Your browser has javascript turned off or blocked. This will lead to some parts of our website to not work properly or at all. Turn on javascript for best performance.

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 Fitipaldi

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

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