Metabolic signatures and risk of type 2 diabetes in a Chinese population: an untargeted metabolomics study using both LC-MS and ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-11

AUTHORS

Yonghai Lu, Yeli Wang, Choon-Nam Ong, Tavintharan Subramaniam, Hyung Won Choi, Jian-Min Yuan, Woon-Puay Koh, An Pan

ABSTRACT

AIMS/HYPOTHESIS: Metabolomics has provided new insight into diabetes risk assessment. In this study we characterised the human serum metabolic profiles of participants in the Singapore Chinese Health Study cohort to identify metabolic signatures associated with an increased risk of type 2 diabetes. METHODS: In this nested case-control study, baseline serum metabolite profiles were measured using LC-MS and GC-MS during a 6-year follow-up of 197 individuals with type 2 diabetes but without a history of cardiovascular disease or cancer before diabetes diagnosis, and 197 healthy controls matched by age, sex and date of blood collection. RESULTS: A total of 51 differential metabolites were identified between cases and controls. Of these, 35 were significantly associated with diabetes risk in the multivariate analysis after false discovery rate adjustment, such as increased branched-chain amino acids (leucine, isoleucine and valine), non-esterified fatty acids (palmitic acid, stearic acid, oleic acid and linoleic acid) and lysophosphatidylinositol (LPI) species (16:1, 18:1, 18:2, 20:3, 20:4 and 22:6). A combination of six metabolites including proline, glycerol, aminomalonic acid, LPI (16:1), 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid and urea showed the potential to predict type 2 diabetes in at-risk individuals with high baseline HbA1c levels (≥6.5% [47.5 mmol/mol]) with an AUC of 0.935. Combined lysophosphatidylglycerol (LPG) (12:0) and LPI (16:1) also showed the potential to predict type 2 diabetes in individuals with normal baseline HbA1c levels (<6.5% [47.5 mmol/mol]; AUC = 0.781). CONCLUSIONS/INTERPRETATION: Our findings show that branched-chain amino acids and NEFA are potent predictors of diabetes development in Chinese adults. Our results also indicate the potential of lysophospholipids for predicting diabetes. More... »

PAGES

2349-2359

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00125-016-4069-2

DOI

http://dx.doi.org/10.1007/s00125-016-4069-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1010774852

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27514531


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": "Amino Acids, Branched-Chain", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Asian Continental Ancestry Group", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Blood Glucose", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Case-Control Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Chromatography, Liquid", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Diabetes Mellitus, Type 2", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Fatty Acids, Nonesterified", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Furans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gas Chromatography-Mass Spectrometry", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Glycated Hemoglobin A", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Glycerol", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Linoleic Acid", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Lysophospholipids", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Metabolomics", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Oleic Acid", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Proline", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Propionates", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Urea", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "National University of Singapore", 
          "id": "https://www.grid.ac/institutes/grid.4280.e", 
          "name": [
            "Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Republic of Singapore"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lu", 
        "givenName": "Yonghai", 
        "id": "sg:person.0720131017.54", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0720131017.54"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National University of Singapore", 
          "id": "https://www.grid.ac/institutes/grid.4280.e", 
          "name": [
            "Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Republic of Singapore"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Yeli", 
        "id": "sg:person.01001002717.01", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01001002717.01"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National University of Singapore", 
          "id": "https://www.grid.ac/institutes/grid.4280.e", 
          "name": [
            "Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Republic of Singapore", 
            "NUS Environmental Research Institute, National University of Singapore, Singapore, Republic of Singapore"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ong", 
        "givenName": "Choon-Nam", 
        "id": "sg:person.01113207236.65", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01113207236.65"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Khoo Teck Puat Hospital", 
          "id": "https://www.grid.ac/institutes/grid.415203.1", 
          "name": [
            "Department of General Medicine, Diabetes Centre, Khoo Teck Puat Hospital, Singapore, Republic of Singapore"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Subramaniam", 
        "givenName": "Tavintharan", 
        "id": "sg:person.01062536554.54", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01062536554.54"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National University of Singapore", 
          "id": "https://www.grid.ac/institutes/grid.4280.e", 
          "name": [
            "Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Republic of Singapore"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Choi", 
        "givenName": "Hyung Won", 
        "id": "sg:person.01164136471.32", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01164136471.32"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Pittsburgh", 
          "id": "https://www.grid.ac/institutes/grid.21925.3d", 
          "name": [
            "Division of Cancer Control and Population Sciences, University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania, USA", 
            "Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yuan", 
        "givenName": "Jian-Min", 
        "id": "sg:person.013027434542.94", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013027434542.94"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National University of Singapore", 
          "id": "https://www.grid.ac/institutes/grid.4280.e", 
          "name": [
            "Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Republic of Singapore", 
            "Office of Clinical Sciences, Duke-NUS Medical School, 8 College Road Level 4, 169857, Singapore, Republic of Singapore"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Koh", 
        "givenName": "Woon-Puay", 
        "id": "sg:person.01046476114.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01046476114.00"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Huazhong University of Science and Technology", 
          "id": "https://www.grid.ac/institutes/grid.33199.31", 
          "name": [
            "Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, 430030, Wuhan, Hubei, People\u2019s Republic of China", 
            "Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Pan", 
        "givenName": "An", 
        "id": "sg:person.01226711612.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01226711612.33"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/nm.2307", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002009671", 
          "https://doi.org/10.1038/nm.2307"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pgen.1002215", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002943793"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11306-015-0804-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003245453", 
          "https://doi.org/10.1007/s11306-015-0804-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11306-015-0804-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003245453", 
          "https://doi.org/10.1007/s11306-015-0804-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/ajpendo.00134.2007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005410517"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.bbalip.2008.03.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005455659"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/db11-1378", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008589452"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/db12-0495", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010275685"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1042/bj2530027", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013711282"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1042/bj2530027", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013711282"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/db14-0509", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015660265"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/db13-0570", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016050719"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0140-6736(09)60619-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018122882"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00726-013-1493-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018446828", 
          "https://doi.org/10.1007/s00726-013-1493-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00726-013-1493-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018446828", 
          "https://doi.org/10.1007/s00726-013-1493-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s001250050793", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018566541", 
          "https://doi.org/10.1007/s001250050793"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s001250050793", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018566541", 
          "https://doi.org/10.1007/s001250050793"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1373/clinchem.2014.235986", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019092868"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/oby.2009.510", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023024736"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/db11-0649", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023840974"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/physiolgenomics.00194.2006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026562336"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1042/bj1340349", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027592031"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1042/bj1340349", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027592031"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/diabetes.51.3.599", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028211724"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/db11-1133", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029175033"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11306-015-0887-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032304404", 
          "https://doi.org/10.1007/s11306-015-0887-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3945/jn.108.103754", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033438981"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1474-9726.2012.00865.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034235233"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1373/clinchem.2014.228965", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035164807"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/db12-0466", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036208550"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/aos/1013699998", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036427477"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1074/jbc.r300025200", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036430371"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1373/clinchem.2012.200527", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042340809"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/dc10-s062", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042714871"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/aje/kwt004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045485187"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11306-015-0890-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046764408", 
          "https://doi.org/10.1007/s11306-015-0890-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0015234", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047725575"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00125-015-3517-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050422611", 
          "https://doi.org/10.1007/s00125-015-3517-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00125-015-3517-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050422611", 
          "https://doi.org/10.1007/s00125-015-3517-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.bbrc.2007.08.078", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053291659"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0013953", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053385042"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac051080y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054997265"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac051080y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054997265"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac051495j", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054997456"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac051495j", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054997456"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/pr070183p", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056291599"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/pr900524z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056295021"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1210/jc.2012-4132", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064294074"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/dc15-2251", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070730144"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/dc15-2251", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070730144"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/diacare.20.7.1183", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070747779"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jn/133.1.261s", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1075213180"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1075230259", 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2016-11", 
    "datePublishedReg": "2016-11-01", 
    "description": "AIMS/HYPOTHESIS: Metabolomics has provided new insight into diabetes risk assessment. In this study we characterised the human serum metabolic profiles of participants in the Singapore Chinese Health Study cohort to identify metabolic signatures associated with an increased risk of type 2 diabetes.\nMETHODS: In this nested case-control study, baseline serum metabolite profiles were measured using LC-MS and GC-MS during a 6-year follow-up of 197 individuals with type 2 diabetes but without a history of cardiovascular disease or cancer before diabetes diagnosis, and 197 healthy controls matched by age, sex and date of blood collection.\nRESULTS: A total of 51 differential metabolites were identified between cases and controls. Of these, 35 were significantly associated with diabetes risk in the multivariate analysis after false discovery rate adjustment, such as increased branched-chain amino acids (leucine, isoleucine and valine), non-esterified fatty acids (palmitic acid, stearic acid, oleic acid and linoleic acid) and lysophosphatidylinositol (LPI) species (16:1, 18:1, 18:2, 20:3, 20:4 and 22:6). A combination of six metabolites including proline, glycerol, aminomalonic acid, LPI (16:1), 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid and urea showed the potential to predict type 2 diabetes in at-risk individuals with high baseline HbA1c levels (\u22656.5% [47.5\u00a0mmol/mol]) with an AUC of 0.935. Combined lysophosphatidylglycerol (LPG) (12:0) and LPI (16:1) also showed the potential to predict type 2 diabetes in individuals with normal baseline HbA1c levels (<6.5% [47.5\u00a0mmol/mol]; AUC\u2009=\u20090.781).\nCONCLUSIONS/INTERPRETATION: Our findings show that branched-chain amino acids and NEFA are potent predictors of diabetes development in Chinese adults. Our results also indicate the potential of lysophospholipids for predicting diabetes.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00125-016-4069-2", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.3804052", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2480768", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1001482", 
        "issn": [
          "0012-186X", 
          "1432-0428"
        ], 
        "name": "Diabetologia", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "11", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "59"
      }
    ], 
    "name": "Metabolic signatures and risk of type 2 diabetes in a Chinese population: an untargeted metabolomics study using both LC-MS and GC-MS", 
    "pagination": "2349-2359", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "3354f0d38a4dca6f16b98ad69fb7210b966323cbe260ea175da0be281aa8d200"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "27514531"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "0006777"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00125-016-4069-2"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1010774852"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00125-016-4069-2", 
      "https://app.dimensions.ai/details/publication/pub.1010774852"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:21", 
    "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/v90/0000000362_0000000362/records_87079_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs00125-016-4069-2"
  }
]
 

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.10.1007/s00125-016-4069-2'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00125-016-4069-2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00125-016-4069-2'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00125-016-4069-2'


 

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

349 TRIPLES      21 PREDICATES      92 URIs      40 LITERALS      28 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00125-016-4069-2 schema:about N0361fe4987aa452380970dacfc7f9a76
2 N0810c18a1b0e405e9eab9f7980eed32a
3 N0a2b4ff45c494ade8172d76de4511e98
4 N1a1062b6ad294f6b839a8c52f8a5f3b0
5 N299ef74b09b74ebfb52cf06c9fc68a86
6 N2ede11d5874c43e7ba90c784c849114b
7 N339f13f2238a4788b78d1b490b44c456
8 N57e4ba259c5c4b1a9ab08f79cfff5eee
9 N82c3528246c74f168b1bdb6b6b59a784
10 N85a1f8e7c8bd490185831b32e4cbddfe
11 N91392c96797646d995662e25d9191cb5
12 N9c160be56dec4c6298ca43726ea81fb4
13 Nb4e5228e714946c7a840224f2fdce571
14 Nbaf84a3d324b43b48bc6bc864b3838f1
15 Nd8df0017f53a45d3923e94cd464c7470
16 Ne42fee1e801f494996e5159815d35e44
17 Ne4ca8f9e72e64957bcc17ab92006b4bf
18 Ned1475ec3f42424997ee0ebc4dab584e
19 Nfb84322855b64dd789f3624ec35f7fbf
20 anzsrc-for:11
21 anzsrc-for:1103
22 schema:author N233b985cb53e41a8be9f5c07feef1c50
23 schema:citation sg:pub.10.1007/s00125-015-3517-8
24 sg:pub.10.1007/s001250050793
25 sg:pub.10.1007/s00726-013-1493-1
26 sg:pub.10.1007/s11306-015-0804-9
27 sg:pub.10.1007/s11306-015-0887-3
28 sg:pub.10.1007/s11306-015-0890-8
29 sg:pub.10.1038/nm.2307
30 https://app.dimensions.ai/details/publication/pub.1075230259
31 https://doi.org/10.1016/j.bbalip.2008.03.004
32 https://doi.org/10.1016/j.bbrc.2007.08.078
33 https://doi.org/10.1016/s0140-6736(09)60619-x
34 https://doi.org/10.1021/ac051080y
35 https://doi.org/10.1021/ac051495j
36 https://doi.org/10.1021/pr070183p
37 https://doi.org/10.1021/pr900524z
38 https://doi.org/10.1038/oby.2009.510
39 https://doi.org/10.1042/bj1340349
40 https://doi.org/10.1042/bj2530027
41 https://doi.org/10.1074/jbc.r300025200
42 https://doi.org/10.1093/aje/kwt004
43 https://doi.org/10.1093/jn/133.1.261s
44 https://doi.org/10.1111/j.1474-9726.2012.00865.x
45 https://doi.org/10.1152/ajpendo.00134.2007
46 https://doi.org/10.1152/physiolgenomics.00194.2006
47 https://doi.org/10.1210/jc.2012-4132
48 https://doi.org/10.1214/aos/1013699998
49 https://doi.org/10.1371/journal.pgen.1002215
50 https://doi.org/10.1371/journal.pone.0013953
51 https://doi.org/10.1371/journal.pone.0015234
52 https://doi.org/10.1373/clinchem.2012.200527
53 https://doi.org/10.1373/clinchem.2014.228965
54 https://doi.org/10.1373/clinchem.2014.235986
55 https://doi.org/10.2337/db11-0649
56 https://doi.org/10.2337/db11-1133
57 https://doi.org/10.2337/db11-1378
58 https://doi.org/10.2337/db12-0466
59 https://doi.org/10.2337/db12-0495
60 https://doi.org/10.2337/db13-0570
61 https://doi.org/10.2337/db14-0509
62 https://doi.org/10.2337/dc10-s062
63 https://doi.org/10.2337/dc15-2251
64 https://doi.org/10.2337/diabetes.51.3.599
65 https://doi.org/10.2337/diacare.20.7.1183
66 https://doi.org/10.3945/jn.108.103754
67 schema:datePublished 2016-11
68 schema:datePublishedReg 2016-11-01
69 schema:description AIMS/HYPOTHESIS: Metabolomics has provided new insight into diabetes risk assessment. In this study we characterised the human serum metabolic profiles of participants in the Singapore Chinese Health Study cohort to identify metabolic signatures associated with an increased risk of type 2 diabetes. METHODS: In this nested case-control study, baseline serum metabolite profiles were measured using LC-MS and GC-MS during a 6-year follow-up of 197 individuals with type 2 diabetes but without a history of cardiovascular disease or cancer before diabetes diagnosis, and 197 healthy controls matched by age, sex and date of blood collection. RESULTS: A total of 51 differential metabolites were identified between cases and controls. Of these, 35 were significantly associated with diabetes risk in the multivariate analysis after false discovery rate adjustment, such as increased branched-chain amino acids (leucine, isoleucine and valine), non-esterified fatty acids (palmitic acid, stearic acid, oleic acid and linoleic acid) and lysophosphatidylinositol (LPI) species (16:1, 18:1, 18:2, 20:3, 20:4 and 22:6). A combination of six metabolites including proline, glycerol, aminomalonic acid, LPI (16:1), 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid and urea showed the potential to predict type 2 diabetes in at-risk individuals with high baseline HbA1c levels (≥6.5% [47.5 mmol/mol]) with an AUC of 0.935. Combined lysophosphatidylglycerol (LPG) (12:0) and LPI (16:1) also showed the potential to predict type 2 diabetes in individuals with normal baseline HbA1c levels (<6.5% [47.5 mmol/mol]; AUC = 0.781). CONCLUSIONS/INTERPRETATION: Our findings show that branched-chain amino acids and NEFA are potent predictors of diabetes development in Chinese adults. Our results also indicate the potential of lysophospholipids for predicting diabetes.
70 schema:genre research_article
71 schema:inLanguage en
72 schema:isAccessibleForFree true
73 schema:isPartOf N1ea9d4ffaa3b4908bb942074b90f63a2
74 N764c84bb34bb46638ee0fb84c10f8415
75 sg:journal.1001482
76 schema:name Metabolic signatures and risk of type 2 diabetes in a Chinese population: an untargeted metabolomics study using both LC-MS and GC-MS
77 schema:pagination 2349-2359
78 schema:productId N42a5eb8a19b245b5887bd1f0e95ec3b3
79 N720bbae4a96a419286795cc022e151b6
80 N7823d25703834574ae9bc79446f90447
81 N8f285078edb04ce6baa76c0e2c0eac39
82 Ne579c0a55f2f4d8c81991d07c8aabae9
83 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010774852
84 https://doi.org/10.1007/s00125-016-4069-2
85 schema:sdDatePublished 2019-04-11T12:21
86 schema:sdLicense https://scigraph.springernature.com/explorer/license/
87 schema:sdPublisher Nb6593f1fe19b46f7ad2757be0b9235a5
88 schema:url https://link.springer.com/10.1007%2Fs00125-016-4069-2
89 sgo:license sg:explorer/license/
90 sgo:sdDataset articles
91 rdf:type schema:ScholarlyArticle
92 N0361fe4987aa452380970dacfc7f9a76 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
93 schema:name Oleic Acid
94 rdf:type schema:DefinedTerm
95 N0810c18a1b0e405e9eab9f7980eed32a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
96 schema:name Chromatography, Liquid
97 rdf:type schema:DefinedTerm
98 N0a2b4ff45c494ade8172d76de4511e98 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
99 schema:name Amino Acids, Branched-Chain
100 rdf:type schema:DefinedTerm
101 N0dd8a1cec14b43bc8743af4dbaebfb81 rdf:first sg:person.013027434542.94
102 rdf:rest N9d2420635a8e4a12b1ef2e39d13f19f0
103 N0e739d863a9344ce8b3717ed6df86814 rdf:first sg:person.01226711612.33
104 rdf:rest rdf:nil
105 N14cdd25171f54ff0865a130a7164b07f rdf:first sg:person.01062536554.54
106 rdf:rest N6d970b159f794e5eb7ab7145085862e7
107 N1a1062b6ad294f6b839a8c52f8a5f3b0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
108 schema:name Metabolomics
109 rdf:type schema:DefinedTerm
110 N1ea9d4ffaa3b4908bb942074b90f63a2 schema:volumeNumber 59
111 rdf:type schema:PublicationVolume
112 N233b985cb53e41a8be9f5c07feef1c50 rdf:first sg:person.0720131017.54
113 rdf:rest Nbc5fe322b3064f7899ec74319caf3dc3
114 N299ef74b09b74ebfb52cf06c9fc68a86 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
115 schema:name Urea
116 rdf:type schema:DefinedTerm
117 N2ede11d5874c43e7ba90c784c849114b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
118 schema:name Proline
119 rdf:type schema:DefinedTerm
120 N339f13f2238a4788b78d1b490b44c456 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
121 schema:name Lysophospholipids
122 rdf:type schema:DefinedTerm
123 N42a5eb8a19b245b5887bd1f0e95ec3b3 schema:name dimensions_id
124 schema:value pub.1010774852
125 rdf:type schema:PropertyValue
126 N57e4ba259c5c4b1a9ab08f79cfff5eee schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
127 schema:name Case-Control Studies
128 rdf:type schema:DefinedTerm
129 N6d970b159f794e5eb7ab7145085862e7 rdf:first sg:person.01164136471.32
130 rdf:rest N0dd8a1cec14b43bc8743af4dbaebfb81
131 N720bbae4a96a419286795cc022e151b6 schema:name nlm_unique_id
132 schema:value 0006777
133 rdf:type schema:PropertyValue
134 N764c84bb34bb46638ee0fb84c10f8415 schema:issueNumber 11
135 rdf:type schema:PublicationIssue
136 N7823d25703834574ae9bc79446f90447 schema:name pubmed_id
137 schema:value 27514531
138 rdf:type schema:PropertyValue
139 N82c3528246c74f168b1bdb6b6b59a784 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
140 schema:name Furans
141 rdf:type schema:DefinedTerm
142 N85a1f8e7c8bd490185831b32e4cbddfe schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
143 schema:name Diabetes Mellitus, Type 2
144 rdf:type schema:DefinedTerm
145 N8f285078edb04ce6baa76c0e2c0eac39 schema:name readcube_id
146 schema:value 3354f0d38a4dca6f16b98ad69fb7210b966323cbe260ea175da0be281aa8d200
147 rdf:type schema:PropertyValue
148 N91392c96797646d995662e25d9191cb5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
149 schema:name Gas Chromatography-Mass Spectrometry
150 rdf:type schema:DefinedTerm
151 N9c160be56dec4c6298ca43726ea81fb4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
152 schema:name Glycerol
153 rdf:type schema:DefinedTerm
154 N9d2420635a8e4a12b1ef2e39d13f19f0 rdf:first sg:person.01046476114.00
155 rdf:rest N0e739d863a9344ce8b3717ed6df86814
156 Nb4e5228e714946c7a840224f2fdce571 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
157 schema:name Asian Continental Ancestry Group
158 rdf:type schema:DefinedTerm
159 Nb6593f1fe19b46f7ad2757be0b9235a5 schema:name Springer Nature - SN SciGraph project
160 rdf:type schema:Organization
161 Nbaf84a3d324b43b48bc6bc864b3838f1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
162 schema:name Linoleic Acid
163 rdf:type schema:DefinedTerm
164 Nbc5fe322b3064f7899ec74319caf3dc3 rdf:first sg:person.01001002717.01
165 rdf:rest Nca3b045533d745f180e0c20cd689428d
166 Nca3b045533d745f180e0c20cd689428d rdf:first sg:person.01113207236.65
167 rdf:rest N14cdd25171f54ff0865a130a7164b07f
168 Nd8df0017f53a45d3923e94cd464c7470 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
169 schema:name Blood Glucose
170 rdf:type schema:DefinedTerm
171 Ne42fee1e801f494996e5159815d35e44 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
172 schema:name Glycated Hemoglobin A
173 rdf:type schema:DefinedTerm
174 Ne4ca8f9e72e64957bcc17ab92006b4bf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
175 schema:name Fatty Acids, Nonesterified
176 rdf:type schema:DefinedTerm
177 Ne579c0a55f2f4d8c81991d07c8aabae9 schema:name doi
178 schema:value 10.1007/s00125-016-4069-2
179 rdf:type schema:PropertyValue
180 Ned1475ec3f42424997ee0ebc4dab584e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
181 schema:name Humans
182 rdf:type schema:DefinedTerm
183 Nfb84322855b64dd789f3624ec35f7fbf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
184 schema:name Propionates
185 rdf:type schema:DefinedTerm
186 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
187 schema:name Medical and Health Sciences
188 rdf:type schema:DefinedTerm
189 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
190 schema:name Clinical Sciences
191 rdf:type schema:DefinedTerm
192 sg:grant.2480768 http://pending.schema.org/fundedItem sg:pub.10.1007/s00125-016-4069-2
193 rdf:type schema:MonetaryGrant
194 sg:grant.3804052 http://pending.schema.org/fundedItem sg:pub.10.1007/s00125-016-4069-2
195 rdf:type schema:MonetaryGrant
196 sg:journal.1001482 schema:issn 0012-186X
197 1432-0428
198 schema:name Diabetologia
199 rdf:type schema:Periodical
200 sg:person.01001002717.01 schema:affiliation https://www.grid.ac/institutes/grid.4280.e
201 schema:familyName Wang
202 schema:givenName Yeli
203 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01001002717.01
204 rdf:type schema:Person
205 sg:person.01046476114.00 schema:affiliation https://www.grid.ac/institutes/grid.4280.e
206 schema:familyName Koh
207 schema:givenName Woon-Puay
208 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01046476114.00
209 rdf:type schema:Person
210 sg:person.01062536554.54 schema:affiliation https://www.grid.ac/institutes/grid.415203.1
211 schema:familyName Subramaniam
212 schema:givenName Tavintharan
213 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01062536554.54
214 rdf:type schema:Person
215 sg:person.01113207236.65 schema:affiliation https://www.grid.ac/institutes/grid.4280.e
216 schema:familyName Ong
217 schema:givenName Choon-Nam
218 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01113207236.65
219 rdf:type schema:Person
220 sg:person.01164136471.32 schema:affiliation https://www.grid.ac/institutes/grid.4280.e
221 schema:familyName Choi
222 schema:givenName Hyung Won
223 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01164136471.32
224 rdf:type schema:Person
225 sg:person.01226711612.33 schema:affiliation https://www.grid.ac/institutes/grid.33199.31
226 schema:familyName Pan
227 schema:givenName An
228 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01226711612.33
229 rdf:type schema:Person
230 sg:person.013027434542.94 schema:affiliation https://www.grid.ac/institutes/grid.21925.3d
231 schema:familyName Yuan
232 schema:givenName Jian-Min
233 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013027434542.94
234 rdf:type schema:Person
235 sg:person.0720131017.54 schema:affiliation https://www.grid.ac/institutes/grid.4280.e
236 schema:familyName Lu
237 schema:givenName Yonghai
238 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0720131017.54
239 rdf:type schema:Person
240 sg:pub.10.1007/s00125-015-3517-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050422611
241 https://doi.org/10.1007/s00125-015-3517-8
242 rdf:type schema:CreativeWork
243 sg:pub.10.1007/s001250050793 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018566541
244 https://doi.org/10.1007/s001250050793
245 rdf:type schema:CreativeWork
246 sg:pub.10.1007/s00726-013-1493-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018446828
247 https://doi.org/10.1007/s00726-013-1493-1
248 rdf:type schema:CreativeWork
249 sg:pub.10.1007/s11306-015-0804-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003245453
250 https://doi.org/10.1007/s11306-015-0804-9
251 rdf:type schema:CreativeWork
252 sg:pub.10.1007/s11306-015-0887-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032304404
253 https://doi.org/10.1007/s11306-015-0887-3
254 rdf:type schema:CreativeWork
255 sg:pub.10.1007/s11306-015-0890-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046764408
256 https://doi.org/10.1007/s11306-015-0890-8
257 rdf:type schema:CreativeWork
258 sg:pub.10.1038/nm.2307 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002009671
259 https://doi.org/10.1038/nm.2307
260 rdf:type schema:CreativeWork
261 https://app.dimensions.ai/details/publication/pub.1075230259 schema:CreativeWork
262 https://doi.org/10.1016/j.bbalip.2008.03.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005455659
263 rdf:type schema:CreativeWork
264 https://doi.org/10.1016/j.bbrc.2007.08.078 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053291659
265 rdf:type schema:CreativeWork
266 https://doi.org/10.1016/s0140-6736(09)60619-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1018122882
267 rdf:type schema:CreativeWork
268 https://doi.org/10.1021/ac051080y schema:sameAs https://app.dimensions.ai/details/publication/pub.1054997265
269 rdf:type schema:CreativeWork
270 https://doi.org/10.1021/ac051495j schema:sameAs https://app.dimensions.ai/details/publication/pub.1054997456
271 rdf:type schema:CreativeWork
272 https://doi.org/10.1021/pr070183p schema:sameAs https://app.dimensions.ai/details/publication/pub.1056291599
273 rdf:type schema:CreativeWork
274 https://doi.org/10.1021/pr900524z schema:sameAs https://app.dimensions.ai/details/publication/pub.1056295021
275 rdf:type schema:CreativeWork
276 https://doi.org/10.1038/oby.2009.510 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023024736
277 rdf:type schema:CreativeWork
278 https://doi.org/10.1042/bj1340349 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027592031
279 rdf:type schema:CreativeWork
280 https://doi.org/10.1042/bj2530027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013711282
281 rdf:type schema:CreativeWork
282 https://doi.org/10.1074/jbc.r300025200 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036430371
283 rdf:type schema:CreativeWork
284 https://doi.org/10.1093/aje/kwt004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045485187
285 rdf:type schema:CreativeWork
286 https://doi.org/10.1093/jn/133.1.261s schema:sameAs https://app.dimensions.ai/details/publication/pub.1075213180
287 rdf:type schema:CreativeWork
288 https://doi.org/10.1111/j.1474-9726.2012.00865.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1034235233
289 rdf:type schema:CreativeWork
290 https://doi.org/10.1152/ajpendo.00134.2007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005410517
291 rdf:type schema:CreativeWork
292 https://doi.org/10.1152/physiolgenomics.00194.2006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026562336
293 rdf:type schema:CreativeWork
294 https://doi.org/10.1210/jc.2012-4132 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064294074
295 rdf:type schema:CreativeWork
296 https://doi.org/10.1214/aos/1013699998 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036427477
297 rdf:type schema:CreativeWork
298 https://doi.org/10.1371/journal.pgen.1002215 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002943793
299 rdf:type schema:CreativeWork
300 https://doi.org/10.1371/journal.pone.0013953 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053385042
301 rdf:type schema:CreativeWork
302 https://doi.org/10.1371/journal.pone.0015234 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047725575
303 rdf:type schema:CreativeWork
304 https://doi.org/10.1373/clinchem.2012.200527 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042340809
305 rdf:type schema:CreativeWork
306 https://doi.org/10.1373/clinchem.2014.228965 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035164807
307 rdf:type schema:CreativeWork
308 https://doi.org/10.1373/clinchem.2014.235986 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019092868
309 rdf:type schema:CreativeWork
310 https://doi.org/10.2337/db11-0649 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023840974
311 rdf:type schema:CreativeWork
312 https://doi.org/10.2337/db11-1133 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029175033
313 rdf:type schema:CreativeWork
314 https://doi.org/10.2337/db11-1378 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008589452
315 rdf:type schema:CreativeWork
316 https://doi.org/10.2337/db12-0466 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036208550
317 rdf:type schema:CreativeWork
318 https://doi.org/10.2337/db12-0495 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010275685
319 rdf:type schema:CreativeWork
320 https://doi.org/10.2337/db13-0570 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016050719
321 rdf:type schema:CreativeWork
322 https://doi.org/10.2337/db14-0509 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015660265
323 rdf:type schema:CreativeWork
324 https://doi.org/10.2337/dc10-s062 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042714871
325 rdf:type schema:CreativeWork
326 https://doi.org/10.2337/dc15-2251 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070730144
327 rdf:type schema:CreativeWork
328 https://doi.org/10.2337/diabetes.51.3.599 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028211724
329 rdf:type schema:CreativeWork
330 https://doi.org/10.2337/diacare.20.7.1183 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070747779
331 rdf:type schema:CreativeWork
332 https://doi.org/10.3945/jn.108.103754 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033438981
333 rdf:type schema:CreativeWork
334 https://www.grid.ac/institutes/grid.21925.3d schema:alternateName University of Pittsburgh
335 schema:name Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
336 Division of Cancer Control and Population Sciences, University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania, USA
337 rdf:type schema:Organization
338 https://www.grid.ac/institutes/grid.33199.31 schema:alternateName Huazhong University of Science and Technology
339 schema:name Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, 430030, Wuhan, Hubei, People’s Republic of China
340 Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People’s Republic of China
341 rdf:type schema:Organization
342 https://www.grid.ac/institutes/grid.415203.1 schema:alternateName Khoo Teck Puat Hospital
343 schema:name Department of General Medicine, Diabetes Centre, Khoo Teck Puat Hospital, Singapore, Republic of Singapore
344 rdf:type schema:Organization
345 https://www.grid.ac/institutes/grid.4280.e schema:alternateName National University of Singapore
346 schema:name NUS Environmental Research Institute, National University of Singapore, Singapore, Republic of Singapore
347 Office of Clinical Sciences, Duke-NUS Medical School, 8 College Road Level 4, 169857, Singapore, Republic of Singapore
348 Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Republic of Singapore
349 rdf:type schema:Organization
 




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


...