Evidence map›Paper›PMID 41043132›Full record

ArticleJournal of medical Internet research2025

Gender Differences in Psychosocial Pathways to Depression and Anxiety: Cross-Sectional and Bayesian Causal Network Study.

Han Zhang, Ye Xia, Peicai Fu, Cun Li, Ke Shi, Yuan Yang

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 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

6 authors.

Han Zhang *Department of Neurology and Psychiatry, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue, 1095, Wuhan, 430030, China, +86 13995561816.ORCID http://orcid.org/0000-0003-0016-8973
Ye Xia *Department of Neurology and Psychiatry, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue, 1095, Wuhan, 430030, China, +86 13995561816.ORCID http://orcid.org/0000-0002-3025-5747
Peicai FuDepartment of Neurology and Psychiatry, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue, 1095, Wuhan, 430030, China, +86 13995561816.ORCID http://orcid.org/0000-0002-1841-4186
Cun LiDepartment of Neurology, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China.ORCID http://orcid.org/0000-0002-0387-9896
Ke ShiDepartment of Neurology and Psychiatry, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue, 1095, Wuhan, 430030, China, +86 13995561816.ORCID http://orcid.org/0009-0007-7975-4647
Yuan YangDepartment of Neurology and Psychiatry, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue, 1095, Wuhan, 430030, China, +86 13995561816.ORCID http://orcid.org/0000-0002-4511-8083

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Depression and anxiety are widespread disorders with documented gender differences in symptom progression and associated psychosocial factors. However, the complex interrelationships between childhood trauma, self-esteem, social support, emotion regulation, and their gender-specific impacts on the development of depression and anxiety remain unclear. Objective: The objective of this study was to investigate the network structures of depression, anxiety, and psychosocial factors and to examine the pathways contributing to the development of depression and anxiety, with a focus on gender-specific differences. Methods: This study included 6105 participants from across China, collecting their sociodemographic characteristics and psychological scale data. Cross-sectional network analysis was used to explore the complex relationships between depression, anxiety, insomnia, somatic symptoms, childhood trauma, self-esteem, social support, and emotional regulation. Subsequently, Bayesian network analysis was used to infer potential causal pathways. Gender differences in the network structures were specifically examined. Results: Network analysis revealed strong associations among depression, anxiety, insomnia, and somatic symptoms. Network strength centrality exhibited the highest stability across overall networks (CS-C=0.75), with high predictability for depression (R²=72.4%) and anxiety (R²=64%), supporting the robustness of the model. The network structure invariance test between male and female participants was significant (P=.001). Furthermore, the Bayesian network analysis showed gender-specific symptom progression, where anxiety preceded depression in male participants, while depression preceded anxiety in female participants (with edges retained in nearly 100% of bootstrap samples). Self-esteem, social support, and insomnia were central nodes in female participants, whereas emotion regulation was more influential in male participants. Additionally, childhood trauma influenced depression or anxiety indirectly through self-esteem and social support in both male and female participants. Conclusions: This study presents a novel application of network analyses to delineate distinct gender-specific pathways in the development of depression and anxiety. The findings underscore insomnia, self-esteem, and social support as intervention targets for women and emotion regulation for men. Findings support gender-sensitive mental health strategies and emphasize the need for longitudinal validation.

Indexed as

AnxietyDepressionAdolescentAdultBayes TheoremChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedSelf ConceptSex FactorsSleep Initiation and Maintenance DisordersSocial SupportYoung AdultanxietyBayesian networkdepressiongender differencesnetwork analysis

Identifiers

PMID41043132
PMCPMC12494359

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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.