Depression and Anxiety最新文献

Objectives: Aging is an inevitable process. Chronic pain and late-life depression frequently coexist in older adults. This study was aimed to explore the association between chronic pain and late-life depression in Chinese centenarians and oldest-old adults.

Study Design: According to the list provided by the Department of Civil Affairs, a household survey was conducted on all centenarian and oldest-old adults residing in 18 cities and counties of Hainan Province.

Methods: The household survey method was used to collect basic information with interview questionnaires, physical examinations, and blood tests conducted by systematically trained doctors and nurses. This study used visual analog scales and numerical rating scale for pain assessment. Geriatric depression scale (GDS) was used for the evaluation of depression.

Results: All 1324 older adults had a median age of 91 years, ranging from 80 to 116 years. Among them, 349 older adults (26.4%) have depression, and 507 (38.3%) suffer from chronic pain. Comorbidity rate of chronic pain and late-life depression was 12.6% (167 participants). Furthermore, late-life depression (odds ratio [OR]: 1.591, 95% confidence interval [CI]: 1.218–2.078, and p = 0.001) was significantly and positively associated with chronic pain in multivariate logistic regression analysis. Chronic pain (OR: 1.581, 95% CI: 1.210–2.067, and p = 0.001) was significant and positive factor associated with late-life depression in multivariate logistic regression analysis.

Conclusions: This study demonstrated that chronic pain and late-life depression are positively associated in Chinese centenarians and oldest-old adults. This suggests that the management of pain should be considered when treating late-life depression in older adults.

Smoking during pregnancy is known to adversely affect offspring health; however, the association between maternal smoking during pregnancy and the risk of depression and anxiety in offspring remains inconsistent. This meta-analysis aimed to clarify this relationship. A systematic search was conducted in PubMed, Web of Science, and OVID databases for articles published between 2000 and 2024. The odds ratio (OR) with a 95% confidence interval (CI) was used to assess the association. A total of 11 studies involving 1,775,220 participants met the inclusion criteria. The meta-analysis revealed that maternal smoking during pregnancy was significantly associated with an increased risk of depression in offspring (OR = 1.33, 95% CI = 1.09–1.63). Stratified analysis by cigaret consumption dose showed that heavy maternal smoking (≥ 10 cigarets/day) further increased the risk of both depression (OR = 1.61, 95% CI = 1.21–2.14) and anxiety (OR = 1.51, 95% CI = 1.32–1.72) in offspring. In conclusion, this meta-analysis provides evidence that maternal smoking during pregnancy may elevate the risk of depression and anxiety in offspring, particularly with heavy smoking. Preventing maternal smoking and reducing exposure to tobacco smoke during pregnancy could have significant benefits for offspring mental health and well-being.

Background: University students often experience high levels of stress and anxiety during exam periods, adversely affecting their well-being and academic performance. This study investigated the short-term effects of evening bright light (BL) exposure on several psychophysiological stress measures during exam preparation.

Methods: In this preregistered randomized controlled pilot study, 35 university students were assigned either to an intervention group exposed to BL (1500 lx, 4000 K; n = 18) or a control light (CL) group with standard lighting (100 lx, 3000 K; n = 17) for 4 h over five consecutive evenings. Outcomes included questionnaires (test anxiety, general anxiety, psychological distress), cognitive performance (2-back, go-/no-go task [GNT]), physiological stress (heart rate variability [HRV]), and subjective and objective sleep quality measures (actigraphy).

Results: The BL group showed significant reductions in test anxiety by the last evening. Both groups improved in working memory performance over time. HRV analysis revealed mixed results, with some indications of reduced stress in the BL group on the first day. No adverse effects of evening BL were found on sleep parameters, and participants reported significantly higher satisfaction with the BL exposure.

Conclusions: Evening BL exposure during exam preparation may help reduce test anxiety without significantly disrupting sleep. Although cognitive performance effects were limited, the perceived usefulness suggests that BL could be a well-accepted supportive measure for students during stressful academic periods. Further research is needed to optimize light-based interventions for student well-being.

Major depressive disorder (MDD) and schizophrenia (SZ) are among the most debilitating psychiatric disorders, characterized by widespread disruptions in large-scale brain networks. However, the commonalities and distinctions in their large-scale network distributions remain unclear. The present study aimed to leverage advanced deep learning techniques to identify these common and distinct patterns, providing insights into the shared and disorder-specific neural mechanisms underlying MDD and SZ. Recent advances in graph neural networks (GNNs) offer a powerful framework for analyzing brain connectivity patterns, enabling automated learning of complex, high-dimensional network features. In this study, we applied state-of-art GNN architectures to classify MDD and SZ patients from healthy controls (HCs), respectively, using a multisite resting-state fMRI dataset. The attention-based hierarchical pooling GNN (SAGPool) model achieved the highest performance, with mean accuracies of 71.50% for MDD and 75.65% for SZ classification. Using a perturbation-based explainability method, we identified prominent functional connections driving model decisions, revealing distinct patterns of the large-scale network disruption across disorders. In MDD, alterations were dominantly observed in the default mode network (DMN), whereas SZ exhibited prominent alterations in the ventral attention network (VAN). Notably, specific functional connections identified by our model showed significant correlations with clinical symptoms, particularly positive and general symptoms measured by the positive and negative syndrome scale (PANSS) in SZ patients. Our findings demonstrate the utility of GNNs for uncovering complex connectivity patterns in psychiatric disorders and provide novel insights into the distinct neural mechanisms underlying MDD and SZ. These results highlight the potential of graph-based models as tools for both diagnostic classification and biomarker discovery in psychiatric research.

Background: Postpartum depression (PPD) and postpartum post-traumatic stress disorder (PP-PTSD) are prevalent among women. However, the specific symptoms that serve as bridges remain unknown between these two disorders.

Aim: The objective of this study is to establish a symptom network model for PPD and PP-PTSD and investigate the bridge symptoms and their interrelationships in cases of comorbid PPD and PP-PTSD.

Methods: A cross-sectional study was conducted at a tertiary hospital in Wuhan from March 2024 to November 2024. PPD was evaluated using the Edinburgh Postnatal Depression Scale, and PP-PTSD was measured using the Chinese version of the Perinatal PTSD Questionnaire. The “Postpartum Depression–Postpartum Post-traumatic Stress Disorder” network model was constructed and analyzed using R software version 4.2.3.

Conclusion: Healthcare professionals should focus on the severe bridge symptoms reported by postpartum women. To enhance awareness and alleviate anxiety levels, it is advisable to implement positive psychological interventions.

Background: The maladaptive metacognition measured by the Metacognition Questionnaire-30 (MCQ-30) is often linked to a wide range of affective disorders. However, few studies have elucidated the neural underpinnings of different metacognition subdimensions. Additionally, the relationship between these functional neural bases and longitudinal changes in individual emotional distresses remains unclear.

Methods: A total of 180 college students completed brain imaging and a battery of behavioral assessments. Employing the connectome-based predictive modeling (CPM), we delineated the functional connectivity (FC) network of each metacognition subdimension. Then, the mediation model was used to explore the relationships between FC networks, metacognition subdimensions, and emotional distresses.

Results: Default mode network (DMN) was found to be the general network of three significant subdimensions. Specifically, the FC network of cognitive self-consciousness (CSC) was scattered and mainly relied on DMN and frontoparietal network; need to control thoughts (NC) was largely consisted of the correlates between DMN and ventral attention network (VAN); negative beliefs about uncontrollability and danger of worry (NEG) was primarily associated with DMN and its correlates with visual network. CSC, NC, and NEG could mediate the relationship between the corresponding FC network and emotional distresses. Additionally, the CSC related and NEG related FCs could effectively predict the change of anxiety positive affect (PA) and negative affect (NA).

Conclusions: These findings demonstrated the common and distinct FC bases of maladaptive metacognition. The excessive FCs of CSC and NEG might be responsible for impaired self-check-related ability and further increase the risk of several affective disorders.

Depression is a growing public health problem in the European Union (EU), with many individuals turning to self-medication (SM) to manage their symptoms. This cross-sectional study uses data from the third wave of the European Health Interview Survey (EHIS; 2018–2020) to examine the prevalence and determinants of SM among people with recognized depression and depressive symptoms. A total of 25,701 respondents were analyzed. Prevalence of SM among individuals with recognized depression and symptoms of depression in the EU is 38.46% in men and 46.84% in women, varying considerably between countries. An important finding of this study is the impact of medication availability, with access to over-the-counter (OTC) medications outside of pharmacies nearly doubling SM likelihood (adjusted odds ratio [AOR] = 1.98). Additionally, the results reveal marked differences in how these men and women self-medicate. Specifically, women are more likely to self-medicate with depressive symptoms versus recognized depression (AOR = 1.28), whereas the opposite is observed in men (AOR = 0.69). Among women, younger age groups with depression symptoms are particularly likely to self-medicate (15–24 AOR = 1.60; 25–44 AOR = 1.93) and the results reinforce education as a strong predictor of SM (higher education vs. no education AOR = 5.63). Visits to medical/surgical specialists are also linked to SM in women (AOR = 1.32). This study also highlights potentially concerning relationships between SM and alcohol use in men with recognized depression (AOR = 1.42) and prescribed medicine (AOR = 1.68). Differences are also observable in the effect of employment on SM (AOR = 1.45) in men with depression symptoms and women with recognized depression. In contrast, physical activity (PA; high vs. low AOR = 1.32) and healthcare barriers (distance/transportation issues AOR = 1.89 in women; AOR = 1.55 in men, inability to afford care AOR = 1.38) display similar positive associations in men and women. Taken together, these findings underscore the complex and multifaceted nature of SM and point to potential gaps in depression care across the EU, emphasizing the need for gender-sensitive public health strategies and a closer look at OTC medication access.

Recent research has emphasized the continuum of depression, highlighting the significance of early intervention for subclinical depression. However, previous studies often focused on specific populations or lacked comparisons across participants and intervention characteristics in the effectiveness of cognitive behavioral therapy (CBT). This systematic review and meta-analysis (CRD42024498284) aimed to address these gaps by examining the effectiveness of CBT in managing subclinical depression and its potential for preventing the transition to major depression. A comprehensive search across seven databases from inception to March 2025, identified 23 randomized controlled trials (RCTs) involving 5877 participants. Meta-regression, sensitivity analysis, and funnel plots were utilized to assess heterogeneity, publication bias, and study quality. CBT significantly improved subclinical depressive symptoms (at postassessment: g = −0.89; 95% confidence interval (CI) = −1.57 to −0.20 and follow-up: g = −0.56; 95% CI: −0.93 to −0.18) and anxiety symptoms (at postassessment: g = −0.92; 95% CI: −1.84 to −0.00 and follow-up: g = −0.70; 95% CI: −1.15 to −0.25), but had no notable impact on quality of life. Meta-regression analysis identified the number of CBT sessions as factors influencing CBT effectiveness in managing depressive symptoms. While there are statistically significant results (RR = 0.62; 95% CI = 0.50–0.77) indicating CBT’s preventive efficacy in transitioning from subclinical to major depression, evidences were limited by the self-reporting data. The majority of included studies came from Europe which limited generalizability, and comparisons between different types of CBT, education levels, and CBT components were limited. In general, CBT has been demonstrated to be effective in managing depressive symptoms over time. Additional research, particularly from diverse regions and comparative studies between CBT and alternative treatments, is imperative to overcome the current study’s limitations.

Suicide is a significant global public health issue, with current risk assessment methods primarily relying on psychiatrists' clinical judgment and scale-based evaluations, which can be challenging to implement. Recently, interest has increased in using vocal and linguistic features to identify suicide risk. This study investigates speech-based methods for assessing suicide risk in two phases involving 90 patients with major depressive disorder (MDD) or bipolar disorder (BD). In Phase 1, three types of question-answer materials with different emotional valences (positive, neutral, and negative) were employed. The model combining acoustic and word frequency features from negative emotional valence materials achieved the highest accuracy at 77.82%. Phase 2 introduced stress factors, highlighting that speech data collected under stress better reflects participants' psychological states, providing more insights into suicide risk. These findings emphasize the potential of speech analysis in suicide prevention, while also calling for further research to validate and expand these results.