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      Associations between habitual diet, metabolic disease, and the gut microbiota using latent Dirichlet allocation

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          Abstract

          Background

          The gut microbiome impacts human health through various mechanisms and is involved in the development of a range of non-communicable diseases. Diet is a well-known factor influencing microbe-host interaction in health and disease. However, very few findings are based on large-scale analysis using population-based studies. Our aim was to investigate the cross-sectional relationship between habitual dietary intake and gut microbiota structure in the Cooperative Health Research in the Region of Augsburg (KORA) FF4 study.

          Results

          Fecal microbiota was analyzed using 16S rRNA gene amplicon sequencing. Latent Dirichlet allocation (LDA) was applied to samples from 1992 participants to identify 20 microbial subgroups within the study population. Each participant’s gut microbiota was subsequently described by a unique composition of these 20 subgroups. Associations between habitual dietary intake, assessed via repeated 24-h food lists and a Food Frequency Questionnaire, and the 20 subgroups, as well as between prevalence of metabolic diseases/risk factors and the subgroups, were assessed with multivariate-adjusted Dirichlet regression models. After adjustment for multiple testing, eight of 20 microbial subgroups were significantly associated with habitual diet, while nine of 20 microbial subgroups were associated with the prevalence of one or more metabolic diseases/risk factors. Subgroups 5 ( Faecalibacterium, Lachnospiracea incertae sedis, Gemmiger, Roseburia) and 14 ( Coprococcus, Bacteroides, Faecalibacterium, Ruminococcus) were particularly strongly associated with diet. For example, participants with a high probability for subgroup 5 were characterized by a higher Alternate Healthy Eating Index and Mediterranean Diet Score and a higher intake of food items such as fruits, vegetables, legumes, and whole grains, while participants with prevalent type 2 diabetes mellitus were characterized by a lower probability for subgroup 5.

          Conclusions

          The associations between habitual diet, metabolic diseases, and microbial subgroups identified in this analysis not only expand upon current knowledge of diet-microbiota-disease relationships, but also indicate the possibility of certain microbial groups to be modulated by dietary intervention, with the potential of impacting human health. Additionally, LDA appears to be a powerful tool for interpreting latent structures of the human gut microbiota. However, the subgroups and associations observed in this analysis need to be replicated in further studies.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s40168-020-00969-9.

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          Most cited references65

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          The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

          SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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            UPARSE: highly accurate OTU sequences from microbial amplicon reads.

            Amplified marker-gene sequences can be used to understand microbial community structure, but they suffer from a high level of sequencing and amplification artifacts. The UPARSE pipeline reports operational taxonomic unit (OTU) sequences with ≤1% incorrect bases in artificial microbial community tests, compared with >3% incorrect bases commonly reported by other methods. The improved accuracy results in far fewer OTUs, consistently closer to the expected number of species in a community.
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              UCHIME improves sensitivity and speed of chimera detection

              Motivation: Chimeric DNA sequences often form during polymerase chain reaction amplification, especially when sequencing single regions (e.g. 16S rRNA or fungal Internal Transcribed Spacer) to assess diversity or compare populations. Undetected chimeras may be misinterpreted as novel species, causing inflated estimates of diversity and spurious inferences of differences between populations. Detection and removal of chimeras is therefore of critical importance in such experiments. Results: We describe UCHIME, a new program that detects chimeric sequences with two or more segments. UCHIME either uses a database of chimera-free sequences or detects chimeras de novo by exploiting abundance data. UCHIME has better sensitivity than ChimeraSlayer (previously the most sensitive database method), especially with short, noisy sequences. In testing on artificial bacterial communities with known composition, UCHIME de novo sensitivity is shown to be comparable to Perseus. UCHIME is >100× faster than Perseus and >1000× faster than ChimeraSlayer. Contact: robert@drive5.com Availability: Source, binaries and data: http://drive5.com/uchime. Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                taylor.breuninger@helmholtz-muenchen.de
                nina.wawro@helmholtz-muenchen.de
                jakob.breuninger@gmail.com
                sandra.reitmeier@tum.de
                tclavel@ukaachen.de
                julia.six-merker@helmholtz-muenchen.de
                giulia.pestoni@uzh.ch
                sabine.rohrmann@uzh.ch
                wolfgang.rathmann@ddz.de
                peters@helmholtz-muenchen.de
                harald.grallert@helmholtz-muenchen.de
                c.meisinger@unika-t.de
                dirk.haller@tum.de
                j.linseisen@unika-t.de
                Journal
                Microbiome
                Microbiome
                Microbiome
                BioMed Central (London )
                2049-2618
                16 March 2021
                16 March 2021
                2021
                : 9
                : 61
                Affiliations
                [1 ]GRID grid.4567.0, ISNI 0000 0004 0483 2525, Independent Research Unit Clinical Epidemiology, , Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), ; Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
                [2 ]GRID grid.5252.0, ISNI 0000 0004 1936 973X, Ludwig-Maximilians-Universität München, , UNIKA-T Augsburg, ; Neusässer Str. 47, 86156 Augsburg, Germany
                [3 ]Delicious Data GmbH, Lichtenbergstr. 8, 85748 Garching, Germany
                [4 ]GRID grid.6936.a, ISNI 0000000123222966, Technische Universität München, ; Gregor-Mendel-Str. 2, 85354 Freising, Germany
                [5 ]GRID grid.6936.a, ISNI 0000000123222966, ZIEL - Institute for Food & Health, , Technische Universität München, ; Weihenstephaner Berg 3, 85354 Freising, Germany
                [6 ]GRID grid.412301.5, ISNI 0000 0000 8653 1507, Functional Microbiome Research Group, Institute of Medical Microbiology, , RWTH University Hospital, ; Pauwelsstrasse 30, 52074 Aachen, Germany
                [7 ]GRID grid.4567.0, ISNI 0000 0004 0483 2525, Institute of Epidemiology, , Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), ; Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
                [8 ]GRID grid.7400.3, ISNI 0000 0004 1937 0650, Division of Chronic Disease Epidemiology, Epidemiology, Biostatistics and Prevention Institute, , University of Zurich, ; Hirschengraben 84, CH-8001 Zurich, Switzerland
                [9 ]GRID grid.429051.b, ISNI 0000 0004 0492 602X, Institute for Biometrics and Epidemiology, , Deutsches Diabetes-Zentrum (DDZ), ; Auf’m Hennekamp 65, 40225 Düsseldorf, Germany
                Author information
                http://orcid.org/0000-0002-8109-9465
                Article
                969
                10.1186/s40168-020-00969-9
                7967986
                33726846
                47e0f61f-643d-4144-aca1-3e7aea467464
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 5 October 2020
                : 6 December 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100002347, Bundesministerium für Bildung und Forschung;
                Award ID: 01EA1409E
                Funded by: Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH) (4209)
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

                enable-cluster,16s rrna gene sequencing,nutrition,dietary intake,diabetes,serum lipids,obesity,hypertension

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