تجزیه ناهمگنی افسردگی: آنالیز عامل استفاده شده در مقیاس های افسردگی Parsing the heterogeneity of depression: An exploratory factor analysis across commonly used depression rating scales

1. Introduction Under DSM-5 criteria, an estimated 227 combinations of symptoms will lead to a diagnosis of a depressive episode. As a result, a wide range of individuals who meet criteria for depression may overlap on only a limited number of symptoms (Ostergaard et al., 2011; Zimmerman et al., 2015). Indeed, the heterogeneity inherent in the diagnosis of major depressive disorder (MDD) has been a consistent obstacle for identifying viable depression-specific biomarkers that could signal the presence of the disorder as well as predict and track treatment response (Leuchter et al., 2010; Zarate et al., 2013). Isolating specific clusters of the depressive syndrome with a particular biological signature may be an important step towards advancing translational research into depression and, concomitantly, developing novel therapeutics. However, the depression rating scales commonly used in clinical trials survey a variety of symptoms that reflect DSM criteria, which limits research in several key ways. For instance, such rating scales are useful in dichotomizing individuals into depressed vs. non-depressed samples, but provide little insight into specific symptom clusters that would lead to more homogeneous subgroups, as advocated by efforts such as the NIMH RDoC (Woody and Gibb, 2015). In this context, using unidimensional depressive symptom constructs could reduce variability in the data and increase the precision of attempts to connect specific symptoms with pathophysiology. However, it can be difficult to translate the multifaceted construct of depression across modalities—that is, from depressed patients to healthy control samples or to preclinical models. For example, a cross-method translational approach might first involve isolating a particular symptom construct (e.g, anhedonia or approach motivation) into specific neural circuits in patient samples, followed by an experimental paradigm to induce anhedonic symptoms in non-depressed healthy control participants, and finally into preclinical models of anhedonia in animal studies (Treadway and Zald, 2011). In a similarly translational fashion, findings from preclinical models of anhedonia could have implications for both healthy control and patient samples. However, this approach may be unnecessarily complicated by use of diffuse constructs like ‘depression’. Moreover, depression symptom domains may not have uniform response to treatment. For example, some symptom clusters may be particularly vulnerable to the placebo effect, some may exhibit differential response latency, and others still may not respond to a given intervention. These properties may have unexpected effects on the efficiency and precision of clinical trials, and it is possible—even likely—that researchers are unnecessarily handicapped by redundant use of multidimensional outcome measures.