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

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 De
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.
Publisher :
Apollo - University of Cambridge Repository

Abstract

Funder: Henning och Johan Throne-Holsts<br />Funder: Hans Werthén<br />Funder: Swedish Foundation for Strategic Research<br />Funder: NIHR clinical senior lecturer fellowship<br />Funder: Wellcome Trust Senior Investigator<br />Funder: NIHR Exeter Clinical Research Facility<br />Funder: Science for Life Laboratory (Plasma Profiling Facility)<br />Funder: Knut and Alice Wallenberg Foundation (Human Protein Atlas)<br />Funder: Erling-Persson Foundation (KTH Centre for Precision Medicine)<br />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 (

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........f834a796a56b33735c8e864146fed2b3