Summary of project PR001095

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001095. The data can be accessed directly via it's Project DOI: 10.21228/M85976 This work is supported by NIH grant, U2C- DK119886.

See: https://www.metabolomicsworkbench.org/about/howtocite.php

Project ID: PR001095
Project DOI:doi: 10.21228/M85976
Project Title:Metabolomic signatures of NAFLD
Project Summary: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 delineate 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, a random forest ensemble classification approach was used to both search for valid disease biomarkers and to compare classification performance of lipids, metabolites and clinical factors in combination. Results: We found that steatosis and fibrosis grades were 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 preliminary metabolite and lipid biomarkers to classify NAFLD fibrosis. Conclusions: 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 NAFLD pathogenesis. Using this cohort as a proof-of-concept, we demonstrate current progress in tuning the accuracy random forest approaches with a view to predicting various subtypes of NAFLD patient using a minimal set of lipidomic and metabolic markers. For the first time, 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.
Institute:Örebro University
Last Name:McGlinchey
First Name:Aidan
Address:School of Medical Sciences, Örebro, Örebro, 70281, Sweden
Email:aidan.mcglinchey@oru.se
Phone:+46736485638

Summary of all studies in project PR001095

Study IDStudy TitleSpeciesInstituteAnalysis
(* : Contains Untargted data)
Release
Date
VersionSamplesDownload
(* : Contains raw data)
ST001710 Metabolic signatures of NAFLD - Lipidomics data (part 1 of 3) Homo sapiens Örebro University MS 2021-05-01 1 627 Uploaded data (23.8G)*
ST001711 Metabolic signatures of NAFLD - Polar metabolomics data (part II) Homo sapiens Örebro University MS 2021-05-01 1 627 Uploaded data (147.5G)*
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