ABSTRACT Objective To provide expert recommendations for the development of a specialty bipolar clinic that promotes best clinical practices for the delivery of safe and effective care. Methods Members of the National Network of Depression Centers (NNDC) Bipolar Clinics Work Group met in a series of 1‐h monthly video calls over 18 months to discuss all aspects of their respective institution's bipolar clinics, including services provided, administrative processes, staffing models, characteristics of patients treated, patient volumes, finances, and research. The group also invited psychiatrists from outside of the NNDC who ran large bipolar clinics to contribute their thoughts and experiences on the subject. In addition, the NNDC sent a 20‐question survey to all 27 sites asking clinic directors specific questions about their bipolar clinics, if applicable. Further meetings and discussions incorporating results from the survey took place to produce a comprehensive set of recommendations. Results Twenty‐one of 27 NNDC sites (81%) responded to the survey. Twelve of 21 (57%) sites had a specific bipolar clinic, and another 4 (19%) were looking to start one. Clinics were based in university settings. The meetings, clinic survey, and discussions resulted in 10 recommendations describing services and operations for bipolar specialty clinics. Conclusions Given the complexity of bipolar disorder, specialty clinics are important to enhance care. Clinics with a wide array of services that include consultation, medication management, bipolar‐specific psychotherapies, and patient/family psychoeducation provide the most comprehensive care. Challenges to these clinics include maintaining ongoing patient access as well as ensuring long‐term financial stability.
Genomic dimensions deconstruct the clinical heterogeneity of bipolar disorder
ABSTRACT Bipolar disorder’s (BD) clinical heterogeneity has an unresolved genetic basis. We meta-analyzed genome-wide association studies (GWAS) of 16 BD subphenotypes in 226,032 individuals from 57 cohorts (38,022 cases); 10 advanced to multivariate and multi-trait analyses. Four factors (compulsive, psychotic, dysregulated, internalizing) explained 82.8% of shared genetic variance. BD1 and BD2 loaded on distinct factors despite a high genetic correlation; 87.0% of common-factor loci were significant in neither subtype. Unipolar mania aligned with psychosis over internalizing, and was distinguishable from BD1, and rapid cycling showed heritable cross-domain liability. We identified 356 risk loci, 158 novel, including the first univariate-GWAS associations for psychosis, unipolar mania, rapid cycling and schizoaffective disorder—and 249 credible genes (89 high-confidence), 12 with approved-drug or clinical-phase annotations. Cell-type association showed a midbrain dopaminergic–GABAergic gradient along the psychotic factor. BD’s genetic architecture appears hierarchical—a general liability resolving into dimensions of course and comorbidity beyond diagnostic subtypes.
Symptom Severity and Treatment Needs Among Racial Groups Seeking Treatment at a Mood Outcomes Program
Circadian clock genes and cell survival in bipolar disorder: Insights into lithium responsiveness and molecular mechanisms in patient‐derived neural progenitor cells and mouse neurons
Bipolar disorder (BD) is characterized by disrupted circadian rhythms and neuronal loss. Lithium amplifies circadian rhythms and is neuroprotective, indicating mechanistic overlap across cellular systems. We examined the role of neuroprotection in determining lithium response and how circadian clock genes regulate apoptosis. In stem‐cell‐derived neuronal progenitor cells (NPCs) from BD patients, and in immortalized mouse hippocampal neurons, lithium was neuroprotective against staurosporine (STS)‐induced apoptosis. However, neuroprotection did not distinguish lithium‐responders (Li‐Rs) from nonresponders (Li‐NRs). Disruption of selected clock genes—period circadian regulator 1 ( PER1 ), basic helix–loop–helix ARNT like 1 ( ARNTL , also known as BMAL1 ), and nuclear receptor subfamily 1 group D member 1 ( NR1D1 ; also known as REV‐ERBα )—with small interfering RNA (siRNA) had distinct effects on apoptosis in mouse neurons and human NPCs. In BD patient NPCs, reduced expression of PER1 and BMAL1 increased apoptosis more in Li‐NR NPCs vs. Li‐R NPCs. Reduced expression of REV‐ERBα caused more extensive apoptosis in BD vs. control NPCs, but did not distinguish Li‐Rs vs. Li‐NRs. The REV‐ERB agonist GSK4112 was neuroprotective in mouse neurons and control NPCs, but did not reduce apoptosis in BD NPCs. Simultaneous treatment of NPCs with STS and GSK4112 revealed BD‐ and Li‐R‐associated differences in apoptosis‐related gene expression. In summary, the pharmacological effects of lithium in promoting neuroprotection are not sufficient to distinguish between Li‐Rs and Li‐NRs. However, genetic and pharmacological manipulations of the circadian clock reveal distinct roles in apoptosis in BD patient NPCs, some of which do distinguish Li‐Rs and Li‐NRs. We conclude that genetic disruption of cellular circadian clocks may predispose neurons to apoptosis preferentially in BD and/or Li‐NR. These findings imply that differences in BD between Li‐Rs/Li‐NRs primarily arise from defining biological characteristics of patient subgroups, not from distinct pharmacodynamic factors following lithium treatment.
Digital Technologies Tracking Active and Passive Data Collection in Depressive Disorders: Lessons Learned From a Case Series
In this case series, we present several examples from participants (2 patients and 1 healthy control) of a 12-week pilot feasibility study to create a digital phenotype of depression (unipolar or bipolar) through active and passive data collection from a smartphone and a wearable device combined with routine clinical care for mood disorders. The selected cases represent real clinical examples that highlight the intrinsic challenges that should be expected when conducting similar studies, including appropriate health data privacy protection, clinical standardization, and interindividual differences in levels of engagement and acceptability of active and passive data collection (ie, self-reported, behavioral, cognitive, and physiological data), particularly with patient-generated data in mobile apps, digital proficiency habituation, and consistent use of wearable devices. In the context of the rapidly growing use of digital technologies in psychiatry, anticipating challenges for the integration of personal mobile devices and smartphone mental health apps as aides to track specific aspects of depressive disorders is critical for a clinically meaningful digital transformation of mood disorders care.
Differential contributions of circadian clock genes to cell survival in bipolar disorder patient derived neuronal progenitor cells distinguishes lithium responders and non-responders
Abstract 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.
Exploring the genetics of lithium response in bipolar disorders
Abstract Background: Lithium (Li) remains the treatment of choice for bipolar disorders (BP). Its mood-stabilizing effects help reduce the long-term burden of mania, depression and suicide risk in patients with BP. It also has been shown to have beneficial effects on disease-associated conditions, including sleep and cardiovascular disorders. However, the individual responses to Li treatment vary within and between diagnostic subtypes of BP (e.g. BP-I and BP-II) according to the clinical presentation. Moreover, long-term Li treatment has been linked to adverse side-effects that are a cause of concern and non-adherence, including the risk of developing chronic medical conditions such as thyroid and renal disease. In recent years, studies by the Consortium on Lithium Genetics (ConLiGen) have uncovered a number of genetic factors that contribute to the variability in Li treatment response in patients with BP. Here, we leveraged the ConLiGen cohort (N=2,064) to investigate the genetic basis of Li effects in BP. For this, we studied how Li response and linked genes associate with the psychiatric symptoms and polygenic load for medical comorbidities, placing particular emphasis on identifying differences between BP-I and BP-II. Results: We found that clinical response to Li treatment, measured with the Alda scale, was associated with a diminished burden of mania, depression, substance and alcohol abuse, psychosis and suicidal ideation in patients with BP-I and, in patients with BP-II, of depression only. Our genetic analyses showed that a stronger clinical response to Li was modestly related to lower polygenic load for diabetes and hypertension in BP-I but not BP-II. Moreover, our results suggested that a number of genes that have been previously linked to Li response variability in BP differentially relate to the psychiatric symptomatology, particularly to the numbers of manic and depressive episodes, and to the polygenic load for comorbid conditions, including diabetes, hypertension and hypothyroidism. Conclusions: Taken together, our findings suggest that the effects of Li on symptomatology and comorbidity in BP are partially modulated by common genetic factors, with differential effects between BP-I and BP-II.
The Human Phenotype Ontology in 2024: phenotypes around the world
Abstract The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.
Lithium Response in Bipolar Disorder is Associated with Focal Adhesion and PI3K-Akt Networks: A Multi-omics Replication Study
Abstract Lithium is the gold standard treatment for bipolar disorder (BD). However, its mechanism of action is incompletely understood, and prediction of treatment outcomes is limited. In our previous multi-omics study of the Pharmacogenomics of Bipolar Disorder (PGBD) sample combining transcriptomic and genomic data, we found that focal adhesion, the extracellular matrix (ECM), and PI3K-Akt signaling networks were associated with response to lithium. In this study, we replicated the results of our previous study using network propagation methods in a genome-wide association study of an independent sample of 2,039 patients from the International Consortium on Lithium Genetics (ConLiGen) study. We identified functional enrichment in focal adhesion and PI3K-Akt pathways, but we did not find an association with the ECM pathway. Our results suggest that deficits in the neuronal growth cone and PI3K-Akt signaling, but not in ECM proteins, may influence response to lithium in BD.
Patterns of pharmacotherapy for bipolar disorder: A
GBC
survey
Abstract 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.