Depressive Disorder Research Articles

Enhancing prediction of major depressive disorder onset in adolescents: A machine learning approach.

Major Depressive Disorder (MDD) is a prevalent mental health condition that often begins in adolescence, with significant long-term implications. Indicated prevention programs targeting adolescents with mild symptoms have shown efficacy, yet the methods for identifying at-risk individuals need improvement. This study aims to evaluate the utility of Partial Least Squares Regression (PLSR) in predicting the onset of MDD among non-depressed adolescents, compared to traditional screening methods. The study recruited 1462 Portuguese adolescents aged 13-16, who were assessed using various self-report measures and followed for two years. Participants were randomly divided into training (70%, N=1023) and testing (30%, N=439) samples. PLSR models were developed to predict the occurrence of a major depressive episode (MDE) within two years, using 331 variables. The model's performance was compared to the Children's Depression Inventory (CDI) in predicting MDE onset. The best-fitting PLSR model with two components explained 19.1% and 16.9% of the variance in the training and testing samples, respectively, significantly outperforming the CDI, which explained 7.7% of the variance. The area under the ROC curve was 0.78 for PLSR, compared to 0.71 for CDI. An empirically derived cut-off point was used to create dichotomous risk categories, and it showed a significant difference in MDE rates between predicted high-risk and low-risk groups. The balanced accuracy of the PLSR model was 0.77, compared to 0.65 for the CDI method. The PLSR model effectively identified adolescents at risk for developing MDD, demonstrating superior predictive power over the CDI. This study supports the potential utility of ML techniques (e.g., PLSR) in enhancing early identification and prevention efforts for adolescent depression.

A novel MSN-II feature extracted from T1-weighted MRI for discriminating between BD patients and MDD patients.

Differentiating between patients with bipolar disorder (BD) and major depressive disorder (MDD) is clinically challenging. This study aimed to explore the potential of radiomic textural features for discriminating BD and MDD. A total 253 subjects (114 patients with BD, 139 patients with MDD) with T1-weighted MRI data were recruited. Radiomics features and gray matter volume (GMV) features were extracted from each brain region. A novel high-level MSN_II feature method based on radiomic features was proposed. And a total of 21 MSN features (5 MSN_I and 16 MSN_II) based on different combinations of the 5 types of radiomic textural feature were calculated. Classification models were constructed using various combinations of MSNs or GMV, and their performance and stability was evaluated through 2000 repeated experiments. The model built with combined features (GMV and GMV+MSN_II_GLCM_GLSZM_NGTDM) showed the best classification performance (AUC=0.896±0.058, ACC=0.831±0.064) in the validation cohort. After MANOVA analysis and FDR correlation, the MSN_II_GLCM_GLSZM_NGTDM values in 4 regions (right rectus gyrus, right temporal pole: middle temporal gyrus, Vermis3 and Vermis10) showed significant difference between BD and MDD. The main limitation of this study is that the data is derived from a single center without an external independent test set. Incorporating the high-level MSN_II based on radiomics features can improve the classification performance compared to models solely relying on GMV features alone. This result implied the potential application of the proposed high level MSN method and radiomics textural features on the MDD and BD clinical studies.

The Association Between Medication Adherence, Internalized Stigma and Social Support Among Outpatients with Major Depressive Disorder in a Malaysian Hospital: A Cross-Sectional Study

The Association Between Medication Adherence, Internalized Stigma and Social Support Among Outpatients with Major Depressive Disorder in a Malaysian Hospital: A Cross-Sectional Study

Federated Bayesian network learning from multi-site data.

Identifying functional connectivity biomarkers of major depressive disorder (MDD) patients is essential to advance the understanding of disorder mechanisms and early intervention. Multi-site data arise naturally which could enhance the statistical power of single-site-based methods. However, the main concern is the inter-site heterogeneity and data sharing barriers between different sites. Our objective is to overcome these barries to learn multiple Bayesian networks (BNs) from rs-fMRI data. We propose a federated joint estimator and the corresponding optimization algorithm, called NOTEARS-PFL. Specifically, we incorporate both shared and site-specific information into NOTEARS-PFL by utilizing the sparse group lasso penalty. Addressing data-sharing constraint, we develop the alternating direction method of multipliers for the optimization of NOTEARS-PFL. This entails processing neuroimaging data locally at each site, followed by the transmission of the learned network structures for central global updates. The effectiveness and accuracy of the NOTEARS-PFL method are validated through its application on both synthetic and real-world multi-site resting-state functional magnetic resonance imaging (rs-fMRI) datasets. This demonstrates its superior efficiency and precision in comparison to alternative approaches. We proposed a toolbox called NOTEARS-PFL to learn the heterogeneous brain functional connectivity in MDD patients using multi-site data efficiently and with the data sharing constraint. The comprehensive experiments on both synthetic data and real-world multi-site rs-fMRI datasets with MDD highlight the excellent efficacy of our proposed method.

The efficacy of vortioxetine in the acute treatment of major depressive disorder: A systematic review and meta-analysis.

Among the available pharmacological treatments for acute major depressive disorder (MDD), vortioxetine, a serotonin transporter inhibitor (SERT), has been widely used for its multimodal action on serotonin neurotransmission, which produces essential changes also on glutamate, gamma amino butyric acid (GABA), norepinephrine, acetylcholine, and dopamine. This systematic review and meta-analysis aimed to evaluate the acute efficacy of vortioxetine across multiple dosing and to evaluate whether there is a dose-response effect and as well there is a dose-response issue with respect to side effects in acute depression. According to PRISMA guidelines, we systematically searched three major electronic databases (PubMed/MEDLINE, PsycINFO, and Cochrane Central Register of Controlled Trials) for Randomized Controlled Trial (RCT) studies published between January 2013 and April 2024. Twenty-four studies were included in the review and two meta-analyses were conducted to determine whether the mean Montgomery-Asberg Depression Rating Scale (MADRS) scale values in the placebo groups differ significantly from the mean MADRS scale values in the group receiving vortioxetine 10 mg or vortioxetine 20 mg. Vortioxetine significantly improved acute depression severity, anxiety symptoms, and cognitive function, with high response and remission rates in acute MDD. It was also well tolerated with a relatively low occurrence of severe or serious treatment-emergent adverse events (TEAEs). Observing the results of the meta-analysis, the effect was significant for both vortioxetine 10 and 20 mg, with a greater effect size for vortioxetine 20 mg. Vortioxetine should be considered efficacious as a first- and second-line therapy.

Iron metabolism dysfunction in neuropsychiatric disorders: Implications for therapeutic intervention.

Iron is a trace metal that takes part in the maintenance of body homeostasis by, for instance, aiding in energy production and immunity. A body of evidence now demonstrates that dysfunction in iron metabolism can have detrimental effects and is intricately associated with the development of neuropsychiatric disorders, including Major Depressive Disorder (MDD), anxiety, and schizophrenia. For instance, changes in serum and central nervous system (CNS) levels of iron and in proteins mediating iron metabolism have been documented in patients grappling with the aforementioned diseases. By contrast, targeting iron metabolism by using iron chelators, for instance, has proven to be effective in alleviating disease burden. Therefore, here we review the state-of-the-art regarding the role of iron metabolism and its dysfunction in the context of neuropsychiatric disorders. Furthermore, we discuss how targeting iron metabolism can be an effective therapeutic option to tackle this class of diseases. Finally, we discuss the mechanisms linking this dysfunction to behavioral changes in these disorders. Harnessing the knowledge of iron metabolism is not only key to the characterization of novel molecular targets and disease biomarkers but also crucial to drug repurposing and drug design.

Persons At Risk for Psychosis Overestimate Social Functioning Compared to Persons with Major Depressive Disorder and Community Controls.

Persons At Risk for Psychosis Overestimate Social Functioning Compared to Persons with Major Depressive Disorder and Community Controls.

Effect of antidepressants and social defeat stress on the activity of dorsal raphe serotonin neurons in free-moving animals.

Major depressive disorder (MDD) is among the most common mental disorders worldwide and is characterized by dysregulated reward processing associated with anhedonia. Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for MDD; however, their onset of action is delayed. Recent reports have shown that serotonin neurons in the dorsal raphe nucleus (DRN) are activated by rewards and play a vital role in reward processing. However, whether antidepressant treatment affects the DRN serotonin neuronal response to rewards in awake animals remains unknown. In this study, we measured the activity of DRN serotonin neurons in awake mice and determined the effects of antidepressants and chronic stress on DRN serotonin neuronal activity. We found that acute treatment with citalopram, an SSRI, significantly decreased sucrose-induced activation of DRN serotonin neurons. The decrease in response to acute citalopram treatment was attenuated by chronic citalopram treatment. Acute treatment with (S)-WAY100135, a 5-HT1A receptor antagonist, dose-dependently inhibited the response to acute citalopram treatment. These results indicate that autoinhibition by activating 5-HT1A receptors via acute SSRI treatment may blunt the reward response, which can be recovered after chronic SSRI treatment.

Time- and Region-specific Effect of Vortioxetine on Central LPS-induced Transcriptional Regulation of NLRP3 Inflammasome.

Inflammasome overactivation, multiprotein complexes that trigger inflammatory responses, plays a critical role in Major Depressive Disorder (MDD) pathogenesis and treatment responses. Indeed, different antidepressants alleviate depression-related behaviours by specifically counteracting the NLRP3 inflammasome signalling pathway. The immunomodulatory effects of vortioxetine (VTX), a multimodal antidepressant with cognitive benefits, were recently revealed to counter memory impairment induced by a peripheral lipopolysaccharide (LPS) injection 24 hours (h) postchallenge. The potential link between VTX and NLRP3, along with other inflammasomes, remains unexplored. Hence, adult C57BL/6J male mice (n = 73) were fed with a standard or VTX-enriched diet (600 mg/kg of food, 28 days), injected with LPS (830 μg/kg) or saline, and sacrificed 6/24 h post-LPS. At these time-points, transcriptional effects of LPS and VTX's on NLRP3, NLRP1, NLRC4, AIM2 (inflammasomes), ASC and CASP1 (related subunits) and NEK7 mediator (NLRP3 regulator) were assessed in dorsal and ventral hippocampal subregions, frontal-prefrontal cortex and hypothalamus, brain regions serving behavioural-cognitive functions impaired in MDD. Varied expression patterns of inflammasomes were revealed, with long-term NLRP3 and ASC transcriptional changes observed in response to LPS. It was discovered that VTX counteracted the LPS-mediated NLRP3 and ASC upregulation in memory-related brain areas like the dorsal hippocampus at 24 h time-point, potentially via regulating NEK7 expression. No VTX-mediated transcriptional effects were observed on other inflammasomes, reinforcing a potentially specific modulation on the NLRP3 inflammasome signalling pathway. Thus, a novel VTX's molecular mechanism in modulating the NLRP3 inflammasome in a time- and area-specific manner in the brain was highlighted, with significant clinical implications in treating depression and cognitive impairments.</p>.

Altered local spontaneous activity and functional connectivity density in major depressive disorder patients with anhedonia.

The purpose of this study was to investigate the alterations of intrinsic brain function in major depressive disorder (MDD) patients with and without anhedonia based on whole-brain level by using two novel measures, four-dimensional spatial-temporal consistency of local neural activity (FOCA) and local functional connectivity density (lFCD). A total of 26 MDD patients with anhedonia (MDD-WA), 29 MDD patients without anhedonia (MDD-WoA), and 30 healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning and intrinsic brain function was explored by FOCA and lFCD. A two-sample t-test was conducted to explore FOCA and lFCD differences between MDD patients and HCs, then analysis of covariance (ANCOVA) and post hoc tests were performed to obtain brain regions with significant differences among three groups. Finally, the diagnostic performance of FOCA and lFCD values with significant inter-group difference was evaluated using receiver operating characteristic (ROC) curves. Compared to HCs, MDD patients showed decreased FOCA in the right cuneus (CUN) and left postcentral gyrus (PoCG), as well as diminished lFCD in the right CUN and left calcarine fissure and surrounding cortex (CAL). Interestingly, the MDD-WA group was more likely to exhibit decreased FOCA in the left PoCG and reduced lFCD in the left CAL after consideration for the effect of anhedonia in MDD patients. The MDD-WA group further showed increased FOCA in the bilateral caudate (CAU) and right ventral anterior nucleus (VA) when comparing to HCs. Additionally, as compared with MDD-WoA group, the MDD-WA group presented decreased FOCA in the right middle occipital gyrus (MOG), which was also negatively associated with the severity of anhedonia in MDD patients. Finally, FOCA values of the right MOG exhibited excellent discriminant validity in differentiating MDD-WA from MDD-WoA, and the other individual or combined indices of FOCA or lFCD values in the aforementioned distinct brain regions presented significant utility in distinguishing between MDD or MDD-WA and HCs. The present findings suggest that aberrant intrinsic brain function in the left CAL, left PoCG, bilateral CAU, right VA, and, especially the right MOG may be associated with anhedonia in patients with MDD. Altered FOCA in the right MOG may have the potential to be a diagnostic neuroimaging biomarker for MDD patients with anhedonia.

The neural correlates of guilt highlight preclinical manifestations between bipolar and major depressive disorders.

Over 25% of bipolar disorder (BD) patients are misdiagnosed with major depressive disorder (MDD). An urgent need exists for a biomarker to differentiate BD from MDD. Various manifestations and intensities of maladaptive guilt processing might uniquely contribute to the pathogenesis of BD compared to MDD. This study adopted a first-person perspective guilt-provoking functional magnetic resonance imaging (fMRI) task, respectively induced by painful and ambiguous scenarios in 111 individuals: 35 with remitted MDD, 38 with remitted bipolar I disorder (BD-I), and 38 matched controls. A significant interaction between group and sense of agency in predicting guilt ratings for ambiguous, rather than painful, scenarios. The association between sense of agency and guilt was significant in MDD but not in BD-I patients or controls. Activation in the dorsomedial prefrontal cortex (dmPFC), pregenual anterior cingulate cortex (pgACC), and right inferior parietal lobule (IPL) was higher in BD-I than MDD subjects in response to ambiguous scenarios, whereas these were comparable to painful ones. The correlation between guilt ratings and activation in the dorsal anterior cingulate cortex (dACC) to ambiguous scenarios was significant in MDD, but not in BD-I or controls. The results of the multivariate pattern classification analysis showed that in the ambiguous scenarios, the accuracy of using brain activation patterns in the dmPFC, pgACC, and IPL to distinguish between participants with MDD and BD-I was 70.0%, 71.5%, and 68.7%, respectively. An additional test of the classification model, employing a combined mask of the three ROIs to distinguish between the two mood disorders in ambiguous scenarios, achieved an accuracy of 78.6% and an AUC value of 0.84. Subjective reports and neural correlates of guilt in ambiguous social situations, as well as a sense of agency, could provide to be a potential biomarker to help distinguish between BD-I and MDD even in the remitted stage.

The state of implementation science in major depressive disorder: A narrative review.

Major depressive disorder (MDD) is one of the most prevalent and disabling mental disorders with high recurrence rate. There is often a gap between scientific evidence related to the effective and cost-effective treatment of depression and clinical practice. Implementation science is a field of inquiry that aims to advance the process of applying evidence-based interventions to real-world problems. Implementation research consists of two broad phases. The first phase seeks to identify the gaps that exist between evidence and practice and their impact on outcomes. The second phase, building on the first, evaluates the effectiveness of implementation strategies by examining various implementation indicators to support the adoption and scale up of interventions. Although implementation research can facilitate the uptake and adoption of evidence-based interventions, it is underused in depression research. In this narrative review, the authors will introduce the definition of implementation science and the state of implementation science in MDD in order to support more widespread use of implementation science for scaling up evidence-based interventions.

Discovery of potential female-specific biomarkers for major depressive disorder by LC-MS-based metabolomics.

The prevalence of major depressive disorder (MDD) is higher in females than males, emphasizing the need to identify gender-specific biomarkers to improve diagnosis accuracy. In this study, a cross-sectional investigation with 258 samples was conducted to evaluate the discriminative power of potential gender-specific biomarkers for MDD. Eighteen MDD-related differential metabolites have been identified, involving pathways of phospholipids, glycerolipids, fatty acids, sphingolipids, cholesterol, vitamin E, and heme. A potential biomarker combination consisting of palmitelaidic acid, gamma carboxyethyl hydroxychroman (gamma-CEHC), and lysoPE(16:0) was confirmed for predicting depression in women using binary logistic regression analysis. To evaluate the panel's specificity, nine generalized anxiety disorder (GAD) samples, which share highly similar clinical symptoms with MDD, were included in the validation set. The discovery and validation sets yielded an area under the receiver operating characteristic curve of 0.86 and 0.83, respectively. All nine female GAD samples were correctly predicted as non-MDD, demonstrating the panel's specificity in diagnosing female MDD. Remarkably, this composite panel achieved a 75 % prediction accuracy in female samples in both the discovery and validation sets, but it did not reach 60 % prediction accuracy in male samples in either set. Our findings highlight the importance of gender-specific molecular diagnostics in developing practical and accurate diagnostic methods for MDD.

Using tree-based models to identify factors contributing to trait negative affect in adults

BackgroundIndividuals with high levels of negative affect (NA) are at an increased risk of experiencing distress and negative self-views. Theoretical models suggest that NA plays a critical role in psychopathology, particularly in Major Depressive Disorder (MDD), and is linked to cognitive-perceptual and affective regulation issues.ObjectiveDetermine whether maladaptive cognitive schemas, attributional style, childhood adversity, and lifestyle factors (including alcohol and drug use and physical activity) could effectively predict negative affect (NA) in adults.MethodsA secondary data analysis was performed on a sample of 342 depressed and non-depressed adults. Beta regression and regression tree analyses were conducted to identify the principal risk factors and their interactions. The regression tree model was trained with 5-fold cross-validation on 75% of the sample, with 25% of observations held for testing.ResultsThe findings revealed that the cognitive schemas of disconnection and rejection and impaired autonomy had a significant impact on the likelihood of higher scores on the State Depression Inventory (IDER) test (p < 0.001), as indicated by both beta regression and regression tree analyses. Additionally, childhood adversity emerged as a crucial factor in determining high levels of NA. The regression tree model achieved strong performance metrics, including an R-squared value of 0.77.ConclusionsThis study represents a significant step forward in the understanding of NA, as it considers a broad range of individual factors, such as cognitive schemas, lifestyle, and demographics, to predict its impact on NA, with potential implications for prevention programs aimed at reducing NA.

Transdiagnostic white matter controllability deficits across patients with affective and anxiety spectrum disorders

BackgroundAffective and anxiety disorders including major depression disorder (MDD), post-traumatic stress disorder (PTSD), and social anxiety disorder (SAD) are characterized by network dysconnectivity. Network controllability quantifies the capability of specific brain regions to impact functional dynamics based on the underlying structural connectome. This study aimed to investigate transdiagnostic and illness-specific network controllability alterations across these three disorders. Materials and methodsThe study enrolled 233 currently untreated and non-comorbid subjects, including 68 MDD patients, 51 PTSD patients, 46 SAD patients, and 68 healthy controls (HCs). White matter network controllability was compared among the four groups, and its associations with symptom severity and duration of untreated illness were evaluated. ResultsCompared with HCs, patients with PTSD, MDD and SAD exhibited reduced average controllability in the somatomotor, subcortical, and default mode network, notably in brain regions such as the superior frontal gyrus, postcentral gyrus, paracentral gyrus, pallidum, posterior cingulate, and putamen. MDD and SAD patients exhibited reduced average controllability in the left lateral occipital gyrus and bilateral accumbens. SAD patients showed reduced average controllability in the dorsal attention network. These controllability changes did not correlate with illness duration or symptom severity. LimitationsThe cross-sectional design limits causal inference, and adjusting for age and sex differences may not completely eliminate their influence on the results. ConclusionThe present study revealed shared and specific alterations of network controllability in MDD, PTSD, and SAD, suggesting reduced ability of specific brain regions/networks in driving the brain system into different functional states across these disorders.

Challenges in maintaining remission after ECT - Insights from a six-month follow up study.

Electroconvulsive therapy (ECT) is a widely recognized treatment for severe depressive disorders, particularly in cases of inadequate response to pharmacological interventions or when rapid symptom relief is essential. Although ECT demonstrates high efficacy, a notable proportion of patients relapse after a successful ECT course. This study investigated clinical baseline characteristics and residual depressive symptoms associated with relapse - defined as a Montgomery-Åsberg Depression Rating Scale (MADRS) score >15, restart of ECT, or suicide attempt - within six months after successful ECT. Data were obtained from the PROTECT study, a single-site, prospective cohort study conducted at the University Psychiatric Center Duffel, Belgium. - Among the 65 patients who completed the ECT course, 40 patients (62%) achieved remission. At six months, 32 patients were reassessed, and 18 (56%) of them experienced relapse. No significant associations were identified between relapse and baseline factors, including age, social circumstances, baseline depression severity, psychomotor symptoms, cognitive functioning, treatment resistance, lithium use, or the presence of psychotic or melancholic features. Residual depressive symptoms at the end of the ECT course also did not predict relapse. - The observed high relapse rate underscores the critical need for robust continuation and maintenance strategies following ECT. Future research should prioritize larger cohorts to better identify predictors of relapse and optimize post-ECT treatment protocols.

Exploring the dynamic relationships between nocturnal heart rate, sleep disruptions, anxiety levels, and depression severity over time in recurrent major depressive disorder.

Elevated night resting heart rate (HR) has been associated with increased depression severity, yet the underlying mechanisms remain elusive. This study aimed to investigate the mediating role of sleep disturbance and the influence of anxiety on the relationship between night resting HR and depression severity. This is a secondary data analysis of data collected in the Remote Assessment of Disease and Relapse (RADAR) Major Depressive Disorder (MDD) longitudinal mobile health study, encompassing 461 participants (1774 observations) across three national centers (Netherlands, Spain, and the UK). Depression severity, anxiety, and sleep disturbance were assessed every three months. Night resting HR parameters in the 2 weeks preceding assessments were measured using a wrist-worn Fitbit device. Linear mixed models and causal mediation analysis were employed to examine the impact of sleep disturbance and anxiety on night resting HR on depression severity. Covariates included age, sex, BMI, smoking, alcohol consumption, antidepressant use, and comorbidities with other medical conditions. Higher night resting HR was linked to subsequent depressive severity, through the mediation of sleep disturbance. Anxiety contributed to an exacerbated level of sleep disturbance, subsequently intensifying depression severity. Anxiety exhibited no direct effect on night resting HR. Our findings underscore the mediating role of sleep disturbance in the effect of night resting HR on depression severity, and anxiety on depression severity. This insight has potential implications for early identification of indicators signalling worsening depression symptoms, enabling clinicians to initiate timely and responsive treatment measures.

Attenuated Doors-Locked P300 Amplitude and Elevated Depressive Symptoms: Effects of Age and Sex in Two Independent Samples of Youth.

Deficits in cognitive and reward-related functions, measured via reductions in the P300 and reward positivity (RewP) event-related potential (ERP) components, are commonly observed in adults suffering with depression. Considering higher risk for depression emerges among females in adolescence, examination of the neurological underpinnings of depression during this critical developmental period can help further elucidate our overall understanding of the etiology of depressive disorders. Therefore, the present study sought to first examine associations between doors-locked P300 amplitude, RewP amplitude, current depression symptoms, and age in an all-female youth sample (sample 1: n = 296; age, 8 to 14). Next, we examined these same associations, as well as sex, in a second independent sample consisting of male and female youths (sample 2: n = 605; age, 11 to 14). Blunted doors-locked P300 was associated with higher depressive symptoms in both samples. Moreover, the association between P300 and depression was stronger among older youths in both samples. Sex further moderated this relationship in sample 2 such that smaller P300 related to greater depression only in females. There were no consistent associations between RewP amplitude and depressive symptoms in either youth sample. These findings suggest that the doors-locked P300 component is a reliable neural correlate of depression in youth that might specifically relate to pathways linked to increased risk for depression among adolescent females.

Associations Between Growth Differentiation Factor 15 and Anxiety and Depression in the General Population: The Akershus Cardiac Examination 1950 Study.

Several studies suggest a bidirectional association between inflammation, and anxiety and depression. Elevated inflammatory cytokines generate and aggravate neuroinflammation, which may play a part in developing psychological symptoms. Growth differentiation factor 15 (GDF-15) is a novel biomarker possibly reflecting fibrosis and inflammation. The aim of the current study was to investigate the associations between levels of GDF-15 and symptoms of anxiety and depression in the general population. We measured GDF-15 in middle-aged persons participating in the Akershus Cardiac Examination 1950 Study. Symptoms of anxiety and depression were assessed using the Hospital Anxiety and Depression Scale (HADS), with HADS ≥8 denoting significant symptoms. We used multivariable regression analysis to assess the associations between GDF-15 and HADS, adjusting for levels of C-reactive protein (CRP), demographics, and comorbidities. A total of 3638 participants had valid assessment of HADS and measurements of GDF-15 and CRP. The mean age was 63.9 (SD 0.65) years, and 48.8% were women. In adjusted models, levels of GDF-15 were associated with the continuous HADS-D score (β = 0.27, 95% confidence interval [CI] = 0.12 to 0.43) and HADS-D score ≥8 (odds ratio = 1.41, 95% CI = 1.12 to 1.78), but not with the continuous HADS-A score (β = 0.06, 95% CI = -0.12 to 0.24) or HADS-A score ≥8 (odds ratio = 1.06, 95% CI = 0.88 to 1.27). Levels of GDF-15 are independently associated with symptoms of depression in the general population. Our results reinforce the notion that inflammation may be a contributing factor for the development of clinical depression. ClinicalTrials.gov identifier NCT01555411 (Akershus Cardiac Examination [ACE] 1950 Study), https://clinicaltrials.gov/study/NCT01555411.

Mental health and well-being in adolescent and young adult refugees in Sweden: A cross-sectional study of accompanied and unaccompanied individuals.

Currently, approximately 100 million people are displaced worldwide, including children and young adults. Previous studies showed exposure to violence and posttraumatic stress disorder (PTSD) are common in this sub-population. However, we still lack comprehensive data on well-being, mental health, and the ability to function. This study included 291 adolescent (aged 12-17years) and young adult (18-25years) refugees recruited nationwide in Sweden between 2019 and 2022. Sociodemographic, mental health and well-being-related data (well-being, post-traumatic stress symptoms, psychiatric diagnoses, and functional ability) were collected using semi-structured interviews with structured components. Associations between accompanied status and well-being/depression/suicidal thoughts, generalized anxiety disorder/panic disorder/PTSD symptoms, and functional ability were analyzed using linear and logistic regression, adjusted for age, gender, parental education, asylum status, and region of origin. Most study participants (mean age 17·9years) originated from the Middle East and North Africa (70·6%) or Sub-Saharan Africa (27·0%). 16·1% of adolescents and 32·3% of young adults were unaccompanied. Nearly the entire study sample had experienced violence (92·8%). However, the sample had a low prevalence of psychiatric diagnoses. For example, only 5·9% fulfilled the criteria for clinical depression. Self-reported well-being (WHO-5, 71·09 (23·91)) and observer-rated functional ability (GAS/GAF, 81·76 (14·15)) were high. There were no significant differences in diagnosis prevalence by gender. However, significant differences existed between accompanied versus unaccompanied groups. Being an unaccompanied refugee individual was associated with a higher risk of suicidal thoughts, adjusted odds ratio, aOR 5·66 (95% CI 2·15-14·88), higher rates of post-traumatic stress disorder symptoms β=0·72 (0·39-1·05), lower mental well-being β=-10·86 95% CI (-18·23- -3·48) and lower functional ability β=-9·38 (-13·84- -4·92). There were no differences in outcomes by gender except for worse well-being in males (β=6·83 (1·01-12·66)). In this sample, we found lower prevalence rates for all psychiatric diagnoses compared to earlier published studies. Being an unaccompanied refugee individual was a risk factor for all adverse outcomes. Future studies need to confirm the relatively low rates of psychiatric diagnoses. Regardless, the results highlight the heterogeneous needs among newly arrived refugees.