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中国躯体症状障碍门诊患者抑郁症状的网络分析

Network analysis of depressive symptoms in Chinese outpatients with somatic symptom disorder.

作者信息

Li Yihui, Fang Junning, Zhong Yunhui, Li Yibo, Liao Yuanping, Tang Hong

机构信息

Department of Psychology, Gannan Medical University, Ganzhou, Ganzhou, Jiangxi, China.

The Third People's Hospital of Ganzhou, Ganzhou, Jiangxi, China.

出版信息

Front Psychiatry. 2025 Sep 3;16:1617999. doi: 10.3389/fpsyt.2025.1617999. eCollection 2025.

DOI:10.3389/fpsyt.2025.1617999
PMID:40969708
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12440883/
Abstract

BACKGROUND

Somatic symptom disorder and depression in clinical practice are strongly correlated. In this study, network analysis was used to assess the depressive symptoms of patients with somatic symptom disorder to identify the most core and influential symptoms. The aim of this study was to provide new perspectives for the treatment and rehabilitation of patients with somatic symptom disorder.

METHODS

A total of 899 individuals were enrolled from Gannan Medical University's First Affiliated Hospital, Ganzhou People's Hospital, and Third People's Hospital of Ganzhou. A version of the Patient Health Questionnaire-9 was administered to assess symptoms of depression. We described the network structure of depressive symptoms, utilizing indicators of "strength," "betweenness," and "closeness" to identify the key symptoms within the network. A bootstrap approach with case-dropping was used to test the network's stability.

RESULTS

Concentration (PHQ7), Motor (PHQ8), and Anhedonia (PHQ1) symptoms had the highest centrality values, the strength values are 1.67, 1.62, and 1.58 respectively. The edge connecting sad mood (PHQ2) and energy (PHQ4) were the most influential in the model, with an edge weight of 0.69, the highest among all edges.

CONCLUSIONS

This network analysis study identifies distinct depressive symptomatology within the Chinese SSD patient population. Core symptoms anhedonia, cognition, and motivation primarily drive depressive symptoms, underscoring the need for clinical focus on these manifestations to prevent exacerbation. Tailored interventions targeting these core symptoms, including the integration of pleasant experiences, dopamine-based medications, attention bias modification training, and behavioral activation therapy, should be considered in treatment strategies.

摘要

背景

在临床实践中,躯体症状障碍与抑郁症密切相关。在本研究中,采用网络分析方法评估躯体症状障碍患者的抑郁症状,以确定最核心和最具影响力的症状。本研究的目的是为躯体症状障碍患者的治疗和康复提供新的视角。

方法

从赣南医学院第一附属医院、赣州市人民医院和赣州市第三人民医院共招募了899名个体。使用患者健康问卷-9的一个版本来评估抑郁症状。我们描述了抑郁症状的网络结构,利用“强度”“中介中心性”和“接近中心性”指标来识别网络中的关键症状。采用基于病例剔除的自助法来测试网络的稳定性。

结果

注意力不集中(PHQ7)、运动症状(PHQ8)和快感缺失(PHQ1)症状的中心性值最高,强度值分别为1.67、1.62和1.58。连接悲伤情绪(PHQ2)和精力(PHQ4)的边在模型中最具影响力,边权重为0.69,在所有边中最高。

结论

这项网络分析研究确定了中国躯体症状障碍患者群体中独特的抑郁症状学。核心症状快感缺失、认知和动机是抑郁症状的主要驱动因素,强调临床需要关注这些表现以防止病情加重。在治疗策略中应考虑针对这些核心症状的定制干预措施,包括融入愉快体验、使用基于多巴胺的药物、注意力偏差修正训练和行为激活疗法。