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Background Emerging evidence links immune activation to antidepressant response in adolescents with major depressive disorder (MDD). We examined how depressive symptoms, quality of life (QOL), and blood markers of inflammation, blood–brain barrier (BBB) integrity, and gut permeability change following antidepressant treatment in this population. Methods Twenty-six adolescents with MDD were evaluated at baseline (pre-treatment), 12 weeks, and 24 weeks after starting therapy. Depression and anxiety were measured by the Hamilton Depression Rating Scale (HAMD) and Hamilton Anxiety Rating Scale (HAMA). QOL was assessed with the QOL Scale for Children and Adolescents (QLSCA) and an adolescent social support instrument. Serum levels of IL-1β, TNF-α, IL-6, IL-4, S100β, claudin-5, LBP, I-FABP, and brain-derived neurotrophic factor (BDNF) were quantified. Results At baseline, pro-inflammatory cytokines (IL-1β, TNF-α, IL-6) and IL-4 strongly correlated with BBB (S100β, claudin-5) and gut-permeability (I-FABP) markers. IL-4 levels rose significantly and were associated with reduced anxiety (HAMA: r  = −0.48, p  < 0.05) and better QOL (QLSCA: r  = 0.57, p  < 0.01). BBB integrity and gut-permeability markers remained stable. In responders only, BDNF increased at 12 weeks ( p  < 0.05) and IL-8 rose at 24 weeks ( p  < 0.05), suggesting their involvement in treatment efficacy. Conclusions Antidepressant therapy in adolescents with MDD is accompanied by dynamic elevations in IL-4, IL-8, and BDNF that parallel clinical improvement and enhanced QOL. These biomarkers warrant further study to develop more targeted interventions for adolescent depression. Clinical trial number Not applicable.

Background Sleep disorders are common among adolescents with depression, yet lack reliable neuroimaging diagnostic techniques. This study aimed to predict sleep disorders in depressed adolescents using brain network features, including betweenness centrality (BC) and functional connectivity (FC). Methods 117 adolescents diagnosed with depression underwent resting-state fMRI. Whole-brain FC (reflecting inter-regional relationships) and BC (quantifying a node’s importance for network information flow) were analyzed. Differences in FC and BC between depressed adolescents with sleep disorders and depressed adolescents without sleep disorders were compared using two-sample t -tests in a discovery dataset ( n  = 86). A support vector machine (SVM) classifier was trained to differentiate these groups. Validation employed leave-one-out cross-validation (LOOCV) internally and an independent dataset ( n  = 31). Results Depressed adolescents with sleep disorders showed elevated BC in the right middle temporal gyrus (MTG.R) and decreased BC in the left median cingulate and paracingulate gyri (DCG.L) and left caudate nucleus (CAU.L), indicating altered information flow hubs. Alterations in FC were observed across several regions, with the most pronounced changes occurring between the left middle occipital gyrus and MTG.R (MOG.L-MTG.R). The SVM model, using combined whole-brain BC and FC features, achieved 81.40% accuracy during LOOCV and identified discriminative features. Predictive performance was validated externally, yielding 74.19% accuracy. Conclusions Significant functional brain network alterations occur in depressed adolescents with sleep disorders. Integrating brain network analysis(BC and FC analysis) with machine learning techniques offers a promising approach to identifying neuroimaging markers for diagnosing sleep disorders in depressed adolescents.

Objectives While high socioeconomic status (SES) indicators generally protect against mental health issues such as mood disorders in overall populations, evidence suggests that these protective effects might be attenuated for marginalized groups such as immigrants, in comparison to groups who are more socially privileged. This phenomenon is referred to as Marginalization-related Diminished Returns (MDRs). Existing knowledge on diminished returns of SES indicators primarily stems from cross-sectional comparisons of racial groups of adults in the US. Therefore, there is a crucial need to investigate these dynamics using longitudinal data of youth in European countries that are host countries for migrants from many different countries. Aims This study aims to compare the impact of family SES indicators (parental education, parental employment, family income), and family composition on subsequent incidence of mood disorders in youth from immigrant and non-immigrant families. Methods Utilizing a retrospective cohort design, we analyzed data from nationwide registers in Sweden from Jan 1, 2001, to Dec 31, 2020, and included immigrant and non-immigrant children 12–18 years. Independent variables included parental education, parental employment, family income, and family composition. The outcome variable was a mood disorder diagnosis based on the national patient register. Sex and study year served as covariates, while immigration background acted as a moderator. Cox regression models were employed for statistical analysis. Results All SES indicators (higher parental education, parental employment, higher family income), and two-parent family composition were associated with a lower risk of mood disorders. However, the protective effects of parental education and employment were weaker for immigrant youth compared to native-born youth, as documented by statistically significant interaction tests. Conclusions Our findings reveal weaker protective effects of parental education and employment for youth from immigrant families compared to youth from native-born families in Sweden. Prevention of mood disorders among immigrant families in Sweden may require two sets of policy interventions: one that supports education and employment in general and one that supports employed and educated families to harness their available human capital, with particular attention to immigrants from Africa and the Middle East.

Background Adolescent depression is a common and disabling disorder that is frequently associated with large-scale sleep disturbances. Adolescents with depression frequently experience poor sleep quality, insomnia, and delayed sleep phase syndrome, which intensify and prolong depressive symptoms. Since mood disorder and sleep disorder are bidirectional, early diagnosis and treatment of sleep disorders in adolescent with depression are clinically critical. Objective This scoping review aimed to synthesize existing evidence on the evaluation and intervention of sleep disorders among adolescents with depression. The objective was to describe assessment methods, synthesize evidence on both non-pharmacological (e.g., CBT-I, mindfulness) and pharmacological (e.g., melatonin) interventions, and identify key research gaps for clinical practice and future trials. Methods The review followed Arksey and O’Malley’s framework and PRISMA-ScR guidelines. Searches of PubMed, Web of Science, CINAHL, PsycINFO, and Scopus were conducted for studies published between January 2000 and January 2024. Twenty-six peer-reviewed studies meeting the inclusion criteria were analyzed. Results From the study, 26 papers met the inclusion criteria. 17 of these were considered CBT-I or CBT-I-based interventions, 3 examined mindfulness-based interventions, 3 evaluated the use of melatonin, 2 considered psychoeducation or sleep hygiene, and 1 focused on group treatment. CBT-I was able to produce consistent results regarding sleep behavior, and many research studies have reported improvements in depressive symptoms that accompany sleep behavior change. Mindfulness-based therapies proved potentially valuable, but the results were more diverse in their inter-population and intervention format. Online CBT-I tools like Sleep Ninja + were found to be feasible and accessible. Melatonin was commonly used as a complementary treatment and generated short-term effects on getting sleep; however, the evidence of long-term safety and sustained effectiveness was limited. Most studies depended on subjective sleep assessment (e.g., PSQI, ISI), while only a small fraction of studies used objective indicators of sleep such as actigraphy and polysomnography. Divergent comparability and different participant characteristics were the limitations of the methodological heterogeneity that hampered the comparability and prevented sound conclusions of any generalization. Conclusion Addressing sleep disturbances is essential for the effective treatment of adolescent depression. Studies indicates that combining CBT-I with other therapies can enhance treatment outcomes, but further studies are necessary to confirm long-term results, assess potential medication risks, and ensure the treatments are suitable for all individuals. Relevance This review provides a foundational synthesis for clinicians and researchers aiming to improve diagnostic precision and treatment outcomes in adolescents experiencing both sleep disorders and depression. Clinical trial number Not applicable.

Objectives Non-suicidal self-injury (NSSI) exhibits a high prevalence among adolescents. It is common for adolescents to employ multiple methods of NSSI, and these behaviors can manifest in diverse combinations. This study employs latent class analysis (LCA) to identify subgroups of adolescents based on the methods of NSSI and investigates the association between peripheral plasma cytokine levels and the identified NSSI patterns. Methods The research enrolled 405 outpatient adolescents, ranging in age from 12 to 18 years, who had exhibited NSSI behavior. These participants completed the Adolescent Non-suicidal Self-Injury Behavior Questionnaire (ANSAQ) and assessments of plasma cytokine levels. Latent class indicators consisted of 12 NSSI variables. Logistic regression analysis was employed to examine the associations between the identified classes and the related cytokines. Results LCA results supported a three-class model, categorizing participants into multiple methods group (24.20%), moderate methods group (43.70%), and mild/singular method group (32.10%). Further multinomial logistic regression analysis revealed that individuals with higher levels of interleukin-2 (IL-2) and interleukin-10 (IL-10) were more likely to be classified into the multiple-methods group compared to those in the mild/singular-method group (OR = 1.233 for IL-2; OR = 1.634 for IL-10). Additionally, individuals with elevated IL-10 levels were more likely to be classified into the multiple-methods group compared to those in the moderate-methods group (OR = 1.454). Furthermore, relative to individuals in the mild/singular-method group, those with higher IL-2 levels were more likely to belong to the moderate-methods group (OR = 1.207). These findings suggest a significant association between IL-2 and IL-10 levels and the severity of NSSI behaviors. Conclusion The classification of adolescents based on NSSI methods indicates that elevated levels of IL-2 and IL-10 increase the risk of engaging in NSSI involving multiple methods in adolescents. This has potential implications for understanding risk factors and developing targeted prevention and treatment strategies.

Background Adolescent depressive patients generally have clinical manifestations such as emotional depression and cognitive decline, and family caregivers play an irreplaceable and important role in the disease management and rehabilitation process. Despite the heavy burden of caring for adolescent depressed patients, it is possible that family caregivers are also experiencing anxiety, which may increase their burden, and previous research has reported that psychological resilience plays an important role in reducing the physical and mental burden on caregivers. Objective This study aims to assess the current status of caregiver burden, psychological resilience, and anxiety among family caregivers of adolescents with depression and to explore whether psychological resilience mediates the relationship between caregiver burden and anxiety. Methods A cross-sectional study design was employed to recruit 256 family caregivers of adolescents with depression. The caregivers completed questionnaires, including general demographic information, the Zarit Caregiver Burden Scale, the Self-Rating Anxiety Scale, and the Psychological Resilience Scale. Data analysis was performed via SPSS 26.0. Results There was a significant positive correlation between care burden and anxiety ( r  = 0.561, P  < 0.01), psychological resilience and care burden and anxiety showed a significant negative correlation ( r = -0.895, -0.556, P  < 0.01), and psychological resilience played a partial mediating role between care burden and anxiety (β = 0.198, P  < 0.01), and the indirect effect accounted for 42.95%. Conclusion Perceived resilience plays a mediating role in the association between caregiving burden and anxiety among family caregivers of adolescent patients with depression, and it can be considered as a longitudinal follow-up or intervention study in the future. Implications for clinical practice This study emphasizes that caregiver burden is associated with higher anxiety levels, and psychological resilience may play a moderating role in this association, suggesting that clinicians need to include resilience assessment in routine screening and implement hierarchical interventions based on the “burden-resilience” combined risk: priority is given to providing structured psychological support to high-burden-low resilience groups. The final realization: (1) reduce the burden on caregivers (2) improve the quality of care.

Background Depression is a leading cause of disability among adolescents and young adults, yet comprehensive assessments of its global burden and associated risk factors in this population remain limited. This study aimed to evaluate the global, regional, and national trends in depression burden among individuals under 30 years of age from 1990 to 2021, and to identify key risk factors and disparities. Methods We analyzed data from the Global Burden of Disease Study 2021, focusing on depression incidence and disability-adjusted life years (DALYs) among individuals aged under 30. Temporal trends were assessed using an age-period-cohort (APC) model. We further examined trends using estimated annual percentage change (EAPC), frontier analysis, and inequality metrics to provide a comprehensive evaluation. Results From 1990 to 2021, both the incidence of depression and related DALYs in individuals under 30 increased by over 50%. Males experienced a larger proportional increase, although females had an age-standardized incidence rate (ASIR) 23 times higher than males in 2021. The highest burden was observed in the 20–24 age group, while the fastest growth occurred among those aged 15–19. Individuals born after 2000 showed a higher prevalence of depression. High sociodemographic index (SDI) regions—particularly high-income North America and Greenland—had the highest ASIR and age-standardized DALY rate (ASDR). The most prominent risk factors were bullying victimization (61.8%), childhood sexual abuse (12.0%), and intimate partner violence (12.6%). Notable regional disparities were observed, with countries like Switzerland, Norway, Monaco, and Germany showing rapid increases in depression burden. Conclusions The burden of depression among young people has risen substantially over the past three decades, with disproportionate impacts on females, post-2000 birth cohorts, and those in high-SDI regions. Interventions targeting modifiable risk factors—particularly bullying, childhood abuse, and intimate partner violence—are essential to reduce the burden. Tailored, region-specific strategies are necessary to effectively address disparities and improve mental health outcomes in youth.

Background Suicidal ideation (SI) is defined as death-centered thoughts preceding suicidal behaviors, and demands prompt identification. While depressed patients are disproportionately affected by SI, particularly when comorbid with anxiety, the key triggers driving its emergence remain poorly understood. Methods This study analyzed clinical data from 9,193 psychiatric outpatients, with in-depth assessments conducted on 281 depressed patients (mean age 41.9 ± 21.5; 73 males) who completed the Self-rating Depression Scale (SDS) and Self-rating Anxiety Scale (SAS). Of these, 85 patients (30.2%) reported SI. To characterize the interplay among symptoms, we applied a dual-network approach: a regularized partial correlation network quantified conditional associations between SDS/SAS symptom dimensions and SI, and a Bayesian network explored potential precursors to SI. Results In the regularized partial correlation network, SI was associated with two symptom dimensions: ‘anxiety and panic’ (edge weight = 0.14) and ‘depressed mood’ (edge weight = 0.16). ‘Anxiety and panic’ exhibited the highest strength centrality (z = 1.43), indicating its pivotal role in mediating the development of SI. Bayesian network analysis further identified ‘anxiety and panic’ as a key mediator in all indirect paths linking depressive symptoms to SI. Conclusions This dual-network approach highlights the utility of computational psychiatry in revealing the relationships among symptoms within a network, yielding clinically actionable information. Here, we uncovered the central role of anxiety within the symptom network underlying SI, offering insights relevant to early identification and suicide prevention of patients with comorbid depression–anxiety.

Background Smartphone addiction has emerged as a widespread public health concern, especially among college students. This study examines the association between smartphone addiction and creativity among Chinese college students, as well as the underlying psychological mechanisms involved. A serial mediation model framework was conducted to explore the effect of smartphone addiction on creativity, taking into consideration the mediating roles of depression and executive function. Methods In this study, a sample of 691 Chinese college students was surveyed using the Mobile Phone Addiction Index, the Centre for Epidemiologic Studies - Depression Scale, the Behavior Rating Inventory of Executive Function - Adult Version, and the University Students’ Creativity Scale to examine the impact mechanism of smartphone addiction on creativity. Correlation and mediation model analyses were performed respectively using SPSS 22.0 and PROCESS 3.1. Results Correlation analysis showed that smartphone addiction was positively correlated with depression ( r = 0.298, P < 0.001) and executive function impairment ( r = 0.677, P < 0.001), while it was negatively associated with creativity ( r = -0.209, P < 0.001). Additionally, depression was positively correlated with executive function impairment ( r = 0.376, P < 0.001). Furthermore, creativity was negatively associated with depression ( r =-0.143, P < 0.001) and executive function impairment ( r = -0.291, P < 0.001). The mediation analysis indicated that smartphone addiction directly negatively predicted creativity, and also indirectly predicted creativity through the serial mediating role of depression and executive function impairment. Conclusion Smartphone addiction not only directly affected creativity, but also indirectly influenced it through both the independent mediating role of executive function, as well as their serial mediation pathway.

Background Mental health disorders remain a critical global public health issue, exacerbated by the coronavirus disease 2019 (COVID-19) pandemic. Post-lockdown mental health assessments are scarce. This study aims to develop interpretable machine learning (ML) models to classify and identify key risk factors for anxiety, depression, and insomnia symptoms in mainland China after the opening of COVID-19 lockdown. Methods Cross-sectional data collected in 2023 and 2024 were used as the training set (with test set divided in a 7:3 ratio) and internal validation set, respectively. The Generalized Anxiety Disorder-7 (GAD-7) scale, Patient Health Questionnaire-9 (PHQ-9) scale, and Insomnia Severity Index (ISI) scale were employed to assess symptoms of anxiety, depression, and insomnia, respectively. Seven interpretable machine learning models incorporating 27 relevant influencing factors were applied for the classification and risk factor identification of three mental health symptoms. Results The survey included 65,292 respondents with 36,996 cases in the training set (with test set) and 28,296 cases in the external validation set. The Categorical Boosting (CatBoost) model achieved the highest Area Under the Curve (AUC) value among seven ML models, with 0.817, 0.829, and 0.819 for anxiety, depression, and insomnia symptoms in the test set, and with 0.815, 0.820, and 0.822 for anxiety, depression, and insomnia symptoms in the external validation set. Several factors associated with a lower prevalence of anxiety symptom, including satisfactory neighborhood relationship, extensive knowledge about COVID-19, maintaining a regular sleep-wake cycle, daily vegetable consumption, and sufficient time spent sunbathing. Conversely, factors associated with a higher prevalence comprised history of anxiety, history of insomnia, history of depression, external noise at home, and fear of COVID-19 infection. Similar results were observed for depression and insomnia symptoms. Conclusions The explainable ML model found that healthy habits and positive social ties were linked to lower anxiety, depression, and insomnia symptoms. In contrast, past mental health issues and stressors were tied to higher levels. Public health efforts should focus on encouraging these modifiable habits and strengthening community support, to help improve residents’ mental health, especially in the aftermath of a pandemic.