Evidence map›Paper›PMID 39810263›Full record

ArticleMicrobiome2025

Discovery of robust and highly specific microbiome signatures of non-alcoholic fatty liver disease.

Emmanouil Nychas, Andrea Marfil-Sánchez, Xiuqiang Chen, Mohammad Mirhakkak, Huating Li, Weiping Jia, Aimin Xu, Henrik Bjørn Nielsen, Max Nieuwdorp, Rohit Loomba and 2 more

Abstract read
In one paragraph

Article in Microbiome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
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

16 citing papers in PubMed.

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  8. [Research progress and future prospects for artificial intelligence in the diagnosis and treatment of fatty liver disease].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025
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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

12 authors.

Emmanouil Nychas *Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Beutenbergstraße 11A, Jena, 07745, Germany.
Andrea Marfil-Sánchez *Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Beutenbergstraße 11A, Jena, 07745, Germany.
Xiuqiang ChenDepartment of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Beutenbergstraße 11A, Jena, 07745, Germany.
Mohammad MirhakkakDepartment of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Beutenbergstraße 11A, Jena, 07745, Germany.
Huating LiDepartment of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Diabetes Institute, Shanghai, 200233, China.
Weiping JiaDepartment of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Diabetes Institute, Shanghai, 200233, China.
Aimin XuThe State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong SAR, China.
Henrik Bjørn NielsenClinical Microbiomics, Fruebjergvej 3, Copenhagen, 2100, Denmark.
Max NieuwdorpAmsterdam UMC, Location AMC, Department of Vascular Medicine, University of Amsterdam, Amsterdam, The Netherlands.
Rohit LoombaDepartment of Medicine, MASLD Research Center, University of California, San Diego, La Jolla, CA, 92093, USA.
Yueqiong NiDepartment of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Beutenbergstraße 11A, Jena, 07745, Germany. yueqiong.ni@connect.hku.hk.
Gianni PanagiotouDepartment of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Beutenbergstraße 11A, Jena, 07745, Germany. gianni.panagiotou@leibniz-hki.de.

Funding

San Diego Digestive Diseases Research CenterP30DK120515 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LARS ECKMANN, Bernd G. Schnabl · 2019 to 2026
$10.8M
NIDDK NIH HHS P30 DK120515
6 · The paper itself

Abstract

backgroundThe pathogenesis of non-alcoholic fatty liver disease (NAFLD) with a global prevalence of 30% is multifactorial and the involvement of gut bacteria has been recently proposed. However, finding robust bacterial signatures of NAFLD has been a great challenge, mainly due to its co-occurrence with other metabolic diseases.

resultsHere, we collected public metagenomic data and integrated the taxonomy profiles with in silico generated community metabolic outputs, and detailed clinical data, of 1206 Chinese subjects w/wo metabolic diseases, including NAFLD (obese and lean), obesity, T2D, hypertension, and atherosclerosis. We identified highly specific microbiome signatures through building accurate machine learning models (accuracy = 0.845-0.917) for NAFLD with high portability (generalizable) and low prediction rate (specific) when applied to other metabolic diseases, as well as through a community approach involving differential co-abundance ecological networks. Moreover, using these signatures coupled with further mediation analysis and metabolic dependency modeling, we propose synergistic defined microbial consortia associated with NAFLD phenotype in overweight and lean individuals, respectively.

conclusionOur study reveals robust and highly specific NAFLD signatures and offers a more realistic microbiome-therapeutics approach over individual species for this complex disease. Video Abstract.

Indexed as

BacteriaGastrointestinal MicrobiomeNon-alcoholic Fatty Liver DiseaseAdultFemaleHumansMachine LearningMaleMetagenomicsMiddle AgedObesityGut microbiotaMachine learningMetabolic diseasesMetabolomicsMicrobial consortiaNAFLDNetwork analysis

Identifiers

PMID39810263
PMCPMC11730835

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.