Factor analysis of lifetime psychopathology and its brain morphometric and genetic correlates in a transdiagnostic sample.

There is a lack of knowledge regarding the relationship between proneness to dimensional psychopathological syndromes and the underlying pathogenesis across major psychiatric disorders, i.e., Major Depressive Disorder (MDD), Bipolar Disorder (BD), Schizoaffective Disorder (SZA), and Schizophrenia (SZ). Lifetime psychopathology was assessed using the OPerational CRITeria (OPCRIT) system in 1,038 patients meeting DSM-IV-TR criteria for MDD, BD, SZ, or SZA. The cohort was split into two samples for exploratory and confirmatory factor analyses. All patients were scanned with 3-T MRI, and data was analyzed with the CAT-12 toolbox in SPM12. Psychopathological factor scores were correlated with gray matter volume (GMV) and cortical thickness (CT). Finally, factor scores were used for exploratory genetic analyses including genome-wide association studies (GWAS) and polygenic risk score (PRS) association analyses. Three factors (paranoid-hallucinatory syndrome, PHS; mania, MA; depression, DEP) were identified and cross-validated. PHS was negatively correlated with four GMV clusters comprising parts of the hippocampus, amygdala, angular, middle occipital, and middle frontal gyri. PHS was also negatively associated with the bilateral superior temporal, left parietal operculum, and right angular gyrus CT. No significant brain correlates were observed for the two other psychopathological factors. We identified genome-wide significant associations for MA and DEP. PRS for MDD and SZ showed a positive effect on PHS, while PRS for BD showed a positive effect on all three factors. This study investigated the relationship of lifetime psychopathological factors and brain morphometric and genetic markers. Results highlight the need for dimensional approaches, overcoming the limitations of the current psychiatric nosology.

An association study of m6A methylation with major depressive disorder.

OBJECTIVE: To find the relationship between N6-methyladenosine (m6A) genes and Major Depressive Disorder (MDD). METHODS: Differential expression of m6A associated genes between normal and MDD samples was initially identified. Subsequent analysis was conducted on the functions of these genes and the pathways they may affect. A diagnostic model was constructed using the expression matrix of these differential genes, and visualized using a nomogram. Simultaneously, an unsupervised classification method was employed to classify all patients based on the expression of these m6A associated genes. Following this, common differential genes among different clusters were computed. By analyzing the functions of the common differential expressed genes among clusters, the role of m6A-related genes in the pathogenesis of MDD patients was elucidated. RESULTS: Differential expression was observed in ELAVL1 and YTHDC2 between the MDD group and the control group. ELAVL1 was associated with comorbid anxiety in MDD patients. A linear regression model based on these two genes could accurately predict whether patients in the GSE98793 dataset had MDD and could provide a net benefit for clinical decision-making. Based on the expression matrix of ELAVL1 and YTHDC2, MDD patients were classified into three clusters. Among these clusters, there were 937 common differential genes. Enrichment analysis was also performed on these genes. The ssGSEA method was applied to predict the content of 23 immune cells in the GSE98793 dataset samples. The relationship between these immune cells and ELAVL1, YTHDC2, and different clusters was analyzed. CONCLUSION: Among all the m6A genes, ELAVL1 and YTHDC2 are closely associated with MDD, ELAVL1 is related to comorbid anxiety in MDD. ELAVL1 and YTHDC2 have opposite associations with immune cells in MDD.

Mendelian randomization identifies causal effects of major depressive disorder on accelerated aging.

BACKGROUND: Evidence links major depressive disorder (MDD) with aging, but it's unclear if MDD accelerates aging and what factors mediate this transition. METHODS: Two-sample Mendelian randomization (MR) analyses were applied to estimate the causal association between MDD and frailty index (FI), telomere length (TL), and appendicular lean mass (ALM) from available genome-wide association studies in populations of European ancestry. Furthermore, we conducted mediation MR analyses to assess the mediating effects of 31 lifestyle factors or diseases on the causal relationship between MDD and aging. RESULTS: MDD was significantly causally associated with increased FI (ßIVW = 0.23, 95 % CI = 0.18 to 0.28, p = 1.20 × 10-17), shorter TL (ßIVW = -0.04, 95 % CI = -0.07 to -0.01, p = 0.01), and decreased ALM (ßIVW = -0.07, 95 % CI = -0.11 to -0.03, p = 3.54 × 10-4). The mediation analysis through two-step MR revealed smoking initiation (9.09 %), hypertension (6.67 %) and heart failure (5.36 %) mediated the causal effect of MDD on FI. Additionally, alcohol use disorders and alcohol dependence on the causal relationship between MDD and TL were found to be 17.52 % and 17.13 % respectively. LIMITATIONS: Confounding, statistical power, and Euro-centric focus limit generalization. CONCLUSION: Overall, individuals with MDD may be at a higher risk of experiencing premature aging, and this risk is partially influenced by the pathways involving smoking, alcohol use, and cardiovascular health. It underscores the importance of early intervention and comprehensive health management in individuals with MDD to promote healthy aging and overall well-being.

Identification of shared gene signatures in major depressive disorder and triple-negative breast cancer.

BACKGROUND: Patients with major depressive disorder (MDD) have an increased risk of breast cancer (BC), implying that these two diseases share similar pathological mechanisms. This study aimed to identify the key pathogenic genes that lead to the occurrence of both triple-negative breast cancer (TNBC) and MDD. METHODS: Public datasets GSE65194 and GSE98793 were analyzed to identify differentially expressed genes (DEGs) shared by both datasets. A protein-protein interaction (PPI) network was constructed using STRING and Cytoscape to identify key PPI genes using cytoHubba. Hub DEGs were obtained from the intersection of hub genes from a PPI network with genes in the disease associated modules of the Weighed Gene Co-expression Network Analysis (WGCNA). Independent datasets (TCGA and GSE76826) and RT-qPCR validated hub gene expression. RESULTS: A total of 113 overlapping DEGs were identified between TNBC and MDD. The PPI network was constructed, and 35 hub DEGs were identified. Through WGCNA, the blue, brown, and turquoise modules were recognized as highly correlated with TNBC, while the brown, turquoise, and yellow modules were similarly correlated with MDD. Notably, G3BP1, MAF, NCEH1, and TMEM45A emerged as hub DEGs as they appeared both in modules and PPI hub DEGs. Within the GSE65194 and GSE98793 datasets, G3BP1 and MAF exhibited a significant downregulation in TNBC and MDD groups compared to the control, whereas NCEH1 and TMEM45A demonstrated a significant upregulation. These findings were further substantiated by TCGA and GSE76826, as well as through RT-qPCR validation. CONCLUSIONS: This study identified G3BP1, MAF, NCEH1 and TMEM45A as key pathological genes in both TNBC and MDD.

Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders.

BACKGROUND: Sex differences exist in the prevalence and clinical manifestation of several mental disorders, suggesting that sex-specific brain phenotypes may play key roles. Previous research used machine learning models to classify sex from imaging data of the whole brain and studied the association of class probabilities with mental health, potentially overlooking regional specific characteristics. METHODS: We here investigated if a regionally constrained model of brain volumetric imaging data may provide estimates that are more sensitive to mental health than whole brain-based estimates. Given its known role in emotional processing and mood disorders, we focused on the limbic system. Using two different cohorts of healthy subjects, the Human Connectome Project and the Queensland Twin IMaging, we investigated sex differences and heritability of brain volumes of limbic structures compared to non-limbic structures, and subsequently applied regionally constrained machine learning models trained solely on limbic or non-limbic features. To investigate the biological underpinnings of such models, we assessed the heritability of the obtained sex class probability estimates, and we investigated the association with major depression diagnosis in an independent clinical sample. All analyses were performed both with and without controlling for estimated total intracranial volume (eTIV). RESULTS: Limbic structures show greater sex differences and are more heritable compared to non-limbic structures in both analyses, with and without eTIV control. Consequently, machine learning models performed well at classifying sex based solely on limbic structures and achieved performance as high as those on non-limbic or whole brain data, despite the much smaller number of features in the limbic system. The resulting class probabilities were heritable, suggesting potentially meaningful underlying biological information. Applied to an independent population with major depressive disorder, we found that depression is associated with male-female class probabilities, with largest effects obtained using the limbic model. This association was significant for models not controlling for eTIV whereas in those controlling for eTIV the associations did not pass significance correction. CONCLUSIONS: Overall, our results highlight the potential utility of regionally constrained models of brain sex to better understand the link between sex differences in the brain and mental disorders.

Psychiatric disorders have different prevalence between sexes, with women being twice as likely to develop depression and anxiety across the lifespan. Previous studies have investigated sex differences in brain structure that might contribute to this prevalence but have mostly focused on a single-structure level, potentially overlooking the interplay between brain regions. Sex differences in structures responsible for emotional regulation (limbic system), affected in many psychiatric disorders, have been previously reported. Here, we apply a machine learning model to obtain an estimate of brain sex for each participant based on the volumes of multiple brain regions. Particularly, we compared the estimates obtained with a model based solely on limbic structures with those obtained with a non-limbic model (entire brain except limbic structures) and a whole brain model. To investigate the genetic determinants of the models, we assessed the heritability of the estimates between identical twins and fraternal twins. The estimates of all our models were heritable, suggesting a genetic component contributing to brain sex. Finally, to investigate the association with mental health, we compared brain sex estimates in healthy subjects and in a depressed population. We found an association between depression and brain sex in females for the limbic model, but not for the non-limbic model. No effect was found in males. Overall, our results highlight the potential utility of machine learning models of brain sex based on relevant structures to better understand the link between sex differences in the brain and mental disorders.

Systems biology dissection of PTSD and MDD across brain regions, cell types, and blood.

The molecular pathology of stress-related disorders remains elusive. Our brain multiregion, multiomic study of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) included the central nucleus of the amygdala, hippocampal dentate gyrus, and medial prefrontal cortex (mPFC). Genes and exons within the mPFC carried most disease signals replicated across two independent cohorts. Pathways pointed to immune function, neuronal and synaptic regulation, and stress hormones. Multiomic factor and gene network analyses provided the underlying genomic structure. Single nucleus RNA sequencing in dorsolateral PFC revealed dysregulated (stress-related) signals in neuronal and non-neuronal cell types. Analyses of brain-blood intersections in >50,000 UK Biobank participants were conducted along with fine-mapping of the results of PTSD and MDD genome-wide association studies to distinguish risk from disease processes. Our data suggest shared and distinct molecular pathology in both disorders and propose potential therapeutic targets and biomarkers.

Integrating human endogenous retroviruses into transcriptome-wide association studies highlights novel risk factors for major psychiatric conditions.

Human endogenous retroviruses (HERVs) are repetitive elements previously implicated in major psychiatric conditions, but their role in aetiology remains unclear. Here, we perform specialised transcriptome-wide association studies that consider HERV expression quantified to precise genomic locations, using RNA sequencing and genetic data from 792 post-mortem brain samples. In Europeans, we identify 1238 HERVs with expression regulated in cis, of which 26 represent expression signals associated with psychiatric disorders, with ten being conditionally independent from neighbouring expression signals. Of these, five are additionally significant in fine-mapping analyses and thus are considered high confidence risk HERVs. These include two HERV expression signatures specific to schizophrenia risk, one shared between schizophrenia and bipolar disorder, and one specific to major depressive disorder. No robust signatures are identified for autism spectrum conditions or attention deficit hyperactivity disorder in Europeans, or for any psychiatric trait in other ancestries, although this is likely a result of relatively limited statistical power. Ultimately, our study highlights extensive HERV expression and regulation in the adult cortex, including in association with psychiatric disorder risk, therefore providing a rationale for exploring neurological HERV expression in complex neuropsychiatric traits.

Advances in biosensors for major depressive disorder diagnostic biomarkers.

Depression is one of the most common mental disorders and is mainly characterized by low mood or lack of interest and pleasure. It can be accompanied by varying degrees of cognitive and behavioral changes and may lead to suicide risk in severe cases. Due to the subjectivity of diagnostic methods and the complexity of patients' conditions, the diagnosis of major depressive disorder (MDD) has always been a difficult problem in psychiatry. With the discovery of more diagnostic biomarkers associated with MDD in recent years, especially emerging non-coding RNAs (ncRNAs), it is possible to quantify the condition of patients with mental illness based on biomarker levels. Point-of-care biosensors have emerged due to their advantages of convenient sampling, rapid detection, miniaturization, and portability. After summarizing the pathogenesis of MDD, representative biomarkers, including proteins, hormones, and RNAs, are discussed. Furthermore, we analyzed recent advances in biosensors for detecting various types of biomarkers of MDD, highlighting representative electrochemical sensors. Future trends in terms of new biomarkers, new sample processing methods, and new detection modalities are expected to provide a complete reference for psychiatrists and biomedical engineers.

Exploring the potential molecular intersection of stroke and major depression disorder.

Stroke and major depression disorder are common neurological diseases, and a large number of clinical studies have shown that there is a close relationship between the two diseases, but whether the two diseases are linked at the genetic level needs to be further explored. The purpose of this study was to explore the comorbidity mechanism of stroke and major depression by using bioinformatics technology and animal experiments. From the GEO database, we gathered transcriptome data of stroke and depression mice (GSE104036, GSE131712, GSE81672, and GSE146845) and identified comorbid gene set through edgR and WGCNA analyses. Further analysis revealed that these genes were enriched in pathways associated with cell death. Programmed cell death gene sets (PCDGs) are generated from genes related to apoptosis, necroptosis, pyroptosis and autophagy. The intersection of PCDGs and comorbid gene set resulted in two hub genes, Mlkl and Nlrp3. Single-cell sequencing analysis indicated that Mlkl and Nlrp3 are mainly influential on endothelial cells and microglia, suggesting that the impairment of these two cell types may be a factor in the relationship between stroke and major depression. This was experimentally confirmed by RT-PCR and immunofluorescence staining. Our research revealed that two specific genes, namely, Mlkl and Nlrp3, play crucial roles in the complex mechanism that links stroke and major depression. Additionally, we have predicted six possible therapeutic agents and the outcomes of docking simulations of target proteins and drug molecules.

ABCB1 C1236T, G2677TA and C3435T Genetic Polymorphisms and Antidepressant Response Phenotypes: Results from a Portuguese Major Depressive Disorder Cohort.

P-glycoprotein (P-GP) is a transporter molecule expressed on the apical surface of capillary endothelial cells of the Blood-Brain Barrier (BBB), whose activity heavily influences drug distribution, including antidepressants. This transporter is encoded by ABCB1 gene, and genetic variations within ABCB1 gene have been proposed to affect drug efflux and have been previously associated with depression. In this context, we aimed to evaluate the role of C1236T, G2677TA and C3435T ABCB1 genetic polymorphisms in antidepressant treatment phenotypes from a cohort of patients harboring Major Depressive Disorder. Patients enrolled in the study consisted of 80 individuals with Major Depressive Disorder, who took part in a 27-month follow-up study at HML, Portugal. To investigate the correlation between ABCB1 polymorphisms and antidepressant response phenotypes, DNA was extracted from peripheral blood, and C1236T, C3435T and G2677TA polymorphisms were genotyped with TaqMan® SNP Genotyping Assays. Despite the fact that the evaluated polymorphisms (C1236T, C3435T and G2677TA) were not associated with treatment resistant depression, or relapse, we observed that patients carrying TT genotype of the C3435T polymorphism remit earlier than the ones carrying CC or CT genotypes (10.2 weeks vs. 14.9 and 21.3, respectively, p = 0.028, Log-rank test). Since we found an association with C3435T and time to remission, and not to the absence of remission, we suggest that this polymorphism could have an impact on antidepressant drug distribution, and thus influence on the time to remission will occur, without influencing the risk of remission itself.

Astrocytic ALKBH5 in stress response contributes to depressive-like behaviors in mice.

Epigenetic mechanisms bridge genetic and environmental factors that contribute to the pathogenesis of major depression disorder (MDD). However, the cellular specificity and sensitivity of environmental stress on brain epitranscriptomics and its impact on depression remain unclear. Here, we found that ALKBH5, an RNA demethylase of N6-methyladenosine (m6A), was increased in MDD patients' blood and depression models. ALKBH5 in astrocytes was more sensitive to stress than that in neurons and endothelial cells. Selective deletion of ALKBH5 in astrocytes, but not in neurons and endothelial cells, produced antidepressant-like behaviors. Astrocytic ALKBH5 in the mPFC regulated depression-related behaviors bidirectionally. Meanwhile, ALKBH5 modulated glutamate transporter-1 (GLT-1) m6A modification and increased the expression of GLT-1 in astrocytes. ALKBH5 astrocyte-specific knockout preserved stress-induced disruption of glutamatergic synaptic transmission, neuronal atrophy and defective Ca2+ activity. Moreover, enhanced m6A modification with S-adenosylmethionine (SAMe) produced antidepressant-like effects. Our findings indicate that astrocytic epitranscriptomics contribute to depressive-like behaviors and that astrocytic ALKBH5 may be a therapeutic target for depression.

Major depression disorder and heart failure: A two-sample bidirectional Mendelian randomization study.

OBJECTIVE: To determine whether a bidirectional causal relationship exists between major depressive disorder (MDD) and heart failure (HF). METHODS: Our two-sample bidirectional Mendelian randomization (MR) study consisted of two parts. In the first part, we conducted a forward MR analysis where MDD was considered as the exposure and HF as the outcome. In the second part, a reverse MR analysis was performed, treating HF as the exposure and MDD as the outcome. Summary data on MDD and HF were obtained from the IEU Open GWAS database. RESULTS: Based on the results of the MR-Egger regression intercept test, there was no evidence of horizontal pleiotropy in this study. Furthermore, the IVW results consistently suggested estimates of causal effect values. The findings revealed that individuals with MDD had a 16.9% increased risk of HF compared to those without MDD (OR = 1.169, 95%CI: 1.044-1.308, P = 0.007). However, there was no evidence to support that HF would increase the risk of MDD (OR = 1.012, 95%CI: 0.932-1.099, P = 0.773). Heterogeneity in SNPs of MDD and HF was observed through the heterogeneity test and funnel plot. Additionally, the leave-one-out method did not identify any instances where a single SNP was biased toward or dependent on causation. CONCLUSION: Our study provides evidence supporting a one-way causal relationship between MDD and HF. Specifically, MDD increases the risk of developing HF. However, our findings did not provide any evidence suggesting that HF increases the risk of developing MDD.

Omega-3 fatty acids and major depression: a Mendelian randomization study.

Omega-3 fatty acids have been implicated in the aetiology of depressive disorders, though trials supplementing omega-3 to prevent major depressive disorder (MDD) have so far been unsuccessful. Whether this association is causal remains unclear. We used two sample Mendelian randomization (MR) to investigate causality. Genetic variants associated with circulating omega-3 and omega-6 fatty acids in UK Biobank (UKBB, n = 115,078) were selected as exposures. The Psychiatric Genomics Consortium (PGC) genome-wide association studies (GWAS) of MDD (n = 430,775; cases = 116,209; controls = 314,566) and recurrent depression (rMDD, n = 80,933; cases = 17,451; controls = 62,482), were used as outcomes. Multivariable MR (MVMR) models were used to account for biologically correlated lipids, such as high- and low-density cholesterol and triglycerides, and to explore the relative importance of longer-chain omega-3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) using data from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE, n = 8866). Genetic colocalization analyses were used to explore the presence of a shared underlying causal variant between traits. Genetically predicted total omega-3 fatty acids reduced the odds of MDD (ORIVW 0.96 per standard deviation (SD, i.e. 0.22 mmol/l) (95% CIs 0.93-0.98, p = 0.003)). The largest point estimates were observed for eicosapentaenoic acid (EPA), a long-chain omega-3 fatty acid (OREPA 0.92; 95% CI 0.88-0.96; p = 0.0002). The effect of omega-3 fatty acids was robust to MVMR models accounting for biologically correlated lipids. 'Leave-one-out' analyses highlighted the FADS gene cluster as a key driver of the effect. Colocalization analyses suggested a shared causal variant using the primary outcome sample, but genomic confounding could not be fully excluded. This study supports a role for omega-3 fatty acids, particularly EPA, in the aetiology of depression, although pleiotropic mechanisms cannot be ruled out. The findings support guidelines highlighting the importance of EPA dose and ratio for MDD and question whether targeted interventions may be superior to universal prevention trials, as modest effect sizes will limit statistical power.

Association of major depression, schizophrenia and bipolar disorder with thyroid cancer: a bidirectional two-sample mendelian randomized study.

BACKGROUND: Major depressive disease (MDD), schizophrenia (SCZ), and bipolar disorder (BD) are common psychiatric disorders, and their relationship with thyroid cancer has been of great interest. This study aimed to investigate the potential causal effects of MDD, SCZ, BD, and thyroid cancer. METHODS: We used publicly available summary statistics from large-scale genome-wide association studies to select genetic variant loci associated with MDD, SCZ, BD, and thyroid cancer as instrumental variables (IVs), which were quality controlled and clustered. Additionally, we used three Mendelian randomization (MR) methods, inverse variance weighted (IVW), MR-Egger regression and weighted median estimator (WME) methods, to estimate the bidirectional causal relationship between psychiatric disorders and thyroid cancer. In addition, we performed heterogeneity and multivariate tests to verify the validity of the IVs. RESULTS: We used two-sample bidirectional MR analysis to determine whether there was a positive causal association between MDD and thyroid cancer risk. The results of the IVW analysis (OR = 3.956 95% CI = 1.177-13.299; P = 0.026) and the WME method (OR = 5.563 95% CI = 0.998-31.008; P = 0.050) confirmed that MDD may increase the risk of thyroid cancer. Additionally, our study revealed a correlation between genetic susceptibility to SCZ and thyroid cancer (OR = 1.532 95% CI = 1.123-2.088; P = 0.007). The results of the WME method analysis based on the median estimate (OR = 1.599 95% CI = 1.014-2.521; P = 0.043) also suggested that SCZ may increase the risk of thyroid cancer. Furthermore, our study did not find a causal relationship between BD and thyroid cancer incidence. In addition, the results of reverse MR analysis showed no significant causal relationships between thyroid cancer and MDD, SCZ, or BD (P > 0.05), ruling out the possibility of reverse causality. CONCLUSIONS: This MR method analysis provides new evidence that MDD and SCZ may be positively associated with thyroid cancer risk while also revealing a correlation between BD and thyroid cancer. These results may have important implications for public health policy and clinical practice. Future studies will help elucidate the biological mechanisms of these associations and potential confounders.

SNORA69 is up-regulated in the lateral habenula of individuals with major depressive disorder.

Major depressive disorder (MDD) is a complex and potentially debilitating illness whose etiology and pathology remains unclear. Non-coding RNAs have been implicated in MDD, where they display differential expression in the brain and the periphery. In this study, we quantified small nucleolar RNA (snoRNA) expression by small RNA sequencing in the lateral habenula (LHb) of individuals with MDD (n = 15) and psychiatrically-healthy controls (n = 15). We uncovered five snoRNAs that exhibited differential expression between MDD and controls (FDR < 0.01). Specifically, SNORA69 showed increased expression in MDD and was technically validated via RT-qPCR. We further investigated the expression of Snora69 in the LHb and peripheral blood of an unpredicted chronic mild stress (UCMS) mouse model of depression. Snora69 was specifically up-regulated in mice that underwent the UCMS paradigm. SNORA69 is known to guide pseudouridylation onto 5.8S and 18S rRNAs. We quantified the relative abundance of pseudouridines on 5.8S and 18S rRNA in human post-mortem LHb samples and found increased abundance of pseudouridines in the MDD group. Overall, our findings indicate the importance of brain snoRNAs in the pathology of MDD. Future studies characterizing SNORA69's role in MDD pathology is warranted.

Interaction between HTR2A rs3125 and negative life events in suicide attempts among patients with major depressive disorder: a cross-sectional study.

BACKGROUND: Both genetic and environmental factors play crucial roles in the development of major depressive disorder (MDD) and suicide attempts (SA). However, the interaction between both items remains unknown. This study aims to explore the interactions between the genetic variants of the serotonin 2 A receptor (HTR2A) and the nitric oxide synthase 1 (NOS1) and environmental factors in patients who experience MDD and SA. METHODS: A total of 334 patients with MDD and a history of SA (MDD-SA) were recruited alongside 518 patients with MDD with no history of SA (MDD-NSA), and 716 healthy controls (HC). The demographic data and clinical characteristics were collected. Sequenom mass spectrometry was used to detect eight tag-single nucleotide polymorphisms (tagSNPs) in HTR2A (rs1328683, rs17068986, and rs3125) and NOS1 (rs1123425, rs2682826, rs3741476, rs527590, and rs7959232). Generalized multifactor dimensionality reduction (GMDR) was used to analyze the gene-environment interactions. RESULTS: Four tagSNPs (rs17068986, rs3125, rs527590, and rs7959232) exhibited significant differences between the three groups. However, these differences were not significant between the MDD-SA and MDD-NSA groups after Bonferroni correction. A logistic regression analysis revealed that negative life events (OR = 1.495, 95%CI: 1.071-2.087, P = 0.018), self-guilt (OR = 2.263, 95%CI: 1.515-3.379, P < 0.001), and negative cognition (OR = 2.252, 95%CI: 1.264-4.013, P = 0.006) were all independently associated with SA in patients with MDD. Furthermore, GMDR analysis indicated a significant interaction between HTR2A rs3125 and negative life events. Negative life events in conjunction with the HTR2A rs3125 CG + GG genotype were associated with a higher SA risk in patients with MDD when compared to the absence of negative life events in conjunction with the CC genotype (OR = 2.547, 95% CI: 1.264-5.131, P = 0.009). CONCLUSION: Several risk factors and a potential interaction between HTR2A rs3125 and negative life events were identified in patients with SA and MDD. The observed interaction likely modulates the risk of MDD and SA, shedding light on the pathogenesis of SA in patients with MDD.

Obstructive sleep apnea and mental disorders: a bidirectional mendelian randomization study.

BACKGROUND: Previous studies have reported associations between obstructive sleep apnea (OSA) and several mental disorders. However, further research is required to determine whether these associations are causal. Therefore, we evaluated the bidirectional causality between the genetic liability for OSA and nine mental disorders by using Mendelian randomization (MR). METHOD: We performed two-sample bidirectional MR of genetic variants for OSA and nine mental disorders. Summary statistics on OSA and the nine mental disorders were extracted from the FinnGen study and the Psychiatric Genomics Consortium. The primary analytical approach for estimating causal effects was the inverse-variance weighted (IVW), with the weighted median and MR Egger as complementary methods. The MR Egger intercept test, Cochran's Q test, Rucker's Q test, and the MR pleiotropy residual sum and outlier (MR-PRESSO) test were used for sensitivity analyses. RESULT: MR analyses showed that genetic liability for major depressive disorder (MDD) was associated with an increased risk of OSA (odds ratio [OR] per unit increase in the risk of MDD, 1.29; 95% CI, 1.11-1.49; P < 0.001). In addition, genetic liability for OSA may be associated with an increased risk of attention-deficit/hyperactivity disorder (ADHD) (OR = 1.26; 95% CI, 1.02-1.56; p = 0.032). There was no evidence that OSA is associated with other mental disorders. CONCLUSION: Our study indicated that genetic liability for MDD is associated with an increased risk of OSA without a bidirectional relationship. Additionally, there was suggestive evidence that genetic liability for OSA may have a causal effect on ADHD. These findings have implications for prevention and intervention strategies targeting OSA and ADHD. Further research is needed to investigate the biological mechanisms underlying our findings and the relationship between OSA and other mental disorders.

Validation of the predictive value of BDNF -87 methylation for antidepressant treatment success in severely depressed patients-a randomized rater-blinded trial.

BACKGROUND: Brain-derived neurotrophic factor (BDNF) is essential for antidepressant treatment of major depressive disorder (MDD). Our repeated studies suggest that DNA methylation of a specific CpG site in the promoter region of exon IV of the BDNF gene (CpG -87) might be predictive of the efficacy of monoaminergic antidepressants such as selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), and others. This trial aims to evaluate whether knowing the biomarker is non-inferior to treatment-as-usual (TAU) regarding remission rates while exhibiting significantly fewer adverse events (AE). METHODS: The BDNF trial is a prospective, randomized, rater-blinded diagnostic study conducted at five university hospitals in Germany. The study's main hypothesis is that {1} knowing the methylation status of CpG -87 is non-inferior to not knowing it with respect to the remission rate while it significantly reduces the AE rate in patients experiencing at least one AE. The baseline assessment will occur upon hospitalization and a follow-up assessment on day 49 (± 3). A telephone follow-up will be conducted on day 70 (± 3). A total of 256 patients will be recruited, and methylation will be evaluated in all participants. They will be randomly assigned to either the marker or the TAU group. In the marker group, the methylation results will be shared with both the patient and their treating physician. In the TAU group, neither the patients nor their treating physicians will receive the marker status. The primary endpoints include the rate of patients achieving remission on day 49 (± 3), defined as a score of ≤ 10 on the Hamilton Depression Rating Scale (HDRS-24), and the occurrence of AE. ETHICS AND DISSEMINATION: The trial protocol has received approval from the Institutional Review Boards at the five participating universities. This trial holds significance in generating valuable data on a predictive biomarker for antidepressant treatment in patients with MDD. The findings will be shared with study participants, disseminated through professional society meetings, and published in peer-reviewed journals. TRIAL REGISTRATION: German Clinical Trial Register DRKS00032503. Registered on 17 August 2023.

The Relationship Between CYP46A1 Polymorphism and Suicide Risk: A Preliminary Investigation.

Suicide is a global public health issue, with a particularly high incidence in individuals suffering from Major Depressive Disorder (MDD). The role of cholesterol in suicide risk remains controversial, prompting investigations into genetic markers that may be implicated. This study examines the association between CYP46A1 polymorphisms, specifically SNPs rs754203 and rs4900442, and suicide risk in a Mexican MDD patient cohort. Our study involved 188 unrelated suicide death victims, 126 MDD patients, and 144 non-suicidal controls. Genotypic and allelic frequencies were assessed using the Real Time-polymerase chain reaction method, and associations with suicide risk were evaluated using chi-square tests. The study revealed significant differences in allelic and genotypic frequencies in rs754203 SNP between suicide death and controls. The CYP46A1 rs754203 genotype G/G was significantly linked with suicide, and the G allele was associated with a higher risk of suicide (OR = 1.370, 95% CI = 1.002-1.873). However, we did not observe any significant differences in genotype distribution or allele frequencies of CYP46A1 rs4900442. Our study suggests that carriers of the CYP46A1 rs754203 G allele (A/G + G/G) may play a role in suicidal behavior, especially in males. Our findings support that the CYP46A1 gene may be involved in susceptibility to suicide, which has not been investigated previously. These results underscore the importance of further research in different populations to elucidate the genetic underpinnings of the role of CYP46A1 in suicide risk and to develop targeted interventions for at-risk populations.

Identification of novel endoplasmic reticulum-related genes and their association with immune cell infiltration in major depressive disorder.

BACKGROUND: Several lines of evidence point to an interaction between genetic predisposition and environmental factors in the onset of major depressive disorder (MDD). This study is aimed to investigate the pathogenesis of MDD by identifying key biomarkers, associated immune infiltration using bioinformatic analysis and human postmortem sample. METHODS: The Gene Expression Omnibus (GEO) database of GSE98793 was adopted to identify hub genes linked to endoplasmic reticulum (ER) stress-related genes (ERGs) in MDD. Another GEO database of GSE76826 was employed to validate the novel target associated with ERGs and immune infiltration in MDD. Moreover, human postmortem sample from MDD patients was utilized to confirm the differential expression analysis of hub genes. RESULTS: We discovered 12 ER stress-related differentially expressed genes (ERDEGs). A LASSO Cox regression analysis helped construct a diagnostic model for these ERDEGs, incorporating immune infiltration analysis revealed that three hub genes (ERLIN1, SEC61B, and USP13) show the significant and consistent expression differences between the two groups. Western blot analysis of postmortem brain samples indicated notably higher expression levels of ERLIN1 and SEC61B in the MDD group, with USP13 also tending to increase compared to control group. LIMITATIONS: The utilization of the MDD gene chip in this analysis was sourced from the GEO database, which possesses a restricted number of pertinent gene chip samples. CONCLUSIONS: These findings indicate that ERDEGs especially including ERLIN1, SEC61B, and USP13 associated the infiltration of immune cells may be potential diagnostic indicators for MDD.