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Microbiome-derived ethanol in nonalcoholic fatty liver disease

Nature Medicine volume 28, pages 2100–2106 (2022)Cite this article

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Abstract

To test the hypothesis that the gut microbiota of individuals with nonalcoholic fatty liver disease (NAFLD) produce enough ethanol to be a driving force in the development and progression of this complex disease, we performed one prospective clinical study and one intervention study. Ethanol was measured while fasting and 120 min after a mixed meal test (MMT) in 146 individuals. In a subset of 37 individuals and in an external validation cohort, ethanol was measured in portal vein blood. In an intervention study, ten individuals with NAFLD and ten overweight but otherwise healthy controls were infused with a selective alcohol dehydrogenase (ADH) inhibitor before an MMT. When compared to fasted peripheral blood, median portal vein ethanol concentrations were 187 (interquartile range (IQR), 17–516) times higher and increased with disease progression from 2.1 mM in individuals without steatosis to 8.0 mM in NAFL 21.0 mM in nonalcoholic steatohepatitis. Inhibition of ADH induced a 15-fold (IQR,1.6- to 20-fold) increase in peripheral blood ethanol concentrations in individuals with NAFLD, although this effect was abolished after antibiotic treatment. Specifically, Lactobacillaceae correlated with postprandial peripheral ethanol concentrations (Spearman’s rho, 0.42; P < 10−5) in the prospective study. Our data show that the first-pass effect obscures the levels of endogenous ethanol production, suggesting that microbial ethanol could be considered in the pathogenesis of this highly prevalent liver disease.

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Fig. 1: Ethanol concentrations in different blood compartments and studies.
Fig. 2: Gut microbiome analyses of the studies.

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Data availability

Fecal metagenomics and liver transcriptomics data have been deposited in the European Nucleotide Archive (ENA; PRJEB47902) and European Genome-Phenome Archive (EGAS00001005704), respectively. A comprehensive data analysis report can be found at https://amcmc.github.io/BARIA_ETHANASH/.Source data which are provided with this paper.

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Acknowledgements

M.N. is supported by a ZONMW VICI grant 2020 (number 09150182010020). H.H. is supported by a Senior Fellowship of the Dutch Diabetes Research Foundation (2019.82.004). A.S.M. is supported by a Lilly/EFSD grant for Young Investigators. O.A. and V.T. are appointed on a NNF GUTMMM grant 2016 NNF15OC0016798 (to M.N., T.W.S. and F.B.). The study reported here was additionally supported by Le Ducq consortium grant 17CVD01 to F.B. and M.N. M.N, H.H. and A.K.G. are supported by a Dutch Heart Foundation CVON IN CONTROL-2 consortium grant. S.F. has a senior clinical research mandate from the Fund for Scientific Research (FWO) Flanders (1802154N). A.G.H. is supported by the Amsterdam UMC Fellowship, two TKI-PPP grants from Health~Holland and the Gilead Research Scholar grant.

Author information

Authors and Affiliations

  1. Departments of Internal and Experimental Vascular Medicine, Amsterdam University Medical Centers, Location AMC, Amsterdam, the Netherlands

    Abraham S. Meijnikman, Mark Davids, Hilde Herrema, Omrum Aydin, Melany Rios-Morales, Han Levels, Adriaan G. Holleboom, Dees Brandjes, Victor E. A. Gerdes, Albert K. Groen & Max Nieuwdorp

  2. Department of Surgery, Spaarne Hospital, Hoofddorp, the Netherlands

    Abraham S. Meijnikman, Omrum Aydin, Sjoerd Bruin, Maurits de Brauw, Victor E. A. Gerdes & Max Nieuwdorp

  3. Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

    Valentina Tremaroli & Fredrik Bäckhed

  4. Department of Pathology, UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam, the Netherlands

    Joanne Verheij

  5. Department of Clinical Pharmacology, Amsterdam University Medical Centers, Location AMC, Amsterdam, the Netherlands

    Marleen Kemper

  6. Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, the Netherlands

    Maarten E. Tushuizen

  7. Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Thue W. Schwartz & Fredrik Bäckhed

  8. Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden

    Jens Nielsen

  9. Department of Diabetology and Endocrinology, University Hospital Antwerp, Antwerp, Belgium

    Eveline Dirinck, An Verrijken & Christophe E. M. De Block

  10. Department of Gastroenterology and Hepatology, University Hospital Antwerp, Antwerp, Belgium

    Jonas Weyler, Luisa Vonghia & Sven Francque

  11. Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, the Netherlands

    Ulrich Beuers

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  1. Abraham S. Meijnikman
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Contributions

A.S., M.N., A.K., FB. and V.G. conceived and designed the studies. A.S.M., H.H., M.D., O.A., S.B., M.L.D.B. and J.V. managed the clinical studies and collection of stool samples and clinical data. M.K. was responsible for the study medication. A.S.M., O.A., A.G.H. and M.T. were responsible for the inclusion of participants. E.D., J.W., A.V., C.D.B., L.V. and S.F. delivered the samples of the validation cohort. M.D., V.T., F.B., T.S. and J.N. oversaw the processing of the data. M.D., A.K. and A.S.M. analyzed the data. A.S.M., M.D., H.H., U.B., A.K. and M.N. wrote the manuscript, with input from all the authors.

Corresponding author

Correspondence to Max Nieuwdorp.

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Competing interests

M.N. is on the scientific board of Caelus Pharmaceuticals, Netherlands. F.B. is on the scientific board of Metabogen AB, Sweden. However, none of these are directly relevant for the current paper. S.F. has acted as advisor and/or lecturer for Roche, Gilead, Abbvie, Bayer, BMS, MSD, Janssen, Actelion, Astellas, Genfit, Inventiva, Intercept, Genentech, Galmed, Promethera, Coherus and NGM Bio. However, none of these are directly relevant for the current paper. A.G.H. has consulted for Novo Nordisk, Gilead, Amgen, Echosens and Julius Clinical. The remaining authors declare no competing interests.

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Nature Medicine thanks Jasmohan Bajaj and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Jennifer Sargent, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Portal ethanol concentrations linked to NAFLD disease markers in two independent cohorts.

Portal ethanol concentrations linked to histological scores for NAFLD (a–d). Portal vein ethanol in individuals with NAFL and NASH compared to those without steatosis (e). Ethanol concentrations between different SAF-Activity scores (f) and Nonalcoholic Fatty Liver Disease Activity Scores (g). Box plots feature the median (center line), upper and lower quartiles (box limits), 1.5× the interquartile range (whiskers), points outside of boxplot range are outliers. Significant differences were determined by two-tailed Mann–Whitney test (****P < 0.0001;***P < 0.001; **P < 0.01; *P < 0.05).

Source data

Extended Data Fig. 2 Peripheral ethanol concentrations linked to NAFLD disease markers in fasted and post prandial state.

Peripheral ethanol concentrations in relation with histological scores for NAFLD (a–d). Ethanol concentrations in individuals with NAFL and NASH compared to those without steatosis (e). Peripheral ethanol concentrations between different SAF-Activity scores (f) and Nonalcoholic Fatty Liver Disease Activity Scores (g). Box plots feature the median (center line), upper and lower quartiles (box limits), 1.5× the interquartile range (whiskers), points outside of boxplot range are outliers. Significant differences were determined by two-tailed Mann–Whitney test (****P < 0.0001;***P < 0.001; **P < 0.01; *P < 0.05).

Source data

Extended Data Fig. 3 Correlations between portal, fasted and post-prandial peripheral ethanol levels and insulin levels.

Spearman’s Rho correlations are given, and lines represent an overall linear fit, with grey areas the standard error with a 0.95 confidence interval.

Source data

Extended Data Fig. 4 Correlations between portal and peripheral ethanol levels.

Correlations between portal and fasting peripheral ethanol (a) and post prandial peripheral ethanol (b) concentrations. Correlation strength was tested with Spearman’s rank correlation. Colored lines represent a linear fit for each of the NAFLD classes while black represents the overall fit.

Source data

Extended Data Fig. 5 MA plots of liver gene expression in association with NAFLD and ethanol concentrations.

MA plots for fasted peripheral (a), post prandial peripheral (b) and portal (c) ethanol concentrations, between No Steatosis and NAFL (d) and NASH (e). Significant differentially abundant genes are highlighted in blue. NADH2A and CYB2E1 specifically are highlighted in red.

Source data

Extended Data Fig. 6 Small intestinal Streptococcus abundance associates with NAFLD and ethanol concentrations.

Relative abundance is increased in NAFL and NASH (a) and correlates with fasted (b) and post-prandial (c) peripheral and portal (d) ethanol concentrations. Difference in abundance was tested with two-tailed Mann–Whitney test Line represents a linear fit with grey areas the standard error with a 0.95 confidence interval. Spearman’s Rho correlation coefficients are shown. Box plots feature the median (center line), upper and lower quartiles (box limits), 1.5× the interquartile range (whiskers), points outside of boxplot range are outliers.

Source data

Extended Data Fig. 7 Prospective cohort microbiome alpha diversity analysis.

Shannon’s diversity Index with NAFLD classes (a) shown with violin plots, portal ethanol concentrations (b), fasted peripheral ethanol concentrations (c), post prandial ethanol concentrations (d). Differences in alpha diversity between NAFLD classes were determined by Kruskal-Wallis test. Correlations were tested using spearman’s rank correlation.

Source data

Extended Data Fig. 8 Prospective cohort microbiome beta diversity analysis.

NAFLD classes (a), portal ethanol concentrations (b), fasted peripheral ethanol concentrations (c), post prandial ethanol concentrations (d). Bray-Curtis was used as a distance metrics and dissimilarity was tested using permanova using 1000 permutations.

Source data

Extended Data Fig. 9 Prospective cohort microbiome associations.

S. cerevisiae abundance associations with NAFLD classes (a), portal ethanol concentrations (b), fasted peripheral ethanol concentrations (c), post prandial ethanol concentrations (d). Significant differences in abundance were determined by two-tailed Mann–Whitney test (*P < 0.05). Correlations were tested using spearman’s rank correlation.

Source data

Extended Data Fig. 10 Microbiome characteristics of the intervention study.

Alpha- diversity in NASH (a), PCoA of Bray-Curtis dissimilarity between NAFLD class (b), PCoA of Bray Curtis dissimilarity with rate of ethanol accumulation after 4-methylpyrazole infusion (c), Volcano plot with differential taxa abundance analysis (d). Alpha diversity metrics was compared using a two-tailed Mann–Whitney test. Box plots feature the median (center line), upper and lower quartiles (box limits), 1.5× the interquartile range (whiskers), points outside of boxplot range are outliers. Beta diversity was tested using permanova using 1000 permutations. Differential taxa abundance was tested using deSeq2 and observed p values were Benjamini & Hochberg corrected.

Source data

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Table 1. Patient characteristics table portal subcohort. Supplementary Table 2. Patient characteristics table portal validation cohort. Supplementary Table 3. Differential expression and gene set enrichment analysis results. Supplementary Table 4. Correlation analysis between ethanol concentrations and fecal microbiome. Supplementary Table 5. Correlation analysis between KEGG orthologous functions and ethanol levels. Supplementary Table 6 DESeq2 analyses of gut microbiome in the intervention study.

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Meijnikman, A.S., Davids, M., Herrema, H. et al. Microbiome-derived ethanol in nonalcoholic fatty liver disease. Nat Med 28, 2100–2106 (2022). https://doi.org/10.1038/s41591-022-02016-6

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