Suicidal ideation (SI) is a major public health concern, especially in patients with major depressive disorder (MDD). Although various psychosocial factors are associated with SI, the complex interrelationships among childhood trauma (CT), anxiety symptoms, depressive symptoms, and anhedonia remain inadequately understood. This study examined these relationships and their associations with SI. This cross-sectional study included 782 patients with MDD consecutively recruited from the inpatient and outpatient clinics of the Department of Psychiatry at Renmin Hospital of Wuhan University between April 2019 and August 2023. Assessments were conducted using validated Chinese versions of the Childhood Trauma Questionnaire-Short Form, Hamilton Anxiety Rating Scale, Snaith-Hamilton Pleasure Scale, Hamilton Depression Scale, Patient Health Questionnaire-15, and Life Event Scale. Statistical analyses comprised chi-square tests, t-tests, binary logistic regression, Pearson’s correlation analysis, and structural equation modeling (SEM). Anxiety symptoms were present in 425 patients (54.35

Although copy number variants (CNVs) represent well-established genetic contributors to schizophrenia (SCZ), their role in bipolar disorder (BD), especially within non-European ancestries, has been inadequately explored. We evaluated the genome-wide load of rare CNVs, encompassing deletions and duplications, in a Han Chinese sample of 3915 BD cases and 7820 ethnically matched controls. We observed a marked overrepresentation of rare deletions in BD patients relative to controls, with affected genes showing enrichment in neural signaling and dosage-dependent networks, indicating that haploinsufficiency in neurodevelopmental loci could underlie a central etiological pathway in BD. Among the 12 previously reported CNV loci from European cohorts, only deletions at 3q29 and 15q11.2 exhibited robust associations with BD susceptibility in Han Chinese individuals. Through genome-wide, gene-centric CNV association testing, we uncovered novel BD-linked loci, including deletions spanning GLIS2 and PAM16 at 16p13.3, GRID2IP at 7p22.1, and CFLAR at 2q33.1, alongside a duplication affecting ZNF878 and ZNF844 at 19p13.2. These disrupted genes are chiefly implicated in neuronal maturation, synaptic modulation, and mitochondrial dynamics. This work delivers the most thorough delineation of BD-associated CNVs in Han Chinese to date, underscoring the imperative for ancestry-inclusive research to comprehensively unravel psychiatric genomics and unveiling fresh mechanistic perspectives on BD etiology.

Despite the continuous advancement of precision medicine, major depressive disorder (MDD), as a highly heterogeneous psychiatric disorder, still requires in-depth exploration of its distinct subtypes and objective biomarkers to improve diagnostic accuracy and personalized treatment. Traditional diagnostic methods rely on subjective symptoms and scale-based evaluations, which often fail to adequately reveal the biological basis of the disease. To address this challenge, we performed subtyping of patients with MDD using blood biochemical and hematological data from the UK Biobank. We adopted a multi-view clustering model that integrates anchor-based weighting and adaptive learning mechanisms. Subsequently, we performed back-testing on the identified subtypes using the CatBoost classification model and recognized three distinct and discriminative major depressive disorder subtypes. Furthermore, we interpreted the classification model via the Shapley Additive Explanations (SHAP) method, revealing the significant characteristic features of each subtype. The research results demonstrate that the subtyping of MDD based on blood biochemical and hematological data holds significant clinical application potential in terms of precise diagnosis and individualized intervention.

Modern psychiatry is shifting from unitary diagnostic models toward identifying biologically distinct depression subtypes. Despite the potential of multi-omics and AI, the field is hindered by non-standardized pipelines and poor reproducibility. We propose a four-pillar computational framework to standardize the subtyping process: (1) standardized preprocessing and feature embedding to ensure data integrity; (2) integrative multi-omics modeling strategies tailored to diverse sample sizes; (3) robust subtype identification and Explainable Artificial Intelligence (XAI) interpretation, where we propose the Minimum Reporting Standards for Computational Psychiatry Subtyping (MiR-CPS) to ensure methodological transparency; and (4) hierarchical clinical validation to benchmark subtype stability and utility. Beyond this core trajectory, we extend the framework to longitudinal trajectories and cross-diagnostic approaches to address temporal and diagnostic heterogeneity. This framework provides a reproducible roadmap for transitioning from raw high-dimensional data to clinically actionable subtypes, advancing evidence-based precision psychiatry.

Background:Vitamin D deficiency is prevalent among individuals with depression; however, clinical findings regarding this association have been inconsistent. Additionally, a significant proportion of depressed patients present with dyslipidemia, yet the interplay between vitamin D status, lipid metabolism, and depression remains poorly understood. We aimed to explore the role of vitamin D in depression and to investigate the potential associations between vitamin D status, lipid metabolism, and depressive symptoms. Methods:We recruited 412 first-episode, drug-naïve patients with depression and 180 age-matched healthy controls. Fasting venous blood samples were collected in the morning to quantify serum vitamin D and lipid profiles. Depressive symptoms were assessed on the day of blood collection using both the Patient Health Questionnaire-9 (PHQ-9) and the 17-item Hamilton Depression Rating Scale (HAMD-17). Spearman's rank correlation was employed to examine associations between serum vitamin D concentrations and depressive symptom severity. Binary logistic regression analysis was subsequently performed to identify potential risk factors for depression. Results:Compared with healthy controls, depressed patients had significantly lower serum vitamin D and high-density lipoprotein cholesterol (HDL-C) levels. This sex-specific pattern showed that male patients had lower vitamin D, while female patients had lower HDL-C. Spearman's correlation analysis revealed significant inverse correlations of vitamin D and triglyceride (TG) with PHQ-9 and HAMD-17 scores among depressed patients. Logistic regression analysis indicated that individuals with higher vitamin D levels had a reduced likelihood of depression compared with those with low vitamin D levels (adjusted odds ratio (OR) = 0.950, 95% confidence interval (CI): 0.920-0.982, p = 0.002). Similarly, subjects with elevated HDL-C levels were associated with a lower likelihood of depression relative to those with diminished HDL-C levels (adjusted OR = 0.317, 95% CI: 0.173-0.583, p < 0.001). Conclusion:Serum vitamin D and HDL-C levels were lower in patients with depression than in healthy individuals. Both vitamin D and HDL-C may be inversely associated with depression.

Major Depressive Disorder (MDD) is a complex psychiatric condition characterized by neuronal and functional disruptions in the dorsolateral prefrontal cortex (dlPFC). We integrated two single-nucleus RNA sequencing (snRNA-seq) datasets, GWAS summary data, and proteomic data from UK Biobank. We identified 273 MDD-associated expression quantitative trait loci (eQTL), while the single-cell disease-relevance score (scDRS) algorithm revealed that excitatory neurons, inhibitory neurons, and oligodendrocyte precursor cells (OPCs) are significantly associated with MDD. Non-negative matrix factorization (NMF) identified four meta-programs in neurons, reflecting functional impairments related to synaptic plasticity, neuronal connectivity, and epigenetic regulation. We characterized distinct pathological states of neuronal subtypes and identified CXCL14+ inhibitory neurons' role in stress perception. Plasma proteomic analyses provided independent peripheral support for five MDD associated risk genes whose encoded proteins showed prognostic value in CoxBoost models. Chronic unpredictable mild stress (CUMS) and chronic restraint stress (CRS) mouse models supported stress-induced alterations in prefrontal Cxcl14 expression. These findings provide a comprehensive understanding of MDD from a multi-omics perspective, offering potential diagnostic and therapeutic targets for MDD.

BACKGROUND:The underlying neurobiology of a recently described subtype of major depressive disorder (MDD), immunometabolic depression (IMD), characterized by low-grade inflammation and metabolic dysregulation, remains unclear. METHODS:We integrated multimodal neuroimaging (structural and functional magnetic resonance imaging [MRI]) and demographic data from 145 patients with MDD and 68 healthy control (HC) participants. After defining a composite IMD score derived from C-reactive protein, body mass index, triglycerides, and high-density lipoprotein cholesterol levels by principal component analysis, we implemented a binary classification task using machine learning to distinguish high IMD score (IMD group, n = 37) from low IMD score (non-IMD group, n = 37) subgroups. Structural MRI (cortical thickness and gray matter volume), resting-state functional MRI (regional homogeneity [ReHo]/fractional amplitude of low-frequency fluctuations [fALFF]), and demographic covariates were integrated as predictors. RESULTS:The multimodal model showed promise in distinguishing the IMD group from the non-IMD group (mean ± SD cross-validated area under the receiver operating characteristic curve [AUC] = 0.826 ± 0.098). Furthermore, its performance appeared somewhat more pronounced for within-MDD subtyping compared with differentiating MDD from HC participants (mean cross-validated AUCs of 0.647 ± 0.151 for non-IMD group vs. HC group and 0.741 ± 0.111 for IMD group vs. HC group), indicating subtype specificity. Key predictors included right amygdala volume and functional activity (ReHo/fALFF) in the hippocampus and midcingulate cortex. Clinically, the IMD group exhibited significantly higher anhedonia (p = .04), but lower somatic symptom scores (p < .05), compared with the non-IMD group. CONCLUSIONS:Our analysis shows that IMD is characterized by a distinct, multimodal neurodemographic signature involving corticolimbic circuitry. This signature demonstrates high specificity for unraveling MDD heterogeneity and is clinically linked to anhedonia, supporting the potential for biologically informed patient stratification.

Post-traumatic stress disorder (PTSD) may be linked to abnormalities in neural circuits that facilitate fear learning and memory processes. The precise degree to which this connection is influenced by genetic factors is still uncertain. This study aimed to investigate the genetic association between PTSD and its corresponding brain circuitry components. We conducted a meta-analysis using the summary of PTSD genome-wide association studies (GWAS) from multiple cohorts to enhance statistical power (sample size = 306,400). Based on the result, and utilizing the lifetime trauma events (LTE) trait as a comparation for PTSD, we investigated the genetic association of PTSD and LTE with 9 brain structure traits related to the brain circuitry (4 cortical, 2 subcortical, and 3 white matter) by various methodologies, including heritability tissue enrichment analysis, global and local genetic correlations, polygenic overlap analysis, and causal inference. As a result, we discovered the enrichment of heritability for PTSD within circuitry-relevant brain regions such as the cingulate cortex and frontal cortex, and we identified genetic correlations between PTSD and these brain regions. We have observed a polygenic overlap and a total of 31 novel jointly significant genetic loci (conjunction FDR < 0.05). These loci are involved in the process of DNA damage and repair as well as the pathway of neurodegenerative diseases. We also identified a potential causal relationship between PTSD and the surface area of the frontal pole. Our findings offer a valuable understanding of the genetic mechanisms underlying PTSD and its associated brain circuitry.

Background: Early identification of individuals at high risk for depression is essential for effective implementation of interventions. This study utilized the UK Biobank database to construct an individual depression risk score using nomogram and explored the potential of traditional risk factors and routine biochemical markers for the auxiliary diagnosis of individual depression. Methods: A total of 369,407 participants were included in the study and divided into training and testing sets. LASSO regression was employed to select predictive variables for depression from 16 traditional risk factors and 28 routine biochemical markers. Following variable selection, two multivariable logistic regression models were constructed. Nomograms were then generated to visualize the relationships between these variables and depression risk, and to facilitate the calculation of individual depression risk scores. Results: Twelve traditional risk factors and nine biochemical markers were selected for model building. Model 1, using only traditional risk factors, achieved the area under the curve (AUC) of 0.913 (95 % CI: 0.910-0.915), while Model 2, incorporating both traditional and routine biochemical markers, yielded an AUC of 0.914 (95 % CI: 0.912-0.917). Based on optimal cut-off values, Model 1 exhibited a sensitivity of 81.99 % and a specificity of 83.76 %, while Model 2 demonstrated a sensitivity of 81.54 % and a specificity of 84.31 %. Limitations: External validation is still needed to confirm the model's generalizability. Conclusions: While the depression risk scoring model built using traditional risk factors effectively identifies highrisk individuals for depression and demonstrates good clinical performance, incorporating routine biochemical markers did not significantly improve the model's performance.

BACKGROUND:There is still no clinical biomarker to diagnose depression. Given the complexity of a multifactorial disease like depression, a single biomarker is unlikely to capture the full heterogeneity of the disease and be applicable in clinical practice, mandating biomarker panels representing several biological targets. METHODS:We examined two proteomic datasets from the UK Biobank: the Cox dataset (N = 19,632) and the diagnostic dataset (N = 19,374). Cox proportional hazards regression modeling was used to identify potential biomarkers of depression within the Cox dataset, and subsequently the diagnostic accuracy of these candidate biomarkers was validated in the diagnostic dataset. Employing four distinct machine learning algorithms and LASSO regression model, we discovered the most effective biomarker panel for depression, assessing model performance through five-fold cross-validation and the area under receiver operating characteristic curve (AUC). RESULTS:Over a mean follow-up of 14 years, 46 plasma proteins were significantly associated with depression after adjusting for confounders. These depression-related proteins were involved in immune-related processes and pathways. When combined with traditional risk factors, the six blood protein biomarkers identified in this study achieved 75.4 % diagnostic accuracy for depression, which was similar to using 46 (maximum 74.9 %) and 2911 (maximum 75.9 %) proteins. CONCLUSIONS:Our findings suggest the potential clinical use of proteomic biomarkers as complementary information for early and population-based detection of depression. With appropriate clinical and experimental validation, the identified depression-related proteins may be used as a biomarker panel for the screening and prediction of depression.

BACKGROUND:Cortical morphometry is an intermediate phenotype that is closely related to the genetics and onset of major depressive disorder (MDD), and cortical morphometric networks are considered more relevant to disease mechanisms than brain regions. We sought to investigate changes in cortical morphometric networks in MDD and their relationship with genetic risk in healthy controls. METHODS:We recruited healthy controls and patients with MDD of Han Chinese descent. Participants underwent DNA extraction and magnetic resonance imaging, including T 1-weighted and diffusion tensor imaging. We calculated polygenic risk scores (PRS) based on previous summary statistics from a genome-wide association study of the Chinese Han population. We used a novel method based on Kullback-Leibler divergence to construct the morphometric inverse divergence (MIND) network, and we included the classic morphometric similarity network (MSN) as a complementary approach. Considering the relationship between cortical and white matter networks, we also constructed a streamlined density network. We conducted group comparison and PRS correlation analyses at both the regional and network level. RESULTS:We included 130 healthy controls and 195 patients with MDD. The results indicated enhanced connectivity in the MIND network among patients with MDD and people with high genetic risk, particularly in the somatomotor (SMN) and default mode networks (DMN). We did not observe significant findings in the MSN. The white matter network showed disruption among people with high genetic risk, also primarily in the SMN and DMN. The MIND network outperformed the MSN network in distinguishing MDD status. LIMITATIONS:Our study was cross-sectional and could not explore the causal relationships between cortical morphological changes, white matter connectivity, and disease states. Some patients had received antidepressant treatment, which may have influenced brain morphology and white matter network structure. CONCLUSION:The genetic mechanisms of depression may be related to white matter disintegration, which could also be associated with decoupling of the SMN and DMN. These findings provide new insights into the genetic mechanisms and potential biomarkers of MDD.

Major depressive disorder (MDD) is a significant contributor to global disease burden, with somatic symptoms frequently complicating its diagnosis and treatment. Recent advances in neuroimaging have provided insights into the neurobiological underpinnings of MDD, yet the role of the glymphatic system remains largely unexplored. This study aimed to assess glymphatic function in drug-naïve somatic depression (SMD) patients using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index. A total of 272 participants, including somatic depression patients (SMD), pure depression (PMD), and healthy controls (HC), were enrolled. We collected T1-weighted (T1w) and DTI (diffusion tensor image) scans and clinical data of all participants. The DTI-ALPS indices were calculated and compared among three groups. Gray matter regions associated with the DTI-ALPS index were identified by voxel-based morphometry analysis (VBM), revealing a cluster located in the thalamus. Then, we performed partial correlation analyses to further investigate the relationships between the DTI-ALPS index, thalamic volume, and clinical data. The DTI-ALPS index was significantly higher in the MDD group compared to the HC group, particularly in the SMD group. Furthermore, a significant positive correlation was observed between the DTI-ALPS index and thalamic volume, with lower DTI-ALPS values associated with reduced thalamic volumes, especially in the SMD group. Our findings suggest heightened glymphatic activity in MDD patients, especially SMD patients, and a potential link between glymphatic function and thalamic vulnerability. Therefore, the thalamus' vulnerability to glymphatic system function may play a role in the pathophysiology of depression, particularly somatic depression, suggesting that both the glymphatic system and the thalamus could serve as potential therapeutic or intervention targets for future treatments.

Background: Dietary patterns are strongly linked to the risk of major depressive disorder (MDD). However, research on the relationship between dietary patterns and MDD with suicidal ideation (MDD + SI) are limited. The Healthy Eating Index (HEI)-2015, Dietary Inflammatory Index (DII), Comprehensive Dietary Antioxidant Index (CDAI), Oxidative Balance Score (OBS), and Dietary Index for Gut Microbiota (DI-GM) are five validated tools for assessing dietary patterns based on inflammation, antioxidant capacity, and gut microbiota diversity. This study aims to investigate the association between these dietary indices and MDD + SI. Methods: A total of 23,621 participants from the 2007-2020 National Health and Nutrition Examination Survey were included in this study. MDD and SI were assessed using the PHQ-9. Weighted multivariable logistic regression, subgroup analyses, and restricted cubic spline (RCS) models were applied to analyze the relationships between five dietary indices and the risks of MDD and MDD + SI. Results: All five dietary indices showed associations with MDD to varying degrees; however, only DI-GM exhibited a significant negative association with MDD + SI after adjustment for confounding factors. Subgroup and stratified linear trend analyses revealed that this association was stronger among former smokers, obese individuals and those with hypertension or diabetes. RCS analysis showed a significant non-linear relationship between DI-GM and MDD, while a significant linear dose-response relationship was observed for DI-GM and MDD + SI. Limitations: Cross-sectional study designs cannot establish causality. Conclusion: The findings of this study revealed a significant association between DI-GM and MDD + SI. Dietary interventions that promote gut microbiota diversity may help reduce the risk of MDD + SI.

Major Depressive Disorder (MDD) is a complex psychiatric condition characterized by neuronal and functional disruptions in the dorsolateral prefrontal cortex (dlPFC). We integrated MDD-associated multi-omics data, including two single-cell RNA sequencing (scRNA-seq) datasets, GWAS summary data, and depression-related proteomic data from UK Biobank. We identified 273 MDD-associated eQTL, while the single-cell disease-relevance score (scDRS) algorithm revealed that excitatory neurons, inhibitory neurons, and oligodendrocyte precursor cells (OPCs) are significantly associated with MDD. Non-negative matrix factorization (NMF) identified four meta-programs (MPs) in excitatory and inhibitory neurons, reflecting functional impairments related to synaptic plasticity, neuronal connectivity, and epigenetic regulation. Differentiation trajectory analysis revealed distinct pathological states of neuronal subtypes. We identified CXCL14+ inhibitory neurons and investigated their role in stress perception and intercellular communication. Plasma proteomics data validated 33 risk genes, five of which encoded proteins predictive of patient survival in CoxBoost regression models. These findings provide a comprehensive understanding of the cellular and molecular underpinnings of MDD, offering potential diagnostic and therapeutic targets and advancing the development of precision medicine approaches for MDD. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by grants from the National Natural Science Foundation of China (U21A20364) and National Key Research and Development Project of China (Grant No. 2024YFC3308400). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All data were obtained from the publicly accessible GEO database under accession number GSE213982 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE213982). Brain-region-specific eQTL results can be accessed via Supplementary File S1. The GWAS summary data can be accessed via martinjzhang/scDRS. Proteomics data can be accessed via UK Biobank at https://www.ukbiobank.ac.uk/. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors.

Depression is clinically and biologically heterogeneous, mandating classification strategies for personalized medicine. This study explored depression subtypes using metabolomics data from the UK Biobank and validated the subtypes in the Whitehall II cohort. The five-step analysis included: (1) identification of distinct subtypes using non-negative matrix factorization (NMF) and four machine learning algorithms; (2) genome-wide association studies (GWAS) to examine associations across subtypes and controls; (3) comparison of clinical characteristics across subtypes; (4) development of 24 subtype-specific diagnostic models and validation in an independent cohort; and (5) construction and comparison of metabolic networks across subtypes. Cluster analysis of 249 metabolomic indicators in individuals with current depressive episodes (n = 7,945) identified three metabolic subtypes of depression. Subtype 1 was characterized by fatty acid dysregulation, subtype 3 had a hyperlipidemia phenotype, while subtype 2 displayed an intermediate phenotype. Metabolic subtypes were not associated with SNPs. Diagnostic models built using the 249 metabolic indicators yielded the area under the curve (AUC) of 0.644 for the total depression sample and 0.785, 0.817, and 0.942 for subtypes 1, 2, and 3, respectively. Twenty-three additional diagnostic models based on combinations of metabolic indicators improved performance by 12.8-39.6% over a binary classification model. Metabolic networks significantly differed between each subtype and healthy controls but not between the total depressed group and controls. This study defines distinct metabolic subtypes of depression. Future research should combine high-throughput metabolomics with prospectively established depression cohorts and tailored interventions to explore subtype-specific diagnostic and therapeutic biomarkers.

The high prevalence of recurrent depressive episodes (RDE) among patients with major depressive disorder (MDD) has resulted in substantial personal, societal, and financial burdens. Although many studies have explored the factors associated with RDE, most have important methodological limitations. This study aims to examine the factors associated with RDE within two years among young adults with first-episode MDD. A total of 324 patients aged 18 to 30 years with a first episode of MDD completed both the baseline assessment and the two-year follow-up. Personal, psychosocial, and illness-related variables were assessed at baseline. The status of RDE and medication use were evaluated at follow-up. Network analysis and logistic regression were performed to examine variables associated with RDE. The two-year prevalence of RDE among patients with first-episode MDD was 42.28

Genome-wide association studies (GWASs) of bipolar disorder (BD) have predominantly included individuals of European (EUR) ancestry, underrepresenting non-EUR populations and limiting insight into disease mechanisms. Here we performed a GWAS of BD in Han Chinese individuals (5,164 cases and 13,460 controls) and conducted comparative and integrative analyses with independent East Asian (EAS, 4,479 cases and 75,725 controls) and EUR (59,287 cases and 781,022 controls) cohorts from the PGC4 GWAS. Our GWAS in EAS ancestry identified two genome-wide significant risk loci, including variants at the major histocompatibility complex (MHC) class II region. Incorporating EAS data into trans-ancestry GWAS revealed 93 significant loci (23 novel). Heritability enrichment analyses implicated a variety of neuronal cell types. Multidimensional post-GWAS prioritization identified 39 high-confidence risk genes, of which 15 were differentially expressed in the brains of patients with BD, 12 modulated BD-relevant behaviors in mice and 18 are pharmacologically tractable. This work advances understanding of the biological underpinnings of BD and provides direction for future research in underrepresented populations.

Low vitamin D (vitD) levels are consistently associated with an increased risk of depression. However, the biological mechanisms underlying this relationship and potential shared genetic overlap remain elusive. We investigated the genetic overlap and causal relationships between depression (N = 589,356) and vitD levels (N = 417,580) using genome-wide association study (GWAS) summary statistics. We performed genome-wide and local genetic correlation analyses, followed by quantification of polygenic overlap variants. Shared genetic loci were identified and mapped to genes, which were further analyzed through gene expression and lifespan brain expression trajectory analyses. Bidirectional causal relationships were examined using multiple Mendelian randomization approaches. We observed significant negative genetic correlations (rg = -0.079) and identified genetic overlap (N = 410 variants). Genes mapped to the 13 shared loci showed opposing expression patterns. Tissue- and cell-specific functional enrichment analyses revealed significant signals related to brain development, with distinct patterns emerging between fetal development and adulthood. Shared genes (TRMT61A, ITIH4, RASGRP1, CTNND1, HERC1, IP6K1, FURIN ESR1, ZMYND and GRM5) exhibited notable expression variation in the brian throughout the lifespan, aligning with functional enrichment findings. Our findings elucidate the shared biological mechanisms underlying the relationship between vitD and depression, suggesting that vitD play an important role in the development of depression through altered early neurodevelopmental processes.

ObjectiveThis study aimed to investigate the independent or synergistic effects of evening chronotype and poor sleep quality on cognitive impairment in patients with major depressive disorder (MDD).MethodsA cross-sectional study was conducted on 249 individuals diagnosed with MDD, recruited from the Mental Health Center of Renmin Hospital of Wuhan University. Chronotype preference was assessed using the reduced Horne and Ostberg Morningness - Eveningness Questionnaire (rMEQ), while sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI). Cognitive function was evaluated through the Digit Symbol Substitution Test (DSST), defining impairment as a DSST score ≤ 56 (the lowest quartile of the cohort). Univariate analysis and logistic regression models were employed to explore the factors associated with cognitive impairment, focusing on the potential interactive effects of evening chronotype and poor sleep quality.ResultsOf the 249 subjects recruited, about 41% were classified as evening chronotype. These individuals exhibited poorer sleep quality and more severe depressive symptoms compared to non-evening chronotype (p < 0.01). Univariate analysis revealed that first episode status, Hamilton Depression Rating Scale (HAMD-17) scores, evening chronotype, and poor sleep quality were significantly associated with cognitive impairment (p < 0.05). Multivariate logistic regression analysis further demonstrated that the co-existence of evening chronotype and poor sleep quality significantly increased the likelihood of cognitive impairment (adjusted odds ratio [AdjOR] = 2.65, 95% confidence interval [CI] = 1.09–6.45, p < 0.05).ConclusionOur findings suggest that evening chronotype, poor sleep quality, and their interaction are important contributors to cognitive impairment in patients with MDD, alongside the severity of depression and first episode status. These results emphasize the need for integrated approaches targeting circadian rhythm disruptions and sleep disturbances in the treatment of cognitive dysfunction in MDD.

BACKGROUND: Many metabolomics studies of depression have been performed, but these have been limited by their scale. A comprehensive in silico analysis of global metabolite levels in large populations could provide robust insights into the pathological mechanisms underlying depression and candidate clinical biomarkers. METHODS: Depression-associated metabolomics was studied in 2 datasets from the UK Biobank database: participants with lifetime depression (N = 123,459) and participants with current depression (N = 94,921). The Whitehall II cohort (N = 4744) was used for external validation. CatBoost machine learning was used for modeling, and Shapley additive explanations were used to interpret the model. Fivefold cross-validation was used to validate model performance, training the model on 3 of the 5 sets with the remaining 2 sets for validation and testing, respectively. Diagnostic performance was assessed using the area under the receiver operating characteristic curve. RESULTS: In the lifetime depression and current depression datasets and sex-specific analyses, 24 significantly associated metabolic biomarkers were identified, 12 of which overlapped in the 2 datasets. The addition of metabolic features slightly improved the performance of a diagnostic model using traditional (nonmetabolomics) risk factors alone (lifetime depression: area under the curve 0.655 vs. 0.658 with metabolomics; current depression: area under the curve 0.711 vs. 0.716 with metabolomics). CONCLUSIONS: The machine learning model identified 24 metabolic biomarkers associated with depression. If validated, metabolic biomarkers may have future clinical applications as supplementary information to guide early and population-based depression detection.