Evidence map›Paper›PMID 39695082›Full record

ArticleTranslational psychiatry2024

The role of gut microbiota and metabolomic pathways in modulating the efficacy of SSRIs for major depressive disorder.

Ying Jiang, Yucai Qu, Lingyi Shi, Mengmeng Ou, Zhiqiang Du, Zhenhe Zhou, Hongliang Zhou, Haohao Zhu

Abstract read
In one paragraph

Article in Translational psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

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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

8 authors.

Ying Jiang *Affiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, China.
Yucai Qu *Affiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, China.
Lingyi Shi *Affiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, China.
Mengmeng OuAffiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, China.
Zhiqiang DuAffiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, China.
Zhenhe ZhouAffiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, China. zhouzh@jiangnan.edu.cn.ORCID 0000-0002-1334-8335
Hongliang ZhouDepartment of Psychology, The Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China. hongliangzh2022@hotmail.com.ORCID 0000-0002-6496-3346
Haohao ZhuAffiliated Mental Health Center of Jiangnan University, Wuxi, Jiangsu, China. zhuhh@jiangnan.edu.cn.ORCID 0000-0003-2352-4854

Funding

National Natural Science Foundation of China (National Science Foundation of China) 82104244
6 · The paper itself

Abstract

This study aims to explore the mechanism by which gut microbiota influences the antidepressant effects of serotonin reuptake inhibitors (SSRIs) through metabolic pathways. A total of 126 patients were analyzed for their gut microbiota and metabolomics. Patients received SSRI treatment and were categorized into responder and non-responder groups based on changes in their Hamilton Depression Rating Scale (HAMD-17) scores before and after treatment. The association between gut microbiota composition and the efficacy of SSRIs was investigated through 16S rRNA gene sequencing and metabolomic analysis, and a predictive model was developed. As a result, the study found significant differences in gut microbiota composition between the responder and resistant groups. Specific taxa, such as Ruminococcus, Bifidobacterium, and Faecalibacterium, were more abundant in the responder group. Functional analysis revealed upregulation of acetate degradation and neurotransmitter synthesis pathways in the responder group. The machine learning model indicated that gut microbiota and metabolites are potential biomarkers for predicting SSRIs efficacy. In conclusion, gut microbiota influences the antidepressant effects of SSRIs through metabolic pathways. The diversity and function of gut microbiota can serve as biomarkers for predicting the treatment response, providing new insights for personalized treatment.

Indexed as

Gastrointestinal MicrobiomeMajor Depressive DisorderMetabolomicsSelective Serotonin Reuptake InhibitorsAdultFemaleHumansMachine LearningMaleMiddle AgedRNA, Ribosomal, 16STreatment OutcomeRNA, Ribosomal, 16SSelective Serotonin Reuptake Inhibitors

Identifiers

PMID39695082
PMCPMC11655517

What Socratic holds

Textmetadata
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 Socratic graph.