European Archives of Psychiatry and Clinical Neuroscience最新文献

Background: Emerging evidence indicates overlapping neurobiological variations across transdiagnostic psychiatric disorders. Considering depression and anxiety as the most common and co-occurring symptoms in affective disorders, we hypothesize shared symptom-related electrophysiological aberrations in the neural rhythmic oscillations across diagnostic boundaries.

Methods: 53 treatment-naïve patients with major depressive disorder, generalized anxiety disorder, or depressive episode in bipolar disorder, and 83 healthy controls (HC), underwent 64-channel electroencephalographic (EEG) recording while watching 10 min of neutral, sad, happy and fearful videos. The phase locking value (PLV) based on individual alpha peak frequency (iAPF) was used to measure phase synchronization alteration of EEG oscillations.

Results: Compared with HCs, patients exhibited widespread phase synchronization disturbances, characterized by prominent gamma PLV reductions and low-frequency (delta/theta/alpha) increases across frontal, central, and parietal-occipital regions. Depressive symptoms correlated with decreased beta PLV in the central region during happy stimuli, increased alpha PLV in the parieto-occipital lobe during sad stimuli, and increased theta PLV in the frontal and parietal lobes during fearful stimuli. Anxious symptoms correlated with decreased gamma PLV in the right frontal and parietal lobes during happy stimuli, increased alpha PLV in right frontal and parietal lobes, and increased gamma PLV in the frontal and parietal lobes during fearful stimuli.

Conclusions: This study supports shared symptom-related aberrations in EEG phase synchronization patterns across transdiagnostic affective disorders, as well as the divergent neural characteristics between depression and anxiety. These findings may provide potential implications for probing and modulating symptom-targeted neural oscillational biomarkers across diagnoses.

Objectives: Sleep disorders are multifactorial conditions influenced by genetic and environmental factors. However, the causal roles of specific cellular senescence-related genes in distinct sleep disorders subtypes remain unclear. This study aimed to dissect these relationships by integrating multi-omics data to identify potential causal pathways in sleep apnea and insomnia.

Methods: We conducted a multi-stage Mendelian randomization (SMR) study. We first performed an exploratory SMR analysis integrating a comprehensive list of senescence-related genes with genome-wide association study (GWAS) data for a broad sleep disorders phenotype from the FinnGen consortium. To address phenotype heterogeneity, we then performed in-depth, phenotype-specific SMR analyses for sleep apnea and insomnia, integrating data across three molecular levels: DNA methylation (mQTL), gene expression (eQTL), and protein abundance (pQTL). Significant findings were validated using colocalization, HEIDI tests, replication cohorts, and two-sample MR sensitivity analyses.

Results: Our discovery analysis on broad sleep disorders phenotype identified several candidate genes. Subsequent phenotype-specific analyses revealed distinct genetic architectures. Genetically predicted lower CTSB expression was associated with a reduced risk of sleep apnea (OR = 0.943, 95% CI = 0.905-0.983, P = 0.006, FDR = 0.23) a finding potentially mediated by methylation at cg19746565. Furthermore, SIRT6 emerged as a shared risk factor. Genetically predicted higher SIRT6 expression was associated with an increased risk of both sleep apnea (OR = 1.279, 95% CI = 1.124-1.456, P = 0.0002, FDR = 0.06) and insomnia (OR = 1.573, 95% CI = 1.099-2.251, P = 0.013, FDR = 0.61), with its effect on insomnia being validated in brain hypothalamus tissue.

Conclusions: Our multi-omics MR analysis has identified BLK, CTSB, TP53INP1, DNMT3A, ITPR1, and SLC16A7 as pivotal factors in the pathogenesis of sleep disorders. These findings provide a foundation for innovative early interventions and therapeutic strategies for sleep disorders.

Background: Observational studies have indicated interplay between lipid profiles and mental disorders, but genetic causal evidence remains limited. This study aimed to assess bidirectional causal links between lipid profiles and five major mental disorders using Mendelian randomization (MR) analysis.

Methods: We performed a bidirectional two-sample MR analysis utilizing genome-wide association study (GWAS) summary statistics from European populations. Genetic instruments were selected for five psychiatric disorders (major depressive disorder (MDD), anxiety, attention-deficit/hyperactivity disorder (ADHD), bipolar disorder (BIP), and schizophrenia (SCZ)) and six lipid traits: triglycerides (TG), total cholesterol (TC), high-/low-density lipoprotein cholesterol (HDL/LDL) and apolipoproteins (Apo A-1, Apo B). The inverse-variance weighted (IVW) method served as the primary analytical approach, complemented by MR-Egger regression to evaluate horizontal pleiotropy.

Results: Forward MR analysis suggested a significant causal relationship between LDL and MDD (p_(MDD) < 0.001, OR (95%CI) = 0.888 (0.833-0.946)), TG and anxiety (p_(anxiety) = 0.016, OR (95%CI) = 1.057(1.010-1.105)). Reverse MR analysis indicated that genetic liability to MDD ( p = 0.017, OR (95%CI) = 1.096(1.016-1.182), and ADHD ( p = 0.009, OR (95%CI) = 1.039( 1.009-1.069)) elevated TG levels, anxiety disorders increased HDL (p = 0.003, OR (95%CI) = 1.288( 1.029-1.520)), LDL (p = 0.001,OR (95%CI) = 1.176(1.066-1.298)), Apo A-1 (p = 0.012,OR(95%CI) = 1.306(1.060-1.609)), and Apo B (p = 0.007,OR(95%CI) = 1.134(1.036-1.241)), whereas SCZ was inversely correlated with lipid profiles. No causal relationships were observed for bipolar disorder.

Conclusions: These findings suggest that genetically predicted TG levels may influence anxiety risk, while genetic predisposition to MDD and ADHD may contribute to dyslipidemia. Our study provides genetic evidence supporting a potential causal interplay between lipid metabolism and mental disorders, highlighting the need for interdisciplinary care and personalized monitoring to address psycho-metabolic comorbidities.

Although neuroticism has been linked to cardiovascular disease (CVD), the etiological relevance of individual neuroticism symptoms remains poorly understood. In this study, we employed Linkage disequilibrium score regression (LDSC) and systematic Mendelian randomization (MR) analyses to examine the potential causal effects of specific neuroticism symptoms on CVD. We obtained summary-level data on 13 specific neuroticism symptoms from the UK Biobank (ranging from 366,726-380,506 participants). Summary statistics for CVD originated from three European-descent GWASs with a total of 799,534 participants. LDSC was performed to investigate the genetic associations between specific neuroticism symptoms and CVD. The inversevariance weighted approach was applied to test the causality for the studied associations, together with extensive validation and sensitivity analyses to verify the main results. LDSC revealed significant genetic correlations between most neuroticism symptoms and CVD ri(r_g range: 0.0837-0.2041), except for "feeling tense" and "worrying after embarrassment". Furthermore, MR analyses indicated that genetically predicted neuroticism sum-score was causally associated with increased risk of ischemic stroke (IS) (OR = 1.23; 95% CI = 1.05-1.44) and myocardial infarction (MI) (OR = 1.20; 95% CI = 1.02-1.41). Among individual symptoms, irritableness was associated with intracerebral hemorrhage (OR = 4.15; 95% CI = 1.17-14.68), mood swings was associated with IS (OR = 1.30; 95% CI = 1.01-1.68), coronary artery disease (OR = 1.72; 95% CI = 1.35-2.20), and MI (OR = 1.74; 95% CI = 1.29-2.35), and feeling fed up was associated with IS (OR = 1.32; 95% CI = 1.02-1.71). These results remained robust across multiple sensitivity analyses. Overall, our study provided genetic and causal evidence supporting the role of neuroticism sum-score, irritableness, mood swings, and feeling fed up as risk factors for CVD, highlighting potential targets for early intervention and prevention.

Background: Previous studies have suggested an association between tinnitus and various psychiatric phenotypes; however, the causal relationship and underlying pathways remain unclear.

Methods: We used univariable Mendelian randomization (MR) to investigate the bidirectional causality between tinnitus and 8 psychiatric phenotypes. Multivariable MR was employed to control for potential confounders. Extensive sensitivity analyses were conducted to validate our results. Additionally, two-step MR and genetic pleiotropy analyses were performed to explore potential pathways.

Results: Genetic susceptibility to tinnitus was significantly associated with an increased risk of major depressive disorder (MDD) (OR = 1.07, 95% CI = 1.03-1.11). Multivariable MR demonstrated that the impact of tinnitus on MDD was independent of potential confounders. No causal effects were observed from the eight psychiatric phenotypes on tinnitus; likewise, no causal effects were found from tinnitus on the remaining seven psychiatric phenotypes. Two-step MR analysis revealed that hearing difficulties with background noise and insomnia mediated the effect of tinnitus on MDD, with mediation proportions of 20.0% (0.0%-40.1%) and 31.4% (0.9%-61.9%), respectively. Genetic pleiotropy analysis identified a global genetic correlation between tinnitus and MDD (r_g = 0.36-0.48). PLACO analysis revealed 66 shared loci, primarily enriched in the cerebellum and cerebellar hemisphere. FUMA identified two independent genomic risk loci (rs58825580 and rs729437) and mapped 91 pleiotropic genes, which were mainly enriched in nucleosome and chromatin-remodeling processes, and immune response dysregulation.

Conclusions: This study provides genetic evidence that genetic susceptibility to tinnitus has a causal effect on the risk of MDD, partially mediated by hearing difficulties and insomnia. Our genetic pleiotropy analysis further highlights the shared genetic architecture between tinnitus and MDD.

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneities in behavioral symptoms and gray matter (GM) volume changes across the brain. However, progressive and causal GM volume changes and their associations with behavioral symptoms remain unclear. Our study aimed to explore the heterogeneity of causal GM volume changes in individuals with ASD across behavioral spectrums.

Methods: Brain structural imaging and clinical data of 131 autistic individuals and 246 neurotypical individuals (NTs) were included, and were clustered into neuroanatomical subtypes. A novel behavioral-causal structural covariance network (BCaSCN) analysis approach was developed. GM volume maps from each ASD subtype were sequenced according to the social responsiveness scale (SRS) value and values from each behavioral domain of SRS, generating pseudo-time series data with disease progression. We performed BCaSCN analysis on the pseudo-time series data to explore the causal relationship of the GM volume changes across behavior spectrums among ASD subtypes.

Results: Two neurosubtypes of ASD with distinct GM volume alterations were observed. CaSCN analysis revealed heterogeneity in causal GM volume alterations between the two ASD neurostypes. Furthermore, BCaSCN analysis across behavior spectrums demonstrated that subtype 1 exhibited higher overall out- and in-degree GC values in the cognition domain, whereas subtype 2 displayed higher overall out- and in-degree GC values in the domain of motivation, mannerism and communication.

Limitation: CaSCN and BCaSCN applied pseudo time-series data rather than real time-series data, longitudinal data are needed to validate the results of this study in the future. Therefore, the results should be interpreted with caution.

Conclusions: These findings suggest that ASD subtypes are associated with heterogeneous causal GM volume changes, which may be related to distinct behavioral domains.