Year: 2017
Type: editorial
Abstract: While many clinicians have believed for decades that selective serotonin reuptake inhibitors (SSRIs) may increase anxiety soon after antidepressant treatment initiation, this effect has never been clearly documented. In the present issue, Näslund et al. 1 reported that in the first week after treatment initiation in trials involving patients with major depressive disorder (MDD), compared to pill placebo, SSRIs were associated with lower incidence rates of psychological signs of anxiety and agitation, and higher levels of somatic symptoms of anxiety, as assessed by the specific items derived from the Hamilton Depression Rating Scale. Empirical data suggest that depression and anxiety disorders are highly comorbid with, for example, 68% of individuals with MDD also meeting criteria for a comorbid lifetime anxiety disorder 2. Similarly, 45%–46% of 3787 out-patients with MDD in the STAR*D study were deemed to meet criteria for anxious depression 3. Näslund et al.'s study 1 illustrates the close relationship between anxiety and depression, and the need for an approach that can integrate this close-knit relationship into the way that anxiety and depression are conceptualized, assessed, studied, and treated. Successfully developing and using such an approach is likely to critically accelerate our understanding of affective disorders and our capacity to care for individuals who suffer from them. The interdependent relationship of depression and anxiety has long been suggested and described, with early conceptualizations of depression giving a central role to anxiety. For example, in the DSM-I 4, depressive reaction, a precursor of MDD, was described as a form of attenuated anxiety: 'The anxiety in this [depressive] reaction is allayed, and hence partially relieved, by depression and self-depreciation.' While the DSM-5 5 does not include anxiety as a core symptom of depression, it uses the specifier 'with anxious distress' to reflect the presence of at least two of five anxiety symptoms associated with MDD such as feeling keyed up or tense, being unusually restless, having trouble concentrating because of worry, having fear that something awful may happen, and feeling that one might lose control of oneself. Furthermore, conditions such as panic disorder and generalized disorder are very commonly reported among both unipolar depression and bipolar disorder patients 6. Over 60 years later, this relationship is illustrated in a number of ways, including by the continuous use of well-established measures of depression that include anxiety symptoms. Thus, it is interesting to note that in Näslund et al.'s study 1, anxiety was assessed using three items of the Hamilton Depression Rating Scale. Vice versa, the gold standard for assessing anxiety symptom severity—the Hamilton Anxiety Rating Scale—includes items assessing symptoms overlapping with MDD (concentration, sleep) as well as depressed mood. Finally, empirical data suggest common neurobiological pathways to depressive and anxiety disorders, including dysfunction in the serotonergic system 7 that may explain the efficacy of SSRIs in both these types of disorders. This close relationship between depression and anxiety suggests that an approach allowing the study—and treatment—of the mood/anxiety spectrum within a common framework may significantly move our field forward. The Research Domain Criteria (RDoC) initiative of the National Institute of Mental Health is a recently proposed transdiagnostic approach that aims to examine dysfunctions in brain circuits underlying clinical features that cut across disorders 8. In this dimensional framework, mental disorders are not seen as unitary disease entities, but as spectra of dysfunctions of brain circuits. This approach aims to identify 'bio-signatures' for certain dysfunctions that would serve to guide targeted therapeutic interventions. For example, negative valence has been proposed as a system spanning across depressive and anxiety disorders and may be a good target for intervention if empirical data support its role in these conditions. The RDoC's vision is that future classification of mental disorders will rely on the identification of these dysfunctions in neurocircuit systems, and that future healthcare providers will use neuroimaging, genomic sequencing, and laboratory tests' data to determine prognosis and appropriate treatment. Recently, another novel transdiagnostic approach to mental disorders has been proposed based on the use of network analyses 9. While the conventional framework to mental disorders posits that specific symptoms are used as indicators of a presumed underlying disease or disorder, the network framework conceptualizes mental disorders as webs of connected symptoms that mutually reinforce each other. The fact that depressive and anxiety symptoms coexist in the same individual is not explained by the co-occurrence of two distinct clinical entities, but rather, by the fact that these symptoms reinforce each other through feedback loops. For example, anxiety may cause insomnia that may cause fatigue, which in turn may affect concentration, which may heighten feelings of worthlessness, that in turn lower the mood… Using data from the STAR*D study 10, Fried et al. 11 thus recently examined the network of DSM and non-DSM (e.g., anxiety) symptoms in MDD and found that both types of symptoms contributed importantly to the MDD network. Furthermore, they also reported that among the most central symptoms (i.e., the most influential in the maintenance of the network) were the core MDD symptoms of diminished interest/pleasure and depressed mood, as well as the anxious and phobic symptoms, further illustrating the close relationship between depression and anxiety. In the network framework, central symptoms play a significant role in the maintenance of the overall network of symptoms, suggesting that targeting and successfully reducing the intensity of symptoms that are central to a condition (such as anxiety for depression), will consequently 'weaken' the network and help achieve overall symptom reduction. Authors have most recently suggested that this network of symptoms might be specific of each individual (i.e., central target symptom differ by individuals) and that developing methods to identify the unique network of symptoms for each individual holds the promise of developing a highly personalized approach to medicine 12. While resting on two different views of the etiological mechanisms underlying mental disorders, both the RDoC and the network approaches have the common goal to improve the assessment of mental disorders by enhancing our ability to personalize treatment options. The furthering of our understanding and development of efficacious treatments for mental disorders will likely require rethinking the way we conduct clinical trials, with a greater emphasis on transdiagnostic studies that span across disorders on the mood/anxiety spectrum. Such efforts have already been initiated; however, greater collaboration and integration between the depression and anxiety research fields would prove critical in advancing our knowledge about these conditions. As we stated above, the RDoC framework of the National Institute of Mental Health will play an interesting role in supporting this type of research, as well as other efforts like the Patient-Centered Outcomes Research Institute's call to conduct pragmatic studies in routine clinical settings, where patients often present with more than one diagnosis. Future directions may involve the recruitment of research samples that include the full spectrum of mood/anxiety symptoms, the use of common assessments measures, the examination of psychosocial or psychopharmacological interventions on specific constructs, on potential targets, or even on symptoms that cut across the two fields— just as Näslund et al. 1 did in their study.
Source: Acta Psychiatrica Scandinavica
DOI: 10.1111/acps.12801
Authors: Eric Bui, Maurizio Fava
Institutions: Harvard University, Massachusetts General Hospital
Language: English
Subfield: Pharmacology (medical)
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W2752913033