Commonality versus specificity among adiposity traits in normal-weight and moderately overweight adults View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-05

AUTHORS

G K Raja, M A Sarzynski, P T Katzmarzyk, W D Johnson, Y Tchoukalova, S R Smith, C Bouchard

ABSTRACT

BACKGROUND: Many adiposity traits have been related to health complications and premature death. These adiposity traits are intercorrelated but their underlying structure has not been extensively investigated. We report on the degree of commonality and specificity among multiple adiposity traits in normal-weight and moderately overweight adult males and females (mean body mass index (BMI)=22.9 kg m(-2), s.d.=2.4). METHODS: A total of 75 healthy participants were assessed for a panel of adiposity traits including leg, arm, trunk, total fat masses and visceral adipose tissue (VAT) derived from dual energy X-ray absorptiometry (DXA), hepatic and muscle lipids from proton magnetic resonance spectroscopy, fat cell volume from an abdominal subcutaneous adipose tissue biopsy (n=36) and conventional anthropometry (BMI and waist girth). Spearman's correlations were calculated and were subjected to factor analysis. RESULTS: Arm, leg, trunk and total fat masses correlated positively (r=0.78-0.95) with each other. VAT correlated weakly with fat mass indicators (r=0.24-0.31). Intrahepatic lipids (IHL) correlated weakly with all fat mass traits (r=0.09-0.34), whereas correlations between DXA depots and intramyocellular lipids (IMCL) were inconsequential. The four DXA fat mass measures, VAT, IHL and IMCL depots segregated as four independent factors that accounted for 96% of the overall adiposity variance. BMI and waist girth were moderately correlated with the arm, leg, trunk and total fat and weakly with VAT, IHL and IMCL. CONCLUSION: Adiposity traits share a substantial degree of commonality, but there is considerable specificity across the adiposity variance space. For instance, VAT, IHL and IMCL are typically poorly correlated with each other and are poorly to weakly associated with the other adiposity traits. The same is true for BMI and waist girth, commonly used anthropometric indicators of adiposity. These results do not support the view that it will be possible to identify adequate anthropometric indicators of visceral, hepatic and muscle lipid content in normal-weight and moderately overweight individuals. More... »

PAGES

719

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/ijo.2013.153

DOI

http://dx.doi.org/10.1038/ijo.2013.153

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1019474055

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23949614


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/2008/1103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Clinical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Absorptiometry, Photon", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adipocytes", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adiposity", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adult", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Body Composition", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Body Mass Index", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Intra-Abdominal Fat", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Lipids", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Overweight", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Predictive Value of Tests", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Subcutaneous Fat", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Waist Circumference", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Pir Mehr Ali Shah Arid Agriculture University", 
          "id": "https://www.grid.ac/institutes/grid.440552.2", 
          "name": [
            "Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA", 
            "Department of Biochemistry, Pir Mehr Ali Shah Arid Agriculture University Rawalpindi, Punjab, Pakistan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Raja", 
        "givenName": "G K", 
        "id": "sg:person.01302574775.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01302574775.26"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Pennington Biomedical Research Center", 
          "id": "https://www.grid.ac/institutes/grid.250514.7", 
          "name": [
            "Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sarzynski", 
        "givenName": "M A", 
        "id": "sg:person.01136002520.90", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136002520.90"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Pennington Biomedical Research Center", 
          "id": "https://www.grid.ac/institutes/grid.250514.7", 
          "name": [
            "Preventive Medicine and Healthy Aging, Pennington Biomedical Research Center, Baton Rouge, LA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Katzmarzyk", 
        "givenName": "P T", 
        "id": "sg:person.010702120177.88", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010702120177.88"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Pennington Biomedical Research Center", 
          "id": "https://www.grid.ac/institutes/grid.250514.7", 
          "name": [
            "Biostatistics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Johnson", 
        "givenName": "W D", 
        "id": "sg:person.01213722103.41", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01213722103.41"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Pennington Biomedical Research Center", 
          "id": "https://www.grid.ac/institutes/grid.250514.7", 
          "name": [
            "Biology of Adipose Tissue Depots Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tchoukalova", 
        "givenName": "Y", 
        "id": "sg:person.01347154460.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01347154460.00"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Translational Research Institute for Metabolism and Diabetes", 
          "id": "https://www.grid.ac/institutes/grid.489332.7", 
          "name": [
            "Translational Research Institute for Metabolism and Diabetes, Florida Hospital, Orlando, FL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Smith", 
        "givenName": "S R", 
        "id": "sg:person.011256250534.28", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011256250534.28"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Pennington Biomedical Research Center", 
          "id": "https://www.grid.ac/institutes/grid.250514.7", 
          "name": [
            "Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bouchard", 
        "givenName": "C", 
        "id": "sg:person.01246353000.05", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01246353000.05"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.nut.2008.09.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000509783"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature05488", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006317191", 
          "https://doi.org/10.1038/nature05488"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature05488", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006317191", 
          "https://doi.org/10.1038/nature05488"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature05488", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006317191", 
          "https://doi.org/10.1038/nature05488"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.atv.10.4.493", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009147056"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/sj.ijo.0803653", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011276820", 
          "https://doi.org/10.1038/sj.ijo.0803653"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/db09-0942", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013643901"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1440-1746.2007.05212.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014952268"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/gut.2003.036566", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015807246"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/oby.2011.367", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016355724"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1172/jci118637", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017012169"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3109/07853899209166997", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020046805"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0025-6196(12)60695-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021749862"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.0904944106", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021934954"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1467-789x.2012.01033.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023924765"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/dc10-2019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024553986"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/oby.2006.10", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025928011"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3945/ajcn.112.047787", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030433508"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3945/ajcn.112.047787", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030433508"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/jmre.1997.1244", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031415033"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/oby.2010.248", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035508632"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/oby.20519", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037728023"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s001250051123", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039281035", 
          "https://doi.org/10.1007/s001250051123"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s001250051123", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039281035", 
          "https://doi.org/10.1007/s001250051123"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02668096", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039838319", 
          "https://doi.org/10.1007/bf02668096"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02668096", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039838319", 
          "https://doi.org/10.1007/bf02668096"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/ajpendo.00377.2009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040312034"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/oby.2011.142", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043609467"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/dc05-2565", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044042242"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/sj.ijo.0802853", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045599109", 
          "https://doi.org/10.1038/sj.ijo.0802853"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/sj.ijo.0802853", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045599109", 
          "https://doi.org/10.1038/sj.ijo.0802853"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1194/jlr.d300001-jlr200", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049333630"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0007038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053655167"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1210/jc.2003-031986", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064287490"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1210/jc.2010-1722", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064292373"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1210/jc.2011-1798", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064292992"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1210/jcem-54-2-254", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064312100"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1259/bjr.70.835.9245885", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064566365"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2174/138161211798157720", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069167876"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/diab.38.3.304", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070735819"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/diabetes.38.3.304", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070741546"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/diabetes.48.8.1600", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070744252"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/ajpendo.2002.282.1.e95", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1074962388"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/ajcn/4.1.20", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1075606772"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/ajcn/87.1.56", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1077586326"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/ajcn/87.2.295", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1077604777"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/ajcn/52.5.946", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1078530260"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1080257246", 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2014-05", 
    "datePublishedReg": "2014-05-01", 
    "description": "BACKGROUND: Many adiposity traits have been related to health complications and premature death. These adiposity traits are intercorrelated but their underlying structure has not been extensively investigated. We report on the degree of commonality and specificity among multiple adiposity traits in normal-weight and moderately overweight adult males and females (mean body mass index (BMI)=22.9\u2009kg\u2009m(-2), s.d.=2.4).\nMETHODS: A total of 75 healthy participants were assessed for a panel of adiposity traits including leg, arm, trunk, total fat masses and visceral adipose tissue (VAT) derived from dual energy X-ray absorptiometry (DXA), hepatic and muscle lipids from proton magnetic resonance spectroscopy, fat cell volume from an abdominal subcutaneous adipose tissue biopsy (n=36) and conventional anthropometry (BMI and waist girth). Spearman's correlations were calculated and were subjected to factor analysis.\nRESULTS: Arm, leg, trunk and total fat masses correlated positively (r=0.78-0.95) with each other. VAT correlated weakly with fat mass indicators (r=0.24-0.31). Intrahepatic lipids (IHL) correlated weakly with all fat mass traits (r=0.09-0.34), whereas correlations between DXA depots and intramyocellular lipids (IMCL) were inconsequential. The four DXA fat mass measures, VAT, IHL and IMCL depots segregated as four independent factors that accounted for 96% of the overall adiposity variance. BMI and waist girth were moderately correlated with the arm, leg, trunk and total fat and weakly with VAT, IHL and IMCL.\nCONCLUSION: Adiposity traits share a substantial degree of commonality, but there is considerable specificity across the adiposity variance space. For instance, VAT, IHL and IMCL are typically poorly correlated with each other and are poorly to weakly associated with the other adiposity traits. The same is true for BMI and waist girth, commonly used anthropometric indicators of adiposity. These results do not support the view that it will be possible to identify adequate anthropometric indicators of visceral, hepatic and muscle lipid content in normal-weight and moderately overweight individuals.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/ijo.2013.153", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.2699248", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2439036", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1035838", 
        "issn": [
          "0307-0565", 
          "1476-5497"
        ], 
        "name": "International Journal of Obesity", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "5", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "38"
      }
    ], 
    "name": "Commonality versus specificity among adiposity traits in normal-weight and moderately overweight adults", 
    "pagination": "719", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "5cc98a5b6c33ea71c76f147344ce5db7b15f62a226f2540d2858d2de77418b74"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "23949614"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101256108"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/ijo.2013.153"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1019474055"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/ijo.2013.153", 
      "https://app.dimensions.ai/details/publication/pub.1019474055"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T14:18", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000372_0000000372/records_117103_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/ijo2013153"
  }
]
 

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.10.1038/ijo.2013.153'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/ijo.2013.153'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/ijo.2013.153'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/ijo.2013.153'


 

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

315 TRIPLES      21 PREDICATES      86 URIs      36 LITERALS      24 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/ijo.2013.153 schema:about N019224b1ca484fb58a408a1be53cc5f7
2 N2061877d85c04ea880702efa8817c48e
3 N32059b6f143b4e82a69de4d8ffa3e481
4 N47e34918aaf544c8baa9116547afea36
5 N48368c8072ac4a9bbede242670951584
6 N4b79a2be085b4c50aea00ef2eb9dacdb
7 N5f36a98901984f8e9ca57ff35feb8087
8 N64fc1223af4a4656858018b2abd3d64d
9 N7fbdb52baa924ebe90b4b25b51a32169
10 Na71eef0bd17343eb8cdcae3565cefa1f
11 Nd1a4b3c5b1384f489a4073973fa9e4f7
12 Ndfe1c968ed97440981a898703e94b0e4
13 Ne7671391c79a43bd8afeb61a146f1188
14 Nee490c1bba334fcc83b7200c05974c1e
15 Nf5a12ddda8344f879091c0eb16d3b017
16 anzsrc-for:11
17 anzsrc-for:1103
18 schema:author Na4c0db0875314885b99a0b7bd8b4ecb8
19 schema:citation sg:pub.10.1007/bf02668096
20 sg:pub.10.1007/s001250051123
21 sg:pub.10.1038/nature05488
22 sg:pub.10.1038/sj.ijo.0802853
23 sg:pub.10.1038/sj.ijo.0803653
24 https://app.dimensions.ai/details/publication/pub.1080257246
25 https://doi.org/10.1002/oby.20519
26 https://doi.org/10.1006/jmre.1997.1244
27 https://doi.org/10.1016/j.nut.2008.09.001
28 https://doi.org/10.1016/s0025-6196(12)60695-8
29 https://doi.org/10.1038/oby.2006.10
30 https://doi.org/10.1038/oby.2010.248
31 https://doi.org/10.1038/oby.2011.142
32 https://doi.org/10.1038/oby.2011.367
33 https://doi.org/10.1073/pnas.0904944106
34 https://doi.org/10.1093/ajcn/4.1.20
35 https://doi.org/10.1093/ajcn/52.5.946
36 https://doi.org/10.1093/ajcn/87.1.56
37 https://doi.org/10.1093/ajcn/87.2.295
38 https://doi.org/10.1111/j.1440-1746.2007.05212.x
39 https://doi.org/10.1111/j.1467-789x.2012.01033.x
40 https://doi.org/10.1136/gut.2003.036566
41 https://doi.org/10.1152/ajpendo.00377.2009
42 https://doi.org/10.1152/ajpendo.2002.282.1.e95
43 https://doi.org/10.1161/01.atv.10.4.493
44 https://doi.org/10.1172/jci118637
45 https://doi.org/10.1194/jlr.d300001-jlr200
46 https://doi.org/10.1210/jc.2003-031986
47 https://doi.org/10.1210/jc.2010-1722
48 https://doi.org/10.1210/jc.2011-1798
49 https://doi.org/10.1210/jcem-54-2-254
50 https://doi.org/10.1259/bjr.70.835.9245885
51 https://doi.org/10.1371/journal.pone.0007038
52 https://doi.org/10.2174/138161211798157720
53 https://doi.org/10.2337/db09-0942
54 https://doi.org/10.2337/dc05-2565
55 https://doi.org/10.2337/dc10-2019
56 https://doi.org/10.2337/diab.38.3.304
57 https://doi.org/10.2337/diabetes.38.3.304
58 https://doi.org/10.2337/diabetes.48.8.1600
59 https://doi.org/10.3109/07853899209166997
60 https://doi.org/10.3945/ajcn.112.047787
61 schema:datePublished 2014-05
62 schema:datePublishedReg 2014-05-01
63 schema:description BACKGROUND: Many adiposity traits have been related to health complications and premature death. These adiposity traits are intercorrelated but their underlying structure has not been extensively investigated. We report on the degree of commonality and specificity among multiple adiposity traits in normal-weight and moderately overweight adult males and females (mean body mass index (BMI)=22.9 kg m(-2), s.d.=2.4). METHODS: A total of 75 healthy participants were assessed for a panel of adiposity traits including leg, arm, trunk, total fat masses and visceral adipose tissue (VAT) derived from dual energy X-ray absorptiometry (DXA), hepatic and muscle lipids from proton magnetic resonance spectroscopy, fat cell volume from an abdominal subcutaneous adipose tissue biopsy (n=36) and conventional anthropometry (BMI and waist girth). Spearman's correlations were calculated and were subjected to factor analysis. RESULTS: Arm, leg, trunk and total fat masses correlated positively (r=0.78-0.95) with each other. VAT correlated weakly with fat mass indicators (r=0.24-0.31). Intrahepatic lipids (IHL) correlated weakly with all fat mass traits (r=0.09-0.34), whereas correlations between DXA depots and intramyocellular lipids (IMCL) were inconsequential. The four DXA fat mass measures, VAT, IHL and IMCL depots segregated as four independent factors that accounted for 96% of the overall adiposity variance. BMI and waist girth were moderately correlated with the arm, leg, trunk and total fat and weakly with VAT, IHL and IMCL. CONCLUSION: Adiposity traits share a substantial degree of commonality, but there is considerable specificity across the adiposity variance space. For instance, VAT, IHL and IMCL are typically poorly correlated with each other and are poorly to weakly associated with the other adiposity traits. The same is true for BMI and waist girth, commonly used anthropometric indicators of adiposity. These results do not support the view that it will be possible to identify adequate anthropometric indicators of visceral, hepatic and muscle lipid content in normal-weight and moderately overweight individuals.
64 schema:genre research_article
65 schema:inLanguage en
66 schema:isAccessibleForFree true
67 schema:isPartOf Nb38ec540ff0b4e2b9e9d107ba3fea90a
68 Nc9489e46f6ff4e029f373a4b60d89bd5
69 sg:journal.1035838
70 schema:name Commonality versus specificity among adiposity traits in normal-weight and moderately overweight adults
71 schema:pagination 719
72 schema:productId N3356d09b475f4ea7b2e281b5cbbe2c17
73 N7fe32219eef34574ac2e820b60871fcf
74 N9d241e8390c4497fa1acd3887ef1292e
75 Nb41dc13cb32148a9a7dab034fde8c145
76 Nbfa5359e6f8a4a23b024d15c08e51c77
77 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019474055
78 https://doi.org/10.1038/ijo.2013.153
79 schema:sdDatePublished 2019-04-11T14:18
80 schema:sdLicense https://scigraph.springernature.com/explorer/license/
81 schema:sdPublisher N2351d265731b4cff9ae5d70524760529
82 schema:url https://www.nature.com/articles/ijo2013153
83 sgo:license sg:explorer/license/
84 sgo:sdDataset articles
85 rdf:type schema:ScholarlyArticle
86 N019224b1ca484fb58a408a1be53cc5f7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
87 schema:name Subcutaneous Fat
88 rdf:type schema:DefinedTerm
89 N2061877d85c04ea880702efa8817c48e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
90 schema:name Female
91 rdf:type schema:DefinedTerm
92 N2351d265731b4cff9ae5d70524760529 schema:name Springer Nature - SN SciGraph project
93 rdf:type schema:Organization
94 N32059b6f143b4e82a69de4d8ffa3e481 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
95 schema:name Humans
96 rdf:type schema:DefinedTerm
97 N3356d09b475f4ea7b2e281b5cbbe2c17 schema:name nlm_unique_id
98 schema:value 101256108
99 rdf:type schema:PropertyValue
100 N47e34918aaf544c8baa9116547afea36 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
101 schema:name Predictive Value of Tests
102 rdf:type schema:DefinedTerm
103 N48368c8072ac4a9bbede242670951584 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
104 schema:name Lipids
105 rdf:type schema:DefinedTerm
106 N497d7586bb9a40b79671d8c833d4b493 rdf:first sg:person.01213722103.41
107 rdf:rest Ndc5bd814267e4b46b1a0689d18a1a8f5
108 N4b79a2be085b4c50aea00ef2eb9dacdb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
109 schema:name Overweight
110 rdf:type schema:DefinedTerm
111 N510cb34997344d99b16c13e207e84138 rdf:first sg:person.01246353000.05
112 rdf:rest rdf:nil
113 N5f36a98901984f8e9ca57ff35feb8087 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
114 schema:name Intra-Abdominal Fat
115 rdf:type schema:DefinedTerm
116 N64fc1223af4a4656858018b2abd3d64d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
117 schema:name Adult
118 rdf:type schema:DefinedTerm
119 N71b9588fbe4b4856a3ce1a9dd65b67d5 rdf:first sg:person.010702120177.88
120 rdf:rest N497d7586bb9a40b79671d8c833d4b493
121 N7fbdb52baa924ebe90b4b25b51a32169 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
122 schema:name Absorptiometry, Photon
123 rdf:type schema:DefinedTerm
124 N7fe32219eef34574ac2e820b60871fcf schema:name readcube_id
125 schema:value 5cc98a5b6c33ea71c76f147344ce5db7b15f62a226f2540d2858d2de77418b74
126 rdf:type schema:PropertyValue
127 N82051d298ad3427ca22ce48d16d48295 rdf:first sg:person.011256250534.28
128 rdf:rest N510cb34997344d99b16c13e207e84138
129 N9d241e8390c4497fa1acd3887ef1292e schema:name dimensions_id
130 schema:value pub.1019474055
131 rdf:type schema:PropertyValue
132 Na4c0db0875314885b99a0b7bd8b4ecb8 rdf:first sg:person.01302574775.26
133 rdf:rest Nd4f43b74091c4e43bd6b493787587477
134 Na71eef0bd17343eb8cdcae3565cefa1f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
135 schema:name Body Composition
136 rdf:type schema:DefinedTerm
137 Nb38ec540ff0b4e2b9e9d107ba3fea90a schema:issueNumber 5
138 rdf:type schema:PublicationIssue
139 Nb41dc13cb32148a9a7dab034fde8c145 schema:name pubmed_id
140 schema:value 23949614
141 rdf:type schema:PropertyValue
142 Nbfa5359e6f8a4a23b024d15c08e51c77 schema:name doi
143 schema:value 10.1038/ijo.2013.153
144 rdf:type schema:PropertyValue
145 Nc9489e46f6ff4e029f373a4b60d89bd5 schema:volumeNumber 38
146 rdf:type schema:PublicationVolume
147 Nd1a4b3c5b1384f489a4073973fa9e4f7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
148 schema:name Adiposity
149 rdf:type schema:DefinedTerm
150 Nd4f43b74091c4e43bd6b493787587477 rdf:first sg:person.01136002520.90
151 rdf:rest N71b9588fbe4b4856a3ce1a9dd65b67d5
152 Ndc5bd814267e4b46b1a0689d18a1a8f5 rdf:first sg:person.01347154460.00
153 rdf:rest N82051d298ad3427ca22ce48d16d48295
154 Ndfe1c968ed97440981a898703e94b0e4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
155 schema:name Body Mass Index
156 rdf:type schema:DefinedTerm
157 Ne7671391c79a43bd8afeb61a146f1188 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
158 schema:name Waist Circumference
159 rdf:type schema:DefinedTerm
160 Nee490c1bba334fcc83b7200c05974c1e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
161 schema:name Male
162 rdf:type schema:DefinedTerm
163 Nf5a12ddda8344f879091c0eb16d3b017 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
164 schema:name Adipocytes
165 rdf:type schema:DefinedTerm
166 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
167 schema:name Medical and Health Sciences
168 rdf:type schema:DefinedTerm
169 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
170 schema:name Clinical Sciences
171 rdf:type schema:DefinedTerm
172 sg:grant.2439036 http://pending.schema.org/fundedItem sg:pub.10.1038/ijo.2013.153
173 rdf:type schema:MonetaryGrant
174 sg:grant.2699248 http://pending.schema.org/fundedItem sg:pub.10.1038/ijo.2013.153
175 rdf:type schema:MonetaryGrant
176 sg:journal.1035838 schema:issn 0307-0565
177 1476-5497
178 schema:name International Journal of Obesity
179 rdf:type schema:Periodical
180 sg:person.010702120177.88 schema:affiliation https://www.grid.ac/institutes/grid.250514.7
181 schema:familyName Katzmarzyk
182 schema:givenName P T
183 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010702120177.88
184 rdf:type schema:Person
185 sg:person.011256250534.28 schema:affiliation https://www.grid.ac/institutes/grid.489332.7
186 schema:familyName Smith
187 schema:givenName S R
188 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011256250534.28
189 rdf:type schema:Person
190 sg:person.01136002520.90 schema:affiliation https://www.grid.ac/institutes/grid.250514.7
191 schema:familyName Sarzynski
192 schema:givenName M A
193 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136002520.90
194 rdf:type schema:Person
195 sg:person.01213722103.41 schema:affiliation https://www.grid.ac/institutes/grid.250514.7
196 schema:familyName Johnson
197 schema:givenName W D
198 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01213722103.41
199 rdf:type schema:Person
200 sg:person.01246353000.05 schema:affiliation https://www.grid.ac/institutes/grid.250514.7
201 schema:familyName Bouchard
202 schema:givenName C
203 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01246353000.05
204 rdf:type schema:Person
205 sg:person.01302574775.26 schema:affiliation https://www.grid.ac/institutes/grid.440552.2
206 schema:familyName Raja
207 schema:givenName G K
208 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01302574775.26
209 rdf:type schema:Person
210 sg:person.01347154460.00 schema:affiliation https://www.grid.ac/institutes/grid.250514.7
211 schema:familyName Tchoukalova
212 schema:givenName Y
213 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01347154460.00
214 rdf:type schema:Person
215 sg:pub.10.1007/bf02668096 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039838319
216 https://doi.org/10.1007/bf02668096
217 rdf:type schema:CreativeWork
218 sg:pub.10.1007/s001250051123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039281035
219 https://doi.org/10.1007/s001250051123
220 rdf:type schema:CreativeWork
221 sg:pub.10.1038/nature05488 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006317191
222 https://doi.org/10.1038/nature05488
223 rdf:type schema:CreativeWork
224 sg:pub.10.1038/sj.ijo.0802853 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045599109
225 https://doi.org/10.1038/sj.ijo.0802853
226 rdf:type schema:CreativeWork
227 sg:pub.10.1038/sj.ijo.0803653 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011276820
228 https://doi.org/10.1038/sj.ijo.0803653
229 rdf:type schema:CreativeWork
230 https://app.dimensions.ai/details/publication/pub.1080257246 schema:CreativeWork
231 https://doi.org/10.1002/oby.20519 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037728023
232 rdf:type schema:CreativeWork
233 https://doi.org/10.1006/jmre.1997.1244 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031415033
234 rdf:type schema:CreativeWork
235 https://doi.org/10.1016/j.nut.2008.09.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000509783
236 rdf:type schema:CreativeWork
237 https://doi.org/10.1016/s0025-6196(12)60695-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021749862
238 rdf:type schema:CreativeWork
239 https://doi.org/10.1038/oby.2006.10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025928011
240 rdf:type schema:CreativeWork
241 https://doi.org/10.1038/oby.2010.248 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035508632
242 rdf:type schema:CreativeWork
243 https://doi.org/10.1038/oby.2011.142 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043609467
244 rdf:type schema:CreativeWork
245 https://doi.org/10.1038/oby.2011.367 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016355724
246 rdf:type schema:CreativeWork
247 https://doi.org/10.1073/pnas.0904944106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021934954
248 rdf:type schema:CreativeWork
249 https://doi.org/10.1093/ajcn/4.1.20 schema:sameAs https://app.dimensions.ai/details/publication/pub.1075606772
250 rdf:type schema:CreativeWork
251 https://doi.org/10.1093/ajcn/52.5.946 schema:sameAs https://app.dimensions.ai/details/publication/pub.1078530260
252 rdf:type schema:CreativeWork
253 https://doi.org/10.1093/ajcn/87.1.56 schema:sameAs https://app.dimensions.ai/details/publication/pub.1077586326
254 rdf:type schema:CreativeWork
255 https://doi.org/10.1093/ajcn/87.2.295 schema:sameAs https://app.dimensions.ai/details/publication/pub.1077604777
256 rdf:type schema:CreativeWork
257 https://doi.org/10.1111/j.1440-1746.2007.05212.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1014952268
258 rdf:type schema:CreativeWork
259 https://doi.org/10.1111/j.1467-789x.2012.01033.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1023924765
260 rdf:type schema:CreativeWork
261 https://doi.org/10.1136/gut.2003.036566 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015807246
262 rdf:type schema:CreativeWork
263 https://doi.org/10.1152/ajpendo.00377.2009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040312034
264 rdf:type schema:CreativeWork
265 https://doi.org/10.1152/ajpendo.2002.282.1.e95 schema:sameAs https://app.dimensions.ai/details/publication/pub.1074962388
266 rdf:type schema:CreativeWork
267 https://doi.org/10.1161/01.atv.10.4.493 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009147056
268 rdf:type schema:CreativeWork
269 https://doi.org/10.1172/jci118637 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017012169
270 rdf:type schema:CreativeWork
271 https://doi.org/10.1194/jlr.d300001-jlr200 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049333630
272 rdf:type schema:CreativeWork
273 https://doi.org/10.1210/jc.2003-031986 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064287490
274 rdf:type schema:CreativeWork
275 https://doi.org/10.1210/jc.2010-1722 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064292373
276 rdf:type schema:CreativeWork
277 https://doi.org/10.1210/jc.2011-1798 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064292992
278 rdf:type schema:CreativeWork
279 https://doi.org/10.1210/jcem-54-2-254 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064312100
280 rdf:type schema:CreativeWork
281 https://doi.org/10.1259/bjr.70.835.9245885 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064566365
282 rdf:type schema:CreativeWork
283 https://doi.org/10.1371/journal.pone.0007038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053655167
284 rdf:type schema:CreativeWork
285 https://doi.org/10.2174/138161211798157720 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069167876
286 rdf:type schema:CreativeWork
287 https://doi.org/10.2337/db09-0942 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013643901
288 rdf:type schema:CreativeWork
289 https://doi.org/10.2337/dc05-2565 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044042242
290 rdf:type schema:CreativeWork
291 https://doi.org/10.2337/dc10-2019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024553986
292 rdf:type schema:CreativeWork
293 https://doi.org/10.2337/diab.38.3.304 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070735819
294 rdf:type schema:CreativeWork
295 https://doi.org/10.2337/diabetes.38.3.304 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070741546
296 rdf:type schema:CreativeWork
297 https://doi.org/10.2337/diabetes.48.8.1600 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070744252
298 rdf:type schema:CreativeWork
299 https://doi.org/10.3109/07853899209166997 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020046805
300 rdf:type schema:CreativeWork
301 https://doi.org/10.3945/ajcn.112.047787 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030433508
302 rdf:type schema:CreativeWork
303 https://www.grid.ac/institutes/grid.250514.7 schema:alternateName Pennington Biomedical Research Center
304 schema:name Biology of Adipose Tissue Depots Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
305 Biostatistics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
306 Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
307 Preventive Medicine and Healthy Aging, Pennington Biomedical Research Center, Baton Rouge, LA, USA
308 rdf:type schema:Organization
309 https://www.grid.ac/institutes/grid.440552.2 schema:alternateName Pir Mehr Ali Shah Arid Agriculture University
310 schema:name Department of Biochemistry, Pir Mehr Ali Shah Arid Agriculture University Rawalpindi, Punjab, Pakistan
311 Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
312 rdf:type schema:Organization
313 https://www.grid.ac/institutes/grid.489332.7 schema:alternateName Translational Research Institute for Metabolism and Diabetes
314 schema:name Translational Research Institute for Metabolism and Diabetes, Florida Hospital, Orlando, FL, USA
315 rdf:type schema:Organization
 




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


...