
Mattias Ohlsson
Professor

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