Abstract

Bipolar disorder (BD) is a major healthcare and socio-economic challenge. Despite its substantial burden on society, the research activity in BD is much smaller than its economic impact appears to demand. There is a consensus that the accurate identification of the underlying pathophysiology for BD is fundamental to realize major health benefits through better treatment and preventive regimens. However, to achieve these goals requires coordinated action and innovative approaches to boost the discovery of the neurobiological underpinnings of BD, and rapid translation of research findings into development and testing of better and more specific treatments. To this end, we here propose that only a large-scale coordinated action can be successful in integrating international big-data approaches with real-world clinical interventions. This could be achieved through the creation of a Global Bipolar Disorder Foundation, which could bring government, industry and philanthropy together in common cause. A global initiative for BD research would come at a highly opportune time given the seminal advances promised for our understanding of the genetic and brain basis of the disease and the obvious areas of unmet clinical need. Such an endeavour would embrace the principles of open science and see the strong involvement of user groups and integration of dissemination and public involvement with the research programs. We believe the time is right for a step change in our approach to understanding, treating and even preventing BD effectively.

Highlights

  • Bipolar disorder (BD) has been classically depicted as the presence of alternating episodes of mood disturbances of opposite polarity interspersed with periods of well-being (Grande et al, 2016)

  • Dissemination of research findings as well as of clinical guidelines, should be available for patients, as recently exemplified for individuals with BD treated with lithium (Tondo et al, 2019)

  • Future multi-site brain-imaging machine learning studies are moving beyond the use of engineered brain features or site-level results, and towards sharing of raw, individual subject data, where unsupervised machine learning techniques may offer potential to better stratify the heterogeneity in this complex disorder

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Summary

Introduction

Bipolar disorder (BD) has been classically depicted as the presence of alternating episodes of mood disturbances of opposite polarity (hypomania/mania versus depression) interspersed with periods of well-being ( defined as free intervals) (Grande et al, 2016). Only a longitudinal perspective can fully account for the suffering endured by patients and their relatives, and the substantial socioeconomic burden of BD: a recent prospective study of individuals with a BD diagnosed in youth, showed the significantly worse psychosocial functioning when mood symptoms were persistent (i.e. a chronic course) (Hower et al, 2019; Scott et al, 2014) These features translate into the high socio-economic costs and decreased quality of life associated with BD (Cloutier et al, 2018; Gustavsson et al, 2011). The goal is to obtain unique synergy by providing relevant clinical data for new analytical approaches and integrate big data results in a clinical trial network for development of personalized treatment regimens This has been made possible by recent technical developments allowing high throughput, large scale genetic and brain imaging data collection, as well as novel clinical information communication technology (ICT) tools for efficient, user based clinical assessments

Role of the patient community perspective
Causal factors – the ‘polygenic architecture’ and interplay with environment
Large-scale brain imaging phenotyping
Transforming big data discoveries to clinically relevant information
Smaller studies focussing on shorter term effects and clinical trials
Moving forward - global bipolar disorder foundation
Building an infrastructure and database
User involvement and dissemination
Open science
A roadmap to the global bipolar disorder foundation
10. Conclusion
Conflict of Interest
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