McInnis, Melvin G

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.

Background: Bipolar disorder (BD) involves significant mood and energy shifts reflected in speech patterns. Detecting these patterns is crucial for diagnosis and monitoring, currently assessed subjectively. Advances in natural language processing offer opportunities to objectively analyze them. Aims: To (i) correlate speech features with manic-depressive symptom severity in BD, (ii) develop predictive models for diagnostic and treatment outcomes, and (iii) determine the most relevant speech features and tasks for these analyses. Methods: This naturalistic, observational study involved longitudinal audio recordings of BD patients at euthymia, during acute manic/depressive phases, and after-response. Patients participated in clinical evaluations, cognitive tasks, standard text readings, and storytelling. After automatic diarization and transcription, speech features, including acoustics, content, formal aspects, and emotionality, will be extracted. Statistical analyses will (i) correlate speech features with clinical scales, (ii) use lasso logistic regression to develop predictive models, and (iii) identify relevant speech features. Results: Audio recordings from 76 patients (24 manic, 21 depressed, 31 euthymic) were collected. The mean age was 46.0 ± 14.4 years, with 63.2% female. The mean YMRS score for manic patients was 22.9 ± 7.1, reducing to 5.3 ± 5.3 post-response. Depressed patients had a mean HDRS-17 score of 17.1 ± 4.4, decreasing to 3.3 ± 2.8 post-response. Euthymic patients had mean YMRS and HDRS-17 scores of 0.97 ± 1.4 and 3.9 ± 2.9, respectively. Following data pre-processing, including noise reduction and feature extraction, comprehensive statistical analyses will be conducted to explore correlations and develop predictive models. Conclusions: Automated speech analysis in BD could provide objective markers for psychopathological alterations, improving diagnosis, monitoring, and response prediction. This technology could identify subtle alterations, signaling early signs of relapse. Establishing standardized protocols is crucial for creating a global speech cohort, fostering collaboration, and advancing BD understanding.

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.

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.

AbstractObjectivesPersistent functional impairment is common in bipolar disorder (BD) and is influenced by a number of demographic, clinical, and cognitive features. The goal of this project was to estimate and compare the influence of key factors on community function in multiple cohorts of well‐characterized samples of individuals with BD.MethodsThirteen cohorts from 7 countries included n = 5882 individuals with BD across multiple sites. The statistical approach consisted of a systematic uniform application of analyses across sites. Each site performed a logistic regression analysis with empirically derived “higher versus lower function” as the dependent variable and selected clinical and demographic variables as predictors.ResultsWe found high rates of functional impairment, ranging from 41 to 75%. Lower community functioning was associated with depressive symptoms in 10 of 12 of the cohorts that included this variable in the analysis. Lower levels of education, a greater number of prior mood episodes, the presence of a comorbid substance use disorder, and a greater total number of psychotropic medications were also associated with low functioning.ConclusionsThe bipolar clinical research community is poised to work together to characterize the multi‐dimensional contributors to impairment and address the barriers that impede patients' complete recovery. We must also identify the core features which enable many to thrive and live successfully with BD. A large‐scale, worldwide, prospective longitudinal study focused squarely on BD and its heterogeneous presentations will serve as a platform for discovery and promote major advances toward optimizing outcomes for every individual with this illness.

Abstract Bipolar disorder (BD) is a complex and dynamic condition with a typical onset in late adolescence or early adulthood followed by an episodic course with intervening periods of subthreshold symptoms or euthymia. It is complicated by the accumulation of comorbid medical and psychiatric disorders. The etiology of BD remains unknown and no reliable biological markers have yet been identified. This is likely due to lack of comprehensive ontological framework and, most importantly, the fact that most studies have been based on small nonrepresentative clinical samples with cross‐sectional designs. We propose to establish large, global longitudinal cohorts of BD studied consistently in a multidimensional and multidisciplinary manner to determine etiology and help improve treatment. Herein we propose collection of a broad range of data that reflect the heterogenic phenotypic manifestations of BD that include dimensional and categorical measures of mood, neurocognitive, personality, behavior, sleep and circadian, life‐story, and outcomes domains. In combination with genetic and biological information such an approach promotes the integrating and harmonizing of data within and across current ontology systems while supporting a paradigm shift that will facilitate discovery and become the basis for novel hypotheses.

AbstractBackgroundBipolar 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.MethodsIn 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.ResultsSubjects 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.ConclusionsExposure 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.

AbstractBackgroundLithium is regarded as a first‐line treatment for bipolar disorder (BD), but partial response and non‐response commonly occurs. There exists a need to identify lithium non‐responders prior to initiating treatment. The Pharmacogenomics of Bipolar Disorder (PGBD) Study was designed to identify predictors of lithium response.MethodsThe PGBD Study was an eleven site prospective trial of lithium treatment in bipolar I disorder. Subjects were stabilized on lithium monotherapy over 4 months and gradually discontinued from all other psychotropic medications. After ensuring a sustained clinical remission (defined by a score of ≤3 on the CGI for 4 weeks) had been achieved, subjects were followed for up to 2 years to monitor clinical response. Cox proportional hazard models were used to examine the relationship between clinical measures and time until failure to remit or relapse.ResultsA total of 345 individuals were enrolled into the study and included in the analysis. Of these, 101 subjects failed to remit or relapsed, 88 achieved remission and continued to study completion, and 156 were terminated from the study for other reasons. Significant clinical predictors of treatment failure (p < 0.05) included baseline anxiety symptoms, functional impairments, negative life events and lifetime clinical features such as a history of migraine, suicidal ideation/attempts, and mixed episodes, as well as a chronic course of illness.ConclusionsIn this PGBD Study of lithium response, several clinical features were found to be associated with failure to respond to lithium. Future validation is needed to confirm these clinical predictors of treatment failure and their use clinically to distinguish who will do well on lithium before starting pharmacotherapy.

IntroductionDepressive episodes are often prevalent among patients with bipolar disorder, but little is known regarding the differential patterns of development over time. We aimed to determine and characterize trajectories of depressive symptoms among adults with bipolar disorder during 6 months of systematic treatment.MethodsThe pragmatic clinical trial, Bipolar Clinical Health Outcomes Initiative in Comparative Effectiveness (CHOICE), randomized 482 outpatients with bipolar disorder to lithium or quetiapine. Depressive symptoms were rated at up to 9 visits using the Montgomery‐Asberg Depression Rating Scale (MADRS). Growth mixture modeling was utilized to identify trajectories and multinomial regression analysis estimated associations with potential predictors.ResultsFour distinct trajectories of depressive symptoms were identified. The responding class (60.3%) with a rapid reduction and subsequent low level; the partial‐responding class (18.4%) with an initial reduction followed by an increase during the remaining weeks; the fluctuating class (11.6%) with a fluctuation in depressive symptoms; and the non‐responding class (9.7%) with sustained moderate‐severe depressive symptoms. Bipolar type I predicted membership of the non‐responding class and randomization to quetiapine predicted membership of either the responding or the non‐responding class.ConclusionApproximately 30% experienced a partial or fluctuating course, and almost 10% had a chronic course with moderate‐severe depression during 6 months. Patients diagnosed with bipolar type 1 had higher risk of being categorized into a class with a worse outcome. While no differences in average overall outcomes occurred between the lithium and quetiapine groups, trajectory analysis revealed that the lithium group had more variable courses.

Objectives Health disparities between individuals of African and European ancestry are well documented. The disparities in bipolar disorder may be driven by racial bias superimposed on established factors contributing to misdiagnosis, including: evolving empirically based diagnostic criteria (International Classification of Diseases [ ICD ], Research Diagnostic Criteria [ RDC ] and Diagnostic and Statistical Manual [ DSM ]), multiple symptom domains (i.e. mania, depression and psychosis), and multimodal medical and additional psychiatric comorbidity. Methods For this paper, we reviewed the phenomenological differences between bipolar individuals of African and European ancestry in the context of diagnostic criteria and clinical factors that may contribute to a potential racial bias. Results Published data show that bipolar persons of African ancestry, compared with bipolar persons of non‐African ancestry, are more often misdiagnosed with a disease other than bipolar disorder (i.e. schizophrenia). Additionally, studies show that there are disparities in recruiting patients of African ancestry to participate in important genomic studies. This gap in biological research in this underrepresented minority may represent a missed opportunity to address potential racial differences in the risk and course of bipolar illness. Conclusion A concerted effort by the research community to increase inclusion of diverse persons in studies of bipolar disorder through community engagement may facilitate fully addressing these diagnostic and treatment disparities in bipolar individuals of African ancestry.