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

Naeimeh Atabaki-Pasdar, Mattias Ohlsson, Ana Viñuela, Francesca Frau, Hugo Pomares-Millan, Mark Haid, Angus G. Jones, E. Louise Thomas, Robert W. Koivula, Azra Kurbasic, Pascal M. Mutie, Hugo Fitipaldi, Juan Fernandez, Adem Y. Dawed, Giuseppe N. Giordano, Ian M. Forgie, Timothy J. McDonald, Femke Rutters, Henna Cederberg, Elizaveta ChabanovaMatilda Dale, Federico de Masi, Cecilia Engel Thomas, Kristine H. Allin, Tue H. Hansen, Alison Heggie, Mun Gwan Hong, Petra J.M. Elders, Gwen Kennedy, Tarja Kokkola, Helle Krogh Pedersen, Anubha Mahajan, Donna McEvoy, Francois Pattou, Violeta Raverdy, Ragna S. Häussler, Sapna Sharma, Henrik S. Thomsen, Jagadish Vangipurapu, Henrik Vestergaard, Leen M. T Hart, Jerzy Adamski, Petra B. Musholt, Soren Brage, Søren Brunak, Emmanouil Dermitzakis, Gary Frost, Torben Hansen, Markku Laakso, Oluf Pedersen, Martin Ridderstråle, Hartmut Ruetten, Andrew T. Hattersley, Mark Walker, Joline W.J. Beulens, Andrea Mari, Jochen M. Schwenk, Ramneek Gupta, Mark I. McCarthy, Ewan R. Pearson, Jimmy D. Bell, Imre Pavo, Paul W. Franks*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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 (<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 < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 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 (<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: and made it available to the community.

Original languageEnglish
Article numbere1003149
JournalPLoS Medicine
Issue number6
Number of pages27
Publication statusPublished - 2020


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