Journal of affective disorders最新文献

Background: Prediction of atypical health trajectories may enable early intervention. We systematically reviewed the existing literature on models for predicting longitudinal depression and/or anxiety trajectories.

Methods: MEDLINE, Embase and APA PsycINFO were searched (from inception to 31-Jan-2025). We included population-based studies of children and adults (aged 3-65 years). Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST-AI) tool.

Results: Seven of the nine included studies were in adult populations with a diagnosis of depression or anxiety at baseline; two focused on child and adolescent populations. Only one study included anxiety trajectories. Identified trajectories typically comprised three to four groups including: chronic/persistent-high, stable-low, increasing/worsening, and improved/remitted groups. Various supervised predictive modelling methods were used. The number of final predictors included in models ranged from three to 152. Family and own/personal psychiatric history were the most common predictors but were not always important for model performance. Models including more predictors did not always perform better. Overall risk of bias was high in all studies. No studies were externally validated and no studies assessed the clinical utility of models.

Conclusion: This review highlights a need for robust, validated models that can forecast future risk of persistent or worsening anxiety and depression, especially in young people where early intervention is possible.

Attention-deficit/hyperactivity (ADHD), bipolar (BD) and borderline personality (BPD) disorders are severe psychiatric illnesses often presenting with overlapping emotion dysregulation symptoms. To date, it is unknown whether these disorders share a continuum on emotion dysregulation. To address this, we employed machine learning (ML) algorithms on psychological and environmental features from 232 adults to predict and discriminate these disorders. We recruited 92 patients diagnosed with either ADHD, BD, or BPD. Considering heritability, we recruited 67 genetically unrelated ADHD, BD and BPD offspring to determine how offspring overlap with or discriminate from their parent disorder. Seventy-three healthy age-matched healthy controls (HC) were also recruited. Features included clinical dimensions associated with emotion dysregulation (e.g., impulsivity, mania, rumination) as well as childhood trauma and parental bonding. BPD showed the greatest independence, discriminating very strongly from HC, strongly from ADHD and moderately from BD and BPD offspring, with mania as most predictive overall. ADHD discriminated very strongly from HC, strongly from BPD, moderately from BD, and weakly from ADHD offspring, with impulsivity as most predictive. BD discriminated moderately from ADHD, BPD and BD offspring, and weakly from HC, with maternal bonding most predictive. HC discriminated weakly from all offspring groups, with perseverance as most predictive. Results suggest ADHD, BD and BPD are independent psychiatric constructs reliably classified by ML via emotion regulation traits and environmental factors, whereas their offspring more closely resemble HC. We thus show ML may allow for cost-effective classification and prediction of emotion dysregulation disorders (ADHD, BD, BPD) relative to HC.

Background: Lung function has been increasingly linked to brain health, prompting investigation into the causal relationships between lung function and brain structures. This study employs Mendelian Randomization (MR) to explore these causal relationships, leveraging genetic variants as proxies to predict the effects of lung function on brain cortical and subcortical structures.

Methods: We conducted univariable and multivariable MR analyses using GWAS summary statistics for lung function (FEV1, FVC, FEV1/FVC) and brain structures from the UK Biobank and ENIGMA consortium. Our analyses included five MR methods-IVW, MR-Egger, weighted median, weighted mode, and simple mode-to ensure robust causal inference. Multivariable MR (MVMR) analyses were performed to adjust for potential confounders. Sensitivity analyses were performed to confirm the stability of our results, and we applied Bonferroni correction for multiple comparisons.

Results: The univariable MR analysis revealed significant associations between lung function and brain structures. Higher FEV1 was associated with increased global cortical surface area (β = 4428.037, SE = 610.453, p = 4.056E-13), higher FVC with increased global cortical surface area (β = 3650.674, SE = 576.736, p = 2.453E-10), and higher FEV1/FVC with increased paracentral surface area (β = 13.076, SE = 3.538, p = 2.193E-04). FVC was similarly associated with increased parsopercularis thickness (β = 0.013, SE = 0.003, p = 1.160E-04). Significant associations persisted in subcortical regions, with higher FEV1 and FVC linked to increased brainstem volume (FEV1: β = 0.226, SE = 0.049, p = 3.617E-06; FVC: β = 0.203, SE = 0.044, p = 4.158E-06). Multivariable MR confirmed these associations, even after adjusting for smoking, education, socioeconomic status, and physical activity. Significant associations persisted in cortical surface area, with higher FVC linked to increased global cortical surface area, and increased paracentral surface area for FEV1/FVC. Sensitivity and pleiotropy analyses indicated no significant heterogeneity or horizontal pleiotropy, confirming the robustness of the results.

Conclusion: Our study provides robust evidence of a causal relationship between lung function and brain structure, emphasizing the protective effects of better respiratory health on brain integrity.

Background: Cardiometabolic multimorbidity (CMM) has emerged as a significant public health concern. Prior research has found the associations between depression and cardiometabolic diseases (CMD). However, the studies about the association between depression and CMM were limited.

Methods: This study was designed as a prospective observational investigation. The study population comprised middle-aged and older adults who participated in the CHARLS in 2011 and follow-up in 2018, with no CMD at baseline. Depressive symptoms were assessed using the CESD-10 scale. The multivariable Cox proportional hazards model was employed to evaluate the association between depression and the risk of CMM.

Results: The study included 9084 participants, with a mean age of 57.69 years. Among them, 3116 individuals were diagnosed with depression. Over a 7-year follow-up period, 431 participants (4.74%) developed CMM. Multivariable Cox proportional hazards regression revealed that participants with depression had a 1.65-fold heightened risk of CMM (HR:2.65, 95%CI: 1.95, 3.61), 0.84-fold in heart disease (HR:1.84, 95%CI: 1.49, 2.26), 0.58-fold in stroke (HR:1.58, 95%CI: 1.16, 2.16), and 0.48-fold in diabetes (HR:1.48, 95%CI: 1.19, 1.85) compared to those without. And the risk of CMM increased progressively with greater severity of depression. Subgroup analysis revealed that depression significantly increased CMM risk in individuals without hypertension (p for interaction = 0.025). The Kaplan-Meier curves indicated that individuals with depression exhibit a higher cumulative incidence of CMM, which further increased with greater severity of depression.

Conclusion: The findings of this study revealed that depression not only significantly heightened the risk of CMD but also the risk of CMM. Furthermore, the risk of CMM rose progressively with greater severity of depressive degree, highlighting the pivotal role of depression in both the onset and progression of cardiometabolic disorders.

Objective: To evaluate the efficacy and safety of a multimodal rapid anti-depression therapy that combines esketamine treatment with dexmedetomidine patient-controlled sleep (PCSL) in patients with treatment-resistant depression (TRD).

Methods: We retrospectively included 233 patients with TRD who received the multimodal treatment. Baseline clinical characteristics were collected. Follow-up assessments were conducted at 1, 3, and 6 months after the first esketamine infusion. Depressive symptoms were assessed using the Hamilton Depression Scale (HAMD), and subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Esketamine administration during the initial course, additional esketamine treatment during follow-up, and PCSL use during follow-up were recorded. Adverse events were documented.

Results: HAMD and PSQI scores decreased significantly from baseline at 1, 3, and 6 months. Antidepressant response rates at these time points were 62.00%, 59.73%, and 58.49%, respectively. In multivariable analyses, PCSL use remained associated with response at 1, 3, and 6 months. Additional esketamine treatment during follow-up was associated with response at 3 months; at 6 months, the effect estimate remained favorable, although it did not reach statistical significance. Among other variables, age and disease duration were also associated with response. No serious adverse events were observed during follow-up.

Conclusion: In this retrospective cohort, the multimodal treatment was associated with sustained improvements in depressive symptoms and sleep quality over 6 months, with an acceptable safety profile.

Background/objectives: Social anxiety disorder (SAD) frequently co-occurs with major depressive disorder (MDD). We examined the prevalence and clinical correlates of SAD across three heterogeneous MDD cohorts.

Methods: Secondary analyses were conducted in adult (CO-MED: n = 482; GSRD: n = 1398) and late-life (IRL-GREY: n = 438) patients. SAD was assessed dimensionally (PDSQ) and categorically (MINI; SCID-1). Cohort-specific instruments were used to assess depressive severity (QIDS; MADRS), suicidality (CHRT; MADRS; SIS) and hypomanic symptoms (ASRM; YMRS). In late-life depression, neurocognitive tests were administered. Multivariate models were adjusted for depression and anxiety levels.

Results: SAD prevalence varied markedly by samples and diagnostic definitions (CO-MED: 46.7% with PDSQ≥6; 17.0% with PDSQ>12; GSRD: 3.0%; IRL-GREY: 8.7%). Across cohorts, SAD was associated with earlier MDD onset (CO-MED: p < 0.001 d = -0.36; GSRD: p = 0.014 d = -0.39; IRL-GREY: p = 0.002 d = -0.54) and greater anxiety comorbidity (CO-MED: GAD: p < 0.001 d = 1.24; panic: p < 0.001 d = 1.29; GSRD: GAD: p < 0.001; panic: p < 0.001; IRL-GREY: agoraphobia: p < 0.001). After controlling for depression and anxiety levels, SAD was linked to higher suicide risk in both adult cohorts (CO-MED: OR 1.04 (1.01-1.08); GSRD: OR 1.29 (1.04-1.64)) and to lower 6-week remission in CO-MED cohort (OR 0.51 (0.29-0.91)).

Conclusions: In MDD, SAD has consistent associations with earlier illness onset and anxiety burden, modest associations with suicidality and antidepressant outcomes, and reduced impact in late life.

Background: Post-stroke cognitive impairments have been shown to increase the risk of depression, however the mechanisms underpinning this association are not well understood. The theory of social isolation in chronic illness proposes that the relationship between symptom severity in chronic conditions and mood disorders is mediated by social isolation. This study therefore aimed to explore the impact of social isolation on depression in stroke survivors with and without cognitive impairments.

Methods: Stroke survivors were recruited ≥6 months post-stroke from the Oxford Screening Programme and completed assessments of cognitive function, social isolation, and depression. Measures of social isolation evaluated both subjective feelings of loneliness and objective social disconnectedness. Multiple linear regressions examined associations of cognition and social isolation with depression symptoms, and serial mediation analyses assessed potential mediating effects of loneliness and social disconnectedness.

Results: Eighty-five participants completed the study measures. Chronic cognitive impairments predicted depressive symptoms in stroke survivors (β = 1.11, p < 0.001). This relationship was mediated by feelings of loneliness, which were associated with higher depression scores in participants with more severe cognitive impairments (indirect effect [IE] = 0.294, p < 0.05). Whilst significant direct effects were observed between all variables in the mediation analyses (all p < 0.05), there was no evidence for indirect effects of social disconnectedness.

Conclusions: Subjective experiences of loneliness, but not objective social disconnectedness, may increase the risk of depression in stroke survivors with cognitive deficits. These findings suggest that feelings of loneliness may be a suitable target for intervention in post-stroke depression.

Motor impulsivity is implicated in the transition from suicidal ideation to suicidal behavior. A prior study used a Go/No-go (GNG) task to show increased motor impulsivity in those with recent suicide attempt; additionally, computational modeling to extract latent cognitive variables from GNG has shown decreased decisional efficiency in those with an upcoming attempt. The current study aimed to examine a simpler version of GNG in a small sample of Veterans with prior history of suicidal behavior. Participants completed multiple GNG sessions over a one-year period. Each session was coded according to whether the participant had (1) an actual suicide attempt (ASA) in the 90 days following that session; (2) another suicide-related event (OtherSE) but not an ASA; or (3) neither (noSE) in the next 90 days. Although miss rates were low across all groups, results showed that, relative to noSE, an upcoming ASA was associated with 1) reduced false alarms in the behavioral data; 2) increased drift efficiency for foils in the computational modeling. These results complement the prior results which showed increased miss rate and increased drift efficiency for foils were associated with upcoming ASA. Importantly, in both studies, these variables were not associated with upcoming OtherSE, suggesting different cognitive processes associated with upcoming ASA, compared to other suicide-related events that fall short of an actual attempt. These two studies suggest the neurocognitive markers may represent both a cognitive risk factor and a behavioral marker of short-term risk for suicide attempt.

Background: Premenstrual syndrome (PMS) is a common condition that negatively affects women's physical, emotional, and occupational functioning. Web-based interventions offer accessible and cost-effective solutions to support symptom management.

Objective: This study aimed to compare the effects of web-based progressive muscle relaxation training (WB-PMR) and a web-based lifestyle intervention (WB-LSI) on symptom severity, psychological symptoms, occupational balance, occupational performance, and quality of life in women with PMS.

Methods: A randomized controlled trial was conducted with 68 women diagnosed with PMS, randomly assigned to WB-PMR (n = 22), WB-LSI (n = 22), or control (n = 24) groups. Data were collected at baseline, post-intervention, and four-week follow-up. Measures included the Visual Analog Scale (VAS), Premenstrual Syndrome Severity Scale (PMSS), Hospital Anxiety and Depression Scale (HADS), Occupational Balance Questionnaire (OBQ-11T), Canadian Occupational Performance Measure (COPM), and Nottingham Health Profile (NHP). Friedman and Kruskal-Wallis tests were used for statistical analysis.

Results: Both interventions significantly improved outcomes compared to control. The WB-PMR group had the lowest follow-up scores in VAS (p = 0.027), PMSS (p = 0.007), and emotional/physical symptoms. Significant time × group effects were observed in HADS anxiety, depression, and NHP subscales (p < 0.01). WB-LSI led to improvements in irritability, depressive thoughts, sleep, and OBQ-11T scores (p < 0.001). COPM performance and satisfaction increased in both intervention groups (p < 0.001).

Conclusion: Web-based PMS interventions may provide effective, sustainable, and accessible support for symptom and occupational management.