Circulating microRNA-21 is an early predictor of ROS-mediated damage in subjects with high risk of developing diabetes and in drug-naïve ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Lucia La Sala, Simona Mrakic-Sposta, Elena Tagliabue, Francesco Prattichizzo, Stefano Micheloni, Elena Sangalli, Claudia Specchia, Anna Chiara Uccellatore, Silvia Lupini, Gaia Spinetti, Paola de Candia, Antonio Ceriello

ABSTRACT

BACKGROUND: Impaired glucose tolerance (IGT) is a risk factor for the development of diabetes and related complications that ensue. Early identification of at-risk individuals might be beneficial to reduce or delay the progression of diabetes and its related complications. Recently, microRNAs emerged as potential biomarkers of diseases. The aim of the present study was to evaluate microRNA-21 as a potential biomarker for the risk of developing diabetes in adults with IGT and to investigate its downstream effects as the generation of reactive oxygen species (ROS), the induction of manganese-superoxide dismutase-2 (SOD2), and the circulating levels of 4-HNE (4-hydroxynonenal). METHODS: To evaluate the prognostic and predictive values of plasmatic microRNA-21 in identifying metabolic derangements, we tested a selected cohort (n = 115) of subjects enrolled in the DIAPASON Study, whom were selected on ADA criteria for 2hPG. Statistical analysis was performed using ANOVA or the Kruskal-Wallis test as appropriate. ROC curves were drawn for diagnostic accuracy of the tests; positive and negative predictive values were performed, and Youden's index was used to seek the cut-off optimum truncation point. ROS, SOD2 and 4-HNE were also evaluated. RESULTS: We observed significant upregulation of microRNA-21 in IGT and in T2D subjects, and microRNA-21 was positively correlated with glycaemic parameters. Diagnostic performance of microRNA-21 was high and accurate. We detected significant overproduction of ROS by electron paramagnetic resonance (EPR), significant accumulation of the lipid peroxidation marker 4-HNE, and defective SOD2 antioxidant response in IGT and newly diagnosed, drug-naïve T2D subjects. In addition, ROC curves demonstrated the diagnostic accuracy of markers used. CONCLUSIONS: our data demonstrate that microRNA-21 is associated with prediabetic status and exhibits predictive value for early detection of glucose imbalances. These data could provide novel clues for miR-based biomarkers to evaluate diabetes. More... »

PAGES

18

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12933-019-0824-2

DOI

http://dx.doi.org/10.1186/s12933-019-0824-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112386039

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30803440


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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "MultiMedica", 
          "id": "https://www.grid.ac/institutes/grid.420421.1", 
          "name": [
            "Department of Cardiovascular and Dysmetabolic Diseases, IRCCS MultiMedica, Via Fantoli 16/15, 20138, Milan, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "La Sala", 
        "givenName": "Lucia", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute of Molecular Bioimaging and Physiology", 
          "id": "https://www.grid.ac/institutes/grid.428490.3", 
          "name": [
            "Institute of Molecular Bioimaging and Physiology, National Research Council, Segrate, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mrakic-Sposta", 
        "givenName": "Simona", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "MultiMedica", 
          "id": "https://www.grid.ac/institutes/grid.420421.1", 
          "name": [
            "Biostatistic Unit, IRCCS MultiMedica, Milan, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tagliabue", 
        "givenName": "Elena", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "MultiMedica", 
          "id": "https://www.grid.ac/institutes/grid.420421.1", 
          "name": [
            "Department of Cardiovascular and Dysmetabolic Diseases, IRCCS MultiMedica, Via Fantoli 16/15, 20138, Milan, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Prattichizzo", 
        "givenName": "Francesco", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "MultiMedica", 
          "id": "https://www.grid.ac/institutes/grid.420421.1", 
          "name": [
            "Department of Cardiovascular and Dysmetabolic Diseases, IRCCS MultiMedica, Via Fantoli 16/15, 20138, Milan, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Micheloni", 
        "givenName": "Stefano", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "MultiMedica", 
          "id": "https://www.grid.ac/institutes/grid.420421.1", 
          "name": [
            "Department of Cardiovascular and Dysmetabolic Diseases, IRCCS MultiMedica, Via Fantoli 16/15, 20138, Milan, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sangalli", 
        "givenName": "Elena", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Brescia", 
          "id": "https://www.grid.ac/institutes/grid.7637.5", 
          "name": [
            "Department of Translational Biomedicine, University of Brescia, Brescia, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Specchia", 
        "givenName": "Claudia", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Milan", 
          "id": "https://www.grid.ac/institutes/grid.4708.b", 
          "name": [
            "University of Milan, Milan, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Uccellatore", 
        "givenName": "Anna Chiara", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Milan", 
          "id": "https://www.grid.ac/institutes/grid.4708.b", 
          "name": [
            "University of Milan, Milan, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lupini", 
        "givenName": "Silvia", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "MultiMedica", 
          "id": "https://www.grid.ac/institutes/grid.420421.1", 
          "name": [
            "Department of Cardiovascular and Dysmetabolic Diseases, IRCCS MultiMedica, Via Fantoli 16/15, 20138, Milan, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Spinetti", 
        "givenName": "Gaia", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "MultiMedica", 
          "id": "https://www.grid.ac/institutes/grid.420421.1", 
          "name": [
            "Department of Cardiovascular and Dysmetabolic Diseases, IRCCS MultiMedica, Via Fantoli 16/15, 20138, Milan, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "de Candia", 
        "givenName": "Paola", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Barcelona", 
          "id": "https://www.grid.ac/institutes/grid.5841.8", 
          "name": [
            "Department of Cardiovascular and Dysmetabolic Diseases, IRCCS MultiMedica, Via Fantoli 16/15, 20138, Milan, Italy", 
            "Institut d\u2019Investigacions Biom\u00e8diques August Pi i Sunyer (IDIBAPS) and Centro de Investigaci\u00f3n Biom\u00e9dica en Red de Diabetes y Enfermedades Metab\u00f3licas Asociadas (CIBERDEM), Barcelona, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ceriello", 
        "givenName": "Antonio", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1155/2014/306179", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004414442"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/bmj.i6538", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004994346"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/diacare.28.5.1187", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006005898"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circresaha.111.300418", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006653007"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circresaha.111.300418", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006653007"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/db11-1086", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008363834"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/cvr/cvp015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008945957"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.7150/ijms.15548", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010719765"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1104343", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011297901"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep31479", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014593608", 
          "https://doi.org/10.1038/srep31479"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.freeradbiomed.2013.06.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017445433"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/dom.12688", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017949900"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/dc15-s005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018088379"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s2213-8587(16)30328-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021100658"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.freeradbiomed.2011.05.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021177145"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cell.2009.01.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023682432"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature07511", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028723946", 
          "https://doi.org/10.1038/nature07511"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.clinthera.2005.11.020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031207866"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/db08-0063", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033267669"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12933-016-0390-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033583427", 
          "https://doi.org/10.1186/s12933-016-0390-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1074/jbc.m110.208066", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037297000"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circresaha.110.226357", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038702492"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circresaha.110.226357", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038702492"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mnfr.200500090", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039038539"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mnfr.200500090", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039038539"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00592-016-0837-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039198488", 
          "https://doi.org/10.1007/s00592-016-0837-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/diabetes.49.12.2170", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043747622"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3389/fimmu.2014.00043", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044504036"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3389/fgene.2013.00094", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044604833"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circresaha.110.223545", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045876868"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circresaha.110.223545", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045876868"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1159/000366374", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046207912"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1046/j.1365-2362.1998.00295.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048025120"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1530/eje.0.1480157", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048067533"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1530/eje.0.1480157", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048067533"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1530/eje.0.1480157", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048067533"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature03076", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050225907", 
          "https://doi.org/10.1038/nature03076"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature03076", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050225907", 
          "https://doi.org/10.1038/nature03076"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/scitranslmed.3003205", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052341351"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cmet.2006.01.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052534178"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1210/jc.2014-2574", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064295328"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2531595", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069977037"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/diab.46.11.1853", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070738033"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3892/mmr.2014.3107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071522388"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3892/mmr.2014.3107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071522388"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/cd16-0067", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083399967"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00125-017-4237-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084018828", 
          "https://doi.org/10.1007/s00125-017-4237-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00125-017-4237-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084018828", 
          "https://doi.org/10.1007/s00125-017-4237-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00125-017-4237-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084018828", 
          "https://doi.org/10.1007/s00125-017-4237-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/db16-1246", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084404971"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12933-017-0604-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091990360", 
          "https://doi.org/10.1186/s12933-017-0604-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12933-017-0604-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091990360", 
          "https://doi.org/10.1186/s12933-017-0604-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3791/56326", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092426654"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0188980", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093164046"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s2213-8587(18)30027-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101243675"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1155/2018/6872635", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103199994"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00592-018-1149-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103836914", 
          "https://doi.org/10.1007/s00592-018-1149-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12933-018-0748-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105775693", 
          "https://doi.org/10.1186/s12933-018-0748-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.molmet.2018.11.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109914476"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.molmet.2018.11.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109914476"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.molmet.2018.11.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109914476"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.molmet.2018.11.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109914476"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.molmet.2018.11.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109914476"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/1479164118816659", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110565357"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/1479164118816659", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110565357"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-12", 
    "datePublishedReg": "2019-12-01", 
    "description": "BACKGROUND: Impaired glucose tolerance (IGT) is a risk factor for the development of diabetes and related complications that ensue. Early identification of at-risk individuals might be beneficial to reduce or delay the progression of diabetes and its related complications. Recently, microRNAs emerged as potential biomarkers of diseases. The aim of the present study was to evaluate microRNA-21 as a potential biomarker for the risk of developing diabetes in adults with IGT and to investigate its downstream effects as the generation of reactive oxygen species (ROS), the induction of manganese-superoxide dismutase-2 (SOD2), and the circulating levels of 4-HNE (4-hydroxynonenal).\nMETHODS: To evaluate the prognostic and predictive values of plasmatic microRNA-21 in identifying metabolic derangements, we tested a selected cohort (n\u2009=\u2009115) of subjects enrolled in the DIAPASON Study, whom were selected on ADA criteria for 2hPG. Statistical analysis was performed using ANOVA or the Kruskal-Wallis test as appropriate. ROC curves were drawn for diagnostic accuracy of the tests; positive and negative predictive values were performed, and Youden's index was used to seek the cut-off optimum truncation point. ROS, SOD2 and 4-HNE were also evaluated.\nRESULTS: We observed significant upregulation of microRNA-21 in IGT and in T2D subjects, and microRNA-21 was positively correlated with glycaemic parameters. Diagnostic performance of microRNA-21 was high and accurate. We detected significant overproduction of ROS by electron paramagnetic resonance (EPR), significant accumulation of the lipid peroxidation marker 4-HNE, and defective SOD2 antioxidant response in IGT and newly diagnosed, drug-na\u00efve T2D subjects. In addition, ROC curves demonstrated the diagnostic accuracy of markers used.\nCONCLUSIONS: our data demonstrate that microRNA-21 is associated with prediabetic status and exhibits predictive value for early detection of glucose imbalances. These data could provide novel clues for miR-based biomarkers to evaluate diabetes.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s12933-019-0824-2", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1031021", 
        "issn": [
          "1475-2840"
        ], 
        "name": "Cardiovascular Diabetology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "18"
      }
    ], 
    "name": "Circulating microRNA-21 is an early predictor of ROS-mediated damage in subjects with high risk of developing diabetes and in drug-na\u00efve T2D", 
    "pagination": "18", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "d33fe2dc514e35d493ad1bdca14b9d5b1811e303cea36ceac2d9ccf65c4d2042"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30803440"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101147637"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s12933-019-0824-2"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112386039"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s12933-019-0824-2", 
      "https://app.dimensions.ai/details/publication/pub.1112386039"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:21", 
    "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/v90/0000000368_0000000368/records_78972_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1186%2Fs12933-019-0824-2"
  }
]
 

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.10.1186/s12933-019-0824-2'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s12933-019-0824-2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12933-019-0824-2'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12933-019-0824-2'


 

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

303 TRIPLES      21 PREDICATES      78 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s12933-019-0824-2 schema:about anzsrc-for:11
2 anzsrc-for:1103
3 schema:author N9fb5d4ba112245db9703aa63f6d4f236
4 schema:citation sg:pub.10.1007/s00125-017-4237-z
5 sg:pub.10.1007/s00592-016-0837-1
6 sg:pub.10.1007/s00592-018-1149-4
7 sg:pub.10.1038/nature03076
8 sg:pub.10.1038/nature07511
9 sg:pub.10.1038/srep31479
10 sg:pub.10.1186/s12933-016-0390-9
11 sg:pub.10.1186/s12933-017-0604-9
12 sg:pub.10.1186/s12933-018-0748-2
13 https://doi.org/10.1002/mnfr.200500090
14 https://doi.org/10.1016/j.cell.2009.01.002
15 https://doi.org/10.1016/j.clinthera.2005.11.020
16 https://doi.org/10.1016/j.cmet.2006.01.005
17 https://doi.org/10.1016/j.freeradbiomed.2011.05.004
18 https://doi.org/10.1016/j.freeradbiomed.2013.06.009
19 https://doi.org/10.1016/j.molmet.2018.11.003
20 https://doi.org/10.1016/s2213-8587(16)30328-x
21 https://doi.org/10.1016/s2213-8587(18)30027-5
22 https://doi.org/10.1046/j.1365-2362.1998.00295.x
23 https://doi.org/10.1074/jbc.m110.208066
24 https://doi.org/10.1093/cvr/cvp015
25 https://doi.org/10.1111/dom.12688
26 https://doi.org/10.1126/science.1104343
27 https://doi.org/10.1126/scitranslmed.3003205
28 https://doi.org/10.1136/bmj.i6538
29 https://doi.org/10.1155/2014/306179
30 https://doi.org/10.1155/2018/6872635
31 https://doi.org/10.1159/000366374
32 https://doi.org/10.1161/circresaha.110.223545
33 https://doi.org/10.1161/circresaha.110.226357
34 https://doi.org/10.1161/circresaha.111.300418
35 https://doi.org/10.1177/1479164118816659
36 https://doi.org/10.1210/jc.2014-2574
37 https://doi.org/10.1371/journal.pone.0188980
38 https://doi.org/10.1530/eje.0.1480157
39 https://doi.org/10.2307/2531595
40 https://doi.org/10.2337/cd16-0067
41 https://doi.org/10.2337/db08-0063
42 https://doi.org/10.2337/db11-1086
43 https://doi.org/10.2337/db16-1246
44 https://doi.org/10.2337/dc15-s005
45 https://doi.org/10.2337/diab.46.11.1853
46 https://doi.org/10.2337/diabetes.49.12.2170
47 https://doi.org/10.2337/diacare.28.5.1187
48 https://doi.org/10.3389/fgene.2013.00094
49 https://doi.org/10.3389/fimmu.2014.00043
50 https://doi.org/10.3791/56326
51 https://doi.org/10.3892/mmr.2014.3107
52 https://doi.org/10.7150/ijms.15548
53 schema:datePublished 2019-12
54 schema:datePublishedReg 2019-12-01
55 schema:description BACKGROUND: Impaired glucose tolerance (IGT) is a risk factor for the development of diabetes and related complications that ensue. Early identification of at-risk individuals might be beneficial to reduce or delay the progression of diabetes and its related complications. Recently, microRNAs emerged as potential biomarkers of diseases. The aim of the present study was to evaluate microRNA-21 as a potential biomarker for the risk of developing diabetes in adults with IGT and to investigate its downstream effects as the generation of reactive oxygen species (ROS), the induction of manganese-superoxide dismutase-2 (SOD2), and the circulating levels of 4-HNE (4-hydroxynonenal). METHODS: To evaluate the prognostic and predictive values of plasmatic microRNA-21 in identifying metabolic derangements, we tested a selected cohort (n = 115) of subjects enrolled in the DIAPASON Study, whom were selected on ADA criteria for 2hPG. Statistical analysis was performed using ANOVA or the Kruskal-Wallis test as appropriate. ROC curves were drawn for diagnostic accuracy of the tests; positive and negative predictive values were performed, and Youden's index was used to seek the cut-off optimum truncation point. ROS, SOD2 and 4-HNE were also evaluated. RESULTS: We observed significant upregulation of microRNA-21 in IGT and in T2D subjects, and microRNA-21 was positively correlated with glycaemic parameters. Diagnostic performance of microRNA-21 was high and accurate. We detected significant overproduction of ROS by electron paramagnetic resonance (EPR), significant accumulation of the lipid peroxidation marker 4-HNE, and defective SOD2 antioxidant response in IGT and newly diagnosed, drug-naïve T2D subjects. In addition, ROC curves demonstrated the diagnostic accuracy of markers used. CONCLUSIONS: our data demonstrate that microRNA-21 is associated with prediabetic status and exhibits predictive value for early detection of glucose imbalances. These data could provide novel clues for miR-based biomarkers to evaluate diabetes.
56 schema:genre research_article
57 schema:inLanguage en
58 schema:isAccessibleForFree true
59 schema:isPartOf N14ad51f20b3b4658a4ef50f4792ddebc
60 N3e5e01ee4e4e4857845f67a29ae8d5ee
61 sg:journal.1031021
62 schema:name Circulating microRNA-21 is an early predictor of ROS-mediated damage in subjects with high risk of developing diabetes and in drug-naïve T2D
63 schema:pagination 18
64 schema:productId N08d43d09298241308a25b62271c9883f
65 N23da779158cf4264b1d0a5f026f18a97
66 N2e4314afa67d4681a6e73ce57d8f783e
67 N78a0c36f893d433d99d87b3f7b1c7153
68 Nf98bfa96e00c4047b860982e334a986c
69 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112386039
70 https://doi.org/10.1186/s12933-019-0824-2
71 schema:sdDatePublished 2019-04-11T13:21
72 schema:sdLicense https://scigraph.springernature.com/explorer/license/
73 schema:sdPublisher N59ca9ba8772a4f3c833e0c7c8ab93df2
74 schema:url https://link.springer.com/10.1186%2Fs12933-019-0824-2
75 sgo:license sg:explorer/license/
76 sgo:sdDataset articles
77 rdf:type schema:ScholarlyArticle
78 N047a04a84445421daf5f1bd55a9cf481 rdf:first Ne19db34950634a649014ef2bd6645d4c
79 rdf:rest Nb3091250f17f47ccaa2ce11910130ddc
80 N08d43d09298241308a25b62271c9883f schema:name dimensions_id
81 schema:value pub.1112386039
82 rdf:type schema:PropertyValue
83 N14ad51f20b3b4658a4ef50f4792ddebc schema:volumeNumber 18
84 rdf:type schema:PublicationVolume
85 N23da779158cf4264b1d0a5f026f18a97 schema:name doi
86 schema:value 10.1186/s12933-019-0824-2
87 rdf:type schema:PropertyValue
88 N2e4314afa67d4681a6e73ce57d8f783e schema:name pubmed_id
89 schema:value 30803440
90 rdf:type schema:PropertyValue
91 N3e18f6958db64b7baf7110e7bc8973f2 rdf:first N692bdd0471b447fdade23876ecd635c9
92 rdf:rest N6ac638138eef4e81a0c8a98e36b4a137
93 N3e5e01ee4e4e4857845f67a29ae8d5ee schema:issueNumber 1
94 rdf:type schema:PublicationIssue
95 N59ca9ba8772a4f3c833e0c7c8ab93df2 schema:name Springer Nature - SN SciGraph project
96 rdf:type schema:Organization
97 N5a8eee3192ad49018048571fc6de2930 schema:affiliation https://www.grid.ac/institutes/grid.420421.1
98 schema:familyName Prattichizzo
99 schema:givenName Francesco
100 rdf:type schema:Person
101 N61471e8e5f4f463887934d494acbc06e rdf:first N7b074872571d484eb33f3b3a1a4e73e6
102 rdf:rest Nf6b73808e84a467b84a8caac77e458ae
103 N6606d31cf5ef406c912c153f191b8243 schema:affiliation https://www.grid.ac/institutes/grid.7637.5
104 schema:familyName Specchia
105 schema:givenName Claudia
106 rdf:type schema:Person
107 N692bdd0471b447fdade23876ecd635c9 schema:affiliation https://www.grid.ac/institutes/grid.420421.1
108 schema:familyName de Candia
109 schema:givenName Paola
110 rdf:type schema:Person
111 N6ac638138eef4e81a0c8a98e36b4a137 rdf:first Nb19053a36d3b406f9b72b19e1ec62496
112 rdf:rest rdf:nil
113 N78a0c36f893d433d99d87b3f7b1c7153 schema:name readcube_id
114 schema:value d33fe2dc514e35d493ad1bdca14b9d5b1811e303cea36ceac2d9ccf65c4d2042
115 rdf:type schema:PropertyValue
116 N7b074872571d484eb33f3b3a1a4e73e6 schema:affiliation https://www.grid.ac/institutes/grid.420421.1
117 schema:familyName Micheloni
118 schema:givenName Stefano
119 rdf:type schema:Person
120 N7b629fe5dd9e46b895f3131bc4de088f schema:affiliation https://www.grid.ac/institutes/grid.4708.b
121 schema:familyName Lupini
122 schema:givenName Silvia
123 rdf:type schema:Person
124 N84b170b47f7448b18a824a84b88ffda0 schema:affiliation https://www.grid.ac/institutes/grid.420421.1
125 schema:familyName Tagliabue
126 schema:givenName Elena
127 rdf:type schema:Person
128 N8bd6f6e43dae45af983fd3fddcbb4283 schema:affiliation https://www.grid.ac/institutes/grid.420421.1
129 schema:familyName Sangalli
130 schema:givenName Elena
131 rdf:type schema:Person
132 N94e605940d0a45548381f56299082130 rdf:first N7b629fe5dd9e46b895f3131bc4de088f
133 rdf:rest Nc676dbc677bc486f8414c7c41ec8041a
134 N99f68846fbac453aab82ae7ef036a713 schema:affiliation https://www.grid.ac/institutes/grid.420421.1
135 schema:familyName La Sala
136 schema:givenName Lucia
137 rdf:type schema:Person
138 N9fb5d4ba112245db9703aa63f6d4f236 rdf:first N99f68846fbac453aab82ae7ef036a713
139 rdf:rest N047a04a84445421daf5f1bd55a9cf481
140 Nb19053a36d3b406f9b72b19e1ec62496 schema:affiliation https://www.grid.ac/institutes/grid.5841.8
141 schema:familyName Ceriello
142 schema:givenName Antonio
143 rdf:type schema:Person
144 Nb3091250f17f47ccaa2ce11910130ddc rdf:first N84b170b47f7448b18a824a84b88ffda0
145 rdf:rest Nfe46f2b16f414f83997039599ac66227
146 Nb37e143915be4b4f969a8ccc41c33f20 schema:affiliation https://www.grid.ac/institutes/grid.4708.b
147 schema:familyName Uccellatore
148 schema:givenName Anna Chiara
149 rdf:type schema:Person
150 Nb3f2e95af61e448399861220dbb4b6d7 schema:affiliation https://www.grid.ac/institutes/grid.420421.1
151 schema:familyName Spinetti
152 schema:givenName Gaia
153 rdf:type schema:Person
154 Nc676dbc677bc486f8414c7c41ec8041a rdf:first Nb3f2e95af61e448399861220dbb4b6d7
155 rdf:rest N3e18f6958db64b7baf7110e7bc8973f2
156 Ndcc26ec965fb4534933d13d0f98d1fda rdf:first Nb37e143915be4b4f969a8ccc41c33f20
157 rdf:rest N94e605940d0a45548381f56299082130
158 Ne19db34950634a649014ef2bd6645d4c schema:affiliation https://www.grid.ac/institutes/grid.428490.3
159 schema:familyName Mrakic-Sposta
160 schema:givenName Simona
161 rdf:type schema:Person
162 Nf6b73808e84a467b84a8caac77e458ae rdf:first N8bd6f6e43dae45af983fd3fddcbb4283
163 rdf:rest Nf935794e650441afaed70247e7c503e5
164 Nf935794e650441afaed70247e7c503e5 rdf:first N6606d31cf5ef406c912c153f191b8243
165 rdf:rest Ndcc26ec965fb4534933d13d0f98d1fda
166 Nf98bfa96e00c4047b860982e334a986c schema:name nlm_unique_id
167 schema:value 101147637
168 rdf:type schema:PropertyValue
169 Nfe46f2b16f414f83997039599ac66227 rdf:first N5a8eee3192ad49018048571fc6de2930
170 rdf:rest N61471e8e5f4f463887934d494acbc06e
171 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
172 schema:name Medical and Health Sciences
173 rdf:type schema:DefinedTerm
174 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
175 schema:name Clinical Sciences
176 rdf:type schema:DefinedTerm
177 sg:journal.1031021 schema:issn 1475-2840
178 schema:name Cardiovascular Diabetology
179 rdf:type schema:Periodical
180 sg:pub.10.1007/s00125-017-4237-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1084018828
181 https://doi.org/10.1007/s00125-017-4237-z
182 rdf:type schema:CreativeWork
183 sg:pub.10.1007/s00592-016-0837-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039198488
184 https://doi.org/10.1007/s00592-016-0837-1
185 rdf:type schema:CreativeWork
186 sg:pub.10.1007/s00592-018-1149-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103836914
187 https://doi.org/10.1007/s00592-018-1149-4
188 rdf:type schema:CreativeWork
189 sg:pub.10.1038/nature03076 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050225907
190 https://doi.org/10.1038/nature03076
191 rdf:type schema:CreativeWork
192 sg:pub.10.1038/nature07511 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028723946
193 https://doi.org/10.1038/nature07511
194 rdf:type schema:CreativeWork
195 sg:pub.10.1038/srep31479 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014593608
196 https://doi.org/10.1038/srep31479
197 rdf:type schema:CreativeWork
198 sg:pub.10.1186/s12933-016-0390-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033583427
199 https://doi.org/10.1186/s12933-016-0390-9
200 rdf:type schema:CreativeWork
201 sg:pub.10.1186/s12933-017-0604-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091990360
202 https://doi.org/10.1186/s12933-017-0604-9
203 rdf:type schema:CreativeWork
204 sg:pub.10.1186/s12933-018-0748-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105775693
205 https://doi.org/10.1186/s12933-018-0748-2
206 rdf:type schema:CreativeWork
207 https://doi.org/10.1002/mnfr.200500090 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039038539
208 rdf:type schema:CreativeWork
209 https://doi.org/10.1016/j.cell.2009.01.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023682432
210 rdf:type schema:CreativeWork
211 https://doi.org/10.1016/j.clinthera.2005.11.020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031207866
212 rdf:type schema:CreativeWork
213 https://doi.org/10.1016/j.cmet.2006.01.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052534178
214 rdf:type schema:CreativeWork
215 https://doi.org/10.1016/j.freeradbiomed.2011.05.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021177145
216 rdf:type schema:CreativeWork
217 https://doi.org/10.1016/j.freeradbiomed.2013.06.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017445433
218 rdf:type schema:CreativeWork
219 https://doi.org/10.1016/j.molmet.2018.11.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109914476
220 rdf:type schema:CreativeWork
221 https://doi.org/10.1016/s2213-8587(16)30328-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1021100658
222 rdf:type schema:CreativeWork
223 https://doi.org/10.1016/s2213-8587(18)30027-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101243675
224 rdf:type schema:CreativeWork
225 https://doi.org/10.1046/j.1365-2362.1998.00295.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1048025120
226 rdf:type schema:CreativeWork
227 https://doi.org/10.1074/jbc.m110.208066 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037297000
228 rdf:type schema:CreativeWork
229 https://doi.org/10.1093/cvr/cvp015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008945957
230 rdf:type schema:CreativeWork
231 https://doi.org/10.1111/dom.12688 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017949900
232 rdf:type schema:CreativeWork
233 https://doi.org/10.1126/science.1104343 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011297901
234 rdf:type schema:CreativeWork
235 https://doi.org/10.1126/scitranslmed.3003205 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052341351
236 rdf:type schema:CreativeWork
237 https://doi.org/10.1136/bmj.i6538 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004994346
238 rdf:type schema:CreativeWork
239 https://doi.org/10.1155/2014/306179 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004414442
240 rdf:type schema:CreativeWork
241 https://doi.org/10.1155/2018/6872635 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103199994
242 rdf:type schema:CreativeWork
243 https://doi.org/10.1159/000366374 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046207912
244 rdf:type schema:CreativeWork
245 https://doi.org/10.1161/circresaha.110.223545 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045876868
246 rdf:type schema:CreativeWork
247 https://doi.org/10.1161/circresaha.110.226357 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038702492
248 rdf:type schema:CreativeWork
249 https://doi.org/10.1161/circresaha.111.300418 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006653007
250 rdf:type schema:CreativeWork
251 https://doi.org/10.1177/1479164118816659 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110565357
252 rdf:type schema:CreativeWork
253 https://doi.org/10.1210/jc.2014-2574 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064295328
254 rdf:type schema:CreativeWork
255 https://doi.org/10.1371/journal.pone.0188980 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093164046
256 rdf:type schema:CreativeWork
257 https://doi.org/10.1530/eje.0.1480157 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048067533
258 rdf:type schema:CreativeWork
259 https://doi.org/10.2307/2531595 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069977037
260 rdf:type schema:CreativeWork
261 https://doi.org/10.2337/cd16-0067 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083399967
262 rdf:type schema:CreativeWork
263 https://doi.org/10.2337/db08-0063 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033267669
264 rdf:type schema:CreativeWork
265 https://doi.org/10.2337/db11-1086 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008363834
266 rdf:type schema:CreativeWork
267 https://doi.org/10.2337/db16-1246 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084404971
268 rdf:type schema:CreativeWork
269 https://doi.org/10.2337/dc15-s005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018088379
270 rdf:type schema:CreativeWork
271 https://doi.org/10.2337/diab.46.11.1853 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070738033
272 rdf:type schema:CreativeWork
273 https://doi.org/10.2337/diabetes.49.12.2170 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043747622
274 rdf:type schema:CreativeWork
275 https://doi.org/10.2337/diacare.28.5.1187 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006005898
276 rdf:type schema:CreativeWork
277 https://doi.org/10.3389/fgene.2013.00094 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044604833
278 rdf:type schema:CreativeWork
279 https://doi.org/10.3389/fimmu.2014.00043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044504036
280 rdf:type schema:CreativeWork
281 https://doi.org/10.3791/56326 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092426654
282 rdf:type schema:CreativeWork
283 https://doi.org/10.3892/mmr.2014.3107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071522388
284 rdf:type schema:CreativeWork
285 https://doi.org/10.7150/ijms.15548 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010719765
286 rdf:type schema:CreativeWork
287 https://www.grid.ac/institutes/grid.420421.1 schema:alternateName MultiMedica
288 schema:name Biostatistic Unit, IRCCS MultiMedica, Milan, Italy
289 Department of Cardiovascular and Dysmetabolic Diseases, IRCCS MultiMedica, Via Fantoli 16/15, 20138, Milan, Italy
290 rdf:type schema:Organization
291 https://www.grid.ac/institutes/grid.428490.3 schema:alternateName Institute of Molecular Bioimaging and Physiology
292 schema:name Institute of Molecular Bioimaging and Physiology, National Research Council, Segrate, Italy
293 rdf:type schema:Organization
294 https://www.grid.ac/institutes/grid.4708.b schema:alternateName University of Milan
295 schema:name University of Milan, Milan, Italy
296 rdf:type schema:Organization
297 https://www.grid.ac/institutes/grid.5841.8 schema:alternateName University of Barcelona
298 schema:name Department of Cardiovascular and Dysmetabolic Diseases, IRCCS MultiMedica, Via Fantoli 16/15, 20138, Milan, Italy
299 Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Barcelona, Spain
300 rdf:type schema:Organization
301 https://www.grid.ac/institutes/grid.7637.5 schema:alternateName University of Brescia
302 schema:name Department of Translational Biomedicine, University of Brescia, Brescia, Italy
303 rdf:type schema:Organization
 




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


...