Liu Yu, Zhu Zirong, Yan Jiaran, Wang Yuanyuan
Department of Neurology, The Air Force Hospital of Northern Theater PLA, Shenyang, 110000, China.
Department of Neurology, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, China.
Sci Rep. 2025 Jul 11;15(1):25030. doi: 10.1038/s41598-025-09746-w.
Anxiety and depressive symptoms are both common in insomnia patients, and they share a critical bidirectional relationship. The available research suggests that transitioning from a disorder-level analysis to a symptom-level analysis may provide a clearer understanding of these relationships. A total of 1571 insomnia patients were enrolled in this study. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), anxiety symptoms were evaluated using the Hamilton Rating Scale for Anxiety (HAM-A), and depressive symptoms were evaluated using the Hamilton Rating Scale for Depression (HAM-D). Subsequently, a network analysis was conducted for the statistical analysis. Among the present sample, the prevalences of depression and anxiety were 87.1% and 88.0%, respectively. We found that the strongest regularized partial correlations existed in the network between "C3: Sleep duration" and "C4: Habitual sleep efficiency" (r = 0.91). Two other strong regularized partial correlations were observed between "B3: Cognitive disturbance" - "B7: Hopelessness" (r = 0.36), "A2: Psychic anxiety" - "B5: Retardation" (r = 0.28). In addition, the item "C3: Sleep duration" had the highest strength centrality in the network, followed by "C1: Subjective sleep quality", "C4: Habitual sleep efficiency", "A2: Psychic anxiety", "B1: Anxiety/Somatization". The findings highlight the crucial role of the strongest regularized partial correlations and bridge symptoms in relation to depression and anxiety. Targeted interventions for clinical control of anxiety and depressive symptoms in insomnia patients are promising.
焦虑和抑郁症状在失眠患者中都很常见，且它们存在重要的双向关系。现有研究表明，从疾病层面分析转向症状层面分析可能会更清晰地理解这些关系。本研究共纳入了1571名失眠患者。使用匹兹堡睡眠质量指数（PSQI）评估睡眠质量，使用汉密尔顿焦虑量表（HAM - A）评估焦虑症状，使用汉密尔顿抑郁量表（HAM - D）评估抑郁症状。随后，进行网络分析以进行统计分析。在本样本中，抑郁和焦虑的患病率分别为87.1%和88.0%。我们发现，“C3：睡眠时间”与“C4：习惯性睡眠效率”之间的网络中存在最强的正则化偏相关（r = 0.91）。在“B3：认知障碍” - “B7：绝望感”（r = 0.36）、“A2：精神性焦虑” - “B5：迟缓”（r = 0.28）之间还观察到另外两个强正则化偏相关。此外，“C3：睡眠时间”这一项目在网络中的强度中心性最高，其次是“C1：主观睡眠质量”、“C4：习惯性睡眠效率”、“A2：精神性焦虑”、“B1：焦虑/躯体化”。这些发现突出了最强正则化偏相关和与抑郁及焦虑相关的桥梁症状的关键作用。针对失眠患者焦虑和抑郁症状进行临床控制的靶向干预具有前景。
