Maes Michael, Almulla Abbas F
Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand.
Cognitive Fitness and Technology Research Unit, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand.
Brain Sci. 2023 Apr 24;13(5):714. doi: 10.3390/brainsci13050714.
Recent studies have proposed valid precision models and valid Research and Diagnostic Algorithmic Rules (RADAR) for recurrent major depressive disorder (MDD). The aim of the current study was to construct precision models and RADAR scores in patients experiencing first-episode MDD and to examine whether adverse childhood experiences (ACE) and negative life events (NLE) are associated with suicidal behaviors (SB), cognitive impairment, and phenome RADAR scores. This study recruited 90 patients with major depressive disorder (MDD) in an acute phase, of whom 71 showed a first-episode MDD (FEM), and 40 controls. We constructed RADAR scores for ACE; NLE encountered in the last year; SB; and severity of depression, anxiety, chronic fatigue, and physiosomatic symptoms using the Hamilton Depression and Anxiety Rating Scales and the FibroFatigue scale. The partial least squares analysis showed that in FEM, one latent vector (labeled the phenome of FEM) could be extracted from depressive, anxiety, fatigue, physiosomatic, melancholia, and insomnia symptoms, SB, and cognitive impairments. The latter were conceptualized as a latent vector extracted from the Verbal Fluency Test, the Mini-Mental State Examination, and ratings of memory and judgement, indicating a generalized cognitive decline (G-CoDe). We found that 60.8% of the variance in the FEM phenome was explained by the cumulative effects of NLE and ACE, in particular emotional neglect and, to a lesser extent, physical abuse. In conclusion, the RADAR scores and plots constructed here should be used in research and clinical settings, rather than the binary diagnosis of MDD based on the DSM-5 or ICD.
最近的研究提出了针对复发性重度抑郁症（MDD）的有效精准模型和有效的研究与诊断算法规则（RADAR）。本研究的目的是构建首发MDD患者的精准模型和RADAR评分，并检验童年不良经历（ACE）和负面生活事件（NLE）是否与自杀行为（SB）、认知障碍及表型RADAR评分相关。本研究招募了90名急性期重度抑郁症（MDD）患者，其中71例为首发MDD（FEM），以及40名对照。我们使用汉密尔顿抑郁与焦虑评定量表及纤维肌痛疲劳量表构建了ACE、过去一年中遇到的NLE、SB以及抑郁、焦虑、慢性疲劳和躯体化症状严重程度的RADAR评分。偏最小二乘分析表明，在FEM中，一个潜在向量（标记为FEM的表型）可从抑郁、焦虑、疲劳、躯体化、忧郁和失眠症状、SB及认知障碍中提取。后者被概念化为从语言流畅性测试、简易精神状态检查以及记忆和判断力评分中提取的潜在向量，表明存在广泛性认知衰退（G-CoDe）。我们发现，NLE和ACE的累积效应，特别是情感忽视以及程度较轻的身体虐待，解释了FEM表型中60.8%的方差。总之，此处构建的RADAR评分和图表应用于研究和临床环境，而非基于《精神疾病诊断与统计手册》第5版（DSM-5）或《国际疾病分类》（ICD）对MDD进行二元诊断。
