A review of systems biology research of anxiety disorders

Worse Depression Profiles Are Associated With Higher Symptom Burden and Poorer Quality of Life in Patients With Gynecologic Cancer

BACKGROUND

Depression is a pervasive symptom in patients with gynecological cancer undergoing chemotherapy.

OBJECTIVES

Purposes were to identify subgroups of patients with distinct depression profiles and evaluate for differences in demographic and clinical characteristics, severity of common symptoms, and quality of life (QOL) outcomes among these subgroups.

METHODS

Patients with gynecological cancer (n = 231) completed the Center for Epidemiologic Studies-Depression Scale 6 times over 2 cycles of chemotherapy. All of the other measures were completed prior to the second or third cycle of chemotherapy. Latent profile analysis was done to identify the distinct depression profiles. Differences were evaluated using parametric and nonparametric tests.

RESULTS

Three distinct profiles were identified: low (60.1%), high (35.1%), and very high (4.8%). Compared with low class, the other 2 classes had lower functional status and were more likely to self-report a diagnosis of depression. Patients in the 2 worse profiles reported a higher comorbidity burden, higher levels of trait and state anxiety, sleep disturbance, and fatigue, as well as lower levels of cognitive function and poorer QOL. State and trait anxiety, evening fatigue, and sleep disturbance scores exhibit a "dose-response effect" (ie, as the depression profile worsened, the severity of these symptoms increased).

CONCLUSIONS

Almost 40% of our sample experienced high or very high levels of depression across 2 cycles of chemotherapy.

IMPLICATIONS FOR PRACTICE

Clinicians can use the identified risk factors to identify high patients risk and provide tailored psychological interventions aimed to decrease symptom burden and prevent decrements in QOL.

Spatiotemporal expression patterns of anxiety disorder-associated genes

Anxiety disorders (ADs) are the most common form of mental disorder that affects millions of individuals worldwide. Although physiological studies have revealed the neural circuits related to AD symptoms, how AD-associated genes are spatiotemporally expressed in the human brain still remains unclear. In this study, we integrated genome-wide association studies of four human AD subtypes-generalized anxiety disorder, social anxiety disorder, panic disorder, and obsessive-compulsive disorder-with spatial gene expression patterns. Our investigation uncovered a novel division among AD-associated genes, marked by significant and distinct expression enrichments in the cerebral nuclei, limbic, and midbrain regions. Each gene cluster was associated with specific anxiety-related behaviors, signaling pathways, region-specific gene networks, and cell types. Notably, we observed a significant negative correlation in the temporal expression patterns of these gene clusters during various developmental stages. Moreover, the specific brain regions enriched in each gene group aligned with neural circuits previously associated with negative decision-making and anxious temperament. These results suggest that the two distinct gene clusters may underlie separate neural systems involved in anxiety. As a result, our findings bridge the gap between genes and neural circuitry, shedding light on the mechanisms underlying AD-associated behaviors.

Oncology Outpatients With Worse Anxiety and Sleep Disturbance Profiles Are at Increased Risk for a Higher Symptom Burden and Poorer Quality of Life

BACKGROUND

Anxiety and sleep disturbance are frequent symptoms during chemotherapy.

OBJECTIVES

Purposes were to identify subgroups of oncology outpatients with distinct joint anxiety and sleep disturbance profiles, as well as evaluate for differences in demographic and clinical characteristics, sleep disturbance characteristics, severity of common symptoms, and quality-of-life outcomes among these subgroups.

METHODS

Oncology outpatients (n = 1331) completed self-report measures of anxiety and sleep disturbance 6 times over 2 chemotherapy cycles. Latent profile analysis was done to identify subgroups of patients with distinct joint anxiety and sleep disturbance profiles.

RESULTS

Three profiles were identified (ie, no anxiety and low sleep disturbance (59.7%), moderate anxiety and high sleep disturbance (32.5%), high anxiety and very high sleep disturbance (7.8%)). Compared with the no anxiety and low sleep disturbance class, the other 2 classes were younger; less likely to be married; had a lower annual household income; and had childcare responsibilities. Patients in the 2 worse profiles had problems with both sleep initiation and maintenance. These patients reported higher levels of depressive symptoms, trait and state anxiety, and evening fatigue, as well as lower levels of morning and evening energy, cognitive function, and poorer quality of life.

CONCLUSIONS

More than 40% of patients had moderate or high levels of anxiety and high or very high levels of sleep disturbance. Modifiable risk factors associated with these profiles may be used to develop targeted interventions for 1 or both symptoms.

IMPLICATIONS FOR PRACTICE

Clinicians need to assess for the co-occurrence of anxiety and sleep disturbance.

Candidate biomarkers in psychiatric disorders: state of the field

The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.

Guiding Drug Repositioning for Cancers Based on Drug Similarity Networks

Drug repositioning aims to discover novel clinical benefits of existing drugs, is an effective way to develop drugs for complex diseases such as cancer and may facilitate the process of traditional drug development. Meanwhile, network-based computational biology approaches, which allow the integration of information from different aspects to understand the relationships between biomolecules, has been successfully applied to drug repurposing. In this work, we developed a new strategy for network-based drug repositioning against cancer. Combining the mechanism of action and clinical efficacy of the drugs, a cancer-related drug similarity network was constructed, and the correlation score of each drug with a specific cancer was quantified. The top 5% of scoring drugs were reviewed for stability and druggable potential to identify potential repositionable drugs. Of the 11 potentially repurposable drugs for non-small cell lung cancer (NSCLC), 10 were confirmed by clinical trial articles and databases. The targets of these drugs were significantly enriched in cancer-related pathways and significantly associated with the prognosis of NSCLC. In light of the successful application of our approach to colorectal cancer as well, it provides an effective clue and valuable perspective for drug repurposing in cancer.

A Voxel-Based Morphometric Study of Gray Matter in Specific Phobia

The objective of this study was to analyze the neurostructural abnormalities of brain areas responsible for the acquisition and maintenance of fear in small animal phobia by comparing gray matter volume (GMV) in individuals with phobia and non-fearful controls. Structural magnetic resonance imaging was obtained from 62 adults (79% female) assigned to one of two groups: 31 were diagnosed with small animal phobia and 31 were non-fearful controls. To investigate structural alterations, a whole-brain voxel-based morphometry analysis was conducted to compare the GMV of the brain areas involved in fear between both groups. The results indicated that individuals with a small animal specific phobia showed smaller GMV in cortical regions, such as the orbitofrontal (OFC) and medial frontal cortex, and greater GMV in the putamen than non-fearful controls. These brain areas are responsible for avoidant behavior (putamen) and emotional regulation processes or inhibitory control (prefrontal cortex (PFC)), which might suggest a greater vulnerability of phobic individuals to acquiring non-adaptive conditioned responses and emotional dysregulation. The findings provide preliminary support for the involvement of structural deficits in OFC and medial frontal cortex in phobia, contributing to clarify the neurobiological substrates for phobias.

Possible association between the lrrk2 gene and anxiety behavior: a systematic literature review

Alterations to the LRRK2 gene have been associated with Parkinson's disease and alcohol consumption in animals and humans. Furthermore, these disorders are strongly related to anxiety disorders (ADs). Thus, we investigated how the LRRK2 gene might influence anxiety in humans and mice. We elaborated a systematic review based on the PRISMA Statement of studies that investigated levels of anxiety in animal or human models with alterations in the LRRK2 gene. The search was conducted in the PubMed, Scopus, and Web of Science databases, and in reference lists with descriptors related to ADs and the LRRK2. From the 62 articles assessed for eligibility, 16 were included: 11 conducted in humans and seven, in mice. Lrrk2 KO mice and the LRRK2 G2019S, LRRK2 R1441G, and LRRK2 R1441C variants were addressed. Five articles reported an increase in anxiety levels concerning the LRRK2 variants. Decreased anxiety levels were observed in two articles, one focusing on the LRRK2 G2019S and the other, on the Lrrk2 KO mice. Eight other articles reported no differences in anxiety levels in individuals with Lrrk2 alterations compared to their healthy controls. This study discusses a possible influence between the LRRK2 gene and anxiety, adding information to the existing knowledge respecting the influence of genetics on anxiety.

From genetics to systems biology of stress-related mental disorders

Many individuals will be exposed to some form of traumatic stress in their lifetime which, in turn, increases the likelihood of developing stress-related disorders such as post-traumatic stress disorder (PTSD), major depressive disorder (MDD) and anxiety disorders (ANX). The development of these disorders is also influenced by genetics and have heritability estimates ranging between ∼30 and 70%. In this review, we provide an overview of the findings of genome-wide association studies for PTSD, depression and ANX, and we observe a clear genetic overlap between these three diagnostic categories. We go on to highlight the results from transcriptomic and epigenomic studies, and, given the multifactorial nature of stress-related disorders, we provide an overview of the gene-environment studies that have been conducted to date. Finally, we discuss systems biology approaches that are now seeing wider utility in determining a more holistic view of these complex disorders.