Depression and Anxiety最新文献

Background. The efficacy and safety of esketamine nasal spray (ESK) were established in registration trials in patients with treatment-resistant depression (TRD). This French real-world study aimed to describe the treatment patterns, effectiveness, and safety of ESK in TRD patients over a 12-month follow-up. Materials and Methods. This study used secondary data from patient files of hospital-based psychiatrists and started during the first French patient early access to ESK. The response and remission rates with ESK were analyzed using the total score of the Montgomery–Åsberg Depression Rating Scale (MADRS). The time to first treatment response and work resumption were described (Kaplan–Meier method). Adverse events (AEs) were analyzed. Results. Prior to ESK initiation, the 157 analyzed patients (age ≤ 65 years, 82.8%; female, 66.2%) had depression for 10.5 years (median, IQR, 4.2–21.2) and received a median of 6 (3–8) previous treatment lines. At ESK initiation, the mean ± SD total MADRS score was 32.1 ± 7.7. At that time, ESK was combined with antidepressants (93.6% of patients; SNRI, 65.0%; SSRI, 57.3%) and/or other potentiation strategy (63.1%; atypical antipsychotics, 36.3%; lithium, 25.6%; antiepileptics, 21.7%). During the 12-month follow-up, 125 patients (79.6%) discontinued ESK. The median duration of ESK treatment was 19.4 weeks (IQR, 4.4–40.1). At 1 month after ESK initiation, 40.2% of still treated patients met criteria for clinical response and 19.7% for remission (median time to response, 5.7 weeks; 95% CI (4.1–8.4)). 82.6% of active patients were on sick leave at ESK initiation; the work resumption rate was 24% (13%–40%) 12 weeks later. AEs were reported in 68.6% of patients, serious AEs in 17.2%, and AEs leading to ESK discontinuation in 14.6%. Conclusion. These real-world effectiveness and safety data were consistent with findings from previous clinical trials, describing the real-life clinical experience of patients receiving ESK and confirming that ESK has its place in therapy for the treatment of TRD.

Background. Major depressive disorder (MDD) and anxiety disorders (AD) have high degrees of comorbidity and show great overlap in symptoms. The analysis of covarying depressive- and anxiety symptoms in longitudinal, sparse data panels has received limited attention. Dynamic time warping (DTW) analysis may help to provide new insights into symptom network properties based on diagnostic- and disease-state stability criteria. Materials and Methods. In the Netherlands Study of Depression and Anxiety depressive-, anxiety-, and worry symptoms were assessed four or five times over the course of 9 years using self-report questionnaires. The sample included 1,649 participants at baseline, comprising controls (n = 360), AD patients (n = 158), MDD patients (n = 265), and comorbid AD–MDD patients (n = 866). With DTW, 1,649 distance matrices were calculated, which yielded symptom networks and enabling comparison of network densities among subgroups. Results. The mean age of the sample was 41.5 years (standard deviations, 13.2), of whom 66.4% were female. The largest distance was between worry symptoms and physiological arousal symptoms, implicating the most dissimilar dynamics over time. The network density in the groups, from lowest to highest, followed the order: controls, AD, MDD, and comorbid AD–MDD. The comorbid group showed strongly connected mood and cognitive symptoms, which contrasted with the more strongly connected somatic and arousal symptoms in the AD and MDD groups. Groups that showed more transitions in disease states over follow-up, regardless of the diagnoses, had the highest network density compared to more stable states of health or disease (beta for quadratic term = −0.095; P  < 0.001). Conclusions. Symptom networks over time can be visualized by applying DTW methods on sparse panel data. Network density was highest in patients with comorbid anxiety and depressive disorders and those with more instability in disease states, suggesting that a stronger internal connectivity may facilitate “critical transitions” within the complex systems framework.

Background. Functional near-infrared spectroscopy (fNIRS) is being extensively explored as a potential primary screening tool for major depressive disorder (MDD) because of its portability, cost-effectiveness, and low susceptibility to motion artifacts. However, the fNIRS-based computer-aided diagnosis (CAD) of MDD using deep learning methods has rarely been studied. In this study, we propose a novel deep learning framework based on a convolutional neural network (CNN) for the fNIRS-based CAD of MDD with high accuracy. Materials and Methods. The fNIRS data of participants—48 patients with MDD and 68 healthy controls (HCs)—were obtained while they performed a Stroop task. The hemodynamic responses calculated from the preprocessed fNIRS data were used as inputs to the proposed CNN model with an ensemble CNN architecture, comprising three 1D depth-wise convolutional layers specifically designed to reflect interhemispheric asymmetry in hemodynamic responses between patients with MDD and HCs, which is known to be a distinct characteristic in previous MDD studies. The performance of the proposed model was evaluated using a leave-one-subject-out cross-validation strategy and compared with those of conventional machine learning and CNN models. Results. The proposed model exhibited a high accuracy, sensitivity, and specificity of 84.48%, 83.33%, and 85.29%, respectively. The accuracies of conventional machine learning algorithms—shrinkage linear discriminator analysis, regularized support vector machine, EEGNet, and ShallowConvNet—were 73.28%, 74.14%, 62.93%, and 62.07%, respectively. Conclusions. In conclusion, the proposed deep learning model can differentiate between the patients with MDD and HCs more accurately than the conventional models, demonstrating its applicability in fNIRS-based CAD systems.

Background. Major depressive disorder with anxiety (MDD-A) is considered as a clinical subphenotype of major depressive disorder (MDD). There continues to be debate regarding the legitimacy of differentiating between the two diagnoses and their neurobiological foundations, given that the symptoms of MDD and MDD-A overlap. However, there is still a dearth of research that delineates the dynamic alteration in the brain activity unique to anxiety in MDD with resting-state functional magnetic resonance imaging (R-fMRI). Methods. 30 patients with MDD, 45 patients with MDD-A, and 46 healthy controls completed R-fMRI scans. Dynamic analysis was utilized to generate many widely used measures, such as voxel-mirrored homotopic connectivity, global signal correlation, regional homogeneity, amplitude of low-frequency fluctuations, and network degree centrality. Concordance between these indices was assessed with Kendall’s W coefficient for both volume and voxel-wise concordance. Finally, the differences in voxel-wise concordance among the groups were looked at, and their relationship to clinical factors was assessed. Results. Compared to the healthy control group, both MDD and MDD-A exhibited decreased dynamic R-fMRI indices in the bilateral calcarine, left postcentral gyrus, inferior parietal lobe, right lingual gyrus, and middle occipital gyrus. In comparison to the MDD group, the MDD-A group displayed a reduction in voxel-wise concordance in the left medial superior frontal gyrus. Furthermore, it was observed that the MDD and MDD-A groups both exhibited a negative correlation between anxiety levels and voxel-wise concordance in the left medial superior frontal gyrus. Conclusions. The aberrant voxel-wise concordance of the left medial superior frontal gyrus may differentiate the neurobiological aspects of MDD with anxiety symptom from MDD. These findings indicate the underlying mechanisms implicated in MDD with anxiety symptom while highlighting the significance of accounting for heterogeneity in depression research.

Introduction. The acquisition of a college education is typically indicative of a resource advantage. However, in recent years, college graduates have faced increasing mental health related issues. The health advantages derived from this resource advantage have become increasingly less pronounced. This study aims to examine the effects of college education on depressive symptoms. Materials and Methods. We used data from China Family Panel Studies (CFPS), and combined this dataset with Chinese Education Examination Yearbook. We took advantage of variations in educational attainment, which was generated by college expansion policy, and adopted instrumental variables (IV) approach to identify the causal relationship. Results. Our findings indicated that college education did not have a causal effect on promoting mental health. The results were supported by the fact that they held within each subgroup. Notably, our limited evidence suggested that college expansion policy promoted equity in educational access. Conclusion. This study provided new and valuable evidence of education-induced health inequalities from the top of the education distribution.

Objective. Ecological momentary assessment (EMA) is increasingly used to monitor depressive symptoms in clinical trials, but little is known about the comparability of its outcomes to those of clinical interviews and questionnaires. In our study, we administered EMA and questionnaires to measure change in depressive symptoms and repetitive negative thinking (RNT) in a clinical trial and investigated (a) the size of intervention effects associated with both techniques and (b) their validity in predicting clinical interview outcomes (i.e., global functioning). Materials and Methods. Seventy-one depressed patients were randomly assigned to one of three psychological interventions. The EMA comprised a concise item set (four items per scale) and was administered three times per day during a 7-week intervention period. Conversely, questionnaires were assessed weekly (WQA), encompassing their full sets of items of depressive symptoms and RNT. Results. While EMA excelled in detecting significant intervention effects, WQA demonstrated greater strength in predicting clinician ratings of global functioning. Additionally, we observed significant differences in time effects (slopes) between the two techniques. WQA scores decreased steeper over time and were more extreme, e.g., higher at baseline and lower postintervention, than EMA scores. Conclusions. Although clinical interviews, questionnaires, and EMA outcomes are related, they assess changes in depression differently. EMA may be more sensitive to intervention effects, but all three methods harbor potential bias, raising validity and reliability questions. Therefore, to enhance the validity and reliability of clinical trial assessments, we emphasize the importance of EMA approaches that combine subjective self-reports with objectively measured behavioral markers. This trial is registered with osf.io/9fuhn.

There is a treatment gap for Common Mental Disorders (CMDs) such as anxiety, depression, and post-traumatic stress disorder (PTSD), as well as non-specific psychological distress (NPD) in low- and middle-income countries (LAMIC), due to the lack of available clinicians and locally appropriate interventions. Task-shifting using non-specialist providers (NSP) and transdiagnostic approaches may address this. Transdiagnostic approaches can be effective at treating CMDs and NPD in high-income countries (HIC), but currently, there is no comprehensive synthesis of evidence regarding their effectiveness in LAMICs. This review addressed this gap by examining the effectiveness of transdiagnostic psychological interventions for symptoms of CMDs and NPD delivered by NSPs in LAMICs. Three databases were searched (Embase, PsycInfo, and PubMed). Hedge’s g’s were calculated using random-effect models to correct for small sample bias. Twenty-one transdiagnostic interventions across 20 studies were included. Moderate reductions at posttreatment were found in psychological distress (g = −0.64; 95% CI: −0.81 to −0.46), symptoms of anxiety (g = −0.61; 95% CI: −0.80 to −0.42), depression (g = −0.59; 95% CI: −0.75 to −0.44), and PTSD/trauma (g = −0.38; 95% CI: −0.59 to −0.16), with significant small reductions maintained at follow-up ranging from 13 to 26 weeks for NPD (SMD = − 0.37; 95% CI: −0.57 to −0.17), anxiety (g = − 0.41; 95% CI: −0.91 to 0.09), depression (g = −0.38; 95% CI: −0.59 to −0.16), and trauma symptoms (g = −0.23; 95% CI: −0.42 to −0.05). These findings are important and suggest that transdiagnostic approaches delivered by NSPs are effective at treating symptoms of CMDs and NPD in LAMICs. Future research should consider comparing task-shifting approaches with disorder-specific interventions and explore the effectiveness of longer sessions across various mental health conditions.

Background. There is a lack of comprehensive data regarding the association of depression and anxiety for the subsequent risk of thyroid disease. Aim. To examine this prospective association between depression and anxiety and the risk of potential thyroid diseases, a large prospective study was conducted using data from UK Biobank. Materials and Methods. Depression and anxiety were measured through self-reported Patient Health Questionnaire-4 (PHQ-4) at baseline, thyroid disease was diagnosed by corresponding hospital data documented through International Classification of Diseases (ICD)-10 codes. Cox proportional hazard models and restricted cubic splines were employed to analyze the associations. Results. In a 13-year follow-up cohort study involving 349,993 participants, 9,877 (2.82%) individuals developed thyroid disease. Significant associations were observed between depression, anxiety, and risk of both hypothyroidism and hyperthyroidism among all participants, the adjusted hazard ratios (HRs) for PHQ scores were as follows: mild 1.27 (95% CI, 1.20–1.36), moderate 1.33 (95% CI, 1.17–1.51), and severe 1.56 (95% CI, 1.31–1.85). For hyperthyroidism, the HRs were mild 1.19 (95% CI, 1.03–1.38), moderate 1.43 (95% CI, 1.08–1.90), and severe 1.84 (95% CI, 1.28–2.66). The adjusted HRs indicating the association between depression, anxiety, and hypothyroidism remained significant for both males and females. The relationship between PHQ score and the risk of thyroid disease shows linear in restricted cubic spline. Conclusion. Depression and anxiety exposure is associated with subsequent risk of thyroid diseases, the more severe the depression and anxiety, the greater the associated risk. Therefore, any level of depression and anxiety should be taken into consideration in thyroid disease risk prediction and prevention.

Bullying victimization is associated with an increased risk of depression among adolescents. However, few studies have examined the association between bullying victimization and depressive symptoms, the impact of particular dimensions of life satisfaction on this relationship, and whether these associations vary by gender. A multilevel model (MLM) was used to explore the relationship between 1,611 Chinese early adolescents’ incidence of bullying victimization and depression and life satisfaction’s effect on this relationship (60.5% boys, M_age = 12.48, SD = 0.48 at baseline). Bullying victimization was found to predict a high risk of depression in early adolescence. Further, we observed that life satisfaction mitigated the relationship between bullying victimization and depression; the MLM analysis indicated that these associations somewhat differed between genders. This study emphasized—based on ecological theory—four special dimensions of life satisfaction and bullying victimization in relation to depression risk. Additionally, this study provides novel insights into the correlation between bullying victimization and depression among Chinese early adolescents.

Music serves as a prevalent emotional regulation tool among young people. However, the correlational and causal relationships between music use, emotion regulation ability, and emotional symptoms remain unclear. This study aimed to investigate the associations and causal relationships between healthy and unhealthy music use, emotion regulation ability, and emotional symptoms, including alexithymia, depression, and anxiety. This study included 16,588 college students nationwide in China. All participants were assessed online with the Healthy-Unhealthy Music Scale (HUMS), the Regulatory Emotional Self-Efficacy Scale (RESE), the Toronto Alexithymia Scale (TAS-20), and the 10-item Kessler Psychological Stress Scale (K10) using a cluster convenience sampling method. We applied a regularized partial correlation network (RPCN) and Bayesian network to analyze the network characteristics of the outcomes. In the RPCN analysis, healthy music use showed the second strongest expected influence (one-step) and correlated positively with emotion regulation self-efficacy while inversely correlating with externally oriented thinking of alexithymia and depression. The Bayesian network indicated that healthy music use was located downstream of the network, positively predicted by managing anger-irritation and expressing positive affect in emotion regulation self-efficacy. In contrast, unhealthy music use in the RPCN displayed the strongest bridge strength and bridge expected influence (one-step). It negatively correlated with expressing positive affect in emotion regulation self-efficacy and positively correlated with alexithymia, anxiety, and depression. The Bayesian network highlighted that unhealthy music use was positively affected by anxiety, depression, and difficulty identifying feelings. In addition, managing despondency-distress influences difficulty identifying feelings through depression, subsequently affecting unhealthy music use and, finally, influencing externally oriented thinking. This study provides a novel framework for understanding the role of emotion regulation self-efficacy and alexithymia in the relationship between music use and emotional symptoms. Emotion regulation and mental health may benefit from music-based interventions and therapies informed by the findings of this study.