Journal of affective disorders最新文献

Background: Emotion regulation is critical for psychological health. Adaptive emotion regulation, in particular, requires the ability to flexibly use different strategies to meet situational demands. Such flexibility is often reflected in greater variability in everyday emotion regulation strategy use. Research on strategy variability has, to date, been positively associated with some emotional and psychological outcomes, but such research has exclusively focused on healthy individuals. Our investigation examines whether variability in emotion regulation strategy use and its implications differ between individuals with Major Depressive Disorder (MDD) and healthy controls.

Method: Using ecological momentary assessments (EMA), we assessed variability in emotion regulation strategy use (i.e., between-strategy variability) and its implications in individuals with current MDD and healthy controls (Ns = 94 and 90). Participants completed six surveys per day for 10 days, rating their emotional experiences and emotion regulation strategy use. They also rated indices of psychological health daily.

Results: Individuals with MDD had higher emotion regulation strategy variability than healthy controls. In healthy controls, higher strategy variability was linked to greater emotion regulation success and was unrelated to daily depressive symptoms. However, in individuals with MDD, higher strategy variability was not only unrelated or even negatively linked to emotion regulation success, but it was also associated with higher daily depressive symptoms.

Limitations: We did not assess the fit between regulatory strategies and contexts, and only included self-report measures collected through smartphones.

Conclusion: Variability in emotion regulation strategy use may capture adaptive flexibility among healthy individuals, but maladaptive volatility among individuals with MDD.

Suicide attempts are more prevalent in people with bipolar I disorder (BD-I) than in the general population. Most prior studies of suicide in BD-I have focused on separate emotion-related assays or clinician-administered scales, whereas a single, brief, and multidimensional battery of self-report measures has not yet been explored. Here, we utilized the NIH Toolbox Emotion Battery (NIHTB-EB) to assess various emotional measures, determine which were cross-sectionally associated with prior suicide attempt in BD-I, evaluate whether the NIHTB-EB could be used to identify past suicide attempt in BD-I with machine learning, and compare model performance versus using clinical mood scales. The study included 39 participants with BD-I and history of suicide attempt, 48 with BD-I without history of suicide attempt, and 58 controls. We found that 9 of the 17 measures were associated with past suicide attempt in BD-I. The initial random forest model indicated that the most important distinguishing variables were perceived stress, emotional support, anger-hostility, anger-physical aggression, perceived rejection, loneliness, and self-efficacy. Overall, the models utilizing NIHTB-EB measures performed better (69.0 % to 70.1 % accuracy) than the model containing clinical mood scale information without the NIHTB-EB measures (57.5 % accuracy). These findings suggest the NIHTB-EB could be a useful and easy-to-deploy tool in understanding the role of emotion-related measures in suicide in BD-I. Furthermore, these results highlight specific emotional subdomains that could be promising targets for longitudinal studies or interventions aimed at reducing suicide in BD-I.

Background: Cognitive dysfunction is a persistent and difficult-to-treat symptom of major depressive disorder (MDD) and is receiving increasing attention. A balanced state of oxidative stress sustained by antioxidants is essential for normal functioning of brain, including learning capacity, emotional regulation, and cognitive function. The correlation between cognition and oxidative stress may also be altered in patients with mental disorders. This study aimed to explore the relationship between serum antioxidant levels and cognitive dysfunction in patients with MDD.

Methods: We collected and matched cognitive performance data using the Measurement and Treatment Research to Improve Cognition in Schizophrenia Consensus Cognitive Battery (MCCB), serum antioxidants, and demographic and clinical characteristics of 105 first-episode drug-naïve patients with MDD and 53 healthy controls (HC) from February 2022 to October 2023, and then analyzed the differences between the two groups. HamiltonDepressionScale-17 and Young Mania Rating Scale (YMRS) were used to evaluate the clinical symptoms of the participants with MDD. Serum antioxidants, including albumin (ALB), uric acid (UA), and superoxide dismutase (SOD) were used to detect the level of oxidative stress in participants and its correlation with cognitive function. We then used a threshold for cognitive dysfunction of 1.5 standard deviations below the mean of the standard score to divide the participants with MDD into two groups to detect the relevant elements of the five different domains of cognitive dysfunction in MDD.

Results: ALB levels were significantly lower in the MDD group (p = 0.001) after adjusting for years of education. The performance in all five domains of cognitive function was significantly worse in the MDD group than in the HC group (p < 0.001). Speed of processing (SOP) in the MDD group correlated with ALB (r = 0.261, p = 0.008), and UA (r = 0.295, p = 0.002) levels. We also explored the correlation between the attention/vigilance (AV) domain and the UA (r = 0.239, p = 0.015). YMRS score was risk factor of impairment of SOP domain in patients with MDD. Yet UA was a protective factor against SOP impairment, with a 0.006-fold reduction in the risk of SOP impairment for each 1-unit increase in UA.

Conclusions: As serum antioxidants, ALB and UA may serve as biomarkers of cognitive function in patients with MDD. Our findings contribute to the understanding of the potential ability of serum antioxidants to predict cognitive decline in patients with MDD.

Background: Due to the rapid increase in Internet penetration in rural areas of China in recent years, Internet usage among middle-aged and older adults in these areas has also increased dramatically. This has facilitated their engagement in more online shopping, entertainment, and learning activities. Consequently, the construction of digital villages is inevitably and closely linked to the depressive symptoms of rural middle-aged and older adults.

Objective: The aim was to use data from the China Health and Retirement Longitudinal Study (CHARLS) 2020 to reveal the relationship between regional digital village construction and individual depressive symptoms among rural middle-aged and older adults.

Methods: The final sample included 8988 rural middle-aged and older adults. The data on digital village construction were derived from prefecture-level city index scores compiled by the Institute of New Rural Development at Peking University in conjunction with the Ali Research Institute. Depressive symptoms were measured using the 10-item Centre for Epidemiological Studies Depression (CESD) Scale from the 2020 CHARLS questionnaire. Given that the explanatory variable, depressive symptoms among rural middle-aged and older adults, is an integer value >0, we used the Poisson mixed-effects model for regression analyses.

Results: On the map, the average level of depressive symptoms was lower in areas with a high level of digital village construction in 2020. In the t-test, depressive symptoms were significantly higher in rural middle-aged and older adults than in their urban counterparts. In the regression analysis using the Poisson mixed-effects model, digital village construction was significantly and negatively correlated with the depressive symptoms of rural middle-aged and older adults, and this result passed the robustness test.

Limitations: The causal relationship between the construction of digital villages and the depressive symptoms of rural middle-aged and older adults is not clear in the results obtained.

Conclusions: We identified three key factors-income welfare, consumption welfare, and cognitive welfare-as mediators between digital village construction and depressive symptoms among middle-aged and older adults. Our findings also show that disadvantaged groups, such as those with low education, limited social networks, and living in non-central areas, derive greater benefits from digital village construction. These insights offer valuable guidance for optimizing digital village policies in developing countries.

Background: Sleep pattern (sleep duration, trouble sleeping, sleep disorder) is associated with both energy density dietary inflammatory index (EDII) and depression. However, whether depression mediates the EDII-sleep pattern relationship is unclear.

Methods: 14,305 participants from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2014 were included in this study. Weighted multivariable logistic regression and mediated effect analysis were conducted to analyze the associations between EDII, depression and sleep pattern.

Results: In the regression model with full confounding variables adjusted, the OR (95 % CI) for the association between EDII and sleep pattern was 2.11 (1.44, 3.08). Similarly, comparing the highest to the lowest EDII scores, the association with depression yielded an identical OR of 2.62 (1.89, 3.64). Mediation models showed depressive symptoms mediated 24.06 % of the EDII-sleep pattern link. Analysis by sleep pattern subtypes found depressive symptoms mediated 18.22 % for sleep duration, 30.53 % for sleep disorder (both with substantial direct EDII effects), and 93.73 % for trouble sleeping, where EDII's direct impact was not considerable.

Conclusion: Positive association of EDII with unhealthy sleep pattern is partly mediated by depression, and anti-inflammatory diet could be beneficial.

Background: Post-traumatic stress disorder (PTSD) is a debilitating chronic mental disorder that leads to reduced quality of life and increased economic burden. Observational studies have found an association between human blood metabolites and PTSD. Nonetheless, these studies have limitations and are subject to confounding factors as well as reverse causation. Herein, we employed a two-sample Mendelian randomization (MR) approach for the systematic analysis of the blood metabolites and PTSD causal link.

Methods: Data for the human blood metabolome, cerebrospinal fluid (CSF) metabolome, and PTSD were obtained from publicly available summary-level genome-wide association studies (GWAS), respectively. The inverse variance weighted (IVW) approach represented the main analytic method for assessing exposure-outcome causal associations, employing multiple sensitivity analyses to verify the results' stability. In addition, replication and meta-analysis, steiger test and reverse MR analysis methods were performed to clarify further that these metabolites have independent causal effects on PTSD. Finally, the results of blood and CSF metabolomics analyses were synthesized to obtain biological markers with a causal link to PTSD.

Results: Conclusively, we identified potential causal associations between six blood metabolites and PTSD. The sensitivity analyses elucidated the absence of pleiotropy or heterogeneity in the MR results. The Steiger test and reverse MR analysis did not reveal reverse causal associations, proving the robustness of our results. Combined blood and CSF metabolome analyses showed the same trend for theophylline.

Conclusion: This study reveals a strong causal link between metabolites and PTSD, which can be used as a biomarker for clinical PTSD disease screening and prevention. This study also provides a new perspective on the mechanism of metabolite-mediated PTSD development by combining genomics and metabolomics.

Background: Electroconvulsive therapy (ECT) is an effective treatment for depression with potential transient cognitive side effects. However, subjective memory impairment can extend over a long period after ECT.

Objectives: This study aimed to assess potential risk factors for long-term subjective memory impairment 6 months after ECT and to explore if the associations are mediated by depressive symptoms.

Methods: This registry-based study used the Swedish National Quality Register for ECT and other national registers. Long-term subjective memory worsening was defined as a minimum 2-step worsening on the memory item from the comprehensive psychopathological rating scale (CPRS-M) from before ECT to 6 months after ECT. Changes on the scale were also analyzed in continuous models. Statistical methods used were logistic regression and linear regression analyses in univariable and multivariable models.

Results: The study population consisted of 1498 patients. Subjective memory worsening occurred in 25.2 % of the population. Long-term subjective memory worsening was associated with more depressive symptoms and lower education levels. No association could be found related to ECT technical factors. The associations between age and psychiatric comorbidities with subjective memory worsening were mediated by depressive symptoms.

Conclusion: Patients can be informed that depressive symptoms are one of the biggest contributing factors to long-term subjective memory impairment after ECT. A successful treatment is therefore important to minimize the long-term experience of memory deficits. The number of sessions or ECT technical factors do not seem to be associated with long-term subjective memory impairment.

Objective: Previous studies have shown an association between Internet access and cognitive function, and have also found that loneliness is a precursor to cognitive impairment. However, little is known about the bidirectional relationship between Internet access and cognitive function, and the potential mechanisms that underlie these three variables. This study examined the reciprocal relationship between Internet access and cognitive function, as well as the mediating role of loneliness among middle-aged and older Chinese adults.

Methods: Data were derived from four waves (2011, 2013, 2015, and 2018) of the China Health and Retirement Longitudinal Study. A total of 6578 participants aged 50 and above with complete cognitive test data at baseline were selected for the analysis. Cross-lagged panel models were applied to explore the relationship between Internet access and cognitive function, with attention to the mediating role of loneliness.

Results: Reciprocal 4-wave lagged associations between Internet access and cognitive function, and the partial mediating role of loneliness were found in this study, controlling for all covariates. Increased Internet access predicts better cognitive function over time, and vice versa. Notably, this association was partially mediated by loneliness, indicating that more Internet access was associated with reduced loneliness, which was then associated with better cognitive performance.

Conclusions: This suggests that Internet access can influence individuals' cognitive function by impacting their feelings of loneliness. Multidisciplinary interventions utilizing digital connections to alleviate loneliness, enhance cognitive resilience, and delay cognitive decline may thus contribute to healthier aging in the digital era.