Strategies and foundations for scientific discovery in longitudinal studies of bipolar disorder

Predominant Polarity for Enhanced Phenotyping and Personalized Treatment of Bipolar Disorder: A Narrative Review on Recent Findings

PURPOSE OF REVIEW

This paper explores Predominant Polarity (PP) in Bipolar Disorder (BD), defined as the predominance of either manic or depressive episodes over a patient's course of illness. We examine its clinical relevance, neurobiological foundations, and potential for guiding personalized treatment strategies. The review seeks to determine whether PP is a reliable course specifier and how it can be utilized to improve clinical outcomes.

RECENT FINDINGS

PP has a significant impact on prognosis and treatment planning in BD. Manic and depressive PP are associated with distinct clinical and neurobiological profiles of BD, while individuals without a clear predominance of either episode type represent a more severe to-treat subgroup of patients. The development of the Polarity Index (PI) facilitates treatment decisions based on PP. PP offers a valuable framework for refining BD treatment and understanding its complexity. Future research should focus on refining PP definitions, validating neurobiological markers, and integrating these insights into comprehensive treatment models to improve patient outcomes.

The Academic Community Early Psychosis Intervention Network: Toward building a novel learning health system across six US states

Introduction

Compared to usual care, specialty services for first-episode psychosis (FES) have superior patient outcomes. The Early Psychosis Intervention Network (EPINET), comprised of eight U.S. regional clinical networks, aims to advance the quality of FES care within the ethos of learning healthcare systems (LHS). Among these, the Academic Community (AC) EPINET was established to provide FES care, collect common data elements, leverage informatics, foster a culture of continuous learning and quality improvement, and engage in practice-based research.

Methods

We designed and implemented a novel LHS of university-affiliated FES programs within a hub (academic leadership team) and spoke (FES clinics) model. A series of site implementation meetings engaged stakeholders, setting the stage for a culture that values data collection and shared learning. We built clinical workflows to collect common data elements at enrollment and at consecutive 6-month intervals in parallel to an informatics workflow to deliver outcome visualizations and drive quality improvement efforts.

Results

All six clinical sites successfully implemented data capture workflows and engaged in the process of designing the informatics platform. Upon developing the structure, processes, and initial culture of the LHS, a total of 614 patients enrolled in AC-EPINET, with the most common primary diagnoses of schizophrenia (32.1%) and unspecified psychotic disorders (23.6%). Visualized outcomes were delivered to clinical teams who began to consider locally relevant quality improvement projects.

Conclusions

AC-EPINET is a novel LHS, with a simultaneous focus on science, informatics, incentives, and culture. The work of developing AC-EPINET thus far has highlighted the need for future LHS' to be mindful of the complexities of data security issues, develop more automated informatic workflows, resource quality assurance efforts, and attend to building the cultural infrastructure with the input of all stakeholders.

Forging a learning health system for early psychosis: Insights from the academic community EPINET

Psychosis can lead to deleterious outcomes with multifaceted personal and societal costs, but the provision of intervention services early in the illness course positively influences disease trajectory. There has been a growing emphasis on innovation and expansion of first episode services (FES). Yet, such care has not universally delivered on the promise of meeting the needs of individuals with first-episode psychosis. The Academic Community Early Psychosis Intervention Network (AC-EPINET) is one of eight regional hubs envisioned by NIH to function as a learning healthcare system (LHS) to advance the quality of early intervention care. AC-EPINET spans three geographically distinct areas of the U.S. (Midwest: Indiana, Michigan, Ohio; Southeastern: Tennessee, Louisiana; and Northeastern: Connecticut, New York) with six academically oriented, community-facing FES' and an informatics site. Our goals were to strengthen existing services, expand them to collect common data elements within clinical workflows, leverage informatics, support practice-based research, and engage stakeholders to ultimately build a culture of continuous learning and quality improvement. We designed and implemented an infrastructure consisting of a centralized two-site administrative team supporting an inclusive steering committee, three work groups, and the six clinical sites. Common data capture at enrollment and consecutive six-month intervals was integrated into clinical workflows. An informatics workflow that included evidence-based benchmarked outcome visualization, for each site and across the entire network, was designed and deployed to enable shared learning and ongoing quality improvement efforts. Design and implementation lessons presented are relevant to LHS development and dissemination of FES quality improvement and research efforts.

Person-Specific Analyses of Smartphone Use and Mental Health: Intensive Longitudinal Study

BACKGROUND

Contrary to popular concerns about the harmful effects of media use on mental health, research on this relationship is ambiguous, stalling advances in theory, interventions, and policy. Scientific explorations of the relationship between media and mental health have mostly been found null or have small associations, with the results often blamed on the use of cross-sectional study designs or imprecise measures of media use and mental health.

OBJECTIVE

This exploratory empirical demonstration aims to answer whether mental health effects are associated with media use experiences by (1) redirecting research investments to granular and intensive longitudinal recordings of digital experiences to build models of media use and mental health for single individuals over the course of 1 year, (2) using new metrics of fragmented media use to propose explanations of mental health effects that will advance person-specific theorizing in media psychology, and (3) identifying combinations of media behaviors and mental health symptoms that may be more useful for studying media effects than single measures of dosage and affect or assessments of clinical symptoms related to specific disorders.

METHODS

The activity on individuals' smartphone screens was recorded every 5 seconds when devices were in use over 1 year, resulting in a dataset of 6,744,013 screenshots and 123 fortnightly surveys from 5 adult participants. Each participant contributed between 0.8 and 2.7 million screens. Six media use metrics were derived from smartphone metadata. Fortnightly surveys captured symptoms of depression, attention-deficit/hyperactivity disorder, state anxiety, and positive affect. Idiographic filter models (p-technique canonical correlation analyses) were applied to explore person-specific associations.

RESULTS

Canonical correlations revealed substantial person-specific associations between media use and mental health, ranging from r=0.82 (P=.008) to r=0.92 (P=.03). The specific combinations of media use metrics and mental health dimensions were different for each person, reflecting significant individual variability. For instance, the media use canonical variate for 1 participant was characterized by higher loadings for app-switching, which, in combination with other behaviors, correlated strongly with a mental health variate emphasizing anxiety symptoms. For another, prolonged screen time, alongside other media use behaviors, contributed to a mental health variate weighted more heavily toward depression symptoms. These within-person correlations are among the strongest reported in this literature.

CONCLUSIONS

Results suggest that the relationships between media use and mental health are highly individualized, with implications for the development of personalized models and precision smartphone-informed interventions in mental health. We discuss how our approach can be extended generally, while still emphasizing the importance of idiographic approaches. This study highlights the potential for granular, longitudinal data to reveal person-specific patterns that can inform theory development, personalized screening, diagnosis, and interventions in mental health.

Understanding the relationship between childhood abuse and affective symptoms in bipolar disorder: New insights from a network analysis

The impact of childhood abuse on the presentation of bipolar disorder could be further elucidated by comparing the networks of affective symptoms among individuals with and with no history of childhood abuse. Data from 476 participants in the Clinical Health Outcomes Initiative in Comparative Effectiveness for Bipolar Disorder study were used to fit several regularised Gaussian Graphical Models. Differences in the presentation of depressive and manic symptoms were uncovered: only among participants with a history of childhood abuse, inadequacy and pessimism were central symptoms in the network of depressive symptoms, while racing thoughts was an important symptom in the network of manic symptoms. Following network theory, focusing treatments at the symptom-level and on central symptoms - like inadequacy, pessimism, and racing thoughts - could be an effective approach for managing affective symptoms among the sizeable proportion of people with bipolar disorder who have experienced childhood abuse. This study contributes a thorough investigation of the networks of affective symptoms among participants with and with no history of childhood abuse, albeit limited by the use of a binary, self-report measure of childhood abuse, thereby emphasising the importance of assessing for childhood abuse and taking needed steps towards identifying novel targets for treating bipolar disorder.

Longitudinal hair cortisol in bipolar disorder and a mechanism based on HPA dynamics

Bipolar disorder (BD) is marked by fluctuating mood states over months to years, often with elevated cortisol levels. Elevated cortisol can also trigger mood episodes. Here, we combine longitudinal hair cortisol and mood measurements with mathematical modeling to provide a potential mechanistic link between cortisol and mood timescales in BD. Using 12 cm hair samples, representing a year of growth, we found enhanced year-scale cortisol fluctuations whose amplitude averaged 4-fold higher in BD (n = 26) participants than controls (n = 59). The proximal 2 cm of hair correlated with recent mood scores. Depression (n = 266) and mania (n = 273) scores from a longitudinal study of BD showed similar frequency spectra. These results suggest a mechanism for BD in which high emotional reactivity excites the slow timescales in the hypothalamic-pituitary-adrenal (HPA) axis to generate elevated months-scale cortisol fluctuations, triggering cortisol-induced mood episodes.

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.

Essential data dimensions for prospective international data collection in older age bipolar disorder (OABD): Recommendations from the GAGE-BD group

BACKGROUND

By 2030, over 50% of individuals living with bipolar disorder (BD) are expected to be aged ≥50 years. However, older age bipolar disorder (OABD) remains understudied. There are limited large-scale prospectively collected data organized in key dimensions capable of addressing several fundamental questions about BD affecting this subgroup of patients.

METHODS

We developed initial recommendations for the essential dimensions for OABD data collection, based on (1) a systematic review of measures used in OABD studies, (2) a Delphi consensus of international OABD experts, (3) experience with harmonizing OABD data in the Global Aging & Geriatric Experiments in Bipolar Disorder Database (GAGE-BD, n ≥ 4500 participants), and (4) critical feedback from 34 global experts in geriatric mental health.

RESULTS

We identified 15 key dimensions and variables within each that are relevant for the investigation of OABD: (1) demographics, (2) core symptoms of depression and (3) mania, (4) cognition screening and subjective cognitive function, (5) elements for BD diagnosis, (6) descriptors of course of illness, (7) treatment, (8) suicidality, (9) current medication, (10) psychiatric comorbidity, (11) psychotic symptoms, (12) general medical comorbidities, (13) functioning, (14) family history, and (15) other. We also recommend particular instruments for capturing some of the dimensions and variables.

CONCLUSION

The essential data dimensions we present should be of use to guide future international data collection in OABD and clinical practice. In the longer term, we aim to establish a prospective consortium using this core set of dimensions and associated variables to answer research questions relevant to OABD.

Circadian biology to advance therapeutics for mood disorders

Mood disorders account for a significant global disease burden, and pharmacological innovation is needed as existing medications are suboptimal. A wide range of evidence implicates circadian and sleep dysfunction in the pathogenesis of mood disorders, and there is growing interest in these chronobiological pathways as a focus for treatment innovation. We review contemporary evidence in three promising areas in circadian-clock-based therapeutics in mood disorders: targeting the circadian system informed by mechanistic molecular advances; time-tailoring of medications; and personalizing treatment using circadian parameters. We also consider the limitations and challenges in accelerating the development of new circadian-informed pharmacotherapies for mood disorders.

3D printing combined with biopredictive dissolution and PBPK/PD modeling optimization and personalization of pharmacotherapy: Are we there yet?

Precision medicine requires selecting the appropriate dosage regimen for a patient using the right drug, at the right time. Model-Informed Precision Dosing (MIPD) is a concept suggesting utilization of model-based prediction methods for optimizing the treatment benefit-harm balance, based on individual characteristics of the patient, disease, treatment method, and other factors. Here, we discuss a theoretical workflow comprising several elements, beginning from the physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models, through 3D printed tablets with the model proposed dose, information range and flow, and the patient themselves. We also describe each of these elements, and the connection between them, highlighting challenges and potential obstacles.

Childhood trauma and depressive symptoms in bipolar disorder: A network analysis

BACKGROUND

Childhood trauma is related to an increased number of depressive episodes and more severe depressive symptoms in bipolar disorder. The evaluation of the networks of depressive symptoms-or the patterns of relationships between individual symptoms-among people with bipolar disorder with and without a history of childhood trauma may assist in further clarifying this complex relationship.

METHODS

Data from over 500 participants from the Heinz C. Prechter Longitudinal Study of Bipolar Disorder were used to construct a series of regularised Gaussian Graphical Models. The networks of individual depressive symptoms-self-reported (Patient Health Questionnaire-9; n = 543) and clinician-rated (Hamilton Depression Rating Scale-17; n = 529)-among participants with bipolar disorder with and without a history of childhood trauma (Childhood Trauma Questionnaire) were characterised and compared.

RESULTS

Across the sets of networks, depressed mood consistently emerged as a central symptom (as indicated by strength centrality and expected influence); regardless of participants' history of childhood trauma. Additionally, feelings of worthlessness emerged as a key symptom in the network of self-reported depressive symptoms among participants with-but not without-a history of childhood trauma.

CONCLUSION

The present analyses-although exploratory-provide nuanced insights into the impact of childhood trauma on the presentation of depressive symptoms in bipolar disorder, which have the potential to aid detection and inform targeted intervention development.

A critical evaluation of dynamical systems models of bipolar disorder

Bipolar disorder (BD) is a mood disorder involving recurring (hypo)manic and depressive episodes. The inherently temporal nature of BD has inspired its conceptualization using dynamical systems theory, which is a mathematical framework for understanding systems that evolve over time. In this paper, we provide a critical review of the dynamical systems models of BD. Owing to the heterogeneity of methodological and experimental designs in computational modeling, we designed a structured approach that parallels the appraisal of animal models by their face, predictive, and construct validity. This tool, the validity appraisal guide for computational models (VAG-CM), is not an absolute measure of validity, but rather a guide for a more objective appraisal of models in this review. We identified 26 studies published before November 18, 2021 that proposed generative dynamical systems models of time-varying signals in BD. Two raters independently applied the VAG-CM to the included studies, obtaining a mean Cohen's κ of 0.55 (95% CI [0.45, 0.64]) prior to establishing consensus ratings. Consensus VAG-CM ratings revealed three model/study clusters: data-driven models with face validity, theory-driven models with predictive validity, and theory-driven models lacking all forms of validity. We conclude that future modeling studies should employ a hybrid approach that first operationalizes BD features of interest using empirical data to achieve face validity, followed by explanations of those features using generative models with components that are homologous to physiological or psychological systems involved in BD, to achieve construct validity. Such models would be best developed alongside long-term prospective cohort studies involving a collection of multimodal time-series data. We also encourage future studies to extend, modify, and evaluate the VAG-CM approach for a wider breadth of computational modeling studies and psychiatric disorders.