Evidence map›Paper›PMID 40295565›Full record

ArticleNPJ biofilms and microbiomes2025

Multi-omics approach identifies gut microbiota variations associated with depression.

Adrián Hernández-Cacho, Jesús F García-Gavilán, Alessandro Atzeni, Prokopis Konstanti, Clara Belzer, Jesús Vioque, Dolores Corella, Montserrat Fitó, Josep Vidal, Virginia Mela and 15 more

Abstract read
In one paragraph

Article in NPJ biofilms and microbiomes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

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  8. Gut-brain axis in health and brain disease.Chinese medical journal · 2026
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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

25 authors.

Adrián Hernández-CachoUniversitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
Jesús F García-GavilánUniversitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain. jesusfrancisco.garcia@urv.cat.
Alessandro AtzeniUniversitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
Prokopis KonstantiLaboratory of Microbiology, Wageningen University, Wageningen, Netherlands.
Clara BelzerLaboratory of Microbiology, Wageningen University, Wageningen, Netherlands.
Jesús VioqueCIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain.
Dolores CorellaCIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
Montserrat FitóCIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
Josep VidalCIBER Diabetes y Enfermedades Metabólicas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain.
Virginia MelaCIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
Liming LiangDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Laura Torres-ColladoCIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain.
Oscar ColtellCIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
Nancy BabioUniversitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
Clary ClishThe Broad Institute of Harvard and MIT, Boston, MA, USA.
Javier Hernando-RedondoCIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
Miguel Á Martínez-GonzálezCIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
Fenglei WangDepartment of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
Isabel Moreno-IndiasCIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
Jiaqi NiUniversitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
Courtney DennisThe Broad Institute of Harvard and MIT, Boston, MA, USA.
Miguel Ruiz-CanelaCIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
Francisco J TinahonesCIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
Frank B HuDepartment of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
Jordi Salas-SalvadóUniversitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain. jordi.salas@urv.cat.

Funding

Lifestyle Interventions, metabolites, microbiome, and diabetes riskR01DK127601 · NIDDK · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI HU, FRANK B, RUIZ-CANELA, MIGUEL · 2021 to 2024
$1.9M
Instituto de Salud Carlos III CPII21/00013Ministerio de Ciencia, Innovación y Universidades, Spain FPU 20/00385NextGenerationEU 2022PMF-INV-01NIDDK NIH HHS R01 DK127601Servicio Público de Empleo Español, Spain 2022PMF-INV-01Universitat Rovira i Virgili 2022PMF-INV-01
6 · The paper itself

Abstract

The gut microbiota plays a potential role in the pathophysiology of depression through the gut-brain axis. This cross-sectional study in 400 participants from the PREDIMED-Plus study investigates the interplay between gut microbiota and depression using a multi-omics approach. Depression was defined as antidepressant use or high Beck Depression Inventory-II scores. Gut microbiota was characterized by 16S rRNA sequencing, and faecal metabolites were analysed via liquid chromatography-tandem mass spectrometry. Participants with depression exhibited significant differences in gut microbial composition and metabolic profiles. Differentially abundant taxa included Acidaminococcus, Christensenellaceae R-7 group, and Megasphaera, among others. Metabolomic analysis revealed 15 significantly altered metabolites, primarily lipids, organic acids, and benzenoids, some of which correlated with gut microbial features. This study highlights the interplay between the gut microbiota and depression, paving the way for future research to determine whether gut microbiota influences depression pathophysiology or reflects changes associated with depression.

Indexed as

BacteriaDepressionGastrointestinal MicrobiomeAgedCross-Sectional StudiesFecesFemaleHumansMaleMetabolomeMetabolomicsMiddle AgedMultiomicsRNA, Ribosomal, 16STandem Mass SpectrometryRNA, Ribosomal, 16S

Identifiers

PMID40295565
PMCPMC12038053

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.