Unit of medical psychology and behavior medicine, school of public health, Guangxi Medical University, Nanning, Guangxi, China.
Unit of Psychiatry, Department of Public Health and Medicinal Administration, & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China.
Mol Psychiatry. 2024 Mar;29(3):767-781. doi: 10.1038/s41380-023-02369-5. Epub 2024 Jan 18.
Although network analysis studies of psychiatric syndromes have increased in recent years, most have emphasized centrality symptoms and robust edges. Broadening the focus to include bridge symptoms within a systematic review could help to elucidate symptoms having the strongest links in network models of psychiatric syndromes. We conducted this systematic review and statistical evaluation of network analyses on depressive and anxiety symptoms to identify the most central symptoms and bridge symptoms, as well as the most robust edge indices of networks.
A systematic literature search was performed in PubMed, PsycINFO, Web of Science, and EMBASE databases from their inception to May 25, 2022. To determine the most influential symptoms and connections, we analyzed centrality and bridge centrality rankings and aggregated the most robust symptom connections into a summary network. After determining the most central symptoms and bridge symptoms across network models, heterogeneity across studies was examined using linear logistic regression.
Thirty-three studies with 78,721 participants were included in this systematic review. Seventeen studies with 23 cross-sectional networks based on the Patient Health Questionnaire (PHQ) and Generalized Anxiety Disorder (GAD-7) assessments of clinical and community samples were examined using centrality scores. Twelve cross-sectional networks based on the PHQ and GAD-7 assessments were examined using bridge centrality scores. We found substantial variability between study samples and network features. 'Sad mood', 'Uncontrollable worry', and 'Worrying too much' were the most central symptoms, while 'Sad mood', 'Restlessness', and 'Motor disturbance' were the most frequent bridge centrality symptoms. In addition, the connection between 'Sleep' and 'Fatigue' was the most frequent edge for the depressive and anxiety symptoms network model.
Central symptoms, bridge symptoms and robust edges identified in this systematic review can be viewed as potential intervention targets. We also identified gaps in the literature and future directions for network analysis of comorbid depression and anxiety.
尽管近年来精神障碍综合征的网络分析研究有所增加，但大多数研究都强调了中心症状和稳健的边缘。在系统评价中拓宽焦点范围，纳入桥接症状，可以帮助阐明在精神障碍综合征网络模型中具有最强联系的症状。我们进行了这项关于抑郁和焦虑症状的网络分析的系统综述和统计评估，以确定最核心的症状和桥接症状，以及网络中最稳健的边缘指标。
从文献数据库的创立开始到 2022 年 5 月 25 日，我们在 PubMed、PsycINFO、Web of Science 和 EMBASE 数据库中进行了系统文献检索。为了确定最有影响力的症状和连接，我们分析了中心性和桥接中心性排名，并将最稳健的症状连接汇总到一个综合网络中。在确定了跨网络模型的最核心症状和桥接症状后，我们使用线性逻辑回归检查了研究之间的异质性。
这项系统综述共纳入了 33 项研究，涉及 78721 名参与者。我们检查了基于患者健康问卷（PHQ）和广泛性焦虑障碍（GAD-7）评估的临床和社区样本的 17 项研究的中心性得分，这些研究包含 23 个横断面网络。我们还检查了基于 PHQ 和 GAD-7 评估的 12 个横断面网络的桥接中心性得分。我们发现研究样本和网络特征之间存在很大的差异。“悲伤情绪”、“无法控制的担忧”和“过度担忧”是最核心的症状，而“悲伤情绪”、“不安”和“运动障碍”是最常见的桥接中心性症状。此外，“睡眠”和“疲劳”之间的连接是抑郁和焦虑症状网络模型中最常见的边缘。
本系统综述中确定的中心症状、桥接症状和稳健边缘可被视为潜在的干预靶点。我们还发现了文献中的空白，并为抑郁和焦虑共病的网络分析提出了未来的方向。
