Department of Psychology, University of Greifswald, Greifswald, Germany.
Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany.
Depress Anxiety. 2021 Mar;38(3):307-317. doi: 10.1002/da.23136. Epub 2021 Jan 19.
Comorbidities in mental disorders are often understood by assuming a common cause. The network theory of mental disorders offers an alternative to this assumption by understanding comorbidities as mutually reinforced problems. In this study, we used network analysis to examine bridge symptoms between anxiety and depression in a large sample.
Using data from a sample of patients diagnosed with both depression and an anxiety disorder before and after inpatient treatment (N = 5,614, mean age: 42.24, 63.59% female, average treatment duration: 48.12 days), network models of depression and anxiety symptoms are estimated. Topology, the centrality of nodes, stability, and changes in network structure are analyzed. Symptoms that drive comorbidity are determined by bridge node analysis. As an alternative to network communities based on categorical diagnosis, we performed a community analysis and propose empirically derived symptom subsets.
The obtained network models are highly stable. Sad mood and the inability to control worry are the most central. Psychomotor agitation or retardation is the strongest bridge node between anxiety and depression, followed by concentration problems and restlessness. Changes in appetite and suicidality were unique to depression. Community analysis revealed four symptom groups.
The estimated network structure of depression and anxiety symptoms proves to be highly accurate. Results indicate that some symptoms are considerably more influential than others and that only a small number of predominantly physical symptoms are strong candidates for explaining comorbidity. Future studies should include physiological measures in network models to provide a more accurate understanding.
精神障碍的合并症通常被理解为具有共同的病因。精神障碍的网络理论提供了一种替代假设，即通过将合并症理解为相互强化的问题来理解合并症。在这项研究中，我们使用网络分析在一个大样本中检查焦虑和抑郁之间的桥接症状。
使用来自一组在住院治疗前后被诊断出患有抑郁症和焦虑症的患者的数据（N=5614，平均年龄：42.24，63.59%女性，平均治疗持续时间：48.12 天），估计抑郁和焦虑症状的网络模型。分析拓扑结构、节点的中心性、稳定性以及网络结构的变化。通过桥接节点分析确定导致共病的症状。作为基于分类诊断的网络社区的替代方法，我们进行了社区分析并提出了经验衍生的症状子集。
获得的网络模型非常稳定。悲伤情绪和无法控制担忧是最核心的。精神运动激越或迟滞是焦虑和抑郁之间最强的桥接节点，其次是注意力问题和不安。食欲和自杀意念的变化是抑郁症所特有的。社区分析揭示了四个症状群。
抑郁和焦虑症状的估计网络结构被证明非常准确。结果表明，一些症状比其他症状更具影响力，并且只有少数主要的身体症状是解释共病的强有力候选者。未来的研究应在网络模型中包含生理测量，以提供更准确的理解。
