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Article(id=1322607866092044484, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322607837742747790, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202504218, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744300800000, receivedDateStr=2025-04-11, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793263888141, onlineDateStr=2026-10-29, pubDate=1761321600000, pubDateStr=2025-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793263888141, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793263888141, creator=13701087609, updateTime=1793263888141, updator=13701087609, issue=Issue{id=1322607837742747790, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='20', pageStart='3649', pageEnd='3840', issueExtLink='null', onlineDate='null', pubDate='1761321600000', pubDateStr='2025-10-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793263881383, creator='13701087609', updateTime=1793264238791, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322609336933794638, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322607837742747790, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322609336933794639, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322607837742747790, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3785, endPage=3791, ext={EN=ArticleExt(id=1322607867727823045, articleId=1322607866092044484, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Study on the relationship between dietary patterns and central obesity of adult residents, Handan City, columnId=1228016572451718132, journalTitle=Modern Preventive Medicine, columnName=Health and Social Behavior, runingTitle=null, highlight=null, articleAbstract=
Objective

To analyze the relationship between dietary patterns and central obesity to provide a theoretical basis for preventing and improving central obesity.

Methods

A multi-stage stratified cluster random sampling method was used to select adult residents aged 18 and above in Handan City for the 2022 monitoring of Chinese residents’ nutrition and health status as the survey subjects. Collect basic and dietary information of survey subjects and conduct physical examinations. Calculate the central obesity rate among residents. Exploratory factor analysis was used to extract dietary patterns, and the quartiles of factor scores for each dietary pattern were ranked from low to high as Q1, Q2, Q3, Q4. Apply binary logistic regression to analyze the relationship between dietary patterns and central obesity.

Results

The standardized central obesity rate of adult residents in Handan was 53.3%. Six dietary patterns were extracted, namely the meat-fish-fruit-fungi pattern, snack-beverage-milk pattern, vegetable-staple-low-legume pattern, nut pattern, egg pattern, and alcohol pattern. Binary logistic regression analysis showed that the meat-fish-fruit-fungi pattern was associated with central obesity (Q4: OR=0.422, 95% CI: 0.216-0.822). The vegetable-staple-low-legume pattern was associated with central obesity (Q4: OR=2.138, 95% CI: 1.052-4.343). The alcohol patterns were associated with central obesity (Q4: OR=2.013, 95% CI: 1.085-3.734).

Conclusion

The central obesity rate of adult residents in Handan is relatively high. The meat-fish-fruit-fungi pattern is a protective factor for central obesity, while the vegetable-staple-low-legume pattern and alcohol pattern are risk factors for central obesity. It is recommended to moderately increase the intake of high-quality proteins such as meat, fish, and legume, as well as fruits and fungi, and reduce the intake of staple food, and avoid excessive alcohol consumption.

, authors=Hui LI, Yi-man ZHANG, Xi GAO, Shan ZHAO, Yong-qing YANG, authorsList=Hui LI, Yi-man ZHANG, Xi GAO, Shan ZHAO, Yong-qing YANG, authorCompany=null, correspAuthors=Yong-qing YANG, 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=1322607869053223114, articleId=1322607866092044484, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=邯郸市成年居民膳食模式与中心性肥胖的关系研究, columnId=1228016572640461823, journalTitle=现代预防医学, columnName=健康与社会行为, runingTitle=null, highlight=null, articleAbstract=
目的

分析居民膳食模式与中心性肥胖的关系，为预防和改善中心性肥胖提供理论依据。

方法

采用多阶段分层整群随机抽样的方法，选择2022年中国居民营养与健康状况监测邯郸市18岁及以上居民作为调查对象。收集对象的基本信息和膳食信息并进行体格检查。计算居民中心性肥胖率。采用探索性因子分析提取膳食模式，各膳食模式因子得分四分位数由低到高为Q1、Q2、Q3、Q4，应用二元logistic回归分析膳食模式与中心性肥胖的关系。

结果

邯郸市成年居民标化中心性肥胖率53.3%。提取到肉鱼-水果-菌类模式、零食-饮料-奶类模式、蔬菜-主食-低豆类模式、坚果类模式、蛋类模式和酒类模式6种膳食模式。二元logistic回归显示，肉鱼-水果-菌类模式与中心性肥胖相关（Q4：OR=0.422，95% CI：0.216~0.822）。蔬菜-主食-低豆类模式与中心性肥胖相关（Q4：OR=2.138，95% CI：1.052~4.343）。酒类模式与中心性肥胖相关（Q4：OR=2.013，95% CI：1.085~3.734）。

结论

邯郸市成年居民中心性肥胖率较高，肉鱼-水果-菌类模式是中心性肥胖的保护因素，蔬菜-主食-低豆类模式和酒类模式是中心性肥胖的危险因素，应适量增加肉鱼类、豆类等优质蛋白和水果、菌类的摄入量，减少主食的摄入量，避免过量饮酒。

, authors=李惠, 张一曼, 高茜, 赵珊, 杨永清, authorsList=李惠, 张一曼, 高茜, 赵珊, 杨永清, authorCompany=null, correspAuthors=杨永清, authorNote=

李惠（1992－），女，硕士，中级职称，研究方向：营养与食品卫生学

, correspAuthorsNote=
杨永清，E-mail:
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李惠（1992－），女，硕士，中级职称，研究方向：营养与食品卫生学

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Relationship between dietary patterns, carbohydrate quality and metabolic syndrome in adults—a cross-sectional study based on the CHNS database [D]. Chongqing: Medical University, 2024. (In Chinese), articleTitle=Relationship between dietary patterns, carbohydrate quality and metabolic syndrome in adults—a cross-sectional study based on the CHNS database, refAbstract=null), Reference(id=1322607887050981665, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607866092044484, doi=null, pmid=null, pmcid=null, year=2020, volume=39, issue=6, pageStart=528, pageEnd=536, url=null, language=null, rfNumber=[24], rfOrder=39, authorNames=Liu RR, Zhao YL, Li Q, journalName=Journal of the American College of Nutrition, refType=null, unstructuredReference=Liu RR, Zhao YL, Li Q, et al. Body fat mass, fat distribution and egg consumption: a Population-Based study in Chinese adults [J]. Journal of the American College of Nutrition, 2020, 39 (6): 528-536., articleTitle=Body fat mass, fat distribution and egg consumption: a Population-Based study in Chinese adults, refAbstract=null), Reference(id=1322607887118090530, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607866092044484, doi=null, pmid=null, pmcid=null, year=2024, volume=46, issue=3, pageStart=309, pageEnd=311, url=null, language=null, rfNumber=[25], rfOrder=40, authorNames=郑艳敏, 王波, 刘珈显, journalName=营养学报, refType=null, unstructuredReference=郑艳敏，王波，刘珈显, 等. 苏州市成年居民膳食模式与超重肥胖关系的研究 [J]. 营养学报, 2024, 46 (3): 309-311., articleTitle=苏州市成年居民膳食模式与超重肥胖关系的研究, refAbstract=null), Reference(id=1322607887382331683, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607866092044484, doi=null, pmid=null, pmcid=null, year=2024, volume=46, issue=3, pageStart=309, pageEnd=311, url=null, language=null, rfNumber=[25], rfOrder=41, authorNames=Zheng YM, Wang B, Liu JX, journalName=Acta Nutrimenta Sinica, refType=null, unstructuredReference=Zheng YM, Wang B, Liu JX, et al. 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(In Chinese), articleTitle=A study on the relationship between dietary patterns and overweight and obesity among adult residents in Suzhou city, refAbstract=null), Reference(id=1322607887453634852, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607866092044484, doi=null, pmid=null, pmcid=null, year=2024, volume=49, issue=10, pageStart=108, pageEnd=117, url=null, language=null, rfNumber=[26], rfOrder=42, authorNames=赵芳, 杨永清, 侯媛媛, journalName=食品科技, refType=null, unstructuredReference=赵芳，杨永清，侯媛媛, 等. 蒲公英精酿啤酒工艺研究及其品质分析 [J]. 食品科技, 2024, 49 (10): 108-117., articleTitle=蒲公英精酿啤酒工艺研究及其品质分析, refAbstract=null), Reference(id=1322607887533326629, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607866092044484, doi=null, pmid=null, pmcid=null, year=2024, volume=49, issue=10, pageStart=108, pageEnd=117, url=null, language=null, rfNumber=[26], rfOrder=43, authorNames=Zhao F, Yang YQ, Hou YY, journalName=Food Science and Technology, refType=null, unstructuredReference=Zhao F, Yang YQ, Hou YY, et al. Research on the craft and quality analysis of dandelion craft beer [J]. Food Science and Technology, 2024, 49 (10): 108-117. (In Chinese), articleTitle=Research on the craft and quality analysis of dandelion craft beer, refAbstract=null)], funds=[Fund(id=1322607879446708470, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607866092044484, awardId=20251320, language=CN, fundingSource=河北省卫生健康委医学科学研究课题计划项目(20251320), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1322607869481042123, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607866092044484, xref=null, ext=[AuthorCompanyExt(id=1322607869489430732, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607866092044484, companyId=1322607869481042123, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Handan Center for Disease Control and Prevention, Handan, Hebei 056008, China), AuthorCompanyExt(id=1322607869497819341, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607866092044484, companyId=1322607869481042123, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=邯郸市疾病预防控制中心，河北　邯郸　056008)])], figs=[ArticleFig(id=1322607877190172910, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607866092044484, language=EN, label=Table 1, caption=

Central obesity rates with different demographic characteristics

, figureFileSmall=null, figureFileBig=null, tableContent=
特征正常中心性肥胖χ2/Z值P值
性别[n(%)]3.1690.075
男89（46.1）104（53.9）
女84（37.5）140（62.5）
年龄[n(%)]17.835<0.001
18~4969（57.0）52（43.0）
50~5959（33.0）120（67.0）
≥6045（38.5）72（61.5）
人均月收入[n(%)]2.1070.349
<40018（51.4）17（48.6）
400~1 200114（41.8）159（58.2）
>1 20041（37.6）68（62.4）
文化程度[n(%)]4.6160.099
小学及以下68（36.2）120（63.8）
初中73（44.2）92（55.8）
高中及以上32（50.0）32（50.0）
职业性体力活动[n(%)]3.4090.182
低强度123（43.8）158（56.2）
中等强度36（34.0）70（66.0）
高强度14（46.7）16（53.3）
婚姻状况[n(%)]17.389<0.001
未婚25（75.8）8（24.2）
已婚141（38.4）226（61.6）
离婚/丧偶7（41.5）10（58.5）
总能量[kcal，M (P25，P75)]1 323.0（957.7，1 867.7）1 504.4（1 075.2，1 978.8）-2.1510.031
), ArticleFig(id=1322607877307613423, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607866092044484, language=CN, label=表1, caption=

不同人口学特征中心性肥胖率

, figureFileSmall=null, figureFileBig=null, tableContent=
特征正常中心性肥胖χ2/Z值P值
性别[n(%)]3.1690.075
男89（46.1）104（53.9）
女84（37.5）140（62.5）
年龄[n(%)]17.835<0.001
18~4969（57.0）52（43.0）
50~5959（33.0）120（67.0）
≥6045（38.5）72（61.5）
人均月收入[n(%)]2.1070.349
<40018（51.4）17（48.6）
400~1 200114（41.8）159（58.2）
>1 20041（37.6）68（62.4）
文化程度[n(%)]4.6160.099
小学及以下68（36.2）120（63.8）
初中73（44.2）92（55.8）
高中及以上32（50.0）32（50.0）
职业性体力活动[n(%)]3.4090.182
低强度123（43.8）158（56.2）
中等强度36（34.0）70（66.0）
高强度14（46.7）16（53.3）
婚姻状况[n(%)]17.389<0.001
未婚25（75.8）8（24.2）
已婚141（38.4）226（61.6）
离婚/丧偶7（41.5）10（58.5）
总能量[kcal，M (P25，P75)]1 323.0（957.7，1 867.7）1 504.4（1 075.2，1 978.8）-2.1510.031
), ArticleFig(id=1322607877412471024, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607866092044484, language=EN, label=Table 2, caption=

Factor loadings of 13 types of food

, figureFileSmall=null, figureFileBig=null, tableContent=
类别肉鱼-水果-菌类模式零食-饮料-奶类模式蔬菜-主食-低豆类模式坚果类模式蛋类模式酒类模式
肉类0.6570.1510.1930.0510.0190.261
菌类0.641-0.094-0.1060.119-0.283-0.202
水果0.6260.0930.147-0.0530.274-0.139
鱼类0.5180.2720.0340.0220.0600.076
零食0.1460.8050.030-0.0540.004-0.005
饮料0.0740.6840.0020.390-0.1530.094
奶类0.3780.431-0.259-0.3550.320-0.272
蔬菜0.139-0.0220.692-0.2420.1730.161
主食0.1690.0030.6640.330-0.208-0.097
豆类0.384-0.085-0.4470.133-0.0610.367
坚果0.0870.115-0.0600.7980.243-0.084
蛋类0.047-0.0910.0170.1900.8500.082
酒类-0.0180.0470.014-0.0800.0770.836
), ArticleFig(id=1322607877731238129, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607866092044484, language=CN, label=表2, caption=

13类食物因子载荷表

, figureFileSmall=null, figureFileBig=null, tableContent=
类别肉鱼-水果-菌类模式零食-饮料-奶类模式蔬菜-主食-低豆类模式坚果类模式蛋类模式酒类模式
肉类0.6570.1510.1930.0510.0190.261
菌类0.641-0.094-0.1060.119-0.283-0.202
水果0.6260.0930.147-0.0530.274-0.139
鱼类0.5180.2720.0340.0220.0600.076
零食0.1460.8050.030-0.0540.004-0.005
饮料0.0740.6840.0020.390-0.1530.094
奶类0.3780.431-0.259-0.3550.320-0.272
蔬菜0.139-0.0220.692-0.2420.1730.161
主食0.1690.0030.6640.330-0.208-0.097
豆类0.384-0.085-0.4470.133-0.0610.367
坚果0.0870.115-0.0600.7980.243-0.084
蛋类0.047-0.0910.0170.1900.8500.082
酒类-0.0180.0470.014-0.0800.0770.836
), ArticleFig(id=1322607878142279922, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607866092044484, language=EN, label=Table 3, caption=

Waist circumference levels at quartiles of dietary patterns (，cm )

, figureFileSmall=null, figureFileBig=null, tableContent=
四分位数肉鱼-水果-菌类模式零食-饮料-奶类模式蔬菜-主食-低豆类模式坚果类模式蛋类模式酒类模式
Q190.98±11.0990.00±11.3387.05±12.9588.51±10.9290.40±11.7987.57±11.81
Q290.17±12.4291.82±12.9389.62±10.6389.48±12.3590.75±13.0288.87±11.43
Q390.73±11.7388.88±10.7190.82±11.73a91.66±11.7589.77±11.2090.53±12.16
Q487.58±11.5488.77±11.7992.00±11.12a89.83±11.8488.56±10.9292.50±11.13a
F值1.8461.5133.4431.3090.6993.521
P值0.1380.2110.0170.2710.5530.015
), ArticleFig(id=1322607878582681843, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607866092044484, language=CN, label=表3, caption=

各膳食模式四分位数下的腰围水平 (x±s，cm )

, figureFileSmall=null, figureFileBig=null, tableContent=
四分位数肉鱼-水果-菌类模式零食-饮料-奶类模式蔬菜-主食-低豆类模式坚果类模式蛋类模式酒类模式
Q190.98±11.0990.00±11.3387.05±12.9588.51±10.9290.40±11.7987.57±11.81
Q290.17±12.4291.82±12.9389.62±10.6389.48±12.3590.75±13.0288.87±11.43
Q390.73±11.7388.88±10.7190.82±11.73a91.66±11.7589.77±11.2090.53±12.16
Q487.58±11.5488.77±11.7992.00±11.12a89.83±11.8488.56±10.9292.50±11.13a
F值1.8461.5133.4431.3090.6993.521
P值0.1380.2110.0170.2710.5530.015
), ArticleFig(id=1322607878691733748, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607866092044484, language=EN, label=Table 4, caption=

Logistic regression analysis of dietary patterns and the risk of central obesity

, figureFileSmall=null, figureFileBig=null, tableContent=
膳食模式模型1模型2
OR（95% CI）值P值OR（95% CI）值P值
肉鱼-水果-菌类模式
Q111
Q20.922（0.527~1.612）0.7760.924（0.501~1.703）0.800
Q30.975（0.557~1.706）0.9290.872（0.470~1.615）0.663
Q40.577（0.332~1.003）0.0510.422（0.216~0.822）0.011
零食-饮料-奶类模式
Q111
Q21.156（0.663~2.015）0.6091.166（0.645~2.110）0.611
Q30.946（0.546~1.639）0.8431.080（0.588~1.983）0.804
Q40.841（0.486~1.455）0.5360.947（0.512~1.753）0.863
蔬菜-主食-低豆类模式
Q111
Q21.617（0.936~2.793）0.0851.248（0.695~2.243）0.458
Q32.106（1.209~3.670）0.0091.858（0.999~3.453）0.050
Q42.609（1.482~4.592）0.0012.138（1.052~4.343）0.036
坚果类模式
Q111
Q21.017（0.589~1.756）0.9511.014（0.564~1.823）0.963
Q31.436（0.822~2.508）0.2031.181（0.647~2.154）0.588
Q41.082（0.625~1.872）0.7800.751（0.391~1.444）0.391
蛋类模式
Q111
Q21.050（0.602~1.831）0.8641.081（0.591~1.977）0.801
Q30.946（0.545~1.643）0.8431.078（0.589~1.974）0.807
Q40.792（0.458~1.369）0.4030.815（0.450~1.477）0.500
酒类模式
Q111
Q21.392（0.806~2.404）0.2361.408（0.771~2.569）0.265
Q31.425（0.823~2.466）0.2061.526（0.839~2.776）0.166
Q41.880（1.079~3.278）0.0262.013（1.085~3.734）0.026
), ArticleFig(id=1322607878993723637, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322607866092044484, language=CN, label=表4, caption=

膳食模式与中心性肥胖患病风险logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
膳食模式模型1模型2
OR（95% CI）值P值OR（95% CI）值P值
肉鱼-水果-菌类模式
Q111
Q20.922（0.527~1.612）0.7760.924（0.501~1.703）0.800
Q30.975（0.557~1.706）0.9290.872（0.470~1.615）0.663
Q40.577（0.332~1.003）0.0510.422（0.216~0.822）0.011
零食-饮料-奶类模式
Q111
Q21.156（0.663~2.015）0.6091.166（0.645~2.110）0.611
Q30.946（0.546~1.639）0.8431.080（0.588~1.983）0.804
Q40.841（0.486~1.455）0.5360.947（0.512~1.753）0.863
蔬菜-主食-低豆类模式
Q111
Q21.617（0.936~2.793）0.0851.248（0.695~2.243）0.458
Q32.106（1.209~3.670）0.0091.858（0.999~3.453）0.050
Q42.609（1.482~4.592）0.0012.138（1.052~4.343）0.036
坚果类模式
Q111
Q21.017（0.589~1.756）0.9511.014（0.564~1.823）0.963
Q31.436（0.822~2.508）0.2031.181（0.647~2.154）0.588
Q41.082（0.625~1.872）0.7800.751（0.391~1.444）0.391
蛋类模式
Q111
Q21.050（0.602~1.831）0.8641.081（0.591~1.977）0.801
Q30.946（0.545~1.643）0.8431.078（0.589~1.974）0.807
Q40.792（0.458~1.369）0.4030.815（0.450~1.477）0.500
酒类模式
Q111
Q21.392（0.806~2.404）0.2361.408（0.771~2.569）0.265
Q31.425（0.823~2.466）0.2061.526（0.839~2.776）0.166
Q41.880（1.079~3.278）0.0262.013（1.085~3.734）0.026
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邯郸市成年居民膳食模式与中心性肥胖的关系研究
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李惠 , 张一曼 , 高茜 , 赵珊 , 杨永清
现代预防医学 | 健康与社会行为 2025,52(20): 3785-3791
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现代预防医学 |健康与社会行为 2025 , 52 (20) : 3785 -3791
邯郸市成年居民膳食模式与中心性肥胖的关系研究
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李惠, 张一曼, 高茜, 赵珊, 杨永清
作者信息
  • 邯郸市疾病预防控制中心，河北　邯郸　056008
通讯作者:
杨永清，E-mail:
作者简介:

李惠（1992－），女，硕士，中级职称，研究方向：营养与食品卫生学

Study on the relationship between dietary patterns and central obesity of adult residents, Handan City
Hui LI, Yi-man ZHANG, Xi GAO, Shan ZHAO, Yong-qing YANG
Affiliations
  • Handan Center for Disease Control and Prevention, Handan, Hebei 056008, China
出版时间: 2025-10-25 doi: 10.20043/j.cnki.MPM.202504218
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摘要
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目的

分析居民膳食模式与中心性肥胖的关系，为预防和改善中心性肥胖提供理论依据。

方法

采用多阶段分层整群随机抽样的方法，选择2022年中国居民营养与健康状况监测邯郸市18岁及以上居民作为调查对象。收集对象的基本信息和膳食信息并进行体格检查。计算居民中心性肥胖率。采用探索性因子分析提取膳食模式，各膳食模式因子得分四分位数由低到高为Q1、Q2、Q3、Q4，应用二元logistic回归分析膳食模式与中心性肥胖的关系。

结果

邯郸市成年居民标化中心性肥胖率53.3%。提取到肉鱼-水果-菌类模式、零食-饮料-奶类模式、蔬菜-主食-低豆类模式、坚果类模式、蛋类模式和酒类模式6种膳食模式。二元logistic回归显示，肉鱼-水果-菌类模式与中心性肥胖相关（Q4：OR=0.422，95% CI：0.216~0.822）。蔬菜-主食-低豆类模式与中心性肥胖相关（Q4：OR=2.138，95% CI：1.052~4.343）。酒类模式与中心性肥胖相关（Q4：OR=2.013，95% CI：1.085~3.734）。

结论

邯郸市成年居民中心性肥胖率较高，肉鱼-水果-菌类模式是中心性肥胖的保护因素，蔬菜-主食-低豆类模式和酒类模式是中心性肥胖的危险因素，应适量增加肉鱼类、豆类等优质蛋白和水果、菌类的摄入量，减少主食的摄入量，避免过量饮酒。

成年居民  /  膳食模式  /  中心性肥胖
Objective

To analyze the relationship between dietary patterns and central obesity to provide a theoretical basis for preventing and improving central obesity.

Methods

A multi-stage stratified cluster random sampling method was used to select adult residents aged 18 and above in Handan City for the 2022 monitoring of Chinese residents’ nutrition and health status as the survey subjects. Collect basic and dietary information of survey subjects and conduct physical examinations. Calculate the central obesity rate among residents. Exploratory factor analysis was used to extract dietary patterns, and the quartiles of factor scores for each dietary pattern were ranked from low to high as Q1, Q2, Q3, Q4. Apply binary logistic regression to analyze the relationship between dietary patterns and central obesity.

Results

The standardized central obesity rate of adult residents in Handan was 53.3%. Six dietary patterns were extracted, namely the meat-fish-fruit-fungi pattern, snack-beverage-milk pattern, vegetable-staple-low-legume pattern, nut pattern, egg pattern, and alcohol pattern. Binary logistic regression analysis showed that the meat-fish-fruit-fungi pattern was associated with central obesity (Q4: OR=0.422, 95% CI: 0.216-0.822). The vegetable-staple-low-legume pattern was associated with central obesity (Q4: OR=2.138, 95% CI: 1.052-4.343). The alcohol patterns were associated with central obesity (Q4: OR=2.013, 95% CI: 1.085-3.734).

Conclusion

The central obesity rate of adult residents in Handan is relatively high. The meat-fish-fruit-fungi pattern is a protective factor for central obesity, while the vegetable-staple-low-legume pattern and alcohol pattern are risk factors for central obesity. It is recommended to moderately increase the intake of high-quality proteins such as meat, fish, and legume, as well as fruits and fungi, and reduce the intake of staple food, and avoid excessive alcohol consumption.

Adult residents  /  Dietary pattern  /  Central obesity
李惠, 张一曼, 高茜, 赵珊, 杨永清. 邯郸市成年居民膳食模式与中心性肥胖的关系研究. 现代预防医学, 2025 , 52 (20) : 3785 -3791 . DOI: 10.20043/j.cnki.MPM.202504218
Hui LI, Yi-man ZHANG, Xi GAO, Shan ZHAO, Yong-qing YANG. Study on the relationship between dietary patterns and central obesity of adult residents, Handan City[J]. Modern Preventive Medicine, 2025 , 52 (20) : 3785 -3791 . DOI: 10.20043/j.cnki.MPM.202504218
正文
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随着我国经济水平的高速发展和生活质量的提升，居民膳食模式及生活方式也发生了极大的转变，随之而来的是肥胖症患者不断增多。大多数亚洲肥胖症患者属于中心性肥胖，过量的脂肪沉积在脏器中[1]。中国成年人在体质指数较低的情况下也患有中心性肥胖，并且会增加新发心血管疾病和全因死亡风险[2-3]。中心性肥胖的影响因素有很多，研究表明膳食等生活方式因素与其密切相关[4]。以往的研究是探索单一的食物种类或营养素与中心性肥胖的关系[5-6]，与我国丰富的食物种类和饮食习惯不甚相符，存在一定的局限性。膳食模式能够反映食物组或营养素对慢性病的综合效应，被越来越多地应用到探索膳食因素与慢性病的关系研究[7-8]。本研究通过膳食模式综合分析膳食因素与中心性肥胖的关联，为居民膳食结构合理化及改善中心性肥胖提供新的思路。
1　对象与方法
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1.1　对象
选择纳入2022年中国居民营养与健康状况监测邯郸市18岁及以上成年人作为研究对象。本次监测经中国疾病预防控制中心营养与健康所伦理委员会审查通过( No. 2022-008 )，所有研究对象均签署知情同意书。采用多阶段分层整群随机抽样的方法选择调查对象。第一阶段和第二阶段采用人口规模排序的方法，由系统随机抽取3个乡镇内的2个行政村；第三阶段将抽中的行政村内居民户以不少于100户为规模分为若干小组，简单随机抽取其中1个小组；第四阶段采用人口规模排序的方法由系统抽取小组内60户，选取户内18岁及以上成年人开展调查，男女各半，组内男女调查比例不均时，在下一编号小组内随机抽取调查对象递补，共计调查484人。剔除膳食调查数据和体检数据不完整者，剔除日能量摄入量<400 kcal和>5 000 kcal者，最终纳入417人作为调查对象。
1.2　方法
1.2.1　问卷调查
由调查人员使用监测项目组提供的标准化问卷进行面对面访谈，主要包括基本信息和膳食信息。膳食调查采用食物频率表，包括主食、豆类、蔬菜类、菌藻类、水果类、乳类、肉类、鱼虾类、蛋类、坚果类、即食食品类、饮料、酒类共计13类食物。询问调查对象过去一年里各种食物的食用频率（次/天、次/周、次/月、次/年）和食用量（g），按照食物频率和食用量计算每种食物的日平均摄入量，并根据《中国食物成分表》计算每个标准人的日能量摄入量。
1.2.2　体格检查
测量调查对象的身高、体重、腰围。
1.2.3　膳食模式提取
采用探索性因子分析方法，将13类食物组纳入模型中，以主成分法提取公因子，利用方差最大化旋转法，结合碎石图和因子特征根（>1）获取公因子数量。将因子载荷绝对值>0.4的因子纳入膳食模式。根据因子可解释性和食物模式组合特点对不同膳食模式进行命名和解释。计算每个调查对象各种膳食模式标准化因子得分，各类食物标准化摄入量与对应因子载荷乘积之和为标准化因子得分，得分越高表示越趋向于该种膳食模式。
1.3　指标定义
中心性肥胖判定标准 根据中华医学会糖尿病学会分会2017年发布的标准，将男性腰围≥90 cm或女性腰围≥85 cm定义为中心性肥胖[9]。
1.4　质量控制
调查员和体检人员经培训考核合格后开展工作。按照国家疾控中心规定配置相同型号的体检测量仪器，测量方法符合《人群健康监测人体测量方法》（WS/T424-2013）要求[10]。问卷回收时立即审核有无缺项并补填。采用Epi Data 3.1软件建立数据库，数据为双人双录入并进行一致性检验。数据经逻辑性检验和审核。
1.5　统计分析
不同人口学特征的中心性肥胖率比较采用χ2检验；采用K-S法（Kolmogorov-Smirnov）进行正态性检验。符合正态分布的计量资料以均值±标准差（）表示，两组间比较采用独立样本t检验，多组间比较采用方差分析，两两比较采用LSD检验（方差齐）和Tamhane检验（方差不齐）；不符合正态分布的计量资料以中位数（四分位数）〔M（QL，QU）〕表示；膳食模式采用探索性因子分析；多因素分析采用二元logistic回归分析。使用SAS 9.4和SPSS 22.0进行数据分析。双侧检验水准为α＝0.05。
2　结果
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2.1　一般资料与中心性肥胖率比较
本次调查417名调查对象，其中男性193（46.3%）人，女性224（53.7%）人，中位年龄58.0（46.4，65.7）岁。中心性肥胖人数244名，中心性肥胖率58.5%，经年龄性别标准化后，中心性肥胖率为53.3%。女性中心性肥胖率为62.5%，略高于男性，但差异无统计学意义。50~60岁年龄段中心性肥胖率最高（67.0%），18~50岁年龄段最低（43.0%），差异有统计学意义。中心性肥胖组总能量摄入高于正常组，差异有统计学意义。不同人口学特征人群中心性肥胖率见表1。
2.2　膳食模式建立
因子分析结果显示，KMO检验结果为0.619，Bartlett球形检验P<0.001。13类食物共提取出6种膳食模式，分别命名为肉鱼-水果-菌类模式、零食-饮料-奶类模式、蔬菜-主食-低豆类模式、坚果类模式、蛋类模式和酒类模式，累计方差贡献率61.569%。各类食物因子载荷见表2。
2.3　不同膳食模式腰围水平
正常人群腰围（79.05±6.61） cm，中心性肥胖人群腰围（97.54±7.92） cm，差异有统计学意义（t=25.12，P<0.001）。蔬菜-主食-低豆类模式Q3和Q4组腰围水平显著高于Q1组。酒类模式Q4组腰围水平显著高于Q1组。每种膳食模式四分位数下的腰围水平见表3。
2.4　膳食模式与中心性肥胖的关系
以是否患中心性肥胖作为因变量（0=否，1=是），膳食模式因子得分四分位数作为自变量（Q1为参照组）进行多因素logistic回归分析，调整性别、年龄、收入、文化程度、职业、职业性体力活动强度、婚姻状况、总能量等混杂因素。结果显示，与Q1组相比，肉鱼-水果-菌类模式Q4组患中心性肥胖的风险更低（OR=0.422，95% CI：0.216~0.822，P=0.011），蔬菜-主食-低豆类模式Q4组患中心性肥胖的风险更高（OR=2.138，95% CI：1.052~4.343，P=0.036），酒类模式Q4组患中心性肥胖的风险更高（OR=2.013，95% CI：1.085~3.734，P=0.026）。见表4。
3　讨论
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经年龄性别标准化后，邯郸市成年居民中心性肥胖率为53.3%。我国成年居民中心性肥胖率为46.9%[11]，河北省和天津市的中心性肥胖率分别为55.9%和50.1%[12-13]。由此可见邯郸市中心性肥胖形式较为严峻。50~59岁人群中心性肥胖率最高，可能由于年轻群体更加注重体育锻炼，而≥60岁的老年人更加注重健康生活方式，50~59岁人群由于工作应酬等增加了高油高盐食物和酒类的摄入，从而导致能量过剩。
本研究共提取到6种膳食模式。模式1是肉鱼-水果-菌类模式。模式1是中心性肥胖的保护因素，该模式Q4组患中心性肥胖的风险较Q1组更低。该模式富含优质蛋白、维生素和膳食纤维。研究表明优质蛋白会增加饱腹感，从而减少膳食能量的摄入，还可以增加能量消耗，改善糖脂代谢，减少腹部脂肪的堆积[14]。肉类中富含的铁、锌和B组维生素等均有助于能量代谢和体重控制[15]。鱼类等水产品富含多不饱和脂肪酸，能够降低中心性肥胖的发病风险[16]。水果和菌类中的维生素和膳食纤维也能预防中心性肥胖[17]。模式2为零食-饮料-奶类模式。虽然奶类也是优质蛋白的主要来源，但该模式下零食饮料等高糖分高热量的食品摄入较多，因而未发现该模式与中心性肥胖的关联。模式3是蔬菜-主食-低豆类模式。模式3会增加中心性肥胖的发生风险，该模式Q3和Q4组腰围水平显著高于Q1组，Q4组患中心性肥胖的风险较Q1组更高。豆类升糖指数较低，能够提供优质蛋白和多种矿物质，大豆蛋白和大豆异黄酮有助于降低血脂和减少肥胖[18]。但人群大豆摄入不足比例逐年升高[19]，北方地区居民豆类摄入量不能满足推荐量[20]，全球疾病负担不合理膳食危险因素中也包括豆类食品摄入不足[21]。豆类摄入较少的成年人过去10年体重增加幅度更大，体质指数和腹部脂肪含量也更高[22]。且该模式主食摄入较多，高碳水模式会增加中心性肥胖的发生风险[5]，而北方膳食中常以面类等精制碳水作为主食，极高的升糖指数不利于血糖和体重的控制。模式4为坚果类模式。国内外关于坚果与肥胖关系的研究尚没有一致结论。坚果含有丰富的不饱和脂肪酸，这些优质脂肪有助于改善血脂水平和中心性肥胖[23]。但坚果能量密度极高，过量食用反而会增加肥胖的风险，油炸或调味坚果则更加剧其风险。因此，本研究坚果类模式与中心性肥胖的关联没有显著性。模式5为蛋类模式。研究表明蛋类可以改善体脂质量和分布，改善中心性肥胖[24]。本研究中蛋类模式Q3组和Q4组腰围水平有降低趋势但无显著性差异，可能和蛋黄中胆固醇的作用有关，也可能和摄入过量或烹饪方式有关。模式6为酒类模式。研究表明酒类模式会增加肥胖风险[25]。本研究中酒类是中心性肥胖的危险因素，酒类模式Q4组患中心性肥胖的风险较Q1组更高，Q4组腰围也显著高于Q1组。酒类是纯热量食物，长期过量饮酒会引起能量过剩。如今盛行的调味酒添加了糖或蜂蜜等甜味剂，更增加了酒的能量密度。出于对功能性食品和健康的需求，人们会选择保健酒或水果植物发酵酒[26]，但仍然难以抵消酒精及其高热量给健康带来的风险。如果饮酒的同时减少蔬菜的摄入，将会引起膳食纤维和维生素摄入不足，进而增加中心性肥胖的患病风险[20]。
本研究发现肉鱼类、水果和菌类会降低中心性肥胖的患病风险，主食、酒类和豆类摄入不足会增加中心性肥胖的风险。人们应适量增加肉类、鱼类、豆类等优质蛋白和菌类、鲜果的摄入，减少主食的摄入量，也可以选择未加工或粗加工主食替代精细米面。尽量避免饮酒或适量饮酒。膳食因素是中心性肥胖诸多影响因素中的一个，但也是较容易改变并取得成效的因素[4]，因此我们建议通过优化膳食结构来控制中心性肥胖的发生发展，从而降低中心性肥胖相关慢性病的发病率。
本研究存在一些局限性。首先，本研究是一项横断面研究，不能反映出膳食模式和中心性肥胖的因果关系。第二，调查对象回忆过去一年内的各类食物的食用频率，可能存在回忆偏倚。第三，研究范围局限在邯郸市，且样本量较少。在未来的研究中，希望可以联合其他地市，通过更大样本量的前瞻性队列研究，用更加准确的评估方法和严谨的调查手段探索膳食模式与中心性肥胖的关联。
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  • 河北省卫生健康委医学科学研究课题计划项目(20251320)
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2025年第52卷第20期
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doi: 10.20043/j.cnki.MPM.202504218
  • 接收时间：2025-04-11
  • 首发时间：2026-10-29
  • 出版时间：2025-10-25
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  • 收稿日期：2025-04-11
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河北省卫生健康委医学科学研究课题计划项目(20251320)
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    邯郸市疾病预防控制中心，河北　邯郸　056008

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杨永清，E-mail:
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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
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
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