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ORIGINAL RESEARCH article

Front. Psychiatry
Sec. Addictive Disorders
Volume 15 - 2024 | doi: 10.3389/fpsyt.2024.1320248

Exploring Core Symptoms of Alcohol Withdrawal Syndrome in Alcohol Use Disorder Patients: A Network Analysis Approach

Provisionally accepted
Gunghui Shen Gunghui Shen 1*Yu-Hsin Chen Yu-Hsin Chen 2Yuyu Wu Yuyu Wu 2Huang Jiahui Huang Jiahui 2Juan Fang Juan Fang 2Tang Jiayi Tang Jiayi 2Kang YImin Kang YImin 3Wei Wang Wei Wang 2Yanlong Liu Yanlong Liu 2Fan Wang Fan Wang 4Li Chen Li Chen 2*
  • 1 Wenzhou Seventh People’s Hospital, Wenzhou, Zhejiang Province, China
  • 2 Wenzhou Medical University, Wenzhou, Zhejiang Province, China
  • 3 Inner Mongolia Medical College, Hohhot, Inner Mongolia Autonomous Region, China
  • 4 Beijing Huilongguan Hospital, Peking University, Beijing, Beijing Municipality, China

The final, formatted version of the article will be published soon.

    Background: Understanding the interplay between psychopathology of alcohol withdrawal syndrome (AWS) in alcohol use disorder (AUD) patients may improve the effectiveness of relapse interventions for AUD. Network theory of mental disorders assumes that mental disorders persist not of a common functional disorder, but from a sustained feedback loop between symptoms, thereby explaining the persistence of AWS and the high relapse rate of AUD. The current study aims to establish a network of AWS, identify its core symptoms and find the bridges between the symptoms which are intervention target to relieve the AWS and break the self-maintaining cycle of AUD. Methods: Graphical lasso network were constructed using psychological symptoms of 553 AUD patients. Global network structure, centrality indices, cluster coefficient, and bridge symptom were used to identify the core symptoms of the AWS network and the transmission pathways between different symptom clusters. Results: The results revealed that: (1) AWS constitutes a stable symptom network with a stability coefficient (CS) of 0.21-0.75. (2) Anger (Strength = 1.52) and hostility (Strength = 0.84) emerged as the core symptom in the AWS network with the highest centrality and low clustering coefficient. (3) Hostility mediates aggression and anxiety, anger mediates aggression and impulsivity in AWS network respectively. Conclusions: Anger and hostility may be considered the best intervention targets for researching and treating AWS. Hostility and anxiety, anger and impulsiveness are independent but related dimensions, suggesting that different neurobiological bases may be involved in withdrawal symptoms, which play a similar role in withdrawal syndrome.

    Keywords: alcohol use disorder, Alcohol withdrawal syndrome, Network analysis, Psychopathology, LASSO

    Received: 12 Oct 2023; Accepted: 07 Aug 2024.

    Copyright: © 2024 Shen, Chen, Wu, Jiahui, Fang, Jiayi, YImin, Wang, Liu, Wang and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Gunghui Shen, Wenzhou Seventh People’s Hospital, Wenzhou, Zhejiang Province, China
    Li Chen, Wenzhou Medical University, Wenzhou, 325035, Zhejiang Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.