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

Atabaki Pasdar, Naeimeh LU orcid ; Ohlsson, Mattias LU orcid ; Pomares-Millan, Hugo LU orcid ; Koivula, Robert LU ; Kurbasic, Azra LU ; Mutie, Pascal LU ; Fitipaldi, Hugo LU ; Fernandez Tajes, Juan LU ; Giordano, Nick LU and Franks, Paul LU (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)
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author collaboration
organization
publishing date
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}},
}