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- McGlinchey, Aidan J, 1984-, et al.
(författare)
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Metabolomics approaches to identify biomarkers of nonalcoholic fatty liver disease
- 2020
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Ingår i: Journal of Hepatology. - : Elsevier. - 0168-8278 .- 1600-0641. ; 73:Suppl. 1, s. S438-S438
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background and Aims: Nonalcoholic fatty liver disease (NAFLD) is a progressive liver disease that is strongly associated with type 2 diabetes. Accurate, non-invasive diagnostic tests to deliniate the different stages: degree of steatosis, grade of nonalcoholic steatohepatitis (NASH) and stage fibrosis represent an unmet medical need. In our previous studies, we successfully identified specific serum molecular lipid signatures which associate with the amount of liver fat as well as with NASH. Here we report underlying associations between clinical data, lipidomic profiles, metabolic profiles and clinical outcomes, including downstream identification of potential biomarkers for various stages of the disease.Method: We leverage several statistical and machine-learning approaches to analyse clinical, lipidomic and metabolomic profiles of individuals from the European Horizon 2020 project: Elucidating Pathways of Steatohepatitis (EPoS). We interrogate data on patients representing the full spectrum of NAFLD/NASH derived from the EPoS European NAFLD Registry (n = 627). We condense the EPoS lipidomic data into lipid clusters and subsequently apply non-rejection-rate-pruned partial correlation network techniques to facilitate network analysis between the datasets of lipidomic, metabolomic and clinical data. For biomarker identification, random forest ensemble classification and neural network machine learning approaches were used to both search for valid disease biomarkers and to assess the relative improvement over clinical-data-only classification versus addition of our lipidomic and metabolomic datasets.Results: We found that steatosis grade was strongly associated with (1) an increase of triglycerides with low carbon number and double bond count as well as (2) a decrease of specific phospholipids, including lysophosphatidylcholines. In addition to the network topology as a result itself, we also present lipid clusters (LCs) of interest to the derived network of proposed interactions in our NAFLD data from the EPoS cohort, along with our proposed biomarkers for various disease outcomes, as put forward by our current machine learning analyses.Conclusion: Our findings suggest that dysregulation of lipid metabolism in progressive stages of NAFLD is reflected in circulation and may thus hold diagnostic value as well as offer new insights about the NAFLD pathogenesis. Using this cohort as a proof-of-concept, we demonstrate current progress in tuning the accuracy of neural network and random forest approaches with a view to predicting various subtypes of NAFLD patient using a minimal set of lipidomic and metabolic markers. A detailed network-based picture emerges between lipids, polar metabolites and clinical variables. Lipidomic/metabolomic markers may provide an alternative method of NAFLD patient classification and risk stratification to guide therapy.
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| 2. |
- Sen, Parho, et al.
(författare)
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Metabolism of human liver on a genome scale in non-alcoholic fatty liver disease
- 2020
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Ingår i: Journal of Hepatology. - : Elsevier. - 0168-8278 .- 1600-0641. ; 73:Suppl. 1, s. S671-S672
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background and Aims: Non-alcoholic fatty liver disease (NAFLD) is a major risk factor leading to chronic liver disease and type 2 diabetes. By using patient-matched liver transcriptomics and serum metabolomics data from the EPoS European NAFLD Registry cohort, we conducted genome-scale metabolic modeling (GSMM) to dissect hepatic metabolism across the full spectrum of NAFLD, from steatosis (NAFL) to NASH-cirrhosis.Method: We compared the genome-scale metabolic networks across different stages of NAFLD together with healthy controls (HC, n = 10), with the patients divided into three groups: steatosis (n = 60), NASH (n = 139; F0: n = 4, F1 n = 28, F2: n = 53, F3: n = 54) and cirrhosis (n = 14). Based on transcriptomics data obtained from the liver biopsy of the patients enrolled in the European NAFLD Registry, genome-scale metabolic models of the liver were developed and contextualized for these conditions. GSMM, as a scaffold, connects metabolic genes (i.e., enzymes) and metabolic pathways. Moreover, genome-scale networks can be constrained with multi-‘omics’ datasets, and thus connect an organism’s genotype to phenotype.Results: GSMM revealed that similar metabolic functions are perturbed in NAFL and NASH, while additional metabolic processes were regulated in advanced fibrosis/cirrhosis. The primary liver processes such as glycerophospholipid metabolism, chondroitin/heparan sulfate, bile acid and fatty acid biosynthesis and oxidation (carnitine shuttle in mitochondria) were affected. Lipid precursors for VLDL particles were upregulated in NAFL. Integrative analysis of transcriptomics and serum metabolomics data also revealed that several microbial pathways are up-regulated in NAFLD and may contribute to pathogenesis.Conclusion: A GSMM approach has identified common and specific liver metabolic pathways across different stages of NAFLD progression. Data were cross-validated by serum metabolomics, where in addition analysis also revealed that specific microbially-produced metabolites are elevated in NAFLD as compared to controls. These results provide important insights into the changes in hepatic metabolism occurring during NAFLD/NASH pathogenesis.
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