Abstract

Background
Depression results from interactions between biological, social, and psychological factors. Literature shows that depression is associated with abnormal brain structure, and that socioeconomic status (SES) is associated with depression and brain structure. However, limited research considers the interaction between each of these factors.
Methods
Multivariate regression analysis was conducted using UK Biobank data on 39,995 participants to examine the relationship between depression and brain volume in 23 cortical regions for the whole sample and then separated by sex. It then examined whether SES affected this relationship.
Results
Eight out of 23 brain areas had significant negative associations with depression in the whole population. However, these relationships were abolished in seven areas when SES was included in the analysis. For females, three regions had significant negative associations with depression when SES was not included, but only one when it was. For males, lower volume in six regions was significantly associated with higher depression without SES, but this relationship was abolished in four regions when SES was included. The precentral gyrus was robustly associated with depression across all analyses.
Limitations
Participants with conditions that could affect the brain were not excluded. UK Biobank is not representative of the general population which may limit generalisability. SES was made up of education and income which were not considered separately.
Conclusions
SES affects the relationship between depression and cortical brain volume. Health practitioners and researchers should consider this when working with imaging data in these populations.

Introduction

Mental health problems are significant public health issues within the UK and worldwide, with depression being one of the most prevalent. Research shows that there is a relationship between depression and brain structure (Tang et al., 2007; Kronmüller et al., 2009; Bos et al., 2018; Maggioni et al., 2019), and that social factors, including socioeconomic status (SES), are associated with both depression (Delgadillo et al., 2016; Marmot, 2010; Freeman et al., 2016) and changes in brain structure (Kweon et al., 2022; Colich et al., 2020; Jednoróg et al., 2012; Noble et al., 2015). However, these three factors are rarely all considered together. Given the impact of depression on both the individual and society, as discussed below, understanding the mechanisms of this disorder, to improve prevention and treatment options, is of critical importance.

Around 1 in 6 adults in England met the criteria for a common mental health disorder in the week prior to being surveyed (McManus et al., 2016). Mental health problems are one of the leading contributors to overall worldwide disease burden and are reported to account for 16 % of Disability Adjusted Life Years (DALYs, Arias et al., 2022). They confer substantial societal and economic implications with an estimated annual cost in England of £77 billion (Kirkwood et al., 2010). Depression is one of the most prevalent mental health problems, affecting approximately 5 % of adults (World Health Organization, WHO, 2023) and 280 million people globally (Institute of Health Metrics and Evaluation, 2023). Depression can be long-lasting with a single chronic episode, or recurrent, with many episodes over a long time, and has a substantial impact on an individual’s ability to function and cope with daily life. The risk of suicide is reported to be about 15 times higher in those with depression than the general population (Cipriani et al., 2005). This is however likely to be an underestimate as many who die by suicide have undiagnosed depressive symptoms.

Conclusion

This study is, to our knowledge, the first study to examine the relationship between depression, cortical volume, and SES in a middle-aged to older sample of this size. Given that SES appears to impact the relationship between depression and brain structure, we posit that SES should be a consideration for clinicians and scientists working with imaging data in these populations. Future research involving large-scale longitudinal data is needed to elucidate the causal relationships between the factors examined in this study.

Abstract

Background
Perinatal depression is a significant concern affecting both women and men during pregnancy and postpartum periods. While maternal postpartum depression has been extensively studied, paternal depression remains under-researched despite its prevalence and impact on family well-being. This study aimed to estimate the trajectories of perinatal and postpartum depression in Japanese parents over ten years and to determine the details of the symptoms of postpartum depression for each trajectory group, considering reciprocal effects between maternal and paternal depression.

Methods
A total of 789 couples used the Edinburgh Postnatal Depression Scale to rate their depressive symptoms prenatally; at 5 weeks, 3 months, 6 months, and 1 year postpartum; and then yearly thereafter until the 10th year. Parallel-process latent class growth analysis was used to group participants according to their longitudinal patterns of depressive symptoms.

Results
For both mothers and fathers, four depressive symptom trajectories fit the data best and were most informative (escalating: 6.5 %; mothers low and fathers moderate: 17.2 %; mothers high and fathers low: 17.9 %; low: 58.4 %). A variance analysis showed significant class-parent interactions across anhedonia, anxiety, and depression subscales, indicating distinct patterns of depressive symptomatology.

Discussion
Tailored mental health programs and universal screening using the Edinburgh Postnatal Depression Scale are recommended to address the specific needs of each trajectory class. This study contributes to the understanding of long-term depressive symptom trajectories in parents and emphasizes the necessity of comprehensive support strategies to enhance family well-being and resilience.

Introduction

Perinatal depression is a significant concern that affects a considerable number of women during pregnancy and after childbirth, with prevalence rates ranging from 7 % to 20 % (Dadi et al., 2022). Similarly, postpartum depression was found to be prevalent in 14.3 % of Japanese women one month after childbirth (Tokumitsu et al., 2020a). However, both men and women are affected by postnatal depression. A meta-analysis of 74 studies with 41,480 participants found that paternal depression was present in 8 % of men in the included studies (Cameron et al., 2016). Approximately 10 % of Japanese men experience perinatal depression, highlighting that this condition is a concern for both sexes (Tokumitsu et al., 2020b). The mental health of both parents during the perinatal and postpartum periods is crucial, as it can significantly impact the overall well-being of children and families. A meta-analysis further revealed that 3.18 % of couples might experience perinatal depression simultaneously (Smythe et al., 2022). Longitudinal studies have also shown that depressive symptom trajectories can persist for up to 11 years postpartum (Mughal et al., 2023). Therefore, this study’s primary objective was to estimate the trajectories of perinatal and postpartum depression in Japanese parents and their associated risk factors, considering the reciprocal effects of postpartum depression on fathers and mothers.

Conclusion

This 10-year longitudinal study identified four distinct joint trajectories of mothers and fathers’ depressive symptoms from pregnancy to postpartum: low and stable for both parents, high for mothers and low for fathers, moderate for fathers and low for mothers, and escalating for both parents. The findings emphasize the importance of early detection and continuous monitoring of depressive symptoms, including anhedonia, anxiety, and depression, in both parents. Tailored mental health support programs are critical, especially for mothers in Class 2 and fathers in Class 4, who exhibit higher and prolonged symptoms. Despite limitations such as sample size reduction and external factors influencing results, the study adds to the growing body of knowledge on postpartum depression, underscoring the need for comprehensive and targeted approaches to support families and improve mental health outcomes through timely and appropriate interventions. Future research should include larger, more diverse populations to enhance generalizability and explore effective long-term intervention strategies.

Abstract

Research on burnout has garnered considerable attention since its inception. However, the ongoing debate persists regarding the conceptual model of burnout and its relationship with depression. Thus, we conducted a network analysis to determine the dimensional structure of burnout and the burnout-depression overlap. The Maslach Burnout Inventory-Student Survey and Patient Health Questionnaire-9 were used to measure burnout and depression among 1096 college students. We constructed networks for burnout, depression, and a burnout-depression co-occurrence network. The results showed that cynicism symptom was the most central to the burnout network. In the co-occurrence network, depressive symptoms (“anhedonia”, “fatigue”) and burnout symptom (“doubting the significance of studies”) were the most significant in causing burnout-depression comorbidity. Community detection revealed three communities within burnout symptoms, aligning closely with their three dimensions identified through factor analysis. Additionally, there was no overlap between burnout and depression. In conclusion, our findings support a multidimensional structure of burnout, affirming it as a distinct concept separate from depression. Cynicism, rather than exhaustion, plays the most important role in burnout and the burnout-depression comorbidity.

Introduction

Burnout has long been recognized as an occupational hazard in various human service occupations (Lacy and Chan, 2018; Maslach and Leiter, 2016; Sullivan et al., 2022; Weigl, 2022). Since its emergence, the concept of burnout has attracted increasing attention worldwide and has gradually expanded to include a more general range of occupations (Aronsson et al., 2017; Embriaco et al., 2007; Shanafelt et al., 2019). Burnout is now defined as a psychological syndrome, caused by prolonged occupational stressors. Additionally, personality traits, particularly neuroticism, play an important role in the development of burnout (Angelini, 2023). People with high levels of neuroticism may adopt maladaptive coping strategies that magnify the impact of adverse events in the workplace, leading to greater anxiety and exhaustion from work. In summary, both external and internal factors influence the development of burnout.

The widely accepted conceptual model of burnout is a multidimensional model that stratifies burnout into three dimensions: exhaustion, cynicism or depersonalization, and a lack of professional efficacy (Maslach and Leiter, 2016). However, in contrast to the increasing research on burnout, minimal advances have been achieved in the establishment of clinical diagnoses. An undetermined definition of burnout is reportedly the primary issue for a diagnostic consensus (Rotenstein et al., 2018). The evolving definitions of burnout contribute to variability in estimating its prevalence, posing challenges in policy development. Initially, exhaustion was identified as the most central and obvious manifestation of burnout (Maslach et al., 2001). Accordingly, in a few northern European countries, the diagnosis of burnout has been simplified to exhaustion; consequently, the focus of the public and policy has been limited to exhaustion alone. Meanwhile, some argued that despite the importance of exhaustion, it was not the most central component of burnout (Maslach and Leiter, 2008) and that focusing on exhaustion devalued the significance of burnout as a distinct construct. A recent study on the relationship between the three dimensions of burnout suggested that exhaustion is not the most closely associated dimension (Wu et al., 2021). Measurements using an alternative burnout structure exhibit better validity (Shoman et al., 2021). To date, there has been minimal consensus on the definition or dimensional structure of burnout, as well as the most important symptoms of burnout (Tavella et al., 2021).

Conclusion

The current study was designed to determine the structure of burnout and the burnout-depression overlap among college students at the network level. It specifically identified cynicism symptoms as crucial factors for burnout and burnout-depression comorbidity. Additionally, community detection analysis suggested a multidimensional structure for burnout, emphasizing that burnout should be considered distinct from the concept of depression. This study provides insights into the structure of burnout from the perspective of network analysis, and delineates the overlap between burnout and depression.

Abstract

Background
Progress has been achieved in many fields in understanding the biological mechanisms of depression, including genome-wide association analysis, neurotransmitter system function, brain regions and neural networks, inflammatory response, neuroplasticity, neuroimaging, and neuro electrophysiology. These progresses provide a reliable basis for developing the medical and physical therapies for depression. However, the current treatments developed from biological mechanisms can only address less than 60% of depressive symptoms and have limited efficacy in improving social functioning and reducing recurrence. Studies have explored the non-biological mechanisms of depression in mental fields. These progresses are helpful to develop more interventions that could alleviate depressive symptoms, improve functional impairments, and reduce recurrence, thereby promoting a more comprehensive recovery in depressed patients. However, there is not a systematic and deep review to highlight the non-biological mechanisms of depression.

Methods
This study summarizes the recent progress in the non-biological fields of depression by searching publications on human studies in PubMed, PMC, and Google Schooler with exclusion of animal studies.

Results
This study reviews the intergenerational transmission characteristics, the relationship between depression and emotional trauma, cognitive deficit, relationship impairment, self-function, sense of the meaning of life, motivation deficit, and psycho-rationality of depression.

Conclusions
This study was clarified the non-biological mechanisms and characteristics of depression and provided a theoretical basis for the development of non-drug interventions.

Introduction

To date, studies on the pathogenesis of depression are mainly focus on its biological mechanisms (Miao & Chang, 2021), including studies on epigenetic alterations, genome-wide association, neurotransmitter system, brain regions and neural networks, inflammatory responses, neuroplasticity, neuroimaging, and neuro electrophysiological changes (Li et al., 2021). The outstanding contributions of these studies provide a theoretical basis for pharmacotherapy and physical therapy of depression (Li et al., 2019). However, the current treatments developed from biological mechanisms can only relieve 58% of depressive symptoms (Ormel et al., 2019). Moreover, these current pharmacotherapy and physical therapy have limited efficacy in improving prognosis and social function, as well as reducing relapse rates (Cieślik et al., 2020). The cognitive behavioral therapy has been shown to be highly effective in treating depression but has limited impact on generational deficits and trauma-related emotional responses in the symptomatic components of depressive symptoms (Johnsen, T. J., & Friborg, O. 2015). The eye movement desensitization and reprocessing therapy has shown some positive outcomes in helping individuals to reprocess traumatic memories of some emotional traumas, but it is not appropriate for traumatized populations of all backgrounds and can exacerbate symptoms in some populations. Therefore, its limitations and side effects need to be considered (Shapiro, F. 2014). Thus, exploration of new mechanisms and subsequent development of new treatments is an urgent need for patients with depression.

Results

3.1. Non-biological transgenerational transmission of depression
Intergenerational transmission of pathological psychology refers to the phenomenon that psychological disorders or problems presented in the previous generation can reappear in the next generation, or that the behavior or psychological trauma of the previous generation causes mental and psychological problems in the next generation. These problems may span generations and manifest in different ways and degrees (Cooke et al., 2019). In recent years, evidence on the intergenerational transmission of depression has been accumulated. In the biomedical field, family and twin studies have repeatedly validated that the intergenerational transmission of depression is often a result of gene-mediated genetic disorders or epigenetic inheritance (Goodman, 2020, Park et al., 2019). However, the non-biological transgenerational mechanisms of depression have also attracted attention recently (Gotlib et al., 2020).

More and more non-biological transmissions have been observed, while the intergenerational phenomenon of depression and its transmission ways have different explanations (Hentges et al., 2021). For example, parental depression may be transmitted to the next generation through multiple pathways: intergenerational transmission of trauma (Hankerson et al., 2022); intergenerational damage to emotions, self-esteem, and cognition; family culture; parenting style; and behavioral consequences etc. (Kang et al., 2021, Risi et al., 2021, Kujawa et al., 2020, Johnco et al., 2021). These transgenerational mechanisms may indirectly influence the biological changes that evolve into the symptoms of depression (Allison et al., 2023). In the intergenerational transmission of depression, offspring may not inherit the symptoms of depression, but instead display a variety of psychological dysfunctions a variety of psychological dysfunctions, including a tendency to react negatively to emotions (Israel & Gibb, 2024), a state of self-esteem hypersensitivity, cognitive biases, and excessive self-focus (Woody et al., 2022). In addition, offspring may also display relationships with alienation, control, and unclear boundaries (Wei et al., 2023), and even the characteristics of illness that appear in early adulthood or suicidal tendencies (Gijzen et al., 2021).

Abstract

Background
Evidence on the association between single nucleotide polymorphisms (SNPs) in the vitamin D receptor (VDR) and depressive symptoms is inconclusive.

Objectives
The primary aim of the study was to investigate the association between SNPs in the VDR gene and depressive symptoms.

Methods
In a sample of older adults from the Longitudinal Ageing Study Amsterdam (n = 922), depressive symptoms were assessed using the Centre for Epidemiological Studies Depression scale (CES-D scale) at baseline and after 3, 6, and 10 y of follow-up. Blood samples for SNP and serum 25-hydroxyvitamin D3 (25(OH)D3) determination were obtained at baseline. The association between 13 SNPs in the VDR gene and the course of depressive symptoms were evaluated using linear mixed models. The interaction between SNPs and serum 25(OH)D3 in relation to depressive symptoms was evaluated using multiple linear regression.

Results
No SNPs were associated with the course of depressive symptoms. Significant interactions between serum 25(OH)D3 and SNPs in the VDR gene were found. Stratified analysis revealed that within the GG genotype strata, 10 nmol/L higher serum 25(OH)D3 was associated with 0.27 (95% CI: −0.50, −0.04) and 0.23 (95% CI: −0.48, 0.02) lower scores on the CES-D scale for Cdx-2 and 1b-G-886A, respectively. This association was not found in persons having the GA or AA genotype.

Conclusions
No SNPs is associated with the course of depressive symptoms. Stratified analysis shows that the effect of serum 25(OH)D3 concentrations on depressive symptoms is different among genotypes of Cdx-2 and 1b-G-886A. Future research should elucidate on the function of Cdx-2 and 1b-G-886A to describe their effect.

Introduction

Depressive symptoms follow a U-shaped relationship with age, increasing again after the age of 60–65 y [1]. This is, however, not solely due to aging and varies widely between people [2]. A higher medical burden [3], aging-related anxiety [4], and nonhealth-related events [5] have been associated with depressive symptoms in older adults. The prevalence of depressive symptoms in older adults is estimated at 17% [6], but they are often overlooked [7] due to the overlap with symptoms, among others [6,8]. The burden will continue to rise due to the aging population and increasing life expectancy [9], thereby increasing years lived with disease [10] and societal costs [11].

Different factors (e.g., biological, social, and psychological) play a role in the onset and course of depressive symptoms [[12], [13], [14], [15]]. Vitamin D – a modifiable risk factor of which the status decreases with age [16] – has been associated with the course of depressive symptoms. In a previous study of the Longitudinal Ageing Study Amsterdam (LASA), an association between low vitamin D status and an increase in depressive symptoms among women was observed [17]. Two recent meta-analyses found a similar association in both men and women [18,19]. Genetic factors, such as single nucleotide polymorphisms (SNPs) in vitamin D-related genes may influence the response to, and therefore the requirement of, vitamin D [20]. The vitamin D receptor (VDR) gene encodes the VDR, the transporter of active vitamin D (1,25(OH)2D), and thereby influences the availability of 1,25(OH)2D [21].

Results

The study sample consisted of 922 participants who were on average 75.6 (SD 6.6) y old, were 51% women, and had a median CES-D score of 13.6 (IQR: 11.6–15.6). The prevalence of depressive symptoms [40] and antidepressant use at baseline was 24% and 2.5%, respectively. Mean serum 25(OH)D3 concentration was 46.6 nmol/L (SD 17.7), mean BMI was 26.6 kg/m2 (SD 5.2), and median physical activity in minutes per week was 128.6 (IQR: 67.7–189.7). The basic characteristics are summarized in Table 1 [35,36,40]. Compared with nonresponders, participants in the study sample were older and more often women, never smokers, moderate-to-excessive alcohol users, and more often had 2 or more comorbidities (all P < 0.05; Supplemental Table 2). The minor allele frequency was smallest for 282 W (n = 63/919; 3%), and no SNPs departed from Hardy–Weinberg equilibrium (Supplemental Table 1).

Abstract

Background
Anxiety disorder, depression, and bipolar disorder are common psychiatric disorders, and their association with autoimmune thyroiditis (AIT) has been of great interest. This study aimed to investigate the potential causal relationship between these psychiatric disorders and AIT.

Methods
We used publicly available summary statistics from large-scale genome-wide association studies to select, quality control and cluster genetic variant loci associated with anxiety disorder, depression, bipolar disorder and AIT as instrumental variables (IVs). The Mendelian randomization (MR) study mainly used inverse variance weighting (IVW) combined with MR-egger regression and weighted median estimation (WME) to estimate bidirectional causality between mental disorders and AIT. In addition, we conducted heterogeneity and multivariate tests to verify the validity of IVW.

Results
Two-sample bidirectional MR analysis revealed a positive causal link between depression and AIT. The forward MR analysis of IVW (OR 1.614, 95 % CI 1.104–2.358, P = 0.013) and WME (OR 2.314, 95 % CI 1.315–4.074, P = 0.004) demonstrated thatdepression potentially elevate the risk of AIT development, while, our investigation did not uncover a causal relationship between anxiety disorder, bipolar disorder and AIT. The results of reverse MR analysis showed that there was no significant causal relationship between AIT and anxiety disorder, depression, and bipolar disorder (P > 0.05).

Conclusions
The results of the forward MR analysis suggest a positive association between depression, and AIT risk, while indicating no support for a causal link between anxiety disorder or bipolar disorder and AIT risk based on the current data. Subsequent studies will be essential for elucidating the biological mechanisms and potential confounders underlying these associations.

Introduction

Autoimmune thyroiditis (AIT) stands as the most prevalent autoimmune endocrine disorder (Ruggeri et al., 2018), affecting an estimated 3–5 % of the general population (McLeod and Cooper, 2012). Predominantly afflicting women, its incidence escalates with advancing age (McLeod et al., 2014; Hollowell et al., 2002), soaring up to 20 % among elderly women (Surks et al., 2004). Furthermore, it serves as the primary cause of hypothyroidism. In the initial stages, AIT usually manifests with common hypothyroidism symptoms, such as mood disturbances, anxiety, or depression (van Zuuren et al., 2013), while, AIT can also precipitate hyperthyroidism.

Anxiety disorder, depression, and bipolar disorder are prevalent mental disorders with genetic and clinical overlap, suggesting that they may share common etiologic mechanisms (Tondo et al., 2017; Trede et al., 2005; Anttila et al., 2018). Research indicates a shared pattern of abnormal regional intrinsic brain activity in depression and bipolar disorder, particularly implicating the insula, medial prefrontal cortex, and cerebellum (Gong et al., 2020). The lifetime prevalence of anxiety disorder among individuals with bipolar disorder is estimated to be at least 40 %, even exceeding 50 % (Seon et al., 2021; Kessler et al., 1997; Vieta et al., 2001). Recent evidence suggests that the comorbidity of anxiety disorder with bipolar disorder is as high as that of monophasic depression (Preti et al., 2016).

Conclusion

We conducted the first bidirectional Mendelian randomization study of anxiety disorder, depression, bipolar disorder and autoimmune thyroiditis. Depression increases the risk of AIT, while no causal relationship has been found between anxiety disorders, bipolar disorder and AIT. Psychiatric disorders lead to AIT mainly through the expression of related genes affecting the levels of T and B lymphocytes or through the hypothalamic-pituitary-thyroid axis affecting the feedback regulation mechanism to induce thyroid dysfunction. We look forward to more clinical and experimental research data to validate the relationship between psychiatric disorders and AIT. In the meantime, we need to focus on more updated and larger data sets to explore whether there is a causal relationship between other psychiatric disorders and AIT.

Abstract

Background
Evidence on the association between single nucleotide polymorphisms (SNPs) in the vitamin D receptor (VDR) and depressive symptoms is inconclusive.

Objectives
The primary aim of the study was to investigate the association between SNPs in the VDR gene and depressive symptoms.

Methods
In a sample of older adults from the Longitudinal Ageing Study Amsterdam (n = 922), depressive symptoms were assessed using the Centre for Epidemiological Studies Depression scale (CES-D scale) at baseline and after 3, 6, and 10 y of follow-up. Blood samples for SNP and serum 25-hydroxyvitamin D3 (25(OH)D3) determination were obtained at baseline. The association between 13 SNPs in the VDR gene and the course of depressive symptoms were evaluated using linear mixed models. The interaction between SNPs and serum 25(OH)D3 in relation to depressive symptoms was evaluated using multiple linear regression.

Results
No SNPs were associated with the course of depressive symptoms. Significant interactions between serum 25(OH)D3 and SNPs in the VDR gene were found. Stratified analysis revealed that within the GG genotype strata, 10 nmol/L higher serum 25(OH)D3 was associated with 0.27 (95% CI: −0.50, −0.04) and 0.23 (95% CI: −0.48, 0.02) lower scores on the CES-D scale for Cdx-2 and 1b-G-886A, respectively. This association was not found in persons having the GA or AA genotype.

Conclusions
No SNPs is associated with the course of depressive symptoms. Stratified analysis shows that the effect of serum 25(OH)D3 concentrations on depressive symptoms is different among genotypes of Cdx-2 and 1b-G-886A. Future research should elucidate on the function of Cdx-2 and 1b-G-886A to describe their effect.

Introduction

Depressive symptoms follow a U-shaped relationship with age, increasing again after the age of 60–65 y [1]. This is, however, not solely due to aging and varies widely between people [2]. A higher medical burden [3], aging-related anxiety [4], and nonhealth-related events [5] have been associated with depressive symptoms in older adults. The prevalence of depressive symptoms in older adults is estimated at 17% [6], but they are often overlooked [7] due to the overlap with symptoms, among others [6,8]. The burden will continue to rise due to the aging population and increasing life expectancy [9], thereby increasing years lived with disease [10] and societal costs [11].

Different factors (e.g., biological, social, and psychological) play a role in the onset and course of depressive symptoms [[12], [13], [14], [15]]. Vitamin D – a modifiable risk factor of which the status decreases with age [16] – has been associated with the course of depressive symptoms. In a previous study of the Longitudinal Ageing Study Amsterdam (LASA), an association between low vitamin D status and an increase in depressive symptoms among women was observed [17]. Two recent meta-analyses found a similar association in both men and women [18,19]. Genetic factors, such as single nucleotide polymorphisms (SNPs) in vitamin D-related genes may influence the response to, and therefore the requirement of, vitamin D [20]. The vitamin D receptor (VDR) gene encodes the VDR, the transporter of active vitamin D (1,25(OH)2D), and thereby influences the availability of 1,25(OH)2D [21].

Results

The study sample consisted of 922 participants who were on average 75.6 (SD 6.6) y old, were 51% women, and had a median CES-D score of 13.6 (IQR: 11.6–15.6). The prevalence of depressive symptoms [40] and antidepressant use at baseline was 24% and 2.5%, respectively. Mean serum 25(OH)D3 concentration was 46.6 nmol/L (SD 17.7), mean BMI was 26.6 kg/m2 (SD 5.2), and median physical activity in minutes per week was 128.6 (IQR: 67.7–189.7). The basic characteristics are summarized in Table 1 [35,36,40]. Compared with nonresponders, participants in the study sample were older and more often women, never smokers, moderate-to-excessive alcohol users, and more often had 2 or more comorbidities (all P < 0.05; Supplemental Table 2). The minor allele frequency was smallest for 282 W (n = 63/919; 3%), and no SNPs departed from Hardy–Weinberg equilibrium (Supplemental Table 1).

Abstract

Objective
Depressive symptoms have been suggested to increase mortality risk but causality remains unproven. Depressive symptoms increase likelihood of smoking which is thus a potential factor modifying the effect of depressive symptoms on mortality. This study aims to assess if the association of depressive symptoms and all-cause mortality is affected by smoking.

Methods
A prospective cohort study in Finnish primary care setting was conducted among 2557 middle-aged cardiovascular disease (CVD) risk persons identified in a population survey. Baseline depressive symptoms were assessed by Beck’s Depression Inventory (BDI) and current smoking by self-report. Data on mortality was obtained from the official statistics. Effect of depressive symptoms and smoking on all-cause mortality after 14-year follow-up was estimated.

Results
Compared to non-depressive non-smokers, the adjusted hazard ratio (HR) for all-cause mortality was 3.10 (95% CI 2.02 to 4.73) and 1.60 (95% CI 1.15 to 2.22) among smoking subjects with and without depressive symptoms, respectively. Compared to the general population, relative survival was higher among non-depressive non-smokers and lower among depressive smokers. Relative standardized mortality ratio (SMR) for all-cause mortality was 1.78 (95% CI 1.31 to 2.44) and 3.79 (95% CI 2.54 to 6.66) among non-depressive and depressive smokers, respectively, compared to non-depressive non-smokers. The HR for all-cause mortality and relative SMR of depressive non-smokers were not increased compared to non-depressive non-smokers.

Conclusion
Current smoking and increased depressive symptoms seem to additively contribute to excess mortality.

Introduction

Depression and subthreshold depressive symptoms have been related to increased mortality risk [1]. This is plausible as depression intervenes with somatic diseases in complex biological pathways [2] and is considered as a risk factor for major causes of mortality, CVD [3] and cancer [4,5]. Depression and depressive symptoms are also associated with other risk factors such as hypertension [6], metabolic disturbances [7,8], sedentary and unhealthy lifestyle including smoking [[9], [10], [11]], and low socioeconomic status [12]. Nevertheless, there is still controversy weather depressive symptoms per se are associated with mortality. Depression can been considered to namely cause excess mortality through its effect on physical health, social factors, and lifestyle [13]. However, two recent meta-analyses concluded that causality remains unproven, in part due to inadequate adjustments for confounding factors [14,15].

Depression and depressive symptoms increase likelihood of smoking [11] and decrease odds to successful quitting [16]. Current smokers have 2 to 3 times higher mortality than never-smokers [17], and although prevalence of smoking has decreased during the past decades, it still accounts for a substantial loss of life-years [18]. This excess mortality has been attributed to at least 26 different disease categories including several cancers, many cardiovascular (CVD) and respiratory diseases, diabetes, renal failure, and some infections [17].

Results

3.1. Characteristics of the subjects
At baseline, 20.6% (106/515) of the subjects with increased depressive symptoms smoked and 16.8% (344/2042) were non-depressive smokers (p = 0.047). The probability of smoking increased from BDI score 10 onwards (Fig. 1).

Baseline characteristics of the subjects are presented in Table 1. Subjects with depressive symptoms were more often women, slightly older, and were living alone more often than non-depressive subjects. They also performed less LTPA, had higher mean AUDIT score, higher BMI and larger WC, higher triglyceride levels, higher 2-h glucose, and more glucose disorders than those without depressive symptoms.

Abstract

Objective
In process-outcome research, there is a growing body of literature investigating the therapeutic mechanisms underlying the promotion of positive change. This study investigated the between- and within-patient effects of problem mastery and motivational clarification on outcome in patients receiving two variations of cognitive therapies for depression.

Methods
This study drew on data of a randomized controlled trial conducted at an outpatient clinic and included 140 patients randomly assigned to 22 sessions of either cognitive-behavioral therapy or exposure-based cognitive therapy. To address the nested structure of the data and analyze mechanism effects, we used multilevel dynamic structural equations models.

Results
We found significant within-patient effects of both problem mastery and motivational clarification on subsequent outcome.

Conclusion
The results suggest that changes in problem mastery and motivational clarification precede symptom improvement during cognitive therapy for depressed patients and thus there may be benefit in fostering these putative mechanisms during psychotherapy.

Introduction

     Several evidence-based treatments have shown to effectively treat major depressive disorders (MDD) (Barkham & Lambert, 2021; Cuijpers et al., 2014, 2020). Each of these therapies is assumed to work according to specific change mechanisms.

     For instance, it has been hypothesized that cognitive-behavioral therapy’s (CBT) effects on depression can be explained by several putative change mechanisms with partial empirical evidence supporting them (for an extensive review see Crits-Christoph & Connolly Gibbons, 2021). A systematic review showed that cognitive change in CBT for depressive disorders was associated with changes in depressive symptoms, although the specificity of cognitive change as a mediator of CBT effects was not supported (Garratt & Ingram, 2007). Similarly, a meta-analysis found that patients participating in CBT differed significantly in post-treatment dysfunctional thinking from patients of control groups but not from patients in other forms of therapy (Cristea et al., 2015). Additionally, some studies have shown that in the treatment of depression improvements in compensatory skills were related to CBT outcome (Barber & DeRubeis, 2001; Connolly Gibbons et al., 2009; Strunk et al., 2007). Furthermore, a study supported the specificity of this mechanism for CBT (i.e., in terms of mediation effects) when compared with psychodynamic therapy (Crits-Christoph et al., 2017). Finally, studies have shown that in CBT for depression enhancing behavioral activation (Christopher et al., 2009) and environmental rewards (Gawrysiak et al., 2009) were associated with changes in depressive symptoms, with one study supporting them as mediators of CBT effects when compared to treatment-as-usual (Dimidjian et al., 2017).

Results

     At session 1, patients’ mean level of WHO-5 was 1.35 (SD = 0.76). In the BPSR-T, at session 1 the therapist had an average mastery score of 2.03 (SD = 0.85) and an average clarification score of 2.68 (SD = 0.89). Across all sessions of treatment, the average level of WHO-5 was 2.07 (SD = 1.04). Additionally, during the whole therapy, the average level of mastery was 3.43 (SD = 1.10), while the average level of clarification was 3.62 (SD = 0.85). During treatment, the correlation between mastery and clarification was r = 0.42 (p < .001). Furthermore, the correlation between mastery and the WHO-5 was r = 0.19 (p < .001) and between clarification and the WHO-5 was r = 0.11 (p < .001).

Abstract

     Patients with anxious depression have more severe symptoms, more side effects, and higher resistance to treatment than patients with non-anxious depression; therefore, it is crucial to clarify the differences between these two types of patients. In this study, a 5-minute resting EEG was recorded in 15 patients with anxious depression and 9 patients with non-anxious depression under eyes open and closed conditions. Sixty-eight subcortical regions were extracted using exact low resolution brain electromagnetic tomography (eLORETA). The directed transfer function was then used to construct brain networks. Specific features based on graph theory including the strength of connectivity and betweenness centrality (BC) were calculated from the networks. Finally, significant features were selected using the Mann-Whitney U test, and patients were classified into anxious and non-anxious depressive groups using the Support Vector Machine (SVM). Results showed that features of outward connectivity strength led to the highest accuracy, F-score, and specificity with 91.66%, 87. 5%, and 100% in the eyes-closed state, respectively. Moreover, we found that the strength of connectivity in both directions increased for the anxious depressive group during the eyes-open state. In particular, higher outward connectivity was observed in the right hemisphere for the anxious depressive group. Further findings also revealed that features with the most significant difference were mainly associated with the beta band. In addition, significant increased inward and outward connectivity and decreased nodal centrality were observed in the posterior regions of the default mode network. These preliminary findings might provide new insights into the recognition of anxious depressed patients.

Introduction

     Major depressive disorder (MDD) affects 6% of the global adult population annually and is one of the most prevalent psychiatric disorders [1]. About half of MDD patients also have anxiety, such as social anxiety disorder (SAD), generalized anxiety disorder (GAD), or panic disorder (PD) [2]. When MDD co-occurs with anxiety, the risk of suicide becomes greater, and patients may require long-term treatment [3,4]. Currently, however, there is a lack of accepted treatment for patients with anxious depression. They usually receive the same treatment strategies that are given for depression or anxiety. Consequently, the rate of treatment resistance is higher for them than for patients with pure depression or anxiety [5]. Moreover, the rate of medical utilization increased rapidly for patients with comorbid depression and anxiety [4]. Therefore, it is necessary to distinguish anxious depressed patients from non-anxious depressed patients in order to improve diagnosis and treatment methods.

Conclusion

     To the best of our knowledge, this is the first study that has investigated differences in the directed brain network in anxious and nonanxious depressed patients using an effective connectivity measure and the EEG source connectivity method. Network metrics comprising directed node strength and BC were analyzed by a statistical test and the machine learning approach. Classification results demonstrate outward connectivity strength had the highest performance in separating the two groups of patients, while all features performed better in the eyes-closed state than in the eyes-open state. Our main findings related to the strength of connectivity consist of increased node strength during the eyes-open condition, especially a higher out-strength in the right hemisphere for the anxious depressive group. In addition, we found that beta oscillations reflect the most altered brain network in terms of node strength and BC. Further analysis revealed that connectivity and centrality in most regions of posterior DMN were changed significantly. To conclude, the obtained results could potentially lead to the understanding of the underlying brain network of patients with comorbid depression-anxiety disorder, however, more research still is needed regarding the anxious depressive disorder.

Abstract

Depression is a disorder that if not treated can hamper the quality of life. EEG has shown great promise in detecting depressed individuals from depression control individuals. It overcomes the limitations of traditional questionnaire-based methods. In this study, a machine learning-based method for detecting depression among young adults using EEG data recorded by the wireless headset is proposed. For this reason, EEG data has been recorded using an Emotiv Epoc+ headset. A total of 32 young adults participated and the PHQ9 screening tool was used to identify depressed participants. Features such as skewness, kurtosis, variance, Hjorth parameters, Shannon entropy, and Log energy entropy from 1 to 5 sec data filtered at different band frequencies were applied to KNN and SVM classifiers with different kernels. At AB band (8–30 Hz) frequency, 98.43 ± 0.15% accuracy was achieved by extracting Hjorth parameters, Shannon entropy, and Log energy entropy from 5 sec samples with a 5-fold CV using a KNN classifier. And with the same features and classifier overall accuracy = 98.10 ± 0.11, NPV = 0.977, precision = 0.984, sensitivity = 0.984, specificity = 0.976, and F1 score = 0.984 was achieved after splitting the data to 70/30 ratio for training and testing with 5-fold CV. From the findings, it can be concluded that EEG data from an Emotiv headset can be used to detect depression with the proposed method.

Introduction

Depressive disorder is a highly prevalent mental illness. Sadness, loss of interest or enjoyment, feelings of guilt or low self-worth, interrupted sleep or food, fatigue, and difficulty concentrating are some characteristics of depression. It may affect a person’s capacity to operate in daily life or at work or school. According to the World Health Organization (WHO) back in 2015, almost 4.4% of the world’s population was suffering from depression [1]. Because of the COVID-19 pandemic, many people suffered from depression due to job loss, study hampering, losing close relatives, staying indoors, etc. A study showed 19.3% increase in depression symptoms among people after COVID-19 in the United States [2]. A study has shown the changes in obsession, depression, and quality of life in schizophrenia patients before and after COVID-19 [3]. When depression is severe it can lead to suicide. Every year around 800 thousand people die because of suicide [1]. In 2017, 13.2% of young adults (aged 18–25) in the U.S. suffered from depression which was 5.1% less in the year 2009 [4]. Of the deaths of young people, around 9.1% are due to suicide [5]. In most suicide cases, people had psychiatric disorders where depression is the most common disorder among others [6]. According to a recent study, insecure attachment styles are linked to greater problems such as depression, social anxiety, and suicidal thoughts [7]. So, depression is a major issue that should be diagnosed and treated at an early age to prevent suicide and for the betterment of the quality of life.

Conclusion

In this work, we have recorded EEG data of young adults (19 depressed and 13 Control) evaluated by the PHQ9 screening tool and proposed a machine learning approach to learn about the EEG properties for depression detection.

We conducted multiple experiments with the reported machine learning (SVM and KNN) classifiers with our recorded data. The first experiment we conducted was on segmentation to find the better sample length suitable for ML. From our experiments, we have identified that 5-second segments are suitable for our work. Then, we have identified a suitable frequency range from various experiments that improve performance using features that are related to depression detection. We have found out that Hjorth parameters along with Shannon entropy and long energy entropy provide better results among other reported features and the beta band (12–30 Hz) gives the highest accuracy of 97.21 ± 0.21% with 25 iterations and 5-fold CV using weighted KNN compared to the other sub-bands. By combining the sub-bands, we have also investigated some other frequency ranges. We have found out that by taking the range from alpha to beta 8–30 Hz (AB), we can improve ML performance and achieve 98.43 ± 0.15% accuracy with 25 iterations and a 5-fold CV with fine KNN classifier. Using AB (8–30 Hz), we can see a significant improvement of 1.22% accuracy and slandered deviation. To further investigate the reliability, we divided the dataset 70/30 for training and testing with 5-fold CV and 10 iterations. In this experiment, we have found out that the ML performance is better by choosing the AB (8–30 Hz) band with fine KNN classifier with an accuracy of 98.10 ± 0.11%, precision of 0.984 ± 0.003, NPV of 0.977 ± 0.002, sensitivity of 0.984 ± 0.002, specificity of 0.976 ± 0.005, and F1 score of 0.984 ± 0.001. Then, we analyzed the ML performance in different regions of the brain and concluded that using the whole brain for depression detection will give the highest accuracy. The proposed method can detect depression among young adults with minimum requirements compared to other related works.

Abstract
Objectives
Postpartum depression estimated prevalence in women is between 5 and 26% and it has adverse effects both on the mother, infant and her partner. Psychological treatments have proved to be effective for women with mild-to-moderate symptoms. Whereas several systematic reviews have assessed the effects of different psychological interventions for postpartum depression, such as cognitive-behavioural therapy or interpersonal therapy, no review assessing psychodynamic therapy has been carried out. A systematic review was conducted to evaluate the efficacy of psychodynamic therapy for postpartum depression.

Methods
Studies were identified using the following databases: PsycINFO, Psycarticles and Pubmed over January 2023. The requirements for the studies were the following: they had to be quantitative, available in English, including a psychodynamic intervention targeting treatment or prevention of postpartum depression which starts during pregnancy or within the first 12 months after giving birth. Case studies, qualitative studies or studies focused on improving parent-infant relationship or infant outcome were excluded from this research.

Results
Seven trials including 521 women met the inclusion criteria. In summary, three randomized controlled trials and four longitudinal studies were found. The most frequently used assessment tool was EPDS, five were individual interventions and the other two were group interventions.

Discussion
All studies reported the efficacy of psychodynamic interventions for postpartum depression, both in home and clinical settings and both in group and individual format. The limited number of trials, small sample sizes and lack of appropriate control groups were the main limitations.

Conclusions for practice
Psychodynamic therapy is probably efficient intervention for postpartum depression. Future research with strong methodological designs is needed to confirm these findings.

Significance
What is already known on this subject? Several systematic reviews have assessed the effects of different psychological interventions for postpartum depression, but no review assessing psychodynamic therapy has been carried out. What this study adds? A systematic review was conducted to evaluate the efficacy of psychodynamic therapy for postpartumdepression. This makes the systematic review a unique contribution to the literature.

Introduction

Historically parent’s mental health during the perinatal period has been overlooked, as it was conceived as a time of joy and emotional stability. Only in 1950’s some authors started to write about maternity blues, postpartum depression, and puerperal psychosis (Besser et al., 2008). Studies have generally targeted mothers and their psychological wellbeing. On the contrary, paternal mental health remains under-investigated even though non-gestational parents may also suffer from mental disorders in the peripartum period.

     Postpartum depression encompasses a depression disorder occurring within the 12 months following childbirth (Branquinho et al., 2021) and the disorder can be compared to a major depressive episode in any moment of a woman’s life. However, symptoms such as anxiety, anhedonia, aggressive obsessional thoughts, restlessness or concentration and decision-making difficulties are more frequent or severe in the immediate postpartum (Batt et al., 2020). Postpartum depression has adverse effects on the woman and, therefore, on the infant as the disorder is closely related to difficulty to carry out different parenting tasks, such as breastfeeding, sleeping, or responding to the infant’s needs (Branquinho et al., 2021; Nanzer et al., 2012). It is also the strongest predictor of paternal depression during the perinatal period (Kaźmierczak et al., 2020). In addition, it is one of the main causes of maternal deaths in the first year after childbirth as the mother may develop suicidal thoughts and intentions. (Al-Halabí et al., 2021). The estimated prevalence of postpartum depression in women varies across countries, assessment criteria or time frame ranging from 5 to 26% (Liu et al., 2022).

Conclusions

     Psychodynamic approach still plays a minor role in the mainstream theory, research, and treatment for postpartum depression, even though the amount of data has kept growing for the last decade. BDP is probably efficient intervention for postpartum depression. Therefore, it is suggested that research should be continued to assess the effectiveness of psychodynamic interventions in postpartum depression compared to other effective treatments.