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Major Depressive Disorder (MDD) is a common, complex disorder that is a leading cause of disability worldwide and a significant risk factor for suicide. In this study, we have performed the largest molecular analysis of MDD in postmortem human brains (846 samples across 458 individuals) in the subgenual Anterior Cingulate Cortex (sACC) and the Amygdala, two regions central to mood regulation and the pathophysiology of MDD. We found extensive expression differences, particularly at the level of specific transcripts, with prominent enrichment for genes associated with the vesicular functioning, the postsynaptic density, GTPase signaling, and gene splicing. We find associated transcriptional features in 107 of 243 genome-wide significant loci for MDD and, through integrative analyses, highlight convergence of genetic risk, gene expression, and network-based analyses on dysregulated glutamatergic signaling and synaptic vesicular functioning. Together, these results provide an initial mechanistic understanding of MDD and highlight potential targets for novel drug discovery. Major Depressive Disorder (MDD) is a heritable psychiatric disorder whose biological basis remains poorly understood. This large-scale postmortem brain study identified extensive gene expression changes associated with genetic risk, highlighting disrupted glutamatergic signaling and synaptic vesicle function in MDD.

Long non-coding RNA Knowledgebase (lncRNAKB) is an integrated resource for exploring lncRNA biology in the context of tissue-specificity and disease association. A systematic integration of annotations from six independent databases resulted in 77,199 human lncRNA (224,286 transcripts). The user-friendly knowledgebase covers a comprehensive breadth and depth of lncRNA annotation. lncRNAKB is a compendium of expression patterns, derived from analysis of RNA-seq data in thousands of samples across 31 solid human normal tissues (GTEx). Thousands of co-expression modules identified via network analysis and pathway enrichment to delineate lncRNA function are also accessible. Millions of expression quantitative trait loci ( cis -eQTL) computed using whole genome sequence genotype data (GTEx) can be downloaded at lncRNAKB that also includes tissue-specificity, phylogenetic conservation and coding potential scores. Tissue-specific lncRNA-trait associations encompassing 323 GWAS (UK Biobank) are also provided. LncRNAKB is accessible at http://www.lncrnakb.org/ , and the data are freely available through Open Science Framework ( https://doi.org/10.17605/OSF.IO/RU4D2 ). Measurement(s) regulation of gene expression • sequence feature annotation • lnc_RNA • tissue-specific expression of lncRNA • Expression Quantitative Trait Locus Technology Type(s) digital curation • computational modeling technique Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12827597

Background The processing and analysis of the large scale data generated by next-generation sequencing (NGS) experiments is challenging and is a burgeoning area of new methods development. Several new bioinformatics tools have been developed for calling sequence variants from NGS data. Here, we validate the variant calling of these tools and compare their relative accuracy to determine which data processing pipeline is optimal. Results We developed a unified pipeline for processing NGS data that encompasses four modules: mapping, filtering, realignment and recalibration, and variant calling. We processed 130 subjects from an ongoing whole exome sequencing study through this pipeline. To evaluate the accuracy of each module, we conducted a series of comparisons between the single nucleotide variant (SNV) calls from the NGS data and either gold-standard Sanger sequencing on a total of 700 variants or array genotyping data on a total of 9,935 single-nucleotide polymorphisms. A head to head comparison showed that Genome Analysis Toolkit (GATK) provided more accurate calls than SAMtools (positive predictive value of 92.55% vs. 80.35%, respectively). Realignment of mapped reads and recalibration of base quality scores before SNV calling proved to be crucial to accurate variant calling. GATK HaplotypeCaller algorithm for variant calling outperformed the UnifiedGenotype algorithm. We also showed a relationship between mapping quality, read depth and allele balance, and SNV call accuracy. However, if best practices are used in data processing, then additional filtering based on these metrics provides little gains and accuracies of >99% are achievable. Conclusions Our findings will help to determine the best approach for processing NGS data to confidently call variants for downstream analyses. To enable others to implement and replicate our results, all of our codes are freely available at http://metamoodics.org/wes .

Objective The American Psychiatric Association (APA) issued a 2023 report on the future of psychiatry, focusing on how the organization should position itself in relation to coming developments over the next 10 years. Here, we follow up with a discussion of how the psychiatrist's role needs to evolve to adapt to the changes ahead. Methods We drew on senior experts and junior trainees within the APA's Council on Healthcare Systems and Financing, along with additional content experts, to choose areas of focus and discuss their interrelationships. Literature review focused on publications with implications of these areas for future training and practice. Results We are only ∼5% of the mental health work force, and we have unique strengths, including training providing us the ability to discern the varied factors contributing to distress, and direct and apply interventions across all available modalities. Psychiatrists make best use of our capabilities when we lead the process of comprehensively formulating patients' problems and generating a multi‐faceted treatment approach. We have chosen six areas where we envision new developments impacting how psychiatrists will practice and residents should train: digital data and precision medicine, measurement‐based care, artificial intelligence (AI), psychotherapy, integrated care, and care for the seriously mentally ill. We provide suggestions regarding next steps that will allow us to make the best use of our training and expand access to high quality diagnosis and care. Conclusions and Relevance to Clinical Practice We will need to handle the most challenging cases: the most psychiatrically complex, medically complex, and treatment‐resistant. We must preserve our skill, unique among physicians, in psychotherapeutic approaches, even as we manage psychiatric illness. We must also adapt and become more tech‐savvy, as digital data, mobile and computer‐based treatments, electronic medical records, and AI algorithms take on increasing prominence in our field. Highlights In these six areas new developments will impact how psychiatrists will practice and residents should train: digital data and precision medicine, measurement‐based care, artificial intelligence (AI), psychotherapy, integrated care, and care for the seriously mentally ill. Psychiatrists, just 5% of the mental health work force, will need to handle the most challenging cases: the most psychiatrically complex, medically complex, and treatment‐resistant ones. Psychiatrists must preserve their skill, unique among physicians, in psychotherapeutic approaches, even as they manage psychiatric disease. Psychiatrists will need to adapt and become more tech‐savvy, as digital data, mobile and computer‐based treatments, electronic medical records, and AI algorithms take on increasing prominence in the mental health field.

Background Numerous genome-wide gene expression studies of bipolar disorder (BP) have been carried out. These studies are heterogeneous, underpowered and use overlapping samples. We conducted a systematic review of these studies to synthesize the current findings. Methods We identified all genome-wide gene expression studies on BP in humans. We then carried out a quantitative mega-analysis of studies done with post-mortem brain tissue. We obtained raw data from each study and used standardized procedures to process and analyze the data. We then combined the data and conducted three separate mega-analyses on samples from 1) any region of the brain (9 studies); 2) the prefrontal cortex (PFC) (6 studies); and 3) the hippocampus (2 studies). To minimize heterogeneity across studies, we focused primarily on the most numerous, recent and comprehensive studies. Results A total of 30 genome-wide gene expression studies of BP done with blood or brain tissue were identified. We included 10 studies with data on 211 microarrays on 57 unique BP cases and 229 microarrays on 60 unique controls in the quantitative mega-analysis. A total of 382 genes were identified as significantly differentially expressed by the three analyses. Eleven genes survived correction for multiple testing with a q-value < 0.05 in the PFC. Among these were FKBP5 and WFS1 , which have been previously implicated in mood disorders. Pathway analyses suggested a role for metallothionein proteins, MAP Kinase phosphotases, and neuropeptides. Conclusion We provided an up-to-date summary of results from gene expression studies of the brain in BP. Our analyses focused on the highest quality data available and provided results by brain region so that similarities and differences can be examined relative to disease status. The results are available for closer inspection on-line at Metamoodics [ http://metamoodics.igm.jhmi.edu/ ], where investigators can look up any genes of interest and view the current results in their genomic context and in relation to leading findings from other genomic experiments in bipolar disorder.

INTRODUCTION Dementia progression is heterogeneous and predicting who will decline quickly remains an open problem. Most work in this area has focused on volunteer‐based cohorts, which are subject to recruitment biases. Instead, we examine predictors of rate of cognitive decline in cognitive assessment scores using electronic health record (EHR) data from a US memory clinic. METHODS Data include patients with their first memory clinic visit (baseline) between January 1, 2014 and May 31, 2024. We used a discrete‐time model to identify significant predictors of baseline and 6 month change in Mini‐Mental State Examination (MMSE) scores (Montreal Cognitive Assessment scores were converted to MMSE equivalents for analysis). Inverse probability weighting was used to account for selection and censoring biases and p values were adjusted for multiple comparisons. RESULTS The cohort included 9583 patients, of which 7113 had a baseline cognitive assessment. Average MMSE at baseline was 23.2. Variables associated with lower baseline MMSE included female sex, non‐White race, Medicaid enrollment, baseline dementia diagnosis, and cholinesterase inhibitor prescription, while higher scores were associated with prior diagnoses of chronic pain or fatigue. Quicker post‐baseline decline was associated with older age, dementia diagnoses, and cholinesterase inhibitor prescription, while slower decline was associated with a higher number of total prescriptions, distance from home to clinic, and Social Deprivation Index. Notably, rate of decline was not associated with mild cognitive impairment, other non‐dementia cognitive impairment, or any of the comorbidities considered. DISCUSSION While several significant predictors were identified, the lack of associations with broad categories of comorbidities and social determinants of health suggest that finer grained predictors may be needed. Additionally, the finding that cholinesterase inhibitor prescriptions predicted quicker decline merits additional investigation in real‐world samples. The model developed in this work may serve as a first step toward an EHR‐based progression risk tool. Highlights In a memory clinic setting, faster decline in Mini‐Mental State Examination scores was associated with age, dementia diagnosis, and cholinesterase inhibitor or memantine prescription. Slower decline was associated with the patient's total number of prescriptions. Neither race nor ethnicity were associated with rate of decline, nor were baseline mild cognitive impairment, other non‐dementia cognitive impairment, diabetes, hypertension, obesity, depression, anxiety, chronic pain/fatigue, or hearing loss.

Objective: Alzheimer’s disease, the most prevalent dementia, is a prominent source of chronic illness in the elderly. Laboratory evidence suggests that nonsteroidal anti-inflammatory drugs (NSAIDs) might prevent the onset of Alzheimer’s disease. Since the early 1990s, numerous observational epidemiological studies have also investigated this possibility. The purpose of this meta-analysis is to summarize and evaluate available evidence regarding exposure to nonaspirin NSAIDs and risk of Alzheimer’s disease using meta-analyses of published studies. Methods: A systematic search was conducted using Medline, Biological Abstracts, and the Cochrane Library for publications from 1960 onwards. All cross-sectional, retrospective, or prospective observational studies of Alzheimer’s disease in relation to NSAID exposure were included in the analysis. At least 2 of 4 independent reviewers characterized each study by source of data and design, including method of classifying exposure and outcome, and evaluated the studies for eligibility. Discrepancies were resolved by consensus of all 4 reviewers. Results: Of 38 publications, 11 met the qualitative criteria for inclusion in the meta-analysis. For the 3 case-control and 4 cross-sectional studies, the combined risk estimate for development of Alzheimer’s disease was 0.51 (95% CI = 0.40–0.66) for NSAID exposure. In the prospective studies, the estimate was 0.74 (95% CI = 0.62–0.89) for the 4 studies reporting lifetime NSAID exposure and it was 0.42 (95% CI = 0.26–0.66) for the 3 studies reporting a duration of use of 2 or more years. Conclusions: Based on analysis of prospective and nonprospective studies, NSAID exposure was associated with decreased risk of Alzheimer’s disease. An issue that requires further exploration in future trials or observational studies is the temporal relationship between NSAID exposure and protection against Alzheimer’s disease.

Digital social activity, defined as interactions on social media and electronic communication platforms, has become increasingly important. Social factors impact mental health and can contribute to depression and anxiety. Therefore, incorporating digital social activity into routine mental health care has the potential to improve outcomes. This study aimed to compare treatment augmented with an electronic dashboard of patient's digital social activity versus treatment-as-usual on patient-rated outcomes symptoms of depression in a randomized trial of patients with mood and anxiety disorders. We developed a personalized electronic dashboard summarizing a participant's digital social activity. This dashboard, collaboratively discussed during mental health visits, was used to augment clinical care and tested in a randomized trial against treatment-as-usual. Clinicians and patients were recruited from outpatient psychiatry clinics. Patients were eligible if they were 12 years or older and were receiving treatment for a mood or anxiety disorder. Psychiatric symptoms measures for depression (primary outcome measure) and anxiety (secondary outcome measure) were obtained at each clinic visit as part of measurement-based standard of care. Baseline and 3-month follow-up assessments included a measure of mental health status and therapeutic alliance measure. Collateral information and clinical action scale were also collected at each visit. A total of 103 patients consented to participate, 97 of whom were randomized to the dashboard arm (n=49) or the treatment-as-usual arm (n=48). There were no differences in psychiatry symptom rating scores or mental health status between the two arms. However, there was a significant increase in the discussion of digital social activity with the intervention, and it did not appear to change patient therapeutic alliance. The incorporation of a personalized electronic dashboard into clinical care was feasible and led to an increased discussion of digital social activity, but there was no impact on mental health outcomes.

FKBP5 is a critical component of the Hypothalamic-Pituitary-Adrenal (HPA) axis, a system which regulates our response to stress. It forms part of a complex of chaperones, which inhibits binding of cortisol and glucocorticoid receptor translocation to the nucleus. Variations in both the HPA axis and FKBP5 have been associated with suicidal behavior. We developed a systematic, targeted sequencing approach to investigate coding and regulatory regions in or near FKBP5 in 476 bipolar disorder suicide attempters and 473 bipolar disorder non-attempters. Following stringent quality control checks, we performed single-variant, gene-level and haplotype tests on the resulting 481 variants. Secondary analyses investigated whether sex-specific variations in FKBP5 increased the risk of attempted suicide. One variant, rs141713011, showed an excess of minor alleles in suicide attempters that was statistically significant following correction for multiple testing (Odds Ratio = 6.65, P-value = 7.5 x 10-4, Permuted P-value = 0.038). However, this result could not be replicated in an independent cohort (Odds Ratio = 0.90, P-value = 0.78). Three female-specific and four male-specific variants of nominal significance were also identified (P-value < 0.05). The gene-level and haplotype association tests did not produce any significant results. This comprehensive study of common and rare variants in FKBP5 focused on both regulatory and coding regions in relation to attempted suicide. One rare variant remained significant following correction for multiple testing but could not be replicated. Further investigation is required in larger sample sets to fully elucidate the association of this variant with suicidal behavior.

Background The interaction of polygenic risk (PRS) and environmental effects on development of bipolar disorder (BD) is understudied, as are high‐risk offspring perceptions of their family environment (FE). We tested the association of offspring‐perceived FE in interaction with BD‐PRS on liability for BD in offspring at high or low familial risk for BD. Methods Offspring of a parent with BD (oBD; n = 266) or no psychiatric disorders (n = 174), aged 12–21 at recruitment, participated in the US and Australia. Empirically‐derived profiles of FE classified offspring by their perceived levels of familial cohesion, flexibility, and conflict. Offspring BD‐PRS were derived from Psychiatric Genomics Consortium BD‐GWAS. Lifetime DSM‐IV bipolar disorders were derived from the Schedule for Affective Disorders and Schizophrenia for School‐Aged Children. We used a novel stepwise approach for latent class modeling with predictors and distal outcomes. Results Fifty‐two offspring were diagnosed with BD. For those with well‐functioning FE (two‐thirds of the sample), higher BD‐PRS tracked positively with liability for BD. However, for those with high‐conflict FEs, the relationship between BD‐PRS and liability to BD was negative, with highest risk for BD observed with lower BD‐PRS. In exploratory analyses, European‐ancestry offspring with BD had elevated history of suicidal ideation in high‐conflict FE compared to well‐functioning‐FE, and of suicide attempt with low‐BD‐PRS and high‐conflict FE. Conclusions The data suggest that the relationship of BD‐PRS and offspring liability for BD differed between well‐functioning versus high‐conflict FE, potentially in line with a multifactorial liability threshold model and supporting future study of and interventions improving family dynamics. We examined the joint contribution of offspring‐perceived family environment and bipolar‐polygenic risk score to the odds of bipolar diagnosis and related clinical features in adolescents at high or low familial risk for bipolar disorder. Findings may suggest different ways of crossing the liability threshold to develop early‐onset bipolar disorder, consistent with a multifactorial liability threshold model. Namely, offspring with lower bipolar‐polygenic risk score, in the presence of interpersonal environmental risk (conflicted family environments), had higher odds of bipolar diagnosis and greater prevalence of suicide attempt, in contrast to offspring with well‐functioning family environment.