Network characteristics of comorbid symptoms in alcohol use disorder.

Annals of medicine Pub Date : 2025-12-01 Epub Date: 2025-01-13 DOI:10.1080/07853890.2024.2446691
Xin Yu, Wen Zhang, Can Wang, Guolin Mi, Xiuzhe Chen, Yanhu Wang, Xu Chen
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Abstract

Background: Individuals with alcohol use disorder (AUD) often experience symptoms such as anxiety, depression, and decreased sleep quality. Although these are not diagnostic criteria, they may increase dependence risk and complicate treatment. This study aims to analyze comorbidities and their complex relationships in AUD patients through epidemiological surveys and network analysis.

Materials and methods: Using multi-stage stratified cluster random sampling, we selected 27,913 individuals and identified those with AUD for the study. All screened subjects were assessed with the General Health Questionnaire, Pittsburgh Sleep Quality Index, and Simple Coping Style Questionnaire, and diagnosed according to DSM-IV criteria. Network analysis and visualization were performed in R 4.4.0. The qgraph and bootnet packages in R were used to obtain partial correlation network analysis and node centrality of mental health, sleep quality, and coping styles in individuals with AUD through the estimateNetwork function. The bootnet package was used to assess the accuracy and stability of the network. The bnlearn package in R was used to construct directed acyclic graph (DAG) for individuals with AUD using the Bayesian hill-climbing algorithm.

Results: In the partial correlation network, among the three major comorbidity categories, 'anxiety/depression' was most strongly associated with 'sleep quality'. 'Anxiety/depression' and 'sleep quality' had the highest node centrality, with 'sleep latency' also showing notable centrality. The DAG results indicated that 'sleep latency' had the highest probability priority, directly affecting 'anxiety/depression' and key sleep quality symptoms such as 'subjective sleep quality', 'sleep disturbances', 'sleep duration', and 'sleep efficiency', while also indirectly influencing other symptoms.

Conclusions: Among the comorbid symptoms of AUD, sleep latency appears to be a key factor in triggering other comorbid symptoms. This study provides a basis for interventions aimed at reducing the comorbid symptoms of AUD and promoting recovery.

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酒精使用障碍共病症状的网络特征
背景:酒精使用障碍(AUD)患者通常会出现焦虑、抑郁和睡眠质量下降等症状。虽然这些不是诊断标准,但它们可能增加依赖风险并使治疗复杂化。本研究旨在通过流行病学调查和网络分析,分析AUD患者的合并症及其复杂关系。材料和方法:采用多阶段分层整群随机抽样的方法,我们选择了27,913名AUD患者进行研究。采用《一般健康问卷》、《匹兹堡睡眠质量指数》和《简单应对方式问卷》进行评估,并按照DSM-IV标准进行诊断。在r4.4.0中进行网络分析和可视化。使用R中的qgraph和bootnet包,通过estimatenetn函数获得AUD个体心理健康、睡眠质量和应对方式的偏相关网络分析和节点中心性。使用bootnet包来评估网络的准确性和稳定性。使用R中的bnlearn包,使用贝叶斯爬坡算法构建AUD个体的有向无环图(DAG)。结果:在部分相关网络中,在三个主要共病类别中,“焦虑/抑郁”与“睡眠质量”的相关性最强。“焦虑/抑郁”和“睡眠质量”的节点中心性最高,“睡眠潜伏期”也显示出显著的中心性。DAG结果表明,“睡眠潜伏期”具有最高的优先概率,直接影响“焦虑/抑郁”和关键的睡眠质量症状,如“主观睡眠质量”、“睡眠障碍”、“睡眠持续时间”和“睡眠效率”,同时也间接影响其他症状。结论:在AUD的合并症症状中,睡眠潜伏期似乎是引发其他合并症的关键因素。本研究为减少AUD合并症和促进康复的干预措施提供了基础。
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