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