Back to Search
Start Over
Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts
Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts
- Publisher :
- Apollo - University of Cambridge Repository
-
Abstract
- Funder: Henning och Johan Throne-Holsts<br />Funder: Hans Werthén<br />Funder: Swedish Foundation for Strategic Research<br />Funder: NIHR clinical senior lecturer fellowship<br />Funder: Wellcome Trust Senior Investigator<br />Funder: NIHR Exeter Clinical Research Facility<br />Funder: Science for Life Laboratory (Plasma Profiling Facility)<br />Funder: Knut and Alice Wallenberg Foundation (Human Protein Atlas)<br />Funder: Erling-Persson Foundation (KTH Centre for Precision Medicine)<br />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
- Database :
- OpenAIRE
- Accession number :
- edsair.doi...........f834a796a56b33735c8e864146fed2b3