Journal article
Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts
Lund University1
Royal Devon & Exeter NHS Foundation Trust2
University of Amsterdam3
University of Helsinki4
University of Copenhagen5
KTH Royal Institute of Technology6
Department of Health Technology, Technical University of Denmark7
Technical University of Denmark8
Bioinformatics, Department of Health Technology, Technical University of Denmark9
Newcastle University10
University of Eastern Finland11
Halmstad University12
Université de Lille13
German Center for Diabetes Research14
Leiden University15
Technical University of Munich16
Sanofi Aventis Deutschland GmbH17
University of Cambridge18
Imperial College London19
University of Southern Denmark20
Novo Nordisk Foundation21
National Research Council of Italy22
University of Geneva23
Disease Data Intelligence, Bioinformatics, Department of Health Technology, Technical University of Denmark24
Eli Lilly GmbH25
Harvard University26
Aventis Pharma Germany GmbH27
Helmholtz Zentrum München - German Research Center for Environmental Health28
University of Exeter29
University of Westminster30
University of Oxford31
University of Dundee32
...and 22 moreBackground 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: https://www.predictliverfat.org/) and made it available to the community.
