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Mapping the Presence of Anxiety Symptoms in Adults With Major Depressive Disorder

  • Fenfen Ge
  • , Jingwen Jiang
  • , Yue Wang
  • , Mentong Wan
  • , Wei Zhang

Rannsóknarafurð: Framlag til fræðitímaritsGreinritrýni

Útdráttur

Background: Patients with major depressive disorder (MDD) often present with co-occurring anxiety symptoms. The network method provides a novel view on understanding the co-occurrence of depressive and anxiety symptoms. Thus, the purpose of our study was to explore it by applying network analysis methods. Methods: We used electronic medical records from West China Hospital in China. In total, 3,424 patients who met the criteria for MDD were included. R-studio 3.6 was used to estimate the network structure. First, we estimated the network structure of depression and anxiety symptoms using the graphic LASSO algorithm. Then, we estimated the centrality indices of nodes to determine which symptoms are more central in the network. We then estimated the bridge centrality indices using the bridge function via the R package networktools. Results: Some strong connections were found like “easy to wake up,” “wake up early,” and “difficulty falling asleep,” “suicidal thoughts,” and “hopelessness.” “Depressed mood,” “somatic anxiety,” “hopelessness,” “anxiety mood,” and “tension” have the higher centrality indices. Results revealed eight bridge symptoms (e.g., concentration/memory difficulty, gastrointestinal symptoms) in the co-occurrence network structure. Conclusions: This research suggests that the described approach in mapping the presence of anxiety symptoms in individuals with major depression might potentially increase diagnostic precision and help choose more targeted interventions and potentially reduce the occurrence of treatment resistance.

Upprunalegt tungumálEnska
Númer greinar595418
FræðitímaritFrontiers in Psychiatry
Bindi12
DOI
ÚtgáfustaðaÚtgefið - 19 maí 2021

Athugasemd

Funding Information: This study was financially supported by The Special Project on Natural Chronic Non-infectious Diseases (grant no. 2016YFC1307201). Publisher Copyright: © Copyright © 2021 Ge, Jiang, Wang, Wan and Zhang.

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