Human Gut Microbiota Changes Reveal the Progression of Glucose Intolerance
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{"title"=>"Human Gut Microbiota Changes Reveal the Progression of Glucose Intolerance", "type"=>"journal", "authors"=>[{"first_name"=>"Xiuying", "last_name"=>"Zhang", "scopus_author_id"=>"55715728600"}, {"first_name"=>"Dongqian", "last_name"=>"Shen", "scopus_author_id"=>"55838680500"}, {"first_name"=>"Zhiwei", "last_name"=>"Fang", "scopus_author_id"=>"55838669300"}, {"first_name"=>"Zhuye", "last_name"=>"Jie", "scopus_author_id"=>"56548311600"}, {"first_name"=>"Xinmin", "last_name"=>"Qiu", "scopus_author_id"=>"55839337300"}, {"first_name"=>"Chunfang", "last_name"=>"Zhang", "scopus_author_id"=>"55554614200"}, {"first_name"=>"Yingli", "last_name"=>"Chen", "scopus_author_id"=>"23487795000"}, {"first_name"=>"Linong", "last_name"=>"Ji", "scopus_author_id"=>"55660724500"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84883181684", "sgr"=>"84883181684", "pui"=>"369686927", "isbn"=>"10.1371/journal.pone.0071108", "pmid"=>"24013136", "doi"=>"10.1371/journal.pone.0071108"}, "id"=>"6b3c5445-9230-36c4-a8a6-4088229db281", "abstract"=>"To explore the relationship of gut microbiota with the development of type 2 diabetes (T2DM), we analyzed 121 subjects who were divided into 3 groups based on their glucose intolerance status: normal glucose tolerance (NGT; n = 44), prediabetes (Pre-DM; n = 64), or newly diagnosed T2DM (n = 13). Gut microbiota characterizations were determined with 16S rDNA-based high-throughput sequencing. T2DM-related dysbiosis was observed, including the separation of microbial communities and a change of alpha diversity between the different glucose intolerance statuses. To assess the correlation between metabolic parameters and microbiota diversity, clinical characteristics were also measured and a significant association between metabolic parameters (FPG, CRP) and gut microbiota was found. In addition, a total of 28 operational taxonomic units (OTUs) were found to be related to T2DM status by the Kruskal-Wallis H test, most of which were enriched in the T2DM group. Butyrate-producing bacteria (e.g. Akkermansia muciniphila ATCCBAA-835, and Faecalibacterium prausnitzii L2-6) had a higher abundance in the NGT group than in the pre-DM group. At genus level, the abundance of Bacteroides in the T2DM group was only half that of the NGT and Pre-DM groups. Previously reported T2DM-related markers were also compared with the data in this study, and some inconsistencies were noted. We found that Verrucomicrobiae may be a potential marker of T2DM as it had a significantly lower abundance in both the pre-DM and T2DM groups. In conclusion, this research provides further evidence of the structural modulation of gut microbiota in the pathogenesis of diabetes.", "link"=>"http://www.mendeley.com/research/human-gut-microbiota-changes-reveal-progression-glucose-intolerance", "reader_count"=>248, "reader_count_by_academic_status"=>{"Unspecified"=>8, "Professor > Associate Professor"=>8, "Librarian"=>1, "Researcher"=>42, "Student > Doctoral Student"=>12, "Student > Ph. D. 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Student"=>52, "Student > Postgraduate"=>10, "Other"=>20, "Student > Master"=>43, "Student > Bachelor"=>43, "Lecturer"=>5, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>12, "Agricultural and Biological Sciences"=>108, "Veterinary Science and Veterinary Medicine"=>2, "Business, Management and Accounting"=>1, "Chemistry"=>3, "Computer Science"=>2, "Engineering"=>1, "Environmental Science"=>3, "Nursing and Health Professions"=>11, "Biochemistry, Genetics and Molecular Biology"=>31, "Materials Science"=>3, "Mathematics"=>1, "Medicine and Dentistry"=>48, "Design"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Physics and Astronomy"=>1, "Psychology"=>1, "Immunology and Microbiology"=>16, "Arts and Humanities"=>1}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>48}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>12}, "Environmental Science"=>{"Environmental Science"=>3}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Design"=>{"Design"=>1}, "Engineering"=>{"Engineering"=>1}, "Chemistry"=>{"Chemistry"=>3}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>16}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>108}, "Computer Science"=>{"Computer Science"=>2}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>11}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>31}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>2}}, "reader_count_by_country"=>{"Canada"=>1, "Austria"=>1, "Netherlands"=>1, "Belgium"=>1, "United States"=>4, "Norway"=>1, "United Kingdom"=>1, "Malaysia"=>1, "Israel"=>1, "France"=>1, "India"=>1}, "group_count"=>7}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1182517"], "description"=>"<p>Data for NGT (n = 44), Pre-DM (n = 64) and T2DM (n = 13) subjects were plotted on the first two principal components of the OTU profiles. The first 2 components (contributing 19.5% of variance) were plotted. Lines connect individuals belonging to the same group and colored circles cover individuals near the center of gravity for each cluster (<1.5<i>σ</i>). The top 7 OTUs (labeled with their annotations) for the main contributors to these groups were determined and plotted by their loadings for these 2 components. PCA was performed with R package ‘ade4’. OTU = operational taxonomic unit.</p>", "links"=>[], "tags"=>["Computational biology", "Sequence analysis", "ecology", "Ecological metrics", "Species diversity", "microbiology", "Medical microbiology", "Anatomy and physiology", "Digestive system", "Diagnostic medicine", "pathology", "Endocrinology", "Diabetic endocrinology", "Diabetes mellitus type 2", "Metabolic disorders", "microbiota", "t2dm"], "article_id"=>782292, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Xiuying Zhang", "Dongqian Shen", "Zhiwei Fang", "Zhuye Jie", "Xinmin Qiu", "Chunfang Zhang", "Yingli Chen", "Linong Ji"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071108.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Principal_component_analysis_PCA_analysis_of_the_similarity_of_microbiota_OTUs_between_the_NGT_Pre_DM_and_T2DM_groups_/782292", "title"=>"Principal component analysis (PCA) analysis of the similarity of microbiota (OTUs) between the NGT, Pre-DM, and T2DM groups.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-27 01:33:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1182518"], "description"=>"<p>The Shannon index (left panel) and Chao1 index (right panel) were computed for all 121 subjects. The box depicts the interquartile range (IQR) between the first and third quartiles (25th and 75th percentiles, respectively) and the line inside the box denotes the median.</p>", "links"=>[], "tags"=>["Computational biology", "Sequence analysis", "ecology", "Ecological metrics", "Species diversity", "microbiology", "Medical microbiology", "Anatomy and physiology", "Digestive system", "Diagnostic medicine", "pathology", "Endocrinology", "Diabetic endocrinology", "Diabetes mellitus type 2", "Metabolic disorders"], "article_id"=>782294, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Xiuying Zhang", "Dongqian Shen", "Zhiwei Fang", "Zhuye Jie", "Xinmin Qiu", "Chunfang Zhang", "Yingli Chen", "Linong Ji"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071108.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Alpha_diversity_of_the_3_groups_/782294", "title"=>"Alpha diversity of the 3 groups.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-27 01:33:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1182520"], "description"=>"<p>The upper left panel shows the CIA of the clinical parameter principal component analysis (PCA) and the microbiota PCA; arrows indicate where samples in the clinical parameter dataset are relative to the microbiota dataset. Red lines represent the NGT group, green lines the Pre-DM group, and blue lines the T2DM group. The upper right panel shows clinical parameter loading data; the lower panel displays the associated microbiota at the OTU level (labeled with their annotations). Only OTUs present in at least 1% of the samples were used in the analysis. CIA was performed with R package ‘ade4’. OTU = operational taxonomic unit.</p>", "links"=>[], "tags"=>["Computational biology", "Sequence analysis", "ecology", "Ecological metrics", "Species diversity", "microbiology", "Medical microbiology", "Anatomy and physiology", "Digestive system", "Diagnostic medicine", "pathology", "Endocrinology", "Diabetic endocrinology", "Diabetes mellitus type 2", "Metabolic disorders", "microbiota", "otu"], "article_id"=>782296, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Xiuying Zhang", "Dongqian Shen", "Zhiwei Fang", "Zhuye Jie", "Xinmin Qiu", "Chunfang Zhang", "Yingli Chen", "Linong Ji"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071108.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Co_inertia_analysis_CIA_of_the_relationship_between_microbiota_at_the_OTU_level_clinical_parameters_and_disease_group_/782296", "title"=>"Co-inertia analysis (CIA) of the relationship between microbiota at the OTU level, clinical parameters, and disease group.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-27 01:33:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1182522"], "description"=>"<p>The dashed line was derived from a simple regression model (<i>P</i>-value for the model was 0.034; R = −0.22).</p>", "links"=>[], "tags"=>["Computational biology", "Sequence analysis", "ecology", "Ecological metrics", "Species diversity", "microbiology", "Medical microbiology", "Anatomy and physiology", "Digestive system", "Diagnostic medicine", "pathology", "Endocrinology", "Diabetic endocrinology", "Diabetes mellitus type 2", "Metabolic disorders", "fasting", "insulin", "microbiota"], "article_id"=>782298, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Xiuying Zhang", "Dongqian Shen", "Zhiwei Fang", "Zhuye Jie", "Xinmin Qiu", "Chunfang Zhang", "Yingli Chen", "Linong Ji"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071108.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlations_between_the_fasting_insulin_FINS_concentration_mIU_L_and_microbiota_diversity_/782298", "title"=>"Correlations between the fasting insulin (FINS) concentration (mIU/L) and microbiota diversity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-27 01:33:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1182523"], "description"=>"<p>BMI = body mass index; CRP = C-reactive protein; FPG = fasting plasma glucose; PERMANOVA = permutational multivariate analysis of variance.</p>", "links"=>[], "tags"=>["Computational biology", "Sequence analysis", "ecology", "Ecological metrics", "Species diversity", "microbiology", "Medical microbiology", "Anatomy and physiology", "Digestive system", "Diagnostic medicine", "pathology", "Endocrinology", "Diabetic endocrinology", "Diabetes mellitus type 2", "Metabolic disorders", "associations", "microbiota"], "article_id"=>782299, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Xiuying Zhang", "Dongqian Shen", "Zhiwei Fang", "Zhuye Jie", "Xinmin Qiu", "Chunfang Zhang", "Yingli Chen", "Linong Ji"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071108.t003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PERMANOVA_tests_of_associations_between_clinical_parameters_and_microbiota_composition_adjusted_for_age_gender_and_BMI_/782299", "title"=>"PERMANOVA tests of associations between clinical parameters and microbiota composition (adjusted for age, gender and BMI).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-08-27 01:33:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1182524"], "description"=>"a<p>The relative abundance of OTUs enriched in the NGT group decreased to 0 with the development of T2DM.</p>b<p>The relative abundance of OTUs increased with the development of T2DM.</p><p>NGT = normal glucose tolerance; OTU = operational taxonomic unit; Pre-DM = prediabetes; T2DM = type 2 diabetes mellitus.</p>", "links"=>[], "tags"=>["Computational biology", "Sequence analysis", "ecology", "Ecological metrics", "Species diversity", "microbiology", "Medical microbiology", "Anatomy and physiology", "Digestive system", "Diagnostic medicine", "pathology", "Endocrinology", "Diabetic endocrinology", "Diabetes mellitus type 2", "Metabolic disorders", "associations", "glucose", "intolerance", "microbiota"], "article_id"=>782300, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Xiuying Zhang", "Dongqian Shen", "Zhiwei Fang", "Zhuye Jie", "Xinmin Qiu", "Chunfang Zhang", "Yingli Chen", "Linong Ji"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071108.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Kruskal_Wallis_H_tests_of_associations_between_glucose_intolerance_status_and_microbiota_composition_OTUs_level_/782300", "title"=>"Kruskal-Wallis H tests of associations between glucose intolerance status and microbiota composition (OTUs level).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-08-27 01:33:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1182525"], "description"=>"a<p>Median (interquartile range).</p>*<p><i>P</i><0.05 for Pre-DM vs NGT;</p>†<p><i>P</i><0.05 for T2DM vs NGT;</p>‡<p><i>P</i><0.05 for T2DM vs Pre-DM.</p><p>BMI = body mass index; CPR = C-reactive protein; DBP = diastolic blood pressure; FINS = fasting insulin; FPG = fasting plasma glucose; HDL-C = high-density lipoprotein cholesterol; 2HINS = 2-hour insulin concentration; 2HPG = 2-hour plasma glucose concentration; IR = insulin resistance index; LDL-C = low-density lipoprotein cholesterol; NGT = normal glucose tolerance; Pre-DM = prediabetes; SBP = systolic blood pressure; TC = total cholesterol; T2DM = type 2 diabetes mellitus; TG = triglyceride.</p>", "links"=>[], "tags"=>["Computational biology", "Sequence analysis", "ecology", "Ecological metrics", "Species diversity", "microbiology", "Medical microbiology", "Anatomy and physiology", "Digestive system", "Diagnostic medicine", "pathology", "Endocrinology", "Diabetic endocrinology", "Diabetes mellitus type 2", "Metabolic disorders", "subjects", "prediabetes"], "article_id"=>782301, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Xiuying Zhang", "Dongqian Shen", "Zhiwei Fang", "Zhuye Jie", "Xinmin Qiu", "Chunfang Zhang", "Yingli Chen", "Linong Ji"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071108.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characteristics_of_the_subjects_with_NGT_prediabetes_and_T2DM_/782301", "title"=>"Characteristics of the subjects with NGT, prediabetes and T2DM.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-08-27 01:33:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1182526"], "description"=>"a<p>These markers were identified in the study of Qin et al. <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0071108#pone.0071108-Qin2\" target=\"_blank\">[16]</a>.</p><p>The data in this table show the average relative abundance and occurrence rates in the three groups.</p>", "links"=>[], "tags"=>["Computational biology", "Sequence analysis", "ecology", "Ecological metrics", "Species diversity", "microbiology", "Medical microbiology", "Anatomy and physiology", "Digestive system", "Diagnostic medicine", "pathology", "Endocrinology", "Diabetic endocrinology", "Diabetes mellitus type 2", "Metabolic disorders", "markers"], "article_id"=>782302, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Xiuying Zhang", "Dongqian Shen", "Zhiwei Fang", "Zhuye Jie", "Xinmin Qiu", "Chunfang Zhang", "Yingli Chen", "Linong Ji"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071108.t004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_T2DM_markers_in_the_study_a_/782302", "title"=>"T2DM markers in the study<sup>a</sup>.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-08-27 01:33:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1182532", "https://ndownloader.figshare.com/files/1182533", "https://ndownloader.figshare.com/files/1182534", "https://ndownloader.figshare.com/files/1182535", "https://ndownloader.figshare.com/files/1182536", "https://ndownloader.figshare.com/files/1182537", "https://ndownloader.figshare.com/files/1182538", "https://ndownloader.figshare.com/files/1182539", "https://ndownloader.figshare.com/files/1182540", "https://ndownloader.figshare.com/files/1182541"], "description"=>"<div><p>To explore the relationship of gut microbiota with the development of type 2 diabetes (T2DM), we analyzed 121 subjects who were divided into 3 groups based on their glucose intolerance status: normal glucose tolerance (NGT; n = 44), prediabetes (Pre-DM; n = 64), or newly diagnosed T2DM (n = 13). Gut microbiota characterizations were determined with 16S rDNA-based high-throughput sequencing. T2DM-related dysbiosis was observed, including the separation of microbial communities and a change of alpha diversity between the different glucose intolerance statuses. To assess the correlation between metabolic parameters and microbiota diversity, clinical characteristics were also measured and a significant association between metabolic parameters (FPG, CRP) and gut microbiota was found. In addition, a total of 28 operational taxonomic units (OTUs) were found to be related to T2DM status by the Kruskal-Wallis H test, most of which were enriched in the T2DM group. Butyrate-producing bacteria (e.g. <i>Akkermansia muciniphila</i> ATCCBAA-835, and <i>Faecalibacterium prausnitzii</i> L2-6) had a higher abundance in the NGT group than in the pre-DM group. At genus level, the abundance of <i>Bacteroides</i> in the T2DM group was only half that of the NGT and Pre-DM groups. Previously reported T2DM-related markers were also compared with the data in this study, and some inconsistencies were noted. We found that <i>Verrucomicrobiae</i> may be a potential marker of T2DM as it had a significantly lower abundance in both the pre-DM and T2DM groups. In conclusion, this research provides further evidence of the structural modulation of gut microbiota in the pathogenesis of diabetes.</p></div>", "links"=>[], "tags"=>["Computational biology", "Sequence analysis", "ecology", "Ecological metrics", "Species diversity", "microbiology", "Medical microbiology", "Anatomy and physiology", "Digestive system", "Diagnostic medicine", "pathology", "Endocrinology", "Diabetic endocrinology", "Diabetes mellitus type 2", "Metabolic disorders", "microbiota", "progression", "glucose"], "article_id"=>782308, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Xiuying Zhang", "Dongqian Shen", "Zhiwei Fang", "Zhuye Jie", "Xinmin Qiu", "Chunfang Zhang", "Yingli Chen", "Linong Ji"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071108.s001", "https://dx.doi.org/10.1371/journal.pone.0071108.s002", "https://dx.doi.org/10.1371/journal.pone.0071108.s003", "https://dx.doi.org/10.1371/journal.pone.0071108.s004", "https://dx.doi.org/10.1371/journal.pone.0071108.s005", "https://dx.doi.org/10.1371/journal.pone.0071108.s006", "https://dx.doi.org/10.1371/journal.pone.0071108.s007", "https://dx.doi.org/10.1371/journal.pone.0071108.s008", "https://dx.doi.org/10.1371/journal.pone.0071108.s009", "https://dx.doi.org/10.1371/journal.pone.0071108.s010"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Human_Gut_Microbiota_Changes_Reveal_the_Progression_of_Glucose_Intolerance_/782308", "title"=>"Human Gut Microbiota Changes Reveal the Progression of Glucose Intolerance", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-08-27 01:33:45"}

PMC Usage Stats | Further Information

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Relative Metric

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