ABSI低于该阈值时，随着ABSI的升高，PE-BMD也升高，而超过该阈值后则转为负相关关系。这一现象提示，在处于骨量下降时期时，腹型肥胖对于骨密度的影响具有复杂性。轻度的腹型肥胖可能通过机械负荷刺激增加或雌激素代偿分泌等机制[17]，对骨骼存在一定正向作用；然而一旦超过一个临界点，其潜在的代谢性损害（如炎症、胰岛素抵抗）将可能占据主导地位，最终导致骨丢失。这一发现为理解“肥胖悖论”的复杂性，以及为中老年人群骨质疏松的风险分层与精准干预提供了关键的量化依据。
为了进一步探究腹型肥胖与骨丢失的因果关联，我们进一步进行了MR分析。MR分析结果清晰地揭示了遗传预测的腹型肥胖（WCadjBMI）对TB-BMD存在负向因果效应。这一结论不仅与我们横断面研究的发现高度吻合，也印证了一项早期基于足跟骨测量的MR研究[18]。值得注意的是，本研究中横断面分析与MR分析的结果形成了强有力的相互支持，显著增强了研究结论的可靠性。具体而言，横断面研究仅能提示ABSI与骨密度间的关联性，但无法排除潜在的混杂因素或反向因果关系。而MR分析利用遗传变异作为工具变量，有效地规避了这些干扰，为腹型肥胖是导致骨密度下降的直接原因提供了更为坚实的证据。值得注意的是，ABSI与WCadjBMI虽名称不同，但其核心都是对腰围进行BMI调整后的度量。因此，尽管两种分析采用了不同的评估指标，但其结果的一致性共同指向一个明确的结论：腹部脂肪的过度堆积会直接引发骨量流失。此外，本研究采用了TB-BMD作为主要结局指标，这是对各个年龄段骨密度进行无偏评估的最可靠方法[13,19]。MR分析进一步揭示了腹型肥胖对骨密度的负面影响贯穿于不同的骨骼发育阶段，这与我们的横断面研究中观察到的年龄特异性模式高度一致。
本研究中观察到的腹型肥胖与骨密度之间的负相关性可能主要归因于内脏脂肪的积累。内脏脂肪组织作为一种活跃的内分泌器官，能够分泌大量促炎细胞因子（如TNF-α，IL-6），这些促炎因子一方面直接促进破骨细胞的骨吸收活性[20-21]，另一方面抑制成骨细胞的增殖与分化[22]，从而破坏骨重塑平衡，导致骨丢失。此外，在横断面研究中我们通过进一步亚组分析发现，腹型肥胖对男性骨量的负面影响似乎比女性更为显著。这种差异可能源于性别之间不同的脂肪分布模式和激素差异。在同等程度的肥胖情况下，男性表现出内脏脂肪组织的优先积累，而女性的脂肪则优先沉积于皮下脂肪组织[23]。男性的内脏脂肪通过释放炎症因子和芳香化酶转化产生的雌激素，抑制下丘脑-垂体-性腺轴，导致睾酮水平下降[24]。睾酮是促进成骨的关键代谢激素，其水平降低将对男性骨骼健康造成不利影响[25]。而女性的皮下脂肪代谢活性较低、炎症水平较弱，并且可提供额外的非卵巢来源的骨保护性雌激素[26-27]，可能减轻了腹型肥胖对女性骨骼健康的潜在危害。
然而，本研究仍存在一定局限性。首先，我们在横断面与MR分析中，分别采用了ABSI和WCadjBMI作为腹型肥胖的代表指标。这一选择是基于数据可及性的考量——前者易于在横断面研究中直接测量，而后者则具备成熟的遗传工具变量用于MR分析。尽管两项指标的核心都是对腰围进行BMI校正，旨在分离腹部脂肪的独立效应，但其计算方式的差异仍可能导致结果解释上的不一致。未来的研究应采用基于影像学直接测量的内脏脂肪组织作为金标准，以获得更精确的因果推断。其次，尽管横断面分析已对多种潜在混杂因素进行了调整，但仍可能存在未被充分测量的生活方式因素等所导致的残余混杂。另一方面，本研究的主要结局指标为骨密度，而非骨质疏松性骨折这一临床硬终点。由于NHANES数据库中关于新发骨折事件的数据有限，我们未能直接评估腹型肥胖与骨折风险之间的因果关联。骨密度虽是骨强度的关键决定因素和骨折风险的重要预测指标，但它并不能完全代表骨骼的整体健康状况。因此，未来的研究方向应聚焦于大规模前瞻性队列，以验证我们发现的非线性关系，并最终阐明腹型肥胖对骨折风险的直接影响，从而为骨质疏松的精准预防提供更强的临床证据。
基金
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  • 国家自然青年科学基金项目(82204847)
文献
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参考文献 引证文献
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doi: 10.20043/j.cnki.MPM.202508178
  • 接收时间：2025-08-12
  • 首发时间：2026-11-02
  • 出版时间：2026-01-10
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  • 收稿日期：2025-08-12
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国家自然青年科学基金项目(82204847)
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    1.四川大学华西医院中西医结合中心，中西医结合内科，四川 成都 610041
    2.四川大学华西医院中西医结合中心，胰腺炎中心

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鲁凌云，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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