Network characteristics of comorbid symptoms in alcohol use disorder

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.

Keywords: Alcohol use disorder; coping styles; epidemiological surveys; mental health; network analysis method; sleep quality.

Figure 1.

Epidemiological survey framework.

Figure 2.

Network structure of comorbid symptoms with AUD. Green lines indicate positive relationships. The thicker the edge, the greater the correlation between the two variables; the thinner the edge, the smaller the correlation; A1: anxiety/depression; A2: social dysfunction; A3: loss of confidence; B1: sleep quality; B2: sleep latency; B3: sleep duration; B4: sleep efficiency; B5: sleep disturbances; B6: sleep medication; B7: daytime dysfunction; C: coping styles.

Figure 3.

The expected influence measure for the network structure among AUD patients (Z). A1: anxiety/depression; A2: social dysfunction; A3: loss of confidence; B1: sleep quality; B2: sleep latency; B3: sleep duration; B4: sleep efficiency; B5: sleep disturbances; B6: sleep medication; B7: daytime dysfunction; C: coping styles.

Figure 4.

Stability of centrality indices by case dropping subset bootstrap. Red lines indicate the average correlation between the expected influence in the original sample and the expected influence in the subsample.

Figure 5.

Bootstrapped confidence intervals of estimated edge weights. Red lines indicate the edge weights in the study sample, black lines represent the average edge weights estimated by the bootstrap, and the gray area represents the confidence intervals derived from the bootstrap.

Figure 6.

DAG of common comorbid symptoms with AUD. The edge thickness represents the significance of model fit; A1: anxiety/depression; A2: social dysfunction; A3: loss of confidence; B1: sleep quality; B2: sleep latency; B3: sleep duration; B4: sleep efficiency; B5: sleep disturbances; B6: sleep medication; B7: daytime dysfunction; C: coping styles.