Depression and Anxiety最新文献

Background: Depressive symptoms in older adults are associated with adverse psychosocial outcomes. Understanding how depressive symptoms interrelate can enhance intervention strategies. While network analysis has advanced our comprehension of depressive symptom structure, few studies have explored dynamic interactions in older populations. This study examined both cross-sectional and longitudinal networks of depressive symptoms in older adults to identify core symptoms and symptom interactions over time.

Methods: Participants aged 60 and older with complete two-wave data (baseline: 2018; follow-up: 2020) from the China Health and Retirement Longitudinal Study (CHARLS) were included (N = 6621). Depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10), administered face-to-face by trained interviewers. Cross-sectional networks were estimated using the Ising model for each time point, and a cross-lagged panel network (CLPN) model was applied to examine longitudinal symptom interactions over time. Network accuracy and stability were assessed through bootstrap procedures.

Results: Participants had a mean age of 67.34 years, 52% male, and 93.7% Han ethnicity. “Felt depressed” (r_s = 1.244 at Wave 1, r_s = 1.251 at Wave 2) demonstrated the highest strength centrality in both cross-sectional networks. Node strength exhibited strong stability (correlation stability [CS]-coefficient = 0.75 for both waves). The presence of edges (φ = 0.802; p < 0.001) and edge weights (ρ = 0.921, p < 0.001) across two cross-sectional networks showed high reproducibility. In the longitudinal network, “lack of happiness” showed the highest out-expected influence (out-EI; r = 1.404), followed by “felt depressed” (r = 0.994). Both in-expected influence (in-EI) and out-EI showed acceptable stability (CS-coefficient = 0.594).

Conclusions: Targeting core symptoms, such as “felt depressed” and “lack of happiness” may disrupt depressive symptom networks and reduce overall depression severity, informing precision interventions in older adults. Clinicians could prioritize these symptoms in screening and treatment. Future research should explore whether symptom-targeted interventions can reshape network structures over time.

Systemic inflammation is associated with depression. Certain oral bacterial species contribute to inflammation; however their potential association with mental disorders remains unclear. This study investigated the associations between oral microbiota pathogens and depressive and anxiety symptoms. Data came from 436 men from the Geelong Osteoporosis Study. Oral microbiota was characterized using 16S rRNA sequencing, and an oral pathogen composite was created comprising Porphyromonas gingivalis, Treponema denticola, Fusobacterium nucleatum, and Prevotella nigrescens species relative abundances. Binary variables were created representing elevated depressive and anxiety symptoms using the Hospital Anxiety and Depression Scale. Logistic regression was used to investigate associations between oral pathogens and elevated depressive/anxiety symptoms. Models were adjusted for confounders: age, socioeconomic status, diet, smoking, alcohol, exercise, obesity, and hypertension. We report a modest (nonsignificant) association between the pathogen composite and elevated depressive (OR 1.35 [95% CI 0.974, 1.87]) but not anxiety symptoms. Moreover, some of the comprising species were significantly associated with elevated depressive symptoms, including P. nigrescens (1.61 [1.21, 2.13]). Our exploratory analyses revealed that several other taxa were significantly associated with depression and anxiety symptoms. The findings suggest that specific oral bacteria may contribute to symptoms of depression, warranting further research through larger and longitudinal investigations.

Objective: While traditional psychometric approaches, such as latent variable modeling, have primarily focused on the association between bedtime procrastination and anxiety, they often fail to capture symptom-level temporal and directional relationships. Therefore, this study aims to explore the temporal dynamics of symptom-level associations between bedtime procrastination and anxiety, examining both within-person and between-person variations over time in an adolescent population.

Methods: This study utilized panel data-based network analyses to examine both within-person effects (temporal and contemporaneous networks) and between-person dynamics across 3,296 adolescents. Specifically, we examined symptom-to-symptom associations of bedtime procrastination and anxiety using both cross-sectional and temporal network analyses and assessed the symptom centrality to identify key drivers of symptom dynamics.

Results: At the within-person level, the temporal network analysis indicated that restlessness (GAD5) was the most stable and predictive node across time. Additionally, nervousness (GAD1) and going to bed later than intended (BPS1) had the most significant influence on other symptoms in the T1→T2 and T2→T3 networks, respectively. In the contemporaneous network, inability to control worry (GAD2), excessive worry (GAD3), and trouble relaxing (GAD4) were identified as the central symptoms. At the between-person level, positive relationships between specific bedtime procrastination symptoms were consistently observed.

Conclusions: Our findings elucidate the potential complex interactions between bedtime procrastination and anxiety symptoms, highlighting central symptoms that vary across temporal and contemporaneous networks. The identification of central symptoms and their dynamic associations within these networks can inform the causal mechanisms underlying bedtime procrastination and anxiety, thereby guiding the design of targeted interventions for adolescents.

Objective: This study aimed to investigate the pathways of emotional suppression among women aged 35 years and older undergoing in vitro fertilization-embryo transfer (IVF-ET) and to provide a theoretical basis for developing personalized programs to reduce emotional suppression.

Methods: A convenience sample of 225 women aged ≥35 years undergoing IVF-ET at a reproductive centre between May 1st and September 30th, 2023, was selected. The participants completed a self-designed general information questionnaire, the Emotional Inhibition Scale (EIS), the Locke-Wallace Marriage Adjustment Test (MAT), and the Self-Compassion Scale (SCS). Data were analysed using SPSS 27.0 and AMOS 24.0. Normality and homogeneity of variance were assessed, with normally distributed data described as mean ± standard deviation (SD). Pearson correlation, structural equation modeling (SEM), and bootstrap resampling (5000 iterations, 95% confidence intervals [CIs]) were employed to test the mediation effects, with a bootstrap analysis used to assess the stability and significance of indirect effects through repeated sampling.

Results: The cohort comprised predominantly Han Chinese (82.2%), first-married (60.9%), urban-dwelling (60.4%) women with secondary infertility (68.0%). Over 40.0% of the total held a tertiary educational level, and 67.1% reported per capita monthly income of the family (3001–10,000 RMB). The mean emotional suppression score was 28.65 ± 6.74. Emotional suppression was negatively correlated with marital adjustment (r = −0.442, p  < 0.01) and self-compassion (r = −0.393, p  < 0.01). SEM with bootstrap validation demonstrated that marital adjustment mediated the relationship between emotional suppression and self-compassion (mediating effect proportion: 24.89%, 95% CI: −0.115 to −0.027).

Conclusion: Marital adjustment mediates emotional suppression and self-compassion in women aged ≥35 years undergoing IVF-ET. Fostering positive marital dynamics may alleviate emotional suppression and enhance self-compassion, promoting psychological resilience during treatment. These findings support targeted interventions to improve patient satisfaction and treatment success rates.

Background: Anxiety disorders pose a considerable global health challenge, ranking as the most prevalent type of mental illness. This study delineates the trends in incidence, prevalence, and years lived with disability (YLDs) for anxiety disorders at global, regional, and national levels between 1992 and 2021, with an emphasis on the independent effects of age, period, and birth cohort.

Methods: Incidence, prevalence, and YLDs were selected as burden indicators of anxiety disorders, following the standardized methodologies of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021, with data presented as numerical counts and age-standardized rates (ASRs) per 100,000 population. An age-period-cohort (APC) model was applied to estimate the overall annual percentage change (net drift), annual percentage change within each age group (local drift), and the relative risks associated with age, period, and cohort.

Results: From 1992 to 2021, the global age-standardized incidence rate (ASIR), age-standardized prevalence rate (ASPR) and age-standardized YLDs rate (ASYR) for anxiety disorders showed an overall increase. The APC model estimated a global net drift of 0.16% (95% confidence interval [CI]: 0.03%, 0.30%) for incidence, 0.07% (95% CI: 0.02%, 0.12%) for prevalence, and 0.07% (95% CI: 0.01%, 0.12%) for YLDs. Regionally, the highest ASIR, ASPR, and ASYR were recorded in the high SDI region in 2021, with the Region of the Americas (AMR) showing the highest rates across all three metrics. Among 204 countries/territories, Portugal, Brazil, Lebanon, Iran, and Paraguay ranked as the top five highest for ASIR, ASPR, and ASYR. The age effects on incidence, prevalence, and YLDs followed similar global and regional patterns, with risk initially increasing before declining in older age. The most substantial increase in the period risk of incidence from 1992 to 2021 occurred in high SDI and AMR countries, while prevalence and YLDs saw the largest rise in low-middle SDI region and AMR. A steady increase in the risk of incidence, prevalence, and YLDs was observed across successive birth cohorts globally and regionally.

Conclusion: The global burden of anxiety disorders demonstrated an overall upward trend, with considerable regional, demographic, and temporal variations. These findings provide critical insights for optimizing resource allocation and developing tailored public health strategies to address anxiety disorders.

Background: Internet addiction and depression frequently co-occur among university students, resulting in amplified functional deterioration and treatment resistance. Despite established bidirectional relationships, existing research has predominantly examined linear associations and treated these conditions as single global constructs. This study integrated person-centered and network-based approaches to identify distinct symptom profiles of Internet addiction and depressive symptoms, examine sociodemographic predictors of profile membership, and uncover interconnected symptom networks within high-risk populations among Chinese university students.

Methods: A multicenter cross-sectional study was conducted from April to July 2024. Data were collected through a web-based survey incorporating validated instruments for Internet addiction, depression, and suicide risk assessment. Latent profile analysis was employed to identify distinct symptom profiles, followed by multivariate logistic regression to examine sociodemographic predictors. Network analysis was performed within the high-risk profile to unveil symptom interactions, central symptoms, bridge symptoms, and symptomatic pathways to suicide risk.

Results: Among 30,992 participants, latent profile analysis identified three distinct groups: Healthy profile (59.31%), at-risk profile (35.06%), and comorbidity profile (5.63%). Students who were female, ethnic minorities, in higher grade levels, and had prolonged Internet use showed increased risks of problematic profiles. Conversely, enrollment in bachelor’s programs, science and medical majors, higher household income, and regular physical activity demonstrated protective effects. Network analysis revealed Internet preoccupation and fatigue as central symptoms, identified key bridge symptoms (e.g., offline negative affect, difficulty concentrating) linking the symptom clusters, and highlighted Internet withdrawal symptoms and depressed mood as critical pathways to suicide risk within the comorbidity profile.

Conclusion: This study identified distinct profiles of Internet addiction and depression comorbidity, with specific sociodemographic and lifestyle predictors informing targeted screening strategies. Network analysis revealed central symptoms and specific bridge symptoms connecting the conditions, while also identifying critical pathways to suicide risk in the Comorbidity profile, providing empirical evidence for developing precise and effective interventions.

Background: Previous studies have documented brain structural changes in major depressive disorder with suicidal ideation (MDDSI). While sex differences in brain structure have been observed in MDD, the specific differences in MDDSI remain underexplored. This study aims to examine sex-specific cortical structural changes in MDDSI patients, with the goal of improving suicide risk assessment and supporting the development of sex-specific interventions.

Methods: High-resolution T1-weighted magnetic resonance imaging (MRI) data were acquired from 177 participants, including 117 patients with MDD (44 males and 73 females) and 60 healthy controls (HC; 23 males and 37 females). Using FreeSurfer, we assessed the main effects of both group and sex, as well as sex-by-group interactions, on cortical thickness (CT), surface area (SA), cortical volume (CV), and local gyrification index (LGI). Pearson partial correlation analysis was performed to examine the associations between sex-specific regions and SI scores.

Results: Patients with MDDSI exhibited significantly reduced LGI in the right inferior and superior parietal cortices, irrespective of sex. Significant sex-by-group interactions were identified and post hoc analyses revealed that male MDDSI patients showed significantly greater CT in the right lingual gyrus (LG) compared to their female counterparts, along with smaller SA in the right lateral occipital cortex (LOC) and reduced CV in the right pericalcarine cortex (PCAL) compared to their sex-matched MDD patients without SI (MDDNonSI). No significant associations were found between these structural changes and SI scores.

Conclusion: This study highlights sex-specific differences in cortical structures in MDDSI patients. These findings contribute to understanding the neurobiological mechanisms underlying MDD with SI.

Trial Registration: Chinese Clinical Trial Registry (ChiCTR): ChiCTR2100049646

Background: Depression and anxiety are among the most common mental health issues of older populations, and as such they are frequently monitored covariates. The possibilities for collecting research data has grown with the recent emergence of user-friendly online survey platforms. However, to what extent the populations of older persons who participate in such research are similar to the general population remains unclear. We investigated the affective health of an open online sample of older adults (65+) in contrast to a representative randomised in-person interview sample.

Methods: The surveys were conducted in 2021 during the COVID-19 pandemic in the Czech Republic after the second wave of anti-COVID-19 vaccination. The online sample (N = 389) was recruited via the Internet. Participants of the in-person study (N = 633) were randomly approached according to quotas for representativeness. The administered questionnaires included a health status self-report, the Geriatric Depression Scale (GDS) and the Geriatric Anxiety Inventory – Short form (GAI-SF).

Results: Online participants reported better mental and general health; that is, they reported fewer depressive and anxiety symptoms than the randomised representative sample. In both samples, women showed higher levels of anxiety than men. Subjective general health was associated with mental health. In the randomised representative sample, in contrast to the online sample, the level of depression increased significantly with age. The open non-randomised calls for participants attracted a higher percentage of women and people with higher education than are in the general older population.

Conclusions: Older research volunteers recruited online can be expected to be subjectively healthier and to differ largely from the general population in their sociodemographic characteristics.

Background: Major depressive disorder (MDD) is a psychiatric disorder characterized by alterations in global signal (GS) topography across various neural networks and brain regions, including the default mode network and sensorimotor-related areas. While previous research has demonstrated the potential of global brain activity measures to differentiate MDD from healthy controls (HCs), specific changes in GS distribution among MDD patients with and without anxiety remain poorly understood. This study aims to investigate anxiety-related alterations in GS topography in MDD and their associations with clinical symptoms.

Methods: Resting-state functional magnetic resonance imaging (fMRI) and T1-weighted imaging data were collected from 334 MDD patients with anxiety, 145 MDD patients without anxiety, and 307 HCs as part of the REST-meta-MDD consortium. We computed GS topography using GS correlation (GSCORR) and assessed structural–functional interaction (SFI) by examining the relationship between gray matter volume and GS for each subject.

Results: Our analysis revealed no significant differences in GS topography among the three groups at either the whole-brain or network levels. However, decreased GSCORR was observed in the right precentral gyrus, insula, and posterior parieto-occipital cortex in anxious MDD patients compared to HC. SFI analyses indicated anxiety-related alterations in the sensorimotor network, precuneus, putamen, and middle temporal gyrus. Moreover, GSCORR in the inferior parietal lobe and cerebellum exhibited specific correlation trends with anxiety and depression symptoms, respectively.

Conclusions: These findings underscore an abnormal topographic shift in global brain activity in MDD patients with anxiety, offering a new insight into understanding brain dysfunction associated with this disorder.

Background: The association between ambient air pollution and the onset of depression and anxiety has attracted significant scholarly interest; however, its underlying mechanisms remain elusive.

Objective: The primary objective of this study is to assess the connection between air pollution and the onset of depression and anxiety, with a specific emphasis on uncovering the potential pathways influenced by high-dimensional proteomic markers.

Design, setting, and participants: This prospective, nationwide population-based cohort study utilized data from the UK Biobank, incorporating an analysis of proteomic data from 38,301 participants.

Method: Air pollution levels for particulate matter (PM) diameters of ≤2.5 μm (PM_2.5), ≤10 μm (PM₁₀), 2.5–10 μm (PM_coarse), nitrogen oxides (NO_x), and nitrogen dioxide (NO₂) were estimated in 2010. The depression and anxiety symptoms were assessed through self-report questionnaires at baseline and at the 7-year follow-up, and diagnoses were determined using ICD-9/10 codes from hospital records. Plasma proteomic data for 1463 proteins was measured using the Olink platform. A two-step regression approach was used to identify proteins associated with both air pollution and depression/anxiety. Mediation analysis was performed using the med4way method.

Results: This longitudinal nationwide study leveraged the UK Biobank cohort to elucidate the protein-mediated effects of five major air pollutants on depression and anxiety. The findings identified 23 proteins mediating the risk of developing depression associated with air pollution, while no mediating proteins were found for anxiety. Additionally, the study discovered 38 proteins linked to the severity of anxiety related to air pollution and nine proteins connected to the severity of depressive symptoms. Notably, KEGG pathway analysis revealed significant associations with critical signaling cascades, such as the estrogen, IL-17, and pathways. Furthermore, STRING analysis underscored the shared roles of specific proteins, including EGFR, IL15, CCL2, and CCL20, in the context of air pollution-induced depression and anxiety, highlighting the involvement of immune-related processes and pathways.

Conclusion: The findings of this large population-based cohort study provide proteomic evidence on the mediating protein associations between air pollution and the onset of depression. The results suggest that the immune system plays a significant role in the biological mechanisms linking air pollution to depression and anxiety.