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Gut Microbes Volume 13, 2021 - Issue 1
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Research Paper

Links between gut microbiome composition and fatty liver disease in a large population sample

Matti O. Ruuskanena Department of Internal Medicine, University of Turku, Turku, Finland;b Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, FinlandCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0003-4221-2880View further author information
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Fredrik Åbergc Transplantation and Liver Surgery Clinic, Helsinki University Hospital, University of Helsinki, Helsinki, Finland;d Transplant Institute, Sahlgrenska University Hospital, Gothenburg, SwedenORCID Iconhttps://orcid.org/0000-0002-3833-0705View further author information
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Ville Männistöe Department of Medicine, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland;f Department of Experimental Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The NetherlandsORCID Iconhttps://orcid.org/0000-0002-0735-400XView further author information
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Aki S. Havulinnab Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland;g Institute for Molecular Medicine Finland, FIMM - HiLIFE, Helsinki, FinlandORCID Iconhttps://orcid.org/0000-0002-4787-8959View further author information
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Guillaume Mérich Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia;i Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria, AustraliaORCID Iconhttps://orcid.org/0000-0001-6288-9958View further author information
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Yang Liuh Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia;j Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, AustraliaView further author information
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Rohit Loombak Department of Medicine, NAFLD Research Center, La Jolla, CA, USA;l Department of Medicine, University of California San Diego, La Jolla, CA, USAORCID Iconhttps://orcid.org/0000-0002-4845-9991View further author information
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Yoshiki Vázquez-Baezam Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USAORCID Iconhttps://orcid.org/0000-0001-6014-2009View further author information
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Anupriya Tripathin Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA;o Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA;p Division of Biological Sciences, University of California San Diego, La Jolla, CA, USAORCID Iconhttps://orcid.org/0000-0001-8912-9684View further author information
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Liisa M. Valstab Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, FinlandView further author information
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Michael Inouyeh Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia;q Department of Public Health and Primary Care, Cambridge University, Cambridge, UKORCID Iconhttps://orcid.org/0000-0001-9413-6520View further author information
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Pekka Jousilahtib Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, FinlandView further author information
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Veikko Salomaab Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, FinlandORCID Iconhttps://orcid.org/0000-0001-7563-5324View further author information
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Mohit Jainl Department of Medicine, University of California San Diego, La Jolla, CA, USA;r Department of Pharmacology, University of California San Diego, La Jolla, California, USAORCID Iconhttps://orcid.org/0000-0001-8628-2069View further author information
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Rob Knightm Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA;s Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA;t Department of Computer Science & Engineering, University of California San Diego, La Jolla, California, USAORCID Iconhttps://orcid.org/0000-0002-0975-9019View further author information
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Leo Lahtiu Deparment of Computing, University of Turku, Turku, FinlandORCID Iconhttps://orcid.org/0000-0001-5537-637XView further author information
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Teemu J. Niiranena Department of Internal Medicine, University of Turku, Turku, Finland;b Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland;v Division of Medicine, Turku University Hospital, Turku, FinlandORCID Iconhttps://orcid.org/0000-0002-7394-7487View further author information
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Article: 1888673 | Received 17 Aug 2020, Accepted 28 Jan 2021, Published online: 02 Mar 2021

Figures & data

Figure 1. Distribution of FLI (a), its components (b), and FLI in quantiles of the first three PC components of the fecal bacterial composition of the participants (c). The cutoff at FLI = 60 used to divide the participants is indicated with a dashed line in panels a and c

Figure 1. Distribution of FLI (a), its components (b), and FLI in quantiles of the first three PC components of the fecal bacterial composition of the participants (c). The cutoff at FLI = 60 used to divide the participants is indicated with a dashed line in panels a and c

Figure 2. Relative effects of predictive balances and covariates on the FLI < 60 and FLI ≥ 60 classification model (AUC = 0.75) predictions. Nodes of the balances are indicated in the cladogram and the relative effect sizes of their clades (opposite sides of each balance) are shown in the associated heatmap. The relative effect sizes of the covariates (age and sex) are shown below the legend with a heatmap on the same scale as was used for the balances. The two liver-specific balances associated with triglyceride and GGT levels are indicated with bold font. Clades with redundant information have been collapsed but their major genera are indicated. The complete tree is included in Figure S8.

Figure 2. Relative effects of predictive balances and covariates on the FLI < 60 and FLI ≥ 60 classification model (AUC = 0.75) predictions. Nodes of the balances are indicated in the cladogram and the relative effect sizes of their clades (opposite sides of each balance) are shown in the associated heatmap. The relative effect sizes of the covariates (age and sex) are shown below the legend with a heatmap on the same scale as was used for the balances. The two liver-specific balances associated with triglyceride and GGT levels are indicated with bold font. Clades with redundant information have been collapsed but their major genera are indicated. The complete tree is included in Figure S8.
Supplemental material

Supplemental Material

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Data availability statement

The analysis code written for this study is included with the Supplementary Information. The datasets generated during and analyzed during the current study are not public but are available based on a written application to the THL Biobank as instructed in https://thl.fi/en/web/thl-biobank/for-researchers.

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