Unpacking Major Depressive Disorder: From Classification to Treatment Selection

In the past decade, there has been a revival of the debate about diagnostic approaches in mental illness. In 1970, Robins and Guze described 5 distinct requirements to justify the diagnosis of a psychiatric syndrome. These involved 1) a consistent clustering of symptoms, 2) laboratory studies to establish a biological substrate, 3) relative specificity of symptoms to distinguish one disorder from another, 4) evidence from follow-up studies of a common course of illness, and 5) some evidence of genetic predisposition based on higher prevalence rates in the families of affected individuals. There are many potential benefits to this approach: internationally accepted taxonomy facilitates studies across multiple research areas, ranging from epidemiology and health economics to disease mechanisms, treatment outcome evaluations, and pursuit of new therapeutics. On the other hand, major depressive disorder (MDD) is a prime example of the limited success of this ‘one-size-fitsall’ approach, where less than 30% of individuals achieve remission during a first observed antidepressant trial. This not only increases the burden of functional impairment across domains of occupation, physical health, and social relationships but also contributes to the unwelcome position of MDD as the leading cause of disability worldwide. Using current criteria in the Diagnostic and Statistical Manual of Mental Disorders (DSM), more than 200 combinations of symptoms may be applied to fulfill the diagnosis of a major depressive episode (MDE), highlighting the significant clinical heterogeneity of the disorder. Even when MDD is classified according to symptom clusters (e.g., melancholic, atypical, and anxious), these clinical phenotypes have not proven useful as predictors of antidepressant outcome and, by implication, treatment selection. How can we do better? The answer may lie in precision medicine, integrating clinical and biological measures that are unique to the individual to select the best treatment while minimizing adverse effects. Biomarkers promise better diagnostic methods and treatment selection—and rather than being unique to any specific DSM disorder, these biomarkers may transcend traditional diagnostic boundaries. The Research Domain Criteria (RDoC) framework, proposed by Insel et al. at the National Institute of Mental Health (NIMH), is agnostic to DSM disorders and proposes an integrated dimensional approach to understanding psychiatric disorders by incorporating data from various modalities on a continuum of human behaviour from normal to abnormal. While RDoC is still in its preliminary stages and has not yet influenced clinical practice, the ability to cut across traditional diagnostic boundaries may prove useful for future conceptualization of disorders. This ‘lumping’ and ‘splitting’ of diagnoses has resulted in significant progress in the treatment of many cancers and immune disorders, with classifications and treatment strategies being guided by underlying biological mechanisms rather than surface clinical manifestations. Although the RDoC model is intended as a framework for conducting research rather than a diagnostic system, it may be useful in representing overlapping phenotypes across disorders, as well as heterogeneity within disorders such as MDD. Indeed, while evidence of phenotypic heterogeneity in MDD can be observed clinically, the full extent of its variability likely spans all RDoC units of analysis, from