DataSheet1_Exploring core symptoms of alcohol withdrawal syndrome in alcohol use disorder patients: a network analysis approach.docx

BackgroundUnderstanding 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.
MethodsGraphical 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.
ResultsThe 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.
ConclusionsAnger 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.

研究背景：明确酒精使用障碍（AUD）患者的酒精戒断综合征（AWS）精神病理相互作用机制，或可提升AUD复发干预的有效性。精神障碍网络理论认为，精神障碍的持续存在并非源于单一的共性功能障碍，而是源自症状间持续存在的反馈环路，这一理论可解释AWS的持续状态与AUD的高复发率。本研究旨在构建AWS的症状网络，识别其核心症状，并挖掘可作为干预靶点以缓解AWS、打破AUD自我维持循环的症状间关联通路。
研究方法：本研究以553名AUD患者的精神心理症状数据为基础，构建图形化套索（graphical lasso）网络。通过全局网络结构、中心性指标、聚类系数以及桥接症状分析，识别AWS网络的核心症状与不同症状集群间的传递通路。
研究结果：研究结果显示：（1）AWS构成了稳定的症状网络，其稳定性系数（CS）区间为0.21~0.75；（2）愤怒（中心性强度=1.52）与敌意（中心性强度=0.84）为AWS网络中的核心症状，二者中心性最高且聚类系数较低；（3）在AWS网络中，敌意分别介导攻击行为与焦虑的关联，愤怒则分别介导攻击行为与冲动性的关联。
研究结论：愤怒与敌意可作为AWS研究与治疗的最优干预靶点。敌意与焦虑、愤怒与冲动性分别属于独立但相互关联的维度，这提示戒断症状可能涉及不同的神经生物学基础，但这些基础在戒断综合征中发挥相似的作用。