A useful model to improve understanding of mood disorder heterogeneity

Abstract

There can be little doubt these days that psychiatry faces formidable challenges in its mission to relieve suffering caused by mental illness, to discover its causes, and pathways to new and better treatments.An example is the more than 30% increase in US suicide rates from 1999 to 2017, despite a 65% increase in antidepressant prescriptions over roughly the same time period. 1 Antidepressants are the third most prescribed drug class in US physician office visits, after analgesics and statins. 1 Although other factors-such as economic hardship and epidemic opiate addiction-clearly contribute, most suicides are associated with a depressive mood disorder. Yet, as Malhi et al observe in their thoughtful review in this issue, 2 only "modest" advances in our understanding has left diagnosis and treatment of mood disorders with shortcomings like delayed detection-particularly in bipolar disorder-and disappointing rates of response and remission. Life-diminishing side effects such as sexual dysfunction and metabolic syndrome continue to weigh against the relief our medications give, and despite parity efforts, insurance coverage of psychotherapy remains poor.A key challenge is the fact that mood disorders, as Malhi et al note, are extremely heterogeneous. 2 Two recent studies-among the largest to date-of the genomic architecture of mood disorders illustrate this. One, by the Major Depressive Disorder (MDD) group of the Psychiatric Genomics Consortium, including 135 458 cases and 344 901 controls, found "a continuous measure of risk" for MDD, that "all humans carry lesser or greater numbers of genetic risk", and that "major depression is not a discrete entity at any level of analysis." 3 The Bipolar Disorder (BD) and Schizophrenia (SCZ) work group, in a study of 20 129 BD and 33 426 SCZ cases, and 54 065 controls, found evidence "that points to these disorders being neither independent nor the same, but sharing particular symptom dimensions that can be captured from the genetics." 4 Indeed, substantial genetic correlations between MDD and SCZ are "consistent with partially shared biological pathways being causal for both disorders." 3 The latter study concludes, "the current classification scheme for major psychiatric disorders does not align well with the underlying genetic basis of these disorders." 3 Defining "symptom dimensions" which map onto genetic architecture is a pathway that could lead our field to more valid diagnostic classifications.In this context, the scholarly and cogent review and model proposed by Malhi et al is timely. 2 The authors concisely summarize relevant psychiatric nosological history, and then propose a lucid, well-illustrated, and practical model of mood disorders based upon symptom domains. The ACE model they propose, "considers mood disorders as a combination of symptoms across three domains: Activity, Cognition, and Emotion; that vary over time." Their tripartite model is consistent with the thoughts-feelings-behavior model of cognitive-behavioral therapy, an important evidence-based moda...

The 2020 Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for mood disorders

“…A key problem is that conceptualising mood disorders in this categorical and largely dichotomous manner fails to capture clinical reality, in which admixtures of the two mood states are common. The ACE model, which emphasises the domains of activity and cognition alongside emotion , provides one means of addressing these problems (Grunebaum, 2019; Malhi et al, 2018a).…”