Transdiagnostic Symptom Clusters and Associations With Brain, Behavior, and Daily Function in Mood, Anxiety, and Trauma Disorders
Transdiagnostic Symptom Clusters and Associations With Brain, Behavior, and Daily Function in Mood, Anxiety, and Trauma Disorders
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DOI：
10.1001/jamapsychiatry.2017.3951
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发表时间：
2018-02-01
期刊：
影响因子：
25.8
通讯作者：
Williams, Leanne M.
中科院分区：
文献类型：
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作者：
Grisanzio, Katherine A.;Goldstein-Piekarski, Andrea N.;Williams, Leanne M.
重要性定义情绪、焦虑和创伤障碍的症状在障碍之间高度重叠，并且在障碍内部具有异质性。目前尚不清楚是否存在跨越多种诊断并在功能（基础认知和脑功能）和临床（日常功能）上表达的连贯亚型。内聚亚型的识别将有助于解开我们目前诊断中的症状重叠，并作为一种工具，用于定制治疗选择。和参与者这一交叉-一项横断面研究分析了来自悉尼大学脑研究和综合神经科学网络基金会数据库的数据和阿德莱德大学在2006年至2010年之间，并在2013年至2017年之间在斯坦福大学复制。该研究包括420名主要诊断为重度抑郁症（n = 100），惊恐障碍（n = 53），创伤后应激障碍（n = 47）或无障碍（健康对照参与者）（n = 220）的个体。数据分析时间为2016年10月至2017年10月。主要结果和指标我们遵循数据驱动的方法，以实现识别跨诊断亚型的主要研究结果。首先，使用分层聚类算法进行机器学习，根据自我报告的负面情绪，焦虑和压力症状对参与者进行分类。其次，在独立样本中检验亚型的稳健性和普遍性。第三，我们评估了症状亚型是否在行为和生理功能水平上表达。第四，我们评估了亚型功能能力的临床意义差异。结果被解释相对于一个互补的诊断框架的reference.Results四百二十参与者的平均（SD）年龄为39.8（14.1）岁，包括在最终分析，256（61.0%）是女性。我们确定了6种不同的亚型，其特征为紧张（n = 81; 19%），焦虑觉醒（n = 55; 13%），广泛焦虑（n = 38; 9%），快感缺乏（n = 29; 7%），焦虑症（n = 37; 9%）和正常情绪（n = 180; 43%），这些亚型在一个独立的样本中重复。亚型通过认知控制的差异来表达（F-5，F-383 = 5.13，P <.001，eta（2）（p）= 0.063），工作记忆（F-5，F-401 = 3.29，P = 0.006，eta（2）（p）= 0.039），静息模式下脑电图记录的β功率（F-5，F-357 = 3.84，P = 0.002，eta（2）（p）= 0.051），情绪范式中脑电图记录的β功率（F-5，F-365 = 3.56，P = 0.004，eta（2）（p）= 0.047），社会功能能力（F-5，F-414 = 21.33，P <.001，eta（2）（p）= 0.205），情绪弹性（F-5，F-376 = 15.10，P <.001，eta（2）（p）= 0.171）。结论和相关性这些发现提供了一个数据驱动的框架，用于识别稳健的亚型，这些亚型表示症状之间的特异性、一致性、有意义的关联，行为、大脑功能和可观察到的现实世界功能，以及DSM-IV定义的重度抑郁症、恐慌症和创伤后应激障碍的诊断。
IMPORTANCE The symptoms that define mood, anxiety, and trauma disorders are highly overlapping across disorders and heterogeneous within disorders. It is unknown whether coherent subtypes exist that span multiple diagnoses and are expressed functionally (in underlying cognition and brain function) and clinically (in daily function). The identification of cohesive subtypes would help disentangle the symptom overlap in our current diagnoses and serve as a tool for tailoring treatment choices.OBJECTIVE To propose and demonstrate 1 approach for identifying subtypes within a transdiagnostic sample.DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study analyzed data from the Brain Research and Integrative Neuroscience Network Foundation Database that had been collected at the University of Sydney and University of Adelaide between 2006 and 2010 and replicated at Stanford University between 2013 and 2017. The study included 420 individuals with a primary diagnosis of major depressive disorder (n = 100), panic disorder (n = 53), posttraumatic stress disorder (n = 47), or no disorder (healthy control participants) (n = 220). Data were analyzed between October 2016 and October 2017.MAIN OUTCOMES AND MEASURES We followed a data-driven approach to achieve the primary study outcome of identifying transdiagnostic subtypes. First, machine learning with a hierarchical clustering algorithm was implemented to classify participants based on self-reported negative mood, anxiety, and stress symptoms. Second, the robustness and generalizability of the subtypes were tested in an independent sample. Third, we assessed whether symptom subtypes were expressed at behavioral and physiological levels of functioning. Fourth, we evaluated the clinically meaningful differences in functional capacity of the subtypes. Findings were interpreted relative to a complementary diagnostic frame of reference.RESULTS Four hundred twenty participants with a mean (SD) age of 39.8 (14.1) years were included in the final analysis; 256 (61.0%) were female. We identified 6 distinct subtypes characterized by tension (n=81; 19%), anxious arousal (n=55; 13%), general anxiety (n=38; 9%), anhedonia (n=29; 7%), melancholia (n=37; 9%), and normative mood (n=180; 43%), and these subtypes were replicated in an independent sample. Subtypes were expressed through differences in cognitive control (F-5,F-383 = 5.13, P < .001, eta(2)(p) = 0.063), working memory (F-5,F-401 = 3.29, P = .006, eta(2)(p) = 0.039), electroencephalography-recorded beta power in a resting paradigm (F-5,F-357 = 3.84, P = .002, eta(2)(p) = 0.051), electroencephalography-recorded beta power in an emotional paradigm (F-5,F-365 = 3.56, P = .004, eta(2)(p) = 0.047), social functional capacity (F-5,F-414 = 21.33, P < .001, eta(2)(p) = 0.205), and emotional resilience (F-5,F-376 = 15.10, P < .001, eta(2)(p) = 0.171).CONCLUSIONS AND RELEVANCE These findings offer a data-driven framework for identifying robust subtypes that signify specific, coherent, meaningful associations between symptoms, behavior, brain function, and observable real-world function, and that cut across DSM-IV-defined diagnoses of major depressive disorder, panic disorder, and posttraumatic stress disorder.