Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts
Creators: name: Atabaki-Pasdar, Naeimeh
name: Ohlsson, Mattias
name: Viñuela, Ana
name: Frau, Francesca
name: Pomares-Millan, Hugo
name: Haid, Mark
name: Jones, Angus G.
name: Thomas, E. Louise
name: Koivula, Robert W.
name: Kurbasic, Azra
name: Mutie, Pascal M.
name: Fitipaldi, Hugo
name: Fernandez, Juan
name: Dawed, Adem Y.
name: Giordano, Giuseppe N.
name: Forgie, Ian M.
name: McDonald, Timothy J.
name: Rutters, Femke
name: Cederberg, Henna
name: Chabanova, Elizaveta
name: Dale, Matilda
name: Masi, Federico De
name: Thomas, Cecilia Engel
name: Allin, Kristine H.
name: Hansen, Tue H.
name: Heggie, Alison
name: Hong, Mun-Gwan
name: Elders, Petra J. M.
name: Kennedy, Gwen
name: Kokkola, Tarja
name: Pedersen, Helle Krogh
name: Mahajan, Anubha
name: McEvoy, Donna
name: Pattou, Francois
name: Raverdy, Violeta
name: Häussler, Ragna S.
name: Sharma, Sapna
name: Thomsen, Henrik S.
name: Vangipurapu, Jagadish
name: Vestergaard, Henrik
name: ‘T Hart, Leen M.
name: Adamski, Jerzy
name: Musholt, Petra B.
name: Brage, Soren
name: Brunak, Søren
name: Dermitzakis, Emmanouil
name: Frost, Gary
name: Hansen, Torben
name: Laakso, Markku
name: Pedersen, Oluf
name: Ridderstråle, Martin
name: Ruetten, Hartmut
name: Hattersley, Andrew T.
name: Walker, Mark
name: Beulens, Joline W. J.
name: Mari, Andrea
name: Schwenk, Jochen M.
name: Gupta, Ramneek
name: McCarthy, Mark I.
name: Pearson, Ewan R.
name: Bell, Jimmy D.
name: Pavo, Imre
name: Franks, Paul W.
Publisher: name: Apollo - University of Cambridge Repository
Year: 2020
Subjects: subject: Research Article
subject: Medicine and health sciences
subject: Biology and life sciences
subject: Research and analysis methods
subject: Computer and information sciences
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences

description: Funder: Henning och Johan Throne-Holsts
description: Funder: Hans Werthén
description: Funder: Swedish Foundation for Strategic Research
description: Funder: NIHR clinical senior lecturer fellowship
description: Funder: Wellcome Trust Senior Investigator
description: Funder: NIHR Exeter Clinical Research Facility
description: Funder: Science for Life Laboratory (Plasma Profiling Facility)
description: Funder: Knut and Alice Wallenberg Foundation (Human Protein Atlas)
description: Funder: Erling-Persson Foundation (KTH Centre for Precision Medicine)
description: 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 (&lt;5% or ≥5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p &lt; 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p &lt; 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (&lt;5% or ≥5%) rather than a continuous one. Conclusions: In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community. Trial registration: ClinicalTrials.gov NCT03814915.