Evidence map›Paper›PMID 35937875›Full record

ArticleFrontiers in neuroscience2022

Gut microbiome: A potential indicator for predicting treatment outcomes in major depressive disorder.

Zaiquan Dong, Xiaoling Shen, Yanni Hao, Jin Li, Haizhen Xu, Li Yin, Weihong Kuang

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 2 pooled it
–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

36 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Effects ofFrontiers in psychiatry · 2026
    Trial
  4. Trial
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. Characterization of the gut microbiome of wildFrontiers in microbiomes · 2026
    Article
  11. Emerging Therapeutic Interventions for Treatment‑Resistant Depression.Advances in experimental medicine and biology · 2026
    Review
  12. Article
  13. Article
  14. Observational
  15. Review
  16. Review
  17. Article
  18. Review
  19. Article
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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

7 authors.

Zaiquan DongMental Health Center of West China Hospital, Sichuan University, Chengdu, China.
Xiaoling ShenMental Health Center of West China Hospital, Sichuan University, Chengdu, China.
Yanni HaoMental Health Center of West China Hospital, Sichuan University, Chengdu, China.
Jin LiMental Health Center of West China Hospital, Sichuan University, Chengdu, China.
Haizhen XuMental Health Center of West China Hospital, Sichuan University, Chengdu, China.
Li YinMental Health Center of West China Hospital, Sichuan University, Chengdu, China.
Weihong KuangMental Health Center of West China Hospital, Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The therapeutic outcomes in major depressive disorder (MDD), one of the most common and heterogeneous mental illnesses, are affected by factors that remain unclear and often yield unsatisfactory results. Herein, we characterized the composition and metabolic function of the gut microbiota of patients with MDD during antidepressant treatment, based on 16S rRNA sequencing and metabolomics. The microbial signatures at baseline differed significantly between responder and non-responder groups. The gut microbiota of the non-responder group was mainly characterized by increased relative abundances of the phylum Actinobacteria, families Christensenellaceae and Eggerthellaceae, and genera

Indexed as

16S rRNA sequencingdepressiongut microbiotametabolomicstreatment responses

Identifiers

PMID35937875
PMCPMC9354493

What OpenQuestion holds

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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.