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Risk for depression is expressed across multiple levels of analysis. For example, parental depression and cognitive vulnerability are known markers of depression risk, but no study has examined their interactive effects on children's cortisol reactivity, a likely mediator of early depression risk. We examined relations across these different levels of vulnerability using cross-sectional and longitudinal methods in two community samples of children. Children were assessed for cognitive vulnerability using self-reports (Study 1; n = 244) and tasks tapping memory and attentional bias (Study 2; n = 205), and their parents were assessed for depression history using structured clinical interviews. In both samples, children participated in standardized stress tasks and cortisol reactivity was assessed. Cross-sectionally and longitudinally, parental depression history and child cognitive vulnerability interacted to predict children's cortisol reactivity; associations between parent depression and elevated child cortisol activity were found when children also showed elevated depressotypic attributions as well as attentional and memory biases. Findings indicate that models of children's emerging depression risk may benefit from the examination of the interactive effects of multiple sources of vulnerability across levels of analysis.

This study aimed to examine the depression risk factors for knowledge workers aged 20–64 in the post-capitalist society of Taiwan. Interview data from 2014 and 2019 were adopted for quantitative analysis of the depression risk by demographic and individual characteristics. The results showed that the depression risks of knowledge workers were not affected by demographic variables in a single period. From 2014 to 2019, the prevalence of high depression risk in knowledge workers aged 20–64 years increased over time. The more attention is paid to gender equality in society, the less the change in the gender depression index gap may be seen. Positive psychological state and family relationships are both depression risk factors and depression protective factors. Being male, married, religious, and aged 45–49 years old were found to be critical risk factors. Variables of individual characteristics could effectively predict depression risk.

Background Personality is suggested to be a major risk factor for depression but large‐scale individual participant meta‐analyses on this topic are lacking. Method Data from 10 prospective community cohort studies with 117,899 participants (mean age 49.0 years; 54.7% women) were pooled for individual participant meta‐analysis to determine the association between personality traits of the five‐factor model and risk of depressive symptoms. Results In cross‐sectional analysis, low extraversion (pooled standardized regression coefficient (B) = –.08; 95% confidence interval = –0.11, –0.04), high neuroticism (B = .39; 0.32, 0.45), and low conscientiousness (B = –.09; –0.10, –0.06) were associated with depressive symptoms. Similar associations were observed in longitudinal analyses adjusted for baseline depressive symptoms (n = 56,735; mean follow‐up of 5.0 years): low extraversion (B = –.03; –0.05, –0.01), high neuroticism (B = .12; 0.10, 0.13), and low conscientiousness (B = –.04; –0.06, –0.02) were associated with an increased risk of depressive symptoms at follow‐up. In turn, depressive symptoms were associated with personality change in extraversion (B = –.07; 95% CI = –0.12, –0.02), neuroticism (B = .23; 0.09, 0.36), agreeableness (B = –.09; –0.15, –0.04), conscientiousness (B = –.14; –0.21, –0.07), and openness to experience (B = –.04; –0.08, 0.00). Conclusions Personality traits are prospectively associated with the development of depressive symptoms. Depressive symptoms, in turn, are associated with changes in personality that may be temporary or persistent.

Multivariable risk algorithms (MVRP) predicting the personal risk of depression will form an important component of personalized preventive interventions. However, it is unknown whether providing personalized depression risk will lead to unintended psychological harms. The objectives of this study were to evaluate the impact of providing personalized depression risk on non-specific psychological distress and functional impairment over 12 months. A mixed-methods randomized controlled trial was conducted in 358 males and 354 females who were at high risk of having a major depressive episode according to sex-specific MVRPs, and who were randomly recruited across Canada. Participants were assessed at baseline, 6 and 12 months. Over 93% of participants were interested in knowing their depression risk. The intervention group had a greater reduction in K10 score over 12 months than the control group; complete-case analysis found a significant between-group difference in mean K10 change score ( = 1.17, 95% CI 0.12-2.23) at 12 months. Participants in the intervention group also reported significantly less functional impairment in the domains of home and work/school activities, than did those in the control group. A majority of the qualitative interviewees commented that personalized depression risk information does not have a negative impact on physical and mental health. This study found no evidence that providing personalized depression risk information will lead to worsening psychological distress, functional impairment, and absenteeism. Provision of personalized depression risk information may have positive impacts on non-specific psychological distress and functioning. ClinicalTrials.gov NCT02943876.

Depression is a major psychological disorder with a growing impact worldwide. Traditional methods for detecting the risk of depression, predominantly reliant on psychiatric evaluations and self-assessment questionnaires, are often criticized for their inefficiency and lack of objectivity. Advancements in deep learning have paved the way for innovations in depression risk detection methods that fuse multimodal data. This paper introduces a novel framework, the Audio, Video, and Text Fusion-Three Branch Network (AVTF-TBN), designed to amalgamate auditory, visual, and textual cues for a comprehensive analysis of depression risk. Our approach encompasses three dedicated branches—Audio Branch, Video Branch, and Text Branch—each responsible for extracting salient features from the corresponding modality. These features are subsequently fused through a multimodal fusion (MMF) module, yielding a robust feature vector that feeds into a predictive modeling layer. To further our research, we devised an emotion elicitation paradigm based on two distinct tasks—reading and interviewing—implemented to gather a rich, sensor-based depression risk detection dataset. The sensory equipment, such as cameras, captures subtle facial expressions and vocal characteristics essential for our analysis. The research thoroughly investigates the data generated by varying emotional stimuli and evaluates the contribution of different tasks to emotion evocation. During the experiment, the AVTF-TBN model has the best performance when the data from the two tasks are simultaneously used for detection, where the F1 Score is 0.78, Precision is 0.76, and Recall is 0.81. Our experimental results confirm the validity of the paradigm and demonstrate the efficacy of the AVTF-TBN model in detecting depression risk, showcasing the crucial role of sensor-based data in mental health detection.

The association between added sugars or sugar-sweetened beverage consumption and the risk of depression, as well as the role of carbohydrate quality in depression risk, remains unclear. Among 15 546 Spanish university graduates from the Seguimiento Universidad de Navarra (SUN) prospective cohort study, diet was assessed with a validated 136-item semi-quantitative FFQ at baseline and at 10-year follow-up. Cumulative average consumption of added sugars, sweetened drinks and an overall carbohydrate quality index (CQI) were calculated. A better CQI was associated with higher whole-grain consumption and fibre intake and lower glycaemic index and consumption of solid (instead of liquid) carbohydrates. Clinical diagnoses of depression during follow-up were classified as incident cases. Multivariable time-dependent Cox regression models were used to estimate hazard ratios (HR) of depression according to consumption of added sugars, sweetened drinks and CQI. We observed 769 incident cases of depression. Participants in the highest quartile of added sugars consumption showed a significant increment in the risk of depression (HR=1·35; 95 % CI 1·09, 1·67, P=0·034), whereas those in the highest quartile of CQI (upper quartile of the CQI) showed a relative risk reduction of 30 % compared with those in the lowest quartile of the CQI (HR=0·70; 95 % CI 0·56, 0·88). No significant association between sugar-sweetened beverage consumption and depression risk was found. Higher added sugars and lower quality of carbohydrate consumption were associated with depression risk in the SUN Cohort. Further studies are necessary to confirm the reported results.

Learning to respond to others' distress with well-regulated empathy is an important developmental task linked to positive health outcomes and moral achievements. However, this important interpersonal skill set may also confer risk for depression and anxiety when present at extreme levels and in combination with certain individual characteristics or within particular contexts. The purpose of this review is to describe an empirically grounded theoretical rationale for the hypothesis that empathic tendencies can be “risky strengths.” We propose a model in which typical development of affective and cognitive empathy can be influenced by complex interplay among intraindividual and interindividual moderators that increase risk for empathic personal distress and excessive interpersonal guilt. These intermediate states in turn precipitate internalizing problems that map onto empirically derived fear/arousal and anhedonia/misery subfactors of internalizing disorders. The intraindividual moderators include a genetically influenced propensity toward physiological hyperarousal, which is proposed to interact with genetic propensity to empathic sensitivity to contribute to neurobiological processes that underlie personal distress responses to others' pain or unhappiness. This empathic personal distress then increases risk for internalizing problems, particularly fear/arousal symptoms. In a similar fashion, interactions between genetic propensities toward negative thinking processes and empathic sensitivity are hypothesized to contribute to excess interpersonal guilt in response to others' distress. This interpersonal guilt then increases the risk for internalizing problems, especially anhedonia/misery symptoms. Interindividual moderators, such as maladaptive parenting or chronic exposure to parents' negative affect, further interact with these genetic liabilities to amplify risk for personal distress and interpersonal guilt as well as for consequent internalizing problems. Age-related increases in the heritability of depression, anxiety, and empathy-related constructs are consistent with developmental shifts toward greater influence of intraindividual moderators throughout childhood and adolescence, with interindividual moderators exerting their greatest influence during early childhood. Efforts to modulate neurobiological and behavioral expressions of genetic dysregulation liabilities and to promote adaptive empathic skills must thus begin early in development.

In this study, we sought to combine two lines of research to better understand risk for the intergenerational transmission of depression. The first focuses on the role of maternal criticism as a potential mechanism of risk for depression in youth while the second builds from interpersonal and stress generation models regarding the potential impact of youth depression on future escalations in maternal criticism. Specifically, we examined the role of maternal criticism within a transactional mediation model using data from a multi-wave study. Participants were 251 mother–offspring pairs consisting of mothers with ( n = 129) and without ( n = 122) a history of major depressive disorder (MDD) during their child’s lifetime who completed assessments every 6 months for 2 years. We found support for the hypothesized transactional mediational model in which maternal expressed emotion-criticism (EE-Crit) mediated the link between maternal history of MDD and residual change in youth’s depressive symptoms over the previous 6 months and, reciprocally, youth depressive symptoms mediated the relation between maternal MDD history and residual change in EE-Crit 6 months later. These results indicate that maternal criticism and offspring depressive symptoms may contribute to a vicious cycle of depression risk, which should be considered for interventions targeted toward youth at risk of developing MDD.

The phenomenon that the depression risk among doctoral students is higher than that of other groups should not be ignored. Despite this, studies specifically addressing depression risk in doctoral students are relatively scarce, and existing findings are not universally applicable. Using neural network feature extraction technology, this study aims to investigate the factors contributing to the high depression risk of doctoral students and effectively identify doctoral students at depression risk, so as to propose corresponding improvement strategies to prevent and intervene doctoral students with depression risk for universities. Based on the data from the 2019 Nature Global Doctoral Student Survey, we first screened 13 highly relevant features from a total of 37 features potentially related to the risk of depression among doctoral students by Random Forest algorithm. Subsequently, we trained the optimal prediction model to predict the doctoral students with depression risk using a Multilayer Perceptron (MLP), achieving an accuracy of 89.09% on the test set. Additionally, this study constructed a group portrait of doctoral students at risk of depression, and found that overwork, poor work-life balance, and poor supervisor-student relationship, etc., were typical characteristics among these students. Finally, we proposed several improvement strategies for higher education institutions. Our research offers a new perspective on utilising artificial intelligence (AI) methods to tackle educational challenges, particularly in the identification and support of doctoral students at risk of depression, thereby enhancing their mental health.