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  • Atabaki Pasdar, NaeimehLund University,Lunds universitet,Genetisk och molekylär epidemiologi,Forskargrupper vid Lunds universitet,Genetic and Molecular Epidemiology,Lund University Research Groups,Lund Univ, Dept Clin Sci, Genet & Mol Epidemiol Unit, Malmö, Sweden.,Department of Clinical Sciences, Lund University, Malmö, Sweden (författare)

Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts

  • Artikel/kapitelEngelska2020

Förlag, utgivningsår, omfång ...

  • 2020-06-19
  • San Francisco :Public Library of Science (PLoS),2020

Nummerbeteckningar

  • LIBRIS-ID:oai:lup.lub.lu.se:3600e2c7-70bb-456a-8559-3e63dfeb7312
  • https://lup.lub.lu.se/record/3600e2c7-70bb-456a-8559-3e63dfeb7312URI
  • https://doi.org/10.1371/journal.pmed.1003149DOI
  • https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278673URI
  • https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43224URI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:art swepub-publicationtype
  • Ämneskategori:ref swepub-contenttype

Anmärkningar

  • QC 20200720
  • Funding text: The work leading to this publication has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n 115317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies' in kind contribution. NAP is supported in part by Henning och Johan Throne-Holsts Foundation, Hans Werth?n Foundation, an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742_NASCENT. HPM is supported by an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742_NASCENT. AGJ is supported by an NIHR Clinician Scientist award (17/0005624). RK is funded by the Novo Nordisk Foundation (NNF18OC0031650) as part of a postdoctoral fellowship, an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742_NASCENT. AK, PM, HF, JF and GNG are supported by an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742_NASCENT. TJM is funded by an NIHR clinical senior lecturer fellowship. S.Bru acknowledges support from the Novo Nordisk Foundation (grants NNF17OC0027594 and NNF14CC0001). ATH is a Wellcome Trust Senior Investigator and is also supported by the NIHR Exeter Clinical Research Facility. JMS acknowledges support from Science for Life Laboratory (Plasma Profiling Facility), Knut and Alice Wallenberg Foundation (Human Protein Atlas) and Erling-Persson Foundation (KTH Centre for Precision Medicine). MIM is supported by the following grants; Wellcome (090532, 098381, 106130, 203141, 212259); NIH (U01-DK105535). PWF is supported by an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742_NASCENT. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
  • BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. METHODS AND FINDINGS: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Ohlsson, MattiasHögskolan i Halmstad,Halmstad University,Lund University,Lunds universitet,Beräkningsbiologi och biologisk fysik - Genomgår omorganisation,Institutionen för astronomi och teoretisk fysik - Genomgår omorganisation,Naturvetenskapliga fakulteten,Computational Biology and Biological Physics - Undergoing reorganization,Department of Astronomy and Theoretical Physics - Undergoing reorganization,Faculty of Science,CAISR Centrum för tillämpade intelligenta system (IS-lab),Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden(Swepub:hh)matohl (författare)
  • Pomares-Millan, HugoLund University,Lunds universitet,Genetisk och molekylär epidemiologi,Forskargrupper vid Lunds universitet,Genetic and Molecular Epidemiology,Lund University Research Groups(Swepub:lu)hu7454po (författare)
  • Koivula, RobertLund University,Lunds universitet,Genetisk och molekylär epidemiologi,Forskargrupper vid Lunds universitet,Genetic and Molecular Epidemiology,Lund University Research Groups,University of Oxford(Swepub:lu)med-rkl (författare)
  • Kurbasic, AzraLund University,Lunds universitet,Genetisk och molekylär epidemiologi,Forskargrupper vid Lunds universitet,Genetic and Molecular Epidemiology,Lund University Research Groups(Swepub:lu)mats-aku (författare)
  • Mutie, PascalLund University,Lunds universitet,Genetisk och molekylär epidemiologi,Forskargrupper vid Lunds universitet,Genetic and Molecular Epidemiology,Lund University Research Groups(Swepub:lu)pa4264mu (författare)
  • Fitipaldi, HugoLund University,Lunds universitet,Genetisk och molekylär epidemiologi,Forskargrupper vid Lunds universitet,Genetic and Molecular Epidemiology,Lund University Research Groups(Swepub:lu)hu3745fi (författare)
  • Fernandez Tajes, JuanLund University,Lunds universitet,Genetisk och molekylär epidemiologi,Forskargrupper vid Lunds universitet,Genetic and Molecular Epidemiology,Lund University Research Groups(Swepub:lu)ju3355fe (författare)
  • Giordano, NickLund University,Lunds universitet,Genetisk och molekylär epidemiologi,Forskargrupper vid Lunds universitet,Genetic and Molecular Epidemiology,Lund University Research Groups(Swepub:lu)med-geg (författare)
  • Franks, PaulLund University,Lunds universitet,Genetisk och molekylär epidemiologi,Forskargrupper vid Lunds universitet,Genetic and Molecular Epidemiology,Lund University Research Groups,Harvard University,Lund Univ, Dept Clin Sci, Genet & Mol Epidemiol Unit, Malmö, Sweden.;Harvard Sch Publ Hlth, Dept Nutr, Boston, MA 02115 USA.(Swepub:lu)med-plf (författare)
  • Dale, MatildaKTH,Affinitets-proteomik,Science for Life Laboratory, SciLifeLab(Swepub:kth)u1n0viuj (författare)
  • Thomas, Cecilia EngelKTH,Affinitets-proteomik,Science for Life Laboratory, SciLifeLab(Swepub:kth)u1ja1scu (författare)
  • Hong, Mun-GwanKTH,Science for Life Laboratory, SciLifeLab(Swepub:kth)u19gmsru (författare)
  • Häussler, Ragna S.KTH,Affinitets-proteomik,Science for Life Laboratory, SciLifeLab(Swepub:kth)u1izv3pd (författare)
  • Schwenk, Jochen M.KTH,Science for Life Laboratory, SciLifeLab,Affinitets-proteomik(Swepub:kth)u1h7wtme (författare)
  • Genetisk och molekylär epidemiologiForskargrupper vid Lunds universitet (creator_code:org_t)
  • et al.

Sammanhörande titlar

  • Ingår i:PLoS MedicineSan Francisco : Public Library of Science (PLoS)17:6, s. 1003149-10031491549-16761549-1277

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