{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Atabaki-Pasdar N"],"funding":["Novo Nordisk Foundation","Innovative Medicines Initiative","Wellcome Trust Senior Investigator","Knut and Alice Wallenberg Foundation","Henning och Johan Throne-Holsts","NNF Center for Basic Metabolic Research","NIH","Erling-Persson Foundation","Science for Life Laboratory","Swedish Foundation for Strategic Research","European Research Council","NIDDK NIH HHS","NIHR Exeter Clinical Research Facility","Steno Diabetes Center Copenhagen (SDCC)","Medical Research Council","National Institute for Health Research (NIHR)","Novo Nordisk Foundation Center for Protein Research","Wellcome Trust","Novo Nordisk Fonden","NIHR clinical senior lecturer fellowship","Hans Werthén"],"pagination":["e1003149"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7304567"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["17(6)"],"pubmed_abstract":["<h4>Background</h4>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.<h4>Methods and findings</h4>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, anth"],"journal":["PLoS medicine"],"pubmed_title":["Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts."],"pmcid":["PMC7304567"],"funding_grant_id":["17/0005624","Hansen Group","NNF18OC0031650","CS-2015-15-018","SDCC 3.F CMP","U01 DK105535","115317 (DIRECT)","106130","U01-DK105535","MC_UU_12015/3","090532","Pedersen Group","NNF17OC0027594","NF-SI-0617-10090","NNF14CC0001","PI Søren Brunak","NNF15OC0016692","681742","203141","MC_UU_00006/4","098381","ERC-2015-CoG - 681742_NASCENT","NF-SI-0616-10080","212259"],"pubmed_authors":["Ohlsson M","McCarthy MI","Cederberg H","Frost G","Haussler RS","Mutie PM","Vinuela A","Vangipurapu J","Bell JD","Dale M","Thomas CE","Hansen TH","Brunak S","Forgie IM","Mahajan A","Fitipaldi H","Rutters F","Kurbasic A","Koivula RW","Frau F","McEvoy D","Dermitzakis E","Walker M","Pavo I","Kennedy G","Sharma S","'t Hart LM","Pedersen HK","Jones AG","Heggie A","Adamski J","Hattersley AT","Atabaki-Pasdar N","Kokkola T","Ridderstrale M","Laakso M","Mari A","Franks PW","Masi F","Haid M","Dawed AY","Allin KH","Musholt PB","Beulens JWJ","Thomsen HS","Raverdy V","Pearson ER","McDonald TJ","Chabanova E","Ruetten H","Elders PJM","Schwenk JM","Gupta R","Fernandez J","Pedersen O","Brage S","Pomares-Millan H","Thomas EL","Giordano GN","Hansen T","Hong MG","Vestergaard H","Pattou F"],"additional_accession":[]},"is_claimable":false,"name":"Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts.","description":"<h4>Background</h4>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.<h4>Methods and findings</h4>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, anth","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Jun","modification":"2026-05-04T10:20:04.935Z","creation":"2025-06-01T12:32:19.38Z"},"accession":"S-EPMC7304567","cross_references":{"pubmed":["32559194"],"doi":["10.1371/journal.pmed.1003149"]}}