Depression and Anxiety

Background. Suicide is a pressing global health concern, and identifying its risk factors is crucial for prevention. Body weight variability (BWV) has been increasingly recognized as a potential factor impacting physical and mental health outcomes. We aimed to explore the relationship between BWV and the risk of suicide mortality using a nationally representative database. Methods. This population-based cohort study used data from the Korean National Health Insurance Database and included a total of 1,983,701 subjects. BWV was assessed using at least three health examination datasets and validated variability indices (variability independent of the mean (VIM), average successive variability, and coefficient of variation), and patients were divided into BWV quartiles (Q1–Q4). The primary endpoint was suicide-related death. Results. During a median of 11.3 years of follow-up, 5,883 suicide deaths occurred. A higher baseline body weight was associated with a lower risk of suicide. However, greater BWV (VIM) was associated with a significantly greater risk of suicide (adjusted hazard ratio [95% confidence interval], 1.35 [1.26–1.45] in the Q4 group), even after adjusting for baseline body mass index (BMI). Similar results were observed regardless of obesity or BMI category. Consistent findings were observed when using different variability indices. Subgroup analyses according to sex, age, diabetes, and depression also supported these findings. Conclusion. Our study highlights the importance of considering BWV as a potential risk factor for suicide.

Background. Chronic stress is associated with a multitude of psychopathological disorders that share similar alterations in neural dynamics and symptomatology. Applying the National Institute of Mental Health’s Research Domain Criteria (RDoC) framework, we probed the stress-diathesis model by identifying how a transdiagnostic psychosocial distress index representing high-dimensional patterns of stress-related aberrations was coupled to the neural oscillatory dynamics serving abstract reasoning. Methods. The sample consisted of 69 adults (mean years, ) who completed the NIH Toolbox Emotion Battery (NIHTB-EB) and a matrix reasoning task during magnetoencephalography (MEG). A transdiagnostic psychosocial distress index was computed using exploratory factor analysis with assessments from the NIHTB-EB. Whole-brain correlations were conducted using the resulting psychosocial distress index for each oscillatory response, and the resulting peak voxels were extracted for mediation analyses to assess the degree to which neural oscillatory activity mediates the interplay between perceived stress and psychosocial distress. Results. We found that elevated psychosocial distress was associated with blunted oscillatory alpha/beta and gamma responses in key cortical association regions. Further, we found that only alpha/beta activity in the right superior temporal sulcus partially mediated the relationship between perceived stress and psychosocial distress. Conclusions. The present study is among the first to couple perceived stress and psychosocial distress with alterations in oscillatory activity during a matrix reasoning task. These findings illuminate the relationship between perceived stress and neural alterations associated with psychopathology.

Background. High stress is associated with coronary heart disease (CHD), but the impact of perceived stress on prognosis with stable CHD remains unclear. This study investigated the impact of high perceived stress (HPS) on cardiovascular events in stable CHD patients. Methods. From March 2015 and December 2020, 2215 stable CHD patients were recruited. The Chinese version of the Perceived Stress Scale-14 (CPSS) was used, with follow-up conducted every 6 months until the occurrence of a cardiovascular event or March 31, 2022. Cardiovascular-related events were used as outcomes, including myocardial infarction, unplanned revascularization, stroke, death, or rehospitalization from angina. Patients were divided into HPS () and nonhigh perceived stress (NHPS) groups. The Kaplan-Meier survival curves were plotted, and the log-rank test compared the incidence of adverse events after adjusting for sociodemographic, lifestyle, and clinical information. Results. The recruited CHD population was 59.6 years old on average, 79.6% male, 27.2 points average CPSS, and median follow-up of 47 months. There were 523 HPS patients, with 98 (18.7%) cardiovascular events, and 1692 NHPS patients with 239 (14.1%) cardiovascular events. The log-rank analysis showed that risk of cardiovascular events with HPS was higher than NHPS (). After adjusting for demographic, lifestyle, and clinical information, the HPS group had significantly increased risk of events within 24 months (HR 1.369, 95% CI 1.037-1.807, ), but less impact after 24 months. Conclusions. HPS predicts subsequent cardiovascular events in patients with stable CHD within 24 months. Therefore, more attention should be given to CHD patients with HPS, which may improve clinical prognosis.

New developments in machine learning-based analysis of speech can be hypothesized to facilitate the long-term monitoring of major depressive disorder (MDD) during and after treatment. To test this hypothesis, we collected 550 speech samples from telephone-based clinical interviews with 267 individuals in routine care. With this data, we trained and evaluated a machine learning system to identify the absence/presence of a MDD diagnosis (as assessed with the Structured Clinical Interview for DSM-IV) from paralinguistic speech characteristics. Our system classified diagnostic status of MDD with an accuracy of 66% (sensitivity: 70%, specificity: 62%). Permutation tests indicated that the machine learning system classified MDD significantly better than chance. However, deriving diagnoses from cut-off scores of common depression scales was superior to the machine learning system with an accuracy of 73% for the Hamilton Rating Scale for Depression (HRSD), 74% for the Quick Inventory of Depressive Symptomatology–Clinician version (QIDS-C), and 73% for the depression module of the Patient Health Questionnaire (PHQ-9). Moreover, training a machine learning system that incorporated both speech analysis and depression scales resulted in accuracies between 73 and 76%. Thus, while findings of the present study demonstrate that automated speech analysis shows the potential of identifying patterns of depressed speech, it does not substantially improve the validity of classifications from common depression scales. In conclusion, speech analysis may not yet be able to replace common depression scales in clinical practice, since it cannot yet provide the necessary accuracy in depression detection. This trial is registered with DRKS00023670.

Background. The global prevalence of depression and anxiety disorders underscores the need for a more profound comprehension of effective treatments. Mindfulness has shown promise in enhancing treatment outcomes and preventing relapse in these conditions, but the underlying mechanisms remain poorly understood. Methods. This study examined the role of emotion regulation as a mediator in the relationship between changes in mindfulness and the reduction of depression and anxiety symptoms during individual cognitive-behavioral therapy (CBT). We tracked longitudinal changes in mindfulness, emotion regulation, depression, and anxiety at baseline (pre), early treatment (session 5), midtreatment (session 15), and posttreatment (session 25) in 162 patients with depression and anxiety disorders. Further, we examined whether the effects of mindfulness on emotion regulation could be enhanced by introducing a brief mindfulness intervention at the beginning of each CBT session, as compared to progressive muscle relaxation and individual psychotherapy without any standardized session-introducing interventions. Results. Multilevel structural equation modeling indicated that decreases in rumination and increases in reappraisal and acceptance mediated the relationship between mindfulness and reductions in depressive symptoms. In contrast, reductions in avoidance explained the association between mindfulness and changes in anxiety symptoms. These links remained unchanged when adding a mindfulness intervention into individual CBT. Conclusion. Results support emotion regulatory properties of mindfulness and highlight distinct pathways of symptom reduction in depression and anxiety. These findings have important implications for understanding the mechanisms of mindfulness and tailoring treatment to individual patient needs. This trial is registered with NTC02270073.

Background. Heterogeneous findings among anxiety disorder studies have hindered elucidation of the underlying pathophysiology and the development of mechanism-based therapies. Purpose. To determine whether structural MRI findings in anxiety disorder studies converge on a common network with therapeutic significance. Materials and Methods. In this retrospective study, a systematic literature search of PubMed and Web of Science databases was performed to identify coordinates of gray matter atrophy in patients with anxiety disorder. Atrophy coordinates were then mapped to an anxiety network constructed from the resting-state functional MRI (rs-fMRI) data of 652 healthy participants using “coordinate network mapping” and validated by specificity tests. The causal association of this network to anxiety symptoms was tested in a cohort of patients with brain lesions and emergent anxiety symptoms. The potential therapeutic utility of this anxiety network was then assessed by examining the clinical efficacy of network-targeted repetitive transcranial magnetic stimulation (rTMS) among a separate anxiety disorder cohort. Statistical analyses of images were performed using nonparametric tests and corrected for family-wise error. Results. Sixteen studies comprising 453 patients with anxiety (245 females; , years) and 460 healthy controls (238 females; years) were included in the analysis. Atrophy coordinates were mapped to an anxiety network with a hub region situated primarily within the superficial amygdala. Lesions associated with emergent anxiety symptoms exhibited stronger connectivity within this anxiety network than lesions not associated with anxiety (; ). Moreover, the connectivity strength of rTMS targets in the anxiety network was correlated with the improvements of anxiety symptom after treatment (, ). Conclusions. Heterogeneous gray matter atrophy among patients with anxiety disorder localize to a common network that may serve as an effective therapeutic target.