A network model of depressive and anxiety symptoms: a statistical evaluation.
Journal
Molecular psychiatry
ISSN: 1476-5578
Titre abrégé: Mol Psychiatry
Pays: England
ID NLM: 9607835
Informations de publication
Date de publication:
18 Jan 2024
18 Jan 2024
Historique:
received:
12
07
2023
accepted:
07
12
2023
revised:
04
12
2023
medline:
19
1
2024
pubmed:
19
1
2024
entrez:
18
1
2024
Statut:
aheadofprint
Résumé
Although network analysis studies of psychiatric syndromes have increased in recent years, most have emphasized centrality symptoms and robust edges. Broadening the focus to include bridge symptoms within a systematic review could help to elucidate symptoms having the strongest links in network models of psychiatric syndromes. We conducted this systematic review and statistical evaluation of network analyses on depressive and anxiety symptoms to identify the most central symptoms and bridge symptoms, as well as the most robust edge indices of networks. A systematic literature search was performed in PubMed, PsycINFO, Web of Science, and EMBASE databases from their inception to May 25, 2022. To determine the most influential symptoms and connections, we analyzed centrality and bridge centrality rankings and aggregated the most robust symptom connections into a summary network. After determining the most central symptoms and bridge symptoms across network models, heterogeneity across studies was examined using linear logistic regression. Thirty-three studies with 78,721 participants were included in this systematic review. Seventeen studies with 23 cross-sectional networks based on the Patient Health Questionnaire (PHQ) and Generalized Anxiety Disorder (GAD-7) assessments of clinical and community samples were examined using centrality scores. Twelve cross-sectional networks based on the PHQ and GAD-7 assessments were examined using bridge centrality scores. We found substantial variability between study samples and network features. 'Sad mood', 'Uncontrollable worry', and 'Worrying too much' were the most central symptoms, while 'Sad mood', 'Restlessness', and 'Motor disturbance' were the most frequent bridge centrality symptoms. In addition, the connection between 'Sleep' and 'Fatigue' was the most frequent edge for the depressive and anxiety symptoms network model. Central symptoms, bridge symptoms and robust edges identified in this systematic review can be viewed as potential intervention targets. We also identified gaps in the literature and future directions for network analysis of comorbid depression and anxiety.
Sections du résumé
BACKGROUND
BACKGROUND
Although network analysis studies of psychiatric syndromes have increased in recent years, most have emphasized centrality symptoms and robust edges. Broadening the focus to include bridge symptoms within a systematic review could help to elucidate symptoms having the strongest links in network models of psychiatric syndromes. We conducted this systematic review and statistical evaluation of network analyses on depressive and anxiety symptoms to identify the most central symptoms and bridge symptoms, as well as the most robust edge indices of networks.
METHODS
METHODS
A systematic literature search was performed in PubMed, PsycINFO, Web of Science, and EMBASE databases from their inception to May 25, 2022. To determine the most influential symptoms and connections, we analyzed centrality and bridge centrality rankings and aggregated the most robust symptom connections into a summary network. After determining the most central symptoms and bridge symptoms across network models, heterogeneity across studies was examined using linear logistic regression.
RESULTS
RESULTS
Thirty-three studies with 78,721 participants were included in this systematic review. Seventeen studies with 23 cross-sectional networks based on the Patient Health Questionnaire (PHQ) and Generalized Anxiety Disorder (GAD-7) assessments of clinical and community samples were examined using centrality scores. Twelve cross-sectional networks based on the PHQ and GAD-7 assessments were examined using bridge centrality scores. We found substantial variability between study samples and network features. 'Sad mood', 'Uncontrollable worry', and 'Worrying too much' were the most central symptoms, while 'Sad mood', 'Restlessness', and 'Motor disturbance' were the most frequent bridge centrality symptoms. In addition, the connection between 'Sleep' and 'Fatigue' was the most frequent edge for the depressive and anxiety symptoms network model.
CONCLUSION
CONCLUSIONS
Central symptoms, bridge symptoms and robust edges identified in this systematic review can be viewed as potential intervention targets. We also identified gaps in the literature and future directions for network analysis of comorbid depression and anxiety.
Identifiants
pubmed: 38238548
doi: 10.1038/s41380-023-02369-5
pii: 10.1038/s41380-023-02369-5
doi:
Types de publication
Systematic Review
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Universidade de Macau (University of Macau)
ID : MYGR2022-000187-FHS
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature Limited.
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