Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Department of Psychiatry, Research School of Behavioural and Cognitive Neurosciences (BCN), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Mol Psychiatry. 2019 Jun;24(6):888-900. doi: 10.1038/s41380-019-0385-5. Epub 2019 Mar 1.
Research into major depressive disorder (MDD) is complicated by population heterogeneity, which has motivated the search for more homogeneous subtypes through data-driven computational methods to identify patterns in data. In addition, data on biological differences could play an important role in identifying clinically useful subtypes. This systematic review aimed to summarize evidence for biological subtypes of MDD from data-driven studies. We undertook a systematic literature search of PubMed, PsycINFO, and Embase (December 2018). We included studies that identified (1) data-driven subtypes of MDD based on biological variables, or (2) data-driven subtypes based on clinical features (e.g., symptom patterns) and validated these with biological variables post-hoc. Twenty-nine publications including 24 separate analyses in 20 unique samples were identified, including a total of ~ 4000 subjects. Five out of six biochemical studies indicated that there might be depression subtypes with and without disturbed neurotransmitter levels, and one indicated there might be an inflammatory subtype. Seven symptom-based studies identified subtypes, which were mainly determined by severity and by weight gain vs. loss. Two studies compared subtypes based on medication response. These symptom-based subtypes were associated with differences in biomarker profiles and functional connectivity, but results have not sufficiently been replicated. Four out of five neuroimaging studies found evidence for groups with structural and connectivity differences, but results were inconsistent. The single genetic study found a subtype with a distinct pattern of SNPs, but this subtype has not been replicated in an independent test sample. One study combining all aforementioned types of data discovered a subtypes with different levels of functional connectivity, childhood abuse, and treatment response, but the sample size was small. Although the reviewed work provides many leads for future research, the methodological differences across studies and lack of replication preclude definitive conclusions about the existence of clinically useful and generalizable biological subtypes.
研究重度抑郁症(MDD)受到人群异质性的影响,这促使人们通过数据驱动的计算方法寻找更同质的亚型,以识别数据中的模式。此外,生物学差异的数据可能在识别临床上有用的亚型方面发挥重要作用。本系统综述旨在总结数据驱动研究中 MDD 的生物学亚型的证据。我们对 PubMed、PsycINFO 和 Embase 进行了系统的文献检索(2018 年 12 月)。我们纳入了基于生物学变量识别(1)MDD 的数据驱动亚型,或(2)基于临床特征(如症状模式)并随后使用生物学变量验证这些亚型的研究。共确定了 29 篇文献,其中包括 20 个独特样本中的 24 个独立分析,共涉及约 4000 名受试者。六项生化研究中有五项表明,可能存在神经递质水平异常的抑郁亚型和无异常的抑郁亚型,一项表明可能存在炎症亚型。七项基于症状的研究确定了亚型,这些亚型主要由严重程度和体重增加或减轻决定。两项研究比较了基于药物反应的亚型。这些基于症状的亚型与生物标志物谱和功能连接的差异有关,但结果尚未充分复制。五项神经影像学研究中有四项发现了具有结构和连接差异的群体的证据,但结果不一致。唯一的一项遗传学研究发现了一个具有独特 SNP 模式的亚型,但该亚型在独立的测试样本中尚未得到复制。一项结合了上述所有类型数据的研究发现了具有不同功能连接水平、儿童期虐待和治疗反应的亚型,但样本量较小。尽管综述工作为未来的研究提供了许多线索,但研究之间的方法学差异和缺乏复制性排除了对临床有用和可推广的生物学亚型存在的明确结论。