Differential contributions of circadian clock genes to cell survival in bipolar disorder patient derived neuronal progenitor cells distinguishes lithium responders and non-responders.

Bipolar disorder (BD) is characterized by disrupted circadian rhythms and neuronal loss. Lithium is neuroprotective and used to treat BD, but outcomes are variable. Past research identified that circadian rhythms in BD patient neurons are associated with lithium response (Li-R) or non-response (Li-NR). However, the underlying cellular mechanisms remain unknown. To study interactions among circadian clock genes and cell survival, and their role in BD and predicting lithium response, we tested selected genes (PER1, BMAL1 and REV-ERBα) and small molecule modulators of ROR/REV-ERB nuclear receptors in models of cell survival using mouse neurons and stem-cell derived neuronal progenitor cells (NPC) from BD patients and controls. In apoptosis assays using staurosporine (STS), lithium was neuroprotective. Knockdown of PER1, BMAL1 and REV-ERBα modified cell survival across models. In NPCs, reduced expression of PER1 and BMAL1 led to more extensive cell death in Li-NR vs. Li-R. Reduced REV-ERBα expression caused more extensive cell death in BD vs. control NPCs, without distinguishing Li-R and Li-NR. In IMHN, The REV-ERB agonist GSK4112 had strong effects on circadian rhythm amplitude, and was neuroprotective in mouse neurons and control NPCs, but not in BD NPCs. Expression of cell survival genes following STS and GSK4112 treatments revealed BD-associated, and Li-R associated differences in expression profiles. We conclude that the neuroprotective response to lithium is similar in NPCs from Li-R and Li-NR. However, knockdown of circadian clock genes or stimulation of REV-ERBs reveal distinct contributions to cell death in BD patient NPCs, some of which distinguish Li-R and Li-NR.

A cross ancestry genetic study of psychiatric disorders from India.

Genome-wide association studies across diverse populations may help validate and confirm genetic contributions to risk of disease. We estimated the extent of population stratification as well as the predictive accuracy of polygenic scores (PGS) derived from European samples to a data set from India. We analysed 2685 samples from two data sets, a population neurodevelopmental study (cVEDA) and a hospital-based sample of bipolar affective disorder (BD) and obsessive-compulsive disorder (OCD). Genotyping was conducted using Illumina's Global Screening Array. Population structure was examined with principal component analysis (PCA), uniform manifold approximation and projection (UMAP), support vector machine (SVM) ancestry predictions, and admixture analysis. PGS were calculated from the largest available European discovery GWAS summary statistics for BD, OCD, and externalizing traits using two Bayesian methods that incorporate local linkage disequilibrium structures (PGS-CS-auto) and functional genomic annotations (SBayesRC). Our analyses reveal global and continental PCA overlap with other South Asian populations. Admixture analysis revealed a north-south genetic axis within India (FST 1.6%). The UMAP partially reconstructed the contours of the Indian subcontinent. The Bayesian PGS analyses indicates moderate-to-high predictive power for BD. This was despite the cross-ancestry bias of the discovery GWAS dataset, with the currently available data. However, accuracy for OCD and externalizing traits was much lower. The predictive accuracy was perhaps influenced by the sample size of the discovery GWAS and phenotypic heterogeneity across the syndromes and traits studied. Our study results highlight the accuracy and generalizability of newer PGS models across ancestries. Further research, across diverse populations, would help understand causal mechanisms that contribute to psychiatric syndromes and traits.

Patterns of pharmacotherapy for bipolar disorder: A GBC survey.

OBJECTIVES: To understand treatment practices for bipolar disorders (BD), this study leveraged the Global Bipolar Cohort collaborative network to investigate pharmacotherapeutic treatment patterns in multiple cohorts of well-characterized individuals with BD in North America, Europe, and Australia. METHODS: Data on pharmacotherapy, demographics, diagnostic subtypes, and comorbidities were provided from each participating cohort. Individual site and regional pooled proportional meta-analyses with generalized linear mixed methods were conducted to identify prescription patterns. RESULTS: This study included 10,351 individuals from North America (n = 3985), Europe (n = 3822), and Australia (n = 2544). Overall, participants were predominantly female (60%) with BD-I (60%; vs. BD-II = 33%). Cross-sectionally, mood-stabilizing anticonvulsants (44%), second-generation antipsychotics (42%), and antidepressants (38%) were the most prescribed medications. Lithium was prescribed in 29% of patients, primarily in the Australian (31%) and European (36%) cohorts. First-generation antipsychotics were prescribed in 24% of the European versus 1% in the North American cohort. Antidepressant prescription rates were higher in BD-II (47%) compared to BD-I (35%). Major limitations were significant differences among cohorts based on inclusion/exclusion criteria, data source, and time/year of enrollment into cohort. CONCLUSIONS: Mood-stabilizing anticonvulsants, second-generation antipsychotics, and antidepressants were the most prescribed medications suggesting prescription patterns that are not necessarily guideline concordant. Significant differences exist in the prescription practices across different geographic regions, especially the underutilization of lithium in the North American cohorts and the higher utilization of first-generation antipsychotics in the European cohorts. There is a need to conduct future longitudinal studies to further explore these differences and their impact on outcomes, and to inform and implement evidence-based guidelines to help improve treatment practices in BD.

Trends in Telepsychiatry and In-Person Psychiatric Care for Depression in an Academic Health System, 2017-2022.

OBJECTIVE: The authors aimed to assess differences in appointment completion rates between telepsychiatry and in-person outpatient psychiatric care for patients with depression in an academic health system. METHODS: Electronic health records of encounters for patients (ages ≥10) with a depression diagnosis and at least one scheduled outpatient psychiatric appointment (N=586,266 appointments; November 2017-October 2022) were assessed for appointment volume and completion of telepsychiatry versus in-person sessions. RESULTS: Telepsychiatry became the dominant care modality after the onset of the COVID-19 pandemic, although the number of telepsychiatry and in-person appointments nearly converged by October 2022. Logistic regression showed that telepsychiatry appointments (July 2020-October 2022) were more likely (OR=1.30, 95% CI=1.27-1.34) to be completed than in-person appointments. CONCLUSIONS: Telepsychiatry appointments were less likely to be canceled or missed than in-person appointments, suggesting that telepsychiatry improved efficiency and continuity of care. As in-person operations resume following the pandemic, maintaining telepsychiatry services may optimize hospital-level and patient outcomes.

Digital Phenotyping for Mood Disorders: Methodology-Oriented Pilot Feasibility Study.

BACKGROUND: In the burgeoning area of clinical digital phenotyping research, there is a dearth of literature that details methodology, including the key challenges and dilemmas in developing and implementing a successful architecture for technological infrastructure, patient engagement, longitudinal study participation, and successful reporting and analysis of diverse passive and active digital data streams. OBJECTIVE: This article provides a narrative rationale for our study design in the context of the current evidence base and best practices, with an emphasis on our initial lessons learned from the implementation challenges and successes of this digital phenotyping study. METHODS: We describe the design and implementation approach for a digital phenotyping pilot feasibility study with attention to synthesizing key literature and the reasoning for pragmatic adaptations in implementing a multisite study encompassing distinct geographic and population settings. This methodology was used to recruit patients as study participants with a clinician-validated diagnostic history of unipolar depression, bipolar I disorder, or bipolar II disorder, or healthy controls in 2 geographically distinct health care systems for a longitudinal digital phenotyping study of mood disorders. RESULTS: We describe the feasibility of a multisite digital phenotyping pilot study for patients with mood disorders in terms of passively and actively collected phenotyping data quality and enrollment of patients. Overall data quality (assessed as the amount of sensor data obtained vs expected) was high compared to that in related studies. Results were reported on the relevant demographic features of study participants, revealing recruitment properties of age (mean subgroup age ranged from 31 years in the healthy control subgroup to 38 years in the bipolar I disorder subgroup), sex (predominance of female participants, with 7/11, 64% females in the bipolar II disorder subgroup), and smartphone operating system (iOS vs Android; iOS ranged from 7/11, 64% in the bipolar II disorder subgroup to 29/32, 91% in the healthy control subgroup). We also described implementation considerations around digital phenotyping research for mood disorders and other psychiatric conditions. CONCLUSIONS: Digital phenotyping in affective disorders is feasible on both Android and iOS smartphones, and the resulting data quality using an open-source platform is higher than that in comparable studies. While the digital phenotyping data quality was independent of gender and race, the reported demographic features of study participants revealed important information on possible selection biases that may result from naturalistic research in this domain. We believe that the methodology described will be readily reproducible and generalizable to other study settings and patient populations given our data on deployment at 2 unique sites.

Classifying and clustering mood disorder patients using smartphone data from a feasibility study.

Differentiating between bipolar disorder and major depressive disorder can be challenging for clinicians. The diagnostic process might benefit from new ways of monitoring the phenotypes of these disorders. Smartphone data might offer insight in this regard. Today, smartphones collect dense, multimodal data from which behavioral metrics can be derived. Distinct patterns in these metrics have the potential to differentiate the two conditions. To examine the feasibility of smartphone-based phenotyping, two study sites (Mayo Clinic, Johns Hopkins University) recruited patients with bipolar I disorder (BPI), bipolar II disorder (BPII), major depressive disorder (MDD), and undiagnosed controls for a 12-week observational study. On their smartphones, study participants used a digital phenotyping app (mindLAMP) for data collection. While in use, mindLAMP gathered real-time geolocation, accelerometer, and screen-state (on/off) data. mindLAMP was also used for EMA delivery. MindLAMP data was then used as input variables in binary classification, three-group k-nearest neighbors (KNN) classification, and k-means clustering. The best-performing binary classification model was able to classify patients as control or non-control with an AUC of 0.91 (random forest). The model that performed best at classifying patients as having MDD or bipolar I/II had an AUC of 0.62 (logistic regression). The k-means clustering model had a silhouette score of 0.46 and an ARI of 0.27. Results support the potential for digital phenotyping methods to cluster depression, bipolar disorder, and healthy controls. However, due to inconsistencies in accuracy, more data streams are required before these methods can be applied to clinical practice.

Determinants of experience & satisfaction in telehealth psychiatry during the COVID-19 pandemic for patients & providers.

Introduction: The objective of this study was to characterize the experiences and overall satisfaction of patients and providers with the March 2020 transition to telehealth in a psychiatric setting (telepsychiatry). The study also investigated how socio-demographic and clinical characteristics impact an individual's experiences and satisfaction with telepsychiatry. Methods: Responses were collected from 604 patients and 154 providers engaged in clinical care at one of three participating Johns Hopkins Medicine outpatient psychiatric clinics between January 2020-March 2021. Survey data were collected by self-report via Qualtrics or telephone follow-up. Results: Respondents were predominately female and White. Over 70% of patients and providers were generally satisfied with telepsychiatry. However, providers were more likely to favor in-person care over telepsychiatry for post-pandemic care 48% to 17% respectively, while 35% rated both modalities equivalently. Patients were more evenly divided with 45% preferring telepsychiatry compared to 42% for in-person care, and only 13% rating them equivalently. Among providers, technical difficulties were significantly associated with both less satisfaction and lower preference for telepsychiatry [odds ratio for satisfaction (ORS) = 0.12; odds ratio for preference (ORP) = 0.13]. For patients, factors significantly associated with both lower satisfaction and lower preference for telepsychiatry included technical difficulties (ORS = 0.20; ORP = 0.41), unstable access to the internet (ORS = 0.46; ORP = 0.50), worsening depression (ORS = 0.38; ORP = 0.36), and worsening anxiety (ORS = 0.41; ORP = 0.40). Factors associated with greater satisfaction and higher preference for telepsychiatry among patients included higher education (ORS = 2.13; ORP = 1.96) and a decrease in technical difficulties over time (ORS = 2.86; ORP = 2.35). Discussion: Patients and providers were satisfied with telepsychiatry. However, there were greater differences between them in preferences for continuing to use telepsychiatry post-pandemic. These findings highlight factors that influence patient and provider preferences and should be addressed to optimize the use of telepsychiatry in the future.

Family environment and polygenic risk in the bipolar high-risk context.

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.

Contributions of circadian clock genes to cell survival in fibroblast models of lithium-responsive bipolar disorder.

Bipolar disorder (BD) is characterized by mood episodes, disrupted circadian rhythms and gray matter reduction in the brain. Lithium is an effective pharmacotherapy for BD, but not all patients respond to treatment. Lithium has neuroprotective properties and beneficial effects on circadian rhythms that may distinguish lithium responders (Li-R) from non-responders (Li-NR). The circadian clock regulates molecular pathways involved in apoptosis and cell survival, but how this overlap impacts BD and/or lithium responsiveness is unknown. In primary fibroblasts from Li-R/Li-NR BD patients and controls, we found patterns of co-expression among circadian clock and cell survival genes that distinguished BD vs. control, and Li-R vs. Li-NR cells. In cellular models of apoptosis using staurosporine (STS), lithium preferentially protected fibroblasts against apoptosis in BD vs. control samples, regardless of Li-R/Li-NR status. When examining the effects of lithium treatment of cells in vitro, caspase activation by lithium correlated with period alteration, but the relationship differed in control, Li-R and Li-NR samples. Knockdown of Per1 and Per3 in mouse fibroblasts altered caspase activity, cell death and circadian rhythms in an opposite manner. In BD cells, genetic variation in PER1 and PER3 predicted sensitivity to apoptosis in a manner consistent with knockdown studies. We conclude that distinct patterns of coordination between circadian clock and cell survival genes in BD may help predict lithium response.

Translating clinical notes into quantitative measures-a real-world observation on the response to cholinesterase inhibitors or selective serotonin reuptake inhibitors prescribed to outpatients with dementia using electronic medical records.

Objective: Cholinesterase inhibitors (CEIs) are prescribed for dementia to maintain or improve memory. Selective serotonin reuptake inhibitors (SSRIs) are also prescribed to manage psychiatric symptoms seen in dementia. What proportion of outpatients actually responds to these drugs is still unclear. Our objective was to investigate the responder rates of these medications in an outpatient setting using the electronic medical record (EMR). Methods: We used the Johns Hopkins EMR system to identify patients with dementia who were prescribed a CEI or SSRI for the first time between 2010 and 2021. Treatment effects were assessed through routinely documented clinical notes and free-text entries in which healthcare providers record clinical findings and impressions of patients. Responses were scored using a three-point Likert scale named the NOte-based evaluation method for Treatment Efficacy (NOTE) in addition to the Clinician's Interview-Based Impression of Change Plus caregiver input (CIBIC-plus), a seven-point Likert scale used in clinical trials. To validate NOTE, the relationships between NOTE and CIBIC-plus and between NOTE and change in MMSE (Mini-Mental State Examination) before and after medication were examined. Inter-rater reliability was evaluated using Krippendorff's alpha. The responder rates were calculated. Results: NOTE showed excellent inter-rater reliability and correlated well with CIBIC-plus and changes in MMSEs. Out of 115 CEI cases, 27.0% reported improvement and 34.8% reported stable symptoms in cognition; out of 225 SSRI cases, 69.3% reported an improvement in neuropsychiatric symptoms. Conclusion: NOTE showed high validity in measuring the pharmacotherapy effects based on unstructured clinical entries. Although our real-world observation included various types of dementia, the results were remarkably similar to what was reported in controlled clinical trials of Alzheimer's disease and its related neuropsychiatric symptoms.

Genome-wide significant risk loci for mood disorders in the Old Order Amish founder population.

Genome-wide association studies (GWAS) of mood disorders in large case-control cohorts have identified numerous risk loci, yet pathophysiological mechanisms remain elusive, primarily due to the very small effects of common variants. We sought to discover risk variants with larger effects by conducting a genome-wide association study of mood disorders in a founder population, the Old Order Amish (OOA, n = 1,672). Our analysis revealed four genome-wide significant risk loci, all of which were associated with >2-fold relative risk. Quantitative behavioral and neurocognitive assessments (n = 314) revealed effects of risk variants on sub-clinical depressive symptoms and information processing speed. Network analysis suggested that OOA-specific risk loci harbor novel risk-associated genes that interact with known neuropsychiatry-associated genes via gene interaction networks. Annotation of the variants at these risk loci revealed population-enriched, non-synonymous variants in two genes encoding neurodevelopmental transcription factors, CUX1 and CNOT1. Our findings provide insight into the genetic architecture of mood disorders and a substrate for mechanistic and clinical studies.

The prevalence of mental health disorders in people with HIV and the effects on the HIV care continuum.

OBJECTIVE: To describe the prevalence of diagnosed depression, anxiety, bipolar disorder, and schizophrenia in people with HIV (PWH) and the differences in HIV care continuum outcomes in those with and without mental health disorders (MHDs). DESIGN: Observational study of participants in the North American AIDS Cohort Collaboration on Research and Design. METHODS: PWH (≥18 years) contributed data on prevalent schizophrenia, anxiety, depressive, and bipolar disorders from 2008 to 2018 based on International Classification of Diseases code mapping. Mental health (MH) multimorbidity was defined as having two or more MHD. Log binomial models with generalized estimating equations estimated adjusted prevalence ratios (aPR) and 95% confidence intervals for retention in care (≥1 visit/year) and viral suppression (HIV RNA ≤200 copies/ml) by presence vs. absence of each MHD between 2016 and 2018. RESULTS: Among 122 896 PWH, 67 643 (55.1%) were diagnosed with one or more MHD: 39% with depressive disorders, 28% with anxiety disorders, 10% with bipolar disorder, and 5% with schizophrenia. The prevalence of depressive and anxiety disorders increased between 2008 and 2018, whereas bipolar disorder and schizophrenia remained stable. MH multimorbidity affected 24% of PWH. From 2016 to 2018 (N = 64 684), retention in care was marginally lower among PWH with depression or anxiety, however those with MH multimorbidity were more likely to be retained in care. PWH with bipolar disorder had marginally lower prevalence of viral suppression (aPR = 0.98 [0.98-0.99]) as did PWH with MH multimorbidity (aPR = 0.99 [0.99-1.00]) compared with PWH without MHD. CONCLUSION: The prevalence of MHD among PWH was high, including MH multimorbidity. Although retention and viral suppression were similar to people without MHD, viral suppression was lower in those with bipolar disorder and MH multimorbidity.

The miR-124-AMPAR pathway connects polygenic risks with behavioral changes shared between schizophrenia and bipolar disorder.

Schizophrenia (SZ) and bipolar disorder (BP) are highly heritable major psychiatric disorders that share a substantial portion of genetic risk as well as their clinical manifestations. This raises a fundamental question of whether, and how, common neurobiological pathways translate their shared polygenic risks into shared clinical manifestations. This study shows the miR-124-3p-AMPAR pathway as a key common neurobiological mediator that connects polygenic risks with behavioral changes shared between these two psychotic disorders. We discovered the upregulation of miR-124-3p in neuronal cells and the postmortem prefrontal cortex from both SZ and BP patients. Intriguingly, the upregulation is associated with the polygenic risks shared between these two disorders. Seeking mechanistic dissection, we generated a mouse model that upregulates miR-124-3p in the medial prefrontal cortex. We demonstrated that the upregulation of miR-124-3p increases GRIA2-lacking calcium-permeable AMPARs and perturbs AMPAR-mediated excitatory synaptic transmission, leading to deficits in the behavioral dimensions shared between SZ and BP.

National Network of Depression Centers' Recommendations on Harmonizing Clinical Documentation of Electroconvulsive Therapy.

ABSTRACT: Electroconvulsive therapy (ECT) is a highly therapeutic and cost-effective treatment for severe and/or treatment-resistant major depression. However, because of the varied clinical practices, there is a great deal of heterogeneity in how ECT is delivered and documented. This represents both an opportunity to study how differences in implementation influence clinical outcomes and a challenge for carrying out coordinated quality improvement and research efforts across multiple ECT centers. The National Network of Depression Centers, a consortium of 26+ US academic medical centers of excellence providing care for patients with mood disorders, formed a task group with the goals of promoting best clinical practices for the delivery of ECT and to facilitate large-scale, multisite quality improvement and research to advance more effective and safe use of this treatment modality. The National Network of Depression Centers Task Group on ECT set out to define best practices for harmonizing the clinical documentation of ECT across treatment centers to promote clinical interoperability and facilitate a nationwide collaboration that would enable multisite quality improvement and longitudinal research in real-world settings. This article reports on the work of this effort. It focuses on the use of ECT for major depressive disorder, which accounts for the majority of ECT referrals in most countries. However, most of the recommendations on clinical documentation proposed herein will be applicable to the use of ECT for any of its indications.

Rare variants implicate NMDA receptor signaling and cerebellar gene networks in risk for bipolar disorder.

Bipolar disorder is an often-severe mental health condition characterized by alternation between extreme mood states of mania and depression. Despite strong heritability and the recent identification of 64 common variant risk loci of small effect, pathophysiological mechanisms remain unknown. Here, we analyzed genome sequences from 41 multiply-affected pedigrees and identified variants in 741 genes with nominally significant linkage or association with bipolar disorder. These 741 genes overlapped known risk genes for neurodevelopmental disorders and clustered within gene networks enriched for synaptic and nuclear functions. The top variant in this analysis - prioritized by statistical association, predicted deleteriousness, and network centrality - was a missense variant in the gene encoding D-amino acid oxidase (DAOG131V). Heterologous expression of DAOG131V in human cells resulted in decreased DAO protein abundance and enzymatic activity. In a knock-in mouse model of DAOG131, DaoG130V/+, we similarly found decreased DAO protein abundance in hindbrain regions, as well as enhanced stress susceptibility and blunted behavioral responses to pharmacological inhibition of N-methyl-D-aspartate receptors (NMDARs). RNA sequencing of cerebellar tissue revealed that DaoG130V resulted in decreased expression of two gene networks that are enriched for synaptic functions and for genes expressed, respectively, in Purkinje neurons or granule neurons. These gene networks were also down-regulated in the cerebellum of patients with bipolar disorder compared to healthy controls and were enriched for additional rare variants associated with bipolar disorder risk. These findings implicate dysregulation of NMDAR signaling and of gene expression in cerebellar neurons in bipolar disorder pathophysiology and provide insight into its genetic architecture.

Exome sequencing in bipolar disorder identifies AKAP11 as a risk gene shared with schizophrenia.

We report results from the Bipolar Exome (BipEx) collaboration analysis of whole-exome sequencing of 13,933 patients with bipolar disorder (BD) matched with 14,422 controls. We find an excess of ultra-rare protein-truncating variants (PTVs) in patients with BD among genes under strong evolutionary constraint in both major BD subtypes. We find enrichment of ultra-rare PTVs within genes implicated from a recent schizophrenia exome meta-analysis (SCHEMA; 24,248 cases and 97,322 controls) and among binding targets of CHD8. Genes implicated from genome-wide association studies (GWASs) of BD, however, are not significantly enriched for ultra-rare PTVs. Combining gene-level results with SCHEMA, AKAP11 emerges as a definitive risk gene (odds ratio (OR) = 7.06, P = 2.83 × 10-9). At the protein level, AKAP-11 interacts with GSK3B, the hypothesized target of lithium, a primary treatment for BD. Our results lend support to BD's polygenicity, demonstrating a role for rare coding variation as a significant risk factor in BD etiology.

Amygdala and anterior cingulate transcriptomes from individuals with bipolar disorder reveal downregulated neuroimmune and synaptic pathways.

Recent genetic studies have identified variants associated with bipolar disorder (BD), but it remains unclear how brain gene expression is altered in BD and how genetic risk for BD may contribute to these alterations. Here, we obtained transcriptomes from subgenual anterior cingulate cortex and amygdala samples from post-mortem brains of individuals with BD and neurotypical controls, including 511 total samples from 295 unique donors. We examined differential gene expression between cases and controls and the transcriptional effects of BD-associated genetic variants. We found two coexpressed modules that were associated with transcriptional changes in BD: one enriched for immune and inflammatory genes and the other with genes related to the postsynaptic membrane. Over 50% of BD genome-wide significant loci contained significant expression quantitative trait loci (QTL) (eQTL), and these data converged on several individual genes, including SCN2A and GRIN2A. Thus, these data implicate specific genes and pathways that may contribute to the pathology of BP.

Correction of depression-associated circadian rhythm abnormalities is associated with lithium response in bipolar disorder.

BACKGROUND: Bipolar disorder (BD) is characterized by episodes of depression and mania and disrupted circadian rhythms. Lithium is an effective therapy for BD, but only 30%-40% of patients are fully responsive. Preclinical models show that lithium alters circadian rhythms. However, it is unknown if the circadian rhythm effects of lithium are essential to its therapeutic properties. METHODS: In secondary analyses of a multi-center, prospective, trial of lithium for BD, we examined the relationship between circadian rhythms and therapeutic response to lithium. Using standardized instruments, we measured morningness, diurnal changes in mood, sleep, and energy (circadian rhythm disturbances) in a cross-sectional study of 386 BD subjects with varying lithium exposure histories. Next, we tracked symptoms of depression and mania prospectively over 12 weeks in a subset of 88 BD patients initiating treatment with lithium. Total, circadian, and affective mood symptoms were scored separately and analyzed. RESULTS: Subjects with no prior lithium exposure had the most circadian disruption, while patients stable on lithium monotherapy had the least. Patients who were stable on lithium with another drug or unstable on lithium showed intermediate levels of disruption. Treatment with lithium for 12 weeks yielded significant reductions in total and affective depression symptoms. Lithium responders (Li-Rs) showed improvement in circadian symptoms of depression, but non-responders did not. There was no difference between Li-Rs and nonresponders in affective, circadian, or total symptoms of mania. CONCLUSIONS: Exposure to lithium is associated with reduced circadian disruption. Lithium response at 12 weeks was selectively associated with the reduction of circadian depressive symptoms. We conclude that stabilization of circadian rhythms may be an important feature of lithium's therapeutic effects. CLINICAL TRIALS REGISTRY: NCT0127253.

Methylomic and transcriptomic predictors of one-month exposure to cortisol in healthy individuals.

Allostatic load (AL) refers to the cumulative "wear and tear" on an organism throughout its lifetime. One of the primary contributing factors to AL is prolonged exposure to stress or its primary catabolic agent cortisol. Chronic exposure to stress or cortisol is associated with numerous diseases, including cardiovascular disease, metabolic disorders, and psychiatric disorders. Therefore, a molecular marker capable of integrating a past history of cortisol exposure would be of great utility for assessing disease risk. To this end, we recruited 87 healthy males and females of European ancestry between 18 and 60 years old, extracted genomic DNA and RNA from leukocytes, and implemented a gene-centric DNA enrichment method coupled with bisulfite sequencing and RNA-Seq of total RNA for the determination of genome-wide methylation and gene transcription, respectively. Sequencing data were analyzed against awakening and bedtime cortisol data to identify differentially methylated regions (DMRs) and CpGs (DMCs) and differentially expressed genes (DEGs). Six candidate DMCs (punadjusted < 0.005) and nine DEGs (punadjusted < 0.0005) were used to construct a prediction model that could capture past 30+ days of both bedtime and awakening cortisol levels. Utilizing a cross-validation approach, we obtained a regression coefficient of R2 = 0.308 for predicting continuous awakening cortisol and an area under the curve (AUC) = 0.753 for dichotomous (high vs. low tertile) awakening cortisol, and R2 = 0.224 and AUC = 0.723 for continuous and dichotomous bedtime cortisol levels, respectively. To our knowledge, the current study represents the first attempt to identify genome-wide predictors of cortisol exposure that utilizes both methylation and transcription targets. The utility of our approach needs to be replicated in an independent cohort of samples for which similar cortisol metrics are available.