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


Change log
Authors
Atabaki-Pasdar, Naeimeh  ORCID logo  https://orcid.org/0000-0001-7229-1888
Frau, Francesca 
Pomares-Millan, Hugo  ORCID logo  https://orcid.org/0000-0001-9245-4576
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 (<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. Trial registration: ClinicalTrials.gov NCT03814915.

Description

Funder: Henning och Johan Throne-Holsts


Funder: Hans Werthén


Funder: Swedish Foundation for Strategic Research


Funder: NIHR clinical senior lecturer fellowship


Funder: Wellcome Trust Senior Investigator


Funder: NIHR Exeter Clinical Research Facility


Funder: Science for Life Laboratory (Plasma Profiling Facility)


Funder: Knut and Alice Wallenberg Foundation (Human Protein Atlas)


Funder: Erling-Persson Foundation (KTH Centre for Precision Medicine)

Keywords
Research Article, Medicine and health sciences, Biology and life sciences, Research and analysis methods, Computer and information sciences
Journal Title
PLOS Medicine
Conference Name
Journal ISSN
1549-1277
1549-1676
Volume Title
17
Publisher
Public Library of Science
Sponsorship
Innovative Medicines Initiative (115317 (DIRECT))
European Research Council (ERC-2015-CoG - 681742_NASCENT)
Novo Nordisk Foundation (NNF18OC0031650)
Novo Nordisk Foundation (NNF17OC0027594)
Novo Nordisk Foundation (NNF14CC0001)
Wellcome (090532)
Wellcome (098381)
Wellcome (106130)
Wellcome (203141)
Wellcome (212259)
NIH (U01-DK105535)