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Article(id=1240722569410703473, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240722566957027366, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202505192, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1747065600000, receivedDateStr=2025-05-13, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773740912057, onlineDateStr=2026-03-17, pubDate=1760025600000, pubDateStr=2025-10-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773740912057, onlineIssueDateStr=2026-03-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773740912057, creator=13701087609, updateTime=1773740912057, updator=13701087609, issue=Issue{id=1240722566957027366, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='19', pageStart='3457', pageEnd='3648', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=0, createTime=1773740911472, creator=13701087609, updateTime=1773740981732, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240722861736906836, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240722566957027366, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240722861736906837, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240722566957027366, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=3503, endPage=3508, ext={EN=ArticleExt(id=1240722569658167415, articleId=1240722569410703473, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Association between the dietary index for gut microbiota and metabolic syndrome, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=

Objective To investigate the association between the dietary index for gut microbiota (DI-GM) and metabolic syndrome (MetS), thereby providing a theoretical basis for preventing MetS through dietary modulation of the gut microbiome. Methods Data from 17 558 adults participating in the 2007—2018 U.S. National Health and Nutrition Examination Survey (NHANES) were analyzed, with MetS determined according to the NCEP ATP III criteria. The DI-GM score was constructed based on 14 food items or nutrients obtained from two 24 hour dietary recalls. Logistic regression models and restricted cubic spline (RCS) analysis were used to evaluate the relationship between DI-GM and MetS, and subgroup as well as sensitivity analyses were conducted. Results In the fully adjusted model, the risk of MetS decreased with increasing DI-GM scores (OR=0.94, 95% CI: 0.91-0.98). Specifically, individuals in the highest DI-GM quartile (Q4) had an 18% lower risk of MetS than those in the lowest quartile (OR=0.82, 95% CI: 0.72-0.95). RCS analysis indicated a linear inverse dose-response relationship between DI-GM and MetS (P for nonlinear=0.185). Subgroup analyses showed the inverse association was stronger in females, non-Hispanic Black individuals, and those with higher education levels(P for interaction<0.05). Sensitivity analyses confirmed the stability of the results(OR=0.94, 95% CI: 0.90-0.98). Conclusion This study demonstrates that maintaining a diet favorable to gut microbiota health can reduce the risk of MetS. These findings suggest that improving dietary patterns to modulate the gut microbiome may offer a novel strategy for the prevention and management of MetS.

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目的 探讨肠道菌群膳食指数（dietary index for gut microbiota，DI-GM）与代谢综合征（metabolic syndrome，MetS）之间的关联性，为通过饮食调控改善肠道菌群进而预防MetS提供依据。方法 本研究选取2007—2018年美国国家健康与营养调查（NHANES）调查中17 558名成年人数据，根据美国国家胆固醇教育计划成人治疗专家小组第三次报告（NCEP ATP III）标准确定MetS。DI-GM评分基于两次24 h小时饮食回顾数据中的14种食物或营养成分构建。采用logistic回归模型和限制性立方样条分析对DI-GM与MetS患病风险间的关联进行评估，并进行亚组分析和敏感性分析。结果 在完全调整模型中，MetS风险随着DI-GM评分升高而降低（OR=0.94，95%CI:0.91～0.98），与最低四分位组的DI-GM相比，最高四分位组（Q4）中MetS风险下降18%（OR=0.82，95%CI:0.72～0.95）。限制性立方样条分析显示DI-GM和MetS患病风险存在线性负向剂量-反应关联（P for nonlinear=0.185）。亚组分析显示DI-GM和MetS的负向关联在女性及非西班牙裔黑人、教育程度高人群中更明显（P交互<0.05）。敏感性分析显示该研究结果的稳定性（OR= 0.94，95%CI:0.90～0.98）。结论 维持利于肠道菌群健康的饮食模式能够降低MetS患病风险，提示通过改善膳食结构以调控肠道菌群可能为MetS的预防和管理提供新策略。

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田晓迎，E-mail:
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=fIPrI8bnyDz7VkETRHtr4Q==, magXml=8+8IW/V2bAmWfmRdIHWndQ==, pdfUrl=null, pdf=IYPSRt2n7q9kunI7++Mhrg==, pdfFileSize=874073, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=QoKMy5iwetsNC9sLCIwvZQ==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=U7goeKFOVew61kNTR7JRHQ==, mapNumber=null, authorCompany=null, fund=null, authors=

李颖莲（2001—），女，硕士在读，研究方向：慢性疾病照护和健康管理

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李颖莲（2001—），女，硕士在读，研究方向：慢性疾病照护和健康管理

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李颖莲（2001—），女，硕士在读，研究方向：慢性疾病照护和健康管理

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注：图中黑线代表OR值；灰色区域代表95%CI。

, figureFileSmall=nNww2Damy3vT8GBaqvI5xA==, figureFileBig=zswiG4OS9NVCrLk3Rh9y3g==, tableContent=null), ArticleFig(id=1240933510626734949, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722569410703473, language=EN, label=Figure 3, caption=Subgroup analysis of the association between DI-GM and MetS, figureFileSmall=YapQ7ztrJvwBl8Y/v25f7A==, figureFileBig=xlyfWJffLuDh6kTQX90bvg==, tableContent=null), ArticleFig(id=1240933510727398248, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722569410703473, language=CN, label=图3, caption=DI-GM与MetS关联的亚组分析, figureFileSmall=YapQ7ztrJvwBl8Y/v25f7A==, figureFileBig=xlyfWJffLuDh6kTQX90bvg==, tableContent=null), ArticleFig(id=1240933510844838765, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722569410703473, language=EN, label=Table 1, caption=

Characteristics of the study population ［M（P25，P75）， n（%）］

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变量总计(n=17 558)是否患有MetSχ2/t值P值
否(n=9 132)是(n=8 426)
年龄（岁）52.00 (37.00, 65.00)45.00 (31.00, 61.00)58.00 (44.00, 68.00)23.248<0.001
BMI(kg/m2)28.70 (24.88, 33.60)25.81 (22.88, 29.36)32.00 (28.40, 36.77)48.965<0.001
性别7.876<0.001
男性8 498 (48.40)4 652 (50.94)3 846 (45.64)
女性9 060 (51.60)4 480 (49.06)4 580 (54.36)
种族5.782<0.001
非西班牙裔白人2 779 (15.83)1 338 (14.65)1 441 (17.10)
墨西哥裔美国人1 882 (10.72)965 (10.57)917 (10.88)
非西班牙裔黑人7 275 (41.43)3 687 (40.37)3 588 (42.58)
其他西班牙裔3 654 (20.81)1 929 (21.12)1 725 (20.47)
其他种族1 968 (11.21)1 213 (13.28)755 (8.96)
教育程度35.691<0.001
高中以下4 341 (24.72)1 992 (21.81)2 349 (27.87)
高中或同等学历3 957 (22.54)1 982 (21.70)1 975 (23.44)
大学或以上9 210 (52.48)5 158 (56.48)4 052 (48.09)
抑郁症81.114<0.001
否15 843 (90.23)8 479 (92.85)7 364 (87.40)
是1 715 (9.77)653 (7.15)1 062 (12.60)
饮酒状况12.8950.147
现在9 991 (56.90)5 445 (59.63)4 546 (53.95)
曾经5 573 (31.74)2 734 (29.94)2 839 (33.69)
从不1 994 (11.36)953 (10.44)1 041 (12.35)
吸烟状况41.697<0.001
现在9 530 (54.28)5 278 (57.80)4 252 (50.46)
曾经4 505 (25.66)2 003 (21.93)2 502 (29.69)
从不3 523 (20.06)1 851 (20.27)1 672 (19.84)
体力活动水平78.051<0.001
积极活动8 855 (50.43)5 195 (56.89)3 660 (43.44)
活动量不足1 925 (10.96)954 (10.45)971 (11.52)
缺乏活动6 778 (38.60)2 983 (32.67)3 795 (45.04)
社会经济状况24.987<0.001
低收入3 455 (21.54)1 700 (20.35)1 755 (22.84)
中等收入6 859 (42.77)3 367 (40.31)3 492 (45.44)
高收入5 723 (35.69)3 285 (39.33)2 438 (31.72)
婚姻状况109.062<0.001
未婚3 224 (18.37)1 998 (21.88)1 226 (14.56)
同居/已婚10 454 (59.54)5 355 (58.61)5 099 (60.51)
丧偶/离婚/分居3 878 (22.09)1 779 (19.49)2 099 (24.93)
尿酸（μmol/L）321.20 (267.70, 380.70)303.30 (255.80, 362.80)345.00 (285.50, 404.50)22.619<0.001
糖化血红蛋白（%）5.60 (5.30, 6.10)5.40 (5.20, 5.70)5.90 (5.50, 6.70)35.601<0.001
DI-GM（未分组）5 (4, 6)5 (4, 6)4 (3, 6)-5.325<0.001
DI-GM（分组）9.899<0.001
Q14 314 (24.57)2 131 (23.34)2 183 (25.91)
Q24 370 (24.89)2 222 (24.33)2 148 (25.49)
Q34 009 (22.83)2 116 (23.17)1 893 (22.47)
Q44 865(27.71)2 663(29.16)2 202(26.13)
), ArticleFig(id=1240933510945502065, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722569410703473, language=CN, label=表1, caption=

研究对象基线特征［M（P25，P75），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总计(n=17 558)是否患有MetSχ2/t值P值
否(n=9 132)是(n=8 426)
年龄（岁）52.00 (37.00, 65.00)45.00 (31.00, 61.00)58.00 (44.00, 68.00)23.248<0.001
BMI(kg/m2)28.70 (24.88, 33.60)25.81 (22.88, 29.36)32.00 (28.40, 36.77)48.965<0.001
性别7.876<0.001
男性8 498 (48.40)4 652 (50.94)3 846 (45.64)
女性9 060 (51.60)4 480 (49.06)4 580 (54.36)
种族5.782<0.001
非西班牙裔白人2 779 (15.83)1 338 (14.65)1 441 (17.10)
墨西哥裔美国人1 882 (10.72)965 (10.57)917 (10.88)
非西班牙裔黑人7 275 (41.43)3 687 (40.37)3 588 (42.58)
其他西班牙裔3 654 (20.81)1 929 (21.12)1 725 (20.47)
其他种族1 968 (11.21)1 213 (13.28)755 (8.96)
教育程度35.691<0.001
高中以下4 341 (24.72)1 992 (21.81)2 349 (27.87)
高中或同等学历3 957 (22.54)1 982 (21.70)1 975 (23.44)
大学或以上9 210 (52.48)5 158 (56.48)4 052 (48.09)
抑郁症81.114<0.001
否15 843 (90.23)8 479 (92.85)7 364 (87.40)
是1 715 (9.77)653 (7.15)1 062 (12.60)
饮酒状况12.8950.147
现在9 991 (56.90)5 445 (59.63)4 546 (53.95)
曾经5 573 (31.74)2 734 (29.94)2 839 (33.69)
从不1 994 (11.36)953 (10.44)1 041 (12.35)
吸烟状况41.697<0.001
现在9 530 (54.28)5 278 (57.80)4 252 (50.46)
曾经4 505 (25.66)2 003 (21.93)2 502 (29.69)
从不3 523 (20.06)1 851 (20.27)1 672 (19.84)
体力活动水平78.051<0.001
积极活动8 855 (50.43)5 195 (56.89)3 660 (43.44)
活动量不足1 925 (10.96)954 (10.45)971 (11.52)
缺乏活动6 778 (38.60)2 983 (32.67)3 795 (45.04)
社会经济状况24.987<0.001
低收入3 455 (21.54)1 700 (20.35)1 755 (22.84)
中等收入6 859 (42.77)3 367 (40.31)3 492 (45.44)
高收入5 723 (35.69)3 285 (39.33)2 438 (31.72)
婚姻状况109.062<0.001
未婚3 224 (18.37)1 998 (21.88)1 226 (14.56)
同居/已婚10 454 (59.54)5 355 (58.61)5 099 (60.51)
丧偶/离婚/分居3 878 (22.09)1 779 (19.49)2 099 (24.93)
尿酸（μmol/L）321.20 (267.70, 380.70)303.30 (255.80, 362.80)345.00 (285.50, 404.50)22.619<0.001
糖化血红蛋白（%）5.60 (5.30, 6.10)5.40 (5.20, 5.70)5.90 (5.50, 6.70)35.601<0.001
DI-GM（未分组）5 (4, 6)5 (4, 6)4 (3, 6)-5.325<0.001
DI-GM（分组）9.899<0.001
Q14 314 (24.57)2 131 (23.34)2 183 (25.91)
Q24 370 (24.89)2 222 (24.33)2 148 (25.49)
Q34 009 (22.83)2 116 (23.17)1 893 (22.47)
Q44 865(27.71)2 663(29.16)2 202(26.13)
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Weighted logistic regression model of the association between DI-GM and MetS (after multiple imputation)

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变量模型1模型2模型3
OR (95%CI)P值OR (95%CI)P值OR (95%CI)P值
DI-GM(未分组)0.93 (0.90～0.96)<0.0010.90 (0.88～0.93)<0.0010.94 (0.91～0.98)<0.001
DI-GM(分组，ref:Q1)
Q20.90 (0.83～0.97)0.0070.87 (0.80～0.94)<0.0010.96 (0.87～1.07)0.501
Q30.87 (0.80～0.95)0.0020.79 (0.72～0.87)<0.0010.89 (0.79～1.01)0.063
Q40.78 (0.71～0.86)<0.0010.70 (0.63～0.77)<0.0010.82 (0.72～0.95)0.008
P 趋势值<0.001<0.0010.003
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DI-GM与MetS的关联的加权logistic回归模型（多重插补后）

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变量模型1模型2模型3
OR (95%CI)P值OR (95%CI)P值OR (95%CI)P值
DI-GM(未分组)0.93 (0.90～0.96)<0.0010.90 (0.88～0.93)<0.0010.94 (0.91～0.98)<0.001
DI-GM(分组，ref:Q1)
Q20.90 (0.83～0.97)0.0070.87 (0.80～0.94)<0.0010.96 (0.87～1.07)0.501
Q30.87 (0.80～0.95)0.0020.79 (0.72～0.87)<0.0010.89 (0.79～1.01)0.063
Q40.78 (0.71～0.86)<0.0010.70 (0.63～0.77)<0.0010.82 (0.72～0.95)0.008
P 趋势值<0.001<0.0010.003
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Logistic regression model of the association between DI-GM and MetS (unweighted)

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变量模型1模型2模型3
OR (95%CI)P值OR (95%CI)P值OR (95%CI)P值
DI-GM(未分组)0.93 (0.89～0.96)<0.0010.90 (0.88～0.94)<0.0010.94 (0.90～0.98)<0.001
DI-GM(分组，ref:Q1)
Q20.90 (0.83～0.97)0.0070.87 (0.80～0.94)<0.0010.96 (0.86～1.07)0.458
Q30.87 (0.80～0.95)0.0020.79 (0.72～0.87)<0.0010.88 (0.78～1.01)0.051
Q40.78 (0.71～0.86)<0.0010.70 (0.63～0.77)<0.0010.82 (0.71～0.94)0.005
P趋势值<0.001<0.0010.005
), ArticleFig(id=1240933511448818563, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722569410703473, language=CN, label=表3, caption=

DI-GM与MetS的关联的logistic回归模型（未加权）

, figureFileSmall=null, figureFileBig=null, tableContent=
变量模型1模型2模型3
OR (95%CI)P值OR (95%CI)P值OR (95%CI)P值
DI-GM(未分组)0.93 (0.89～0.96)<0.0010.90 (0.88～0.94)<0.0010.94 (0.90～0.98)<0.001
DI-GM(分组，ref:Q1)
Q20.90 (0.83～0.97)0.0070.87 (0.80～0.94)<0.0010.96 (0.86～1.07)0.458
Q30.87 (0.80～0.95)0.0020.79 (0.72～0.87)<0.0010.88 (0.78～1.01)0.051
Q40.78 (0.71～0.86)<0.0010.70 (0.63～0.77)<0.0010.82 (0.71～0.94)0.005
P趋势值<0.001<0.0010.005
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肠道菌群膳食指数与代谢综合征的关联性研究
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李颖莲 1 , 杨帅 1 , 裴芹 1 , 尹艺禧 1 , 牟佳 2 , 田晓迎 1
现代预防医学 | 流行病与统计方法 2025,52(19): 3503-3508
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现代预防医学 | 流行病与统计方法 2025, 52(19): 3503-3508
肠道菌群膳食指数与代谢综合征的关联性研究
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李颖莲1, 杨帅1, 裴芹1, 尹艺禧1, 牟佳2, 田晓迎1
作者信息
  • 1.暨南大学护理学院，广东 广州 510632
  • 2.中山大学附属第六医院

    • 李颖莲（2001—），女，硕士在读，研究方向：慢性疾病照护和健康管理

通讯作者:

田晓迎，E-mail:
Association between the dietary index for gut microbiota and metabolic syndrome
Ying-lian LI1, Shuai YANG1, Qin PEI1, Yi-xi YIN1, Jia MOU2, Xiao-ying TIAN1
Affiliations
  • College of Nursing, Jinan University, Guangzhou, Guangdong 510632, China
出版时间: 2025-10-10 doi: 10.20043/j.cnki.MPM.202505192
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摘要
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目的 探讨肠道菌群膳食指数（dietary index for gut microbiota，DI-GM）与代谢综合征（metabolic syndrome，MetS）之间的关联性，为通过饮食调控改善肠道菌群进而预防MetS提供依据。方法 本研究选取2007—2018年美国国家健康与营养调查（NHANES）调查中17 558名成年人数据，根据美国国家胆固醇教育计划成人治疗专家小组第三次报告（NCEP ATP III）标准确定MetS。DI-GM评分基于两次24 h小时饮食回顾数据中的14种食物或营养成分构建。采用logistic回归模型和限制性立方样条分析对DI-GM与MetS患病风险间的关联进行评估，并进行亚组分析和敏感性分析。结果 在完全调整模型中，MetS风险随着DI-GM评分升高而降低（OR=0.94，95%CI:0.91～0.98），与最低四分位组的DI-GM相比，最高四分位组（Q4）中MetS风险下降18%（OR=0.82，95%CI:0.72～0.95）。限制性立方样条分析显示DI-GM和MetS患病风险存在线性负向剂量-反应关联（P for nonlinear=0.185）。亚组分析显示DI-GM和MetS的负向关联在女性及非西班牙裔黑人、教育程度高人群中更明显（P交互<0.05）。敏感性分析显示该研究结果的稳定性（OR= 0.94，95%CI:0.90～0.98）。结论 维持利于肠道菌群健康的饮食模式能够降低MetS患病风险，提示通过改善膳食结构以调控肠道菌群可能为MetS的预防和管理提供新策略。

肠道菌群  /  膳食指数  /  代谢综合征  /  NHANES  /  代谢健康

Objective To investigate the association between the dietary index for gut microbiota (DI-GM) and metabolic syndrome (MetS), thereby providing a theoretical basis for preventing MetS through dietary modulation of the gut microbiome. Methods Data from 17 558 adults participating in the 2007—2018 U.S. National Health and Nutrition Examination Survey (NHANES) were analyzed, with MetS determined according to the NCEP ATP III criteria. The DI-GM score was constructed based on 14 food items or nutrients obtained from two 24 hour dietary recalls. Logistic regression models and restricted cubic spline (RCS) analysis were used to evaluate the relationship between DI-GM and MetS, and subgroup as well as sensitivity analyses were conducted. Results In the fully adjusted model, the risk of MetS decreased with increasing DI-GM scores (OR=0.94, 95% CI: 0.91-0.98). Specifically, individuals in the highest DI-GM quartile (Q4) had an 18% lower risk of MetS than those in the lowest quartile (OR=0.82, 95% CI: 0.72-0.95). RCS analysis indicated a linear inverse dose-response relationship between DI-GM and MetS (P for nonlinear=0.185). Subgroup analyses showed the inverse association was stronger in females, non-Hispanic Black individuals, and those with higher education levels(P for interaction<0.05). Sensitivity analyses confirmed the stability of the results(OR=0.94, 95% CI: 0.90-0.98). Conclusion This study demonstrates that maintaining a diet favorable to gut microbiota health can reduce the risk of MetS. These findings suggest that improving dietary patterns to modulate the gut microbiome may offer a novel strategy for the prevention and management of MetS.

Gut microbiota  /  Dietary index for gut microbiota  /  Metabolic syndrome  /  NHANES  /  Metabolic health
李颖莲, 杨帅, 裴芹, 尹艺禧, 牟佳, 田晓迎. 肠道菌群膳食指数与代谢综合征的关联性研究. 现代预防医学, 2025 , 52 (19) : 3503 -3508 . DOI: 10.20043/j.cnki.MPM.202505192
Ying-lian LI, Shuai YANG, Qin PEI, Yi-xi YIN, Jia MOU, Xiao-ying TIAN. Association between the dietary index for gut microbiota and metabolic syndrome[J]. Modern Preventive Medicine, 2025 , 52 (19) : 3503 -3508 . DOI: 10.20043/j.cnki.MPM.202505192
正文
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代谢综合征（metabolic syndrome，MetS）的主要特征包含腹部肥胖、胰岛素抵抗、血糖异常、高血压及血脂紊乱等情况[1]。一项全球数据的荟萃分析报告显示，成人当中MetS的患病率达到了28.2%，在60岁及以上人群中更是高达40%左右[2]。这种高发病率以及由此带来的巨大健康负担，让MetS成为全球公共卫生面临的一项重大挑战[1]。
肠道菌群在维持宿主代谢稳态中起着至关重要的作用，其失调与代谢综合征密切相关，特定代谢产物还会加剧炎症反应[3]。饮食是调控肠道菌群的关键因素，不同饮食模式对菌群及代谢状态影响各不相同[4-6]。肠道菌群膳食指数（dietary index for gut microbiota，DI-GM）是一项新颖的指标，其目的在于综合量化能够影响肠道菌群组成与功能的饮食模式[7]。该指数整合了包括发酵乳制品、全谷物、膳食纤维等有利于肠道健康的食物成分，同时考虑了红肉和高脂饮食等不利因素，进而形成一个反映饮食质量与肠道菌群平衡的综合评分。较高的DI-GM评分表明个体摄入的饮食更有助于维持肠道菌群多样性及功能，对改善代谢健康可能会产生积极影响。虽然有研究将饮食模式或肠道菌群和MetS联系起来[8-10]，但DI-GM与MetS之间的具体关联性尚未得到充分探索。本研究基于美国国家健康与营养调查（National Health and Nutrition Examination Survey,NHANES）数据，探讨DI-GM评分与MetS之间的关联，为通过调整饮食模式预防MetS探索新的策略。
1　材料与方法
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1.1　研究人群
本研究采用了由美国国家健康统计中心（National Center for Health Statistics, NCHS）组织的NHANES数据库。相关数据可通过NHANES官方网站（https://www.cdc.gov）获取。本研究选取了NHANES数据库2007—2018年的研究人群，排除了年龄小于20岁的参与者（n22 402）、孕妇（n=274）、缺失DI-GM数据的对象（n=3 977）以及缺乏完整MetS数据的参与者（n=12 961）。最后共纳入17 558名符合条件的成年人。研究人群筛选流程见图1。
1.2　MetS的评估
MetS的诊断标准是根据美国国家胆固醇教育计划成人治疗专家小组第三次报告（NCEP ATP III）的要求，具体为满足以下五项条件中的三项及以上：甘油三酯水平≥1.7 mmol/L；高密度脂蛋白胆固醇<1.03 mmol/L（男性）或<1.29 mmol/L（女性）；男性腰围大于102 cm，女性大于88 cm；空腹血糖水平≥5.6 mmol/L或正在接受降糖药物或胰岛素治疗；以及被确诊为高血压，血压由专业医师依照标准化操作流程进行三次测量后取平均值，收缩压达到或超过130 mm Hg或舒张压达到或超过85 mm Hg[11]。
1.3　DI-GM的评估
DI-GM评分利用NHANES中两次24 h饮食回忆访谈数据构建，其中一次在流动体检中心（MEC）面对面进行，另一次通过电话访谈获取详细的饮食摄入信息[7]。参照Bezawit E. Kase等人[7]的方法，DI-GM整合了14项饮食成分，包括10项有利于维持肠道菌群健康的成分（包括发酵乳制品、鹰嘴豆、豆制品、全谷物、膳食纤维、蔓越莓、鳄梨、西兰花、咖啡以及绿茶）以及四项可能不利于肠道菌群平衡的不良因素（包含红肉、加工肉制品、精制谷物以及高脂饮食）；对于其中的有益成分，若个体摄入量超过性别特异性中位数则记1分，否则记0分；而对于不利成分，则摄入量低于性别中位数记1分，高于中位数记0分。DI-GM的总分范围为0～14分，评分越高表明饮食模式越倾向于促进肠道菌群多样性与平衡。
1.4　协变量评估
基于其他学者研究的文献回顾[12-15]，本研究纳入了多项可能影响MetS的潜在混杂因素作为协变量，包括社会人口学变量（年龄、性别、种族、教育水平、婚姻状况以及家庭收入与贫困比率）、生活方式因素（吸烟状况、饮酒状况、体力活动水平）、临床情况（抑郁症、尿酸、糖化血红蛋白）
1.5　统计分析
本研究的统计分析采用R软件（版本4.4.3）进行。所有分析均采用NHANES的抽样权重、分层信息和聚类变量，以确保所得估计具有全国代表性。采用多重插补法对缺失值进行插补（各变量缺失率均低于15%），以减少选择偏倚并降低信息损失。将DI-GM评分分为四组（Q1:0～4分；Q2:5分；Q3:6分；Q4:7～13分），并计算各组中MetS的患病率。对于连续变量，使用Kolmogorov-Smirnov检验判断数据是否符合正态分布；符合正态分布的变量以（）表示，组间差异采用加权单因素方差分析或t检验进行比较；不符合正态分布的连续变量则以［M（P25，P75）］进行描述，采用Mann-Whitney U检验比较组间差异。分类变量以加权频数和百分比表示，采用χ2检验或Fisher确切概率法进行比较。采用加权多变量logistic回归模型评估DI-GM与MetS之间的关联，以最低DI-GM四分位组（Q1）作为参照。构建了三个逐步调整的模型：模型1为未调整模型；模型2调节了社会人口学因素，包括性别、年龄、种族、教育水平、婚姻状况及家庭收入与贫困比率；模型3在模型2的基础上进一步调整了体力活动、吸烟状况、饮酒情况、抑郁症、尿酸和糖化血红蛋白。采用限制性立方样条分析阐明DI-GM和MetS患病风险之间的剂量-反应关联。为探讨不同亚群中DI-GM与MetS之间的关联差异，对受试者按性别、年龄等因素进行了亚组分析。本研究还进行了敏感性分析，以检验结果的稳健性。所有统计检验均采用双侧检验，检验水准α=0.05。
2　结果
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2.1　研究对象基线特征
本研究共纳入17 558名研究对象，其中男性有8 498名，占48.40%；有8 426人诊断为MetS，占47.99%。MetS患者的中位年龄为58.00（44.00，68.00）岁，高于非MetS患者的平均年龄的45.00（31.00，61.00）岁。MetS患者的DI-GM得分（中位数为4.00分）低于非MetS患者（中位数为5.00分）（P<0.001）。DI-GM高四分位组（Q4）在MetS患者的占比为26.13%，低于非MetS患者的29.16%（P<0.001）。另外，BMI、种族、教育程度、婚姻状况、社会经济状况、吸烟状况、体力活动水平、抑郁症、尿酸及糖化血红蛋白在两组间均存在显著性差异（均P<0.05）。研究对象基线特征见表1。
2.2　DI-GM和MetS的加权logistic分析
加权logistic回归分析显示，在完全调整模型中，DI-GM得分与MetS风险之间呈现显著的负向关联（OR= 0.94，95%CI:0.91～0.98）。与Q1相比，较高分组的MetS风险逐渐降低。在未调整模型中，DI-GM得分每增加1分，MetS风险降低约7%（OR=0.93，95%CI:0.90～0.96）；在部分调整（OR= 0.90，95%CI:0.88～0.93）和完全调整模型中（OR= 0.94，95%CI:0.91～0.98），该负向关联依然保持显著。在完全调整模型中，与DI-GM得分Q1组相比，Q4组MetS的风险下降约18%（OR=0.82,95%CI:0.72～0.95）。见表2。
2.3　DI-GM和MetS患病风险的剂量-反应关联
限制性立方样条分析显示，在调整多个协变量后，DI-GM和MetS患病风险存在线性负向剂量-反应关联(P for overall<0.05)，未发现二者之间存在非线性关联(P for nonlinear>0.05)。剂量-反应关联分析结果见图2。
2.4　亚组分析
较高DI-GM评分的保护作用在特定亚组中更为明显（P<0.05），包括女性、非西班牙裔黑人、教育程度高者、高收入者、未婚、现在饮酒者、现在吸烟者、以及积极活动者。性别、种族和教育程度的亚组对DI-GM与MetS关联具有显著的调节作用，交互作用均达到统计学显著（P交互<0.05）。亚组分析结果见图3。
2.5　敏感性分析
采用未加权回归模型进行敏感性分析，DI-GM得分与MetS之间呈现显著的负向关联。在完全调整模型中，Q4组风险相比Q1组下降约18%（OR=0.82,95%CI:0.71～0.94），与本研究采用的加权方法后得出的结果一致。见表3。
3　讨论
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本研究基于2007—2018年美国NHANES数据库对DI-GM与MetS风险的关联进行了分析，结果显示，两者呈显著负相关，提示以改善肠道菌群状态为目标的饮食干预对MetS具有潜在的防控价值。这一结论与越来越多的关于肠道菌群在代谢健康中作用的研究证据相一致[16-17]。而一项研究在非裔美国女性中未发现MetS与饮食质量存在显著关联[18]，说明饮食质量还不能完全解释MetS的发病机制。
已有研究表明，不健康的饮食模式会导致肠道菌群失衡，增加肠道通透性，使细菌产物（如内毒素）进入循环系统，引发“代谢性内毒素血症”[16]。内毒素会激活TLR4信号通路，引发全身性炎症，导致胰岛素抵抗，而胰岛素抵抗又会进一步扰乱葡萄糖代谢，促进脂肪堆积，加剧MetS的发生发展[19]。健康的肠道饮食可增加有益菌群种类（如嗜黏蛋白阿克曼菌和双歧杆菌）的丰度，这些菌群已被证明可增强肠道屏障功能、减少内毒素血症和减轻炎症[20]。此外肠道菌群的代谢产物，特别是丁酸盐等短链脂肪酸（SCFAs），能够通过与G蛋白偶联受体（如GPR41和GPR43）相互作用，增强胰岛素敏感性并维持能量稳态，也有利于维持肠道屏障完整性和调节免疫应答[21]。除此之外，肠道菌群也会影响胆汁酸代谢，从而改善糖和脂代谢[22]。
亚组分析显示，DI-GM与MetS之间的关联在性别、种族、教育水平等特定人群中存在显著异质性。与男性相比，女性的肠道菌群可能更易受到饮食变化的影响。有研究表明，在MetS患者中，男性的Faecalibacterium和Prevotella菌群丰度更高，而女性的Collinsella和Alistipes菌群丰度更高，且这些菌群与肥胖、胰岛素抵抗及炎症反应密切相关[23]。年轻个体肠道菌群适应性更强，饮食干预效果更佳[24]。在种族中，非西班牙裔黑人的DI-GM与MetS的负相关更为显著，这可能与不同种族人群的饮食习惯差异及其对肠道菌群的影响相关[16]。社会经济因素（如教育、收入和婚姻状况）会影响饮食质量，以及获取多样化食物和选择有益肠道菌群食物的机会[25]。在生活方式方面，吸烟、饮酒以及体力活动等健康行为也会导致肠道菌群组成和功能的差异，健康的生活习惯可以增加有益肠道菌群类别的丰度[26]。
本研究通过限制性立方样条方法对DI-GM与MetS患病风险的关联进行剂量-反应分析，结果显示两者呈线性负相关，DI-GM评分越高，MetS患病风险越低。这一结果与logistic回归分析一致，表明高DI-GM得分的饮食可以提供足够的益生元底物，促进有益菌生长并抑制有害菌，从而降低MetS风险[27]。同时，在敏感性分析中，本研究的结论仍保持稳定。
本研究的优势在于与当前主要关注肠道菌群[16]或饮食模式[28]的研究不同，使用融合两者的创新性指标DI-GM[7]，为饮食在代谢健康中的作用机制提供了新的视角。然而，本研究仍存在局限性：由于本研究为横断面设计，无法确立因果关系，未来需要进行纵向研究来进一步验证这些关联；NHANES数据库的饮食信息主要依靠受试者回忆获得，可能存在信息偏倚；尽管本研究控制了许多已知的混杂因素，但仍可能存在未测量或测量不充分的变量所带来的残留混杂因素的影响，需在后续研究中进一步改进。
综上，本研究显示DI-GM与MetS呈显著负相关，突出了健康饮食对改善肠道菌群及促进代谢健康的重要作用，为饮食干预和预防MetS提供了新思路。不同人群差异提示需要制定个性化的高DI-GM评分膳食计划。
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2025年第52卷第19期
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doi: 10.20043/j.cnki.MPM.202505192
  • 接收时间：2025-05-13
  • 首发时间：2026-03-17
  • 出版时间：2025-10-10
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  • 收稿日期：2025-05-13
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广东省医学科学技术研究基金项目(20231116101011723)
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    1.暨南大学护理学院，广东 广州 510632
    2.中山大学附属第六医院

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田晓迎，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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