Evidence map›Paper›PMID 37641148›Full record

ArticleMicrobiome2023

Multi-omics reveal microbial determinants impacting the treatment outcome of antidepressants in major depressive disorder.

Yaping Wang, Jingjing Zhou, Junbin Ye, Zuoli Sun, Yi He, Yingxin Zhao, Siyu Ren, Guofu Zhang, Min Liu, Peng Zheng and 2 more

Open access · goldAbstract readVideo-Audio Media
In one paragraph

Article in Microbiome, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 1 pooled it
8.2field-weighted citation impact, top 1% 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

54 citing papers in PubMed, 1 synthesis or guideline pooled it, 83 citations in OpenAlex.

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  18. New Personalized Medicine Model for Medication Management.Journal of personalized medicine · 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

12 authors at 4 institutions in 1 country.

Yaping Wang *Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
Jingjing Zhou *Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
Junbin YeBeijing WeGenome Paradigm Co., Ltd, Beijing, China.
Zuoli SunBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
Yi HeBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
Yingxin ZhaoBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
Siyu RenBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
Guofu ZhangBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
Min LiuBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
Peng ZhengDepartment of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
Gang WangBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China. gangwangdoc@ccmu.edu.cn.
Jian YangBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China. yangjian@ccmu.edu.cn.
Capital Medical University · CNBeijing Anding Hospital · CNChinese Institute for Brain Research · CNChongqing Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is a growing body of evidence suggesting that disturbance of the gut-brain axis may be one of the potential causes of major depressive disorder (MDD). However, the effects of antidepressants on the gut microbiota, and the role of gut microbiota in influencing antidepressant efficacy are still not fully understood.

resultsTo address this knowledge gap, a multi-omics study was undertaken involving 110 MDD patients treated with escitalopram (ESC) for a period of 12 weeks. This study was conducted within a cohort and compared to a reference group of 166 healthy individuals. It was found that ESC ameliorated abnormal blood metabolism by upregulating MDD-depleted amino acids and downregulating MDD-enriched fatty acids. On the other hand, the use of ESC showed a relatively weak inhibitory effect on the gut microbiota, leading to a reduction in microbial richness and functions. Machine learning-based multi-omics integrative analysis revealed that gut microbiota contributed to the changes in plasma metabolites and was associated with several amino acids such as tryptophan and its gut microbiota-derived metabolite, indole-3-propionic acid (I3PA). Notably, a significant correlation was observed between the baseline microbial richness and clinical remission at week 12. Compared to non-remitters, individuals who achieved remission had a higher baseline microbial richness, a lower dysbiosis score, and a more complex and well-organized community structure and bacterial networks within their microbiota. These findings indicate a more resilient microbiota community in remitters. Furthermore, we also demonstrated that it was not the composition of the gut microbiota itself, but rather the presence of sporulation genes at baseline that could predict the likelihood of clinical remission following ESC treatment. The predictive model based on these genes revealed an area under the curve (AUC) performance metric of 0.71.

conclusionThis study provides valuable insights into the role of the gut microbiota in the mechanism of ESC treatment efficacy for patients with MDD. The findings represent a significant advancement in understanding the intricate relationship among antidepressants, gut microbiota, and the blood metabolome. Additionally, this study offers a microbiota-centered perspective that can potentially improve antidepressant efficacy in clinical practice. By shedding light on the interplay between these factors, this research contributes to our broader understanding of the complex mechanisms underlying the treatment of MDD and opens new avenues for optimizing therapeutic approaches. Video Abstract.

Indexed as

Major Depressive DisorderAmino AcidsAntidepressive AgentsEscitalopramHumansMultiomicsTreatment OutcomeAmino AcidsAntidepressive AgentsEscitalopramAntidepressantsGut microbiotaMajor depressive disorderSporulation gene

Identifiers

PMID37641148
PMCPMC10464022
OpenAlexW4386222262

What OpenQuestion holds

Textmetadata
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.