Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

The effects of amyloidosis and aging on glutamatergic and GABAergic synapses, and interneurons in the barrel cortex and non-neocortical brain regions

Previous studies on changes in the distribution of GABAergic interneurons and excitation/inhibition (E/I) balance in Alzheimer's disease (AD) and aging were mainly conducted in the neocortex and hippocampus. However, the limbic system is the primary and crucial location for AD progression. Therefore, in this study, we utilized AD and aging mouse models to investigate the E/I balance and the distribution of parvalbumin (PV)- and somatostatin (SST)-expressing cells in S1BF (barrel field of primary somatosensory cortex, barrel cortex), CA1 hippocampal area and brain regions beyond the neocortex and hippocampus, including retrosplenial cortex (RSC, which is composed of RSG and RSA), piriform cortex (Pir), amygdala (BMA), and hypothalamus (DM). We discovered that amyloidosis may disrupt the alignment of excitatory pre- and postsynaptic quantities. Amyloidosis reduces the quantity of synapses and SST cells, but does not impact the counts of PV cells. By contrast, aging is linked to a decline in synapses, I/E ratios, SST and PV cells. Amyloidosis affects the S1BF and BMA, while aging may harm all studied regions, including the S1BF, RSC, hippocampus, Pir, BMA, and DM. Aging mostly affects synapses and I/E ratios in Pir, BMA, and DM, and PV and SST interneurons in the hippocampus.

A network medicine approach to investigating ME/CFS pathogenesis in severely ill patients: a pilot study

This pilot study harnessed the power of network medicine to unravel the complex pathogenesis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). By utilizing a network analysis on whole genome sequencing (WGS) data from the Severely Ill Patient Study (SIPS), we identified ME/CFS-associated proteins and delineated the corresponding network-level module, termed the SIPS disease module, together with its relevant pathways. This module demonstrated significant overlap with genes implicated in fatigue, cognitive disorders, and neurodegenerative diseases. Our pathway analysis revealed potential associations between ME/CFS and conditions such as COVID-19, Epstein-Barr virus (EBV) infection, neurodegenerative diseases, and pathways involved in cortisol synthesis and secretion, supporting the hypothesis that ME/CFS is a neuroimmune disorder. Additionally, our findings underscore a potential link between ME/CFS and estrogen signaling pathways, which may elucidate the higher prevalence of ME/CFS in females. These findings provide insights into the pathogenesis of ME/CFS from a network medicine perspective and highlight potential therapeutic targets. Further research is needed to validate these findings and explore their implications for improving diagnosis and treatment.

Associations between cerebellum and major psychiatric disorders: a bidirectional Mendelian randomization study

Despite its small size the cerebellum is an anatomically complex and functionally important part of the brain. Previous studies have demonstrated associations between characteristic features/anatomic anomalies of cerebellum and psychiatric disorders. However, the potential causal relationships are unknown. In this study, a bidirectional two-sample Mendelian randomization approach was employed to investigate single nucleotide polymorphism (SNP) heritability and genetic causal associations between 77 imaging derived phenotypes (IDPs) of the cerebellum and major psychiatric disorder, including major depressive disorder (MDD), bipolar disorder (BD), schizophrenia (SCZ) and attention deficit hyperactivity disorder (ADHD). We identified thirty IDPs for which there was evidence of a causal effect on risk of MDD, BD, SCZ and ADHD. For example, 1 s.d. increase in the mean diffusivity (MD) of the left superior cerebellar peduncle was associated with 32% lower odds of BD risk. Reverse MR indicated that psychiatric disorders was associated with fourteen IDPs. For example, MDD were causally associated with three IDPs of gray matter volume (GMV) of right and left X cerebellum, and vermis crus II cerebellum. These results suggested that there were genetic causal associations between psychiatric disorders and certain cerebellum regions, such as the cognitive function of posterior cerebellar lobes and the connection of cerebellar to cerebrum. Further investigations need to enhance prediction and intervention strategies for potential psychiatric disorder risks.

Trans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies

In a genome-wide association study (GWAS) meta-analysis of 688,808 individuals with major depression (MD) and 4,364,225 controls from 29 countries across diverse and admixed ancestries, we identify 697 associations at 635 loci, 293 of which are novel. Using fine-mapping and functional tools, we find 308 high-confidence gene associations and enrichment of postsynaptic density and receptor clustering. A neural cell-type enrichment analysis utilizing single-cell data implicates excitatory, inhibitory, and medium spiny neurons and the involvement of amygdala neurons in both mouse and human single-cell analyses. The associations are enriched for antidepressant targets and provide potential repurposing opportunities. Polygenic scores trained using European or multi-ancestry data predicted MD status across all ancestries, explaining up to 5.8% of MD liability variance in Europeans. These findings advance our global understanding of MD and reveal biological targets that may be used to target and develop pharmacotherapies addressing the unmet need for effective treatment.

Association between sleep duration and depression in adolescents and young adults: a system review of observational studies and a genetic research of Mendelian randomization analysis

OBJECTIVES

This study aimed to explore the causal association between sleep duration and depression in adolescents and young adults.

METHODS

We conducted a systematic review and Mendelian randomization (MR) to research the causal relationship between short sleep duration and adolescent depression risk from an observational and genetic perspective. In the systematic review, we searched observational studies from the PubMed, Embase, and Cochrane Library databases. In the MR analysis part, we screened Single Nucleotide Polymorphism (SNP) significantly relative to short sleep and conforming MR concept to investigate the genetic causality.

RESULTS

All research evidence shows that adolescents who sleep <6 h have the highest rates of depression. According to the MR result, short sleep duration significantly affected the depression risk (odds ratio, 1.034; 95% confidence interval, 1.012-1.058, P = .003). Sleep duration of 7-8 h has the lowest depression incidence. Insufficient sleep (≤6 h) and excessive sleep (≥8 h) also elevates adolescent depression risk. Genetic evidence shows that short sleep duration (<6 h) has significant causal effects on depression risk.

CONCLUSIONS

Sleep duration was causally associated with depression in adolescents and young adults. Sleep duration of <6 h or >8 h daily increases the depression risk in adolescents and young adults.

Triangulated evidence provides no support for bidirectional causal pathways between diet/physical activity and depression/anxiety

BACKGROUND

Previous studies (various designs) present contradicting insights on the potential causal effects of diet/physical activity on depression/anxiety (and vice versa). To clarify this, we employed a triangulation framework including three methods with unique strengths/limitations/potential biases to examine possible bidirectional causal effects of diet/physical activity on depression/anxiety.

METHODS

Study 1: 3-wave longitudinal study (n = 9,276 Dutch University students). Using random intercept cross-lagged panel models to study temporal associations. Study 2: cross-sectional study (n = 341 monozygotic and n = 415 dizygotic Australian adult twin pairs). Using a co-twin control design to separate genetic/environmental confounding. Study 3: Mendelian randomization utilizing data (European ancestry) from genome-wide association studies (n varied between 17,310 and 447,401). Using genetic variants as instrumental variables to study causal inference.

RESULTS

Study 1 did not provide support for bidirectional causal effects between diet/physical activity and symptoms of depression/anxiety. Study 2 did provide support for causal effects between fruit/vegetable intake and symptoms of depression/anxiety, mixed support for causal effects between physical activity and symptoms of depression/anxiety, and no support for causal effects between sweet/savoury snack intake and symptoms of depression/anxiety. Study 3 provides support for a causal effect from increased fruit intake to the increased likelihood of anxiety. No support was found for other pathways. Adjusting the analyses including diet for physical activity (and vice versa) did not change the conclusions in any study.

CONCLUSIONS

Triangulating the evidence across the studies did not provide compelling support for causal effects of diet/physical activity on depression/anxiety or vice versa.

Dissociable genetic influences on eye movements during abstract versus naturalistic social scene viewing in infancy

Eye-movement metrics like fixation location and duration are increasingly being used in infancy research. We tested whether fixation durations during meaningful social stimulus viewing involve common or different familial influences than fixation durations during viewing of abstract stimulus. We analysed the duration of fixations, and the allocation of fixations to face and motion, from 536 dizygotic and monozygotic 5-month-old twins in: naturalistic scenes including low- and high-level social features, and abstract scenes only having low-level features. We observed significant genetic influences in both conditions (h2naturalistic = 0.30, 95% confidence interval (CI) 0.14 to 0.44; h2abstract = 0.25, 95% CI 0.09 to 0.39), while shared environmental influences were negligible. Although some genetic influences were shared between the two conditions, unique genetic factors were linked to naturalistic scene viewing, indicating that fixation durations index different phenomena dependent on the context. Heritability for face looking was moderate (h2 = 0.19, 95% CI 0.03 to 0.34), and no familial influences were found for motion looking. Exploratory polygenic score analyses revealed no significant associations with fixation measures. This study underscores the dissociable genetic influences on infants' visual exploration of abstract versus naturalistic stimuli and the importance of considering context when interpreting eye-tracking data.

Genetic insights into psychotic major depressive disorder: bridging the mood-psychotic disorder spectrum

BACKGROUND

Psychotic major depressive disorder (MDD), a subtype of MDD characterised by psychotic symptoms that occur exclusively during mood episode, is clinically significant yet underexplored genetically due to its rarity. This study comprehensively examines the genetic basis of psychotic MDD and elucidates its position within the mood-psychotic spectrum.

METHODS

This population-based cohort study used Swedish and Danish registry data for over 5.1 M individuals born between 1958 and 1993/1996. Specialist-diagnosed psychotic MDD was defined using ICD-10 sub-codes of MDD, F32.2/F32.3. We estimated familial aggregation/coaggregation using generalised estimating equations, heritability and genetic correlations using structural equation modelling. We also analysed ∼30,000 genotyped MDD cases from the UK Biobank and a Swedish cohort to explore which polygenic risk score (PRS) may predispose individuals to psychotic MDD.

FINDINGS

With over 10,000 psychotic MDD identified from the two nationwide patient registers, this study highlights the familial aggregation of psychotic MDD, co-aggregation with mood and psychotic disorders, and its stronger genetic correlation with schizophrenia compared to non-psychotic MDD. The familial risks increased with closer biological relatedness, suggesting genetic influence. Pedigree-heritability of psychotic MDD was 30.17% (95% CI 23.53-36.80%). While the genetic correlation between psychotic and non-psychotic MDD was high (0.82, 95% CI 0.73-0.92), the psychotic subgroup showed a higher genetic correlation with schizophrenia than non-psychotic MDD (0.67 vs 0.46, p-value 7.55∗10-4). Within 30,000 genotyped MDD cases, individuals with psychotic MDD had higher mean PRS for schizophrenia and BD but a lower MDD PRS than non-psychotic MDD. PRS for BD type-I was associated with increased odds of psychotic MDD, while BD type-II PRS showed no significant association with psychotic MDD.

INTERPRETATION

This study provides evidence for the genetic basis of psychotic MDD, underscoring its unique position bridging the spectrum of mood and psychotic disorders. These findings advance our understanding of the aetiology of psychotic MDD and contribute to the limited body of evidence on this phenotype by utilising large-scale population-based data.

FUNDING

European Research Council; US National Institutes of Mental Health; European Union Horizon 2020 Program; Swedish Research Council; Research Council of Norway; Swedish Foundation for Strategic Research; Hjärnfonden.

More and Less Fear in Serotonin Transporter Knockout Mice

Recent theories suggest that reduced serotonin transporter (5-HTT) function, which increases serotonin (5-HT) levels at the synapse, enhances neural plasticity and affects sensitivity to environmental cues. This may promote learning about emotionally relevant events. However, the boundaries that define such emotional learning remain to be established. This was investigated using 5-HTT knockout (5-HTTKO) mice which provide a model of long-term elevated 5-HT transmission and are associated with increased anxiety. Compared to wild-type controls, 5-HTTKO mice were faster to discriminate between an auditory cue that predicted footshock (CS+) and a cue predicting no footshock (CS-). Notably, this enhanced discrimination performance was driven not by faster learning that the CS+ predicted footshock, but rather by faster learning that the CS- cue signals the absence of footshock and thus provides temporary relief from fear/anxiety. Similarly, 5-HTTKO mice were also faster to reduce their fear of the CS+ cue during subsequent extinction. These findings are consistent with facilitated inhibitory learning that predicts the absence of potential threats in 5-HTTKO mice. However, 5-HTTKO mice also exhibited increased generalisation of fear learning about ambiguous aversive cues in a novel context, different from the training context. Thus, 5-HTTKO mice can exhibit both more and less fear compared to wild-type controls. Taken together, our results support the idea that loss of 5-HTT function, and corresponding increases in synaptic 5-HT availability, may facilitate learning by priming of aversive memories. This both facilitates inhibitory learning for fear memories but also enhances generalisation of fear.

Identifying genetic differences between bipolar disorder and major depression through multiple genome-wide association analyses

BACKGROUND

Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD).

AIMS

We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis.

METHOD

Based on individual genotypes from case-control cohorts of BPD and MDD shared through the Psychiatric Genomics Consortium, we compile case-case-control cohorts, applying a careful quality control procedure. In a resulting cohort of 51 149 individuals (15 532 BPD patients, 12 920 MDD patients and 22 697 controls), we perform a variety of GWAS and PRS analyses.

RESULTS

Although our GWAS is not well powered to identify genome-wide significant loci, we find significant chip heritability and demonstrate the ability of the resulting PRS to distinguish BPD from MDD, including BPD cases with depressive onset (BPD-D). We replicate our PRS findings in an independent Danish cohort (iPSYCH 2015, N = 25 966). We observe strong genetic correlation between our case-case GWAS and that of case-control BPD.

CONCLUSIONS

We find that MDD and BPD, including BPD-D are genetically distinct. Our findings support that controls, MDD and BPD patients primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BPD and, importantly, BPD-D from MDD.

Associations between post-traumatic stress disorder and neurological disorders: A genetic correlation and Mendelian randomization study

BACKGROUND

Observational studies have reported a close relationship between post-traumatic stress disorder (PTSD) and neurological disorders, but the existence of a causal link remains uncertain. The aim of this study is to investigate these relationships and potential mediators via Mendelian randomization (MR) analysis.

METHODS

We sourced pooled data for genome-wide association study (GWAS) of PTSD (n = 1,222,882) from the psychiatric genomics consortium. Summary-level data for eight neurological traits were derived from large-scale GWASs. Genetic correlations were computed using linkage disequilibrium (LD) score regression. The inverse variance weighted (IVW) method served as the primary analysis method for MR. We employed a range of sensitivity analysis methods to ensure result robustness. A two-step approach was utilized to ascertain the effects and proportions of mediations.

RESULTS

We identified significant genetic associations between PTSD and any dementia, cognitive performance, multiple sclerosis, and migraine. MR analysis revealed a significant association between PTSD and an increased risk of migraine (P = 0.02). This was substantiated by the results of several sensitivity analyses. Notably, the robust association between PTSD and migraine persisted even after adjustment for major depressive disorder and anxiety. Mediation analysis revealed that both alcohol intake frequency and insomnia partially mediated the association between PTSD and migraine.

LIMITATIONS

Participants in the MR analysis were of European descent, and verification in other ethnicities was not possible due to data limitations.

CONCLUSION

Our findings indicate a close association between PTSD and migraine. Alcohol intake frequency and insomnia serve as intermediate factors, partially explaining the relationship between PTSD and migraine.

Genetic architecture of RNA editing, splicing and gene expression in schizophrenia

Genome wide association studies (GWAS) have been conducted over the past decades to investigate the underlying genetic origin of neuropsychiatric diseases, such as schizophrenia (SCZ). While these studies demonstrated the significance of disease-phenotype associations, there is a pressing need to fully characterize the functional relevance of disease-associated genetic variants. Functional genetic loci can affect transcriptional and post-transcriptional phenotypes that may contribute to disease pathology. Here, we investigate the associations between genetic variation and RNA editing, splicing, and overall gene expression through identification of quantitative trait loci (QTL) in the CommonMind Consortium SCZ cohort. We find that editing QTL (edQTL), splicing QTL (sQTL) and expression QTL (eQTL) possess both unique and common gene targets, which are involved in many disease-relevant pathways, including brain function and immune response. We identified two QTL that fall into all three QTL categories (seedQTL), one of which, rs146498205, targets the lincRNA gene, RP11-156P1.3. In addition, we observe that the RNA binding protein AKAP1, with known roles in neuronal regulation and mitochondrial function, had enriched binding sites among edQTL, including the seedQTL, rs146498205. We conduct colocalization with various brain disorders and find that all QTL have top colocalizations with SCZ and related neuropsychiatric diseases. Furthermore, we identify QTL within biologically relevant GWAS loci, such as in ELA2, an important tRNA processing gene associated with SCZ risk. This work presents the investigation of multiple QTL types in parallel and demonstrates how they target both distinct and overlapping SCZ-relevant genes and pathways.

Bidirectional relationship between sleep and depression

Patients with depression almost inevitably exhibit abnormalities in sleep, such as shortened latency to enter rapid eye movement (REM) sleep and decrease in electroencephalogram delta power during non-REM sleep. Insufficient sleep can be stressful, and the accumulation of stress leads to the deterioration of mental health and contributes to the development of psychiatric disorders. Thus, it is likely that depression and sleep are bidirectionally related, i.e. development of depression contributes to sleep disturbances and vice versa. However, the relation between depression and sleep seems complicated. For example, acute sleep deprivation can paradoxically improve depressive symptoms. Thus, it is difficult to conclude whether sleep has beneficial or harmful effects in patients with depression. How antidepressants affect sleep in patients with depression might provide clues to understanding the effects of sleep, but caution is required considering that antidepressants have diverse effects other than sleep. Recent animal studies support the bidirectional relation between depression and sleep, and animal models of depression are expected to be beneficial for the identification of neuronal circuits that connect stress, sleep, and depression. This review provides a comprehensive overview regarding the current knowledge of the relationship between depression and sleep.

Genetic predisposition for negative affect predicts mental health burden during the COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) pandemic was accompanied by an increase in mental health challenges including depression, stress, loneliness, and anxiety. Common genetic variants can contribute to the risk for psychiatric disorders and may present a risk factor in times of crises. However, it is unclear to what extent polygenic risk played a role in the mental health response to the COVID-19 pandemic. In this study, we investigate whether polygenic scores (PGSs) for mental health-related traits can distinguish between four resilience-vulnerability trajectories identified during the COVID-19 pandemic and associated lockdowns in 2020/21. We used multinomial regression in a genotyped subsample (n = 1316) of the CovSocial project. The most resilient trajectory characterized by the lowest mental health burden and the highest recovery rates served as the reference group. Compared to this most resilient trajectory, a higher value on the PGS for the well-being spectrum decreased the odds for individuals to be in one of the more vulnerable trajectories (adjusted R-square = 0.3%). Conversely, a higher value on the PGS for neuroticism increased the odds for individuals to be in one of the more vulnerable trajectories (adjusted R-square = 0.2%). Latent change in mental health burden extracted from the resilience-vulnerability trajectories was not associated with any PGS. Although our findings support an influence of PGS on mental health during COVID-19, the small added explained variance suggests limited utility of such genetic markers for the identification of vulnerable individuals in the general population.

Genomics-driven integrative analysis highlights immune-related plasma proteins for psychiatric disorders

BACKGROUND

Genome-wide association studies (GWAS) have identified numerous variants associated with psychiatric disorders. However, it remains largely unknown on how GWAS risk variants contribute to psychiatric disorders.

METHODS

Through integrating two largest, publicly available, independent protein quantitative trait loci datasets of plasma protein and nine large-scale GWAS summary statistics of psychiatric disorders, we first performed proteome-wide association study (PWAS) to identify psychiatric disorders-associated plasma proteins, followed by enrichment analysis to reveal the underlying biological processes and pathways. Then, we conducted Mendelian randomization (MR) and Bayesian colocalization (COLOC) analyses, with both discovery and parallel replication datasets, to further identify protein-disorder pairs with putatively causal relationships. We finally prioritized the potential drug targets using Drug Gene Interaction Database.

RESULTS

PWAS totally identified 112 proteins, which were significantly enriched in biological processes relevant to immune regulation and response to stimulus including regulation of immune system process (adjusted P = 1.69 × 10-7) and response to external stimulus (adjusted P = 4.13 × 10-7), and viral infection related pathways, including COVID-19 (adjusted P = 2.94 × 10-2). MR and COLOC analysis further identified 26 potentially causal protein-disorder pairs in both discovery and replication analysis. Notably, eight protein-coding genes were immune-related, such as IRF3, CSK, and ACE, five among 16 druggable genes were reported to interact with drugs, including ACE, CSK, PSMB4, XPNPEP1, and MICB.

CONCLUSIONS

Our findings highlighted the immunological hypothesis and identified potentially causal plasma proteins for psychiatric disorders, providing biological insights into the pathogenesis and benefit the development of preventive or therapeutic drugs for psychiatric disorders.

Linking Genome-Wide Association Studies to Pharmacological Treatments for Psychiatric Disorders

Importance

Large-scale genome-wide association studies (GWAS) should ideally inform the development of pharmacological treatments, but whether GWAS-identified mechanisms of disease liability correspond to the pathophysiological processes targeted by current pharmacological treatments is unclear.

Objective

To investigate whether functional information from a range of open bioinformatics datasets can elucidate the relationship between GWAS-identified genetic variation and the genes targeted by current treatments for psychiatric disorders.

Design, Setting, and Participants

Associations between GWAS-identified genetic variation and pharmacological treatment targets were investigated across 4 psychiatric disorders-attention-deficit/hyperactivity disorder, bipolar disorder, schizophrenia, and major depressive disorder. Using a candidate set of 2232 genes listed as targets for all approved treatments in the DrugBank database, each gene was independently assigned 2 scores for each disorder-one based on its involvement as a treatment target and the other based on the mapping between GWAS-implicated single-nucleotide variants (SNVs) and genes according to 1 of 4 bioinformatic data modalities: SNV position, gene distance on the protein-protein interaction (PPI) network, brain expression quantitative trail locus (eQTL), and gene expression patterns across the brain. Study data were analyzed from November 2023 to September 2024.

Main Outcomes and Measures

Gene scores for pharmacological treatments and GWAS-implicated genes were compared using a measure of weighted similarity applying a stringent null hypothesis-testing framework that quantified the specificity of the match by comparing identified associations for a particular disorder with a randomly selected set of treatments.

Results

Incorporating information derived from functional bioinformatics data in the form of a PPI network revealed links for bipolar disorder (P permutation [P-perm] = 7 × 10-4; weighted similarity score, empirical [ρ-emp] = 0.1347; mean [SD] weighted similarity score, random [ρ-rand] = 0.0704 [0.0163]); however, the overall correspondence between treatment targets and GWAS-implicated genes in psychiatric disorders rarely exceeded null expectations. Exploratory analysis assessing the overlap between the GWAS-identified genetic architecture and treatment targets across disorders identified that most disorder pairs and mapping methods did not show a significant correspondence.

Conclusions and Relevance

In this bioinformatic study, the relatively low degree of correspondence across modalities suggests that the genetic architecture driving the risk for psychiatric disorders may be distinct from the pathophysiological mechanisms currently used for targeting symptom manifestations through pharmacological treatments. Novel approaches incorporating insights derived from GWAS based on refined phenotypes including treatment response may assist in mapping disorder risk genes to pharmacological treatments in the long term.

Mapping the cellular etiology of schizophrenia and complex brain phenotypes

Psychiatric disorders are multifactorial and effective treatments are lacking. Probable contributing factors to the challenges in therapeutic development include the complexity of the human brain and the high polygenicity of psychiatric disorders. Combining well-powered genome-wide and brain-wide genetics and transcriptomics analyses can deepen our understanding of the etiology of psychiatric disorders. Here, we leverage two landmark resources to infer the cell types involved in the etiology of schizophrenia, other psychiatric disorders and informative comparison of brain phenotypes. We found both cortical and subcortical neuronal associations for schizophrenia, bipolar disorder and depression. These cell types included somatostatin interneurons, excitatory neurons from the retrosplenial cortex and eccentric medium spiny-like neurons from the amygdala. In contrast we found T cell and B cell associations with multiple sclerosis and microglial associations with Alzheimer's disease. We provide a framework for a cell-type-based classification system that can lead to drug repurposing or development opportunities and personalized treatments. This work formalizes a data-driven, cellular and molecular model of complex brain disorders.

Structural neural plasticity evoked by rapid-acting antidepressant interventions

A feature in the pathophysiology of major depressive disorder (MDD), a mood disorder, is the impairment of excitatory synapses in the prefrontal cortex. Intriguingly, different types of treatment with fairly rapid antidepressant effects (within days or a few weeks), such as ketamine, electroconvulsive therapy and non-invasive neurostimulation, seem to converge on enhancement of neural plasticity. However, the forms and mechanisms of plasticity that link antidepressant interventions to the restoration of excitatory synaptic function are still unknown. In this Review, we highlight preclinical research from the past 15 years showing that ketamine and psychedelic drugs can trigger the growth of dendritic spines in cortical pyramidal neurons. We compare the longitudinal effects of various psychoactive drugs on neuronal rewiring, and we highlight rapid onset and sustained time course as notable characteristics for putative rapid-acting antidepressant drugs. Furthermore, we consider gaps in the current understanding of drug-evoked in vivo structural plasticity. We also discuss the prospects of using synaptic remodelling to understand other antidepressant interventions, such as repetitive transcranial magnetic stimulation. Finally, we conclude that structural neural plasticity can provide unique insights into the neurobiological actions of psychoactive drugs and antidepressant interventions.

Genetic evidence of the causal relationships between psychiatric disorders and cardiovascular diseases

OBJECTIVE

Our primary objective is to investigate the causal relationships between 12 psychiatric disorders (PDs) and atrial fibrillation (AF), coronary artery disease (CAD), myocardial infarction (MI), and heart failure (HF).

METHODS

Firstly, we used linkage disequilibrium score regression to calculate the genetic correlations between 12 PDs and 4 cardiovascular diseases (CVDs). Subsequently, we performed two-sample and bidirectional Mendelian randomization (MR) analyses of phenotypes with significant genetic correlations to explore the causal relationships between PDs and CVDs. Inverse variance weighted with modified weights (MW-IVW), Robust Adjusted Profile Score, Inverse Variance Weighted, weighted median and weighted mode were used to evaluate causal effects, with MW-IVW being the main analysis method. And to validate the MR results, we conducted the replicate analyses using data from the FinnGen database.

RESULTS

Conducting MR analyses in phenotypes with significant genetic correlations, we identified bidirectional causal relationships between depression (DEP) and MI (DEP as exposure: OR = 1.1324, 95 % confidence interval (CI): 1.0984-1.1663, P < 0.0001; MI as exposure: OR = 1.0268, 95 % CI: 1.0160-1.0375, P < 0.0001). Similar relationships were observed in Attention Deficit/Hyperactivity Disorder (ADHD) and HF (ADHD as exposure: OR = 1.0270, 95 % CI: 1.0144-1.0395, P < 0.0001; HF as exposure: OR = 1.0980, 95 % CI: 1.0502-1.1458, P < 0.0001).

CONCLUSIONS

In our study, we conducted the comprehensive analyses between 12 PDs and CVDs. By bidirectional MR analysis, we observed significant causal relationships between MI and DEP, HF and ADHD. These findings suggest possible complex causal relationships between PDs and CVDs.

Mapping the microRNA landscape in the older adult brain and its genetic contribution to neuropsychiatric conditions

MicroRNAs (miRNAs) play a crucial role in regulating gene expression and influence many biological processes. Despite their importance, understanding of how genetic variation affects miRNA expression in the brain and how this relates to brain disorders remains limited. Here we investigated these questions by identifying microRNA expression quantitative trait loci (miR-QTLs), or genetic variants associated with brain miRNA levels, using genome-wide small RNA sequencing profiles from dorsolateral prefrontal cortex samples of 604 older adult donors of European ancestry. Here we show that nearly half (224 of 470) of the analyzed miRNAs have associated miR-QTLs, many of which fall in regulatory regions such as brain promoters and enhancers. We also demonstrate that intragenic miRNAs often have genetic regulation independent from their host genes. Furthermore, by integrating our findings with 16 genome-wide association studies of psychiatric and neurodegenerative disorders, we identified miRNAs that likely contribute to bipolar disorder, depression, schizophrenia and Parkinson's disease. These findings advance understanding of the genetic regulation of miRNAs and their role in brain health and disease.

Enhancing prediction of major depressive disorder onset in adolescents: A machine learning approach

Major Depressive Disorder (MDD) is a prevalent mental health condition that often begins in adolescence, with significant long-term implications. Indicated prevention programs targeting adolescents with mild symptoms have shown efficacy, yet the methods for identifying at-risk individuals need improvement. This study aims to evaluate the utility of Partial Least Squares Regression (PLSR) in predicting the onset of MDD among non-depressed adolescents, compared to traditional screening methods. The study recruited 1462 Portuguese adolescents aged 13-16, who were assessed using various self-report measures and followed for two years. Participants were randomly divided into training (70%, N = 1023) and testing (30%, N = 439) samples. PLSR models were developed to predict the occurrence of a major depressive episode (MDE) within two years, using 331 variables. The model's performance was compared to the Children's Depression Inventory (CDI) in predicting MDE onset. The best-fitting PLSR model with two components explained 19.1% and 16.9% of the variance in the training and testing samples, respectively, significantly outperforming the CDI, which explained 7.7% of the variance. The area under the ROC curve was 0.78 for PLSR, compared to 0.71 for CDI. An empirically derived cut-off point was used to create dichotomous risk categories, and it showed a significant difference in MDE rates between predicted high-risk and low-risk groups. The balanced accuracy of the PLSR model was 0.77, compared to 0.65 for the CDI method. The PLSR model effectively identified adolescents at risk for developing MDD, demonstrating superior predictive power over the CDI. This study supports the potential utility of ML techniques (e.g., PLSR) in enhancing early identification and prevention efforts for adolescent depression.

Sex Influences Genetic Susceptibility to Depression-Like Behaviors in Chronic Unpredictable Mild Stress-Exposed Wistar Rats

Depression is one of the most common mood disorders among psychiatric diseases. It affects about 10% of the adult population. However, its etiopathogenesis remains poorly understood. Exploring the dynamics of stress-susceptibility and resilience will help in understanding the molecular and biological mechanisms underlying the etiopathogenesis of depression. This study aimed to determine the differences and/or similarities in factors responsible for susceptibility to depression-like behaviors in male and female Wistar rats subjected to chronic unpredictable mild stress (CUMS). Sixty Wistar rats (30 male and 30 female) weighing between 120 and 150 g were used for this study. The rats were divided into two sub-groups: control (10) and test (20) groups. Rats in the test groups were subjected to CUMS. Depression-like behaviors were assessed using light-dark box, sucrose preference, and tail suspension tests. Rats that showed depression-like behaviors following the behavioral tests (CUMS-susceptible group) were sacrificed, and their hippocampi were excised. Genomic deoxyribonucleic acid (gDNA) was purified from the hippocampal samples. Purified gDNA was subjected to whole genome sequencing (WGS). Base-calling of sequence reads from raw sequencing signal (FAST5) files was carried out, and variants were called from alignment BAM files. The corresponding VCF files generated from the variant calling experiment were filtered. Genes were identified, their impacts estimated, and variants annotated. Functional enrichment analysis was then carried out. Approximately 41% of the male and 49% of the female rats subjected to CUMS showed significant (p < 0.05) depression-like behaviors following assessment on behavioral tests. WGS of the hippocampal DNA revealed 289,839 single nucleotide polymorphisms variant types, 7002 insertions, and 34,459 deletions in males, and 1,570,186 single nucleotide polymorphisms variant types, 109,860 insertions, and 597,241 deletions in female Wistar rats. Three genes with high-impact variants were identified in male and 22 in female Wistar rats, respectively. In conclusion, female Wistar rats are more susceptible to depression-like behaviors after exposure to CUMS than males. They also have more gene variants (especially high-impact variants) than male Wistar rats.

NEK4: prediction of available drug targets and common genetic linkages in bipolar disorder and major depressive disorder

Background

Bipolar disorder (BD) is a mental illness characterized by alternating episodes of elevated mood and depression, while major depressive disorder (MDD) is a debilitating condition that ranks second globally in terms of disease burden. Pharmacotherapy plays a crucial role in managing both BD and MDD. We investigated the genetic differences in populations of individuals with MDD and BD, and from a genetic perspective, we offered new insights into potential drug targets. This will provide clues to potential drug targets.

Methods

This study employed genome-wide association studies (GWAS) and summary-data-based Mendelian randomization (SMR) methods to investigate the genetic underpinnings of patients with bipolar disorder (BD) and major depressive disorder (MDD) and to predict potential drug target genes. Genetic variants associated with BD and MDD were identified through large-scale GWAS datasets. For BD, the study utilized a comprehensive meta-analysis comprising 57 BD cohorts from Europe, North America, and Australia, including 41,917 BD cases and 371,549 controls of European ancestry. This dataset included both type 1 and type 2 BD cases diagnosed based on DSM-IV, ICD-9, or ICD-10 criteria through standardized assessments. For MDD, we used data from a meta-analysis by Howard DM et al., which integrated the largest GWAS studies of MDD, totaling 246,363 cases and 561,190 controls. The SMR approach, combined with expression quantitative trait loci (eQTL) data, was then applied to assess causal associations between these genetic variants and gene expression, aiming to identify genetic markers and potential drug targets associated with BD and MDD. Furthermore, two-sample Mendelian randomization (TSMR) analyses were performed to explore causal links between protein quantitative trait loci (pQTL) and these disorders.

Results

The SMR analysis revealed 41 druggable genes associated with BD, of which five genes appeared in both brain tissue and blood eQTL datasets and were significantly associated with BD risk. Furthermore, 45 druggable genes were found to be associated with MDD by SMR analysis, of which three genes appeared simultaneously in both datasets and were significantly associated with MDD risk. NEK4, a common drug candidate gene for BD and MDD, was also significantly associated with a high risk of both diseases and may help differentiate between type 1 and type 2 BD. Specifically, NEK4 showed a strong association with BD (β brain=0.126, P FDR=0.001; βblood=1.158, P FDR=0.003) and MDD (β brain=0.0316, P FDR=0.022; βblood=0.254, P FDR=0.045). Additionally, NEK4 was notably linked to BD type 1 (βbrain=0.123, P FDR=2.97E-05; βblood=1.018, P FDR=0.002), but showed no significant association with BD type 2.Moreover, TSMR analysis identified four proteins (BMP1, F9, ITIH3, and SIGIRR) affecting the risk of BD, and PSMB4 affecting the risk of MDD.

Conclusion

Our study identified NEK4 as a key gene linked to both bipolar disorder (BD) and major depressive disorder (MDD), suggesting its potential as a drug target and a biomarker for differentiating BD subtypes. Using GWAS, SMR, and TSMR approaches, we revealed multiple druggable genes and protein associations with BD and MDD risk, providing new insights into the genetic basis of these disorders. These findings offer promising directions for precision medicine and novel therapeutic strategies in mental health treatment.

Identification and validation of depression-associated genetic variants in the UK Biobank cohort with transcriptome and DNA methylation analyses in independent cohorts

Depression is one of the most common psychiatric conditions resulting from a complex interaction of genetic, epigenetic and environmental factors. The present study aimed to identify independent genetic variants in the protein-coding genes that associate with depression and to analyze their transcriptomic and methylation profile. Data from the GWAS Catalogue was used to identify independent genetic variants for depression. The identified genetic variants were validated in the UK Biobank cohort and used to calculate a genetic risk score for depression. Data was also used from publicly available cohorts to conduct transcriptome and methylation analyses. Eight SNPs corresponding to six protein-coding genes (TNXB, NCAM1, LTBP3, BTN3A2, DAG1, FHIT) were identified that were highly associated with depression. These validated genetic variants for depression were used to calculate a genetic risk score that showed a significant association with depression (p < 0.05) but not with co-morbid traits. The transcriptome and methylation analyses suggested nominal significance for some gene probes (TNXB- and NCAM1) with depressed phenotype. The present study identified six protein-coding genes associated with depression and primarily involved in inflammation (TNXB), neuroplasticity (NCAM1 and LTBP3), immune response (BTN3A2), cell survival (DAG1) and circadian clock modification (FHIT). Our findings confirmed previous evidence for TNXB- and NCAM1 in the pathophysiology of depression and suggested new potential candidate genes (LTBP3, BTN3A2, DAG1 and FHIT) that warrant further investigation.

Causal Impacts of Psychiatric Disorders on Cognition and the Mediating Effect of Oxidative Stress: A Mendelian Randomization Study

Many psychiatric disorders are associated with major cognitive deficits. However, it is uncertain whether these deficits develop as a result of psychiatric disorders and what shared risk factors might mediate this relationship. Here, we utilized the Mendelian randomization (MR) analysis to investigate the complex causal relationship between nine major psychiatric disorders and three cognitive phenotypes, while also examining the potential mediating role of oxidative stress as a shared biological underpinning. Schizophrenia (SZ), major depressive disorder (MDD), and attention deficit hyperactivity disorder (ADHD) showed a decreasing effect on cognitive performance, intelligence, and education, while bipolar disorder (BPD) increased educational attainment. MR-Clust results exhibit the shared genetic basis between SZ and other psychiatric disorders in relation to cognitive function. Furthermore, when oxidative stress was considered as a potential mediating factor, the associations between SZ and the three dimensions of cognition, as well as between MDD and intelligence and ADHD and intelligence, exhibited larger effect sizes than the overall. Mediation MR analysis also supported the causal effects between psychiatric disorders and cognition via oxidative stress traits, including carotene, vitamin E, bilirubin, and uric acid. Finally, summary-based MR identified 29 potential causal associations of oxidative stress genes with both cognitive performance and psychiatric disorders. Our findings highlight the importance of considering oxidative stress in understanding and potentially treating cognitive impairments associated with psychiatric conditions.

Genomic Analysis of Trichotillomania

Trichotillomania (TTM) is a psychiatric condition in which people feel an overwhelming urge to pull out their hair, resulting in noticeable hair loss and significant distress. Twin, family and candidate gene studies suggest that TTM is at least partly genetic, but no genome-wide analyses have been completed. To fill the gap in this field, we have conducted a case-control study of genotype array data from 101 European ancestry TTM cases and 488 ancestry-matched unaffected controls. TTM cases were ascertained in the USA through web-based recruitment, patient support groups, and conferences organized by the Trichotillomania Learning Center. Following clinical confirmation of a TTM diagnosis, patients completed self-report assessments of frequency and duration of hair pulling, other psychiatric symptoms, and family history. Unaffected controls were also ascertained in the USA and were matched to cases by ancestry. In the first formal genome-wide association study of TTM, we did not identify any common variants with a genome-wide significant (P < 5×10 -8 ) association level with case status. We found that TTM cases carry a higher load of common polygenic risk for psychiatric disorders than unaffected controls ( P = 0.008). We also detected copy number variants previously associated with neuropsychiatric disorders in TTM cases (specifically, deletions in NRXN1, CSMD1 , and 15q11.2). These results further support genetics' role in the etiology of TTM and suggest that larger studies are likely to identify risk variation and, ultimately, specific risk genes associated with the condition.

Mental illness and antibody responses after COVID-19 vaccination in a prospective population-based study in Catalonia

Background Mental illnesses have been overlooked as a potential factor influencing antibody responses to COVID-19 vaccine. Associations between mental disorders and antibody response might vary by specific disorders, depend on the long-term course of the illness and relate to psychotropic treatment.

METHODS

The association between mental illness diagnoses (mood affective disorders, anxiety disorders, other) over ten years and psychotropic drug prescription based on electronic health records with antibody levels (IgG and IgA) post COVID-19 vaccination was assessed in 939 vaccinated adults from Catalonia, Spain. We employed linear regression models to assess associations between specific mental illnesses and psychotropic drugs with antibody levels, correcting for demographics, comorbidities and lifestyle factors. In a genotyped subset (n = 247) we assessed the effect of polygenic risk scores (PRS) for mental illnesses and performed a two-sample mendelian randomization (MR) analysis to examine causality between mental illness and antibody responses.

RESULTS

Mood affective disorders were associated with lower IgG to receptor binding domain (RBD) [percentage change = -26.37 (95 % CI, -42.00, -6.54)]. Diagnosis of anxiety disorders was not associated with the outcome. The group of other diagnoses (mainly including insomnia and nicotine dependence) were associated with lower IgG RBD levels [percentage change: -21.53 (95 % CI, -35.38, -4.71)] and recent onset cases (≤5 years ago) showed greater decline in antibody levels. Participants on second-generation antipsychotics and multiple classes of psychotropic drugs in the last 6 months exhibited lower antibody levels. In the genotyped population, higher genetic liability (higher PRS) to schizophrenia was associated with lower IgG RBD levels [percentage change = -35.49 (95 % CI, -56.55, -4.23)]. MR analysis revealed a causal relationship between major depression genetic instrumental variables and lower IgG RBD and S levels.

CONCLUSIONS

These findings raise concerns about the efficacy of COVID-19 vaccines and potentially of other vaccines as well, in individuals with mood affective disorders, current/recent insomnia and nicotine dependence and people on multiple psychotropic drugs. Whether these associations are translated into increased risk for breakthrough infections and immune mediated long-term sequels of the SARS-CoV-2 infection warrants further investigation.

Causal relationships between hippocampal volumetric traits and the risk of Alzheimer's disease: a Mendelian randomization study

Alzheimer's disease, a common and progressive neurodegenerative disorder, is associated with alterations in hippocampal volume, as revealed by neuroimaging research. However, the causal links between the volumes of the hippocampus and its subfield structures with Alzheimer's disease remain unknown. A genetic correlation analysis using linkage disequilibrium score regression was conducted to identify hippocampal volumetric traits linked to Alzheimer's disease. Following this, to examine the causal links between Alzheimer's disease and hippocampal volumetric traits, we applied a two-sample Mendelian randomization approach, utilizing a bidirectional framework. Seven hippocampal volumetric traits were found as genetically correlated with Alzheimer's disease in the genetic correlation analysis and were then included in the Mendelian randomization analyses. Inverse variance weighted Mendelian randomization analyses revealed that increased volumes in the left whole hippocampus, left hippocampal body, right presubiculum head and right cornu ammonis 1 head were causally related to higher risks of Alzheimer's disease. Conversely, a higher risk of Alzheimer's disease was causally associated with decreased volumes of the left hippocampal body and left whole hippocampus. These results were validated through other Mendelian randomization approaches and sensitivity analysis. Our findings uncover bidirectional causal relationships between Alzheimer's disease and hippocampal volumetric traits, suggesting not only the potential significance of these traits in predicting Alzheimer's disease but also the reciprocal influence of Alzheimer's disease on hippocampal volumes.

Nonlinear association between depressive symptoms and homeostasis model assessment of insulin resistance: a cross-sectional analysis in the American population

Background

Depressive symptom, a pervasive mental health disorder, has garnered increasing attention due to its intricate interconnections with various physiological processes. One emerging avenue of investigation delves into the potential association between depressive symptom and Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), a parameter reflecting insulin resistance. The intricate interplay between these two domains holds promising implications for understanding the multifaceted nature of depressive symptom and its impact on metabolic health.

Methods

We used weighted multivariable logistic regression models with subgroup analysis to explore the relationship between depressive symptom and homeostasis model assessment of insulin resistance. Non-linear correlations were explored using fitted smoothing curves. Then, we constructed a two-piece linear regression model and performed a recursive algorithm to calculate the inflection point.

Results

The study included 20,282 participants in the United States. In the regression model adjusted for all confounding variables, the odds ratio (OR) for the correlation between depressive symptom and the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) was 1.01 (95% CI: 1.00, 1.01). However, a significant discrepancy between trend tests and regression analyses suggests a potential non-linear relationship between depressive symptom and the assessment of insulin resistance using the Homeostasis Model. Constrained cubic spline analysis confirmed this non-linear relationship, identifying an inflection point at 10.47. Before the inflection point, depressive symptom exhibited a significantly positive correlation with the assessment of insulin resistance using the Homeostasis Model. However, after the inflection point, a negative correlation was observed, though it did not reach statistical significance.

Conclusion

We found a curve-like relationship between depressive symptom and homeostasis model assessment of insulin resistance.

Causal associations between iron levels in subcortical brain regions and psychiatric disorders: a Mendelian randomization study

Despite observational studies linking brain iron levels to psychiatric disorders, the exact causal relationship remains poorly understood. This study aims to examine the relationship between iron levels in specific subcortical brain regions and the risk of psychiatric disorders. Utilizing two-sample Mendelian randomization (MR) analysis, this study investigates the causal associations between iron level changes in 16 subcortical nuclei and eight major psychiatric disorders, including schizophrenia (SCZ), major depressive disorder (MDD), autism spectrum disorders (ASD), attention-deficit/hyperactivity disorder, bipolar disorder, anxiety disorders, obsessive-compulsive disorder, and insomnia. The genetic instrumental variables linked to iron levels and psychiatric disorders were derived from the genome-wide association studies data of the UK Biobank Brain Imaging and Psychiatric Genomics Consortium. Bidirectional causal estimation was primarily obtained using the inverse variance weighting (IVW) method. Iron levels in the left substantia nigra showed a negative association with the risk of MDD (ORIVW = 0.94, 95% CI = 0.91-0.97, p < 0.001) and trends with risk of SCZ (ORIVW = 0.90, 95% CI = 0.82-0.98, p = 0.020). Conversely, iron levels in the left putamen were positively associated with the risk of ASD (ORIVW = 1.11, 95% CI = 1.04-1.19, p = 0.002). Additionally, several bidirectional trends were observed between subcortical iron levels and the risk for psychiatric disorders. Lower iron levels in the left substantia nigra may increase the risk of MDD, and potentially increase the risk of SCZ, indicating a potential shared pathogenic mechanism. Higher iron levels in the left putamen may lead to the development of ASD. The observed bidirectional trends between subcortical iron levels and psychiatric disorders, indicate the importance of the underlying biomechanical interactions between brain iron regulation and these disorders.

A disturbed communication between hypothalamic-pituitary-ovary axis and gut microbiota in female infertility: is diet to blame?

Female infertility is a multifactorial condition influenced by various genetic, environmental, and lifestyle factors. Recent research has investigated the significant impact of gut microbiome dysbiosis on systemic inflammation, metabolic dysfunction, and hormonal imbalances, which can potentially impair fertility. The gut-brain axis, a bidirectional communication system between the gut and the brain, also plays a significant role in regulating reproductive functions. Emerging evidence suggests that the gut microbiome can influence brain functions and behavior, further emphasizing the importance of the microbiota-gut-brain axis in reproduction. Given their role as a major modulator of the gut microbiome, diet and dietary factors, including dietary patterns and nutrient intake, have been implicated in the development and management of female infertility. Hence, this review aims to highlight the impact of dietary patterns, such as the Western diet (WD) and Mediterranean diet (MD), and to decipher their modulatory action on the microbiota-gut-brain axis in infertile women. By contrasting the detrimental effects of WD with the therapeutic potential of MD, we emphasize the pivotal role of a balanced diet rich in nutrients in promoting a healthy gut microbiome. These insights underscore the potential of targeted dietary interventions and lifestyle modifications as promising strategies to enhance reproductive outcomes in subfertile women.

Mapping the Cerebrospinal Fluid Proteome in Bipolar Disorder

BACKGROUND

Bipolar disorder (BD) is a severe psychiatric condition with unclear etiology and no established biomarkers. Here, we aimed to characterize the cerebrospinal fluid (CSF) proteome in euthymic individuals with BD to identify potential protein biomarkers.

METHODS

We used nano-flow liquid chromatography coupled to high-resolution mass spectrometry to quantify over 2000 CSF proteins in 374 individuals from two independent clinical cohorts (n = 164 cases + 89 controls and 66 cases + 55 controls, respectively). A subset of the cases was followed longitudinally and reexamined after a median of 6.5 years.

RESULTS

Differential abundance analysis revealed 41 proteins with robust case-control association in both cohorts. These included lower levels of synaptic proteins (e.g., APP, CLSTN1, NPTX2, NRXN1) and axon guidance and cell adhesion molecules (e.g., NEO1, NCAM1, SEMA7A) and higher levels of blood-brain barrier integrity proteins (e.g., VTN, SERPIN3) and complement components (e.g., C1RL, C3, C5). The findings were consistently driven by the BD type 1 subtype. Comparing BD type 1 participants with control participants increased discoverability, revealing 86 replicated associations despite a loss of statistical power. Moreover, longitudinal analyses of coexpression modules revealed dynamic changes in the CSF proteome composition that correlated with clinical outcomes, including disease severity, future manic episodes, and symptom improvement. Finally, we conducted association analyses of CSF proteins with genetic risk loci for BD and schizophrenia.

CONCLUSIONS

This study represents the first large-scale untargeted profiling of the CSF proteome in BD, unveiling potential biomarkers and providing in vivo support for altered synaptic and brain connectivity processes, impaired neurovascular integrity, and complement activation in the pathology of BD.

Integrating genetics and transcriptomics to characterize shared mechanisms in digestive diseases and psychiatric disorders

Digestive and psychiatric disorders tend to co-occur, yet mechanisms remain unclear. Leveraging genetic and transcriptomic data integration, we conduct multi-trait analysis of GWAS (MTAG) and weighted gene co-expression network analysis (WGCNA) to explore shared mechanism between psychiatric and gastrointestinal disorders. Significant genetic correlations were found between these disorders, especially in irritable bowel syndrome (IBS), gastroesophageal reflux disease (GERD), depression (DEP), and neuroticism (NE). MTAG identify 60 novel pleiotropic loci for IBS and 14 for GERD, predominantly located near genes associated with neurological pathways. Further WGCNA identifies multiple co-expression modules enriched with genes involved in neurological pathways in digestive tissues, with some modules strongly preserved across brain and digestive tissues. Moreover, our network analysis suggests BSN, CELF4, and NRXN1 as central players in the regulation of the gut-brain axis (GBA). This study enhances our understanding of the GBA and underscores BSN, CELF4, and NRXN1 as crucial targets for future research.

From single nucleotide variations to genes: identifying the genetic links between sleep and psychiatric disorders

STUDY OBJECTIVES

Sleep disorders and psychiatric disorders frequently coexist and interact, yet the shared genetic basis linking these two domains remains poorly understood.

METHODS

We investigated the genetic correlation and overlap between seven sleep/circadian traits and three psychiatric disorders at the level of genome-wide association studies (GWAS), utilizing LDSC, HDL, and GPA. To identify potential polygenic single nucleotide variations (SNVs) within each trait pair, we used PLACO, while gene-level analyses were performed using MAGMA and POPS. Furthermore, the functions and biological mechanisms, enriched phenotypes, tissues, cellular features, and pathways were thoroughly investigated using FUMA, deTS, and enrichment analyses at the biological pathway level.

RESULTS

Our study revealed extensive genetic associations and overlaps in all 21 trait pairs. We identified 18 494 SNVs and 543 independent genomic risk loci, with 113 confirmed as causative through colocalization analysis. These loci collectively spanned 196 unique chromosomal regions. We pinpointed 43 distinct pleiotropic genes exhibiting significant enrichment in behavioral/physiological phenotypes, nervous system phenotypes, and brain tissue. Aberrations in synaptic structure and function, neurogenesis and development, as well as immune responses, particularly involving the MAPK pathway, emerged as potential underpinnings of the biology of sleep/circadian traits and psychiatric disorders.

CONCLUSIONS

We identified shared loci and specific sets of genes between sleep/circadian traits and psychiatric disorders, shedding light on the genetic etiology. These discoveries hold promise as potential targets for novel drug interventions, providing valuable insights for the development of therapeutic strategies for these disorders.

Exploring the genetic landscape of the brain-heart axis: A comprehensive analysis of pleiotropic effects between heart disease and psychiatric disorders

BACKGROUND

The genetic links between heart disease and psychiatric disorders are complex and not well understood. This study uses genome-wide association studies (GWAS) and advanced multilevel analyses to explore these connections.

METHODS

We analyzed GWAS data from seven psychiatric disorders and five types of heart disease. Genetic correlations and overlaps were examined using linkage disequilibrium score regression (LDSC), high-definition likelihood (HDL), and Genetic analysis incorporating Pleiotropy and Annotation (GPA). Pleiotropic single-nucleotide variations (SNVs) were identified with pleiotropic analysis under the composite null hypothesis (PLACO) and annotated via Functional mapping and annotation of genetic associations (FUMA). Potential pleiotropic genes were identified using Multi-marker Analysis of GenoMic Annotation (MAGMA) and Summary data-based Mendelian Randomization (SMR).

RESULTS

Among 35 trait pairs, 32 showed significant genetic correlations or overlaps. PLACO identified 15,077 SNVs, with 287 recognized as pleiotropic loci and 20 colocalization sites. MAGMA and SMR revealed 75 potential pleiotropic genes involved in diverse pathways, including cancer, neurodevelopment, and cellular organization. Mouse Genome Informatics (MGI) queries provided evidence linking multiple genes to heart or psychiatric disorders.

CONCLUSIONS

This analysis reveals loci and genes with pleiotropic effects between heart disease and psychiatric disorders, highlighting shared biological pathways. These findings illuminate the genetic mechanisms underlying the brain-heart axis and suggest shared biological foundations for these conditions, offering potential targets for future prevention and treatment strategies.

Antidepressants account for the causal effect of major depressive disorder on type 2 diabetes

BACKGROUND

Patients with major depressive disorder (MDD) face an elevated risk of type 2 diabetes (T2D). However, the contribution of the disease itself versus the side effects of antidepressants to this increased risk remains unclear.

OBJECTIVE

This study aimed to investigate the overall and independent effects of MDD and exposure to antidepressants on T2D risk.

METHODS

Summary genome-wide association study datasets were utilized for the Mendelian randomization (MR) and multivariable MR (MVMR) analyses, including ones for MDD (N = 500,199), antidepressants (N = 175,161), and T2D (N = 933,970). Bayesian colocalization analysis was used to reveal shared genetic variation between MDD, antidepressants, and T2D.

RESULTS

We found that both MDD (OR: 1.15, CI: 1.03-1.30, P = 0.016) and antidepressants (OR: 1.37, CI: 1.22-1.53, P = 2.75E-08) have overall causal effects on T2D. While T2D was associated with the risk of antidepressant use (OR: 1.08, CI: 1.06-1.11, P = 8.80E-10), but not with the risk of MDD (OR: 1.00, CI: 0.98-1.01, P = 0.661). Our MVMR analysis showed that the use of antidepressants is associated with higher risks of T2D (OR: 1.21, CI: 1.07-1.37, P = 7.19E-04), while MDD is not linked to the risk of T2D (OR: 1.01, CI: 0.86-1.18, P = 0.799). Colocalization analysis identified two shared genetic loci between antidepressants and T2D.

CONCLUSIONS

The elevated T2D risk in MDD patients is chiefly caused by antidepressant use. These findings emphasize the importance of considering the impact of antidepressants on metabolic health in individuals with MDD.

Chronic overlapping pain conditions and nociplastic pain

Chronic overlapping pain conditions (COPCs) are a subset of chronic pain conditions commonly comorbid with one another and more prevalent in women and individuals assigned female at birth (AFAB). Pain experience in these conditions may better fit with a new mechanistic pain descriptor, nociplastic pain, and nociplastic pain may represent a shared underlying factor among COPCs. We applied GenomicSEM common-factor genome-wide association study (GWAS) and multivariate transcriptome-wide association (TWAS) analyses to existing GWAS output for six COPCs in order to find genetic variation associated with nociplastic pain, followed by genetic correlation (linkage disequilibrium score regression), gene set, and tissue enrichment analyses. We found 24 independent single nucleotide polymorphisms (SNPs), and 127 unique genes significantly associated with nociplastic pain, and showed nociplastic pain to be a polygenic trait with significant SNP heritability. We found significant genetic overlap between multisite chronic pain and nociplastic pain, and to a smaller extent with rheumatoid arthritis and a neuropathic pain phenotype. Tissue enrichment analyses highlighted cardiac and thyroid tissue, and gene set enrichment analyses emphasized potential shared mechanisms in cognitive, personality, and metabolic traits and nociplastic pain along with distinct pathology in migraine and headache. We used a well-powered network approach to investigate nociplastic pain using existing COPC GWAS output, and show nociplastic pain to be a complex, heritable trait, in addition to contributing to understanding of potential mechanisms in development of nociplastic pain.

Genome-Wide Insights into Internalizing Symptoms in Admixed Latin American Children

BACKGROUND/OBJECTIVES

Internalizing disorders, including depression and anxiety, are major contributors to the global burden of disease. While the genetic architecture of these disorders in adults has been extensively studied, their early-life genetic mechanisms remain underexplored, especially in non-European populations. This study investigated the genetic mechanisms underlying internalizing symptoms in a cohort of Latin American children.

METHODS

This study included 1244 Brazilian children whose legal guardians completed the Child Behavior Checklist (CBCL) questionnaire. Genotyping was performed using the Illumina HumanOmni 2.5-8v1 BeadChip.

RESULTS

The genome-wide association analysis revealed a significant association of rs7196970 (p = 4.5 × 10-8, OR = 0.61), in the ABCC1 gene, with internalizing symptoms. Functional annotation highlighted variants in epigenetically active regulatory regions, with multiple variants linked to differential expression of ABCC1 across several human tissues. Pathway enrichment analysis identified 42 significant pathways, with notable involvement in neurobiological processes such as glutamatergic, GABAergic, and dopaminergic synapses.

CONCLUSIONS

This study identifies ABCC1 variants as novel genetic factors potentially associated with early-life internalizing symptoms. These results may contribute to future research on targeted interventions for childhood internalizing conditions.

The genetic architecture of differentiating behavioral and emotional problems in early life

BACKGROUND

Early in life, behavioral and cognitive traits associated with risk for developing a psychiatric condition are broad and undifferentiated. As children develop, these traits differentiate into characteristic clusters of symptoms and behaviors that ultimately form the basis of diagnostic categories. Understanding this differentiation process - in the context of genetic risk for psychiatric conditions, which is highly generalized - can improve early detection and intervention.

METHODS

We modeled the differentiation of behavioral and emotional problems from age 1.5-5 years (behavioral problems - emotional problems = differentiation score) in a pre-registered study of ∼79,000 children from the population-based Norwegian Mother, Father, and Child Cohort Study. We used genomic structural equation modeling to identify genetic signal in differentiation and total problems, investigating their links with 11 psychiatric and neurodevelopmental conditions. We examined associations of polygenic scores (PGS) with both outcomes and assessed the relative contributions of direct and indirect genetic effects in ∼33,000 family trios.

RESULTS

Differentiation was primarily genetically correlated with psychiatric conditions via a "neurodevelopmental" factor. Total problems were primarily associated with the "neurodevelopmental" factor and "p"-factor. PGS analyses revealed an association between liability to ADHD and differentiation (β=0.11 [0.10,0.12]), and a weaker association with total problems (β=0.06 [0.04,0.07]). Trio-PGS analyses showed predominantly direct genetic effects on both outcomes.

CONCLUSIONS

We uncovered genomic signal in the differentiation process, mostly related to common variants associated with neurodevelopmental conditions. Investigating the differentiation of early life behavioral and emotional problems may enhance our understanding of the developmental emergence of different psychiatric and neurodevelopmental conditions.

Relationship between major depressive disorder and myalgic encephalomyelitis/chronic fatigue syndrome: a two-sample mendelian randomization study analysis

Major depressive disorder (MDD) and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) frequently occur together; yet their causal relationship remains unclear. To investigate the potential genetic causal link between these conditions, we conducted a two-sample Mendelian randomization (MR) analysis. Summary data from Genome-Wide Association Studies (GWAS) for MDD were sourced from the UK Biobank and the Psychiatric Genomics Consortium, while GWAS data for ME/CFS were retrieved from the UK Biobank. Inverse-variance weighting (IVW), the MR-Egger method, and weighted median, simple and weighted modes were used to perform the MR analysis. In addition, Cochrane's Q-test was used to detect heterogeneity among the MR results. Horizontal pleiotropy was detected using the MR-Egger intercept and the MR pleiotropy residual sum and outlier (MR-PRESSO) tests. Leave-one-out analysis was performed to investigate the sensitivity of the association between MDD and ME/CFS. The results of the MR analysis revealed no causal relationship between MDD and ME/CFS. The pleiotropy test revealed that causality bias was improbable, and no evidence of heterogeneity was found among the genetic variants. Finally, the leave-one-out test confirmed the stability and robustness of our findings.

Developing a translational research framework for MDD: combining biomolecular mechanisms with a spiraling risk factor model

Objective

The global incidence and burden of Major Depressive Disorder (MDD) are increasing annually, with current antidepressant treatments proving ineffective for 30-40% of patients. Biomolecular mechanisms within the microbiota-gut-brain axis (MGBA) may significantly contribute to MDD, potentially paving the way for novel treatment approaches. However, integrating the MGBA with the psychological and environmental aspects of MDD remains challenging. This manuscript aims to: 1) investigate the underlying biomolecular mechanisms of MDD using a modeling approach, and 2) integrate this knowledge into a comprehensive 'spiraling risk factor model' to develop a biopsychosocial translational research framework for the prevention and treatment of MDD.

Methods

For the first aim, a systematic review (PROSPERO registration) was conducted using PubMed, Embase, and Scopus to query literature published between 2016-2020, with select additional sources. A narrative review was performed for the second aim.

Results

In addition to genetics and neurobiology, research consistently indicates that hyperactivation of the HPA axis and a pro-inflammatory state are interrelated components of the MGBA and likely underlying mechanisms of MDD. Dysregulation of the MGBA, along with imbalances in mental and physical conditions, lifestyle factors, and pre-existing treatments, can trigger a downward spiral of stress and anxiety, potentially leading to MDD.

Conclusions

MDD is not solely a brain disorder but a heterogeneous condition involving biomolecular, psychological, and environmental risk factors. Future interdisciplinary research can utilize the integrated biopsychosocial insights from this manuscript to develop more effective lifestyle-focused multimodal treatment interventions, enhance diagnosis, and stimulate early-stage prevention of MDD.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42020215412.

GWAS highlights the neuronal contribution to multiple sclerosis susceptibility

Multiple Sclerosis (MS) is a chronic inflammatory and neurodegenerative disease affecting the brain and spinal cord. Genetic studies have identified many risk loci, that were thought to primarily impact immune cells and microglia. Here, we performed a multi-ancestry genome-wide association study with 20,831 MS and 729,220 control participants, identifying 236 susceptibility variants outside the Major Histocompatibility Complex, including four novel loci. We derived a polygenic score for MS and, optimized for European ancestry, it is informative for African-American and Latino participants. Integrating single-cell data from blood and brain tissue, we identified 76 genes affected by MS risk variants. Notably, while T cells showed the strongest enrichment, inhibitory neurons emerged as a key cell type. The expression of IL7 and STAT3 are affected only in inhibitory neurons, highlighting the importance of neuronal and glial dysfunction in MS susceptibility.

Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity

Identifying cell types and brain regions critical for psychiatric disorders and brain traits is essential for targeted neurobiological research. By integrating genomic insights from genome-wide association studies with a comprehensive single-cell transcriptomic atlas of the adult human brain, we prioritized specific neuronal clusters significantly enriched for the SNP-heritabilities for schizophrenia, bipolar disorder, and major depressive disorder along with intelligence, education, and neuroticism. Extrapolation of cell-type results to brain regions reveals the whole-brain impact of schizophrenia genetic risk, with subregions in the hippocampus and amygdala exhibiting the most significant enrichment of SNP-heritability. Using functional MRI connectivity, we further confirmed the significance of the central and lateral amygdala, hippocampal body, and prefrontal cortex in distinguishing schizophrenia cases from controls. Our findings underscore the value of single-cell transcriptomics in understanding the polygenicity of psychiatric disorders and suggest a promising alignment of genomic, transcriptomic, and brain imaging modalities for identifying common biological targets.

Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders

DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain disorders. We present INTERACT, a transformer-based deep learning model to predict regulatory variants affecting DNAm levels in specific brain cell types, leveraging existing single-nucleus DNAm data from the human brain. We show that INTERACT accurately predicts cell type-specific DNAm profiles, achieving an average area under the receiver operating characteristic curve of 0.99 across cell types. Furthermore, INTERACT predicts cell type-specific DNAm regulatory variants, which reflect cellular context and enrich the heritability of brain-related traits in relevant cell types. We demonstrate that incorporating predicted variant effects and DNAm levels of CpG sites enhances the fine mapping for three brain disorders-schizophrenia, depression, and Alzheimer's disease-and facilitates mapping causal genes to particular cell types. Our study highlights the power of deep learning in identifying cell type-specific regulatory variants, which will enhance our understanding of the genetics of complex traits.

Leveraging haplotype information in heritability estimation and polygenic prediction

Polygenic prediction has yet to make a major clinical breakthrough in precision medicine and psychiatry, where the application of polygenic risk scores is expected to improve clinical decision-making. Most widely used approaches for estimating polygenic risk scores are based on summary statistics from external large-scale genome-wide association studies, which rely on assumptions of matching data distributions. This may hinder the impact of polygenic risk scores in modern diverse populations due to small differences in genetic architectures. Reference-free estimators of polygenic scores are instead based on genomic best linear unbiased predictions and model the population of interest directly. We introduce a framework, named hapla, with a novel algorithm for clustering haplotypes in phased genotype data to estimate heritability and perform reference-free polygenic prediction in complex traits. We utilize inferred haplotype clusters to compute accurate heritability estimates and polygenic scores in a simulation study and the iPSYCH2012 case-cohort for depression disorders and schizophrenia. We demonstrate that our haplotype-based approach robustly outperforms standard genotype-based approaches, which can help pave the way for polygenic risk scores in the future of precision medicine and psychiatry.

Eating Problems Among Adolescent Boys and Girls Before and During the Covid-19 Pandemic

OBJECTIVE

Studies suggest that adolescents reported more eating problems during the pandemic. Using a population-based sample, we compared eating problems-and how they associate with a range of personal characteristics and genetic factors-among adolescents before (June 2017-April 2020) versus during (April 2020-December 2022) the pandemic.

METHOD

Based on a preregistered analysis plan, we used cross-sectional data collected from 22,706 14-16-year-olds over 6 years (55% during the pandemic) in the Norwegian Mother, Father, and Child Cohort. We used measurement invariance analyses to compare the level of eating restraint and body concern before and during the pandemic, and multi-group structural equation models to estimate pre-pandemic and pandemic patterns of associations.

RESULTS

Pandemic responders generally reported more eating problems than pre-pandemic responders, specifically on dieting and body dissatisfaction. However, after adjusting for a general linear increase in eating problems across all 6 years of data collection, the pandemic itself seems to be associated with more eating problems only among girls, reporting more eating restraints (meanΔ = 0.14 [CI: 0.07, 0.20]) and body concern (meanΔ = 0.17 [CI: 0.11, 0.23]). Associations between eating problems and a range of other characteristics did not differ across the pandemic and pre-pandemic groups.

CONCLUSIONS

There was a general increase in eating problems among 14-16-year-olds over time. Adjusting for this trend, the pandemic seems to exacerbate problems among girls. Although the mechanisms are unclear, our results point to factors susceptible to change that could have been intensified during the pandemic (e.g., screen time, mental distress). Our results highlight the importance of recognizing sex-specific differences in eating problems.

Polygenic prediction of major depressive disorder and related traits in African ancestries UK Biobank participants

Genome-Wide Association Studies (GWAS) over-represent European ancestries, neglecting all other ancestry groups and low-income nations. Consequently, polygenic risk scores (PRS) more accurately predict complex traits in Europeans than African Ancestries groups. Very few studies have looked at the transferability of European-derived PRS for behavioural and mental health phenotypes to Africans. We assessed the comparative accuracy of depression PRS trained on European and African Ancestries GWAS studies to predict major depressive disorder (MDD) and related traits in African ancestry participants from the UK Biobank. UK Biobank participants were selected based on Principal component analysis clustering with an African genetic similarity reference population, MDD was assessed with the Composite International Diagnostic Interview (CIDI). PRS were computed using PRSice2 software using either European or African Ancestries GWAS summary statistics. PRS trained on European ancestry samples (246,363 cases) predicted case control status in Africans of the UK Biobank with similar accuracies (R2 = 2%, β = 0.32, empirical p-value = 0.002) to PRS trained on far much smaller samples of African Ancestries participants from 23andMe, Inc. (5045 cases, R² = 1.8%, β = 0.28, empirical p-value = 0.008). This suggests that prediction of MDD status from Africans to Africans had greater efficiency relative to discovery sample size than prediction of MDD from Europeans to Africans. Prediction of MDD status in African UK Biobank participants using GWAS findings of likely causal risk factors from European ancestries was non-significant. GWAS of MDD in European ancestries are inefficient for improving polygenic prediction in African samples; urgent MDD studies in Africa are needed.

Modifiable factors for irritable bowel syndrome: evidence from Mendelian randomisation approach

ABSTRACT

Background

The potential modifiable factors influencing irritable bowel syndrome (IBS) have not been thoroughly documented. We aimed to systematically investigate the modifiable factors associated with IBS, while accounting for the impact of unobserved confounders and coexisting disorders.

Methods

Genetic correlation and Mendelian randomisation (MR) analyses were integrated to identify potential modifiable factors and coexisting disorders linked to IBS. Subsequently, multiresponse MR (MR2) was employed to further examine these associations. Summary-level genome-wide association data were used. Modifiable factors and coexisting disorders (ie, gastrointestinal and psychiatric disorders) were identified based on evidence from cohort studies and meta-analysis. In all analyses, IBS was the primary outcome, while in the MR2 analysis, coexisting disorders were also treated as outcomes alongside IBS.

Results

Most identified modifiable factors and coexisting disorders exhibited genetic correlations with IBS. MR analyses revealed strong causation between IBS and multisite chronic pain (OR=2.20, 95% CI 1.82 to 2.66), gastro-oesophageal reflux disease (OR=1.31, 95% CI 1.23 to 1.39), well-being spectrum (OR=0.17, 95% CI 0.13 to 0.21), life satisfaction (OR=0.31, 95% CI 0.25 to 0.38), positive affect (OR=0.30, 95% CI 0.24 to 0.37), neuroticism score (OR=1.20, 95% CI 1.16 to 1.25) and depression (OR=1.50, 95% CI 1.37 to 1.66). Additionally, smoking, alcohol frequency, college or university degree, intelligence, childhood maltreatment, frailty index, diverticular disease of the intestine and schizophrenia were suggestively associated with IBS. Robust associations were found between multisite chronic pain and both IBS and coexisting disorders.

Conclusions

Our study identified a comprehensive array of potential modifiable factors and coexisting disorders associated with IBS, supported by genetic evidence, including genetic correlation and multiple MR analyses. The presence of multisite chronic pain may offer a promising avenue for the concurrent prevention of IBS and its coexisting disorders.

Immuno-metabolic depression: from concept to implementation

Major depressive disorder is a common, disabling mental disorder characterized by extensive etiological and phenotypic heterogeneity. This heterogeneity makes treatment approaches imprecise and often ineffective. Insight into the underlying biological mechanisms underpinning depression and its subtypes may enable more personalized treatments. In this review, we provide an overview of immuno-metabolic depression and illustrate that significant immuno-metabolic dysregulations are present in about 20-30% of people with depression. Such immuno-metabolic depression is characterized by the clustering of 1) atypical, energy-related depressive symptoms such as hypersomnia, fatigue, hyperphagia, and possibly anhedonia, 2) systemic low-grade inflammation with elevated levels of e.g., C-reactive protein, cytokines and glycoprotein acetyls, and 3) metabolic abnormalities involving e.g., obesity, dyslipidaemia, insulin and leptin resistance. Persons with immuno-metabolic depression are at a higher risk for cardiometabolic diseases and seem to respond less well to standard antidepressant treatment. Interventions targeting inflammation, metabolism or lifestyle may be more effective treatment options for individuals with immuno-metabolic depression, in line with principles of precision psychiatry.

Causal association between mitochondrial function and psychiatric disorders: Insights from a bidirectional two-sample Mendelian randomization study

BACKGROUND

Previous observational studies have suggested that there appears to be a close association between mitochondrial function and psychiatric disorders, but whether a causal role exists remains unclear.

METHODS

We extracted genetic instruments for 67 mitochondrial-related proteins and 10 psychiatric disorders from publicly available genome-wide association studies, and employed five distinct MR methods and false discovery rate correction to detect causal associations between them. Additionally, we conducted a series of sensitivity tests and additional model analysis to ensure the robustness of the results. For potential causal associations, we further performed reverse MR analyses to assess the impact of reverse causality.

RESULTS

We identified a total of 2 significant causal associations and 24 suggestive causal associations. Specifically, Phenylalanine-tRNA ligase was found to increase the risk of Alzheimer's disease, while Mitochondrial glutamate carrier 2 decreased the risk of autism spectrum disorder. Furthermore, there was no evidence of significant pleiotropy, heterogeneity, or reverse causality.

LIMITATIONS

This study was limited to individuals of European ancestry, and the conclusions drawn are merely revelatory.

CONCLUSION

This study provides novel insights into the relationship between mitochondria and psychiatric disorders, as well as the pathogenesis and treatment strategies for psychiatric disorders.