Arrhythmic Gut Microbiome Signatures Predict Risk of Type 2 Diabetes
Lifestyle, obesity, and the gut microbiome are important risk factors for metabolic disorders. We demonstrate in 1,976 subjects of a German population cohort (KORA) that specific microbiota members show 24-h oscillations in their relative abundance and identified 13 taxa with disrupted rhythmicity i...
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creator | Reitmeier, Sandra Kiessling, Silke Clavel, Thomas List, Markus Almeida, Eduardo L. Ghosh, Tarini S. Neuhaus, Klaus Grallert, Harald Linseisen, Jakob Skurk, Thomas Brandl, Beate Breuninger, Taylor A. Troll, Martina Rathmann, Wolfgang Linkohr, Birgit Hauner, Hans Laudes, Matthias Franke, Andre Le Roy, Caroline I. Bell, Jordana T. Spector, Tim Baumbach, Jan O’Toole, Paul W. Peters, Annette Haller, Dirk |
description | Lifestyle, obesity, and the gut microbiome are important risk factors for metabolic disorders. We demonstrate in 1,976 subjects of a German population cohort (KORA) that specific microbiota members show 24-h oscillations in their relative abundance and identified 13 taxa with disrupted rhythmicity in type 2 diabetes (T2D). Cross-validated prediction models based on this signature similarly classified T2D. In an independent cohort (FoCus), disruption of microbial oscillation and the model for T2D classification was confirmed in 1,363 subjects. This arrhythmic risk signature was able to predict T2D in 699 KORA subjects 5 years after initial sampling, being most effective in combination with BMI. Shotgun metagenomic analysis functionally linked 26 metabolic pathways to the diurnal oscillation of gut bacteria. Thus, a cohort-specific risk pattern of arrhythmic taxa enables classification and prediction of T2D, suggesting a functional link between circadian rhythms and the microbiome in metabolic diseases.
[Display omitted]
•Human gut microbiome exhibits diurnal rhythmicity across populations and individuals•Obese and T2D individuals show disrupted circadian rhythms in the gut microbiome•Arrhytmic bacterial signatures contribute to risk classification and prediction of T2D•These risk signatures show regional differences in applicability across three cohorts
Reitmeier et al. show that specific gut microbes exhibit rhythmic oscillations in relative abundance and identified taxa with disrupted rhythmicity in individuals with type 2 diabetes (T2D). This arrhythmic signature contributed to the classification and prediction of T2D, suggesting functional links between circadian rhythmicity and the microbiome in metabolic diseases. |
doi_str_mv | 10.1016/j.chom.2020.06.004 |
format | Article |
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[Display omitted]
•Human gut microbiome exhibits diurnal rhythmicity across populations and individuals•Obese and T2D individuals show disrupted circadian rhythms in the gut microbiome•Arrhytmic bacterial signatures contribute to risk classification and prediction of T2D•These risk signatures show regional differences in applicability across three cohorts
Reitmeier et al. show that specific gut microbes exhibit rhythmic oscillations in relative abundance and identified taxa with disrupted rhythmicity in individuals with type 2 diabetes (T2D). This arrhythmic signature contributed to the classification and prediction of T2D, suggesting functional links between circadian rhythmicity and the microbiome in metabolic diseases.</description><identifier>ISSN: 1931-3128</identifier><identifier>EISSN: 1934-6069</identifier><identifier>DOI: 10.1016/j.chom.2020.06.004</identifier><identifier>PMID: 32619440</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>amplicon sequencing ; Bacteria - classification ; Bacteria - genetics ; Bacteria - isolation & purification ; Bacteria - metabolism ; Circadian Clocks - physiology ; Circadian Rhythm - physiology ; circadian rhythms ; Diabetes Mellitus, Type 2 - epidemiology ; Diabetes Mellitus, Type 2 - microbiology ; Diabetes Mellitus, Type 2 - pathology ; diurnal oscillations ; Feces - microbiology ; Gastrointestinal Microbiome - genetics ; Gastrointestinal Microbiome - physiology ; Germany - epidemiology ; human intestinal microbiota ; Humans ; machine learning ; Metagenome - genetics ; metagenomics ; Metagenomics - methods ; obesity ; Obesity - microbiology ; Obesity - pathology ; population-based cohorts ; prediction ; type 2 diabetes</subject><ispartof>Cell host & microbe, 2020-08, Vol.28 (2), p.258-272.e6</ispartof><rights>2020 Elsevier Inc.</rights><rights>Copyright © 2020 Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c466t-5498ac1034eb1a06f6b7d180ecafeb8877131c97c30d9ffc2a056e15bffab7833</citedby><cites>FETCH-LOGICAL-c466t-5498ac1034eb1a06f6b7d180ecafeb8877131c97c30d9ffc2a056e15bffab7833</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1931312820303437$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32619440$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Reitmeier, Sandra</creatorcontrib><creatorcontrib>Kiessling, Silke</creatorcontrib><creatorcontrib>Clavel, Thomas</creatorcontrib><creatorcontrib>List, Markus</creatorcontrib><creatorcontrib>Almeida, Eduardo L.</creatorcontrib><creatorcontrib>Ghosh, Tarini S.</creatorcontrib><creatorcontrib>Neuhaus, Klaus</creatorcontrib><creatorcontrib>Grallert, Harald</creatorcontrib><creatorcontrib>Linseisen, Jakob</creatorcontrib><creatorcontrib>Skurk, Thomas</creatorcontrib><creatorcontrib>Brandl, Beate</creatorcontrib><creatorcontrib>Breuninger, Taylor A.</creatorcontrib><creatorcontrib>Troll, Martina</creatorcontrib><creatorcontrib>Rathmann, Wolfgang</creatorcontrib><creatorcontrib>Linkohr, Birgit</creatorcontrib><creatorcontrib>Hauner, Hans</creatorcontrib><creatorcontrib>Laudes, Matthias</creatorcontrib><creatorcontrib>Franke, Andre</creatorcontrib><creatorcontrib>Le Roy, Caroline I.</creatorcontrib><creatorcontrib>Bell, Jordana T.</creatorcontrib><creatorcontrib>Spector, Tim</creatorcontrib><creatorcontrib>Baumbach, Jan</creatorcontrib><creatorcontrib>O’Toole, Paul W.</creatorcontrib><creatorcontrib>Peters, Annette</creatorcontrib><creatorcontrib>Haller, Dirk</creatorcontrib><title>Arrhythmic Gut Microbiome Signatures Predict Risk of Type 2 Diabetes</title><title>Cell host & microbe</title><addtitle>Cell Host Microbe</addtitle><description>Lifestyle, obesity, and the gut microbiome are important risk factors for metabolic disorders. We demonstrate in 1,976 subjects of a German population cohort (KORA) that specific microbiota members show 24-h oscillations in their relative abundance and identified 13 taxa with disrupted rhythmicity in type 2 diabetes (T2D). Cross-validated prediction models based on this signature similarly classified T2D. In an independent cohort (FoCus), disruption of microbial oscillation and the model for T2D classification was confirmed in 1,363 subjects. This arrhythmic risk signature was able to predict T2D in 699 KORA subjects 5 years after initial sampling, being most effective in combination with BMI. Shotgun metagenomic analysis functionally linked 26 metabolic pathways to the diurnal oscillation of gut bacteria. Thus, a cohort-specific risk pattern of arrhythmic taxa enables classification and prediction of T2D, suggesting a functional link between circadian rhythms and the microbiome in metabolic diseases.
[Display omitted]
•Human gut microbiome exhibits diurnal rhythmicity across populations and individuals•Obese and T2D individuals show disrupted circadian rhythms in the gut microbiome•Arrhytmic bacterial signatures contribute to risk classification and prediction of T2D•These risk signatures show regional differences in applicability across three cohorts
Reitmeier et al. show that specific gut microbes exhibit rhythmic oscillations in relative abundance and identified taxa with disrupted rhythmicity in individuals with type 2 diabetes (T2D). This arrhythmic signature contributed to the classification and prediction of T2D, suggesting functional links between circadian rhythmicity and the microbiome in metabolic diseases.</description><subject>amplicon sequencing</subject><subject>Bacteria - classification</subject><subject>Bacteria - genetics</subject><subject>Bacteria - isolation & purification</subject><subject>Bacteria - metabolism</subject><subject>Circadian Clocks - physiology</subject><subject>Circadian Rhythm - physiology</subject><subject>circadian rhythms</subject><subject>Diabetes Mellitus, Type 2 - epidemiology</subject><subject>Diabetes Mellitus, Type 2 - microbiology</subject><subject>Diabetes Mellitus, Type 2 - pathology</subject><subject>diurnal oscillations</subject><subject>Feces - microbiology</subject><subject>Gastrointestinal Microbiome - genetics</subject><subject>Gastrointestinal Microbiome - physiology</subject><subject>Germany - epidemiology</subject><subject>human intestinal microbiota</subject><subject>Humans</subject><subject>machine learning</subject><subject>Metagenome - genetics</subject><subject>metagenomics</subject><subject>Metagenomics - methods</subject><subject>obesity</subject><subject>Obesity - microbiology</subject><subject>Obesity - pathology</subject><subject>population-based cohorts</subject><subject>prediction</subject><subject>type 2 diabetes</subject><issn>1931-3128</issn><issn>1934-6069</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kEtP6zAQhS0E4v0HWFx5ySZhxnGcRGKDeEsgEI-15TjjW5emKXaC1H9PSoElq5nFd450PsaOEFIEVCfT1E66NhUgIAWVAsgNtotVJhMFqtr8-jHJUJQ7bC_GKUCeQ4HbbCcTCispYZddnIUwWfaT1lt-PfT83tvQ1b5riT_7_3PTD4EifwzUeNvzJx_feOf4y3JBXPALb2rqKR6wLWdmkQ6_7z57vbp8Ob9J7h6ub8_P7hIrleqTXFalsQiZpBoNKKfqosESyBpHdVkWBWZoq8Jm0FTOWWEgV4R57ZypizLL9tnxuncRuveBYq9bHy3NZmZO3RC1kAJQYgVyRMUaHefEGMjpRfCtCUuNoFf29FSv7OmVPQ1Kw1fo33f_ULfU_EZ-dI3A6RqgceWHp6Cj9TS3o51AttdN5__q_wS_E3_L</recordid><startdate>20200812</startdate><enddate>20200812</enddate><creator>Reitmeier, Sandra</creator><creator>Kiessling, Silke</creator><creator>Clavel, Thomas</creator><creator>List, Markus</creator><creator>Almeida, Eduardo L.</creator><creator>Ghosh, Tarini S.</creator><creator>Neuhaus, Klaus</creator><creator>Grallert, Harald</creator><creator>Linseisen, Jakob</creator><creator>Skurk, Thomas</creator><creator>Brandl, Beate</creator><creator>Breuninger, Taylor A.</creator><creator>Troll, Martina</creator><creator>Rathmann, Wolfgang</creator><creator>Linkohr, Birgit</creator><creator>Hauner, Hans</creator><creator>Laudes, Matthias</creator><creator>Franke, Andre</creator><creator>Le Roy, Caroline I.</creator><creator>Bell, Jordana T.</creator><creator>Spector, Tim</creator><creator>Baumbach, Jan</creator><creator>O’Toole, Paul W.</creator><creator>Peters, Annette</creator><creator>Haller, Dirk</creator><general>Elsevier Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20200812</creationdate><title>Arrhythmic Gut Microbiome Signatures Predict Risk of Type 2 Diabetes</title><author>Reitmeier, Sandra ; Kiessling, Silke ; Clavel, Thomas ; List, Markus ; Almeida, Eduardo L. ; Ghosh, Tarini S. ; Neuhaus, Klaus ; Grallert, Harald ; Linseisen, Jakob ; Skurk, Thomas ; Brandl, Beate ; Breuninger, Taylor A. ; Troll, Martina ; Rathmann, Wolfgang ; Linkohr, Birgit ; Hauner, Hans ; Laudes, Matthias ; Franke, Andre ; Le Roy, Caroline I. ; Bell, Jordana T. ; Spector, Tim ; Baumbach, Jan ; O’Toole, Paul W. ; Peters, Annette ; Haller, Dirk</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c466t-5498ac1034eb1a06f6b7d180ecafeb8877131c97c30d9ffc2a056e15bffab7833</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>amplicon sequencing</topic><topic>Bacteria - classification</topic><topic>Bacteria - genetics</topic><topic>Bacteria - isolation & purification</topic><topic>Bacteria - metabolism</topic><topic>Circadian Clocks - physiology</topic><topic>Circadian Rhythm - physiology</topic><topic>circadian rhythms</topic><topic>Diabetes Mellitus, Type 2 - epidemiology</topic><topic>Diabetes Mellitus, Type 2 - microbiology</topic><topic>Diabetes Mellitus, Type 2 - pathology</topic><topic>diurnal oscillations</topic><topic>Feces - microbiology</topic><topic>Gastrointestinal Microbiome - genetics</topic><topic>Gastrointestinal Microbiome - physiology</topic><topic>Germany - epidemiology</topic><topic>human intestinal microbiota</topic><topic>Humans</topic><topic>machine learning</topic><topic>Metagenome - genetics</topic><topic>metagenomics</topic><topic>Metagenomics - methods</topic><topic>obesity</topic><topic>Obesity - microbiology</topic><topic>Obesity - pathology</topic><topic>population-based cohorts</topic><topic>prediction</topic><topic>type 2 diabetes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Reitmeier, Sandra</creatorcontrib><creatorcontrib>Kiessling, Silke</creatorcontrib><creatorcontrib>Clavel, Thomas</creatorcontrib><creatorcontrib>List, Markus</creatorcontrib><creatorcontrib>Almeida, Eduardo L.</creatorcontrib><creatorcontrib>Ghosh, Tarini S.</creatorcontrib><creatorcontrib>Neuhaus, Klaus</creatorcontrib><creatorcontrib>Grallert, Harald</creatorcontrib><creatorcontrib>Linseisen, Jakob</creatorcontrib><creatorcontrib>Skurk, Thomas</creatorcontrib><creatorcontrib>Brandl, Beate</creatorcontrib><creatorcontrib>Breuninger, Taylor A.</creatorcontrib><creatorcontrib>Troll, Martina</creatorcontrib><creatorcontrib>Rathmann, Wolfgang</creatorcontrib><creatorcontrib>Linkohr, Birgit</creatorcontrib><creatorcontrib>Hauner, Hans</creatorcontrib><creatorcontrib>Laudes, Matthias</creatorcontrib><creatorcontrib>Franke, Andre</creatorcontrib><creatorcontrib>Le Roy, Caroline I.</creatorcontrib><creatorcontrib>Bell, Jordana T.</creatorcontrib><creatorcontrib>Spector, Tim</creatorcontrib><creatorcontrib>Baumbach, Jan</creatorcontrib><creatorcontrib>O’Toole, Paul W.</creatorcontrib><creatorcontrib>Peters, Annette</creatorcontrib><creatorcontrib>Haller, Dirk</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Cell host & microbe</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Reitmeier, Sandra</au><au>Kiessling, Silke</au><au>Clavel, Thomas</au><au>List, Markus</au><au>Almeida, Eduardo L.</au><au>Ghosh, Tarini S.</au><au>Neuhaus, Klaus</au><au>Grallert, Harald</au><au>Linseisen, Jakob</au><au>Skurk, Thomas</au><au>Brandl, Beate</au><au>Breuninger, Taylor A.</au><au>Troll, Martina</au><au>Rathmann, Wolfgang</au><au>Linkohr, Birgit</au><au>Hauner, Hans</au><au>Laudes, Matthias</au><au>Franke, Andre</au><au>Le Roy, Caroline I.</au><au>Bell, Jordana T.</au><au>Spector, Tim</au><au>Baumbach, Jan</au><au>O’Toole, Paul W.</au><au>Peters, Annette</au><au>Haller, Dirk</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Arrhythmic Gut Microbiome Signatures Predict Risk of Type 2 Diabetes</atitle><jtitle>Cell host & microbe</jtitle><addtitle>Cell Host Microbe</addtitle><date>2020-08-12</date><risdate>2020</risdate><volume>28</volume><issue>2</issue><spage>258</spage><epage>272.e6</epage><pages>258-272.e6</pages><issn>1931-3128</issn><eissn>1934-6069</eissn><abstract>Lifestyle, obesity, and the gut microbiome are important risk factors for metabolic disorders. We demonstrate in 1,976 subjects of a German population cohort (KORA) that specific microbiota members show 24-h oscillations in their relative abundance and identified 13 taxa with disrupted rhythmicity in type 2 diabetes (T2D). Cross-validated prediction models based on this signature similarly classified T2D. In an independent cohort (FoCus), disruption of microbial oscillation and the model for T2D classification was confirmed in 1,363 subjects. This arrhythmic risk signature was able to predict T2D in 699 KORA subjects 5 years after initial sampling, being most effective in combination with BMI. Shotgun metagenomic analysis functionally linked 26 metabolic pathways to the diurnal oscillation of gut bacteria. Thus, a cohort-specific risk pattern of arrhythmic taxa enables classification and prediction of T2D, suggesting a functional link between circadian rhythms and the microbiome in metabolic diseases.
[Display omitted]
•Human gut microbiome exhibits diurnal rhythmicity across populations and individuals•Obese and T2D individuals show disrupted circadian rhythms in the gut microbiome•Arrhytmic bacterial signatures contribute to risk classification and prediction of T2D•These risk signatures show regional differences in applicability across three cohorts
Reitmeier et al. show that specific gut microbes exhibit rhythmic oscillations in relative abundance and identified taxa with disrupted rhythmicity in individuals with type 2 diabetes (T2D). This arrhythmic signature contributed to the classification and prediction of T2D, suggesting functional links between circadian rhythmicity and the microbiome in metabolic diseases.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>32619440</pmid><doi>10.1016/j.chom.2020.06.004</doi><oa>free_for_read</oa></addata></record> |
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subjects | amplicon sequencing Bacteria - classification Bacteria - genetics Bacteria - isolation & purification Bacteria - metabolism Circadian Clocks - physiology Circadian Rhythm - physiology circadian rhythms Diabetes Mellitus, Type 2 - epidemiology Diabetes Mellitus, Type 2 - microbiology Diabetes Mellitus, Type 2 - pathology diurnal oscillations Feces - microbiology Gastrointestinal Microbiome - genetics Gastrointestinal Microbiome - physiology Germany - epidemiology human intestinal microbiota Humans machine learning Metagenome - genetics metagenomics Metagenomics - methods obesity Obesity - microbiology Obesity - pathology population-based cohorts prediction type 2 diabetes |
title | Arrhythmic Gut Microbiome Signatures Predict Risk of Type 2 Diabetes |
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