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Psychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. We used high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N = 1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, showed general well-being. Clusters 1–3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. Depressed patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N = 622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction area under the receiver operating characteristic curve (AUC) = 81%; significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatments.

Mental stress caused by city life By 2050, two-thirds of the world's population will be living in cities. Although city living has many advantages, rapidly increasing urbanization has major health implications — schizophrenia is more common in people born in cities than in those from less heavily populated districts, and living in cities increases the rates of depression and anxiety disorders. It has been suggested that social stress plays a part in these effects, but the mechanisms involved are unknown. Now, in a study of healthy German volunteers using functional magnetic resonance imaging, a key brain structure for negative emotion (the amygdala) was found to be more active during stress in city dwellers, and a regulatory brain area (the cingulate cortex) more active in people born in cities. These results identify potential mechanisms linking social environment and mental illness, and might contribute to planning healthier urban surroundings. More than half of the world’s population now lives in cities, making the creation of a healthy urban environment a major policy priority 1 . Cities have both health risks and benefits 1 , but mental health is negatively affected: mood and anxiety disorders are more prevalent in city dwellers 2 and the incidence of schizophrenia is strongly increased in people born and raised in cities 3 , 4 , 5 , 6 . Although these findings have been widely attributed to the urban social environment 2 , 3 , 7 , 8 , the neural processes that could mediate such associations are unknown. Here we show, using functional magnetic resonance imaging in three independent experiments, that urban upbringing and city living have dissociable impacts on social evaluative stress processing in humans. Current city living was associated with increased amygdala activity, whereas urban upbringing affected the perigenual anterior cingulate cortex, a key region for regulation of amygdala activity, negative affect 9 and stress 10 . These findings were regionally and behaviourally specific, as no other brain structures were affected and no urbanicity effect was seen during control experiments invoking cognitive processing without stress. Our results identify distinct neural mechanisms for an established environmental risk factor, link the urban environment for the first time to social stress processing, suggest that brain regions differ in vulnerability to this risk factor across the lifespan, and indicate that experimental interrogation of epidemiological associations is a promising strategy in social neuroscience.

Electroconvulsive therapy (ECT) is a quick-acting and powerful antidepressant treatment considered to be effective in treating severe and pharmacotherapy-resistant forms of depression. Recent studies have suggested that epigenetic mechanisms can mediate treatment response and investigations about the relationship between the effects of ECT and DNA methylation have so far largely taken candidate approaches. In the present study, we examined the effects of ECT on the methylome associated with response in depressed patients (n = 34), testing for differentially methylated CpG sites before the first and after the last ECT treatment. We identified one differentially methylated CpG site associated with the effect of ECT response (defined as >50% decrease in Hamilton Depression Rating Scale score, HDRS), TNKS (q < 0.05; p = 7.15 × 10−8). When defining response continuously (ΔHDRS), the top suggestive differentially methylated CpG site was in FKBP5 (p = 3.94 × 10−7). Regional analyses identified two differentially methylated regions on chromosomes 8 (Šídák’s p = 0.0031) and 20 (Šídák’s p = 4.2 × 10−5) associated with ΔHDRS. Functional pathway analysis did not identify any significant pathways. A confirmatory look at candidates previously proposed to be involved in ECT mechanisms found CpG sites associated with response only at the nominally significant level (p < 0.05). Despite the limited sample size, the present study was able to identify epigenetic change associated with ECT response suggesting that this approach, especially when involving larger samples, has the potential to inform the study of mechanisms involved in ECT and severe and treatment-resistant depression.

Alcohol Use Disorder (AUD) is a major contributor to global mortality and morbidity. Postmortem human brain tissue enables the investigation of molecular mechanisms of AUD in the neurocircuitry of addiction. We aimed to identify differentially expressed (DE) genes in the ventral and dorsal striatum between individuals with AUD and controls, and to integrate the results with findings from genome- and epigenome-wide association studies (GWAS/EWAS) to identify functionally relevant molecular mechanisms of AUD. DNA-methylation and gene expression (RNA-seq) data was generated from postmortem brain samples of 48 individuals with AUD and 51 controls from the ventral striatum (VS) and the dorsal striatal regions caudate nucleus (CN) and putamen (PUT). We identified DE genes using DESeq2, performed gene-set enrichment analysis (GSEA), and tested enrichment of DE genes in results of GWASs using MAGMA. Weighted correlation network analysis (WGCNA) was performed for DNA-methylation and gene expression data and gene overlap was tested. Differential gene expression was observed in the dorsal (FDR < 0.05), but not the ventral striatum of AUD cases. In the VS, DE genes at FDR < 0.25 were overrepresented in a recent GWAS of problematic alcohol use. The ARHGEF15 gene was upregulated in all three brain regions. GSEA in CN and VS pointed towards cell-structure associated GO-terms and in PUT towards immune pathways. The WGCNA modules most strongly associated with AUD showed strong enrichment for immune response and inflammation pathways. Our integrated analysis of multi-omics data sets provides further evidence for the importance of immune- and inflammation-related processes in AUD.

To assess the prevalence of depression in the general population of Beijing and its association with ocular diseases. The population-based Beijing Eye Study was conducted in a rural and an urban region of Greater Beijing. The study participants underwent a detailed ophthalmological examination and an interview including questions on the socioeconomic background. Depressive symptoms were evaluated using a Chinese depression scale adapted from Zung´s self-rated depression scale. The total score of depression symptoms was 80. Depression was defined as having a depression score >44. Out of 3468 study participants, 3267 (94.2%) individuals (1419 men) with an age of 64.5±9.7 years (range: 50-93 years) participated in the interview and answered all questions on depression. The mean depression score was 25.0±5.9 (median: 23.3; range:20-64). Depression (depression score >44) was present in 66 individuals (2.0%; 95% confidence interval (CI): 1.5, 2.5), and 5 individuals (0.2%; 95%CI: 0.02,0.3) had a depression score ≥59. In multivariate regression, analysis, a higher depression score was associated (regression coefficient r2: 0.22) with a higher number of days with dry eye feeling (P<0.001; standardized regression coefficient beta: 0.09; non-standardized regression coefficient B: 0.20; 95%CI: 0.12,0.29) and shorter corneal curvature radius (P = 0.03;beta:-0.04; B:1.01; 95%CI: -1.90,-0.12), after adjusting for age, gender, region of habitation, body mass index, cognitive function score, life quality score and blood concentration of triglycerides. Adding age-related macular degeneration (P = 0.10), glaucoma (P = 0.77), diabetic retinopathy (P = 0.77), nuclear cataract (P = 0.35), cortical cataract (P = 0.58) or posterior subcapsular cataract (P = 0.28) as single parameters to the model revealed no significant correlation with the depression score. Lower best corrected visual acuity showed a marginal significant association (P = 0.05; beta: 0.04; B: 1.56; 95%CI: -0.01, 3.13). Dry eye feeling was the only common ocular disorder associated with an increased depression score, while the occurrence of age-related macular degeneration, any type of glaucoma, diabetic retinopathy, any type of cataract and keratoconus were not significantly associated with an increased depression score. Lower visual acuity was marginally associated. The prevalence of depression in the population aged 50+ years in Greater Beijing was 2.0% (96%CI: 1.5, 2.5).

Alcohol use disorder (AUD) is closely linked to the brain regions forming the neurocircuitry of addiction. Postmortem human brain tissue enables the direct study of the molecular pathomechanisms of AUD. This study aims to identify these mechanisms by examining differential DNA-methylation between cases with severe AUD (n = 53) and controls (n = 58) using a brain-region-specific approach, in which sample sizes ranged between 46 and 94. Samples of the anterior cingulate cortex (ACC), Brodmann Area 9 (BA9), caudate nucleus (CN), ventral striatum (VS), and putamen (PUT) were investigated. DNA-methylation levels were determined using the Illumina HumanMethylationEPIC Beadchip. Epigenome-wide association analyses were carried out to identify differentially methylated CpG-sites and regions between cases and controls in each brain region. Weighted correlation network analysis (WGCNA), gene-set, and GWAS-enrichment analyses were performed. Two differentially methylated CpG-sites were associated with AUD in the CN, and 18 in VS (q < 0.05). No epigenome-wide significant CpG-sites were found in BA9, ACC, or PUT. Differentially methylated regions associated with AUD case-/control status (q < 0.05) were found in the CN (n = 6), VS (n = 18), and ACC (n = 1). In the VS, the WGCNA-module showing the strongest association with AUD was enriched for immune-related pathways. This study is the first to analyze methylation differences between AUD cases and controls in multiple brain regions and consists of the largest sample to date. Several novel CpG-sites and regions implicated in AUD were identified, providing a first basis to explore epigenetic correlates of AUD.

Major Depression and Bipolar Disorder Type I (BIP-I) and Type II (BIP-II), are characterized by depressed, manic, and hypomanic episodes in which specific changes of physical activity, circadian rhythm, and sleep are observed. It is known that genetic factors contribute to variation in mood disorders and biological rhythms, but unclear to what extent there is an overlap between their underlying genetics. In the present study, data from genome-wide association studies were used to examine the genetic relationship between mood disorders and biological rhythms. We tested the genetic correlation of depression, BIP-I, and BIP-II with physical activity (overall physical activity, moderate activity, sedentary behaviour), circadian rhythm (relative amplitude), and sleep features (sleep duration, daytime sleepiness). Genetic correlations of depression, BIP-I, and BIP-II with biological rhythms were compared to discover commonalities and differences. A gene-based analysis tested for associations of single genes and common circadian genes with mood disorders. Depression was negatively correlated with overall physical activity and positively with sedentary behaviour, while BIP-I showed associations in the opposite direction. Depression and BIP-II had negative correlations with relative amplitude. All mood disorders were positively correlated with daytime sleepiness. Overall, we observed both genetic commonalities and differences across mood disorders in their relationships with biological rhythms: depression and BIP-I differed the most, while BIP-II was in an intermediate position. Gene-based analysis suggested potential targets for further investigation. The present results suggest shared genetic underpinnings for the clinically observed associations between mood disorders and biological rhythms. Research considering possible joint mechanisms may offer avenues for improving disease detection and treatment.

Impulsivity is a key feature of bipolar disorder (BD) associated with various negative outcomes. Recent use of ecological momentary assessment (EMA) has allowed for nuanced examination of the mechanisms of mood and impulsivity dysregulation. However, few existing studies have used an ecological momentary assessment of impulsivity in multiplex families with BD and examined its associations with mood. Using EMA, this study investigated the concurrent and predictive relationships between impulsivity and mood. Multiplex family members with BD (BDF, n = 8), unaffected family members (FC, n = 6), individuals with BD not from families (BDC, n = 8) and healthy controls (HC, n = 8), completed daily EMA surveys about mood and impulsivity for 6-12 weeks. Mixed-effects regression concurrent and lagged models were employed to analyze the relationship between impulsivity and mood. The BDF (Diff = -31.70, p = 0.001) and BDC (Diff = -25.74, p = 0.007) groups had a significantly lower mean in mood scores compared to the HC group but not compared to the FC group. There were no significant differences in the mean impulsivity scores between the groups. Time-lagged analyses revealed a significant negative association between prior impulsivity and mood at the next assessment independent of diagnosis (OR=0.939, p = 0.002). However, the opposite relationship between prior mood and impulsivity was not significant (OR=0.996, p = 0.135). These results contribute to the understanding of the complex interactions between BD, the genetic load of the disorder, impulsivity and mood. Furthermore, these findings indicate the potential benefits of addressing impulsivity as a means to improve mood outcomes at an early stage.

Intelligence is a highly polygenic trait and genome-wide association studies (GWAS) have identified thousands of DNA variants contributing with small effects. Polygenic scores (PGS) can aggregate those effects for trait prediction in independent samples. As large-scale light-phenotyping GWAS operationalized intelligence as performance in rather superficial tests, the question arises which intelligence facets are actually captured. We used deep-phenotyping to investigate the molecular determinants of individual differences in cognitive ability. We, therefore, studied the association between PGS of intelligence (IQ-PGS), cognitive performance (CP-PGS), and educational attainment (EA-PGS) with a wide range of intelligence facets in a sample of 557 healthy adults. IQ-PGS, CP-PGS, and EA-PGS had the highest incremental R 2 s for general (2.71%; 4.27%; 2.06%), verbal (3.30%; 4.64%; 1.61%), and numerical intelligence (3.06%; 3.24%; 1.26%) and the weakest for non-verbal intelligence (0.89%; 1.47%; 0.70%) and memory (0.80%; 1.06%; 0.67%). These results indicate that PGS derived from light-phenotyping GWAS do not reflect different facets of intelligence equally well, and thus should not be interpreted as genetic indicators of intelligence per se. The findings refine our understanding of how PGS are related to other traits or life outcomes.

Childhood maltreatment is associated with cognitive deficits that in turn have been predictive for therapeutic outcome in psychiatric patients. However, previous studies have either investigated maltreatment associations with single cognitive domains or failed to adequately control for confounders such as depression, socioeconomic environment, and genetic predisposition. We aimed to isolate the relationship between childhood maltreatment and dysfunction in diverse cognitive domains, while estimating the contribution of potential confounders to this relationship, and to investigate gene–environment interactions. We included 547 depressive disorder and 670 healthy control participants (mean age: 34.7 years, SD = 13.2). Cognitive functioning was assessed for the domains of working memory, executive functioning, processing speed, attention, memory, and verbal intelligence using neuropsychological tests. Childhood maltreatment and parental education were assessed using self-reports, and psychiatric diagnosis was based on DSM-IV criteria. Polygenic scores for depression and for educational attainment were calculated. Multivariate analysis of cognitive domains yielded significant associations with childhood maltreatment (η²p = 0.083, P < 0.001), depression (η²p = 0.097, P < 0.001), parental education (η²p = 0.085, P < 0.001), and polygenic scores for depression (η²p = 0.021, P = 0.005) and educational attainment (η²p = 0.031, P < 0.001). Each of these associations remained significant when including all of the predictors in one model. Univariate tests revealed that maltreatment was associated with poorer performance in all cognitive domains. Thus, environmental, psychopathological, and genetic risk factors each independently affect cognition. The insights of the current study may aid in estimating the potential impact of different loci of interventions for cognitive dysfunction. Future research should investigate if customized interventions, informed by individual risk profiles and related cognitive preconditions, might enhance response to therapeutic treatments.