A machine learning-assisted model for renal urate underexcretion with genetic and clinical variables among Chinese men with gout View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-03-09

AUTHORS

Mingshu Sun, Wenyan Sun, Xuetong Zhao, Zhiqiang Li, Nicola Dalbeth, Aichang Ji, Yuwei He, Hongzhu Qu, Guangmin Zheng, Lidan Ma, Jiayi Wang, Yongyong Shi, Xiangdong Fang, Haibing Chen, Tony R. Merriman, Changgui Li

ABSTRACT

ObjectivesThe objective of this study was to develop and validate a prediction model for renal urate underexcretion (RUE) in male gout patients.MethodsMen with gout enrolled from multicenter cohorts in China were analyzed as the development and validation data sets. The RUE phenotype was defined as fractional excretion of uric acid (FEUA) <5.5%. Candidate genetic and clinical features were screened by the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation. Machine learning algorithms (stochastic gradient descent (SGD), logistic regression, support vector machine) were performed to construct a predictive classifier of RUE. Models were assessed by the area under the receiver operating characteristic curve (AUC) and the precision-recall curve (PRC).ResultsOne thousand two hundred thirty-eight and two thousand twenty-three patients were enrolled as the development and validation cohorts, with 1220 and 754 randomly chosen patients genotyped, respectively. Rs3775948.GG of SLC2A9/GLUT9, rs504915.AA of NRXN2/URAT1, and 7 clinical features (age, hypertension, nephrolithiasis, blood glucose, serum urate, urea nitrogen, and creatinine) were generated by LASSO. Two additional SNP variants (rs2231142.GG of ABCG2 and rs11231463.GG of SLC22A9/OAT7) were selected based on their contributions to gout in the development cohort and their reported effects on renal urate handling. The optimized classifiers yielded AUCs of ~0.914 and PRCs of ~0.980 using these 11 variables. The SGD model was conducted in the validation cohort with an AUC of 0.899 and the PRC of 0.957.ConclusionsA prediction model for RUE composed of four SNPs and readily accessible clinical features was established with acceptable accuracy for men with gout. More... »

PAGES

67

References to SciGraph publications

  • 2018-04-24. Urinary excretion of uric acid is negatively associated with albuminuria in patients with chronic kidney disease: a cross-sectional study in BMC NEPHROLOGY
  • 2019-11-12. Population-specific factors associated with fractional excretion of uric acid in ARTHRITIS RESEARCH & THERAPY
  • 2015-05-13. Genome-wide association analysis identifies three new risk loci for gout arthritis in Han Chinese in NATURE COMMUNICATIONS
  • 2012-01. Decreased extra-renal urate excretion is a common cause of hyperuricemia in NATURE COMMUNICATIONS
  • 2018-05-08. An update on the genetics of hyperuricaemia and gout in NATURE REVIEWS RHEUMATOLOGY
  • 2019-10-02. Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels in NATURE GENETICS
  • 2019-04-08. Genome-wide meta-analysis identifies multiple novel loci associated with serum uric acid levels in Japanese individuals in COMMUNICATIONS BIOLOGY
  • 2019-11-25. Risk Stratification for Sudden Cardiac Death in Non-Ischaemic Dilated Cardiomyopathy in CURRENT CARDIOLOGY REPORTS
  • 2017-07-04. Prevalence and incidence of gout in Korea: data from the national health claims database 2007–2015 in RHEUMATOLOGY INTERNATIONAL
  • 2015-03-20. Gene-expression molecular subtyping of triple-negative breast cancer tumours: importance of immune response in BREAST CANCER RESEARCH
  • 2020-06-15. Global epidemiology of gout: prevalence, incidence, treatment patterns and risk factors in NATURE REVIEWS RHEUMATOLOGY
  • 2010-12-16. A proposal for identifying the low renal uric acid clearance phenotype in ARTHRITIS RESEARCH & THERAPY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13075-022-02755-4

    DOI

    http://dx.doi.org/10.1186/s13075-022-02755-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1146163469

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/35264217


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

    JSON-LD is the canonical representation for SciGraph data.

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

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Medical and Health Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1103", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Clinical Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1107", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Immunology", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1117", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Public Health and Health Services", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Asians", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Glucose Transport Proteins, Facilitative", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Gout", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Hyperuricemia", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Machine Learning", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Male", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Polymorphism, Single Nucleotide", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Uric Acid", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China", 
              "id": "http://www.grid.ac/institutes/grid.412521.1", 
              "name": [
                "Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Sun", 
            "givenName": "Mingshu", 
            "id": "sg:person.012364156423.55", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012364156423.55"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China", 
              "id": "http://www.grid.ac/institutes/grid.412521.1", 
              "name": [
                "Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Sun", 
            "givenName": "Wenyan", 
            "id": "sg:person.013310476631.38", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013310476631.38"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China", 
              "id": "http://www.grid.ac/institutes/grid.464209.d", 
              "name": [
                "CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zhao", 
            "givenName": "Xuetong", 
            "id": "sg:person.07450220377.35", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07450220377.35"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China", 
              "id": "http://www.grid.ac/institutes/grid.412521.1", 
              "name": [
                "Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Li", 
            "givenName": "Zhiqiang", 
            "id": "sg:person.0674613177.69", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0674613177.69"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Medicine, University of Auckland, Auckland, New Zealand", 
              "id": "http://www.grid.ac/institutes/grid.9654.e", 
              "name": [
                "Department of Medicine, University of Auckland, Auckland, New Zealand"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Dalbeth", 
            "givenName": "Nicola", 
            "id": "sg:person.01141577000.02", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01141577000.02"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China", 
              "id": "http://www.grid.ac/institutes/grid.412521.1", 
              "name": [
                "Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ji", 
            "givenName": "Aichang", 
            "id": "sg:person.015177141137.92", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015177141137.92"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China", 
              "id": "http://www.grid.ac/institutes/grid.412521.1", 
              "name": [
                "Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "He", 
            "givenName": "Yuwei", 
            "id": "sg:person.011763651031.40", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011763651031.40"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China", 
              "id": "http://www.grid.ac/institutes/grid.464209.d", 
              "name": [
                "CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Qu", 
            "givenName": "Hongzhu", 
            "id": "sg:person.01035060767.30", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01035060767.30"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China", 
              "id": "http://www.grid.ac/institutes/grid.464209.d", 
              "name": [
                "CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zheng", 
            "givenName": "Guangmin", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China", 
              "id": "http://www.grid.ac/institutes/grid.412521.1", 
              "name": [
                "Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ma", 
            "givenName": "Lidan", 
            "id": "sg:person.016416130433.46", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016416130433.46"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Pharmacy, Peking University First Hospital, Beijing, China", 
              "id": "http://www.grid.ac/institutes/grid.411472.5", 
              "name": [
                "Department of Pharmacy, Peking University First Hospital, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Wang", 
            "givenName": "Jiayi", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China", 
              "id": "http://www.grid.ac/institutes/grid.412521.1", 
              "name": [
                "Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Shi", 
            "givenName": "Yongyong", 
            "id": "sg:person.01066657177.25", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01066657177.25"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China", 
              "id": "http://www.grid.ac/institutes/grid.464209.d", 
              "name": [
                "CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Fang", 
            "givenName": "Xiangdong", 
            "id": "sg:person.01144323677.05", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01144323677.05"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Endocrinology and Metabolism, Shanghai 10th People\u2019s Hospital, Tongji University, Shanghai, China", 
              "id": "http://www.grid.ac/institutes/grid.24516.34", 
              "name": [
                "Department of Endocrinology and Metabolism, Shanghai 10th People\u2019s Hospital, Tongji University, Shanghai, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Chen", 
            "givenName": "Haibing", 
            "id": "sg:person.01154564455.86", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01154564455.86"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Medicine, University of Alabama Birmingham, Birmingham, AL, USA", 
              "id": "http://www.grid.ac/institutes/grid.265892.2", 
              "name": [
                "Department of Biochemistry, University of Otago, Dunedin, New Zealand", 
                "Department of Medicine, University of Alabama Birmingham, Birmingham, AL, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Merriman", 
            "givenName": "Tony R.", 
            "id": "sg:person.0641412005.79", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0641412005.79"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China", 
              "id": "http://www.grid.ac/institutes/grid.412521.1", 
              "name": [
                "Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Li", 
            "givenName": "Changgui", 
            "id": "sg:person.0676564741.16", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0676564741.16"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1038/s41584-020-0441-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1128481532", 
              "https://doi.org/10.1038/s41584-020-0441-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ncomms8041", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031524864", 
              "https://doi.org/10.1038/ncomms8041"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s42003-019-0339-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1113305924", 
              "https://doi.org/10.1038/s42003-019-0339-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00296-017-3768-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1090351593", 
              "https://doi.org/10.1007/s00296-017-3768-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13058-015-0550-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008234629", 
              "https://doi.org/10.1186/s13058-015-0550-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41584-018-0004-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103851103", 
              "https://doi.org/10.1038/s41584-018-0004-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41588-019-0504-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1121426348", 
              "https://doi.org/10.1038/s41588-019-0504-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11886-019-1236-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1122868470", 
              "https://doi.org/10.1007/s11886-019-1236-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12882-018-0892-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103661595", 
              "https://doi.org/10.1186/s12882-018-0892-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/ar3191", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002554310", 
              "https://doi.org/10.1186/ar3191"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13075-019-2016-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1122511056", 
              "https://doi.org/10.1186/s13075-019-2016-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ncomms1756", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051043532", 
              "https://doi.org/10.1038/ncomms1756"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2022-03-09", 
        "datePublishedReg": "2022-03-09", 
        "description": "ObjectivesThe objective of this study was to develop and validate a prediction model for renal urate underexcretion (RUE) in male gout patients.MethodsMen with gout enrolled from multicenter cohorts in China were analyzed as the development and validation data sets. The RUE phenotype was defined as fractional excretion of uric acid (FEUA) <5.5%. Candidate genetic and clinical features were screened by the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation. Machine learning algorithms (stochastic gradient descent (SGD), logistic regression, support vector machine) were performed to construct a predictive classifier of RUE. Models were assessed by the area under the receiver operating characteristic curve (AUC) and the precision-recall curve (PRC).ResultsOne thousand two hundred thirty-eight and two thousand twenty-three patients were enrolled as the development and validation cohorts, with 1220 and 754 randomly chosen patients genotyped, respectively. Rs3775948.GG of SLC2A9/GLUT9, rs504915.AA of NRXN2/URAT1, and 7 clinical features (age, hypertension, nephrolithiasis, blood glucose, serum urate, urea nitrogen, and creatinine) were generated by LASSO. Two additional SNP variants (rs2231142.GG of ABCG2 and rs11231463.GG of SLC22A9/OAT7) were selected based on their contributions to gout in the development cohort and their reported effects on renal urate handling. The optimized classifiers yielded AUCs of ~0.914 and PRCs of ~0.980 using these 11 variables. The SGD model was conducted in the validation cohort with an AUC of 0.899 and the PRC of 0.957.ConclusionsA prediction model for RUE composed of four SNPs and readily accessible clinical features was established with acceptable accuracy for men with gout.", 
        "genre": "article", 
        "id": "sg:pub.10.1186/s13075-022-02755-4", 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.8347503", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.8877821", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.8368779", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1297499", 
            "issn": [
              "1478-6354", 
              "1478-6362"
            ], 
            "name": "Arthritis Research & Therapy", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "24"
          }
        ], 
        "keywords": [
          "clinical features", 
          "validation cohort", 
          "male gout patients", 
          "urate underexcretion", 
          "renal urate handling", 
          "multicenter cohort", 
          "fractional excretion", 
          "gout patients", 
          "development cohort", 
          "clinical variables", 
          "Chinese men", 
          "urate handling", 
          "ObjectivesThe objective", 
          "cohort", 
          "patients", 
          "gout", 
          "least absolute shrinkage", 
          "uric acid", 
          "characteristic curve", 
          "underexcretion", 
          "absolute shrinkage", 
          "AUC", 
          "men", 
          "precision-recall curve", 
          "predictive classifier", 
          "SNP variants", 
          "excretion", 
          "GLUT9", 
          "URAT1", 
          "validation data sets", 
          "phenotype", 
          "thirties", 
          "selection operator", 
          "prediction model", 
          "development", 
          "study", 
          "variants", 
          "variables", 
          "features", 
          "SNPs", 
          "curves", 
          "effect", 
          "objective", 
          "acid", 
          "candidates", 
          "model", 
          "receiver", 
          "area", 
          "handling", 
          "shrinkage", 
          "acceptable accuracy", 
          "SGD model", 
          "China", 
          "contribution", 
          "LASSO", 
          "accuracy", 
          "data sets", 
          "classifier", 
          "set", 
          "machine", 
          "operators", 
          "algorithm"
        ], 
        "name": "A machine learning-assisted model for renal urate underexcretion with genetic and clinical variables among Chinese men with gout", 
        "pagination": "67", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1146163469"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/s13075-022-02755-4"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "35264217"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/s13075-022-02755-4", 
          "https://app.dimensions.ai/details/publication/pub.1146163469"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-12-01T06:43", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_923.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1186/s13075-022-02755-4"
      }
    ]
     

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

    HOW TO GET THIS DATA PROGRAMMATICALLY:

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

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s13075-022-02755-4'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s13075-022-02755-4'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s13075-022-02755-4'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13075-022-02755-4'


     

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

    340 TRIPLES      21 PREDICATES      110 URIs      88 LITERALS      16 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/s13075-022-02755-4 schema:about N0853e0071eda45e28f39d360bf785e58
    2 N0b9a540d3d15430e9e03a9912c589d5d
    3 N184bd4f70ed54187ad3a0d2353ad4bcc
    4 N2582138e3c9f485eb6b9d1094f39b51d
    5 N59e29057005048d09ecb8981b22ef5ab
    6 N8374f28749884782afb218433b222ee0
    7 Na73ab3b31e5b4179b34f834afbbb4602
    8 Nd08a23157ae04baaac91990dd39fa4e5
    9 Ne6c19434b34042f5873bdf2904858b54
    10 anzsrc-for:11
    11 anzsrc-for:1103
    12 anzsrc-for:1107
    13 anzsrc-for:1117
    14 schema:author Na27484e0f08e419cab816f05e5bfb48a
    15 schema:citation sg:pub.10.1007/s00296-017-3768-4
    16 sg:pub.10.1007/s11886-019-1236-3
    17 sg:pub.10.1038/ncomms1756
    18 sg:pub.10.1038/ncomms8041
    19 sg:pub.10.1038/s41584-018-0004-x
    20 sg:pub.10.1038/s41584-020-0441-1
    21 sg:pub.10.1038/s41588-019-0504-x
    22 sg:pub.10.1038/s42003-019-0339-0
    23 sg:pub.10.1186/ar3191
    24 sg:pub.10.1186/s12882-018-0892-7
    25 sg:pub.10.1186/s13058-015-0550-y
    26 sg:pub.10.1186/s13075-019-2016-6
    27 schema:datePublished 2022-03-09
    28 schema:datePublishedReg 2022-03-09
    29 schema:description ObjectivesThe objective of this study was to develop and validate a prediction model for renal urate underexcretion (RUE) in male gout patients.MethodsMen with gout enrolled from multicenter cohorts in China were analyzed as the development and validation data sets. The RUE phenotype was defined as fractional excretion of uric acid (FEUA) <5.5%. Candidate genetic and clinical features were screened by the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation. Machine learning algorithms (stochastic gradient descent (SGD), logistic regression, support vector machine) were performed to construct a predictive classifier of RUE. Models were assessed by the area under the receiver operating characteristic curve (AUC) and the precision-recall curve (PRC).ResultsOne thousand two hundred thirty-eight and two thousand twenty-three patients were enrolled as the development and validation cohorts, with 1220 and 754 randomly chosen patients genotyped, respectively. Rs3775948.GG of SLC2A9/GLUT9, rs504915.AA of NRXN2/URAT1, and 7 clinical features (age, hypertension, nephrolithiasis, blood glucose, serum urate, urea nitrogen, and creatinine) were generated by LASSO. Two additional SNP variants (rs2231142.GG of ABCG2 and rs11231463.GG of SLC22A9/OAT7) were selected based on their contributions to gout in the development cohort and their reported effects on renal urate handling. The optimized classifiers yielded AUCs of ~0.914 and PRCs of ~0.980 using these 11 variables. The SGD model was conducted in the validation cohort with an AUC of 0.899 and the PRC of 0.957.ConclusionsA prediction model for RUE composed of four SNPs and readily accessible clinical features was established with acceptable accuracy for men with gout.
    30 schema:genre article
    31 schema:isAccessibleForFree true
    32 schema:isPartOf Nab08997181324c46b3affcd0ee3e93e1
    33 Ncf9841c933994f7bbd61163d72ecf143
    34 sg:journal.1297499
    35 schema:keywords AUC
    36 China
    37 Chinese men
    38 GLUT9
    39 LASSO
    40 ObjectivesThe objective
    41 SGD model
    42 SNP variants
    43 SNPs
    44 URAT1
    45 absolute shrinkage
    46 acceptable accuracy
    47 accuracy
    48 acid
    49 algorithm
    50 area
    51 candidates
    52 characteristic curve
    53 classifier
    54 clinical features
    55 clinical variables
    56 cohort
    57 contribution
    58 curves
    59 data sets
    60 development
    61 development cohort
    62 effect
    63 excretion
    64 features
    65 fractional excretion
    66 gout
    67 gout patients
    68 handling
    69 least absolute shrinkage
    70 machine
    71 male gout patients
    72 men
    73 model
    74 multicenter cohort
    75 objective
    76 operators
    77 patients
    78 phenotype
    79 precision-recall curve
    80 prediction model
    81 predictive classifier
    82 receiver
    83 renal urate handling
    84 selection operator
    85 set
    86 shrinkage
    87 study
    88 thirties
    89 underexcretion
    90 urate handling
    91 urate underexcretion
    92 uric acid
    93 validation cohort
    94 validation data sets
    95 variables
    96 variants
    97 schema:name A machine learning-assisted model for renal urate underexcretion with genetic and clinical variables among Chinese men with gout
    98 schema:pagination 67
    99 schema:productId N48b02fb533754f0683126ac42130d00c
    100 N5f4d3d8d10354960a062c3acaa1f3259
    101 Nd3786d3c635f47fcba6bd2ca417fbba6
    102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1146163469
    103 https://doi.org/10.1186/s13075-022-02755-4
    104 schema:sdDatePublished 2022-12-01T06:43
    105 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    106 schema:sdPublisher N3dd2acf19b4543859bede208f2fa23c4
    107 schema:url https://doi.org/10.1186/s13075-022-02755-4
    108 sgo:license sg:explorer/license/
    109 sgo:sdDataset articles
    110 rdf:type schema:ScholarlyArticle
    111 N0853e0071eda45e28f39d360bf785e58 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    112 schema:name Uric Acid
    113 rdf:type schema:DefinedTerm
    114 N0b9a540d3d15430e9e03a9912c589d5d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    115 schema:name Polymorphism, Single Nucleotide
    116 rdf:type schema:DefinedTerm
    117 N184bd4f70ed54187ad3a0d2353ad4bcc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    118 schema:name Asians
    119 rdf:type schema:DefinedTerm
    120 N2582138e3c9f485eb6b9d1094f39b51d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    121 schema:name Glucose Transport Proteins, Facilitative
    122 rdf:type schema:DefinedTerm
    123 N2d9f8f1e6b0644f7b18083a0eb6cb2e2 rdf:first sg:person.01035060767.30
    124 rdf:rest Ne101672b756247899cbc971b11ca58c0
    125 N301f04197c064067994a4bbc90b9b279 rdf:first sg:person.0674613177.69
    126 rdf:rest N80f9f407609441ea9410acc3eb40382b
    127 N323a09af36a94594ab6f9a41905782db schema:affiliation grid-institutes:grid.411472.5
    128 schema:familyName Wang
    129 schema:givenName Jiayi
    130 rdf:type schema:Person
    131 N3dd2acf19b4543859bede208f2fa23c4 schema:name Springer Nature - SN SciGraph project
    132 rdf:type schema:Organization
    133 N4559e62fd40c423b88d190ecee2c7ed4 schema:affiliation grid-institutes:grid.464209.d
    134 schema:familyName Zheng
    135 schema:givenName Guangmin
    136 rdf:type schema:Person
    137 N475b4bd7b02b46fea2283e746867519c rdf:first sg:person.013310476631.38
    138 rdf:rest N50fe495a2fad4607b4ab1a848708632d
    139 N48b02fb533754f0683126ac42130d00c schema:name dimensions_id
    140 schema:value pub.1146163469
    141 rdf:type schema:PropertyValue
    142 N50fe495a2fad4607b4ab1a848708632d rdf:first sg:person.07450220377.35
    143 rdf:rest N301f04197c064067994a4bbc90b9b279
    144 N59e29057005048d09ecb8981b22ef5ab schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    145 schema:name Gout
    146 rdf:type schema:DefinedTerm
    147 N5f4d3d8d10354960a062c3acaa1f3259 schema:name doi
    148 schema:value 10.1186/s13075-022-02755-4
    149 rdf:type schema:PropertyValue
    150 N6f869f4a6894481095bb56aa50a0ac13 rdf:first sg:person.0641412005.79
    151 rdf:rest Nee708f949a41416e8b66549a8e51b0e6
    152 N80f9f407609441ea9410acc3eb40382b rdf:first sg:person.01141577000.02
    153 rdf:rest Nd946a64a64404c929b340a304b29d024
    154 N81f8902cd0fd4d25abb0dad7b07249b8 rdf:first sg:person.01066657177.25
    155 rdf:rest Ned918cc9384f4133a4f140a2036069f8
    156 N8374f28749884782afb218433b222ee0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    157 schema:name Machine Learning
    158 rdf:type schema:DefinedTerm
    159 Na27484e0f08e419cab816f05e5bfb48a rdf:first sg:person.012364156423.55
    160 rdf:rest N475b4bd7b02b46fea2283e746867519c
    161 Na73ab3b31e5b4179b34f834afbbb4602 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    162 schema:name Male
    163 rdf:type schema:DefinedTerm
    164 Nab08997181324c46b3affcd0ee3e93e1 schema:volumeNumber 24
    165 rdf:type schema:PublicationVolume
    166 Nace970f0890943509cea13a42a1888b1 rdf:first sg:person.011763651031.40
    167 rdf:rest N2d9f8f1e6b0644f7b18083a0eb6cb2e2
    168 Nb9a72e36b4254dde98bd2c3452352457 rdf:first N323a09af36a94594ab6f9a41905782db
    169 rdf:rest N81f8902cd0fd4d25abb0dad7b07249b8
    170 Ncf9841c933994f7bbd61163d72ecf143 schema:issueNumber 1
    171 rdf:type schema:PublicationIssue
    172 Nd08a23157ae04baaac91990dd39fa4e5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    173 schema:name Hyperuricemia
    174 rdf:type schema:DefinedTerm
    175 Nd3786d3c635f47fcba6bd2ca417fbba6 schema:name pubmed_id
    176 schema:value 35264217
    177 rdf:type schema:PropertyValue
    178 Nd946a64a64404c929b340a304b29d024 rdf:first sg:person.015177141137.92
    179 rdf:rest Nace970f0890943509cea13a42a1888b1
    180 Ne101672b756247899cbc971b11ca58c0 rdf:first N4559e62fd40c423b88d190ecee2c7ed4
    181 rdf:rest Nfc629abb416d459c927fdbf16bb83d57
    182 Ne6c19434b34042f5873bdf2904858b54 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    183 schema:name Humans
    184 rdf:type schema:DefinedTerm
    185 Ned918cc9384f4133a4f140a2036069f8 rdf:first sg:person.01144323677.05
    186 rdf:rest Nfdf460b9856f4aaa86e4918ed7a7dc63
    187 Nee708f949a41416e8b66549a8e51b0e6 rdf:first sg:person.0676564741.16
    188 rdf:rest rdf:nil
    189 Nfc629abb416d459c927fdbf16bb83d57 rdf:first sg:person.016416130433.46
    190 rdf:rest Nb9a72e36b4254dde98bd2c3452352457
    191 Nfdf460b9856f4aaa86e4918ed7a7dc63 rdf:first sg:person.01154564455.86
    192 rdf:rest N6f869f4a6894481095bb56aa50a0ac13
    193 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    194 schema:name Medical and Health Sciences
    195 rdf:type schema:DefinedTerm
    196 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
    197 schema:name Clinical Sciences
    198 rdf:type schema:DefinedTerm
    199 anzsrc-for:1107 schema:inDefinedTermSet anzsrc-for:
    200 schema:name Immunology
    201 rdf:type schema:DefinedTerm
    202 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
    203 schema:name Public Health and Health Services
    204 rdf:type schema:DefinedTerm
    205 sg:grant.8347503 http://pending.schema.org/fundedItem sg:pub.10.1186/s13075-022-02755-4
    206 rdf:type schema:MonetaryGrant
    207 sg:grant.8368779 http://pending.schema.org/fundedItem sg:pub.10.1186/s13075-022-02755-4
    208 rdf:type schema:MonetaryGrant
    209 sg:grant.8877821 http://pending.schema.org/fundedItem sg:pub.10.1186/s13075-022-02755-4
    210 rdf:type schema:MonetaryGrant
    211 sg:journal.1297499 schema:issn 1478-6354
    212 1478-6362
    213 schema:name Arthritis Research & Therapy
    214 schema:publisher Springer Nature
    215 rdf:type schema:Periodical
    216 sg:person.01035060767.30 schema:affiliation grid-institutes:grid.464209.d
    217 schema:familyName Qu
    218 schema:givenName Hongzhu
    219 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01035060767.30
    220 rdf:type schema:Person
    221 sg:person.01066657177.25 schema:affiliation grid-institutes:grid.412521.1
    222 schema:familyName Shi
    223 schema:givenName Yongyong
    224 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01066657177.25
    225 rdf:type schema:Person
    226 sg:person.01141577000.02 schema:affiliation grid-institutes:grid.9654.e
    227 schema:familyName Dalbeth
    228 schema:givenName Nicola
    229 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01141577000.02
    230 rdf:type schema:Person
    231 sg:person.01144323677.05 schema:affiliation grid-institutes:grid.464209.d
    232 schema:familyName Fang
    233 schema:givenName Xiangdong
    234 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01144323677.05
    235 rdf:type schema:Person
    236 sg:person.01154564455.86 schema:affiliation grid-institutes:grid.24516.34
    237 schema:familyName Chen
    238 schema:givenName Haibing
    239 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01154564455.86
    240 rdf:type schema:Person
    241 sg:person.011763651031.40 schema:affiliation grid-institutes:grid.412521.1
    242 schema:familyName He
    243 schema:givenName Yuwei
    244 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011763651031.40
    245 rdf:type schema:Person
    246 sg:person.012364156423.55 schema:affiliation grid-institutes:grid.412521.1
    247 schema:familyName Sun
    248 schema:givenName Mingshu
    249 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012364156423.55
    250 rdf:type schema:Person
    251 sg:person.013310476631.38 schema:affiliation grid-institutes:grid.412521.1
    252 schema:familyName Sun
    253 schema:givenName Wenyan
    254 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013310476631.38
    255 rdf:type schema:Person
    256 sg:person.015177141137.92 schema:affiliation grid-institutes:grid.412521.1
    257 schema:familyName Ji
    258 schema:givenName Aichang
    259 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015177141137.92
    260 rdf:type schema:Person
    261 sg:person.016416130433.46 schema:affiliation grid-institutes:grid.412521.1
    262 schema:familyName Ma
    263 schema:givenName Lidan
    264 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016416130433.46
    265 rdf:type schema:Person
    266 sg:person.0641412005.79 schema:affiliation grid-institutes:grid.265892.2
    267 schema:familyName Merriman
    268 schema:givenName Tony R.
    269 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0641412005.79
    270 rdf:type schema:Person
    271 sg:person.0674613177.69 schema:affiliation grid-institutes:grid.412521.1
    272 schema:familyName Li
    273 schema:givenName Zhiqiang
    274 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0674613177.69
    275 rdf:type schema:Person
    276 sg:person.0676564741.16 schema:affiliation grid-institutes:grid.412521.1
    277 schema:familyName Li
    278 schema:givenName Changgui
    279 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0676564741.16
    280 rdf:type schema:Person
    281 sg:person.07450220377.35 schema:affiliation grid-institutes:grid.464209.d
    282 schema:familyName Zhao
    283 schema:givenName Xuetong
    284 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07450220377.35
    285 rdf:type schema:Person
    286 sg:pub.10.1007/s00296-017-3768-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090351593
    287 https://doi.org/10.1007/s00296-017-3768-4
    288 rdf:type schema:CreativeWork
    289 sg:pub.10.1007/s11886-019-1236-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1122868470
    290 https://doi.org/10.1007/s11886-019-1236-3
    291 rdf:type schema:CreativeWork
    292 sg:pub.10.1038/ncomms1756 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051043532
    293 https://doi.org/10.1038/ncomms1756
    294 rdf:type schema:CreativeWork
    295 sg:pub.10.1038/ncomms8041 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031524864
    296 https://doi.org/10.1038/ncomms8041
    297 rdf:type schema:CreativeWork
    298 sg:pub.10.1038/s41584-018-0004-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1103851103
    299 https://doi.org/10.1038/s41584-018-0004-x
    300 rdf:type schema:CreativeWork
    301 sg:pub.10.1038/s41584-020-0441-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1128481532
    302 https://doi.org/10.1038/s41584-020-0441-1
    303 rdf:type schema:CreativeWork
    304 sg:pub.10.1038/s41588-019-0504-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1121426348
    305 https://doi.org/10.1038/s41588-019-0504-x
    306 rdf:type schema:CreativeWork
    307 sg:pub.10.1038/s42003-019-0339-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1113305924
    308 https://doi.org/10.1038/s42003-019-0339-0
    309 rdf:type schema:CreativeWork
    310 sg:pub.10.1186/ar3191 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002554310
    311 https://doi.org/10.1186/ar3191
    312 rdf:type schema:CreativeWork
    313 sg:pub.10.1186/s12882-018-0892-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103661595
    314 https://doi.org/10.1186/s12882-018-0892-7
    315 rdf:type schema:CreativeWork
    316 sg:pub.10.1186/s13058-015-0550-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1008234629
    317 https://doi.org/10.1186/s13058-015-0550-y
    318 rdf:type schema:CreativeWork
    319 sg:pub.10.1186/s13075-019-2016-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1122511056
    320 https://doi.org/10.1186/s13075-019-2016-6
    321 rdf:type schema:CreativeWork
    322 grid-institutes:grid.24516.34 schema:alternateName Department of Endocrinology and Metabolism, Shanghai 10th People’s Hospital, Tongji University, Shanghai, China
    323 schema:name Department of Endocrinology and Metabolism, Shanghai 10th People’s Hospital, Tongji University, Shanghai, China
    324 rdf:type schema:Organization
    325 grid-institutes:grid.265892.2 schema:alternateName Department of Medicine, University of Alabama Birmingham, Birmingham, AL, USA
    326 schema:name Department of Biochemistry, University of Otago, Dunedin, New Zealand
    327 Department of Medicine, University of Alabama Birmingham, Birmingham, AL, USA
    328 rdf:type schema:Organization
    329 grid-institutes:grid.411472.5 schema:alternateName Department of Pharmacy, Peking University First Hospital, Beijing, China
    330 schema:name Department of Pharmacy, Peking University First Hospital, Beijing, China
    331 rdf:type schema:Organization
    332 grid-institutes:grid.412521.1 schema:alternateName Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China
    333 schema:name Shandong Provincial Clinical Research Center for Immune Diseases and Gout & Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China
    334 rdf:type schema:Organization
    335 grid-institutes:grid.464209.d schema:alternateName CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
    336 schema:name CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
    337 rdf:type schema:Organization
    338 grid-institutes:grid.9654.e schema:alternateName Department of Medicine, University of Auckland, Auckland, New Zealand
    339 schema:name Department of Medicine, University of Auckland, Auckland, New Zealand
    340 rdf:type schema:Organization
     




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


    ...