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. 2022 Mar 26;4(5):100477.
doi: 10.1016/j.jhepr.2022.100477. eCollection 2022 May.

Metabolic signatures across the full spectrum of non-alcoholic fatty liver disease

Aidan J McGlinchey  1 , Olivier Govaere  2 , Dawei Geng  3 , Vlad Ratziu  4 , Michael Allison  5 , Jerome Bousier  6 , Salvatore Petta  7 , Claudia de Oliviera  8 , Elisabetta Bugianesi  9 , Jörn M Schattenberg  10 , Ann K Daly  2 , Tuulia Hyötyläinen  3 , Quentin M Anstee  2   11 , Matej Orešič  1   12
Affiliations

Metabolic signatures across the full spectrum of non-alcoholic fatty liver disease

Aidan J McGlinchey et al. JHEP Rep. .

Abstract

Background & aims: Non-alcoholic fatty liver disease (NAFLD) is a progressive liver disease with potentially severe complications including cirrhosis and hepatocellular carcinoma. Previously, we have identified circulating lipid signatures associating with liver fat content and non-alcoholic steatohepatitis (NASH). Here, we develop a metabolomic map across the NAFLD spectrum, defining interconnected metabolic signatures of steatosis (non-alcoholic fatty liver, NASH, and fibrosis).

Methods: We performed mass spectrometry analysis of molecular lipids and polar metabolites in serum samples from the European NAFLD Registry patients (n = 627), representing the full spectrum of NAFLD. Using various univariate, multivariate, and machine learning statistical approaches, we interrogated metabolites across 3 clinical perspectives: steatosis, NASH, and fibrosis.

Results: Following generation of the NAFLD metabolic network, we identify 15 metabolites unique to steatosis, 18 to NASH, and 15 to fibrosis, with 27 common to all. We identified that progression from F2 to F3 fibrosis coincides with a key pathophysiological transition point in disease natural history, with n = 73 metabolites altered.

Conclusions: Analysis of circulating metabolites provides important insights into the metabolic changes during NAFLD progression, revealing metabolic signatures across the NAFLD spectrum and features that are specific to NAFL, NASH, and fibrosis. The F2-F3 transition marks a critical metabolic transition point in NAFLD pathogenesis, with the data pointing to the pathophysiological importance of metabolic stress and specifically oxidative stress.

Clinical trials registration: The study is registered at Clinicaltrials.gov (NCT04442334).

Lay summary: Non-alcoholic fatty liver disease is characterised by the build-up of fat in the liver, which progresses to liver dysfunction, scarring, and irreversible liver failure, and is markedly increasing in its prevalence worldwide. Here, we measured lipids and other small molecules (metabolites) in the blood with the aim of providing a comprehensive molecular overview of fat build-up, liver fibrosis, and diagnosed severity. We identify a key metabolic 'watershed' in the progression of liver damage, separating severe disease from mild, and show that specific lipid and metabolite profiles can help distinguish and/or define these cases.

Keywords: 2-HB, 2-hydroxybutanoic acid; 3-HB, 3-hydroxybutanoic acid; ALT, alanine aminotransferase; AST, aspartate aminotransferase; CE, cholesterol ester; Cer, ceramide; FFA, free fatty acid; FLIP, Fatty Liver Inhibition of Progression; Fibrosis; GC, gas chromatography; HCC, hepatocellular carcinoma; HSD, honest significant difference; LC, lipid cluster; LDL, low-density lipoprotein; LM, lipid and metabolite; LMC, lipid, metabolite, and clinical variable; LPC, lysophosphatidylcholine; Lipidomics; Mass spectrometry; Metabolomics; NAFL, non-alcoholic fatty liver; NAFLD, non-alcoholic fatty liver disease; NAS, NASH activity score; NASH, non-alcoholic steatohepatitis; NIDDK NASH-CRN, National Institute of Digestive Diseases and Kidney NASH Clinical Research Network; NRR, non-rejection rate; Non-alcoholic steatohepatitis; PC(O), ether PC; PC, phosphatidylcholine; PCA, principal component analysis; PE, phosphatidylethanolamine; QTOFMS, quadrupole-time-of-flight mass spectrometry; ROC, receiving operator characteristic; SAF, steatosis, activity, and fibrosis; SM, sphingomyelin; T2DM, type 2 diabetes mellitus; TG, triacylglycerol; UHPLC, ultrahigh-performance liquid chromatography.

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Conflict of interest statement

QMA is Coordinator of the EU IMI-2 LITMUS consortium, which is funded by the EU Horizon 2020 programme and EFPIA. He reports research grant funding: Allergan/Tobira, AstraZeneca, GlaxoSmithKline, Glympse Bio, Novartis Pharma AG, and Pfizer Ltd. Consultancy: 89Bio, Allergan/Tobira, Altimmune, AstraZeneca, Axcella, Blade, BMS, BNN Cardio, Cirius, CymaBay, EcoR1, E3Bio, Eli Lilly & Company Ltd., Galmed, Genentech, Genfit SA, Gilead, Grunthal, HistoIndex, Indalo, Intercept Pharma Europe Ltd., Inventiva, IQVIA, Janssen, Madrigal, MedImmune, Medpace, Metacrine, NGMBio, North Sea Therapeutics, Novartis, Novo Nordisk A/S, PathAI, Pfizer Ltd., Poxel, ProSciento, Raptor Pharma, Roche, Servier, Terns, The Medicines Company, and Viking Therapeutics. Speaker: Abbott Laboratories, Allergan/Tobira, BMS, Clinical Care Options, Falk, Fishawack, Genfit SA, Gilead, Integritas Communications, Kenes, and Medscape. Royalties: Elsevier Ltd. Please refer to the accompanying ICMJE disclosure forms for further details.

Figures

None
Graphical abstract
Fig. 1
Fig. 1
Putative partial correlation network. Associations between all study variables were filtered by the application of a NRR (<0.4; see the Materials andMethods section and Fig. S1B) to remove spurious associations. The remaining associations of interest are given as an interaction network, with edge thicknesses representing the strength of association, and the colours showing the direction of association (orange for positive association and blue for negative association). Nodes are collared purely by the dataset from which they originate, for clarity. Colours: LCs (orange), metabolites (red), clinical variables (blue), and blood-derived measures (grey). ALT, alanine aminotransferase; AST, aspartate aminotransferase; LC, lipid cluster; NAS, NASH activity score; NASH, non-alcoholic steatohepatitis; NRR, non-rejection rate; T2DM, type 2 diabetes mellitus.
Fig. 2
Fig. 2
Lipid and polar metabolites associated with NAFLD fibrosis. Heat maps of features changing significantly (p <0.05 in ANOVA and/or Tukey HSD analyses) across (A) Kleiner fibrosis scores and (B) Kleiner steatosis scores. Each coloured cell represents the median value of a given feature across all samples in that fibrosis group. Colour bars denote magnitude of change in the level of that lipid/polar metabolite, with orange/blue depicting relatively higher/lower levels (within feature). Rows are clustered for clarity (dendrogram removed for clarity). All cells in the heat map represent the median value of a given lipid/polar metabolite for all individuals in that fibrosis (A) or steatosis (B) group. (C) A volcano plot depicting the median fold changes (x-axis) occurring in both lipids and metabolites between clinically rated NAFL vs. NASH, those in orange/blue having significantly increased/decreased in NASH. The 4 greatest significances for both increases and decreases in the levels are listed as examples. CE, cholesterol ester; Cer, ceramide; HSD, honest significant difference; LPC, lysophosphatidylcholine; NAFL, non-alcoholic fatty liver; NAFLD, non-alcoholic fatty liver disease; NASH, non-alcoholic steatohepatitis; PC, phosphatidylcholine; PE, phosphatidylethanolamine; SM, sphingomyelin; TG, triacylglycerol.
Fig. 3
Fig. 3
Representative examples of lipid and metabolite changes across fibrosis and steatosis and between NAFL/NASH states. (A) Number of changing metabolites across Kleiner fibrosis stages in all (n = 627) participants, showing the number of metabolites changing between each stage (F1–F4) as compared with F0. (B) Venn diagram depicting which metabolites significantly change across fibrosis stages (light green) and steatosis grades (green) or between NAFL/NASH diagnoses (purple), revealing the existence of both overlapping and, crucially, specific metabolic signatures of 3 clinical perspectives of NAFLD. (C) Levels of lipids/metabolites across the 5 stages of fibrosis as per the Kleiner scoring system (bottom row), the 4 steatosis scores (top row), and between NAFL and NASH (middle row). For each row, 3 representative lipids and 1 metabolite are given. ANOVA and Tukey HSD p values shown in the upper left of each panel only given if they satisfy significance threshold of (p <0.05). Cer, ceramide; HSD, honest significant difference; LPC, lysophosphatidylcholine; NAFL, non-alcoholic fatty liver; NAFLD, non-alcoholic fatty liver disease; NASH, non-alcoholic steatohepatitis; SM, sphingomyelin; TG, triacylglycerol.
Fig. 4
Fig. 4
Lipids and metabolites’ classification of patients with NAFLD before and after the metabolic tipping point of NAFLD. Results of random forest predicting (A and B) NAFLD Kleiner fibrosis score (0–1) vs. (2–4), and (C and D) Kleiner fibrosis score (0–2) vs. (3–4). (A) and (C) show the progress of recursive feature addition, recursively adding the most important features, plotting median AUC of the model as more features are added. (B) and (D) show the ROC curves (main panel) and distribution of AUCs (inset box plot) when using the minimal feature sets (n = 2,001 iterations). LM, lipid and metabolite; LPC, lysophosphatidylcholine; NAFLD, non-alcoholic fatty liver disease; PC, phosphatidylcholine; PE, phosphatidylethanolamine; SM, sphingomyelin; TG, triacylglycerol.
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References

    1. Anstee Q.M., Targher G., Day C.P. Progression of NAFLD to diabetes mellitus, cardiovascular disease or cirrhosis. Nat Rev Gastroenterol Hepatol. 2013;10:330–344. - PubMed
    1. Younossi Z., Anstee Q.M., Marietti M., Hardy T., Henry L., Eslam M., et al. Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention. Nat Rev Gastroenterol Hepatol. 2018;15:11–20. - PubMed
    1. Byrne C.D., Targher G. NAFLD: a multisystem disease. J Hepatol. 2015;62(Suppl. 1):S47–64. - PubMed
    1. Friedman S.L., Neuschwander-Tetri B.A., Rinella M., Sanyal A.J. Mechanisms of NAFLD development and therapeutic strategies. Nat Med. 2018;24:908–922. - PMC - PubMed
    1. Rinella M.E., Tacke F., Sanyal A.J. Anstee QM, participants of the AASLD/EASL Workshop. Report on the AASLD/EASL joint workshop on clinical trial endpoints in NAFLD. J Hepatol. 2019;71:823–833. - PubMed

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