Article(id=1311350009619116066, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311349984730116963, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202601069, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1767542400000, receivedDateStr=2026-01-05, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790579805997, onlineDateStr=2026-09-28, pubDate=1784908800000, pubDateStr=2026-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790579805997, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790579805997, creator=13701087609, updateTime=1790579805997, updator=13701087609, issue=Issue{id=1311349984730116963, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='14', pageStart='2497', pageEnd='2688', issueExtLink='null', onlineDate='null', pubDate='1784908800000', pubDateStr='2026-07-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790579800064, creator='13701087609', updateTime=1790588348354, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311385838924169506, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311349984730116963, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311385838924169507, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311349984730116963, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2531, endPage=2537, ext={EN=ArticleExt(id=1311350009833025571, articleId=1311350009619116066, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Intestinal microbial characteristics in patients with advanced diabetic nephropathy based on metagenomics, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the differences in the composition and function of intestinal microbiota among healthy individuals, patients with diabetes, and those with diabetic nephropathy, and to analyze the characteristics of intestinal flora imbalance in patients with advanced diabetic nephropathy, providing references for the mechanism research and prevention and treatment strategy formulation of this disease.

Methods

A total of 32 patients with advanced diabetic nephropathy, 79 patients with diabetes mellitus and 55 healthy controls were enrolled from the West China Fourth Hospital and Southwest China natural population cohort. Clinical data were collected and fecal samples were subjected to metagenomic sequencing. Linear discriminant analysis was used to screen for differentially abundant species and functional pathways. Linear regression analysis was performed to investigate the correlation between population phenotypes and differentially abundant species. Random forest and SPIEC-EASI algorithms were used to establish species classifiers and interaction networks, respectively.

Results

There were significant differences in the β-diversity of the intestinal microbiota among the three groups, but no statistically significant differences were observed in the α-diversity. In the advanced stage of diabetic nephropathy patients, the intestines were enriched with various opportunistic pathogenic bacteria (such as Eggerthella lenta, Clostridium innocuum, Hungatella hathewayi, etc.) and harmful pathways such as anaerobic energy metabolism and L-histidine degradation, and were significantly correlated with renal function indicators. The intestines of diabetic patients were characterized by the enrichment of opportunistic pathogenic bacteria (such as Escherichia coli and Klebsiella pneumoniae) and the activation of glycolytic pathways. The random forest model performed best in differentiating the diabetic nephropathy group from the healthy control group (AUC=0.920). The co-abundance network analysis further revealed that the intestinal microbial interaction network of diabetic nephropathy patients was significantly sparser and the topological parameters changed, suggesting that there was an imbalance in the microbial ecological network during the disease process.

Conclusion

From diabetes to the advanced stage of diabetic nephropathy, the intestinal microbiota shows a progressive functional disorder, with an increase in the abundance of harmful bacterial species and metabolic pathways, which is closely related to the progression of the disease.

, authors=Yi CAO, Yu-qi ZHANG, Lin DENG, Na GUO, Jin-li TUO, Lin XIONG, Pei-bin ZENG, Zhong LI, authorsList=Yi CAO, Yu-qi ZHANG, Lin DENG, Na GUO, Jin-li TUO, Lin XIONG, Pei-bin ZENG, Zhong LI, authorCompany=null, correspAuthors=Zhong LI, 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=1311350013452709944, articleId=1311350009619116066, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=基于宏基因组学的糖尿病肾病晚期患者肠道微生物特征分析, 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

探究健康人群、糖尿病以及糖尿病肾病患者之间的肠道微生物组成和功能差异,解析晚期糖尿病肾病患者肠道菌群失调特征,为该病的机制研究与防治策略制定提供参考。

方法

纳入来自华西第四医院和西南区域自然人群队列的32例糖尿病肾病晚期患者、79例糖尿病患者和55名健康对照,收集临床资料并进行粪便样本宏基因组测序。通过线性判别分析筛选差异物种和功能通路,使用线性回归分析人群表型与差异物种之间的相关性,并利用随机森林和SPIEC-EASI算法分别建立物种分类器和互作网络。

结果

三组间肠道菌群β多样性存在显著差异,但α多样性无统计学差异。糖尿病肾病晚期患者组肠道中富集多种机会性致病菌(如Eggerthella lenta、Clostridium innocuum、Hungatella hathewayi等)及厌氧能量代谢、L-组氨酸降解等有害通路,且与肾功能指标显著相关。糖尿病患者肠道则以条件致病菌(如Escherichia coli和Klebsiella pneumoniae)富集及糖酵解通路激活为特征。随机森林模型在区分糖尿病肾病组与健康对照组时表现最佳(AUC=0.920)。共丰度网络分析进一步揭示,糖尿病肾病患者的肠道微生物互作网络显著稀疏化,且拓扑参数发生改变,提示疾病进程中存在微生物生态网络的失调。

结论

从糖尿病到糖尿病肾病晚期,肠道菌群呈现渐进式功能失调,有害菌种及代谢通路丰度上升,与疾病进展密切相关。

, authors=曹易, 张宇骐, 邓林, 郭娜, 庹金丽, 熊琳, 曾沛斌, 李钟, authorsList=曹易, 张宇骐, 邓林, 郭娜, 庹金丽, 熊琳, 曾沛斌, 李钟, authorCompany=null, correspAuthors=李钟, authorNote=

曹易(2002—),男,硕士在读,研究方向:微生物、公众健康与检验

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李钟,E-mail:
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曹易(2002—),男,硕士在读,研究方向:微生物、公众健康与检验

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Journal of Clinical Microbiology, 2015, 53(1): 302-308., articleTitle=Biochemical characteristics, adhesion, and cytotoxicity of environmental and clinical isolates of Herbaspirillum spp, refAbstract=null), Reference(id=1311350020633358478, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350009619116066, doi=null, pmid=null, pmcid=null, year=1998, volume=64, issue=8, pageStart=3110, pageEnd=3113, url=null, language=null, rfNumber=[23], rfOrder=23, authorNames=Tanner MA, Goebel BM, Dojka MA, journalName=Applied and Environment Microbiology, refType=null, unstructuredReference=Tanner MA, Goebel BM, Dojka MA, et al. Specific ribosomal DNA sequences from diverse environmental settings correlate with experimental contaminants[J]. Applied and Environment Microbiology, 1998, 64(8): 3110-3113., articleTitle=Specific ribosomal DNA sequences from diverse environmental settings correlate with experimental contaminants, refAbstract=null)], funds=[Fund(id=1311350018813030518, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350009619116066, awardId=2024ZYD0098, language=CN, fundingSource=四川省科技厅-中央引导地方科技发展项目(2024ZYD0098), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1311350013666619449, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350009619116066, xref=null, ext=[AuthorCompanyExt(id=1311350013679202362, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350009619116066, companyId=1311350013666619449, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=West China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China), AuthorCompanyExt(id=1311350013687590971, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350009619116066, companyId=1311350013666619449, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=四川大学华西公共卫生学院/华西第四医院,四川 成都 610041)])], figs=[ArticleFig(id=1311350016141258858, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350009619116066, language=EN, label=Fig.1, caption=Comparison of diversity indices (A) Shannon index (B) Simpson index (C) PCoA visualization of beta diversity, figureFileSmall=FFJ8a1JJGsOwMkzNxFnsOg==, figureFileBig=KnO39ongaKrniUnoiRWROg==, tableContent=null), ArticleFig(id=1311350016208367723, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350009619116066, language=CN, label=图1, caption=多样性指数比较(A)Shannon指数(B)Simpson指数(C)PCoA可视化beta多样性, figureFileSmall=FFJ8a1JJGsOwMkzNxFnsOg==, figureFileBig=KnO39ongaKrniUnoiRWROg==, tableContent=null), ArticleFig(id=1311350017974169708, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350009619116066, language=EN, label=Fig.2, caption=LEfSe analysis of differential species and differential pathways (A) Species-level differences (B) Metabolic pathway differences, figureFileSmall=E4wDg98Ql86hvelFiidsiA==, figureFileBig=F50qtaU+UD6MjZQPgxJirw==, tableContent=null), ArticleFig(id=1311350018066444397, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350009619116066, language=CN, label=图2, caption=差异物种和差异通路LEfSe分析(A)种水平差异(B)代谢通路差异, figureFileSmall=E4wDg98Ql86hvelFiidsiA==, figureFileBig=F50qtaU+UD6MjZQPgxJirw==, tableContent=null), ArticleFig(id=1311350018146136174, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350009619116066, language=EN, label=Fig.3, caption=Heatmap of differential species related to phenotypes calculated using linear regression, figureFileSmall=ecvQOcZZRD9Kq71HfFl/5A==, figureFileBig=P5dUPnQ+i6QbyHfW+LNMgQ==, tableContent=null), ArticleFig(id=1311350018230022255, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350009619116066, language=CN, label=图3, caption=利用线性回归计算与表型相关的差异物种热图

注:对原始物种丰度执行中心对数比转换(CLR),校正年龄、性别、BMI及粪便性状等潜在混杂变量;并采用分层聚类,通过关联Z分数(Z-score)聚类差异物种。

, figureFileSmall=ecvQOcZZRD9Kq71HfFl/5A==, figureFileBig=P5dUPnQ+i6QbyHfW+LNMgQ==, tableContent=null), ArticleFig(id=1311350018322296944, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350009619116066, language=EN, label=Fig.4, caption=Intestinal microbiota network interaction graphs based on the SPIEC-EASI algorithm and differences in network topological parameters between pairwise comparison groups, figureFileSmall=eSD32ym+de/Ive0g50ILmw==, figureFileBig=TsfxEeWSQNTSEKH+i8EfoQ==, tableContent=null), ArticleFig(id=1311350018410377329, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350009619116066, language=CN, label=图4, caption=基于SPIEC-EASI算法的肠道菌群网络互作图及两两比较组间网络拓扑参数差异

注:A,B,C分别为健康组、糖尿病组和糖尿病肾病组网络互作图;D为两两比较组间网络拓扑参数差异柱状图;n代表节点数,l代表边数;***代表P<0.001,*代表P<0.05。

, figureFileSmall=eSD32ym+de/Ive0g50ILmw==, figureFileBig=TsfxEeWSQNTSEKH+i8EfoQ==, tableContent=null), ArticleFig(id=1311350018473291890, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350009619116066, language=EN, label=Fig.5, caption=Boruta Feature Analysis Chart Based on Random Forest(A) and ROC curve graph(B), figureFileSmall=xKkbIX4H9QfOvpOGaXoRWg==, figureFileBig=cBNFZZqBF+FODTPClrSzsw==, tableContent=null), ArticleFig(id=1311350018532012147, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350009619116066, language=CN, label=图5, caption=基于随机森林的Boruta特征分析图(A)和ROC工作曲线(B), figureFileSmall=xKkbIX4H9QfOvpOGaXoRWg==, figureFileBig=cBNFZZqBF+FODTPClrSzsw==, tableContent=null), ArticleFig(id=1311350018611703924, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350009619116066, language=EN, label=Table 1, caption=

Basic Characteristics of the Study Subjects

, figureFileSmall=null, figureFileBig=null, tableContent=
基本特征DKD组
(n=32)
DM组
(n=79)
HC组
(n=55)
统计量P值
性别[n(%)]:6.9020.032
男7(21.9)35(44.3)15(27.3)
女25(78.1)44(55.7)40(72.7)
年龄(岁,)66.2±10.8a59.0±11.4b63.1±8.34a9.4070.006
BMI(kg/m2,)22.5±3.52a26.1±3.00b23.2±2.88a35.199<0.001
总胆固醇(mmol/L,)3.46±0.90a5.03±1.21b5.07±0.83b46.422<0.001
甘油三酯(mmol/L,[M(P25, P75)])1.64(1.04,2.63)a1.58(1.28,2.62)b1.29(0.93,2.03)a9.0770.011
低密度脂蛋白(mmol/L,)1.65±0.67a3.27±0.80b3.29±0.66a63.455<0.001
高密度脂蛋白(mmol/L,[M(P25, P75)])1.02(0.80,1.26)a1.16(1.02,1.40)b1.39(1.15,1.66)c26.510<0.001
空腹血糖(mmol/L,[M(P25, P75)])7.80(6.38,9.58)a7.31(6.30,9.03)a4.88(4.62,5.17)b93.108<0.001
糖化血红蛋白[n(%)]6.70(5.90,7.38)a7.20(6.70,8.25)b5.40(5.20,5.50)c109.350<0.001
血清肌酐(mmol/L,[M(P25, P75)])727(519,1028)a63.6(54.5,77.2)b73.2(64.6,81.3)c82.568<0.001
血清尿酸(mmol/L,)393±119345±79.1347±80.15.9230.118
血清尿素(mmol/L,[M(P25, P75)])22.6(17.0,28.4)a5.54(4.62,6.39)b5.18(4.34,6.02)b77.704<0.001
估算肾小球滤过率(mL/min/1.73m2,[M(P25, P75)])6.85(4.69,9.69)a102(91.2,119)b99.0(84.9,108)b80.844<0.001
饮酒频率[n(%)]-<0.001
从不或几乎不30(93.8)41(51.9)25(45.5)
仅在重要日子饮酒0(0.00)13(16.5)7(12.7)
每月喝但少于每周一次2(6.25)5(6.33)4(7.27)
大于每周一次0(0.00)20(25.3)19(34.5)
吸烟[n(%)]:19.7930.001
不吸烟13(40.6)53(67.1)30(54.5)
正在吸烟5(15.6)18(22.8)17(30.9)
已戒烟14(43.8)8(10.1)8(14.5)
每周静坐时间
(小时,[M(P25, P75)])42.0(31.5,56.0)21.0(14.0,29.0)21.0(14.0,28.0)32.522<0.001
), ArticleFig(id=1311350018695590005, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350009619116066, language=CN, label=表1, caption=

研究对象基本特征

, figureFileSmall=null, figureFileBig=null, tableContent=
基本特征DKD组
(n=32)
DM组
(n=79)
HC组
(n=55)
统计量P值
性别[n(%)]:6.9020.032
男7(21.9)35(44.3)15(27.3)
女25(78.1)44(55.7)40(72.7)
年龄(岁,)66.2±10.8a59.0±11.4b63.1±8.34a9.4070.006
BMI(kg/m2,)22.5±3.52a26.1±3.00b23.2±2.88a35.199<0.001
总胆固醇(mmol/L,)3.46±0.90a5.03±1.21b5.07±0.83b46.422<0.001
甘油三酯(mmol/L,[M(P25, P75)])1.64(1.04,2.63)a1.58(1.28,2.62)b1.29(0.93,2.03)a9.0770.011
低密度脂蛋白(mmol/L,)1.65±0.67a3.27±0.80b3.29±0.66a63.455<0.001
高密度脂蛋白(mmol/L,[M(P25, P75)])1.02(0.80,1.26)a1.16(1.02,1.40)b1.39(1.15,1.66)c26.510<0.001
空腹血糖(mmol/L,[M(P25, P75)])7.80(6.38,9.58)a7.31(6.30,9.03)a4.88(4.62,5.17)b93.108<0.001
糖化血红蛋白[n(%)]6.70(5.90,7.38)a7.20(6.70,8.25)b5.40(5.20,5.50)c109.350<0.001
血清肌酐(mmol/L,[M(P25, P75)])727(519,1028)a63.6(54.5,77.2)b73.2(64.6,81.3)c82.568<0.001
血清尿酸(mmol/L,)393±119345±79.1347±80.15.9230.118
血清尿素(mmol/L,[M(P25, P75)])22.6(17.0,28.4)a5.54(4.62,6.39)b5.18(4.34,6.02)b77.704<0.001
估算肾小球滤过率(mL/min/1.73m2,[M(P25, P75)])6.85(4.69,9.69)a102(91.2,119)b99.0(84.9,108)b80.844<0.001
饮酒频率[n(%)]-<0.001
从不或几乎不30(93.8)41(51.9)25(45.5)
仅在重要日子饮酒0(0.00)13(16.5)7(12.7)
每月喝但少于每周一次2(6.25)5(6.33)4(7.27)
大于每周一次0(0.00)20(25.3)19(34.5)
吸烟[n(%)]:19.7930.001
不吸烟13(40.6)53(67.1)30(54.5)
正在吸烟5(15.6)18(22.8)17(30.9)
已戒烟14(43.8)8(10.1)8(14.5)
每周静坐时间
(小时,[M(P25, P75)])42.0(31.5,56.0)21.0(14.0,29.0)21.0(14.0,28.0)32.522<0.001
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基于宏基因组学的糖尿病肾病晚期患者肠道微生物特征分析
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曹易 , 张宇骐 , 邓林 , 郭娜 , 庹金丽 , 熊琳 , 曾沛斌 , 李钟
现代预防医学 | 流行病与统计方法 2026,53(14): 2531-2537
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现代预防医学 |流行病与统计方法 2026 , 53 (14) : 2531 -2537
基于宏基因组学的糖尿病肾病晚期患者肠道微生物特征分析
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曹易, 张宇骐, 邓林, 郭娜, 庹金丽, 熊琳, 曾沛斌, 李钟
作者信息
  • 四川大学华西公共卫生学院/华西第四医院,四川 成都 610041
通讯作者:
李钟,E-mail:
作者简介:

曹易(2002—),男,硕士在读,研究方向:微生物、公众健康与检验

Intestinal microbial characteristics in patients with advanced diabetic nephropathy based on metagenomics
Yi CAO, Yu-qi ZHANG, Lin DENG, Na GUO, Jin-li TUO, Lin XIONG, Pei-bin ZENG, Zhong LI
Affiliations
  • West China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
出版时间: 2026-07-25 doi: 10.20043/j.cnki.MPM.202601069
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目的

探究健康人群、糖尿病以及糖尿病肾病患者之间的肠道微生物组成和功能差异,解析晚期糖尿病肾病患者肠道菌群失调特征,为该病的机制研究与防治策略制定提供参考。

方法

纳入来自华西第四医院和西南区域自然人群队列的32例糖尿病肾病晚期患者、79例糖尿病患者和55名健康对照,收集临床资料并进行粪便样本宏基因组测序。通过线性判别分析筛选差异物种和功能通路,使用线性回归分析人群表型与差异物种之间的相关性,并利用随机森林和SPIEC-EASI算法分别建立物种分类器和互作网络。

结果

三组间肠道菌群β多样性存在显著差异,但α多样性无统计学差异。糖尿病肾病晚期患者组肠道中富集多种机会性致病菌(如Eggerthella lenta、Clostridium innocuum、Hungatella hathewayi等)及厌氧能量代谢、L-组氨酸降解等有害通路,且与肾功能指标显著相关。糖尿病患者肠道则以条件致病菌(如Escherichia coli和Klebsiella pneumoniae)富集及糖酵解通路激活为特征。随机森林模型在区分糖尿病肾病组与健康对照组时表现最佳(AUC=0.920)。共丰度网络分析进一步揭示,糖尿病肾病患者的肠道微生物互作网络显著稀疏化,且拓扑参数发生改变,提示疾病进程中存在微生物生态网络的失调。

结论

从糖尿病到糖尿病肾病晚期,肠道菌群呈现渐进式功能失调,有害菌种及代谢通路丰度上升,与疾病进展密切相关。

糖尿病肾病  /  肠道微生物组  /  宏基因组学
Objective

To explore the differences in the composition and function of intestinal microbiota among healthy individuals, patients with diabetes, and those with diabetic nephropathy, and to analyze the characteristics of intestinal flora imbalance in patients with advanced diabetic nephropathy, providing references for the mechanism research and prevention and treatment strategy formulation of this disease.

Methods

A total of 32 patients with advanced diabetic nephropathy, 79 patients with diabetes mellitus and 55 healthy controls were enrolled from the West China Fourth Hospital and Southwest China natural population cohort. Clinical data were collected and fecal samples were subjected to metagenomic sequencing. Linear discriminant analysis was used to screen for differentially abundant species and functional pathways. Linear regression analysis was performed to investigate the correlation between population phenotypes and differentially abundant species. Random forest and SPIEC-EASI algorithms were used to establish species classifiers and interaction networks, respectively.

Results

There were significant differences in the β-diversity of the intestinal microbiota among the three groups, but no statistically significant differences were observed in the α-diversity. In the advanced stage of diabetic nephropathy patients, the intestines were enriched with various opportunistic pathogenic bacteria (such as Eggerthella lenta, Clostridium innocuum, Hungatella hathewayi, etc.) and harmful pathways such as anaerobic energy metabolism and L-histidine degradation, and were significantly correlated with renal function indicators. The intestines of diabetic patients were characterized by the enrichment of opportunistic pathogenic bacteria (such as Escherichia coli and Klebsiella pneumoniae) and the activation of glycolytic pathways. The random forest model performed best in differentiating the diabetic nephropathy group from the healthy control group (AUC=0.920). The co-abundance network analysis further revealed that the intestinal microbial interaction network of diabetic nephropathy patients was significantly sparser and the topological parameters changed, suggesting that there was an imbalance in the microbial ecological network during the disease process.

Conclusion

From diabetes to the advanced stage of diabetic nephropathy, the intestinal microbiota shows a progressive functional disorder, with an increase in the abundance of harmful bacterial species and metabolic pathways, which is closely related to the progression of the disease.

Diabetic nephropathy  /  Gut microbiome  /  Metagenomics
曹易, 张宇骐, 邓林, 郭娜, 庹金丽, 熊琳, 曾沛斌, 李钟. 基于宏基因组学的糖尿病肾病晚期患者肠道微生物特征分析. 现代预防医学, 2026 , 53 (14) : 2531 -2537 . DOI: 10.20043/j.cnki.MPM.202601069
Yi CAO, Yu-qi ZHANG, Lin DENG, Na GUO, Jin-li TUO, Lin XIONG, Pei-bin ZENG, Zhong LI. Intestinal microbial characteristics in patients with advanced diabetic nephropathy based on metagenomics[J]. Modern Preventive Medicine, 2026 , 53 (14) : 2531 -2537 . DOI: 10.20043/j.cnki.MPM.202601069
糖尿病肾病(Diabetic kidney disease,DKD)是一种由糖尿病(Diabetes mellitus,DM)引起的慢性肾脏病(Chronic kidney disease,CKD),其发病机制复杂,临床特征表现为持续性白蛋白尿排泄增加,和(或)肾小球滤过率(glomerular filtration rate,GFR)进行性下降,最终可发展为终末期肾脏疾病(End-stage renal disease,ESRD)[1]。据报道,在过去的二十年里,全球糖尿病患者的数量增长超过两倍,大约三分之一的DM患者最终会发展为DKD,其中大约50%的人可能会发展为ESRD[2]。ESRD是DKD发展的严重阶段,患者肾功能严重衰竭,需依赖肾脏替代治疗,其高致残率与死亡率给患者家庭及社会医疗系统带来沉重负担[3-4]。近年来,调节肠道微生态已成为延缓慢性肾脏病进展的新兴潜在策略[5]。针对DKD的肠道菌群研究亦逐步开展,已有研究报道DKD患者存在肠道菌群结构改变,部分机会致病菌丰度升高,产短链脂肪酸菌丰度降低[6-7]。但是目前针对中国汉族人群,尤其是已进展至ESRD的DKD患者,其肠道菌群的特征仍缺乏报道。此外,该领域多数研究依赖于16S rRNA基因测序[6,8,9],其分辨率多限于属水平,难以在物种及功能层面进行深入解析。因此,本研究基于宏基因组测序,探究健康人群、糖尿病以及糖尿病肾病晚期患者之间的肠道微生物组成和功能差异,为糖尿病肾病的预防和治疗提供研究基础。
本研究从四川大学华西第四医院肾脏内科188名终末期肾脏病患者中进行筛选,依据糖尿病肾病诊断标准[10]和医师临床诊断最终纳入了的32名被确诊为DKD且已进入ESRD的患者。对照组研究对象均源自西南区域自然人群队列,根据血糖和糖化血红蛋白水平区分健康对照组和糖尿病组,再通过校正地区、性别、体重指数(Body Mass Index,BMI)、年龄因素匹配到了55名健康对照(Healthy Control, HC)和79名糖尿病(Diabetes mellitus, DM)患者。
本研究分组的纳入排除标准包括:(1)DKD终末期患者的估算肾小球滤过率(Estimated Glomerular Filtration Rate,eGFR)<15 ml/(min·1.73m2),长期进行透析治疗,且排除其他非糖尿病性原因导致的慢性肾脏病;(2)糖尿病患者空腹血糖(Fasting Blood Glucose, FBG)≥7.0 mmol/L或糖化血红蛋白(glycated hemoglobin, HbAlc)≥ 6.5%,无肾脏疾病史;(3)健康对照组无糖尿病及其他重大慢性疾病史,FBG<6.1 mmol/L且HbA1c<5.7%;(4)排除了藏族的研究对象;(5)排除了1个月内使用抗生素的个体。所有研究对象均已签署知情同意书,且经过四川大学华西第四医院/华西公共卫生学院伦理委员会批准(HXSY-EC-2024087和Gwll2025025)。
所有研究对象在至少空腹8小时后采集其静脉血,血液样本送检,完成血常规、血脂、空腹血糖、肾功能等生化指标检测,并收集其新鲜中段粪便样本,置于冰上运输,尽快存入-80℃冰箱保存,用于后续宏基因组测序。其余的基础信息以及生活、行为方式等信息通过结构化问卷收集。
宏基因组文库的构建与测序工作委托诺禾致源生物科技有限公司完成。为了保证质量控制水平,并去除与人类基因组比对上的读长,因此使用了开源软件KneadData(v.0.10.0)。物种注释方面,采用MetaPhlAn 3(v 3.0.13)及其内置数据库进行微生物物种组成分析。对于代谢通路分析,采用了MEGAHIT(v.1.2.9)和MetaBAT2(v.2.12.1)完成读长的组装和分箱,并且使用HUMAnN(v 3.0.1)对编码微生物代谢通路的基因进行定量分析。
所有统计分析及数据可视化均在R语言环境(v 4.3.2)中完成。首先,采用vegan包计算香农指数、辛普森指数来表征Alpha多样性,组间比较使用Kruskal-Wallis检验及事后Dunn检验。基于Bray-Curtis距离进行主坐标分析,并用PERMANOVA检验评估组间Beta多样性差异。其次,基于线性判别分析(linear discriminant analysis, LDA)效应大小(LDA effect size, LEfSe)识别组间差异显著的微生物物种及代谢通路。为探究肠道菌群与宿主临床表型之间的潜在关联,采用多重线性回归模型。多重线性回归模型控制了年龄、性别、BMI和粪便性状等混杂因素,且为控制多重比较导致的假阳性,所有P值均经过错误发现率校正,统计显著性设定为FDR q值<0.1。多重检验中使用FDR(false discovery rate)校正P值。此外,针对肠道微生物数据使用Boruta包进行默认参数1 000次的迭代特征选择,并基于randomForest包构建五重交叉验证的随机森林模型。最后,为探究肠道微生物物种间的生态互作关系,本研究采用 SPIEC-EASI(SParse InversE Covariance Estimation for Ecological ASsociation Inference)算法。
如表1所示,本研究共纳入166名来自四川省的汉族研究对象,并将其分为健康对照组(HC组)、糖尿病组(DM组)和糖尿病肾病组(DKD组)。在人口学特征方面,分组时虽已以最小化标准化均值差(Standardized Mean Difference, SMD)为目标,对地区、性别、年龄及体重指数(BMI)进行了匹配,但三组间在性别、年龄和BMI上仍存在一定差异。在生活方式方面,DKD组晚期患者饮酒(P<0.001)及吸烟率(P=0.001)均明显低于DM组和HC组,而每日静坐时间更长(P<0.001)。在临床指标方面,DKD组和DM组空腹血糖和HbA1c都显著高于HC组(P<0.001),且DKD组HbA1c显著高于DM组(P<0.001),但是两组间空腹血糖无统计学差异(P=0.49)。在肾功能指标上,DKD组血清肌酐和尿素显著高于DM组和HC组(P<0.001),而估算肾小球滤过率显著低于其余两组(P<0.001),表明DKD患者已存在显著的肾功能损害。
如图1所示,在α多样性上,Shannon多样性指数(P=0.267)和Simpson多样性指数(P=0.088)均显示三组人群之间差异无统计学意义。此外,基于Bray-Curtis距离的Adonis检验显示,三组人群肠道菌群结构存在统计学差异(F=4.2,R2=0.049,P<0.001),但分组仅解释 4.9 % 的变异。两两ANOSIM 比较表明,DM组菌群结构显著区别于HC组(R=0.245,FDR<0.001)和DKD组(R=0.182,FDR<0.001),而DKD人群与HC人群之间差异不显著(R=-0.009,FDR=0.65)。
如图2A所示,基于LEfSe分析(LDA>3.0,P<0.05),三组间共鉴定出42个具有显著差异的微生物物种,提示从健康状态到糖尿病及糖尿病肾病进程中肠道菌群存在动态演变。在DKD患者中,差异菌种以产乳酸俄罗斯杆菌(Ruthenibacterium lactatiformans,LDA=4.25,P<0.001)、嗜黏蛋白阿克曼菌(Akkermansia muciniphila,LDA=4.21,P<0.001)、共生梭菌(Clostridium symbiosum,LDA=3.65,P<0.001)、缓慢爱格士氏菌(Eggerthella lenta,LDA=3.06,P<0.001)和哈氏亨盖特菌(Hungatella hathewayi,LDA=3.63,P<0.001)等为代表。DM患者肠道中富集普雷沃氏菌(Prevotella copri,LDA=4.36,P=0.008)、大肠埃希氏菌(Escherichia coli,LDA=4.43,P<0.001)、肺炎克雷伯菌(Klebsiella pneumoniae,LDA=3.91,P<0.001)和平常拟杆菌(Bacteroides plebeius,LDA=4.41,P<0.001)。相比之下,健康人群中直肠真杆菌(Eubacterium rectale,LDA=4.26,P<0.001)、细绳形巨单胞菌(Megamonas funiformis,LDA=3.96,P=0.004)和卵形拟杆菌(Bacteroides ovatus,LDA=3.86,P=0.003)等物种相对丰度较高。
对代谢通路进行LEfSe分析(图2B显示),LDA值排名前20名的差异代谢通路中,DKD患者在厌氧能量代谢(PWY-7383,LDA=2.98,P<0.001)、L-组氨酸降解(HISDEG-PWY,LDA=2.88,P=0.001)、糖异生(PWY66-399,LDA=2.82,P<0.001)、乙醇胺利用(PWY-8134,LDA=2.76,P<0.001)、L-瓜氨酸生物合成(CITRULBIO-PWY,LDA=2.76,P=0.003)和尿素循环(PWY-4984,LDA=2.72,P=0.006)等代谢通路上富集。DM患者则在L -苯丙氨酸的生物合成(PWY-6628,LDA=2.86,P=0.001)以及葡萄糖和葡萄糖-1-磷酸降解(GLUCOSE1PMETAB-PWY,LDA=2.75,P=0.008)通路上相对丰度较高。S-腺苷甲硫氨酸回收(PWY-6151,LDA=2.96,P=0.002)和脂肪酸生物合成起始(PWY66-429,LDA=2.84,P=0.003)等代谢通路是作为HC组的特异性代谢通路。
通过校正年龄、BMI、性别和粪便性状等混杂因素,构建线性回归模型计算表型和差异物种之间的关联,如图3所示,在DKD组中富集的菌种,如产乳酸俄罗斯杆菌(Ruthenibacterium lactatiformans)、嗜黏蛋白阿克曼菌(Akkermansia muciniphila)、惰性真杆菌(Eubacterium siraeum)、哈氏亨盖特菌(Hungatella hathewayi)以及部分梭菌属细菌,与肾功能指标存在显著关联。这些细菌丰度总体上与血清肌酐、尿素水平呈正相关,和估算肾小球滤过率呈负相关。此外,DKD患者中富集的差异菌种总体上与总胆固醇、低密度脂蛋白呈负相关。
本研究使用 SPIEC-EASI 算法的 MB(Meanderizing Bootstrap)方法构建微生物关联网络,结果显示(图4),HC组内产生297个节点物种,存在589种关联,DM组内产生289个节点物种,存在532种关联,DKD组内产生234个节点物种,存在260种关联。HC和DM组的肠道微生物网络更加复杂,而DKD组的网络则相对稀疏,且DKD组的网络中连通分量数增加,提示存在更多的孤立关联。共丰度网络拓扑参数的KS 检验(Kolmogorov-Smirnov test)两两对比显示(图4D),度、介数中心性和接近中心性指数在各组间差异均具有显著性(P<0.05),表明了DKD发展过程中,肠道微生物组内部互作网络也随疾病进程逐步出现失调。
首先,如图5所示,基于随机森林的Boruta特征分析方法,共鉴定出20种特征物种,部分特征潜在致病菌物种与上文LEfSe方法鉴定出的差异物种有一定重叠。其次,根据ROC曲线两两比较分析,发现随机森林模型在HC组和DKD组比较中表现最佳(图5),AUC为0.920, 95%置信区间为0.860到0.980,总体模型的宏AUC为0.787, 95%置信区间为0.746到0.828。上述结果表明,糖尿病肾病晚期患者的肠道菌群构成发生了显著改变,可有效区别于健康人群及糖尿病患者,提示了肠道微生物具有用于构建诊断模型的潜在作用。
越来越多的证据表明,肠道微生物群的组成和功能变化与糖尿病及其并发症(如糖尿病肾病)的发生发展密切相关[11]。本研究通过宏基因组测序方法比较了健康人群、糖尿病患者及晚期糖尿病肾病患者肠道菌群的组成与功能。结果显示,DKD组肠道菌群呈现出独特的物种组成特征,且与肾功能指标呈现显著相关。和Fan[7]等研究结果类似,DKD患者肠道中机会性致病菌丰度上升,如缓慢爱格士氏菌(Eggerthella lenta)、无害梭菌(Clostridium innocuum)。此外,哈氏亨盖特菌(Hungatella hathewayi)亦在DKD患者肠道中富集,该菌被认为是三甲胺氧化物(trimethylamine-N-oxide,TMAO)的产生菌[12],具有促炎作用。值得注意的是,被视为潜在下一代益生菌的嗜黏蛋白阿克曼菌(Akkermansia muciniphila)[13]在DKD患者肠道中也呈现富集,这与部分先前研究结果趋势相反[14]。然而,近年来越多研究表明[15-16],嗜黏蛋白阿克曼菌在某些条件下具有干扰宿主黏液稳态、加重肠道炎症的能力。这一矛盾提示,Akkermansia muciniphila的生物学作用可能受宿主状态、菌株特异性及肠道微环境等多种因素影响,其在DKD中的具体作用仍需深入探究。相比之下,健康人群对照中富含多种产短链脂肪酸的菌种,如直肠真杆菌(Eubacterium rectale)、卵形拟杆菌(Bacteroides ovatus)和食葡糖罗斯拜瑞氏菌(Roseburia inulinivorans)等,这些细菌对维持肠道屏障完整性与免疫调节具有积极作用。
功能分析揭示,从健康对照组、糖尿病组到糖尿病肾病组,肠道微生物的代谢功能呈现渐进式紊乱。在HC组人群中富集的S-腺苷甲硫氨酸回收(PWY-6151)、脂肪酸生物合成起始(PWY66-429)、丙酮酸发酵生成乙酸和乳酸(P41-PWY)等通路反映了健康人群肠道微生物较强的营养合成及代谢调控能力。糖尿病组中葡萄糖与葡萄糖-1-磷酸降解通路(GLUCOSE1PMETAB-PWY)的富集,提示在高糖环境下菌群倾向于通过糖酵解途径高效获取能量。而在DKD组中,肠道菌群功能失调表现在促进肾脏损害的有害代谢功能的亢进,如厌氧能量代谢通路(PWY-7383)和一些尿毒症毒素前体合成通路。有研究表明,糖尿病肾病高糖环境下,足细胞无氧代谢产物(乳酸)积累会驱动肾损伤[17]。包括L-组氨酸降解(HISDEG-PWY)、乙醇胺利用(PWY0-1477)、尿素循环(PWY-4984)和肌苷- 5 ’ -磷酸的生物合成(PWY-6124)在内的代谢通路涉及含氮化合物(如氨基酸、组胺、氨、尿酸)的分解与转化[18-20],其过度活跃可能导致过量含氮废物产生,加重机体氮负荷,进而损害肾功能。
基于随机森林算法筛选出的特征菌在一定程度上验证了基于LEfSe分析鉴定的差异物种,如嗜黏蛋白阿克曼菌(Akkermansia muciniphila)、共生梭菌(Clostridium symbiosum)、缓慢爱格士氏菌(Eggerthella lenta)和哈氏亨盖特菌(Hungatella hathewayi)等,佐证了以上物种在DKD患者肠道内富集,并反映了糖尿病肾病晚期病人肠道菌群的严重失调。然而,重要性评分最高的微杆菌属(Microbacterium sp T32)和草小螺菌属(Herbaspirillum sp)两种细菌被报道广泛存在于土壤和水体等自然环境中[21-22],且Herbaspirillum可能是DNA提取试剂盒或测序试剂中极常见的“污染菌”[23],二者的阳性结果可能源于宏基因组测序中的试剂污染。最后,通过物种网络关联分析发现了晚期糖尿病肾病患者的肠道菌群存在物种网络稀疏的现象,提示疾病状态下微生物相互作用网络更加碎片化,不同功能的微生物群体相互隔离,从整体的角度上说明了晚期糖尿病肾病患者的肠道菌群组成结构的失调。
尽管本研究发现晚期糖尿病肾病患者和对照人群的肠道微生物在组成和功能上具有差异,初步阐释了肠道菌群在DKD发生发展中的潜在作用,但是仍存在一些局限性。首先,为明确DKD相关的特异性菌群改变,本研究同时设置了糖尿病组与健康对照组,但未纳入单纯慢性肾脏病对照组。其次,本研究纳入的DKD患者均为终末期肾病患者且长期接受透析治疗,透析本身可能作为重要的混杂因素影响肠道菌群的组成与功能。因此,本研究所观察到的菌群特征可能同时反映了疾病进程与透析治疗的共同作用,二者难以完全分离。第三,随机森林分析结果提示测序中可能存在试剂污染的情况,从而影响结果的可靠性。第四,糖尿病肾病患者的样本量较小,并且例如随机森林模型的建立也缺乏外部数据集的验证,可能限制研究结论的外推性。最后,横断面设计无法推断因果关系,未来需借助纵向队列或动物实验进一步验证。
综上所述,基于宏基因组测序,本研究主要描述了糖尿病肾病患者的肠道微生物特征,揭示了肠道菌群失调在DKD疾病发生发展中的潜在作用,为后续探索驱动DKD疾病进程的微生物靶点和相关机制奠定了基础。
  • 四川省科技厅-中央引导地方科技发展项目(2024ZYD0098)
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doi: 10.20043/j.cnki.MPM.202601069
  • 接收时间:2026-01-05
  • 首发时间:2026-09-28
  • 出版时间:2026-07-25
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  • 收稿日期:2026-01-05
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四川省科技厅-中央引导地方科技发展项目(2024ZYD0098)
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    四川大学华西公共卫生学院/华西第四医院,四川 成都 610041

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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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