Cerebrospinal Fluid Metabolomics, Brain Morphometry, and Psychiatric Disorders: A Mendelian Randomization Study

Cerebrospinal Fluid Metabolomics, Brain Morphometry, and Psychiatric Disorders: A Mendelian Randomization Study | 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 Cerebrospinal Fluid Metabolomics, Brain Morphometry, and Psychiatric Disorders: A Mendelian Randomization Study Na Lin, Zi-Wei Gao, Dan-Feng Wang, Xiao-Chun Zheng, Pei-Sen Yao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7571315/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To identify the causal roles of cerebrospinal fluid (CSF) metabolomics and brain morphometry in the development of psychiatric disorders, we conducted a bidirectional Mendelian Randomization (MR) study. Methods We performed a two-sample MR analysis to estimate the causal effects of 338 CSF metabolites and 83 brain-wide volumes on the risk of 6 psychiatric disorders: Attention Deficit Hyperactivity Disorder (ADHD), alcohol dependence, bipolar disorder (BD), cannabis use disorder (CUD), major depressive disorder (MDD), and schizophrenia (SCZ). Imputation and strict quality control were applied to CSF metabolite and genotype data, resulting in a final dataset of 291 baseline visits from 689 unrelated individuals of European ancestry (338 CSF metabolites quantified). GWAS summary data for brain morphometry were derived from the UK Biobank (36,778 unrelated white European participants, 54% female, with neuroimaging data). Summary statistics for psychiatric disorders were obtained from the Psychiatric Genomics Consortium (PGC): ADHD (38,691 cases/186,843 controls), alcohol dependence (10,206 cases/28,480 controls), BD (41,917 cases/371,549 controls), CUD (14,080 cases/343,726 controls), MDD (17,339 cases/53,426 controls), SCZ (68,125 cases/90,984 controls). We further explored whether brain morphometry mediates the pathway from CSF metabolomics to psychiatric disorders. Results After inverse variance weighting (IVW) and sensitivity analyses, 3 CSF metabolites showed causal effects on psychiatric disorders: 1. CSF cysteine levels increased BD risk (odds ratio [OR] = 1.23, 95% confidence interval [CI] = 1.11–1.37, P = 1.22×10⁻⁴), validated by an independent GWAS (OR = 2.46, 95% CI = 1.17–5.18, P = 0.02); 2. CSF betaine levels increased MDD risk (OR = 1.40, 95% CI = 1.18–1.66, P = 1.25×10⁻⁴); 3. CSF N-acetylglutamine levels increased MDD risk (OR = 1.42, 95% CI = 1.21–1.68, P = 4.47×10⁻⁴). Reverse MR showed no reverse causality (genetically predicted BD/MDD did not affect respective metabolite levels, P > 0.05). Brain morphometry had no causal effect on BD, and did not mediate the cysteine-BD association. Conclusions CSF metabolites (cysteine, betaine, N-acetylglutamine) have causal associations with BD or MDD, providing potential targets for diagnosis and treatment of these psychiatric disorders. Cerebrospinal fluid metabolomics Psychiatric disorder Brain morphometry Mendelian randomization Causal effect Figures Figure 1 Figure 2 Introduction Psychiatric disorders are among the top 10 global causes of disease burden, encompassing conditions such as bipolar disorder (BD), schizophrenia (SCZ), major depressive disorder (MDD), Attention Deficit Hyperactivity Disorder (ADHD), and substance use disorders (SUD) [ 1 ]. Over the past two decades, this burden has not decreased [ 1 ], and the underlying mechanisms of most psychiatric disorders remain unclear—limiting progress in diagnosis and treatment. Metabolomics has emerged as a key tool to unravel disease mechanisms, with disease-associated metabolites offering potential biomarkers for diagnosis and prognosis. Most human metabolomics studies focus on accessible samples (e.g., blood, urine), but cerebrospinal fluid (CSF) is uniquely relevant to psychiatric disorders: it directly reflects the biochemical environment of the central nervous system (CNS), the primary site of pathological changes in these conditions. Preliminary studies have linked CSF metabolites to psychiatric disorders—for example, MDD patients may show state-dependent changes in CSF 5-hydroxyindoleacetic acid (5-HIAA) and homovanillic acid (HVA) [ 7 ], while BD/SCZ patients have higher CSF lactate levels than healthy controls or MDD patients [ 8 ]. However, these observational findings do not establish causality. Brain morphometry has furthered understanding of the neural substrates of psychiatric disorders. MRI studies link MDD to atrophy in the frontal cortex, hippocampus, and basal ganglia [ 11 , 13 ]; both SCZ and BD show gray matter volume (GMV) reductions in frontotemporal regions, with SCZ exhibiting more widespread loss [ 12 ]. Yet the tripartite relationship between CSF metabolomics, brain morphometry, and psychiatric disorders remains underexplored. Mendelian randomization (MR) addresses limitations of observational studies by using genetic variants as instrumental variables (IVs): genetic variation is fixed at conception, reducing bias from confounding and reverse causality [ 16 , 17 ]. We conducted a bidirectional two-sample MR study to: (1) estimate causal effects of 338 CSF metabolites and 83 brain-wide volumes on 6 psychiatric disorders; (2) test if brain morphometry mediates CSF metabolite-psychiatric disorder pathways; (3) investigate reverse causality (genetic susceptibility to psychiatric disorders affecting CSF metabolites/brain morphometry). Methods Study Design We used two-sample MR to evaluate causal relationships between CSF metabolomics, brain morphometry, and psychiatric disorder risk. Valid MR requires three assumptions [18]: (1) IVs are strongly associated with exposures (CSF metabolites/brain morphometry); (2) IVs are independent of confounders; (3) IVs affect outcomes (psychiatric disorders) only through exposures (no horizontal pleiotropy). Assumptions 2–3 were tested via sensitivity analyses. The study followed the Strengthening the Reporting of Mendelian Randomization Studies (STROBE-MR) guidelines (Supplementary Table S1) [21]. The study workflow is shown in Fig. 1. GWAS Summary Datasets CSF Metabolomics Data were from two longitudinal Alzheimer’s disease (AD) cohorts: the Wisconsin Alzheimer’s Disease Research Center (WADRC) and Wisconsin Registry for Alzheimer’s Prevention (WRAP). Initial inclusion: 689 participants (532 from WADRC, 168 from WRAP) with distinct CSF samples for metabolite analysis. After imputation and strict quality control (details in [25]), the final dataset included 291 baseline visits from unrelated European ancestry individuals (338 CSF metabolites quantified). GWAS summary statistics are available in the NHGRI-EBI GWAS Catalog (Supplementary Table S2) and via ftp://ftp.biostat.wisc.edu/pub/lu_group/Projects/MWAS/. Brain Morphometry GWAS summary data for 83 brain-wide volumes were from the UK Biobank (36,778 unrelated white European participants, 54% female, mean age 63.3 years [range 40.0–81.8 years] at neuroimaging). Genomic principal components analysis (PCA) controlled for population stratification (Supplementary Table S3). Summary statistics are available in the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/). Psychiatric Disorders Summary statistics for 6 psychiatric disorders were from the Psychiatric Genomics Consortium (PGC), restricted to European ancestry (consistent with exposure datasets): ADHD (38,691 cases/186,843 controls), alcohol dependence (10,206 cases/28,480 controls), BD (41,917 cases/371,549 controls), CUD (14,080 cases/343,726 controls), MDD (17,339 cases/53,426 controls), SCZ (68,125 cases/90,984 controls). Data are available at https://www.med.unc.edu/pgc/results-and-downloads. An independent BD GWAS (7,481 cases/9,250 controls) [27] was used for validation. Selection of Genetic Instruments Single-nucleotide polymorphisms (SNPs) associated with exposures (CSF metabolites/brain morphometry) were selected as IVs using: 1. Genome-wide significance: SNPs with P < 1×10⁻⁵; 2. LD pruning: Clumping (r² 0.42 or incompatible alleles (e.g., A/G vs. A/C) excluded; 4. Weak instrument exclusion: F-statistic > 10 (F < 10 indicates weak instruments) [18]. Mendelian Randomization Analyses Primary analysis: Inverse variance weighting (IVW), which weights SNP-exposure and SNP-outcome associations by the inverse variance of SNP-exposure effects. Sensitivity analyses included: MR-Egger regression: Tests horizontal pleiotropy (non-zero intercept indicates pleiotropy) [29]; Weighted median: Unbiased if ≥50% of IVs are valid [29]; Simple/weighted mode: Identifies the most consistent effect size [29]. Heterogeneity was assessed via Cochran’s Q test. Results with significant heterogeneity (P < 0.05) or pleiotropy (MR-Egger intercept P < 0.05) were excluded. Confounding Analysis LDlink’s LDtrait tool (https://ldlink.nih.gov/?tab=ldtrait) tested if SNPs associated with psychiatric disorders were linked to confounders (stressful life events, illness trajectory, childhood developmental delay, offspring psychiatric disorders). No associations were found. Ethics Original GWAS datasets (WADRC, WRAP, UK Biobank, PGC) received ethical approval from their institutional review boards. This study used publicly available summary data, so no additional approval was required. Statistics Analyses were performed in R 4.2.1 (Two-Sample-MR package 0.5.6). Significance was set at two-sided P < 0.05. For CSF metabolite-outcome analyses, Bonferroni correction (338 metabolites per disorder) gave a threshold of P 10, indicating no weak instrument bias. Causal Effects of CSF Metabolites on Psychiatric Disorders Three CSF metabolites showed causal effects on psychiatric disorders (Table 1): 1. Cysteine and BD: CSF cysteine levels increased BD risk (OR = 1.23, 95% CI = 1.11–1.37, P = 1.22×10⁻⁴), validated independently (OR = 2.46, 95% CI = 1.17–5.18, P = 0.02). Reverse MR showed no effect of genetically predicted BD on CSF cysteine (P = 0.46, Supplementary Table S4). 2. Betaine and MDD: CSF betaine levels increased MDD risk (OR = 1.40, 95% CI = 1.18–1.66, P = 1.25×10⁻⁴). 3. N-acetylglutamine and MDD: CSF N-acetylglutamine levels increased MDD risk (OR = 1.42, 95% CI = 1.21–1.68, P = 4.47×10⁻⁴). Reverse MR showed no effect of genetically predicted MDD on betaine (P = 0.24) or N-acetylglutamine (P = 0.80, Supplementary Table S4). Betaine/N-acetylglutamine-MDD associations could not be validated independently (Supplementary Table S4). IVW, MR-Egger, and weighted median showed consistent effect directions. No heterogeneity (Cochran’s Q P > 0.05) or pleiotropy (MR-Egger intercept P > 0.05) was detected. For cysteine-BD, scatter plots, leave-one-out analyses, and funnel plots confirmed no outliers/pleiotropy (Fig. 2). Association Between Brain Morphometry and BD Nine brain morphometric traits were associated with BD (Supplementary Table S5): left superior frontal volume (P = 2.01×10⁻⁵), right superior frontal volume (P = 4.41×10⁻⁵), right inferior temporal volume (P = 6.69×10⁻³), left precentral volume (P = 4.41×10⁻²), etc. However, ORs were near 1 (0.99–1.01, negligible practical effect), and significant heterogeneity was detected (Cochran’s Q P < 0.05). Mediation Analysis: CSF Cysteine → Brain Morphometry → BD Genetically predicted CSF cysteine levels were negatively correlated with: Right inferior temporal volume (β = -131.93, 95% CI = -252.18 to -11.68, P = 0.032; no heterogeneity/pleiotropy, Supplementary Table S6); Left precentral volume (β = -156.28, 95% CI = -307.18 to -5.39, P = 0.04; significant heterogeneity, Supplementary Table S6). Correlation magnitudes were small (β near 0), and brain morphometry did not mediate the cysteine-BD association. Discussion This is the first large-scale bidirectional MR study to explore causal relationships among CSF metabolomics, brain morphometry, and 6 psychiatric disorders. Key findings: (1) CSF cysteine, betaine, and N-acetylglutamine have causal effects on BD or MDD; (2) brain morphometry has no meaningful causal effect on BD; (3) brain morphometry does not mediate the cysteine-BD association. CSF Cysteine and BD Cysteine is a conserved amino acid involved in redox regulation and metal trafficking [30]. Homocysteine (HCY)—a cysteine-methionine metabolism intermediate—is elevated in BD [32,33], but cross-sectional studies could not establish causality. Our MR findings confirm that elevated CSF cysteine *causally* increases BD risk, supporting the role of cysteine-HCY metabolism disturbances in BD pathogenesis [30,33]. CSF Betaine, N-Acetylglutamine, and MDD Betaine (a choline pathway nutrient) has reported antidepressant effects—e.g., elevated plasma betaine reduces post-stroke depression risk [39]. However, we found elevated CSF betaine increases MDD risk. This discrepancy may reflect betaine’s dual immune role: while anti-inflammatory in most contexts [40], excess betaine may trigger chronic inflammation [40]—a known MDD driver [41]. N-acetylglutamine (NAG), a glutamine derivative [35], is linked to MDD via glutamatergic signaling. Previous studies show higher plasma/CSF glutamine in MDD [36,37], and our MR findings confirm a causal NAG-MDD association, highlighting glutamatergic metabolism as a MDD target. Brain Morphometry and Psychiatric Disorders Nine brain volumes were associated with BD, but ORs near 1 and significant heterogeneity indicate no meaningful causal effect. This aligns with inconsistent structural findings in BD [12,13], suggesting brain morphometric changes may be secondary (e.g., to medication/illness chronicity) rather than causal. Limitations 1.Population restriction: All datasets included European ancestry participants—findings may not generalize to other ethnic groups; 2. Disorder scope: Only 6 psychiatric disorders were included; 3. Mediation limits: No independent validation of betaine/N-acetylglutamine-MDD associations, and other potential mediators (e.g., inflammation) were not tested. Conclusion CSF metabolites (cysteine, betaine, N-acetylglutamine) have causal associations with BD or MDD, offering potential diagnostic and therapeutic targets. Brain morphometry has no meaningful causal role in BD and does not mediate the cysteine-BD association. Future studies should validate findings in diverse populations and explore underlying mechanisms (e.g., cysteine-HCY metabolism, glutamatergic signaling). Declarations Data Availability Statement All GWAS summary statistics are publicly available: CSF metabolomics: NHGRI-EBI GWAS Catalog (Supplementary Table S2) and ftp://ftp.biostat.wisc.edu/pub/lu_group/Projects/MWAS/; Brain morphometry: NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/); Psychiatric disorders: PGC website (https://www.med.unc.edu/pgc/results-and-downloads). Conflict of Interest Statement All authors declare no conflicts of interest: no financial support from organizations with a vested interest in the work; no relevant financial relationships in the past 3 years; no activities that could influence the work. Funding This study was supported by the Natural Science Foundation of Fujian Province [grant number 2024J011002]，the Excellent Talent Project of the First Affiliated Hospital of Fujian Medical University (YYXQN-YPS2021 to Pei-Sen Yao)，and the Natural Science Foundation of Fujian Province (2023J01565 to Pei-Sen Yao). 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Supplementary Files Tables.xlsx Supplementary material: Supplementary Tables S1–S6 are available online with this article. supplementfile1.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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16:13:54","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":102321,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7571315/v1/73d37ed6b2703ae97f4461c8.html"},{"id":95566311,"identity":"00e6369e-e1b9-4c9e-a64b-d1c5987a2270","added_by":"auto","created_at":"2025-11-10 16:18:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27510,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of the Mendelian randomization study. Exposures: 338 cerebrospinal fluid (CSF) metabolites, 83 brain-wide volumes. Outcomes: 6 psychiatric disorders (ADHD, alcohol dependence, bipolar disorder [BD], cannabis use disorder [CUD], major depressive disorder [MDD], schizophrenia [SCZ]). Analyses included bidirectional causal effect testing and mediation analysis (brain morphometry as mediator).\u003c/p\u003e","description":"","filename":"Binder11.png","url":"https://assets-eu.researchsquare.com/files/rs-7571315/v1/18e5e963c2d71988a57cf325.png"},{"id":95566313,"identity":"2e2f1e88-5581-458e-ab1b-3af227ba981c","added_by":"auto","created_at":"2025-11-10 16:18:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":638761,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity analyses for the causal association between CSF cysteine and BD. A Scatter plot: Each point represents a SNP; x-axis = SNP effect on CSF cysteine, y-axis = SNP effect on BD; solid line = inverse variance weighting (IVW) estimate. B Leave-one-out plot: IVW estimate after excluding one SNP (no outliers detected). C Funnel plot: Symmetric SNP distribution (no publication bias/pleiotropy).\u003c/p\u003e","description":"","filename":"Binder12.png","url":"https://assets-eu.researchsquare.com/files/rs-7571315/v1/e369b879f13e77b0a71c95e4.png"},{"id":95659998,"identity":"9d670da8-9f35-48a5-86fa-1b09ddf296e6","added_by":"auto","created_at":"2025-11-11 16:30:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1260017,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7571315/v1/e60535f9-0648-4aeb-9ebc-dca131af1391.pdf"},{"id":95566315,"identity":"fc435128-320e-4b4b-8aa6-6c3ea6ecd8a4","added_by":"auto","created_at":"2025-11-10 16:18:30","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":526524,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary material: Supplementary Tables S1–S6 are available online with this article.\u003c/p\u003e","description":"","filename":"Tables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7571315/v1/fc9f41d33ab02114285685fe.xlsx"},{"id":95654802,"identity":"515f2466-e132-4e96-888c-b6f0e467098a","added_by":"auto","created_at":"2025-11-11 16:13:10","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17364,"visible":true,"origin":"","legend":"","description":"","filename":"supplementfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7571315/v1/e81b0a4a0ebb0a4c48319c43.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cerebrospinal Fluid Metabolomics, Brain Morphometry, and Psychiatric Disorders: A Mendelian Randomization Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePsychiatric disorders are among the top 10 global causes of disease burden, encompassing conditions such as bipolar disorder (BD), schizophrenia (SCZ), major depressive disorder (MDD), Attention Deficit Hyperactivity Disorder (ADHD), and substance use disorders (SUD) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Over the past two decades, this burden has not decreased [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and the underlying mechanisms of most psychiatric disorders remain unclear\u0026mdash;limiting progress in diagnosis and treatment.\u003c/p\u003e\u003cp\u003eMetabolomics has emerged as a key tool to unravel disease mechanisms, with disease-associated metabolites offering potential biomarkers for diagnosis and prognosis. Most human metabolomics studies focus on accessible samples (e.g., blood, urine), but cerebrospinal fluid (CSF) is uniquely relevant to psychiatric disorders: it directly reflects the biochemical environment of the central nervous system (CNS), the primary site of pathological changes in these conditions. Preliminary studies have linked CSF metabolites to psychiatric disorders\u0026mdash;for example, MDD patients may show state-dependent changes in CSF 5-hydroxyindoleacetic acid (5-HIAA) and homovanillic acid (HVA) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], while BD/SCZ patients have higher CSF lactate levels than healthy controls or MDD patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, these observational findings do not establish causality.\u003c/p\u003e\u003cp\u003eBrain morphometry has furthered understanding of the neural substrates of psychiatric disorders. MRI studies link MDD to atrophy in the frontal cortex, hippocampus, and basal ganglia [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]; both SCZ and BD show gray matter volume (GMV) reductions in frontotemporal regions, with SCZ exhibiting more widespread loss [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Yet the tripartite relationship between CSF metabolomics, brain morphometry, and psychiatric disorders remains underexplored.\u003c/p\u003e\u003cp\u003eMendelian randomization (MR) addresses limitations of observational studies by using genetic variants as instrumental variables (IVs): genetic variation is fixed at conception, reducing bias from confounding and reverse causality [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. We conducted a bidirectional two-sample MR study to: (1) estimate causal effects of 338 CSF metabolites and 83 brain-wide volumes on 6 psychiatric disorders; (2) test if brain morphometry mediates CSF metabolite-psychiatric disorder pathways; (3) investigate reverse causality (genetic susceptibility to psychiatric disorders affecting CSF metabolites/brain morphometry).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used two-sample MR to evaluate causal relationships between CSF metabolomics, brain morphometry, and psychiatric disorder risk. Valid MR requires three assumptions [18]: (1) IVs are strongly associated with exposures (CSF metabolites/brain morphometry); (2) IVs are independent of confounders; (3) IVs affect outcomes (psychiatric disorders) only through exposures (no horizontal pleiotropy). Assumptions 2–3 were tested via sensitivity analyses. The study followed the Strengthening the Reporting of Mendelian Randomization Studies (STROBE-MR) guidelines (Supplementary Table S1) [21]. The study workflow is shown in Fig. 1. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGWAS Summary Datasets\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCSF Metabolomics \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were from two longitudinal Alzheimer’s disease (AD) cohorts: the Wisconsin Alzheimer’s Disease Research Center (WADRC) and Wisconsin Registry for Alzheimer’s Prevention (WRAP). Initial inclusion: 689 participants (532 from WADRC, 168 from WRAP) with distinct CSF samples for metabolite analysis. After imputation and strict quality control (details in [25]), the final dataset included 291 baseline visits from unrelated European ancestry individuals (338 CSF metabolites quantified). GWAS summary statistics are available in the NHGRI-EBI GWAS Catalog (Supplementary Table S2) and via ftp://ftp.biostat.wisc.edu/pub/lu_group/Projects/MWAS/. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBrain Morphometry \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGWAS summary data for 83 brain-wide volumes were from the UK Biobank (36,778 unrelated white European participants, 54% female, mean age 63.3 years [range 40.0–81.8 years] at neuroimaging). Genomic principal components analysis (PCA) controlled for population stratification (Supplementary Table S3). Summary statistics are available in the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePsychiatric Disorders \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSummary statistics for 6 psychiatric disorders were from the Psychiatric Genomics Consortium (PGC), restricted to European ancestry (consistent with exposure datasets): ADHD (38,691 cases/186,843 controls), alcohol dependence (10,206 cases/28,480 controls), BD (41,917 cases/371,549 controls), CUD (14,080 cases/343,726 controls), MDD (17,339 cases/53,426 controls), SCZ (68,125 cases/90,984 controls). Data are available at https://www.med.unc.edu/pgc/results-and-downloads. An independent BD GWAS (7,481 cases/9,250 controls) [27] was used for validation. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelection of Genetic Instruments \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-nucleotide polymorphisms (SNPs) associated with exposures (CSF metabolites/brain morphometry) were selected as IVs using: \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1. Genome-wide significance: SNPs with P \u0026lt; 1×10⁻⁵; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. LD pruning: Clumping (r² \u0026lt; 0.001, 1 Mb window) to ensure IV independence; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. Allele harmonization: Consistency in effect alleles between exposure and outcome datasets; SNPs with effect allele frequency (EAF) \u0026gt; 0.42 or incompatible alleles (e.g., A/G vs. A/C) excluded; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4. Weak instrument exclusion: F-statistic \u0026gt; 10 (F \u0026lt; 10 indicates weak instruments) [18]. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMendelian Randomization Analyses \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrimary analysis: Inverse variance weighting (IVW), which weights SNP-exposure and SNP-outcome associations by the inverse variance of SNP-exposure effects. Sensitivity analyses included: \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMR-Egger regression: Tests horizontal pleiotropy (non-zero intercept indicates pleiotropy) [29]; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWeighted median: Unbiased if ≥50% of IVs are valid [29]; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimple/weighted mode: Identifies the most consistent effect size [29]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHeterogeneity was assessed via Cochran’s Q test. Results with significant heterogeneity (P \u0026lt; 0.05) or pleiotropy (MR-Egger intercept P \u0026lt; 0.05) were excluded. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfounding Analysis \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLDlink’s LDtrait tool (https://ldlink.nih.gov/?tab=ldtrait) tested if SNPs associated with psychiatric disorders were linked to confounders (stressful life events, illness trajectory, childhood developmental delay, offspring psychiatric disorders). No associations were found.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOriginal GWAS datasets (WADRC, WRAP, UK Biobank, PGC) received ethical approval from their institutional review boards. This study used publicly available summary data, so no additional approval was required. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistics \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses were performed in R 4.2.1 (Two-Sample-MR package 0.5.6). Significance was set at two-sided P \u0026lt; 0.05. For CSF metabolite-outcome analyses, Bonferroni correction (338 metabolites per disorder) gave a threshold of \u0026nbsp;P \u0026lt; 1.48×10⁻⁴. \u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eGenetic Instruments and Weak Instrument Bias \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll IVs had F-statistics \u0026gt; 10, indicating no weak instrument bias. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCausal Effects of CSF Metabolites on Psychiatric Disorders\u003c/p\u003e\n\u003cp\u003eThree CSF metabolites showed causal effects on psychiatric disorders (Table 1): \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1. Cysteine and BD: CSF cysteine levels increased BD risk (OR = 1.23, 95% CI = 1.11–1.37, P = 1.22×10⁻⁴), validated independently (OR = 2.46, 95% CI = 1.17–5.18, P = 0.02). Reverse MR showed no effect of genetically predicted BD on CSF cysteine (P = 0.46, Supplementary Table S4). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. Betaine and MDD: CSF betaine levels increased MDD risk (OR = 1.40, 95% CI = 1.18–1.66, P = 1.25×10⁻⁴). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. N-acetylglutamine and MDD: CSF N-acetylglutamine levels increased MDD risk (OR = 1.42, 95% CI = 1.21–1.68, P = 4.47×10⁻⁴). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eReverse MR showed no effect of genetically predicted MDD on betaine (P = 0.24) or N-acetylglutamine (P = 0.80, Supplementary Table S4). Betaine/N-acetylglutamine-MDD associations could not be validated independently (Supplementary Table S4). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIVW, MR-Egger, and weighted median showed consistent effect directions. No heterogeneity (Cochran’s Q P \u0026gt; 0.05) or pleiotropy (MR-Egger intercept P \u0026gt; 0.05) was detected. For cysteine-BD, scatter plots, leave-one-out analyses, and funnel plots confirmed no outliers/pleiotropy (Fig. 2). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation Between Brain Morphometry and BD \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNine brain morphometric traits were associated with BD (Supplementary Table S5): left superior frontal volume (P = 2.01×10⁻⁵), right superior frontal volume (P = 4.41×10⁻⁵), right inferior temporal volume (P = 6.69×10⁻³), left precentral volume (P = 4.41×10⁻²), etc. However, ORs were near 1 (0.99–1.01, negligible practical effect), and significant heterogeneity was detected (Cochran’s Q P \u0026lt; 0.05). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMediation Analysis: CSF Cysteine → Brain Morphometry → BD \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenetically predicted CSF cysteine levels were negatively correlated with: \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRight inferior temporal volume (β = -131.93, 95% CI = -252.18 to -11.68, P = 0.032; no heterogeneity/pleiotropy, Supplementary Table S6); \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLeft precentral volume (β = -156.28, 95% CI = -307.18 to -5.39, P = 0.04; significant heterogeneity, Supplementary Table S6). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorrelation magnitudes were small (β near 0), and brain morphometry did not mediate the cysteine-BD association.\u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first large-scale bidirectional MR study to explore causal relationships among CSF metabolomics, brain morphometry, and 6 psychiatric disorders. Key findings: (1) CSF cysteine, betaine, and N-acetylglutamine have causal effects on BD or MDD; (2) brain morphometry has no meaningful causal effect on BD; (3) brain morphometry does not mediate the cysteine-BD association.\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCSF Cysteine and BD \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCysteine is a conserved amino acid involved in redox regulation and metal trafficking [30]. Homocysteine (HCY)—a cysteine-methionine metabolism intermediate—is elevated in BD [32,33], but cross-sectional studies could not establish causality. Our MR findings confirm that elevated CSF cysteine *causally* increases BD risk, supporting the role of cysteine-HCY metabolism disturbances in BD pathogenesis [30,33]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCSF Betaine, N-Acetylglutamine, and MDD \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetaine (a choline pathway nutrient) has reported antidepressant effects—e.g., elevated plasma betaine reduces post-stroke depression risk [39]. However, we found elevated CSF betaine increases MDD risk. This discrepancy may reflect betaine’s dual immune role: while anti-inflammatory in most contexts [40], excess betaine may trigger chronic inflammation [40]—a known MDD driver [41]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eN-acetylglutamine (NAG), a glutamine derivative [35], is linked to MDD via glutamatergic signaling. Previous studies show higher plasma/CSF glutamine in MDD [36,37], and our MR findings confirm a causal NAG-MDD association, highlighting glutamatergic metabolism as a MDD target. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBrain Morphometry and Psychiatric Disorders \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNine brain volumes were associated with BD, but ORs near 1 and significant heterogeneity indicate no meaningful causal effect. This aligns with inconsistent structural findings in BD [12,13], suggesting brain morphometric changes may be secondary (e.g., to medication/illness chronicity) rather than causal. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.Population restriction: All datasets included European ancestry participants—findings may not generalize to other ethnic groups; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. Disorder scope: Only 6 psychiatric disorders were included; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. Mediation limits: No independent validation of betaine/N-acetylglutamine-MDD associations, and other potential mediators (e.g., inflammation) were not tested. \u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eCSF metabolites (cysteine, betaine, N-acetylglutamine) have causal associations with BD or MDD, offering potential diagnostic and therapeutic targets. Brain morphometry has no meaningful causal role in BD and does not mediate the cysteine-BD association. Future studies should validate findings in diverse populations and explore underlying mechanisms (e.g., cysteine-HCY metabolism, glutamatergic signaling). \u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll GWAS summary statistics are publicly available: \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCSF metabolomics: NHGRI-EBI GWAS Catalog (Supplementary Table S2) and ftp://ftp.biostat.wisc.edu/pub/lu_group/Projects/MWAS/; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBrain morphometry: NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/); \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePsychiatric disorders: PGC website (https://www.med.unc.edu/pgc/results-and-downloads). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflicts of interest: no financial support from organizations with a vested interest in the work; no relevant financial relationships in the past 3 years; no activities that could influence the work.\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Natural Science Foundation of Fujian Province [grant number 2024J011002]，the Excellent Talent Project of the First Affiliated Hospital of Fujian Medical University (YYXQN-YPS2021 to Pei-Sen Yao)，and the Natural Science Foundation of Fujian Province (2023J01565 to Pei-Sen Yao). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the participants and investigators of WADRC, WRAP, UK Biobank, and PGC for sharing data. We also acknowledge the NHGRI-EBI GWAS Catalog for hosting metabolomic and brain morphometric data. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGBD 2019 Mental Disorders Collaborators. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022;9(2):137\u0026ndash;150. \u003c/li\u003e\n\u003cli\u003eSuhre K, Meisinger C, D\u0026ouml;ring A, et al. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One. 2010;5(11):e13953. \u003c/li\u003e\n\u003cli\u003eOberbach A, Bl\u0026uuml;her M, Wirth H, et al. Combined proteomic and metabolomic profiling of serum reveals association of the complement system with obesity and identifies novel markers of body fat mass changes. 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Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat Genet. 2011;43(10):977\u0026ndash;983. \u003c/li\u003e\n\u003cli\u003eCai J, He L, Wang H, et al. Genetic liability for prescription opioid use and risk of cardiovascular diseases: a multivariable Mendelian randomization study. Addiction. 2022;117(5):1382\u0026ndash;1391. \u003c/li\u003e\n\u003cli\u003eBowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512\u0026ndash;525. \u003c/li\u003e\n\u003cli\u003eFan N, Zhao W, Yun Y, et al. Homocysteine levels in first-episode patients with psychiatric disorders. Front Psychiatry. 2024;15:1380900. \u003c/li\u003e\n\u003cli\u003eChancel R, Lopez-Castroman J, Baca-Garcia E, Mateos Alvarez R, Courtet P, Conejero I. Biomarkers of Bipolar Disorder in Late Life: An Evidence-Based Systematic Review. 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Front Pharmacol. 2020;11:508. \u003c/li\u003e\n\u003cli\u003eMathis P, Schmitt L, Benatia M, Granier F, Ghisolfi J, Moron P. [Plasma amino acid disturbances and depression]. Encephale. 1988;14(2):77\u0026ndash;82. \u003c/li\u003e\n\u003cli\u003eMadeira C, Vargas-Lopes C, Brand\u0026atilde;o CO, et al. Elevated Glutamate and Glutamine Levels in the Cerebrospinal Fluid of Patients With Probable Alzheimer\u0026apos;s Disease and Depression. Front Psychiatry. 2018;9:561. \u003c/li\u003e\n\u003cli\u003eLevine J, Panchalingam K, Rapoport A, Gershon S, McClure RJ, Pettegrew JW. Increased cerebrospinal fluid glutamine levels in depressed patients. Biol Psychiatry. 2000;47(7):586\u0026ndash;593. \u003c/li\u003e\n\u003cli\u003eMiao M, Du J, Che B, et al. Circulating choline pathway nutrients and depression after ischemic stroke. Eur J Neurol. 2022;29(2):459\u0026ndash;468. \u003c/li\u003e\n\u003cli\u003eZhang G, Huang F, Wang C, et al. Betaine in Inflammation: Mechanistic Aspects and Applications. Front Immunol. 2018;9:2741. \u003c/li\u003e\n\u003cli\u003eBennabi E, Tanti M, Naassila M. The Bidirectional Relationship of Depression and Inflammation: Double Trouble. Neuron. 2020;107(2):234\u0026ndash;248. \u003c/li\u003e\n\u003cli\u003eJones LD, Payne ME, Messer DF, et al. Temporal lobe volume in bipolar disorder: relationship with diagnosis and antipsychotic medication use. J Affect Disord. 2009;114(1\u0026ndash;3):50\u0026ndash;57. \u003c/li\u003e\n\u003cli\u003eGao Y, Duan X, Li W, et al. Elevated Homocysteine Levels and Hypertension Relate to Cognitive Impairment via Increased White Matter Hyperintensity Volume. J Alzheimers Dis. 2023;96(4):1739\u0026ndash;1746. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cerebrospinal fluid metabolomics, Psychiatric disorder, Brain morphometry, Mendelian randomization, Causal effect","lastPublishedDoi":"10.21203/rs.3.rs-7571315/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7571315/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003eTo identify the causal roles of cerebrospinal fluid (CSF) metabolomics and brain morphometry in the development of psychiatric disorders, we conducted a bidirectional Mendelian Randomization (MR) study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eWe performed a two-sample MR analysis to estimate the causal effects of 338 CSF metabolites and 83 brain-wide volumes on the risk of 6 psychiatric disorders: Attention Deficit Hyperactivity Disorder (ADHD), alcohol dependence, bipolar disorder (BD), cannabis use disorder (CUD), major depressive disorder (MDD), and schizophrenia (SCZ). Imputation and strict quality control were applied to CSF metabolite and genotype data, resulting in a final dataset of 291 baseline visits from 689 unrelated individuals of European ancestry (338 CSF metabolites quantified). GWAS summary data for brain morphometry were derived from the UK Biobank (36,778 unrelated white European participants, 54% female, with neuroimaging data). Summary statistics for psychiatric disorders were obtained from the Psychiatric Genomics Consortium (PGC): ADHD (38,691 cases/186,843 controls), alcohol dependence (10,206 cases/28,480 controls), BD (41,917 cases/371,549 controls), CUD (14,080 cases/343,726 controls), MDD (17,339 cases/53,426 controls), SCZ (68,125 cases/90,984 controls). We further explored whether brain morphometry mediates the pathway from CSF metabolomics to psychiatric disorders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e After inverse variance weighting (IVW) and sensitivity analyses, 3 CSF metabolites showed causal effects on psychiatric disorders:\u003c/p\u003e\n\u003cp\u003e1. CSF cysteine levels increased BD risk (odds ratio [OR] = 1.23, 95% confidence interval [CI] = 1.11–1.37, P = 1.22×10⁻⁴), validated by an independent GWAS (OR = 2.46, 95% CI = 1.17–5.18, P = 0.02);\u003c/p\u003e\n\u003cp\u003e2. CSF betaine levels increased MDD risk (OR = 1.40, 95% CI = 1.18–1.66, P = 1.25×10⁻⁴);\u003c/p\u003e\n\u003cp\u003e3. CSF N-acetylglutamine levels increased MDD risk (OR = 1.42, 95% CI = 1.21–1.68, P = 4.47×10⁻⁴).\u003c/p\u003e\n\u003cp\u003eReverse MR showed no reverse causality (genetically predicted BD/MDD did not affect respective metabolite levels, P \u0026gt; 0.05). Brain morphometry had no causal effect on BD, and did not mediate the cysteine-BD association.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e CSF metabolites (cysteine, betaine, N-acetylglutamine) have causal associations with BD or MDD, providing potential targets for diagnosis and treatment of these psychiatric disorders.\u003c/p\u003e","manuscriptTitle":"Cerebrospinal Fluid Metabolomics, Brain Morphometry, and Psychiatric Disorders: A Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 16:18:25","doi":"10.21203/rs.3.rs-7571315/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b864418-c12f-46ae-b9fa-84ee95ee8cce","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:18:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 16:18:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7571315","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7571315","identity":"rs-7571315","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}