Indexed in

License and use

Citations

Cited 2 times in

Cited 2 times in

Altmetrics

Grant support

The authors thank all PREDIMED-Plus participants and investigators. CIBEROBN, CIBERESP, and CIBERDEM are initiative of the Instituto de Salud Carlos III (ISCIII), Madrid, Spain. The authors also thank the PREDIMED-Plus Biobank Network as a part of the National Biobank Platform of the ISCIII for storing and managing the PREDIMED-Plus biological samples. This work was supported by the official Spanish Institutions for funding scientific biomedical research, CIBER Fisiopatologia de la Obesidad y Nutricion (CIBEROBN) and Instituto de Salud Carlos III (ISCIII), through the Fondo de Investigacion para la Salud (FIS), which is co-funded by the European Regional Development Fund (six coordinated FIS projects lead by J.S.-S. and J.Vi., including the following projects: PI13/00233, PI13/00728, PI13/00462, PI14/01206, PI14/ 00696, PI16/00533, PI16/00366, PI16/00501, PI17/01441, PI17/00855, PI19/00017, PI19/00781, PI19/00576, PI20/00557, PI21/0046; the Especial Action Project entitled: Implementacion y evaluacion de una intervencion intensiva sobre la actividad fisica Cohorte PREDIMED-Plus grant to J.S.-S.; the Recercaixa (number 2013ACUP00194) grant to J.S.-S.; grants from the Consejeria de Salud de la Junta de Andalucia (PI0458/2013, PS0358/2016, PI0137/2018); the PROMETEO/ 2017/017 and PROMETEO/2021/21 grants from the Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital from the Generalitat Valenciana; and by NIH grant R01DK127601. This research was also partially funded by the Eat2beNICE/H2020-SFS-2016-2 EU- H2020 European grant, and the Horizon 2020 PRIME study (Prevention and Remediation of Insulin Multimorbidity in Europe; grant agreement #847879). J.S.-S., the senior author of this paper, was partially supported by ICREA under the ICREA Academia programme. A.H.-C. is supported by a predoctoral grant from Marti Franques- INVESTIGO research fellowship funded and supported by NextGenerationEU, Servicio Publico de Empleo Estatal and Universitat Rovira i Virgili (2022PMF-INV-01). I.M.-I. was supported by a Miguel Servet type II grant from Instituto de Salud Carlos III, Madrid, Spain (CPII21/00013). J.N. is supported by a predoctoral grant from Ministerio de Ciencia, Innovacion y Universidades (FPU 20/00385). None of the funding sources took part in the design, collection, analysis, interpretation of the data, writing the report, or in the decision to submit the manuscript for publication.

Analysis of institutional authors

Share

Multi-omics approach identifies gut microbiota variations associated with depression

Publicated to:Npj Biofilms And Microbiomes. 11 (1): 68- - 2025-04-28 11(1), DOI: 10.1038/s41522-025-00707-9

Authors: Hernandez-Cacho, Adrian; Garcia-Gavilan, Jesus F; Atzeni, Alessandro; Konstanti, Prokopis; Belzer, Clara; Vioque, Jesus; Corella, Dolores; Fito, Montserrat; Vidal, Josep; Mela, Virginia; Liang, Liming; Torres-Collado, Laura; Coltell, Oscar; Babio, Nancy; Clish, Clary; Hernando-Redondo, Javier; Martinez-Gonzalez, Miguel A; Wang, Fenglei; Moreno-Indias, Isabel; Ni, Jiaqi; Dennis, Courtney; Ruiz-Canela, Miguel; Tinahones, Francisco J; Hu, Frank B; Salas-Salvado, Jordi

Affiliations

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.

Keywords