Evidence map›Paper›PMID 40394516›Full record

ArticleBMC psychiatry2025

Association of psychosocial factors and biological pathways identified from rare-variant analysis with longitudinal trajectories of treatment response in major depressive disorder.

Haiping Tang, Yan Xia, Chenjie Gao, Yufan Cai, Yongqi Shao, Wenji Chen, Yonggui Yuan, Chunyu Liu, Zhijun Zhang, Zhi Xu

Abstract read
In one paragraph

Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Trial
  2. Symptom-specific genetics reveal heterogeneity within major depressive disorder.medRxiv : the preprint server for health sciences · 2026
    Article
  3. Review
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

10 authors.

Haiping Tang *Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China.
Yan Xia *Department of Molecular Biophysics and Biochemistry, Yale University, CT, New Haven, United States.
Chenjie GaoDepartment of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China.
Yufan CaiDepartment of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China.
Yongqi ShaoDepartment of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China.
Wenji ChenDepartment of General Practice, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China.
Yonggui YuanDepartment of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China.
Chunyu LiuCenter for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China.
Zhijun ZhangDepartment of Neurology, Affiliated Zhongda Hospital, Southeast University, Nanjing, Jiangsu, China. janemengzhang@vip.163.com.
Zhi XuDepartment of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China. slowtherapy@126.com.

Funding

China Science and Technology Innovation 2030 - Major Project 2022ZD0211701, 2021ZD0200700Key Research & Developement Program (Social Development) foundation of Jiangsu Province BE2019714National Natural Science Key Foundation of China 82130042, 81830040National Nature Science Foundation of China 82371534Open Project Programme of the Key Base for Standardized Training for General Physicans, Zhongda Hospital, Southeast University 2022ZYJD15Science and Technology Program of Guangdong 2018B030334001STI2030-Major Projects 2021ZD0200600
6 · The paper itself

Abstract

backgroundAntidepressant efficacy is influenced by a multitude of factors, yet predicting treatment outcomes remains challenging. This difficulty is partly due to the commonly employed dichotomous classifications of treatment response that rely on a single primary endpoint.

methodsThe study enrolled 972 patients diagnosed with depression, including both first-episode and recurrent cases. All patients received treatment with a single class of antidepressant medication over an eight-week period. Treatment response trajectories were identified through cluster analysis using normalized score change ratios from the 17-item Hamilton Rating Scale for Depression (HAMD-17) at baseline and weeks 2, 4, 6, and 8. The impact of psychosocial factors-including childhood trauma experience, social support, and family environment-on these response patterns was evaluated using ANOVA and Tukey's HSD tests. Additionally, targeted exome sequencing was conducted to perform rare-variant burden and enrichment analyses to investigate genetic influences on antidepressant response.

resultsThree patterns of antidepressant treatment response were identified: gradual response (C1 cluster), early response (C2 cluster), and fluctuating response (C3 cluster). Notably, patients in the C3 cluster exhibited higher levels of suicidal ideation, alexithymia, and anhedonia after the treatment period, along with the highest baseline levels of family control (a subscale of the family environment). Our rare-variant analysis revealed genes associated with response efficiency between C1 and C2 clusters to be significantly enriched in the neurotrophin signaling pathway (odds ratio = 23.94; p-adjusted = 6.96e-05). In addition, genes linked to response volatility between C1 and C3 clusters were enriched in the regulation of inflammatory mediators of transient receptor potential (TRP) channels (odds ratio = 31.5; p-adjusted = 1.83e-07).

conclusionsOur findings suggest that patients exhibiting a fluctuating response to antidepressant treatment may endure more severe clinical symptoms throughout the treatment course. The involvement of the neurotrophin signaling pathway and TRP channels in these response patterns highlights their potential as novel targets for therapeutic intervention in depression. This underscores the importance of personalized treatment strategies that consider the underlying genetic and psychological factors influencing antidepressant efficacy.

Indexed as

Antidepressive AgentsMajor Depressive DisorderAdultAdverse Childhood ExperiencesAnhedoniaFemaleHumansLongitudinal StudiesMaleMiddle AgedSocial SupportSuicidal IdeationTreatment OutcomeAntidepressive AgentsAntidepressant efficacyGenetic factorsPsychosocial factorsRare variantsResponse trajectoriesTarget exome sequencing

Identifiers

PMID40394516
PMCPMC12090571

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.