Analyses of gut microbiota and plasma bile acids enable stratification of patients for antidiabetic treatment
Antidiabetic medication may modulate the gut microbiota and thereby alter plasma and faecal bile acid (BA) composition, which may improve metabolic health. Here we show that treatment with Acarbose, but not Glipizide, increases the ratio between primary BAs and secondary BAs and plasma levels of unc...
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| Published in | Nature communications Vol. 8; no. 1; pp. 1785 - 12 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
27.11.2017
Nature Publishing Group Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Antidiabetic medication may modulate the gut microbiota and thereby alter plasma and faecal bile acid (BA) composition, which may improve metabolic health. Here we show that treatment with Acarbose, but not Glipizide, increases the ratio between primary BAs and secondary BAs and plasma levels of unconjugated BAs in treatment-naive type 2 diabetes (T2D) patients, which may beneficially affect metabolism. Acarbose increases the relative abundances of
Lactobacillus
and
Bifidobacterium
in the gut microbiota and depletes
Bacteroides
, thereby changing the relative abundance of microbial genes involved in BA metabolism. Treatment outcomes of Acarbose are dependent on gut microbiota compositions prior to treatment. Compared to patients with a gut microbiota dominated by
Prevotella
, those with a high abundance of
Bacteroides
exhibit more changes in plasma BAs and greater improvement in metabolic parameters after Acarbose treatment. Our work highlights the potential for stratification of T2D patients based on their gut microbiota prior to treatment.
The authors examine the effects of antidiabetic medication on the gut microbiome and bile acid composition and show that these data can be used to stratify treatment regimens for type 2 diabetes. |
|---|---|
| AbstractList | Antidiabetic medication may modulate the gut microbiota and thereby alter plasma and faecal bile acid (BA) composition, which may improve metabolic health. Here we show that treatment with Acarbose, but not Glipizide, increases the ratio between primary BAs and secondary BAs and plasma levels of unconjugated BAs in treatment-naive type 2 diabetes (T2D) patients, which may beneficially affect metabolism. Acarbose increases the relative abundances of Lactobacillus and Bifidobacterium in the gut microbiota and depletes Bacteroides, thereby changing the relative abundance of microbial genes involved in BA metabolism. Treatment outcomes of Acarbose are dependent on gut microbiota compositions prior to treatment. Compared to patients with a gut microbiota dominated by Prevotella, those with a high abundance of Bacteroides exhibit more changes in plasma BAs and greater improvement in metabolic parameters after Acarbose treatment. Our work highlights the potential for stratification of T2D patients based on their gut microbiota prior to treatment.Antidiabetic medication may modulate the gut microbiota and thereby alter plasma and faecal bile acid (BA) composition, which may improve metabolic health. Here we show that treatment with Acarbose, but not Glipizide, increases the ratio between primary BAs and secondary BAs and plasma levels of unconjugated BAs in treatment-naive type 2 diabetes (T2D) patients, which may beneficially affect metabolism. Acarbose increases the relative abundances of Lactobacillus and Bifidobacterium in the gut microbiota and depletes Bacteroides, thereby changing the relative abundance of microbial genes involved in BA metabolism. Treatment outcomes of Acarbose are dependent on gut microbiota compositions prior to treatment. Compared to patients with a gut microbiota dominated by Prevotella, those with a high abundance of Bacteroides exhibit more changes in plasma BAs and greater improvement in metabolic parameters after Acarbose treatment. Our work highlights the potential for stratification of T2D patients based on their gut microbiota prior to treatment. Antidiabetic medication may modulate the gut microbiota and thereby alter plasma and faecal bile acid (BA) composition, which may improve metabolic health. Here we show that treatment with Acarbose, but not Glipizide, increases the ratio between primary BAs and secondary BAs and plasma levels of unconjugated BAs in treatment-naive type 2 diabetes (T2D) patients, which may beneficially affect metabolism. Acarbose increases the relative abundances of Lactobacillus and Bifidobacterium in the gut microbiota and depletes Bacteroides , thereby changing the relative abundance of microbial genes involved in BA metabolism. Treatment outcomes of Acarbose are dependent on gut microbiota compositions prior to treatment. Compared to patients with a gut microbiota dominated by Prevotella , those with a high abundance of Bacteroides exhibit more changes in plasma BAs and greater improvement in metabolic parameters after Acarbose treatment. Our work highlights the potential for stratification of T2D patients based on their gut microbiota prior to treatment. The authors examine the effects of antidiabetic medication on the gut microbiome and bile acid composition and show that these data can be used to stratify treatment regimens for type 2 diabetes. Antidiabetic medication may modulate the gut microbiota and thereby alter plasma and faecal bile acid (BA) composition, which may improve metabolic health. Here we show that treatment with Acarbose, but not Glipizide, increases the ratio between primary BAs and secondary BAs and plasma levels of unconjugated BAs in treatment-naive type 2 diabetes (T2D) patients, which may beneficially affect metabolism. Acarbose increases the relative abundances of Lactobacillus and Bifidobacterium in the gut microbiota and depletes Bacteroides, thereby changing the relative abundance of microbial genes involved in BA metabolism. Treatment outcomes of Acarbose are dependent on gut microbiota compositions prior to treatment. Compared to patients with a gut microbiota dominated by Prevotella, those with a high abundance of Bacteroides exhibit more changes in plasma BAs and greater improvement in metabolic parameters after Acarbose treatment. Our work highlights the potential for stratification of T2D patients based on their gut microbiota prior to treatment. The authors examine the effects of antidiabetic medication on the gut microbiome and bile acid composition and show that these data can be used to stratify treatment regimens for type 2 diabetes. Antidiabetic medication may modulate the gut microbiota and thereby alter plasma and faecal bile acid (BA) composition, which may improve metabolic health. Here we show that treatment with Acarbose, but not Glipizide, increases the ratio between primary BAs and secondary BAs and plasma levels of unconjugated BAs in treatment-naive type 2 diabetes (T2D) patients, which may beneficially affect metabolism. Acarbose increases the relative abundances of Lactobacillus and Bifidobacterium in the gut microbiota and depletes Bacteroides , thereby changing the relative abundance of microbial genes involved in BA metabolism. Treatment outcomes of Acarbose are dependent on gut microbiota compositions prior to treatment. Compared to patients with a gut microbiota dominated by Prevotella , those with a high abundance of Bacteroides exhibit more changes in plasma BAs and greater improvement in metabolic parameters after Acarbose treatment. Our work highlights the potential for stratification of T2D patients based on their gut microbiota prior to treatment. |
| ArticleNumber | 1785 |
| Author | Feng, Qiang Xie, Xiaoyan Gu, Yanyun Li, Junhua Xu, Xun Madsen, Lise Peng, Yongde Ren, Huahui Wang, Xiaokai Zhang, Hongmei Xia, Huihua Kristiansen, Karsten Xu, Xiaoqiang Hong, Jie Gu, Weiqiong Su, Qing Yang, Huanming Xu, Guowang Ning, Guang Zhang, Dongya Zhao, Xinjie Li, Fang Zhong, Huanzi Liu, Wei Zhang, Yifei Bi, Yufang Wang, Weiqing Ma, Jing Liu, Ruixin Wang, Xiaoling Yang, Jialin |
| Author_xml | – sequence: 1 givenname: Yanyun orcidid: 0000-0002-4323-1648 surname: Gu fullname: Gu, Yanyun organization: Shanghai National Research Centre for Endocrine and Metabolic Diseases, State Key Laboratory of Medical Genomics, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine – sequence: 2 givenname: Xiaokai surname: Wang fullname: Wang, Xiaokai organization: BGI-Shenzhen, China National GeneBank-Shenzhen, BGI Education Centre, University of Chinese Academy of Sciences – sequence: 3 givenname: Junhua orcidid: 0000-0001-6784-1873 surname: Li fullname: Li, Junhua organization: BGI-Shenzhen, China National GeneBank-Shenzhen, Shenzhen Key Laboratory of Human commensal microorganisms and Health Research, BGI-Shenzhen – sequence: 4 givenname: Yifei surname: Zhang fullname: Zhang, Yifei organization: Shanghai National Research Centre for Endocrine and Metabolic Diseases, State Key Laboratory of Medical Genomics, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine – sequence: 5 givenname: Huanzi orcidid: 0000-0001-9512-1750 surname: Zhong fullname: Zhong, Huanzi organization: BGI-Shenzhen, China National GeneBank-Shenzhen, Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen – sequence: 6 givenname: Ruixin surname: Liu fullname: Liu, Ruixin organization: Shanghai National Research Centre for Endocrine and Metabolic Diseases, State Key Laboratory of Medical Genomics, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine – sequence: 7 givenname: Dongya surname: Zhang fullname: Zhang, Dongya organization: BGI-Shenzhen, China National GeneBank-Shenzhen – sequence: 8 givenname: Qiang surname: Feng fullname: Feng, Qiang organization: BGI-Shenzhen, China National GeneBank-Shenzhen – sequence: 9 givenname: Xiaoyan surname: Xie fullname: Xie, Xiaoyan organization: Shanghai National Research Centre for Endocrine and Metabolic Diseases, State Key Laboratory of Medical Genomics, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine – sequence: 10 givenname: Jie surname: Hong fullname: Hong, Jie organization: Shanghai National Research Centre for Endocrine and Metabolic Diseases, State Key Laboratory of Medical Genomics, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine – sequence: 11 givenname: Huahui surname: Ren fullname: Ren, Huahui organization: BGI-Shenzhen, China National GeneBank-Shenzhen, Shenzhen Key Laboratory of Human commensal microorganisms and Health Research, BGI-Shenzhen, Shenzhen Engineering Laboratory of Detection and Intervention of human intestinal microbiome, BGI-Shenzhen – sequence: 12 givenname: Wei surname: Liu fullname: Liu, Wei organization: Renji Hospital affiliated to Shanghai Jiaotong University Medical School – sequence: 13 givenname: Jing surname: Ma fullname: Ma, Jing organization: Renji Hospital affiliated to Shanghai Jiaotong University Medical School – sequence: 14 givenname: Qing surname: Su fullname: Su, Qing organization: Xinhua Hospital affiliated to Shanghai Jiaotong University Medical School – sequence: 15 givenname: Hongmei surname: Zhang fullname: Zhang, Hongmei organization: Xinhua Hospital affiliated to Shanghai Jiaotong University Medical School – sequence: 16 givenname: Jialin surname: Yang fullname: Yang, Jialin organization: MinHang Central Hospital affiliated to Fudan University Medical School – sequence: 17 givenname: Xiaoling surname: Wang fullname: Wang, Xiaoling organization: Dalian Institute of Chemical Physics, Chinese Academy of Science – sequence: 18 givenname: Xinjie surname: Zhao fullname: Zhao, Xinjie organization: Dalian Institute of Chemical Physics, Chinese Academy of Science – sequence: 19 givenname: Weiqiong surname: Gu fullname: Gu, Weiqiong organization: Shanghai National Research Centre for Endocrine and Metabolic Diseases, State Key Laboratory of Medical Genomics, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine – sequence: 20 givenname: Yufang surname: Bi fullname: Bi, Yufang organization: Shanghai National Research Centre for Endocrine and Metabolic Diseases, State Key Laboratory of Medical Genomics, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine – sequence: 21 givenname: Yongde surname: Peng fullname: Peng, Yongde organization: Shanghai General Hospital, Shanghai Jiaotong University – sequence: 22 givenname: Xiaoqiang surname: Xu fullname: Xu, Xiaoqiang organization: BGI-Shenzhen, China National GeneBank-Shenzhen, BGI Education Centre, University of Chinese Academy of Sciences – sequence: 23 givenname: Huihua surname: Xia fullname: Xia, Huihua organization: BGI-Shenzhen, China National GeneBank-Shenzhen, Shenzhen Key Laboratory of Human commensal microorganisms and Health Research, BGI-Shenzhen – sequence: 24 givenname: Fang surname: Li fullname: Li, Fang organization: BGI-Shenzhen, China National GeneBank-Shenzhen, Shenzhen Key Laboratory of Human commensal microorganisms and Health Research, BGI-Shenzhen, Shenzhen Engineering Laboratory of Detection and Intervention of human intestinal microbiome, BGI-Shenzhen – sequence: 25 givenname: Xun surname: Xu fullname: Xu, Xun organization: BGI-Shenzhen, China National GeneBank-Shenzhen – sequence: 26 givenname: Huanming surname: Yang fullname: Yang, Huanming organization: BGI-Shenzhen, China National GeneBank-Shenzhen, James D. Watson Institute of Genome Sciences – sequence: 27 givenname: Guowang orcidid: 0000-0003-4298-3554 surname: Xu fullname: Xu, Guowang organization: Dalian Institute of Chemical Physics, Chinese Academy of Science – sequence: 28 givenname: Lise orcidid: 0000-0003-4468-1947 surname: Madsen fullname: Madsen, Lise organization: BGI-Shenzhen, China National GeneBank-Shenzhen, BGI Education Centre, University of Chinese Academy of Sciences, National Institute of Nutrition and Seafood Research (NIFES) – sequence: 29 givenname: Karsten orcidid: 0000-0002-6024-0917 surname: Kristiansen fullname: Kristiansen, Karsten email: kk@bio.ku.dk organization: BGI-Shenzhen, China National GeneBank-Shenzhen, Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen – sequence: 30 givenname: Guang surname: Ning fullname: Ning, Guang email: gning@sibs.ac.cn organization: Shanghai National Research Centre for Endocrine and Metabolic Diseases, State Key Laboratory of Medical Genomics, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine – sequence: 31 givenname: Weiqing surname: Wang fullname: Wang, Weiqing email: wqingw61@163.com organization: Shanghai National Research Centre for Endocrine and Metabolic Diseases, State Key Laboratory of Medical Genomics, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29176714$$D View this record in MEDLINE/PubMed |
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| Snippet | Antidiabetic medication may modulate the gut microbiota and thereby alter plasma and faecal bile acid (BA) composition, which may improve metabolic health.... The authors examine the effects of antidiabetic medication on the gut microbiome and bile acid composition and show that these data can be used to stratify... |
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| SubjectTerms | 631/326/2565/2134 631/326/2565/2142 631/443/319/1642/137 692/308/575 Abundance Acarbose Antidiabetics Bacteroides Bile Bile acids Diabetes mellitus Humanities and Social Sciences Intestinal microflora Metabolism Microbiota Microorganisms multidisciplinary Patients Plasma levels Relative abundance Science Science (multidisciplinary) |
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| Title | Analyses of gut microbiota and plasma bile acids enable stratification of patients for antidiabetic treatment |
| URI | https://link.springer.com/article/10.1038/s41467-017-01682-2 https://www.ncbi.nlm.nih.gov/pubmed/29176714 https://www.proquest.com/docview/1968412227 https://www.proquest.com/docview/1969920020 https://pubmed.ncbi.nlm.nih.gov/PMC5702614 https://doaj.org/article/086f86b810fe44deb049093bd1706028 |
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