Department of Psychology, California State University, Fresno, Fresno, CA 93740, United States.
Department of Psychology, Stanford University, Stanford, CA 94305, United States.
Psychiatry Res. 2020 Dec;294:113399. doi: 10.1016/j.psychres.2020.113399. Epub 2020 Sep 11.
Although many investigators have examined symptoms of major depressive disorder (MDD), the multivariate relations among these features of depression and their relative associations with overall severity of depression are not well understood. The present study is the first to examine the underlying factor structure of depression across a broad set of constructs and to model the multivariate association of these factors with the overall severity of depression. We conducted a large-scale factor analysis and multiple regression in a sample of participants diagnosed with MDD (N = 233) and healthy controls (N = 235). We obtained a five-factor solution composed of the following factors: (1) anxiety; (2) behavioral activation; (3) core symptoms; (4) rumination; and (5) emotional intensity. The core symptoms factor, composed primarily of DSM-5 diagnostic criteria for MDD, was the only factor that showed a consistent, significant association with overall severity of depression and functional impairment. Rumination combined with behavioral inhibition and positive and negative affect combined with each other to form coherent constructs that may be useful in examining differences among depressed individuals. These findings provide an important data-driven framework for the multidimensional symptom structure of depression and suggest several actionable ways for improving clinical assessment and treatment for individuals with MDD.
尽管许多研究人员已经研究了重度抑郁症(MDD)的症状,但这些抑郁特征之间的多变量关系及其与抑郁严重程度的相对关联尚不清楚。本研究首次检查了横跨广泛的结构的抑郁的潜在因子结构,并对这些因子与抑郁严重程度的整体关联进行了多元回归分析。我们对患有 MDD 的参与者(N=233)和健康对照组(N=235)进行了大规模的因子分析和多元回归。我们得到了一个由五个因子组成的解决方案:(1)焦虑;(2)行为激活;(3)核心症状;(4)反刍;(5)情绪强度。核心症状因子主要由 DSM-5 对 MDD 的诊断标准组成,是唯一一个与抑郁严重程度和功能障碍有一致、显著关联的因子。反刍与行为抑制相结合,积极和消极影响相互结合,形成了连贯的结构,这可能有助于检查抑郁个体之间的差异。这些发现为抑郁的多维症状结构提供了一个重要的基于数据的框架,并为改善 MDD 个体的临床评估和治疗提供了一些可行的方法。