Shared Genetic Underpinnings of Gray Matter Volume Alterations and Metabolic Traits in Major Depressive Disorder

Shared Genetic Underpinnings of Gray Matter Volume Alterations and Metabolic Traits in Major Depressive Disorder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Shared Genetic Underpinnings of Gray Matter Volume Alterations and Metabolic Traits in Major Depressive Disorder Xiangzheng Wu, Piaoran Wang, Lang Xu, Qingwei Guo, Yurong Jiang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7971333/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Feb, 2026 Read the published version in BMC Psychiatry → Version 1 posted 6 You are reading this latest preprint version Abstract Background Major depressive disorder (MDD) is linked to extensive gray matter volume (GMV) reductions and frequently co-occurs with metabolic dysfunction. However, the shared genetic basis linking neurostructural abnormalities and metabolic traits remains poorly understood. Methods Using a coordinate-based meta-analytic framework, we synthesized findings from 57 voxel-based morphometry (VBM) studies to characterize GMV alterations in MDD. Spatial transcriptomic correlation analysis was performed using the Allen Human Brain Atlas to identify genes associated with these alterations. In parallel, conjunctional false discovery rate (conjFDR) analysis was applied to genome-wide association study (GWAS) summary statistics from MDD and five metabolic traits—glucose, hemoglobin A1c (HbA1c), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG)—to identify pleiotropic loci. Functional characterization of the intersecting genes was conducted by applying gene ontology enrichment and protein–protein interaction (PPI) analyses. Results We identified consistent GMV reductions in the left superior temporal gyrus, inferior frontal gyrus, and insula. A total of 2,585 genes were spatially correlated with GMV alterations. ConjFDR analysis revealed 20–195 pleiotropic loci across metabolic traits and MDD. Gene-level overlap analysis identified 13–73 shared genes per trait, with FADS2 emerging as a common gene across all five traits. Functional annotation highlighted pathways related to lipid metabolism and synaptic signaling. Conclusion This integrative multi-omics study reveals shared genetic mechanisms linking brain structure in MDD with systemic metabolic traits. FADS2 may serve as a molecular hub underlying this convergence, offering potential targets for future mechanistically informed interventions. Major depressive disorder gray matter volume metabolic traits gene expression transcriptome-neuroimaging association Allen Human Brain Atlas. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Major depressive disorder (MDD) represents a highly prevalent psychiatric condition that leads to significant disability, characterized by long-lasting depressive symptoms, cognitive deficits, and declines in overall functioning.Epidemiological data from the World Health Organization (WHO) indicate that MDD affects an estimated 280 million individuals worldwide, with a prevalence of about 5% in adults and 5.7% in older adults[ 1 ]. MDD contributes significantly to the global burden disease, accounting for nearly 50% of all suicide deaths worldwide[ 2 ]. Although the pathogenesis of MDD is shaped by the interplay of genetic predisposition and environmental influences.[ 3 , 4 ], the underlying neurobiological mechanisms remain incompletely understood. Gray matter volume (GMV) alterations have been widely observed in MDD using voxel-based morphometry (VBM). Regional GMV reductions have been reported in the anterior cingulate cortex, hippocampus, superior temporal gyrus, inferior temporal gyrus, inferior frontal gyrus, right amygdala and thalamus[ 5 – 7 ]. However, findings across studies on GMV alterations in MDD are often inconsistent, likely due to limited small sample size, and methodological heterogeneity. Recent neuroimaging meta-analyses have demonstrated highly reproducible GMV reductions in the hippocampus, superior temporal gyrus, and inferior frontal gyrus of MDD patients, highlighting them as potential imaging markers for disease progression[ 8 , 9 ]. Despite these findings, the molecular mechanisms underlying these structural alterations remain largely unclear. Beyond structural brain abnormalities, metabolic dysregulation—including disturbances in glucose and lipid metabolism—has been increasingly recognized as a frequent comorbidity in MDD. Epidemiological evidence indicates that MDD is associated with an elevated risk of metabolic syndrome, which includes hyperglycemia, insulin resistance, and dyslipidemia[ 10 – 12 ]. Additionally, metabolically unhealthy obesity is commonly observed in MDD populations[ 13 ]. Emerging evidence suggests that these metabolic disturbances are not merely peripheral features, but may contribute to cognitive deficits and brain structural and functional abnormalities observed in MDD[ 14 – 16 ]. However, despite the known co-occurrence of GMV alterations and metabolic abnormalities in MDD, the underlying shared genetic mechanisms remain largely elusive. Several risk loci related to MDD[ 17 ] and metabolic traits[ 18 – 20 ] have been discovered via genome-wide association studies (GWAS). While conventional GWAS have enhanced our understanding of genetic architecture, they are limited in identifying pleiotropic effects across complex phenotypes, such as comorbid psychiatric and metabolic conditions. To address this, conjunctional false discovery rate (conjFDR) analysis has been developed as a robust statistical approach to detect shared genetic variants across complex traits[ 21 – 23 ]. Meanwhile, transcriptome-neuroimaging association analysis has emerged as a powerful interdisciplinary approach for linking spatial gene expression patterns with neuroimaging phenotypes[ 24 ]. The Allen Human Brain Atlas (AHBA) provides high-resolution, spatially mapped gene expression data across the human brain, enabling the exploration of transcriptional correlates of regional brain alterations[ 24 – 26 ]. Although prior studies utilizing AHBA have begun to uncover gene expression gradients associated with MDD[ 27 , 28 ], no studies to date have systematically linked these patterns to metabolic genetic variations. In this study, we propose a multi-omics integrative framework to unravel the molecular basis of GMV alterations in MDD and their shared genetic mechanisms with metabolic dysregulation. First, we performed a coordinate-based neuroimaging meta-analysis to identify reproducible GMV alterations in MDD. Then, transcriptome-neuroimaging association analysis was used to identify genes spatially associated with MDD-related GMV alterations. In parallel, we conducted a pleiotropy-informed conjFDR analysis to identify shared genetic loci between MDD and five metabolic traits. Finally, intersecting gene sets from both pipelines were subjected to enrichment analysis to explore biological pathways of interest. Our work highlights a novel systems-level perspective on the convergence between neurostructural and metabolic dimensions of MDD, and identifies key molecular candidates potentially relevant for biomarker discovery and therapeutic development. A schematic overview of the analysis framework is shown in Fig. 1 . 2. Materials and methods 2.1. Search strategy and selection criteria We searched the PubMed and Web of Science databases for articles available before September 2024 to ensure comprehensive coverage of the literature. We used the following search terms: (("gray matter volume" OR "GMV" OR "gray matter" OR "grey matter" OR "grey matter volume") AND ("major depressive disorder" OR "depression" OR "depress" OR "depressive " OR "depressed" OR "major depression" OR "major depressive episode" OR "depressive disorder" OR "unipolar depression" OR "unipolar disorder" OR "melancholia")). Studies that satisfied the following conditions were included: (1) English-language publications in peer-reviewed journals; (2) employed VBM to estimate whole-brain GMV[ 29 ]; (3) participants were required to have a diagnosis of MDD based on standardized criteria, including DSM-III-R, DSM-IV, DSM-5, ICD-10, or the MINI, and studies also needed to include a healthy control (HC) group; (4) reported peak coordinates of gray matter volume alterations in either the Montreal Neurological Institute (MNI) or Talairach reference space, or indicated null findings; (5) employed a consistent whole-brain statistical threshold. (6) limited participants to adult populations. Exclusion criteria included the following conditions: (1) review articles, and theoretical papers; (2) studies that did not report peak coordinates or yielded no statistically significant results; (3) studies involving participants with comorbid psychiatric or neurological disorders; (4) duplicate publications or repeated analyses from the same sample. In cases where participants were reported in multiple publications, we retained the study with the largest sample size. Articles describing more than one independent patient cohort were regarded as providing separate datasets. For longitudinal designs, analyses were restricted to baseline patient–control comparisons. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)[ 30 ], this meta-analysis was carried out to ensure methodological rigor. The study selection workflow was presented in Fig. 2 . 2.2. Neuroimaging meta-analysis of GMV alterations in patients with MDD Meta-analysis was performed to investigate GMV alterations in patients with MDD, aiming to systematically assess disease-related structural brain alterations. The analysis was conducted using SDM-PSI software (version 6.23, available for download on the SDM website). This method integrates multiple advanced processing techniques, including multiple imputation of peak coordinates and subject-level maps, random-effects modeling, and voxel-wise meta-analyses of imputed study images. Final estimates were combined using Rubin's rule, which enhances statistical power and reduces methodological heterogeneity across studies[ 31 ]. To account for multiple comparisons, statistical significance was assessed using threshold-free cluster enhancement (TFCE) with family-wise error (FWE) correction, applying a voxel-level threshold of p < 0.05 and requiring a minimum cluster size of 10 voxels. [ 32 , 33 ]. Heterogeneity among studies was evaluated with Cochran’s Q statistic and the I² statistic [ 34 ], and publication bias was examined using Egger’s test [ 35 ]. For exploratory analysis, meta-regression was conducted to examine the association between clinical variables (e.g., illness duration, mean age) and GMV alterations. Additionally, subgroup analyses were carried out to assess the consistency of findings across studies stratified by clinical or methodological characteristics, thereby improving interpretability and robustness. Detailed technical methods, statistical parameters, and data processing procedures can be found in the Supplementary Methods . 2.3. Transcriptome-neuroimaging association analysis To investigate the spatial transcriptional correlates of MDD-related GMV alterations, we performed a transcriptome–neuroimaging association analysis using data from the AHBA, a publicly available dataset comprising genome-wide expression profile from 3,702 brain tissue samples across 6 adult human donors, covering 20,737 genes and 58,692 probes[ 36 ]. Among the donors, two provided bilateral hemisphere samples, while the remaining four included only left hemisphere data. Demographic details are displayed in Table S1 . Gene expression preprocessing was carried out via the abagen toolbox[ 37 ] ( https://www.github.com/netneurolab/abagen ), adhering to a standardized procedure: Firstly, the microarray probes were re-annotated based on the latest annotation file[ 38 ]. Subsequently, probes with expression values fell beneath background signals in ≥ 50% of the samples were removed. For multiple probes corresponding to a single gene, the probe with the highest differential stability was selected as the representative. The preprocessed expression data were standardized across genes by the scaled robust sigmoid (SRS) method to eliminate the influence of donor-to-donor variation. Given the significant heterogeneity in gene expression across different brain regions (cortex, subcortex/brainstem, cerebellum), we further implemented region-specific normalization processing. We then performed partial least squares (PLS) regression analysis[ 39 ] to identify multivariate association between the regional gene expression profiles (predictive variables, n = 15,633 genes across 1,159 regions) and the case-control GMV effect size value maps (response variables). Both the gene expression matrix and GMV maps were z -score standardized. The latent components were established through singular value decomposition, and ranked by explained variance. Components with an explained variance > 10% and passing the permutation test (permutation p < 0.05) were retained. To ensure spatial specificity and statistical robustness, we generated 1,000 spatial null models using the BrainSMASH toolkit (v0.1.3)[ 40 ]. The stability of gene weights was estimated through 1,000 bootstrap resampling iterations, and standardized weights ( z = weight/standard error) were calculated. To identify statistically significant genes for downstream analysis, bonferroni correction was employed. 2.4. Identification of shared genes between MDD and metabolic traits using conjFDR To investigate genes jointly associated with MDD and metabolic traits, We conducted a conjFDR analysis based on publicly accessible GWAS summary statistics. For MDD, we used data from the largest available GWAS to date. To avoid overlapping samples, participants from the UK Biobank (UKBB) were excluded, leaving a final dataset of 357,636 cases and 1,281,936 controls of European ancestry [ 17 ]. GWAS summary statistics for five metabolic traits, including glucose ( N = 459,772), HbA1c ( N = 146,864), HDL-C ( N = 1,320,016), LDL-C ( N = 1,320,016), TG ( N = 1,320,016), were derived from the UKBB datasets of European ancestry[ 18 , 19 ]. Details of all GWAS datasets are provided in the Supplementary Methods. The conjFDR analysis method, built on the conditional false discovery rate (condFDR) framework, employs empirical Bayesian statistics to jointly analyze SNP associations across two phenotypes[ 41 , 42 ]. Briefly, condFDR re-ranks SNP association statistics of the primary phenotype conditioned on the strength of association with the secondary phenotype. On this basis, the inverse condFDR value can be calculated by swapping the roles of the primary and secondary phenotypes. The conjFDR value is defined as the maximum of the original condFDR and the inverse condFDR. This method ensures the reliability of the common association signals through a dual screening mechanism. We applied a significance threshold of conjFDR < 0.05, consistent with prior studies[ 43 , 44 ]. To visualize pleiotropic enrichment, conditional quantile-quantile (Q-Q) plots were generated. In these plots, SNPs stratified by their level of significance in the secondary phenotype ( p < 0.100, p < 0.010, p < 0.001) showed systematic leftward deflection in the primary phenotype, indicating cross-trait genetic enrichment. To eliminate the interference of complex linkage disequilibrium (LD) on the analysis, we systematically excluded three high-LD regions before constructing the cond/conjFDR model: the extended major histocompatibility complex on chromosome 6 (25,119,106–33,854,733), the 8p23.1 locus on chromosome 8 (7,200,000–12,500,000), and the region adjacent to the MAPT gene on chromosome 17 (40,000,000–47,000,000)[ 45 ]. Significant loci were annotated and mapped using the Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) genome analysis platform[ 46 ]. The specific process is as follows: (1) Independence determination of loci: SNP with conjFDR < 0.05 and LD r² < 0.6 are selected as independent significant loci, where r² < 0.1 is defined as the lead SNP; (2) Boundaries definition: SNPs with LD r² ≥ 0.6 and located in the adjacent region of the independent significant SNPs (conjFDR < 0.1) are included in the candidate set, and the boundaries of the genotypes are defined accordingly; (3) Locus merging: adjacent loci with a spatial distance of < 250 kb are merged, and the final dominant SNP is determined by the minimum FDR value; (4) Function annotation: candidate SNPs are functionally predicted using CADD pathogenicity scores (v1.6)[ 47 ], RegulomeDB scores[ 48 ], and chromatin state information[ 49 ]; (5) Gene mapping: positional mapping was used to determine the shared genes between MDD and metabolic traits. 2.5. Shared gene identification To examine shared genetic architecture, we intersected the gene set MDD-related GMV alterations (identified via transcriptome-neuroimaging association analysis) and the conjFDR-derived gene set shared between MDD and each metabolic marker. This yielded five gene sets, corresponding to genes jointly implicated in MDD-related GMV alterations and MDD-metabolic marker pairs of GLU, HbA1c, HDL-C, LDL-C, and TG, respectively. Additionally, we derived the intersection across all five sets to investigate a core group of pleiotropic genes potentially underlying a common mechanism linking metabolic dysregulation and neurodegeneration in MDD. To facilitate visualization of gene overlaps, we created a Venn diagram. 2.6. Functional enrichment analysis Gene enrichment analysis was conducted to functional annotate the intersecting gene sets and elucidate their roles in biological processes[ 50 ]. Gene Ontology (GO) database ( https://geneontology.org/ ) systematically described across a biological process, molecular function and cellular composition of Gene functions[ 51 ]. GO enrichment analysis was performed using the clusterProfiler package in R, and multiple comparison correction was applied using the false discovery rate (FDR) method ( p < 0.05)[ 52 ]. In addition, a protein-protein interaction (PPI) network encompassing the shared genes was established via STRING (version 12.0, https://string-db.org/ ), adopting a medium confidence threshold of 0.4.[ 53 ]. 3. Results 3.1. Included studies and sample characteristics As shown in Fig. 2 , 57 studies were included, encompassing 4,393 MDD patients and 4,109 HCs. Demographic and clinical characteristics, along with the relevant imaging and technical parameters, are summarized in Table S2 . 3.2. Neuroimaging meta-analysis reveals GMV alterations in MDD The neuroimaging meta-analysis revealed significant GMV reductions in the left superior temporal gyrus, the left inferior frontal gyrus and the left insula in individuals with MDD compared to HCs (Fig. 3 A and Table 1 ). No regions showed significantly increased GMV. Subsequently, no significant heterogeneity was detected among the studies based on Cochran’s Q test and I² statistics. The Egger’s test further confirmed that the cluster did not exhibit significant publication bias ( Table S5 ). Results from meta-regression and subgroup analyses are reported in the Supplementary Results and Tables S3–S4 . Table 1 Results of meta-analysis between MDD patients and HCs. Brain regions SDM-Z p value Peak MNI coordinates Cluster size Heterogeneity test Egger’s test X Y Z (voxels) Q ( p value) I 2 (%) p value MDD < HCs Left insula/Superior temporal gyrus/Inferior frontal gyrus -7.376 0.001 -48 4 -4 1721 37.444(0.988) 0.890 0.738 Abbreviations : HCs, healthy controls; MDD, major depressive disorder; MNI, Montreal Neurological Institute; Q , Cochran’s Q statistic; SDM, seed-based d mapping. 3.3. Transcriptome-neuroimaging association analysis Using PLS regression on AHBA gene expression dataset, we found that the first latent component (PLS1) explained more than 10% of the variance in GMV abnormalities ( Figure S2 ). Notably, the PLS1 component score was significantly correlated positively with the GMV z-map (Pearson's r = 0.37, p = 0.02; Fig. 3 B). The spatial autocorrelation-preserved permutation test further validated the robustness of the PLS1 component, demonstrating that the explained variance of PLS1 significantly exceeded that of the null distribution generated by surrogate maps (permutation p = 0.0029). Finally, we identified a total of 2,585 genes significantly associated with PLS1 (Bonferroni-corrected p < 0.05), of which 1,142 had positive weights (PLS1+) and 1,443 had negative weights (PLS1−), suggesting differential transcriptional regulation underlying GMV alterations in MDD. The complete gene lists and statistical details are shown in Table S7-S8 . 3.4. Shared genes between MDD and metabolic traits revealed by conjFDR The conditional Q-Q plots were used to evaluate the pleiotropic enrichment effect between MDD and five metabolic traits (Figs. 4 A-B). With the threshold of conjFDR < 0.05, we pinpointed the following shared genomic loci: 20 loci for MDD-GLU, 20 loci for MDD-HbA1c, 195 loci for MDD-HDL-C, 83 loci for MDD-LDL-C, and 186 loci for MDD-TG (Fig. 4 C). Details of these independent genomic loci and functional annotation results are provided in Table S9-S18 . Further, the candidate SNPs were localized to 100 (MDD-glucose), 129 (MDD-HbA1c), 533 (MDD-HDL-C), 360 (MDD-LDL-C), and 603 (MDD-TG) potential pathogenic genes through a multi-gene mapping strategy ( Table S19-S23 ). We intersected these genes with the 2,585 significant genes associated with MDD-related GMV alterations, identifying 13 genes shared with GLU, 14 with HbA1c, 73 with HDL-C, 37 with LDL-C and 70 with TG ( Table S24-S28 ). In addition, we cross-validated these five groups of genes and found that there is a common regulatory gene FADS2 . To visualize these results, we created a Venn diagram (Fig. 3 C). 3.5. Enrichment analysis We performed GO enrichment analysis in R to investigate the biological functions of the previously identified genes. The gene list of HbA1c is mainly concentrated in the synaptic function, presynaptic active zone and GABAergic synapse. In addition, the genes are also involved in oxidoreductase activity and phosphoinositol kinase activity, suggesting that they may affect neural function by regulating oxidative stress and signal transduction (Fig. 5 A, Table S29 ). The results of HDL-C showed that these genes were significantly enriched in biological processes such as synaptic function, neuronal signaling and postsynaptic membrane structure ( Fig. 5 B, Table S30) . The PPI network of 37 LDL-C–associated overlapping genes featured eight edges, significantly surpassing the one edge predicted under the null hypothesis ( p = 1.37 × 10⁻⁴, Fig. 5 C). Similarly, the HDL-C network comprising 73 overlapping genes displayed 21 edges, exceeding the expected nine. Within this network, GRIA1 served a pivotal role. ( p = 3.38×10 − 4 , Fig. 5 D). 4. Discussion This study is the first to systematically explore the shared genetic architecture underlying GMV alterations in MDD with glucose- and lipid-related metabolic traits. We combined neuroimaging meta-analysis and transcriptome-neuroimaging association analysis to detect genes spatially linked to GMV reductions observed in MDD. Leveraging large-scale GWAS summary statistics and applying the conjFDR approach, we further pinpointed pleiotropic genes associated with MDD and five key metabolic traits. Our cross-analysis revealed substantial genetic overlap, identifying 13 genes shared between MDD-related GMV alterations and GLU, 14 with HbA1c, 73 with HDL-C, 37 with LDL-C, and 70 with TG. Notably, FADS2 emerged as a common gene across all five traits, suggesting convergent pathways linking metabolic dysregulation and structural brain abnormalities in MDD. Neuroimaging meta-analysis identified that GMV alterations in MDD patients are distributed across multiple brain regions, including the superior temporal gyrus, inferior frontal gyrus, and insula. The superior temporal gyrus is crucial for language processing, emotional regulation, and social cognition[ 54 , 55 ], while the insula plays a central role in emotional perception, self-awareness, and somatosensory processing. Additionally, the inferior frontal gyrus also contributes to emotional regulation[ 56 – 59 ]. Previous research has demonstrated that GMV reductions in these regions are closely related to cognitive impairment, emotional dysregulation, and deficits in social cognition in MDD patients[ 8 , 9 , 60 ]. These abnormalities may constitute the neurobiological basis underlying the impaired emotional regulation, self-awareness, and social functioning observed in MDD. In summary, our findings reinforce the hypothesis that MDD involves widespread neurodegeneration affecting multiple functional networks. MDD is increasingly recognized as a multifactorial disorder, with metabolic dysfunction as a critical comorbid and potentially causal component[ 10 – 12 ]. The conjFDR-based pleiotropy analysis revealed hundreds of loci jointly contributing to both MDD and metabolic phenotypes. Functional enrichment analysis of these genes showed their primary involvement in inflammatory responses, neurotransmitter regulation, and lipid metabolism[ 61 – 63 ]. For example, among genes associated with the five metabolic traits, HbA1c-related genes were enriched in synaptic function, presynaptic active zones, and GABAergic synapses. Studies have indicated that the GABAergic system plays a crucial role in emotional regulation, anxiety disorders, and depression, and abnormalities in GABA receptors are closely linked to the pathogenesis of MDD[ 64 , 65 ]. Additionally, HDL-C-related genes were enriched in synaptic protein localization and postsynaptic neurotransmitter receptor level regulation, suggesting that HDL-C may influence brain structure and function in MDD patients by modulating synaptic homeostasis and neurotransmitter function. Among the pleiotropic loci, FADS2 stood out as a shared gene across all metabolic traits and MDD-related GMV alterations. FADS2 , as a key co-regulatory gene across all factors, plays an important role in lipid metabolism and inflammation regulation[ 66 ]. It encodes desaturase enzymes that participate in omega-3 and omega-6 fatty acid metabolism, and abnormalities in fatty acid metabolism are closely related to inflammatory responses and neurotransmitter imbalances in MDD[ 67 , 68 ]. Further analysis of FADS2 may provide new genetic evidence linking structural brain reductions and metabolic abnormalities in MDD, and offers theoretical support for potential therapeutic targets. Moreover, PPI analysis underscored GRIA1 as a pivotal component of the HDL-C–specific network. GRIA1 encodes a subunit of AMPA-type glutamate receptors, which are tetrameric ligand-gated ion channels mediating glutamatergic neurotransmission in the brain. The subunits of these receptors exhibit tissue-specific expression patterns. In addition, growing evidence suggests that GRIA1 is implicated in psychiatric disorders. GRIA1 polymorphisms have been associated with schizophrenia and mood disorders[ 69 ]. In postmortem brain tissues of schizophrenia patients, GRIA1 expression was found to be regionally dysregulated[ 70 ]. Studies suggest that ligands targeting ionotropic glutamate receptors exhibit potential therapeutic effects in animal models of schizophrenia and depression[ 71 ]. These findings suggest that targeting glutamatergic signaling, possibly via GRIA1 , could offer therapeutic potential for metabolic–psychiatric comorbidities. 5. Limitations Several limitations should be noted. First, the genomic data were exclusively obtained from publicly available GWAS datasets of European ancestry, which may reduce the generalizability of the conclusions to non-European populations. To strengthen the robustness of shared loci, future investigations should involve cohorts of multiple ethnic backgrounds. Second, although our multi-omics framework integrates imaging, transcriptomic, and genetic data, it does not explicitly address gene–environment interactions, which are known to modulate both metabolic and psychiatric risk. Future studies should incorporate longitudinal cohort designs, epigenomic profiling, and environmental exposure data (e.g., diet, stress, lifestyle) to elucidate how external factors influence the expression of shared risk genes. Third, the application of single-cell RNA sequencing technology provides new ideas for understanding the complexity of gene expression, and combining single-cell RNA sequencing with the analysis of brain tissue samples of MDD patients will help reveal how systemic metabolic dysregulation influences cellular and regional vulnerability in the brain. 6. Conclusions In summary, our study presents an integrative multi-omics framework that reveals the shared genetic landscape linking MDD-related GMV alterations with glucose and lipid metabolism traits. By integrating neuroimaging meta-analysis, transcriptome-neuroimaging association analysis, and GWAS-based pleiotropy analysis using the conjFDR method, we identified a set of convergent genes, including the core gene FADS2 , which may mechanistically link brain structural alterations and systemic metabolic abnormalities in MDD. Functional enrichment analysis further highlighted genes involved in synaptic transmission, lipid metabolism, and glutamatergic signaling, offering new targets for translational research.The study's revelations deepen our grasp of the complex metabolic roots tied to GMV alterations in MDD, laying the groundwork for the creation of treatment approaches that focus on the intricate relationship between brain architecture and overall metabolic health. Declarations Funding This work was supported by the Tianjin Major Special Project on Public Health Science and Technology (24ZXGQSY00040), the Tianjin Key Medical Discipline Construction Project (TJYXZDXK-3-008C), the Young Talent Fund of Xi’an Association for Science and Technology (959202413050), and the National Natural Science Foundation of China (62301304). CRediT authorship contribution statement Xiangzheng Wu: Data curation, Formal analysis, Validation, Visualization, Writing - original draft. Piaoran Wang: Investigation, Methodology, Software, Validation . Xu lang: Methodology, Formal analysis. Qingwei Guo: Methodology, Data curation. Yurong Jiang: Conceptualization , Resources, Software. Jinglei Xu: Data curation , Resources. Qiuhui Wang: Methodology, Supervision. Feng Liu: Conceptualization , Project administration , Writing – review & editing . Hao Ding: Methdology, Software, Validation. Huaigui Liu: Conceptualization , Funding acquisition , Methodology , Project administration , Supervision , Writing – review & editing . Data availability The GWAS summary statistics used in this study are publicly available as follows: MDD: https://pgc.unc.edu/for-researchers/download-results/; glucose: https://magicinvestigators.org/downloads/index.html; HDL-C, LDL-C, and TG: https://csg.sph.umich.edu/willer/public/glgc-lipids2021/. All analyses were based on publicly available summary-level data; no individual-level data were used. Declaration of Interest Statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Clinical trial registration Clinical trial number: not applicable. Acknowledgments None. References Diseases GBD, Injuries C. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet (London England). 2020;396(10258):1204–22. Chesney E, Goodwin GM, Fazel S. Risks of all-cause and suicide mortality in mental disorders: a meta-review. World psychiatry: official J World Psychiatric Association (WPA). 2014;13(2):153–60. Geschwind DH, Flint J. Genetics and genomics of psychiatric disease. Sci (New York NY). 2015;349(6255):1489–94. Li M, D'Arcy C, Meng X. 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13:37:28","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":52630,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7971333/v1/d98e50fd4d7a21cc8604fc5b.png"},{"id":97250314,"identity":"60a90713-449b-4ff3-a763-72ac135c1ab4","added_by":"auto","created_at":"2025-12-02 13:14:16","extension":"xml","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":159427,"visible":true,"origin":"","legend":"","description":"","filename":"47a9ab627eca4a9cacfd8c91125d13921structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7971333/v1/7b33bf59264bc1d20c27bc8a.xml"},{"id":97250064,"identity":"2e89b7f6-cc06-4f28-bf1c-6a301e2b0331","added_by":"auto","created_at":"2025-12-02 13:13:50","extension":"html","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":174106,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7971333/v1/0e6567ed01dcefa27442175f.html"},{"id":97165410,"identity":"ae1a26d2-7ed4-4534-a8a2-047e5ad515f8","added_by":"auto","created_at":"2025-12-01 13:37:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4674233,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic workflow of the research framework.\u003c/strong\u003e A neuroimaging meta-analysis was first conducted to identify GMV alterations associated with MDD. Transcriptome-neuroimaging association analysis was then performed using the AHBA to determine genes whose spatial expression patterns were significantly correlated with MDD-related GMV alterations. Meanwhile, conjFDR analysis was applied to GWAS summary statistics to identify shared genetic loci between MDD and five metabolic traits. The resulting gene lists from both pipelines were intersected to identify shared genes implicated in both metabolic regulation and GMV alterations in MDD. These shared genes were further characterized through enrichment analysis to uncover their potential biological functions and molecular pathways. \u003cstrong\u003eAbbreviations:\u003c/strong\u003eAHBA, Allen Human Brain Atlas; conjFDR, conjunctional false discovery rate; GO, Gene Ontology; GWAS, genome-wide association study; MDD, major depressive disorder; PPI, protein-protein interaction.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7971333/v1/cc6a87411fa09a4765d14ea9.png"},{"id":97165411,"identity":"06401f60-defe-49e1-98ed-a09ab22437b8","added_by":"auto","created_at":"2025-12-01 13:37:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1476692,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA flow diagram showing study selection and literature screening. \u003c/strong\u003eStudy identification, screening, eligibility evaluation, and inclusion are summarized in the flowchart, following the guidelines of the PRISMA\u003cstrong\u003e.\u003c/strong\u003e After removing duplicates and applying inclusion/exclusion criteria, 57 eligible studies were retained for VBM meta-analysis of GMV alterations MDD. \u003cstrong\u003eAbbreviations:\u003c/strong\u003e \u003cem\u003eN\u003c/em\u003e, number; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; ROI, region of interest; VBM, voxel-based morphometry.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7971333/v1/0fecaf744a5a76fcaf593c4f.png"},{"id":97165413,"identity":"18552ef3-2b0c-402b-a46f-46203cdfb906","added_by":"auto","created_at":"2025-12-01 13:37:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5661753,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of meta-analysis and related gene expression analysis.\u003c/strong\u003e \u003cstrong\u003eA.\u003c/strong\u003e Areas of significantly reduced GMV in MDD patients are displayed in cold colors, with the bar indicating SDM-Z values. \u003cstrong\u003eB\u003c/strong\u003e. Relationship between PLS1 score (x-axis) and case–control z-score (y-axis) presented as a scatter plot (Pearson’s \u003cem\u003er\u003c/em\u003e = 0.37, \u003cem\u003ep\u003c/em\u003e = 0.02).\u003cstrong\u003eC.\u003c/strong\u003e Venn diagram, obtained by intersecting the five groups of genes finally selected, resulting in a shared gene (\u003cem\u003eFADS2\u003c/em\u003e). \u003cstrong\u003eAbbreviations:\u003c/strong\u003e GLU, glucose; HbA1c, Hemoglobin A1c; HDL-C, High-Density Lipoprotein Cholesterol; LDL-C, Low-Density Lipoprotein Cholesterol; L, left; PLS1, the first component of the partial least squares regression; R, right; SDM, seed-based \u003cem\u003ed\u003c/em\u003e mapping; TG, triglycerides.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7971333/v1/464d851ec899abb113e6ceda.png"},{"id":97248706,"identity":"874b0e6f-208b-426a-923a-5a5e1ac11d34","added_by":"auto","created_at":"2025-12-02 13:06:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4755243,"visible":true,"origin":"","legend":"\u003cp\u003eThe conjFDR analysis results between MDD and metabolic traits. A. The conditional Q-Q plot of the -log10 values of nominal and empirical p-values in MDD, with the p-values conditioned on metabolic traits. B. Conversely, the p-values are p \u0026lt; 0.100, p \u0026lt; 0.010, and p \u0026lt; 0.001 respectively. The blue line includes all SNPs, and the dotted line represents the null hypothesis. \u003cstrong\u003eC.\u003c/strong\u003eManhattan plot displaying −log10 conjFDR values (y-axis) plotted against chromosome position (x-axis). The dotted horizontal line marks the cutoff (conjFDR = 0.05) for relevant associations. \u003cstrong\u003eAbbreviations:\u003c/strong\u003e conjFDR, conjunctional false discovery rate; MDD, Major Depressive Disorder.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7971333/v1/ca2f4f591e97fd085741df66.png"},{"id":97249082,"identity":"65dfb1fe-7b2e-4782-ba2c-5fd8ed87d0a2","added_by":"auto","created_at":"2025-12-02 13:10:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1356212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment of overlapping genes. A-B. \u003c/strong\u003eRepresentative enrichment terms of HbA1c/HDL-C overlapping genes. The X-axis corresponds to enrichment degree (Count), while the Y-axis lists enriched GO terms. The color scheme illustrates enrichment significance: deep red reflects highly significant results, whereas deep blue indicates nonsignificant findings.\u003cstrong\u003e C-D. \u003c/strong\u003ePPI networks of LDL-C/HDL-C overlapping genes. \u003cstrong\u003eAbbreviations: \u003c/strong\u003eBP, biological processes; CC, cellular components; MF, Molecular Function; PPI, protein-protein interaction.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7971333/v1/9c4e01ee422cee5fc5da49e6.png"},{"id":103251230,"identity":"e31751f2-4fa5-4658-8a44-ced47ef01811","added_by":"auto","created_at":"2026-02-23 16:06:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":19014433,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7971333/v1/56ab92f5-18e2-43cf-8ca6-6d1fd653d530.pdf"},{"id":97165428,"identity":"64268635-a24c-4f34-a238-0aa1feae38de","added_by":"auto","created_at":"2025-12-01 13:37:28","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":10632811,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7971333/v1/08b1351d92935610c2b5f1f8.xlsx"},{"id":97248647,"identity":"119040d0-9fb2-4783-aabb-3447087858ac","added_by":"auto","created_at":"2025-12-02 13:04:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":511181,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7971333/v1/9603f9d18a61185c439dcc58.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Shared Genetic Underpinnings of Gray Matter Volume Alterations and Metabolic Traits in Major Depressive Disorder","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMajor depressive disorder (MDD) represents a highly prevalent psychiatric condition that leads to significant disability, characterized by long-lasting depressive symptoms, cognitive deficits, and declines in overall functioning.Epidemiological data from the World Health Organization (WHO) indicate that MDD affects an estimated 280\u0026nbsp;million individuals worldwide, with a prevalence of about 5% in adults and 5.7% in older adults[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. MDD contributes significantly to the global burden disease, accounting for nearly 50% of all suicide deaths worldwide[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although the pathogenesis of MDD is shaped by the interplay of genetic predisposition and environmental influences.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], the underlying neurobiological mechanisms remain incompletely understood.\u003c/p\u003e\u003cp\u003eGray matter volume (GMV) alterations have been widely observed in MDD using voxel-based morphometry (VBM). Regional GMV reductions have been reported in the anterior cingulate cortex, hippocampus, superior temporal gyrus, inferior temporal gyrus, inferior frontal gyrus, right amygdala and thalamus[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, findings across studies on GMV alterations in MDD are often inconsistent, likely due to limited small sample size, and methodological heterogeneity. Recent neuroimaging meta-analyses have demonstrated highly reproducible GMV reductions in the hippocampus, superior temporal gyrus, and inferior frontal gyrus of MDD patients, highlighting them as potential imaging markers for disease progression[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite these findings, the molecular mechanisms underlying these structural alterations remain largely unclear.\u003c/p\u003e\u003cp\u003eBeyond structural brain abnormalities, metabolic dysregulation\u0026mdash;including disturbances in glucose and lipid metabolism\u0026mdash;has been increasingly recognized as a frequent comorbidity in MDD. Epidemiological evidence indicates that MDD is associated with an elevated risk of metabolic syndrome, which includes hyperglycemia, insulin resistance, and dyslipidemia[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, metabolically unhealthy obesity is commonly observed in MDD populations[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Emerging evidence suggests that these metabolic disturbances are not merely peripheral features, but may contribute to cognitive deficits and brain structural and functional abnormalities observed in MDD[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, despite the known co-occurrence of GMV alterations and metabolic abnormalities in MDD, the underlying shared genetic mechanisms remain largely elusive.\u003c/p\u003e\u003cp\u003eSeveral risk loci related to MDD[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and metabolic traits[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] have been discovered via genome-wide association studies (GWAS). While conventional GWAS have enhanced our understanding of genetic architecture, they are limited in identifying pleiotropic effects across complex phenotypes, such as comorbid psychiatric and metabolic conditions. To address this, conjunctional false discovery rate (conjFDR) analysis has been developed as a robust statistical approach to detect shared genetic variants across complex traits[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Meanwhile, transcriptome-neuroimaging association analysis has emerged as a powerful interdisciplinary approach for linking spatial gene expression patterns with neuroimaging phenotypes[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The Allen Human Brain Atlas (AHBA) provides high-resolution, spatially mapped gene expression data across the human brain, enabling the exploration of transcriptional correlates of regional brain alterations[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Although prior studies utilizing AHBA have begun to uncover gene expression gradients associated with MDD[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], no studies to date have systematically linked these patterns to metabolic genetic variations.\u003c/p\u003e\u003cp\u003eIn this study, we propose a multi-omics integrative framework to unravel the molecular basis of GMV alterations in MDD and their shared genetic mechanisms with metabolic dysregulation. First, we performed a coordinate-based neuroimaging meta-analysis to identify reproducible GMV alterations in MDD. Then, transcriptome-neuroimaging association analysis was used to identify genes spatially associated with MDD-related GMV alterations. In parallel, we conducted a pleiotropy-informed conjFDR analysis to identify shared genetic loci between MDD and five metabolic traits. Finally, intersecting gene sets from both pipelines were subjected to enrichment analysis to explore biological pathways of interest. Our work highlights a novel systems-level perspective on the convergence between neurostructural and metabolic dimensions of MDD, and identifies key molecular candidates potentially relevant for biomarker discovery and therapeutic development. A schematic overview of the analysis framework is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Search strategy and selection criteria\u003c/h2\u003e\u003cp\u003eWe searched the PubMed and Web of Science databases for articles available before September 2024 to ensure comprehensive coverage of the literature. We used the following search terms: ((\"gray matter volume\" OR \"GMV\" OR \"gray matter\" OR \"grey matter\" OR \"grey matter volume\") AND (\"major depressive disorder\" OR \"depression\" OR \"depress\" OR \"depressive \" OR \"depressed\" OR \"major depression\" OR \"major depressive episode\" OR \"depressive disorder\" OR \"unipolar depression\" OR \"unipolar disorder\" OR \"melancholia\")).\u003c/p\u003e\u003cp\u003eStudies that satisfied the following conditions were included: (1) English-language publications in peer-reviewed journals; (2) employed VBM to estimate whole-brain GMV[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]; (3) participants were required to have a diagnosis of MDD based on standardized criteria, including DSM-III-R, DSM-IV, DSM-5, ICD-10, or the MINI, and studies also needed to include a healthy control (HC) group; (4) reported peak coordinates of gray matter volume alterations in either the Montreal Neurological Institute (MNI) or Talairach reference space, or indicated null findings; (5) employed a consistent whole-brain statistical threshold. (6) limited participants to adult populations. Exclusion criteria included the following conditions: (1) review articles, and theoretical papers; (2) studies that did not report peak coordinates or yielded no statistically significant results; (3) studies involving participants with comorbid psychiatric or neurological disorders; (4) duplicate publications or repeated analyses from the same sample. In cases where participants were reported in multiple publications, we retained the study with the largest sample size. Articles describing more than one independent patient cohort were regarded as providing separate datasets. For longitudinal designs, analyses were restricted to baseline patient\u0026ndash;control comparisons. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], this meta-analysis was carried out to ensure methodological rigor. The study selection workflow was presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Neuroimaging meta-analysis of GMV alterations in patients with MDD\u003c/h2\u003e\u003cp\u003eMeta-analysis was performed to investigate GMV alterations in patients with MDD, aiming to systematically assess disease-related structural brain alterations. The analysis was conducted using SDM-PSI software (version 6.23, available for download on the SDM website). This method integrates multiple advanced processing techniques, including multiple imputation of peak coordinates and subject-level maps, random-effects modeling, and voxel-wise meta-analyses of imputed study images. Final estimates were combined using Rubin's rule, which enhances statistical power and reduces methodological heterogeneity across studies[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. To account for multiple comparisons, statistical significance was assessed using threshold-free cluster enhancement (TFCE) with family-wise error (FWE) correction, applying a voxel-level threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and requiring a minimum cluster size of 10 voxels. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Heterogeneity among studies was evaluated with Cochran\u0026rsquo;s Q statistic and the I\u0026sup2; statistic [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and publication bias was examined using Egger\u0026rsquo;s test [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. For exploratory analysis, meta-regression was conducted to examine the association between clinical variables (e.g., illness duration, mean age) and GMV alterations. Additionally, subgroup analyses were carried out to assess the consistency of findings across studies stratified by clinical or methodological characteristics, thereby improving interpretability and robustness. Detailed technical methods, statistical parameters, and data processing procedures can be found in the \u003cb\u003eSupplementary Methods\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Transcriptome-neuroimaging association analysis\u003c/h2\u003e\u003cp\u003eTo investigate the spatial transcriptional correlates of MDD-related GMV alterations, we performed a transcriptome\u0026ndash;neuroimaging association analysis using data from the AHBA, a publicly available dataset comprising genome-wide expression profile from 3,702 brain tissue samples across 6 adult human donors, covering 20,737 genes and 58,692 probes[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Among the donors, two provided bilateral hemisphere samples, while the remaining four included only left hemisphere data. Demographic details are displayed in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eGene expression preprocessing was carried out via the abagen toolbox[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.github.com/netneurolab/abagen\u003c/span\u003e\u003cspan address=\"https://www.github.com/netneurolab/abagen\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), adhering to a standardized procedure: Firstly, the microarray probes were re-annotated based on the latest annotation file[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Subsequently, probes with expression values fell beneath background signals in \u0026ge;\u0026thinsp;50% of the samples were removed. For multiple probes corresponding to a single gene, the probe with the highest differential stability was selected as the representative. The preprocessed expression data were standardized across genes by the scaled robust sigmoid (SRS) method to eliminate the influence of donor-to-donor variation. Given the significant heterogeneity in gene expression across different brain regions (cortex, subcortex/brainstem, cerebellum), we further implemented region-specific normalization processing.\u003c/p\u003e\u003cp\u003eWe then performed partial least squares (PLS) regression analysis[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] to identify multivariate association between the regional gene expression profiles (predictive variables, n\u0026thinsp;=\u0026thinsp;15,633 genes across 1,159 regions) and the case-control GMV effect size value maps (response variables). Both the gene expression matrix and GMV maps were \u003cem\u003ez\u003c/em\u003e-score standardized. The latent components were established through singular value decomposition, and ranked by explained variance. Components with an explained variance\u0026thinsp;\u0026gt;\u0026thinsp;10% and passing the permutation test (permutation \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were retained.\u003c/p\u003e\u003cp\u003eTo ensure spatial specificity and statistical robustness, we generated 1,000 spatial null models using the BrainSMASH toolkit (v0.1.3)[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The stability of gene weights was estimated through 1,000 bootstrap resampling iterations, and standardized weights (\u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;weight/standard error) were calculated. To identify statistically significant genes for downstream analysis, bonferroni correction was employed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Identification of shared genes between MDD and metabolic traits using conjFDR\u003c/h2\u003e\u003cp\u003eTo investigate genes jointly associated with MDD and metabolic traits, We conducted a conjFDR analysis based on publicly accessible GWAS summary statistics. For MDD, we used data from the largest available GWAS to date. To avoid overlapping samples, participants from the UK Biobank (UKBB) were excluded, leaving a final dataset of 357,636 cases and 1,281,936 controls of European ancestry [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. GWAS summary statistics for five metabolic traits, including glucose (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;459,772), HbA1c (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;146,864), HDL-C (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,320,016), LDL-C (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,320,016), TG (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,320,016), were derived from the UKBB datasets of European ancestry[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Details of all GWAS datasets are provided in the \u003cb\u003eSupplementary Methods.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe conjFDR analysis method, built on the conditional false discovery rate (condFDR) framework, employs empirical Bayesian statistics to jointly analyze SNP associations across two phenotypes[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Briefly, condFDR re-ranks SNP association statistics of the primary phenotype conditioned on the strength of association with the secondary phenotype. On this basis, the inverse condFDR value can be calculated by swapping the roles of the primary and secondary phenotypes. The conjFDR value is defined as the maximum of the original condFDR and the inverse condFDR. This method ensures the reliability of the common association signals through a dual screening mechanism. We applied a significance threshold of conjFDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, consistent with prior studies[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. To visualize pleiotropic enrichment, conditional quantile-quantile (Q-Q) plots were generated. In these plots, SNPs stratified by their level of significance in the secondary phenotype (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.100, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.010, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed systematic leftward deflection in the primary phenotype, indicating cross-trait genetic enrichment. To eliminate the interference of complex linkage disequilibrium (LD) on the analysis, we systematically excluded three high-LD regions before constructing the cond/conjFDR model: the extended major histocompatibility complex on chromosome 6 (25,119,106\u0026ndash;33,854,733), the 8p23.1 locus on chromosome 8 (7,200,000\u0026ndash;12,500,000), and the region adjacent to the MAPT gene on chromosome 17 (40,000,000\u0026ndash;47,000,000)[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSignificant loci were annotated and mapped using the Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) genome analysis platform[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The specific process is as follows: (1) Independence determination of loci: SNP with conjFDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and LD \u003cem\u003er\u0026sup2;\u003c/em\u003e \u0026lt; 0.6 are selected as independent significant loci, where \u003cem\u003er\u0026sup2;\u003c/em\u003e \u0026lt; 0.1 is defined as the lead SNP; (2) Boundaries definition: SNPs with LD \u003cem\u003er\u0026sup2;\u003c/em\u003e \u0026ge; 0.6 and located in the adjacent region of the independent significant SNPs (conjFDR\u0026thinsp;\u0026lt;\u0026thinsp;0.1) are included in the candidate set, and the boundaries of the genotypes are defined accordingly; (3) Locus merging: adjacent loci with a spatial distance of \u0026lt;\u0026thinsp;250 kb are merged, and the final dominant SNP is determined by the minimum FDR value; (4) Function annotation: candidate SNPs are functionally predicted using CADD pathogenicity scores (v1.6)[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], RegulomeDB scores[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], and chromatin state information[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]; (5) Gene mapping: positional mapping was used to determine the shared genes between MDD and metabolic traits.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Shared gene identification\u003c/h2\u003e\u003cp\u003eTo examine shared genetic architecture, we intersected the gene set MDD-related GMV alterations (identified via transcriptome-neuroimaging association analysis) and the conjFDR-derived gene set shared between MDD and each metabolic marker. This yielded five gene sets, corresponding to genes jointly implicated in MDD-related GMV alterations and MDD-metabolic marker pairs of GLU, HbA1c, HDL-C, LDL-C, and TG, respectively. Additionally, we derived the intersection across all five sets to investigate a core group of pleiotropic genes potentially underlying a common mechanism linking metabolic dysregulation and neurodegeneration in MDD. To facilitate visualization of gene overlaps, we created a Venn diagram.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Functional enrichment analysis\u003c/h2\u003e\u003cp\u003eGene enrichment analysis was conducted to functional annotate the intersecting gene sets and elucidate their roles in biological processes[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Gene Ontology (GO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://geneontology.org/\u003c/span\u003e\u003cspan address=\"https://geneontology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) systematically described across a biological process, molecular function and cellular composition of Gene functions[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. GO enrichment analysis was performed using the clusterProfiler package in R, and multiple comparison correction was applied using the false discovery rate (FDR) method (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In addition, a protein-protein interaction (PPI) network encompassing the shared genes was established via STRING (version 12.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), adopting a medium confidence threshold of 0.4.[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Included studies and sample characteristics\u003c/h2\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, 57 studies were included, encompassing 4,393 MDD patients and 4,109 HCs. Demographic and clinical characteristics, along with the relevant imaging and technical parameters, are summarized in \u003cb\u003eTable S2\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Neuroimaging meta-analysis reveals GMV alterations in MDD\u003c/h2\u003e\u003cp\u003eThe neuroimaging meta-analysis revealed significant GMV reductions in the left superior temporal gyrus, the left inferior frontal gyrus and the left insula in individuals with MDD compared to HCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA \u003cb\u003eand\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No regions showed significantly increased GMV. Subsequently, no significant heterogeneity was detected among the studies based on Cochran\u0026rsquo;s \u003cem\u003eQ\u003c/em\u003e test and \u003cem\u003eI\u0026sup2;\u003c/em\u003e statistics. The Egger\u0026rsquo;s test further confirmed that the cluster did not exhibit significant publication bias (\u003cb\u003eTable S5\u003c/b\u003e). Results from meta-regression and subgroup analyses are reported in the \u003cb\u003eSupplementary Results\u003c/b\u003e and \u003cb\u003eTables S3\u0026ndash;S4\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of meta-analysis between MDD patients and HCs.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBrain regions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSDM-Z\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003ePeak MNI coordinates\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCluster size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eHeterogeneity test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eEgger\u0026rsquo;s test\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eY\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(voxels)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eQ\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDD\u0026thinsp;\u0026lt;\u0026thinsp;HCs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft insula/Superior temporal gyrus/Inferior frontal gyrus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-7.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e37.444(0.988)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.738\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: HCs, healthy controls; MDD, major depressive disorder; MNI, Montreal Neurological Institute; \u003cem\u003eQ\u003c/em\u003e, Cochran\u0026rsquo;s \u003cem\u003eQ\u003c/em\u003e statistic; SDM, seed-based \u003cem\u003ed\u003c/em\u003e mapping.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Transcriptome-neuroimaging association analysis\u003c/h2\u003e\u003cp\u003eUsing PLS regression on AHBA gene expression dataset, we found that the first latent component (PLS1) explained more than 10% of the variance in GMV abnormalities (\u003cb\u003eFigure S2\u003c/b\u003e). Notably, the PLS1 component score was significantly correlated positively with the GMV z-map (Pearson's \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.37, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The spatial autocorrelation-preserved permutation test further validated the robustness of the PLS1 component, demonstrating that the explained variance of PLS1 significantly exceeded that of the null distribution generated by surrogate maps (permutation \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0029). Finally, we identified a total of 2,585 genes significantly associated with PLS1 (Bonferroni-corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), of which 1,142 had positive weights (PLS1+) and 1,443 had negative weights (PLS1\u0026minus;), suggesting differential transcriptional regulation underlying GMV alterations in MDD. The complete gene lists and statistical details are shown in \u003cb\u003eTable S7-S8\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Shared genes between MDD and metabolic traits revealed by conjFDR\u003c/h2\u003e\u003cp\u003eThe conditional Q-Q plots were used to evaluate the pleiotropic enrichment effect between MDD and five metabolic traits (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B). With the threshold of conjFDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, we pinpointed the following shared genomic loci: 20 loci for MDD-GLU, 20 loci for MDD-HbA1c, 195 loci for MDD-HDL-C, 83 loci for MDD-LDL-C, and 186 loci for MDD-TG (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Details of these independent genomic loci and functional annotation results are provided in \u003cb\u003eTable S9-S18\u003c/b\u003e. Further, the candidate SNPs were localized to 100 (MDD-glucose), 129 (MDD-HbA1c), 533 (MDD-HDL-C), 360 (MDD-LDL-C), and 603 (MDD-TG) potential pathogenic genes through a multi-gene mapping strategy (\u003cb\u003eTable S19-S23\u003c/b\u003e). We intersected these genes with the 2,585 significant genes associated with MDD-related GMV alterations, identifying 13 genes shared with GLU, 14 with HbA1c, 73 with HDL-C, 37 with LDL-C and 70 with TG (\u003cb\u003eTable S24-S28\u003c/b\u003e). In addition, we cross-validated these five groups of genes and found that there is a common regulatory gene \u003cem\u003eFADS2\u003c/em\u003e. To visualize these results, we created a Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Enrichment analysis\u003c/h2\u003e\u003cp\u003eWe performed GO enrichment analysis in R to investigate the biological functions of the previously identified genes. The gene list of HbA1c is mainly concentrated in the synaptic function, presynaptic active zone and GABAergic synapse. In addition, the genes are also involved in oxidoreductase activity and phosphoinositol kinase activity, suggesting that they may affect neural function by regulating oxidative stress and signal transduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cb\u003eTable S29\u003c/b\u003e). The results of HDL-C showed that these genes were significantly enriched in biological processes such as synaptic function, neuronal signaling and postsynaptic membrane structure \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, \u003cb\u003eTable S30)\u003c/b\u003e. The PPI network of 37 LDL-C\u0026ndash;associated overlapping genes featured eight edges, significantly surpassing the one edge predicted under the null hypothesis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.37 \u0026times; 10⁻⁴, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Similarly, the HDL-C network comprising 73 overlapping genes displayed 21 edges, exceeding the expected nine. Within this network, GRIA1 served a pivotal role. (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.38\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study is the first to systematically explore the shared genetic architecture underlying GMV alterations in MDD with glucose- and lipid-related metabolic traits. We combined neuroimaging meta-analysis and transcriptome-neuroimaging association analysis to detect genes spatially linked to GMV reductions observed in MDD. Leveraging large-scale GWAS summary statistics and applying the conjFDR approach, we further pinpointed pleiotropic genes associated with MDD and five key metabolic traits. Our cross-analysis revealed substantial genetic overlap, identifying 13 genes shared between MDD-related GMV alterations and GLU, 14 with HbA1c, 73 with HDL-C, 37 with LDL-C, and 70 with TG. Notably, \u003cem\u003eFADS2\u003c/em\u003e emerged as a common gene across all five traits, suggesting convergent pathways linking metabolic dysregulation and structural brain abnormalities in MDD.\u003c/p\u003e\u003cp\u003eNeuroimaging meta-analysis identified that GMV alterations in MDD patients are distributed across multiple brain regions, including the superior temporal gyrus, inferior frontal gyrus, and insula. The superior temporal gyrus is crucial for language processing, emotional regulation, and social cognition[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], while the insula plays a central role in emotional perception, self-awareness, and somatosensory processing. Additionally, the inferior frontal gyrus also contributes to emotional regulation[\u003cspan additionalcitationids=\"CR57 CR58\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Previous research has demonstrated that GMV reductions in these regions are closely related to cognitive impairment, emotional dysregulation, and deficits in social cognition in MDD patients[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. These abnormalities may constitute the neurobiological basis underlying the impaired emotional regulation, self-awareness, and social functioning observed in MDD. In summary, our findings reinforce the hypothesis that MDD involves widespread neurodegeneration affecting multiple functional networks. MDD is increasingly recognized as a multifactorial disorder, with metabolic dysfunction as a critical comorbid and potentially causal component[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The conjFDR-based pleiotropy analysis revealed hundreds of loci jointly contributing to both MDD and metabolic phenotypes. Functional enrichment analysis of these genes showed their primary involvement in inflammatory responses, neurotransmitter regulation, and lipid metabolism[\u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. For example, among genes associated with the five metabolic traits, HbA1c-related genes were enriched in synaptic function, presynaptic active zones, and GABAergic synapses. Studies have indicated that the GABAergic system plays a crucial role in emotional regulation, anxiety disorders, and depression, and abnormalities in GABA receptors are closely linked to the pathogenesis of MDD[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Additionally, HDL-C-related genes were enriched in synaptic protein localization and postsynaptic neurotransmitter receptor level regulation, suggesting that HDL-C may influence brain structure and function in MDD patients by modulating synaptic homeostasis and neurotransmitter function. Among the pleiotropic loci, \u003cem\u003eFADS2\u003c/em\u003e stood out as a shared gene across all metabolic traits and MDD-related GMV alterations. \u003cem\u003eFADS2\u003c/em\u003e, as a key co-regulatory gene across all factors, plays an important role in lipid metabolism and inflammation regulation[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. It encodes desaturase enzymes that participate in omega-3 and omega-6 fatty acid metabolism, and abnormalities in fatty acid metabolism are closely related to inflammatory responses and neurotransmitter imbalances in MDD[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Further analysis of \u003cem\u003eFADS2\u003c/em\u003e may provide new genetic evidence linking structural brain reductions and metabolic abnormalities in MDD, and offers theoretical support for potential therapeutic targets.\u003c/p\u003e\u003cp\u003eMoreover, PPI analysis underscored \u003cem\u003eGRIA1\u003c/em\u003e as a pivotal component of the HDL-C\u0026ndash;specific network. \u003cem\u003eGRIA1\u003c/em\u003e encodes a subunit of AMPA-type glutamate receptors, which are tetrameric ligand-gated ion channels mediating glutamatergic neurotransmission in the brain. The subunits of these receptors exhibit tissue-specific expression patterns. In addition, growing evidence suggests that \u003cem\u003eGRIA1\u003c/em\u003e is implicated in psychiatric disorders. \u003cem\u003eGRIA1\u003c/em\u003e polymorphisms have been associated with schizophrenia and mood disorders[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. In postmortem brain tissues of schizophrenia patients, \u003cem\u003eGRIA1\u003c/em\u003e expression was found to be regionally dysregulated[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Studies suggest that ligands targeting ionotropic glutamate receptors exhibit potential therapeutic effects in animal models of schizophrenia and depression[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. These findings suggest that targeting glutamatergic signaling, possibly via \u003cem\u003eGRIA1\u003c/em\u003e, could offer therapeutic potential for metabolic\u0026ndash;psychiatric comorbidities.\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eSeveral limitations should be noted. First, the genomic data were exclusively obtained from publicly available GWAS datasets of European ancestry, which may reduce the generalizability of the conclusions to non-European populations. To strengthen the robustness of shared loci, future investigations should involve cohorts of multiple ethnic backgrounds. Second, although our multi-omics framework integrates imaging, transcriptomic, and genetic data, it does not explicitly address gene\u0026ndash;environment interactions, which are known to modulate both metabolic and psychiatric risk. Future studies should incorporate longitudinal cohort designs, epigenomic profiling, and environmental exposure data (e.g., diet, stress, lifestyle) to elucidate how external factors influence the expression of shared risk genes. Third, the application of single-cell RNA sequencing technology provides new ideas for understanding the complexity of gene expression, and combining single-cell RNA sequencing with the analysis of brain tissue samples of MDD patients will help reveal how systemic metabolic dysregulation influences cellular and regional vulnerability in the brain.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eIn summary, our study presents an integrative multi-omics framework that reveals the shared genetic landscape linking MDD-related GMV alterations with glucose and lipid metabolism traits. By integrating neuroimaging meta-analysis, transcriptome-neuroimaging association analysis, and GWAS-based pleiotropy analysis using the conjFDR method, we identified a set of convergent genes, including the core gene \u003cem\u003eFADS2\u003c/em\u003e, which may mechanistically link brain structural alterations and systemic metabolic abnormalities in MDD. Functional enrichment analysis further highlighted genes involved in synaptic transmission, lipid metabolism, and glutamatergic signaling, offering new targets for translational research.The study's revelations deepen our grasp of the complex metabolic roots tied to GMV alterations in MDD, laying the groundwork for the creation of treatment approaches that focus on the intricate relationship between brain architecture and overall metabolic health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Tianjin Major Special Project on Public Health Science and Technology (24ZXGQSY00040), the Tianjin Key Medical Discipline Construction Project (TJYXZDXK-3-008C), the Young Talent Fund of Xi\u0026rsquo;an Association for Science and Technology (959202413050), and the National Natural Science Foundation of China (62301304).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXiangzheng Wu:\u003c/strong\u003e\u003cstrong\u003eData curation, Formal analysis, Validation, Visualization, Writing - original draft.\u003c/strong\u003e\u003cstrong\u003ePiaoran Wang:\u003c/strong\u003e\u003cstrong\u003eInvestigation, Methodology, Software, Validation\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eXu lang:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Methodology, Formal analysis.\u003c/strong\u003e\u003cstrong\u003eQingwei Guo:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Methodology, Data curation.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eYurong Jiang:\u003c/strong\u003e\u003cstrong\u003eConceptualization\u003c/strong\u003e\u003cstrong\u003e, Resources, Software.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eJinglei Xu:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Data curation\u003c/strong\u003e\u003cstrong\u003e, Resources.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eQiuhui Wang:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Methodology, Supervision.\u003c/strong\u003e\u003cstrong\u003eFeng Liu:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Conceptualization\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eProject administration\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Writing\u003c/strong\u003e\u003cstrong\u003e\u0026ndash; review \u0026amp; editing\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003eHao Ding:\u003c/strong\u003e\u003cstrong\u003eMethdology, Software, Validation.\u003c/strong\u003e\u003cstrong\u003eHuaigui Liu:\u003c/strong\u003e\u003cstrong\u003eConceptualization\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding acquisition\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eProject administration\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSupervision\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eWriting \u0026ndash; review \u0026amp; editing\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GWAS summary statistics used in this study are publicly available as follows: MDD:\u0026nbsp;https://pgc.unc.edu/for-researchers/download-results/; glucose:\u0026nbsp;https://magicinvestigators.org/downloads/index.html; HDL-C, LDL-C, and TG:\u0026nbsp;https://csg.sph.umich.edu/willer/public/glgc-lipids2021/. All analyses were based on publicly available summary-level data; no individual-level data were used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDiseases GBD, Injuries C. 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Psychopharmacology. 2005;179(1):154\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Major depressive disorder, gray matter volume, metabolic traits, gene expression, transcriptome-neuroimaging association, Allen Human Brain Atlas.","lastPublishedDoi":"10.21203/rs.3.rs-7971333/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7971333/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMajor depressive disorder (MDD) is linked to extensive gray matter volume (GMV) reductions and frequently co-occurs with metabolic dysfunction. However, the shared genetic basis linking neurostructural abnormalities and metabolic traits remains poorly understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eUsing a coordinate-based meta-analytic framework, we synthesized findings from 57 voxel-based morphometry (VBM) studies to characterize GMV alterations in MDD. Spatial transcriptomic correlation analysis was performed using the Allen Human Brain Atlas to identify genes associated with these alterations. In parallel, conjunctional false discovery rate (conjFDR) analysis was applied to genome-wide association study (GWAS) summary statistics from MDD and five metabolic traits\u0026mdash;glucose, hemoglobin A1c (HbA1c), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG)\u0026mdash;to identify pleiotropic loci. Functional characterization of the intersecting genes was conducted by applying gene ontology enrichment and protein\u0026ndash;protein interaction (PPI) analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe identified consistent GMV reductions in the left superior temporal gyrus, inferior frontal gyrus, and insula. A total of 2,585 genes were spatially correlated with GMV alterations. ConjFDR analysis revealed 20\u0026ndash;195 pleiotropic loci across metabolic traits and MDD. Gene-level overlap analysis identified 13\u0026ndash;73 shared genes per trait, with \u003cem\u003eFADS2\u003c/em\u003e emerging as a common gene across all five traits. Functional annotation highlighted pathways related to lipid metabolism and synaptic signaling.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis integrative multi-omics study reveals shared genetic mechanisms linking brain structure in MDD with systemic metabolic traits. \u003cem\u003eFADS2\u003c/em\u003e may serve as a molecular hub underlying this convergence, offering potential targets for future mechanistically informed interventions.\u003c/p\u003e","manuscriptTitle":"Shared Genetic Underpinnings of Gray Matter Volume Alterations and Metabolic Traits in Major Depressive Disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 13:37:23","doi":"10.21203/rs.3.rs-7971333/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"129128099140141747531554445744194151898","date":"2025-11-27T17:31:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-27T07:07:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-11T04:32:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-04T14:11:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-04T03:31:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2025-11-04T03:14:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"afc7e340-36a9-4f93-8a61-c79f1be5888b","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:03:07+00:00","versionOfRecord":{"articleIdentity":"rs-7971333","link":"https://doi.org/10.1186/s12888-026-07895-4","journal":{"identity":"bmc-psychiatry","isVorOnly":false,"title":"BMC Psychiatry"},"publishedOn":"2026-02-21 15:59:31","publishedOnDateReadable":"February 21st, 2026"},"versionCreatedAt":"2025-12-01 13:37:23","video":"","vorDoi":"10.1186/s12888-026-07895-4","vorDoiUrl":"https://doi.org/10.1186/s12888-026-07895-4","workflowStages":[]},"version":"v1","identity":"rs-7971333","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7971333","identity":"rs-7971333","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}