Evidence map›Paper›PMID 33726846›Full record

ArticleMicrobiome2021

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

Taylor A Breuninger, Nina Wawro, Jakob Breuninger, Sandra Reitmeier, Thomas Clavel, Julia Six-Merker, Giulia Pestoni, Sabine Rohrmann, Wolfgang Rathmann, Annette Peters and 4 more

Open access · goldAbstract readVideo-Audio Media
In one paragraph

Article in Microbiome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed, 2 pooled it
5.4field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

55 citing papers in PubMed, 2 syntheses or guidelines pooled it, 82 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 5 institutions in 2 countries.

Taylor A BreuningerIndependent Research Unit Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany. taylor.breuninger@helmholtz-muenchen.de.ORCID 0000-0002-8109-9465
Nina WawroIndependent Research Unit Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.
Jakob BreuningerDelicious Data GmbH, Lichtenbergstr. 8, 85748, Garching, Germany.
Sandra ReitmeierTechnische Universität München, Gregor-Mendel-Str. 2, 85354, Freising, Germany.
Thomas ClavelZIEL - Institute for Food & Health, Technische Universität München, Weihenstephaner Berg 3, 85354, Freising, Germany.
Julia Six-MerkerInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.
Giulia PestoniDivision of Chronic Disease Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, CH-8001, Zurich, Switzerland.
Sabine RohrmannDivision of Chronic Disease Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, CH-8001, Zurich, Switzerland.
Wolfgang RathmannInstitute for Biometrics and Epidemiology, Deutsches Diabetes-Zentrum (DDZ), Auf'm Hennekamp 65, 40225, Düsseldorf, Germany.
Annette PetersInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.
Harald GrallertInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.
Christa MeisingerIndependent Research Unit Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.
Dirk HallerTechnische Universität München, Gregor-Mendel-Str. 2, 85354, Freising, Germany.
Jakob LinseisenIndependent Research Unit Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.
University of Augsburg · DEHelmholtz Zentrum München · DETechnical University of Munich · DEUniversity of Zurich · CHZimmer Biomet (Germany) · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

resultsFecal 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.

conclusionsThe 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. Video abstract.

Indexed as

Diabetes Mellitus, Type 2Gastrointestinal MicrobiomeCross-Sectional StudiesDietFecesHumansRNA, Ribosomal, 16SRNA, Ribosomal, 16S16S rRNA gene sequencingDiabetesDietary intakeenable-ClusterHypertensionNutritionObesitySerum lipids

Identifiers

PMID33726846
PMCPMC7967986
OpenAlexW3138205321

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.