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

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

To investigate the causal relevance of melatonin metabolism, which provides the biological basis for circulating melatonin levels, to specific depression symptom subtypes, we performed a targeted systematic review of melatonin metabolism pathways in the human brain and liver. Using two-sample Mendelian randomization (MR), we assessed the causal effects of metabolism pathways and/or individual genes on major depressive disorder (MDD) and nine symptom subtypes derived from Patient Health Questionnaire-9 (PHQ-9). Instrumental variables (IVs) were expression quantitative trait loci (eQTL) for eight individual genes, one synthesis route, and three degradation routes. Results were assessed using Bayesian colocalization and phenome-wide association analyses. At the pathway-level, the genetically proxied synthesis-route signal was associated with PHQ-9 Assessment 5 (PHQ9A5, OR: 0.89, 95% CI: 0.85-0.93), but sensitivity analyses suggested this association was primarily driven by TPH1 and may reflect serotonin-related biology. In contrast, higher brain melatonin degradation raised the risk of both PHQ9A1 (OR: 1.03, 95% CI: 1.02-1.04) and PHQ9A7 (OR: 1.03, 95% CI: 1.02-1.03). Within degradation, up-regulation of the kynurenine sub-pathway increased the odds of PHQ9A3 (OR: 1.05, 95% CI: 1.02-1.07), PHQ9A4 (OR = 1.04, 95% CI: 1.02-1.06) and PHQ9A7 (OR: 1.05, 95% CI: 1.02-1.07). Gene-level analyses were largely concordant, except for SULT1A1, whose higher expression was genetically protective for PHQ9A3 but risk-increased for PHQ9A1 and PHQ9A4. Overall, these results demonstrate that melatonin metabolism exerts symptom-specific and pathway-specific causal effects on depression. A stratified view of melatonin's role may help optimize the application of exogenous melatonin supplementation.

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

These findings provide a hypothesis-generating reframing of the traditional comorbidity model, suggesting that divergent molecular programs may converge on shared pathways and offer a preliminary foundation for exploring therapeutic strategies at the mood–metabolism interface.

Immune disturbances cluster around specific depressive phenotypes under conditions of structural adversity

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

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.

Mapping genetic convergence across brain structure, mental health, and cardiometabolic disease

Individuals with psychiatric disorders frequently experience comorbid cardiometabolic conditions, complicating treatment and worsening health outcomes. Both psychiatric and cardiometabolic disorders have been individually associated with alterations in brain structure. Yet, it remains unclear whether these associations stem from a shared genetic basis that underlies their frequent co-occurrence. We analyzed genome-wide association summary statistics from large international consortia of individuals of European ancestry, including psychiatric disorder GWAS with case–control sample sizes ranging from ~18,000 to ~158,000 cases, cardiometabolic disease GWAS with up to ~242,000 cases, and cortical morphology GWAS from UK Biobank comprising ~39,000 individuals. We applied complementary multivariate, causal, and mediation genetic analyses to disentangle genetic factors underlying brain alterations and comorbidity. Here we show that patterns of genetic overlap differ across disorders. Schizophrenia exhibits substantial polygenic overlap with cortical thickness and type 2 diabetes, despite low genetic correlation. In contrast, attention-deficit/hyperactivity disorder (ADHD) is more strongly correlated with cardiometabolic disease but shows limited overlap with cortical morphology. Notably, cortical surface area partly mediates the genetic association between ADHD and type 2 diabetes. Pathway analyses highlight metabolic stress processes in ADHD as well as neurodevelopmental and immune processes in schizophrenia. These findings indicate that psychiatric–cardiometabolic comorbidity arises through both shared and disorder-specific genetic pathways. This work clarifies the genetic architecture of multimorbidity and highlights opportunities for trait-targeted prevention strategies in psychiatry. Kopal et al. integrate large-scale genetic data to map the shared architecture across psychiatric disorders, cardiometabolic disease, and brain morphology. Their findings reveal divergent patterns: schizophrenia shows extensive overlap with cortical thickness and type 2 diabetes, whereas ADHD displays a metabolic-linked profile. Many people with psychiatric disorders also experience physical health problems, such as heart disease or diabetes. It remains unclear whether these links are due to shared genetic factors. In this study, we used large genetic datasets from hundreds of thousands of participants to investigate how psychiatric disorders, brain structure, and cardiometabolic diseases are genetically connected. We applied statistical methods to identify shared genetic influences and to explore whether one trait may partly influence another. We found that schizophrenia and ADHD show distinct genetic patterns: schizophrenia shares more genetic factors with brain structure and diabetes, while ADHD is more strongly linked to metabolic pathways. These findings highlight that different mental disorders may involve different biological routes, which could inform prevention and treatment strategies in the future.

Joint Effects of Adverse Childhood Experiences and Genetic Burden of Depression on Multiple Depressive Symptoms.

Adverse childhood experiences (ACEs) and genetic burden of depression are key factors influencing depression, but their joint effects remain unclear. This study explores the joint effects on depressive symptoms and examines the mediating role of ACEs. This study was based on data from the prospective cohort Health and Retirement Study (HRS), in which 6204 adults of European ancestry aged 50 years and older were analysed. ACEs were measured using the Psychosocial and Lifestyle Questionnaire (PLQ), and genetic burden was assessed via polygenic risk scores (PRS). Depressive symptoms (total, cognitive-affective, somatic) were evaluated with the Centre for Epidemiological Studies Depression Scale (CES-D). Multivariate Cox regression models were used to assess independent and joint effects, and mediation analysis tested whether genetic burden increased depression risk through ACEs. ACEs were associated with higher risks of total (HR = 1.305, 95% CI: 1.174, 1.451), somatic (1.337, 1.206-1.482), and cognitive-affective (1.247, 1.114-1.395) depressive symptoms. Increased genetic burden was also associated with elevated risks of total (HR = 1.088, 95% CI: 1.033, 1.147) and somatic (1.089, 1.035-1.147) depressive symptoms. Compared to those with no ACEs and low genetic burden, individuals with ACEs and high genetic burden had significantly higher risks of depressive symptoms (total: 60%, somatic: 63%, cognitive-affective: 43%). ACEs mediated the link between genetic burden and somatic symptoms. Both ACEs and genetic burden of depression are associated with an increased risk of depressive symptoms, and their joint effects significantly increase the risk of all types of depressive symptoms. Furthermore, genetic burden also indirectly elevates the risk of somatic depressive symptoms by influencing ACEs.