Depression and Anxiety最新文献

Although patients with major depressive disorder (MDD) achieve remission after antidepressant treatment, >90% of those in remission have at least one residual depressive symptom, which may be due to neural damage linked with MDD. To better understand the structural impairments in patients with remitted MDD, we conducted a meta-analysis comparing grey matter volume (GMV) abnormalities between patients with remitted MDD and healthy controls (HCs). There were 11 cross-sectional datasets that investigated 275 patients with remitted MDD versus 437 HCs, and 7 longitudinal datasets that investigated 167 patients with remitted MDD. We found that GMV in the left insula, inferior parietal gyri, amygdala, and right superior parietal gyrus was decreased in patients with remitted MDD than in HCs. Additionally, patients with remitted MDD had lower GMV in the bilateral gyrus rectus than those in the nonremission state. Moreover, increased GMV in the bilateral anterior cingulate cortex, right striatum, middle temporal gyrus, and superior frontal gyrus was observed in patients with remitted MDD than in HCs. Furthermore, patients with remitted MDD had a larger GMV in the bilateral median cingulate/paracingulate gyri, left striatum, putamen, amygdala, hippocampus, and parahippocampal gyrus at follow-up than at baseline. Based on the brain morphological abnormalities in patients with remitted MDD after electroconvulsive therapy and pharmacological treatment, we proposed a schematic diagram of targeted intervention approaches for residual symptoms. In summary, our findings provide neurobiology-based evidence for multitarget treatment of depression to reduce residual symptoms and improve social function in patients with MDD.

Suicide is a major public health problem caused by a complex interaction of various factors. Major depressive disorder (MDD) is the most prevalent psychiatric disorder associated with suicide; therefore, it is essential to prioritize suicide prediction and prevention within this population. Integrated information from different dimensions, including personality, cognitive function, and social and genetic factors, is necessary to improve the performance of predictive models. Besides, recent studies have indicated the critical roles for EPHX2/P2X2 in the pathophysiology of MDD. Our previous studies found an association of EPHX2 and P2X2 with suicide in MDD. This study is aimed at (1) establishing predictive models with integrated information to distinguish MDD from healthy volunteers, (2) estimating the suicide risk of MDD, and (3) determining the contribution of EPHX2/P2X2. This cross-sectional study was conducted on 472 prospectively collected participants. The machine learning (ML) technique using Extreme Gradient Boosting (XGBoost) classifier was employed to evaluate the performance and relative importance of the extracted characteristics in recognising patients with MDD and depressed suicide attempters (DSA). In independent validation set, the model with clinical and cognitive information could recognise MDD with an area under the receiver operating characteristic curve (AUC) of 0.938 (95% confidence interval (CI), 0.898–0.977), and genetic information did not improve classification performance. The model with clinical, cognitive, and genetic information resulted in a significantly higher AUC of 0.801 (95% CI, 0.719–0.884) for identifying DSA than the model with only clinical information, in which the three single nucleotide polymorphisms of EPHX2 showed important roles. This study successfully established step-by-step predictive ML models to estimate the risk of suicide attempts in MDD. We found that EPHX2 can help improve the performance of suicidal predictive models. This trial is registered with NCT05575713.

Our objective was to examine the influence of COVID-19 vaccination on recent (i.e., past month) moderate or severe symptoms of anxiety (GAD-7 ≥ 10) or depression (PHQ-8 ≥ 10) before and after the COVID-19 vaccine became universally available for adults in the U.S. Participants belonged to the Communities, Households, and SARS-CoV-2 Epidemiology Cohort (CHASING COVID), a national longitudinal study. Our analytic population included 4,832 participants who reported vaccination status from December 2020 to December 2021 with follow-up outcomes assessed through March 2022. We emulated a hypothetical randomized experiment, a target trial, to estimate the effect of COVID-19 vaccination on symptoms of anxiety or depression. Before vaccines were universally available, participants who were vaccinated versus not had significantly lower adjusted odds of symptoms of moderate or severe anxiety (aOR: 0.79; 95% CI: 0.70-0.89). In the universal vaccine era, vaccination was associated with marginally higher adjusted odds of symptoms of moderate or severe anxiety (aOR: 1.23; 95% CI: 1.00-1.50). Vaccination did not influence subsequent moderate or severe depressive symptoms in the preuniversal vaccine era (aOR: 0.92; 95% CI: 0.82-1.03) or universal vaccine era (aOR: 1.11; 95% CI: 0.91-1.36). Research into the longitudinal relationship between COVID-19 vaccination and symptoms of depression and anxiety is warranted, with a focus on advancing understanding of potential mediators on the pathway between vaccination and mental health as well as modifiable factors, such as vaccine hesitancy or vaccine beliefs, that may help identify populations for whom vaccination may be particularly beneficial to their mental health.

Background. Previous research on panic risk factors within the US population has been limited. This cross-sectional study is aimed at exploring the association between smoking and panic among adults in the United States. Methods. We conducted an analysis of data from the National Health and Nutrition Examination Survey. Results. The study included 2,222 participants. Those diagnosed with panic disorder were more likely to be female, unmarried, have lower income, engage in higher rates of smoking, and exhibit greater alcohol consumption. Participants who smoke cigarettes occasionally indicated a significant increase in panic disorder (unadjusted OR 95% CI: 4.396 [2.032-9.513]; P < 0.001). The significance of our results remained even after performing the multivariate analysis (full-adjusted OR 95% CI: 2.89 [1.30-6.42]). Furthermore, participants who never smoked cigarettes demonstrated strong and significantly low odds for panic disorder, regardless of adjustment (unadjusted OR 95% CI: 0.180 [0.055-0.591]). There was no association between pipe and cigar smoking and panic disorder in both unadjusted and full-adjusted models. Conclusion. This study highlights that smoking remains a significant risk factor for panic disorder, even after accounting for potential confounding variables. Further prospective longitudinal research should be done to investigate the causality between smoking and panic disorder.

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 age = 44.77 years, SD = 13.66) 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 (CPSS ≥ 31) 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 (P = 0.012). 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, P = 0.027), 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.