The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity

Patients with major depressive disorder (MDD) vary substantially in illness course and treatment response. Recognition of this variation has led researchers to search for depression subtypes defined by distinctions assessed at the beginning of treatment, such as supposed causes (e.g., postnatal depression) (Cooper et al., 2007; Cooper & Murray, 1995), clinical presentation (e.g., atypical or melancholic depression) (Fink et al., 2007; Uher et al., 2011), and empirically-derived (e.g., factor analysis, latent class analysis) symptom profiles (Lamers et al., 2012; Vrieze et al., 2014), in hopes that these subtypes would tap into underlying psychopathological processes that predict treatment response or course of illness (Baumeister & Parker, 2012; Carragher et al., 2009). While some promising results have emerged regarding significant associations of baseline biomarkers (e.g., Pizzagalli, 2011) and psychosocial variables (e.g., Candrian et al., 2007) with depression treatment response, subtyping distinctions based on empirically-derived symptom profiles have been disappointing due to profile instability (Baumeister & Parker, 2012; Hasler & Northoff, 2011; van Loo et al., 2012). However, an alternative approach to symptom-based subtyping given the desire to predict treatment response and course of illness would be to define subtypes using recursive partitioning (Strobl et al., 2009; Zhang & Singer, 2010) and related machine learning methods (James et al., 2013; van der Laan & Rose, 2011) that search for synergistic associations of baseline measures with subsequent outcomes. The latter methods have been useful in discovering stable synergistic predictors of clinical outcomes in others areas of medicine (Chang et al., 2012; Chao et al., 2012). Other than small studies of depression treatment response (Andreescu et al., 2008; Jain et al., 2013; Nelson et al., 2012; Rabinoff et al., 2011; Riedel et al., 2011), though, we are aware of only one previous study using machine learning to search for depression subtypes in predicting course of illness. That study, by van Loo and colleagues (van Loo et al., 2014), analyzed retrospectively reported data on associations of DSM-IV MDD symptoms in incident episodes with four measures of long-term illness persistence-severity in a sample of 8,261 respondents with lifetime MDD in the WHO World Mental Health (WMH) surveys. Significant subtyping distinctions were found based on the conjunction of child-adolescent onset, suicidality, and symptoms of anxiety occurring during incident depressive episodes. Respondents in the high-risk cluster (fewer than one-third of respondents) accounted for 53–71% of high persistence-severity. The predictors in the van Loo analysis were limited, though, to variables characterizing incident episode symptoms. A question can be raised whether an expanded set of predictors might improve subtyping accuracy. In particular, information about prior lifetime comorbidities might be especially valuable given that van Loo found symptoms of anxiety to be powerful predictors of illness course and that evidence exists in the larger literature that comorbidity is related to the course of MDD (Steinert et al., 2014). The current report presents an expanded WMH analysis evaluating whether information about temporally primary comorbid disorders improves on the van Loo results.