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Foto på Mattias Ohlsson

Mattias Ohlsson

Professor

Foto på Mattias Ohlsson

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

Författare

  • Naeimeh Atabaki Pasdar
  • Mattias Ohlsson
  • Hugo Pomares-Millan
  • Robert Koivula
  • Azra Kurbasic
  • Pascal Mutie
  • Hugo Fitipaldi
  • Juan Fernandez Tajes
  • Nick Giordano
  • Paul Franks

Summary, in English

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 (

Avdelning/ar

  • Genetisk och molekylär epidemiologi
  • EXODIAB: Excellence of Diabetes Research in Sweden
  • eSSENCE: The e-Science Collaboration
  • Beräkningsbiologi och biologisk fysik - Har omorganiserats
  • EpiHealth: Epidemiology for Health

Publiceringsår

2020

Språk

Engelska

Sidor

1003149-1003149

Publikation/Tidskrift/Serie

PLoS Medicine

Volym

17

Issue

6

Dokumenttyp

Artikel i tidskrift

Förlag

Public Library of Science (PLoS)

Ämne

  • Gastroenterology and Hepatology

Status

Published

Projekt

  • AIR Lund - Artificially Intelligent use of Registers

Forskningsgrupp

  • Genetic and Molecular Epidemiology

ISBN/ISSN/Övrigt

  • ISSN: 1549-1676