失眠严重程度的焦虑-抑郁- crp网络结构差异:来自英国生物银行的证据

IF 3.3 2区 医学 Q1 PSYCHIATRY
Xue Luo, Shuqiong Zheng, Yihong Cheng, Shuai Liu, Shufei Zeng, Leqin Fang, Shixu Du, Weimin Li, Hangyi Yang, Zhiting Huang, Bin Zhang
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引用次数: 0

摘要

背景:本研究利用网络分析研究了不同失眠严重程度的焦虑、抑郁症状和c反应蛋白(CRP)之间的关系,并检查了这些网络中的结构差异。方法:对英国生物银行数据(N = 143,027)进行最小绝对收缩和选择算子(LASSO)正则化的高斯图形模型网络分析。抑郁和焦虑症状分别采用9项患者健康问卷(PHQ-9)和7项广泛性焦虑障碍量表(GAD-7)进行评估。采用免疫比浊法-高灵敏度分析定量CRP。参与者根据失眠频率(从不/很少,有时和通常)进行分类。强度症状和预期影响确定核心症状,而桥架预期影响(桥架EI)确定桥架症状。在三组中两两进行网络比较测试(nct),以评估整体强度和边缘权重的差异。结果:在所有网络中,“抑郁情绪”表现出最高的强度中心性,而“易怒”表现出最高的桥接EI。在从未/很少失眠组中,“抑郁情绪”的预期影响中心性最高,而在其他组中,“无法控制的担忧”的预期影响中心性最高。nct显示,从不/很少和经常失眠组在总体强度(S = 0.178, p < 0.01)和边缘权重(M = 0.062, p < 0.01)上存在显著差异,在经常失眠组中,抑郁症状(能量/食欲)和CRP之间存在更强的联系(p < 0.001)。结论:抑郁情绪、无法控制的担忧和易怒在所有失眠严重程度组的焦虑-抑郁- crp网络中的核心作用表明,这些症状是未来干预研究的潜在目标。值得注意的是,网络结构因失眠严重程度而异;在经常失眠的组中,抑郁症状和CRP之间的联系加强,这表明失眠的严重程度可能是理解情感过程和炎症过程之间关系的一个重要因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Differential Anxiety–Depression–CRP Network Structures Across Insomnia Severity Levels: Evidence From UK Biobank

Differential Anxiety–Depression–CRP Network Structures Across Insomnia Severity Levels: Evidence From UK Biobank

Background: This study investigated the relationships between anxiety, depression symptoms, and C-reactive protein (CRP) across insomnia severity levels using network analysis and examined the structural differences within these networks.

Methods: Gaussian graphical model network analysis with Least Absolute Shrinkage and Selection Operator (LASSO) regularization was conducted on UK Biobank data (N = 143,027). Depression and anxiety symptoms were assessed using the 9-item Patient Health Questionnaire (PHQ-9) and 7-item Generalized Anxiety Disorder Scale (GAD-7), respectively. CRP was quantified using immunoturbidimetric-high-sensitivity analysis. Participants were categorized by insomnia frequency (never/rarely, sometimes, and usually). The strength symptoms and expected influence identified core symptoms, while bridge expected influence (bridge EI) determined bridge symptoms. Network comparison tests (NCTs) were performed pairwise across the three groups to assess differences in global strength and edge weights.

Results: Across all networks, “Depressed mood” demonstrated the highest strength centrality, while “Irritability” exhibited the highest bridge EI. “Depressed mood” had the highest expected influence centrality in the never/rarely insomnia group and “Uncontrollable worry” in other groups. NCTs revealed significant differences in global strength (S = 0.178, p < 0.01) and edge weights (M = 0.062, p < 0.01) between the never/rarely and usually insomnia groups, with stronger connections between depressive symptoms (energy/appetite) and CRP in the usually insomnia group (p < 0.001).

Conclusions: The central roles of depressed mood, uncontrollable worry, and irritability in the anxiety–depression–CRP network across all insomnia severity groups suggest that these symptoms represent potential targets for future intervention research. Notably, network structure differed across insomnia severity; the strengthened associations between depressive symptoms and CRP in the usually insomnia group suggest that insomnia severity may be an important factor to consider in understanding the relationships between affective and inflammatory processes.

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来源期刊
Depression and Anxiety
Depression and Anxiety 医学-精神病学
CiteScore
15.00
自引率
1.40%
发文量
81
审稿时长
4-8 weeks
期刊介绍: Depression and Anxiety is a scientific journal that focuses on the study of mood and anxiety disorders, as well as related phenomena in humans. The journal is dedicated to publishing high-quality research and review articles that contribute to the understanding and treatment of these conditions. The journal places a particular emphasis on articles that contribute to the clinical evaluation and care of individuals affected by mood and anxiety disorders. It prioritizes the publication of treatment-related research and review papers, as well as those that present novel findings that can directly impact clinical practice. The journal's goal is to advance the field by disseminating knowledge that can lead to better diagnosis, treatment, and management of these disorders, ultimately improving the quality of life for those who suffer from them.
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