There are no patients without comorbidity

Pharmacological Strategies for Bipolar Disorders in Acute Phases and Chronic Management with a Special Focus on Lithium, Valproic Acid, and Atypical Antipsychotics

Bipolar disorders (BDs) are a heterogeneous group of severe affective disorders generally described by the alternation of (hypo)manic, depressive, and mixed phases, with euthymic intervals of variable duration. BDs are burdened with high psychiatric and physical comorbidity, increased suicide risk and reduced life expectancy. In addition, BDs can progress into complicated forms (e.g., mixed states, rapid/irregular cycling), which are more difficult to treat and often require personalized pharmacological combinations. Mood stabilizers, particularly Lithium and Valproic acid (VPA), still represent the cornerstones of both acute and chronic pharmacotherapies of BDs. Lithium is the gold standard in BD-I and BDII with typical features, while VPA seems more effective for atypical forms (e.g., mixed-prevalence and rapid-cycling). However, despite appropriate mood stabilization, many patients show residual symptoms, and more than a half recur within 1-2 years, highlighting the need of additional strategies. Among these, the association of atypical antipsychotics (AAPs) with mood stabilizers is recurrent in the treatment of acute phases, but it is also being growingly explored in the maintenance pharmacotherapy. These combinations are clinically more aggressive and often needed in the acute phases, whereas simplifying pharmacotherapies to mood stabilizers only is preferable in the long-term, whenever possible. When mood stabilizers are not enough for maintenance treatment, Quetiapine and, less consistently, Aripiprazole have been proposed as the most advisable adjunctive strategies, for their safety and tolerability profiles. However, in view of the increased risk of serious adverse effects, a careful patient-centered balance between costs and benefits is mandatory.

Investigation of Psychoactive Medications: Challenges and a Practical and Scalable New Path

In the last two decades the validity of clinical trials in psychiatry has been subject to discussion. The most accepted clinical study method in the medical area, randomized controlled trial (RCT), faces significant problems when applied to the psychiatric world. One of the causes for this scenario is the strict participant inclusion and exclusion criteria that may not represent the real world. The inconsistency of the different endpoint parameters that are used in the field is another cause. We think that psychiatric RCTs' challenges, together with the underlying complexity of psychiatry, lead to a problematic clinical practice. Today, psychoactive drugs are routinely tested not in an official clinical trial setting. Off-label psychoactive drugs are commonly prescribed, and other substances, such as herbal remedies, are also regularly consumed. Learning from those real-life experiments can teach us useful lessons. Real-world data (RWD) includes information about heterogeneous patient populations, and it can be measured with standardized parameters. Collecting RWD can also address the need for systematically documenting and sharing case reports' outcomes. We suggest using digital tools to capture objective and continuous behavioral data from patients passively. New conclusions will be constantly drawn, possibly allowing more personalized treatment outcomes. The relevant next-generation decision support tools are already available.

Focusing on Comorbidity-A Novel Meta-Analytic Approach and Protocol to Disentangle the Specific Neuroanatomy of Co-occurring Mental Disorders

BACKGROUND

In mental health, comorbidities are the norm rather than the exception. However, current meta-analytic methods for summarizing the neural correlates of mental disorders do not consider comorbidities, reducing them to a source of noise and bias rather than benefitting from their valuable information.

OBJECTIVES

We describe and validate a novel neuroimaging meta-analytic approach that focuses on comorbidities. In addition, we present the protocol for a meta-analysis of all major mental disorders and their comorbidities.

METHODS

The novel approach consists of a modification of Seed-based d Mapping-with Permutation of Subject Images (SDM-PSI) in which the linear models have no intercept. As in previous SDM meta-analyses, the dependent variable is the brain anatomical difference between patients and controls in a voxel. However, there is no primary disorder, and the independent variables are the percentages of patients with each disorder and each pair of potentially comorbid disorders. We use simulations to validate and provide an example of this novel approach, which correctly disentangled the abnormalities associated with each disorder and comorbidity. We then describe a protocol for conducting the new meta-analysis of all major mental disorders and their comorbidities. Specifically, we will include all voxel-based morphometry (VBM) studies of mental disorders for which a meta-analysis has already been published, including at least 10 studies. We will use the novel approach to analyze all included studies in two separate single linear models, one for children/adolescents and one for adults.

DISCUSSION

The novel approach is a valid method to focus on comorbidities. The meta-analysis will yield a comprehensive atlas of the neuroanatomy of all major mental disorders and their comorbidities, which we hope might help develop potential diagnostic and therapeutic tools.

A New Characterization of Mental Health Disorders Using Digital Behavioral Data: Evidence from Major Depressive Disorder

Mental health disorders are ambiguously defined and diagnosed. The established diagnosis technique, which is based on structured interviews, questionnaires and data subjectively reported by the patients themselves, leaves the mental health field behind other medical areas. We support these statements with examples from major depressive disorder (MDD). The National Institute of Mental Health (NIMH) launched the Research Domain Criteria (RDoC) project in 2009 as a new framework to investigate psychiatric pathologies from a multidisciplinary point of view. This is a good step in the right direction. Contemporary psychiatry considers mental illnesses as diseases that manifest in the mind and arise from the brain, expressed as a behavioral condition; therefore, we claim that these syndromes should be characterized primarily using behavioral characteristics. We suggest the use of smartphones and wearable devices to passively collect quantified behavioral data from patients by utilizing digital biomarkers of mental disorder symptoms. Various digital biomarkers of MDD symptoms have already been detected, and apps for collecting this longitudinal behavioral data have already been developed. This quantified data can be used to determine a patient’s diagnosis and personalized treatment, and thereby minimize the diagnosis rate of comorbidities. As there is a wide spectrum of human behavior, such a fluidic and personalized approach is essential.