| Title: |
Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts |
| Authors: |
Atabaki-Pasdar, Naeimeh; Ohlsson, Mattias; Viñuela, Ana; Frau, Francesca; Pomares-Millan, Hugo; Haid, Mark; Jones, Angus G.; Thomas, E. Louise; Koivula, Robert W.; Kurbasic, Azra; Mutie, Pascal M.; Fitipaldi, Hugo; Fernandez, Juan; Dawed, Adem Y.; Giordano, Giuseppe N.; Forgie, Ian M.; Mcdonald, Timothy J.; Rutters, Femke; Cederberg, Henna; Chabanova, Elizaveta; Dale, Matilda; Masi, Federico; Thomas, Cecilia Engel; Allin, Kristine H.; Hansen, Tue H.; Heggie, Alison; Hong, Mun-Gwan; Elders, Petra J. M.; Kennedy, Gwen; Kokkola, Tarja; Pedersen, Helle Krogh; Mahajan, Anubha; McEvoy, Donna; Pattou, Francois; Raverdy, Violeta; Häussler, Ragna S.; Sharma, Sapna; Thomsen, Henrik S.; Vangipurapu, Jagadish; Vestergaard, Henrik; 't Hart, Leen M.; Adamski, Jerzy; Musholt, Petra B.; Brage, Soren; Brunak, Søren; Dermitzakis, Emmanouil; Frost, Gary; Hansen, Torben; Laakso, Markku; Pedersen, Oluf; Ridderstråle, Martin; Ruetten, Hartmut; Hattersley, Andrew T.; Walker, Mark; Beulens, Joline W. J.; Mari, Andrea; Schwenk, Jochen M.; Gupta, Ramneek; Mccarthy, Mark I.; Pearson, Ewan R.; Bell, Jimmy D.; Pavo, Imre; Franks, Paul W. |
| Contributors: |
Université de Lille; Inserm; CHU Lille; Lund University; Halmstad University; Swiss Institute of Bioinformatics Genève SIB; Department of Genetic Medicine and Development Geneva; Sanofi-Aventis Deutschland GmbH Francfort, Allemagne; Helmholtz Zentrum München = German Research Center for Environmental Health HMGU; University of Exeter; University of Westminster London UOW; Oxford Centre for Diabetes, Endocrinology and Metabolism OCDEM; Ninewells Hospital and Medical School Dundee; University of Dundee; Amsterdam University Medical Centers Amsterdam UMC; Helsinki University Hospital Finland HUS; Herlev and Gentofte Hospital; School of Engineering Sciences in Chemistry, Biotechnology and Health Stockholm CBH; Danmarks Tekniske Universitet = Technical University of Denmark DTU; Novo Nordisk Foundation Center for Basic Metabolic Research CBMR; Bispebjerg University Hospital BUH; Newcastle University Newcastle; Science for Life Laboratory Solna; University of Eastern Finland; The Wellcome Trust Centre for Human Genetics Oxford; Institute of Cellular Medicine Newcastle; Recherche translationnelle sur le diabète (RTD) - U1190; German Center for Diabetes Research - Deutsches Zentrum für Diabetesforschung Neuherberg DZD; Leiden University Medical Center LUMC; Technische Universität Munchen - Technical University Munich - Université Technique de Munich TUM; National University of Singapore NUS; University of Cambridge UK CAM; University of Copenhagen = Københavns Universitet UCPH; Institute of Genetics and Genomics in Geneva iGE3; Imperial College London; University of Southern Denmark SDU; University of Kuopio; University of Exeter Medical School; Julius Center for Health Sciences and Primary Care; University of Oxford; Genentech, Inc. San Francisco; Harvard School of Public Health |
| Publisher Information: |
Public Library of Science |
| Publication Year: |
2024 |
| Collection: |
LillOA (Lille Open Archive - Université de Lille) |
| Description: |
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 ( |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/octet-stream; application/rdf+xml; charset=utf-8; application/pdf |
| Language: |
English |
| ISSN: |
32559194 |
| Relation: |
info:eu-repo/grantAgreement/EC/FP7/115317/EU/DIabetes REsearCh on patient sTratification/DIRECT; PLoS Medicine; PLoS Med.; http://hdl.handle.net/20.500.12210/103411 |
| Availability: |
https://hdl.handle.net/20.500.12210/103411 |
| Rights: |
CC0 1.0 Universal ; info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.DFF42C82 |
| Database: |
BASE |