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

Autor: Atabaki-Pasdar N; Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden., Ohlsson M; Computational Biology and Biological Physics Unit, Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.; Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden., Viñuela A; Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.; Institute for Genetics and Genomics in Geneva, University of Geneva Medical School, Geneva, Switzerland.; Swiss Institute of Bioinformatics, Geneva, Switzerland., Frau F; Sanofi-Aventis Deutschland, Frankfurt am Main, Germany., Pomares-Millan H; Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden., Haid M; Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Neuherberg, Germany., Jones AG; Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, United Kingdom., Thomas EL; Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom., Koivula RW; Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden.; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom., Kurbasic A; Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden., Mutie PM; Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden., Fitipaldi H; Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden., Fernandez J; Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden., Dawed AY; Division of Population Health and Genomics, School of Medicine, University of Dundee, Ninewells Hospital, Dundee, United Kingdom., Giordano GN; Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden., Forgie IM; Division of Population Health and Genomics, School of Medicine, University of Dundee, Ninewells Hospital, Dundee, United Kingdom., McDonald TJ; Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, United Kingdom.; Blood Sciences, Royal Devon and Exeter NHS Foundation Trust, Exeter, United Kingdom., Rutters F; Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands., Cederberg H; Department of Endocrinology, Abdominal Centre, Helsinki University Hospital, Helsinki, Finland., Chabanova E; Department of Diagnostic Radiology, Copenhagen University Hospital Herlev Gentofte, Herlev, Denmark., Dale M; Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden., Masi F; Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark., Thomas CE; Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden., Allin KH; Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.; Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark., Hansen TH; Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.; Department of Cardiology and Endocrinology, Slagelse Hospital, Slagelse, Denmark., Heggie A; Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom., Hong MG; Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden., Elders PJM; Department of General Practice, Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands., Kennedy G; Immunoassay Biomarker Core Laboratory, School of Medicine, University of Dundee, Ninewells Hospital, Dundee, United Kingdom., Kokkola T; Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland., Pedersen HK; Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark., Mahajan A; Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom., McEvoy D; Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom., Pattou F; University of Lille, Inserm, UMR 1190, Translational Research in Diabetes, Department of Endocrine Surgery, CHU Lille, Lille, France., Raverdy V; University of Lille, Inserm, UMR 1190, Translational Research in Diabetes, Department of Endocrine Surgery, CHU Lille, Lille, France., Häussler RS; Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden., Sharma S; German Center for Diabetes Research, Neuherberg, Germany.; Unit of Molecular Epidemiology, Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany., Thomsen HS; Department of Diagnostic Radiology, Copenhagen University Hospital Herlev Gentofte, Herlev, Denmark., Vangipurapu J; Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland., Vestergaard H; Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.; Steno Diabetes Center Copenhagen, Gentofte, Denmark., 't Hart LM; Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands.; Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands.; Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands., Adamski J; Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Neuherberg, Germany.; Lehrstuhl für Experimentelle Genetik, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Technische Universität München, Freising, Germany.; Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore., Musholt PB; Diabetes Division, Research and Development, Sanofi, Frankfurt, Germany., Brage S; MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom., Brunak S; Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.; Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark., Dermitzakis E; Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.; Institute for Genetics and Genomics in Geneva, University of Geneva Medical School, Geneva, Switzerland.; Swiss Institute of Bioinformatics, Geneva, Switzerland., Frost G; Section for Nutrition Research, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom., Hansen T; Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.; Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark., Laakso M; Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.; Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland., Pedersen O; Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark., Ridderstråle M; Clinical Pharmacology and Translational Medicine, Novo Nordisk, Søborg, Denmark., Ruetten H; Sanofi-Aventis Deutschland, Frankfurt am Main, Germany., Hattersley AT; Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, United Kingdom., Walker M; Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom., Beulens JWJ; Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands.; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands., Mari A; Institute of Neuroscience, National Research Council, Padua, Italy., Schwenk JM; Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden., Gupta R; Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark., McCarthy MI; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.; Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom.; OMNI Human Genetics, Genentech, South San Francisco, California, United States of America., Pearson ER; Division of Population Health and Genomics, School of Medicine, University of Dundee, Ninewells Hospital, Dundee, United Kingdom., Bell JD; Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom., Pavo I; Eli Lilly Regional Operations, Vienna, Austria., Franks PW; Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden.; Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, United States of America.
Jazyk: angličtina
Zdroj: PLoS medicine [PLoS Med] 2020 Jun 19; Vol. 17 (6), pp. e1003149. Date of Electronic Publication: 2020 Jun 19 (Print Publication: 2020).
DOI: 10.1371/journal.pmed.1003149
Abstrakt: 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.
Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: PWF is a consultant for Novo Nordisk, Lilly, and Zoe Global Ltd., and has received research grants from numerous diabetes drug companies. HR is an employee and shareholder of Sanofi. MIM: The views expressed in this article are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. MIM has served on advisory panels for Pfizer, NovoNordisk and Zoe Global, has received honoraria from Merck, Pfizer, Novo Nordisk and Eli Lilly, and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. As of June 2019, MIM is an employee of Genentech, and a holder of Roche stock. AM is a consultant for Lilly and has received research grants from several diabetes drug companies.
Databáze: MEDLINE
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