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The Capital Region of Denmark - a part of Copenhagen University Hospital

Translating big data to better treatment in bipolar disorder - a manifesto for coordinated action

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  • European College of Neuropsychopharmacology (ECNP) Bipolar Disorders Network
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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.

Original languageEnglish
JournalEuropean neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology
Pages (from-to)121-136
Number of pages16
Publication statusPublished - 1 Jul 2020

    Research areas

  • Machine learning, Open science, Philanthropy, Precision medicine, Risk prediction

ID: 60949799