The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Default user image.

Hugo Pomares-Millan

Doctoral student

Default user image.

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

Author

  • 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 (

Department/s

  • Genetic and Molecular Epidemiology
  • EXODIAB: Excellence of Diabetes Research in Sweden
  • eSSENCE: The e-Science Collaboration
  • Computational Biology and Biological Physics
  • EpiHealth: Epidemiology for Health

Publishing year

2020

Language

English

Pages

1003149-1003149

Publication/Series

PLoS Medicine

Volume

17

Issue

6

Document type

Journal article

Publisher

Public Library of Science (PLoS)

Topic

  • Gastroenterology and Hepatology

Status

Published

Project

  • AIR Lund - Artificially Intelligent use of Registers

Research group

  • Genetic and Molecular Epidemiology

ISBN/ISSN/Other

  • ISSN: 1549-1676