Population study of the gut microbiome: associations with diet, lifestyle, and cardiometabolic disease View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-12-17

AUTHORS

Rebecca L. Walker, Hera Vlamakis, Jonathan Wei Jie Lee, Luke A. Besse, Vanessa Xanthakis, Ramachandran S. Vasan, Stanley Y. Shaw, Ramnik J. Xavier

ABSTRACT

BackgroundThe human gut harbors trillions of microbes that play dynamic roles in health. While the microbiome contributes to many cardiometabolic traits by modulating host inflammation and metabolism, there is an incomplete understanding regarding the extent that and mechanisms by which individual microbes impact risk and development of cardiovascular disease (CVD). The Framingham Heart Study (FHS) is a multi-generational observational study following participants over decades to identify risk factors for CVD by correlating genetic and phenotypic factors with clinical outcomes. As a large-scale population-based cohort with extensive clinical phenotyping, FHS provides a rich landscape to explore the relationships between the gut microbiome and cardiometabolic traits.MethodsWe performed 16S rRNA gene sequencing on stool from 1423 participants of the FHS Generation 3, OMNI2, and New Offspring Spouse cohorts. Data processing and taxonomic assignment were performed with the 16S bioBakery workflow using the UPARSE pipeline. We conducted statistical analyses to investigate trends in overall microbiome composition and diversity in relation to disease states and systematically examined taxonomic associations with a variety of clinical traits, disease phenotypes, clinical blood markers, and medications.ResultsWe demonstrate that overall microbial diversity decreases with increasing 10-year CVD risk and body mass index measures. We link lifestyle factors, especially diet and exercise, to microbial diversity. Our association analyses reveal both known and unreported microbial associations with CVD and diabetes, related prescription medications, as well as many anthropometric and blood test measurements. In particular, we observe a set of microbial species that demonstrate significant associations with CVD risk, metabolic syndrome, and type 2 diabetes as well as a number of shared associations between microbial species and cardiometabolic subphenotypes.ConclusionsThe identification of significant microbial taxa associated with prevalent CVD and diabetes, as well as risk for developing CVD, adds to increasing evidence that the microbiome may contribute to CVD pathogenesis. Our findings support new hypothesis generation around shared microbe-mediated mechanisms that influence metabolic syndrome, diabetes, and CVD risk. Further investigation of the gut microbiomes of CVD patients in a targeted manner may elucidate microbial mechanisms with diagnostic and therapeutic implications. More... »

PAGES

188

References to SciGraph publications

  • 2006-12. An obesity-associated gut microbiome with increased capacity for energy harvest in NATURE
  • 2020-05-20. Interaction between microbiota and immunity in health and disease in CELL RESEARCH
  • 2013-04-07. Intestinal microbiota metabolism of l-carnitine, a nutrient in red meat, promotes atherosclerosis in NATURE MEDICINE
  • 2015-12-02. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota in NATURE
  • 2012-03-08. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms in THE ISME JOURNAL: MULTIDISCIPLINARY JOURNAL OF MICROBIAL ECOLOGY
  • 2012-06-13. Structure, function and diversity of the healthy human microbiome in NATURE
  • 2017-04-13. Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort in GENOME BIOLOGY
  • 2012-01. Symptomatic atherosclerosis is associated with an altered gut metagenome in NATURE COMMUNICATIONS
  • 2013-12-11. Diet rapidly and reproducibly alters the human gut microbiome in NATURE
  • 2017-10-10. The gut microbiome in atherosclerotic cardiovascular disease in NATURE COMMUNICATIONS
  • 2013-05-01. Immunity, atherosclerosis and cardiovascular disease in BMC MEDICINE
  • 2013-05-29. Gut metagenome in European women with normal, impaired and diabetic glucose control in NATURE
  • 2012-09-26. A metagenome-wide association study of gut microbiota in type 2 diabetes in NATURE
  • 2013-08-28. Richness of human gut microbiome correlates with metabolic markers in NATURE
  • 2017-05-22. Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug in NATURE MEDICINE
  • 2019-05-29. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases in NATURE
  • 2020-10-15. Health and disease markers correlate with gut microbiome composition across thousands of people in NATURE COMMUNICATIONS
  • 2013-08-25. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences in NATURE BIOTECHNOLOGY
  • 2011-04-06. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease in NATURE
  • 2020-01-17. Impact of commonly used drugs on the composition and metabolic function of the gut microbiota in NATURE COMMUNICATIONS
  • 2008-11-30. A core gut microbiome in obese and lean twins in NATURE
  • 2020-03-25. Implication of the gut microbiome composition of type 2 diabetic patients from northern China in SCIENTIFIC REPORTS
  • 2017-02-01. Gut microbiota dysbiosis contributes to the development of hypertension in MICROBIOME
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13073-021-01007-5

    DOI

    http://dx.doi.org/10.1186/s13073-021-01007-5

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1143945140

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/34915914


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Medical and Health Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1102", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Cardiorespiratory Medicine and Haematology", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Cardiovascular Diseases", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Diabetes Mellitus, Type 2", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Diet", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Gastrointestinal Microbiome", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Life Style", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "RNA, Ribosomal, 16S", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Broad Institute of MIT and Harvard, Cambridge, MA, USA", 
              "id": "http://www.grid.ac/institutes/grid.66859.34", 
              "name": [
                "Broad Institute of MIT and Harvard, Cambridge, MA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Walker", 
            "givenName": "Rebecca L.", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA", 
              "id": "http://www.grid.ac/institutes/grid.116068.8", 
              "name": [
                "Broad Institute of MIT and Harvard, Cambridge, MA, USA", 
                "Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Vlamakis", 
            "givenName": "Hera", 
            "id": "sg:person.0773476547.34", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0773476547.34"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Division of Gastroenterology and Hepatology, National University Health System, Singapore, Singapore", 
              "id": "http://www.grid.ac/institutes/grid.410759.e", 
              "name": [
                "Broad Institute of MIT and Harvard, Cambridge, MA, USA", 
                "Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore", 
                "Division of Gastroenterology and Hepatology, National University Health System, Singapore, Singapore"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lee", 
            "givenName": "Jonathan Wei Jie", 
            "id": "sg:person.01074623131.68", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01074623131.68"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Broad Institute of MIT and Harvard, Cambridge, MA, USA", 
              "id": "http://www.grid.ac/institutes/grid.66859.34", 
              "name": [
                "Broad Institute of MIT and Harvard, Cambridge, MA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Besse", 
            "givenName": "Luke A.", 
            "id": "sg:person.07424335323.83", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07424335323.83"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA", 
              "id": "http://www.grid.ac/institutes/grid.189504.1", 
              "name": [
                "Boston University and NHLBI\u2019s Framingham Heart Study, Framingham, MA, USA", 
                "Department of Medicine, Section of Preventive Medicine and Epidemiology, Boston University School of Medicine, Boston, MA, USA", 
                "Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Xanthakis", 
            "givenName": "Vanessa", 
            "id": "sg:person.0676733310.78", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0676733310.78"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Medicine, Section of Cardiology, Boston University School of Medicine, Boston, MA, USA", 
              "id": "http://www.grid.ac/institutes/grid.189504.1", 
              "name": [
                "Boston University and NHLBI\u2019s Framingham Heart Study, Framingham, MA, USA", 
                "Department of Medicine, Section of Preventive Medicine and Epidemiology, Boston University School of Medicine, Boston, MA, USA", 
                "Department of Medicine, Section of Cardiology, Boston University School of Medicine, Boston, MA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Vasan", 
            "givenName": "Ramachandran S.", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Division of Cardiovascular Medicine, Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, MA, USA", 
              "id": "http://www.grid.ac/institutes/grid.38142.3c", 
              "name": [
                "Broad Institute of MIT and Harvard, Cambridge, MA, USA", 
                "Division of Cardiovascular Medicine, Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, MA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Shaw", 
            "givenName": "Stanley Y.", 
            "id": "sg:person.01070313237.32", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01070313237.32"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA", 
              "id": "http://www.grid.ac/institutes/grid.66859.34", 
              "name": [
                "Broad Institute of MIT and Harvard, Cambridge, MA, USA", 
                "Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA", 
                "Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA", 
                "Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Xavier", 
            "givenName": "Ramnik J.", 
            "id": "sg:person.0717130066.82", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0717130066.82"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1038/nature12820", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013830289", 
              "https://doi.org/10.1038/nature12820"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nm.4345", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085459595", 
              "https://doi.org/10.1038/nm.4345"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ncomms2266", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021161767", 
              "https://doi.org/10.1038/ncomms2266"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt.2676", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034019934", 
              "https://doi.org/10.1038/nbt.2676"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature12198", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002791386", 
              "https://doi.org/10.1038/nature12198"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1741-7015-11-117", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027152234", 
              "https://doi.org/10.1186/1741-7015-11-117"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s40168-016-0222-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1083399679", 
              "https://doi.org/10.1186/s40168-016-0222-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41467-017-00900-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092092284", 
              "https://doi.org/10.1038/s41467-017-00900-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature05414", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023893418", 
              "https://doi.org/10.1038/nature05414"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature09922", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043309820", 
              "https://doi.org/10.1038/nature09922"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41586-019-1237-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1115981538", 
              "https://doi.org/10.1038/s41586-019-1237-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13059-017-1194-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084807339", 
              "https://doi.org/10.1186/s13059-017-1194-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature12506", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003823032", 
              "https://doi.org/10.1038/nature12506"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nm.3145", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004485531", 
              "https://doi.org/10.1038/nm.3145"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-020-62224-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1125895021", 
              "https://doi.org/10.1038/s41598-020-62224-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature11450", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004546178", 
              "https://doi.org/10.1038/nature11450"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ismej.2012.8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038992953", 
              "https://doi.org/10.1038/ismej.2012.8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41467-020-18871-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1131748222", 
              "https://doi.org/10.1038/s41467-020-18871-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41467-019-14177-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1124147887", 
              "https://doi.org/10.1038/s41467-019-14177-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature07540", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030170002", 
              "https://doi.org/10.1038/nature07540"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature11234", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007740093", 
              "https://doi.org/10.1038/nature11234"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature15766", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014535385", 
              "https://doi.org/10.1038/nature15766"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41422-020-0332-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1127737788", 
              "https://doi.org/10.1038/s41422-020-0332-7"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2021-12-17", 
        "datePublishedReg": "2021-12-17", 
        "description": "BackgroundThe human gut harbors trillions of microbes that play dynamic roles in health. While the microbiome contributes to many cardiometabolic traits by modulating host inflammation and metabolism, there is an incomplete understanding regarding the extent that and mechanisms by which individual microbes impact risk and development of cardiovascular disease (CVD). The Framingham Heart Study (FHS) is a multi-generational observational study following participants over decades to identify risk factors for CVD by correlating genetic and phenotypic factors with clinical outcomes. As a large-scale population-based cohort with extensive clinical phenotyping, FHS provides a rich landscape to explore the relationships between the gut microbiome and cardiometabolic traits.MethodsWe performed 16S rRNA gene sequencing on stool from 1423 participants of the FHS Generation 3, OMNI2, and New Offspring Spouse cohorts. Data processing and taxonomic assignment were performed with the 16S bioBakery workflow using the UPARSE pipeline. We conducted statistical analyses to investigate trends in overall microbiome composition and diversity in relation to disease states and systematically examined taxonomic associations with a variety of clinical traits, disease phenotypes, clinical blood markers, and medications.ResultsWe demonstrate that overall microbial diversity decreases with increasing 10-year CVD risk and body mass index measures. We link lifestyle factors, especially diet and exercise, to microbial diversity. Our association analyses reveal both known and unreported microbial associations with CVD and diabetes, related prescription medications, as well as many anthropometric and blood test measurements. In particular, we observe a set of microbial species that demonstrate significant associations with CVD risk, metabolic syndrome, and type 2 diabetes as well as a number of shared associations between microbial species and cardiometabolic subphenotypes.ConclusionsThe identification of significant microbial taxa associated with prevalent CVD and diabetes, as well as risk for developing CVD, adds to increasing evidence that the microbiome may contribute to CVD pathogenesis. Our findings support new hypothesis generation around shared microbe-mediated mechanisms that influence metabolic syndrome, diabetes, and CVD risk. Further investigation of the gut microbiomes of CVD patients in a targeted manner may elucidate microbial mechanisms with diagnostic and therapeutic implications.", 
        "genre": "article", 
        "id": "sg:pub.10.1186/s13073-021-01007-5", 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.2439002", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.10015615", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.4729555", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.7030409", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1040124", 
            "issn": [
              "1756-994X"
            ], 
            "name": "Genome Medicine", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "13"
          }
        ], 
        "keywords": [
          "Framingham Heart Study", 
          "cardiovascular disease", 
          "CVD risk", 
          "gut microbiome", 
          "metabolic syndrome", 
          "large-scale population-based cohort", 
          "body mass index measures", 
          "cardiometabolic traits", 
          "clinical blood markers", 
          "prevalent cardiovascular disease", 
          "population-based cohort", 
          "type 2 diabetes", 
          "trillions of microbes", 
          "FHS Generation 3", 
          "blood test measurements", 
          "overall microbiome composition", 
          "clinical outcomes", 
          "lifestyle factors", 
          "cardiometabolic diseases", 
          "prescription medications", 
          "blood markers", 
          "CVD pathogenesis", 
          "risk factors", 
          "CVD patients", 
          "host inflammation", 
          "observational study", 
          "extensive clinical phenotyping", 
          "clinical phenotyping", 
          "Heart Study", 
          "therapeutic implications", 
          "diabetes", 
          "significant association", 
          "ConclusionsThe identification", 
          "disease states", 
          "clinical traits", 
          "population studies", 
          "human gut", 
          "medications", 
          "disease phenotype", 
          "microbiome composition", 
          "risk", 
          "syndrome", 
          "cohort", 
          "association", 
          "disease", 
          "further investigation", 
          "microbial diversity", 
          "phenotypic factors", 
          "microbial species", 
          "microbiome", 
          "diet", 
          "overall microbial diversity", 
          "incomplete understanding", 
          "microbe-mediated mechanisms", 
          "participants", 
          "inflammation", 
          "statistical analysis", 
          "patients", 
          "stool", 
          "pathogenesis", 
          "anthropometric", 
          "factors", 
          "MethodsWe", 
          "microbial taxa", 
          "subphenotypes", 
          "individual microbes", 
          "rRNA gene", 
          "taxonomic associations", 
          "ResultsWe", 
          "study", 
          "taxonomic assignment", 
          "microbial associations", 
          "microbial mechanisms", 
          "outcomes", 
          "gut", 
          "association analysis", 
          "exercise", 
          "markers", 
          "new hypothesis generation", 
          "health", 
          "metabolism", 
          "lifestyle", 
          "mechanism", 
          "generation 3", 
          "traits", 
          "phenotype", 
          "diversity", 
          "phenotyping", 
          "microbes", 
          "dynamic role", 
          "findings", 
          "species", 
          "targeted manner", 
          "evidence", 
          "genes", 
          "role", 
          "measures", 
          "rich landscape", 
          "taxa", 
          "analysis", 
          "hypothesis generation", 
          "manner", 
          "index measure", 
          "extent", 
          "identification", 
          "development", 
          "relationship", 
          "number", 
          "investigation", 
          "decades", 
          "implications", 
          "trillions", 
          "trends", 
          "understanding", 
          "landscape", 
          "variety", 
          "relation", 
          "measurements", 
          "composition", 
          "state", 
          "generation", 
          "workflow", 
          "pipeline", 
          "processing", 
          "assignment", 
          "test measurements", 
          "set", 
          "data processing"
        ], 
        "name": "Population study of the gut microbiome: associations with diet, lifestyle, and cardiometabolic disease", 
        "pagination": "188", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1143945140"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/s13073-021-01007-5"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "34915914"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/s13073-021-01007-5", 
          "https://app.dimensions.ai/details/publication/pub.1143945140"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-09-02T16:06", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220902/entities/gbq_results/article/article_899.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1186/s13073-021-01007-5"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s13073-021-01007-5'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s13073-021-01007-5'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s13073-021-01007-5'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13073-021-01007-5'


     

    This table displays all metadata directly associated to this object as RDF triples.

    387 TRIPLES      21 PREDICATES      183 URIs      152 LITERALS      14 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/s13073-021-01007-5 schema:about N06ee0a80fd9c4a1a9edcdc1b114424ad
    2 N10de0d96e27244f78a3969cf84b56d0c
    3 N2fc3ddd07a17456b83ac3eeb59257154
    4 N73f43b0948ee4ae39a9ad6d776c28c3f
    5 N9cf6158f3ad64004b6519cc3f372d2bd
    6 Nb7f99efd9cd5474aaa49cd857718fc03
    7 Nd10571818fbd4a67a016fc571dd0ca00
    8 anzsrc-for:11
    9 anzsrc-for:1102
    10 schema:author N313533622ab242c9a9696ed001bfd601
    11 schema:citation sg:pub.10.1038/ismej.2012.8
    12 sg:pub.10.1038/nature05414
    13 sg:pub.10.1038/nature07540
    14 sg:pub.10.1038/nature09922
    15 sg:pub.10.1038/nature11234
    16 sg:pub.10.1038/nature11450
    17 sg:pub.10.1038/nature12198
    18 sg:pub.10.1038/nature12506
    19 sg:pub.10.1038/nature12820
    20 sg:pub.10.1038/nature15766
    21 sg:pub.10.1038/nbt.2676
    22 sg:pub.10.1038/ncomms2266
    23 sg:pub.10.1038/nm.3145
    24 sg:pub.10.1038/nm.4345
    25 sg:pub.10.1038/s41422-020-0332-7
    26 sg:pub.10.1038/s41467-017-00900-1
    27 sg:pub.10.1038/s41467-019-14177-z
    28 sg:pub.10.1038/s41467-020-18871-1
    29 sg:pub.10.1038/s41586-019-1237-9
    30 sg:pub.10.1038/s41598-020-62224-3
    31 sg:pub.10.1186/1741-7015-11-117
    32 sg:pub.10.1186/s13059-017-1194-2
    33 sg:pub.10.1186/s40168-016-0222-x
    34 schema:datePublished 2021-12-17
    35 schema:datePublishedReg 2021-12-17
    36 schema:description BackgroundThe human gut harbors trillions of microbes that play dynamic roles in health. While the microbiome contributes to many cardiometabolic traits by modulating host inflammation and metabolism, there is an incomplete understanding regarding the extent that and mechanisms by which individual microbes impact risk and development of cardiovascular disease (CVD). The Framingham Heart Study (FHS) is a multi-generational observational study following participants over decades to identify risk factors for CVD by correlating genetic and phenotypic factors with clinical outcomes. As a large-scale population-based cohort with extensive clinical phenotyping, FHS provides a rich landscape to explore the relationships between the gut microbiome and cardiometabolic traits.MethodsWe performed 16S rRNA gene sequencing on stool from 1423 participants of the FHS Generation 3, OMNI2, and New Offspring Spouse cohorts. Data processing and taxonomic assignment were performed with the 16S bioBakery workflow using the UPARSE pipeline. We conducted statistical analyses to investigate trends in overall microbiome composition and diversity in relation to disease states and systematically examined taxonomic associations with a variety of clinical traits, disease phenotypes, clinical blood markers, and medications.ResultsWe demonstrate that overall microbial diversity decreases with increasing 10-year CVD risk and body mass index measures. We link lifestyle factors, especially diet and exercise, to microbial diversity. Our association analyses reveal both known and unreported microbial associations with CVD and diabetes, related prescription medications, as well as many anthropometric and blood test measurements. In particular, we observe a set of microbial species that demonstrate significant associations with CVD risk, metabolic syndrome, and type 2 diabetes as well as a number of shared associations between microbial species and cardiometabolic subphenotypes.ConclusionsThe identification of significant microbial taxa associated with prevalent CVD and diabetes, as well as risk for developing CVD, adds to increasing evidence that the microbiome may contribute to CVD pathogenesis. Our findings support new hypothesis generation around shared microbe-mediated mechanisms that influence metabolic syndrome, diabetes, and CVD risk. Further investigation of the gut microbiomes of CVD patients in a targeted manner may elucidate microbial mechanisms with diagnostic and therapeutic implications.
    37 schema:genre article
    38 schema:isAccessibleForFree true
    39 schema:isPartOf N91806e237c8a447c8b2e6a4bb794c3b3
    40 Nbef2c72e43e74c1f8e48f6e302a9f85d
    41 sg:journal.1040124
    42 schema:keywords CVD pathogenesis
    43 CVD patients
    44 CVD risk
    45 ConclusionsThe identification
    46 FHS Generation 3
    47 Framingham Heart Study
    48 Heart Study
    49 MethodsWe
    50 ResultsWe
    51 analysis
    52 anthropometric
    53 assignment
    54 association
    55 association analysis
    56 blood markers
    57 blood test measurements
    58 body mass index measures
    59 cardiometabolic diseases
    60 cardiometabolic traits
    61 cardiovascular disease
    62 clinical blood markers
    63 clinical outcomes
    64 clinical phenotyping
    65 clinical traits
    66 cohort
    67 composition
    68 data processing
    69 decades
    70 development
    71 diabetes
    72 diet
    73 disease
    74 disease phenotype
    75 disease states
    76 diversity
    77 dynamic role
    78 evidence
    79 exercise
    80 extensive clinical phenotyping
    81 extent
    82 factors
    83 findings
    84 further investigation
    85 generation
    86 generation 3
    87 genes
    88 gut
    89 gut microbiome
    90 health
    91 host inflammation
    92 human gut
    93 hypothesis generation
    94 identification
    95 implications
    96 incomplete understanding
    97 index measure
    98 individual microbes
    99 inflammation
    100 investigation
    101 landscape
    102 large-scale population-based cohort
    103 lifestyle
    104 lifestyle factors
    105 manner
    106 markers
    107 measurements
    108 measures
    109 mechanism
    110 medications
    111 metabolic syndrome
    112 metabolism
    113 microbe-mediated mechanisms
    114 microbes
    115 microbial associations
    116 microbial diversity
    117 microbial mechanisms
    118 microbial species
    119 microbial taxa
    120 microbiome
    121 microbiome composition
    122 new hypothesis generation
    123 number
    124 observational study
    125 outcomes
    126 overall microbial diversity
    127 overall microbiome composition
    128 participants
    129 pathogenesis
    130 patients
    131 phenotype
    132 phenotypic factors
    133 phenotyping
    134 pipeline
    135 population studies
    136 population-based cohort
    137 prescription medications
    138 prevalent cardiovascular disease
    139 processing
    140 rRNA gene
    141 relation
    142 relationship
    143 rich landscape
    144 risk
    145 risk factors
    146 role
    147 set
    148 significant association
    149 species
    150 state
    151 statistical analysis
    152 stool
    153 study
    154 subphenotypes
    155 syndrome
    156 targeted manner
    157 taxa
    158 taxonomic assignment
    159 taxonomic associations
    160 test measurements
    161 therapeutic implications
    162 traits
    163 trends
    164 trillions
    165 trillions of microbes
    166 type 2 diabetes
    167 understanding
    168 variety
    169 workflow
    170 schema:name Population study of the gut microbiome: associations with diet, lifestyle, and cardiometabolic disease
    171 schema:pagination 188
    172 schema:productId N13e3d418bedb4f589e6f2815b5560922
    173 Nc745ea7347b5453faaac290ee7ada81c
    174 Nde47ad2e370945f2a29ef6a3b2100c29
    175 schema:sameAs https://app.dimensions.ai/details/publication/pub.1143945140
    176 https://doi.org/10.1186/s13073-021-01007-5
    177 schema:sdDatePublished 2022-09-02T16:06
    178 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    179 schema:sdPublisher N8aa12ad5aaae4bf0939d515e9f03c93c
    180 schema:url https://doi.org/10.1186/s13073-021-01007-5
    181 sgo:license sg:explorer/license/
    182 sgo:sdDataset articles
    183 rdf:type schema:ScholarlyArticle
    184 N06ee0a80fd9c4a1a9edcdc1b114424ad schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    185 schema:name Gastrointestinal Microbiome
    186 rdf:type schema:DefinedTerm
    187 N0c1d52b60277496388207ccd3a32e81c rdf:first sg:person.07424335323.83
    188 rdf:rest Nc533a17f156945aa8472eafee6ea2a24
    189 N10de0d96e27244f78a3969cf84b56d0c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    190 schema:name Cardiovascular Diseases
    191 rdf:type schema:DefinedTerm
    192 N13e3d418bedb4f589e6f2815b5560922 schema:name dimensions_id
    193 schema:value pub.1143945140
    194 rdf:type schema:PropertyValue
    195 N2fc3ddd07a17456b83ac3eeb59257154 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    196 schema:name Humans
    197 rdf:type schema:DefinedTerm
    198 N313533622ab242c9a9696ed001bfd601 rdf:first N7f24d54d111c4d8599ed26a41c22598b
    199 rdf:rest N6a51182aebe34ef4b91c928bb80790df
    200 N5f0d755300034e36824210d1e8a08168 rdf:first sg:person.0717130066.82
    201 rdf:rest rdf:nil
    202 N6a51182aebe34ef4b91c928bb80790df rdf:first sg:person.0773476547.34
    203 rdf:rest N849d452c935b41fcad655ff97f5abaca
    204 N73f43b0948ee4ae39a9ad6d776c28c3f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    205 schema:name Diabetes Mellitus, Type 2
    206 rdf:type schema:DefinedTerm
    207 N7f24d54d111c4d8599ed26a41c22598b schema:affiliation grid-institutes:grid.66859.34
    208 schema:familyName Walker
    209 schema:givenName Rebecca L.
    210 rdf:type schema:Person
    211 N849d452c935b41fcad655ff97f5abaca rdf:first sg:person.01074623131.68
    212 rdf:rest N0c1d52b60277496388207ccd3a32e81c
    213 N8aa12ad5aaae4bf0939d515e9f03c93c schema:name Springer Nature - SN SciGraph project
    214 rdf:type schema:Organization
    215 N8bf63aa3b19842dfb803da4bc52e9ab5 rdf:first Ne3a025b8f4554395addad93761be645c
    216 rdf:rest Nce17dc879b054768b849502679b1d559
    217 N91806e237c8a447c8b2e6a4bb794c3b3 schema:volumeNumber 13
    218 rdf:type schema:PublicationVolume
    219 N9cf6158f3ad64004b6519cc3f372d2bd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    220 schema:name Diet
    221 rdf:type schema:DefinedTerm
    222 Nb7f99efd9cd5474aaa49cd857718fc03 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    223 schema:name RNA, Ribosomal, 16S
    224 rdf:type schema:DefinedTerm
    225 Nbef2c72e43e74c1f8e48f6e302a9f85d schema:issueNumber 1
    226 rdf:type schema:PublicationIssue
    227 Nc533a17f156945aa8472eafee6ea2a24 rdf:first sg:person.0676733310.78
    228 rdf:rest N8bf63aa3b19842dfb803da4bc52e9ab5
    229 Nc745ea7347b5453faaac290ee7ada81c schema:name doi
    230 schema:value 10.1186/s13073-021-01007-5
    231 rdf:type schema:PropertyValue
    232 Nce17dc879b054768b849502679b1d559 rdf:first sg:person.01070313237.32
    233 rdf:rest N5f0d755300034e36824210d1e8a08168
    234 Nd10571818fbd4a67a016fc571dd0ca00 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    235 schema:name Life Style
    236 rdf:type schema:DefinedTerm
    237 Nde47ad2e370945f2a29ef6a3b2100c29 schema:name pubmed_id
    238 schema:value 34915914
    239 rdf:type schema:PropertyValue
    240 Ne3a025b8f4554395addad93761be645c schema:affiliation grid-institutes:grid.189504.1
    241 schema:familyName Vasan
    242 schema:givenName Ramachandran S.
    243 rdf:type schema:Person
    244 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    245 schema:name Medical and Health Sciences
    246 rdf:type schema:DefinedTerm
    247 anzsrc-for:1102 schema:inDefinedTermSet anzsrc-for:
    248 schema:name Cardiorespiratory Medicine and Haematology
    249 rdf:type schema:DefinedTerm
    250 sg:grant.10015615 http://pending.schema.org/fundedItem sg:pub.10.1186/s13073-021-01007-5
    251 rdf:type schema:MonetaryGrant
    252 sg:grant.2439002 http://pending.schema.org/fundedItem sg:pub.10.1186/s13073-021-01007-5
    253 rdf:type schema:MonetaryGrant
    254 sg:grant.4729555 http://pending.schema.org/fundedItem sg:pub.10.1186/s13073-021-01007-5
    255 rdf:type schema:MonetaryGrant
    256 sg:grant.7030409 http://pending.schema.org/fundedItem sg:pub.10.1186/s13073-021-01007-5
    257 rdf:type schema:MonetaryGrant
    258 sg:journal.1040124 schema:issn 1756-994X
    259 schema:name Genome Medicine
    260 schema:publisher Springer Nature
    261 rdf:type schema:Periodical
    262 sg:person.01070313237.32 schema:affiliation grid-institutes:grid.38142.3c
    263 schema:familyName Shaw
    264 schema:givenName Stanley Y.
    265 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01070313237.32
    266 rdf:type schema:Person
    267 sg:person.01074623131.68 schema:affiliation grid-institutes:grid.410759.e
    268 schema:familyName Lee
    269 schema:givenName Jonathan Wei Jie
    270 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01074623131.68
    271 rdf:type schema:Person
    272 sg:person.0676733310.78 schema:affiliation grid-institutes:grid.189504.1
    273 schema:familyName Xanthakis
    274 schema:givenName Vanessa
    275 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0676733310.78
    276 rdf:type schema:Person
    277 sg:person.0717130066.82 schema:affiliation grid-institutes:grid.66859.34
    278 schema:familyName Xavier
    279 schema:givenName Ramnik J.
    280 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0717130066.82
    281 rdf:type schema:Person
    282 sg:person.07424335323.83 schema:affiliation grid-institutes:grid.66859.34
    283 schema:familyName Besse
    284 schema:givenName Luke A.
    285 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07424335323.83
    286 rdf:type schema:Person
    287 sg:person.0773476547.34 schema:affiliation grid-institutes:grid.116068.8
    288 schema:familyName Vlamakis
    289 schema:givenName Hera
    290 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0773476547.34
    291 rdf:type schema:Person
    292 sg:pub.10.1038/ismej.2012.8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038992953
    293 https://doi.org/10.1038/ismej.2012.8
    294 rdf:type schema:CreativeWork
    295 sg:pub.10.1038/nature05414 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023893418
    296 https://doi.org/10.1038/nature05414
    297 rdf:type schema:CreativeWork
    298 sg:pub.10.1038/nature07540 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030170002
    299 https://doi.org/10.1038/nature07540
    300 rdf:type schema:CreativeWork
    301 sg:pub.10.1038/nature09922 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043309820
    302 https://doi.org/10.1038/nature09922
    303 rdf:type schema:CreativeWork
    304 sg:pub.10.1038/nature11234 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007740093
    305 https://doi.org/10.1038/nature11234
    306 rdf:type schema:CreativeWork
    307 sg:pub.10.1038/nature11450 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004546178
    308 https://doi.org/10.1038/nature11450
    309 rdf:type schema:CreativeWork
    310 sg:pub.10.1038/nature12198 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002791386
    311 https://doi.org/10.1038/nature12198
    312 rdf:type schema:CreativeWork
    313 sg:pub.10.1038/nature12506 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003823032
    314 https://doi.org/10.1038/nature12506
    315 rdf:type schema:CreativeWork
    316 sg:pub.10.1038/nature12820 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013830289
    317 https://doi.org/10.1038/nature12820
    318 rdf:type schema:CreativeWork
    319 sg:pub.10.1038/nature15766 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014535385
    320 https://doi.org/10.1038/nature15766
    321 rdf:type schema:CreativeWork
    322 sg:pub.10.1038/nbt.2676 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034019934
    323 https://doi.org/10.1038/nbt.2676
    324 rdf:type schema:CreativeWork
    325 sg:pub.10.1038/ncomms2266 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021161767
    326 https://doi.org/10.1038/ncomms2266
    327 rdf:type schema:CreativeWork
    328 sg:pub.10.1038/nm.3145 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004485531
    329 https://doi.org/10.1038/nm.3145
    330 rdf:type schema:CreativeWork
    331 sg:pub.10.1038/nm.4345 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085459595
    332 https://doi.org/10.1038/nm.4345
    333 rdf:type schema:CreativeWork
    334 sg:pub.10.1038/s41422-020-0332-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1127737788
    335 https://doi.org/10.1038/s41422-020-0332-7
    336 rdf:type schema:CreativeWork
    337 sg:pub.10.1038/s41467-017-00900-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092092284
    338 https://doi.org/10.1038/s41467-017-00900-1
    339 rdf:type schema:CreativeWork
    340 sg:pub.10.1038/s41467-019-14177-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1124147887
    341 https://doi.org/10.1038/s41467-019-14177-z
    342 rdf:type schema:CreativeWork
    343 sg:pub.10.1038/s41467-020-18871-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1131748222
    344 https://doi.org/10.1038/s41467-020-18871-1
    345 rdf:type schema:CreativeWork
    346 sg:pub.10.1038/s41586-019-1237-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1115981538
    347 https://doi.org/10.1038/s41586-019-1237-9
    348 rdf:type schema:CreativeWork
    349 sg:pub.10.1038/s41598-020-62224-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1125895021
    350 https://doi.org/10.1038/s41598-020-62224-3
    351 rdf:type schema:CreativeWork
    352 sg:pub.10.1186/1741-7015-11-117 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027152234
    353 https://doi.org/10.1186/1741-7015-11-117
    354 rdf:type schema:CreativeWork
    355 sg:pub.10.1186/s13059-017-1194-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084807339
    356 https://doi.org/10.1186/s13059-017-1194-2
    357 rdf:type schema:CreativeWork
    358 sg:pub.10.1186/s40168-016-0222-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1083399679
    359 https://doi.org/10.1186/s40168-016-0222-x
    360 rdf:type schema:CreativeWork
    361 grid-institutes:grid.116068.8 schema:alternateName Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA
    362 schema:name Broad Institute of MIT and Harvard, Cambridge, MA, USA
    363 Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA
    364 rdf:type schema:Organization
    365 grid-institutes:grid.189504.1 schema:alternateName Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
    366 Department of Medicine, Section of Cardiology, Boston University School of Medicine, Boston, MA, USA
    367 schema:name Boston University and NHLBI’s Framingham Heart Study, Framingham, MA, USA
    368 Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
    369 Department of Medicine, Section of Cardiology, Boston University School of Medicine, Boston, MA, USA
    370 Department of Medicine, Section of Preventive Medicine and Epidemiology, Boston University School of Medicine, Boston, MA, USA
    371 rdf:type schema:Organization
    372 grid-institutes:grid.38142.3c schema:alternateName Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
    373 schema:name Broad Institute of MIT and Harvard, Cambridge, MA, USA
    374 Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
    375 rdf:type schema:Organization
    376 grid-institutes:grid.410759.e schema:alternateName Division of Gastroenterology and Hepatology, National University Health System, Singapore, Singapore
    377 schema:name Broad Institute of MIT and Harvard, Cambridge, MA, USA
    378 Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
    379 Division of Gastroenterology and Hepatology, National University Health System, Singapore, Singapore
    380 rdf:type schema:Organization
    381 grid-institutes:grid.66859.34 schema:alternateName Broad Institute of MIT and Harvard, Cambridge, MA, USA
    382 Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
    383 schema:name Broad Institute of MIT and Harvard, Cambridge, MA, USA
    384 Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
    385 Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA
    386 Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
    387 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...