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

Research output: Contribution to journalArticle

Abstract

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

Authors
Organisations
External organisations
  • Halmstad University
  • University of Oxford
  • Harvard University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Gastroenterology and Hepatology
Original languageEnglish
Pages (from-to)e1003149
JournalPLoS Medicine
Volume17
Issue number6
Publication statusPublished - 2020
Publication categoryResearch
Peer-reviewedYes