Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts
(2020) In PLoS Medicine 17(6). p.1003149-1003149- 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... (More)
- 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 ( (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3600e2c7-70bb-456a-8559-3e63dfeb7312
- author
- Atabaki Pasdar, Naeimeh LU ; Ohlsson, Mattias LU ; Pomares-Millan, Hugo LU ; Koivula, Robert LU ; Kurbasic, Azra LU ; Mutie, Pascal LU ; Fitipaldi, Hugo LU ; Fernandez Tajes, Juan LU ; Giordano, Nick LU and Franks, Paul LU
- author collaboration
- organization
- publishing date
- 2020
- type
- Contribution to journal
- publication status
- published
- subject
- in
- PLoS Medicine
- volume
- 17
- issue
- 6
- pages
- 1003149 - 1003149
- publisher
- Public Library of Science (PLoS)
- external identifiers
-
- scopus:85086754493
- pmid:32559194
- ISSN
- 1549-1676
- DOI
- 10.1371/journal.pmed.1003149
- project
- AIR Lund - Artificially Intelligent use of Registers
- language
- English
- LU publication?
- yes
- id
- 3600e2c7-70bb-456a-8559-3e63dfeb7312
- date added to LUP
- 2020-07-08 13:45:17
- date last changed
- 2024-04-03 09:48:51
@article{3600e2c7-70bb-456a-8559-3e63dfeb7312, 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 (}}, author = {{Atabaki Pasdar, Naeimeh and Ohlsson, Mattias and Pomares-Millan, Hugo and Koivula, Robert and Kurbasic, Azra and Mutie, Pascal and Fitipaldi, Hugo and Fernandez Tajes, Juan and Giordano, Nick and Franks, Paul}}, issn = {{1549-1676}}, language = {{eng}}, number = {{6}}, pages = {{1003149--1003149}}, publisher = {{Public Library of Science (PLoS)}}, series = {{PLoS Medicine}}, title = {{Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts}}, url = {{http://dx.doi.org/10.1371/journal.pmed.1003149}}, doi = {{10.1371/journal.pmed.1003149}}, volume = {{17}}, year = {{2020}}, }