Li Xiuwen, Zhang Huimin, Han Xue, Guo Lan, Ceban Felicia, Liao Yuhua, Shi Jingman, Wang Wanxin, Liu Yifeng, Song Weidong, Zhu Dongjian, Wang Hongqiong, Li Lingjiang, Fan Beifang, Lu Ciyong, McIntyre Roger S
Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Sun Yat-sen University, Guangzhou, China.
Front Psychiatry. 2023 Feb 14;14:999047. doi: 10.3389/fpsyt.2023.999047. eCollection 2023.
The presence of heterogenous somatic symptoms frequently obscures the recognition of depression in primary care. We aimed to explore the association between somatic symptoms and subthreshold depression (SD) and Major Depressive Disorder (MDD), as well as to determine the predictive potential of somatic symptoms in identifying SD and MDD in primary care.
Data were derived from the Depression Cohort in China study (ChiCTR registry number: 1900022145). The Patient Health Questionnaire-9 (PHQ-9) was used to assess SD by trained general practitioners (GPs), and the Mini International Neuropsychiatric Interview depression module was used to diagnose MDD by professional psychiatrists. Somatic symptoms were assessed using the 28-item Somatic Symptoms Inventory (SSI).
In total of 4,139 participants aged 18-64 years recruited from 34 primary health care settings were included. The prevalence of all 28 somatic symptoms increased in a dose-dependent manner from non-depressed controls to SD, and to MDD ( for trend <0.001). Hierarchical clustering analysis grouped the 28 heterogeneous somatic symptoms into three clusters (Cluster 1: energy-related symptoms, Cluster 2: vegetative symptoms, and Cluster 3: muscle, joint, and central nervous symptoms). Following adjustment for potential confounders and the other two clusters of symptoms, per 1 increase of energy-related symptoms exhibited significant association with SD ( = 1.24, 95% , 1.18-1.31) and MDD ( = 1.50, 95% , 1.41-1.60) The predictive performance of energy-related symptoms in identifying individuals with SD ( = 0.715, 95% , 0.697-0.732) and MDD ( = 0.941, 95% , 0.926-0.963) was superior to the performance of total SSI and the other two clusters ( < 0.05).
Somatic symptoms were associated with the presence of SD and MDD. In addition, somatic symptoms, notably those related to energy, showed good predictive potential in identifying SD and MDD in primary care. The clinical implication of the present study is that GPs should consider the closely related somatic symptoms for early recognition for depression in practice.
在基层医疗中,多种躯体症状的存在常常掩盖了对抑郁症的识别。我们旨在探讨躯体症状与亚阈值抑郁症(SD)和重度抑郁症(MDD)之间的关联,并确定躯体症状在基层医疗中识别SD和MDD的预测潜力。
数据来源于中国抑郁症队列研究(中国临床试验注册中心注册号:1900022145)。由经过培训的全科医生(GP)使用患者健康问卷-9(PHQ-9)评估SD,由专业精神科医生使用迷你国际神经精神病学访谈抑郁模块诊断MDD。使用28项躯体症状量表(SSI)评估躯体症状。
共纳入了从34个基层医疗机构招募的4139名年龄在18至64岁之间的参与者。从无抑郁对照组到SD组,再到MDD组,所有28种躯体症状的患病率呈剂量依赖性增加(趋势检验P<0.001)。层次聚类分析将28种异质性躯体症状分为三个聚类(聚类1:与能量相关的症状,聚类2:植物神经症状,聚类3:肌肉、关节和中枢神经症状)。在对潜在混杂因素和其他两组症状进行调整后,每增加1种与能量相关的症状,与SD(比值比=1.24,95%置信区间,1.18至1.31)和MDD(比值比=1.50,95%置信区间,1.41至1.60)均呈现显著关联。与能量相关的症状在识别SD个体(曲线下面积=0.715,95%置信区间,0.697至0.732)和MDD个体(曲线下面积=0.941,95%置信区间,0.926至0.963)方面的预测性能优于总SSI和其他两组症状(P<0.05)。
躯体症状与SD和MDD的存在相关。此外,躯体症状,尤其是与能量相关的症状,在基层医疗中识别SD和MDD方面显示出良好的预测潜力。本研究的临床意义在于,全科医生在实践中应考虑密切相关的躯体症状以早期识别抑郁症。