Genetic evidence for a causal relationship between melatonin metabolism and depression.

Immune-metabolic mediation of depression risk: Causal evidence from antibody and metabolite GWAS.

Depression is a major contributor to global disability, yet its underlying biological mechanisms remain incompletely understood. Immune activation and metabolic alterations, particularly involving lipid metabolism, have been implicated, but their roles in depression remain unclear. We used large-scale genome-wide association study (GWAS) summary statistics to examine the relationships among antibody responses, plasma metabolites, and depression. Mendelian randomization (MR) was applied to evaluate genetically predicted associations of antibody traits and metabolites with depression risk. Generalized summary-data-based Mendelian randomization (GSMR) was used as a complementary analysis, and two-step mediation analyses were performed to assess whether selected metabolites were compatible with mediating immune-related effects on depression. Higher Epstein-Barr virus (EBV) ZEBRA antibody levels were associated with increased depression risk, whereas arachidonate-enriched metabolites tended to show inverse associations with depression and several linoleoyl-related metabolites showed positive associations. Mediation analyses suggested that 1,2-dilinoleoyl-GPC showed a positive indirect effect, whereas 1-arachidonylglycerol showed a negative indirect effect. Two glycerolipid ratios also showed indirect effects consistent with partial mediation. Complementary GSMR analyses showed directionally concordant results for the principal associations. Overall, these findings support an immunometabolic interpretation in which EBV-related immune responses and plasma metabolite remodeling may be relevant to depression vulnerability, and they prioritize candidate metabolites and pathways for future mechanistic and clinical investigation.

Genetic clusters of BMI reveal symptom-specific causal effects on depression.

While BMI is phenotypically and genetically associated with depression, the extent to which BMI causes depression and the mechanisms underlying this effect remain unclear. We estimated the causal effect of BMI on depression symptoms, accounting for complexity in both the exposure and the outcome. We applied PheWAS-based Clustering of Mendelian Randomization instruments (PWC-MR) to partition 324 BMI-associated SNPs into six genetic clusters and estimated the effect of each cluster on nine depression symptoms from the largest available genome-wide meta-analysis of PHQ-9 depression symptoms (N = 224,535-308,421). Across all six clusters, BMI had the largest effect on appetite changes (β ranges from 0.19 to 0.34), which was robust across estimators, homogeneous across clusters, and survived correction for multiple testing. Smaller effects were observed for several other symptoms, including concentration changes, fatigue, anhedonia, and sleep problems, but these were heterogeneous across clusters and often attenuated under pleiotropy-robust estimators, indicating likely bias from horizontal pleiotropy. Little evidence of an effect was detected for depressed mood and suicidal ideation. Cluster-level heterogeneity was observed for the majority of symptoms, demonstrating that the causal effect of BMI varies by instrument group and indicating likely violation of the exclusion restriction assumption. These findings indicate that the causal effect of BMI on depression detected in typical non-clustered Mendelian randomization is driven to an extent by appetite, with no or inconsistent evidence for effects on core psychological symptoms such as anhedonia and depressed mood.

Genetic evidence for a causal role of depression in late-onset Alzheimer disease: A Mendelian randomization study

Observational studies link psychiatric disorders to Alzheimer disease (AD), but whether these associations are causal remains unclear due to confounding and reverse causality. We aimed to dissect these relationships using genetic evidence. We performed a two-sample Mendelian randomization (MR) study to assess the causal effects of 7 psychiatric and neurodevelopmental disorders on AD and its subtypes. The primary analysis utilized the inverse-variance weighted (IVW) method, supported by comprehensive sensitivity analyses (MR Steiger test, MR-Egger, and MR-PRESSO) and a supplementary analysis excluding single nucleotide polymorphisms (SNPs) associated with potential confounders. Depression showed a significant causal association with late-onset AD (odds ratio [OR] = 1.0736, 95% confidence interval [CI]: 1.0084–1.1431, P = .0264). No significant causal associations were found for other psychiatric disorders (all P > .05). A directionality test conducted by MR Steiger confirmed our estimation of potential causal direction (P < .001). Sensitivity analyses excluding pleiotropic SNPs yielded consistent results. The causal association for depression and late-onset AD remained significant after excluding pleiotropic SNPs linked to confounders (OR = 1.0726, 95% CI: 1.0069–1.1425, P = .03). Our study provides genetic evidence supporting a causal role for depression in the etiology of late-onset AD, a link not observed for other major psychiatric disorders tested. These findings highlight the specific importance of managing depression as a potential strategy for mitigating AD risk and suggest distinct etiological pathways between different mental health conditions and neurodegeneration.

Identification of novel loci regulating circulating melatonin and its causal relationship with hypertension

Genetic associations and candidate functional genes linking depression and obesity: a multi-omics integrative study

Introduction Major depressive disorder (MDD) and obesity are intersecting global crises. Despite observational links, a clinical paradox persists: antidepressants often improve metabolic status, while weight loss rarely alleviates core depressive symptoms. This prompts closer examination of whether the depression–obesity relationship reflects asymmetric genetic architecture, shared liability, or statistical constraints that obscure definitive conclusions. Methods We developed an integrative multi-omics framework leveraging large-scale population data from the National Health and Nutrition Examination Survey (NHANES) and East Asian genetic data. Epidemiological regression was applied to NHANES to characterize real-world phenotypic cross-talk. We utilized bidirectional Mendelian randomization (MR) to explore the direction of association, targeted summary-data-based MR (SMR) with heterogeneity in dependent instruments (HEIDI) testing to prioritize candidate functional genes, and single-cell RNA sequencing (scRNA-seq) of regulatory T cells (Tregs). In silico cell composition adjustment and virtual knockout (VKO) simulations were implemented to distinguish intrinsic cellular remodeling from compositional shifts and to infer convergent downstream programs. Results Bidirectional MR yielded a nominally significant association from MDD to obesity risk (β = 0.0458, P = 0.0209), whereas the reverse path was inconclusive due to low statistical power (<10%), precluding definitive conclusions about directionality. SMR/HEIDI identified multiple FDR-significant obesity-associated genes, including NT5C2, ACYP2, and TMEM180, whereas on the depression side only ACAT1 reached nominal significance, positioning it as a borderline hypothesis-generating candidate. Cell composition adjustment suggested that transcriptional signals reflected intrinsic remodeling, preserving up to 98% of effect sizes for top candidates. At the molecular level, the conditions diverged: obesity risk was dominated by immune-compartment inflammation and post-transcriptional splicing dysregulation, whereas MDD risk was characterized by ribosomal translation perturbations. Strikingly, VKO simulations revealed convergence on a shared downstream program anchored in cytoskeletal reorganization and E2F-target modulation. Exploratory druggability screening nominated FDFT1 (with a phase 3 inhibitor) and ADORA2A as potential repurposing candidates requiring experimental validation. Conclusion Our findings provide a hypothesis-generating reframing of the traditional comorbidity model, suggesting that divergent molecular programs may converge on shared pathways. Although the full extent of bidirectional genetic relationships remains unconfirmed, these findings offer a preliminary foundation for exploring therapeutic strategies at the mood–metabolism interface.

Exploring the Causal Relationship Between Sex Hormones and Depression by Means of Two-Sample Mendelian Randomization Analysis.

BACKGROUND To investigate the potential causal association between sex hormones and depression, a twosample mendelian randomization (MR) analysis was conducted. METHODS Summary statistics from Genome-wide Association Study (GWAS) on sex hormones and depression were collected. Sex-specific instruments were used to analyze seven sex hormones, including progesterone (PROG) and bioavailable testosterone (BAT). The inverse variance weighted (IVW) method was employed as the primary analysis, and sensitivity analyses were conducted to assess the robustness of the findings. RESULTS The IVW analysis revealed genetically significant association between PROG and depression (odds ratio (OR): 0.95, 95% confidence interval (CI): 0.92-0.98, p = 0.002), which remained significant after Bonferroni correction (p < 0.0036). A nominally significant association was observed for BAT (OR: 0.90, 95% CI: 0.82-1.00, p = 0.049) and depression; however, this association did not survive Bonferroni correction. Upon stratification by gender, these associations were no longer significant (p > 0.05). Furthermore, no substantial associations were observed between depression and other sex hormones, including total testosterone (TT), estradiol (E2), follicle-stimulating hormone, prolactin, and luteinizing hormone (p > 0.05). Leave-one-out analyses and funnel plots (indicating balanced pleiotropy), confirmed the reliability of these findings, supporting the robustness of the results. Significant heterogeneity was observed in seven exposures: E2_Female, TT_Male, TT_Both, BAT_Both, BAT_Male, BAT_Female, and TT_Female. Additionally, Mendelian Randomization Pleiotropy Residual Sum and Outlier (MR-PRESSO) analysis identified outliers for BAT_Both, BAT_Female, E2_Female, TT_Both, and TT_Female; however, excluding these outliers did not alter the results. CONCLUSIONS This study indicates a potential causal association between genetically predicted PROG levels and a decreased risk of depression. While BAT also suggested a potential protective effect, this association was considered suggestive after multiple testing correction and requires further validation.