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Advancing biomarker research: utilizing ‘Big Data’ approaches for the characterization and prevention of bipolar disorder

Authors

  • Roger S McIntyre,

    Corresponding author
    1. Department of Psychiatry, University of Toronto, Toronto, ON, Canada
    2. Department of Pharmacology, University of Toronto, Toronto, ON, Canada
    3. Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
    4. Institute of Medical Science, University of Toronto, Toronto, ON, Canada
    • Corresponding author:

      Roger S. McIntyre, M.D., F.R.C.P.C.

      Mood Disorders Psychopharmacology Unit

      Department of Psychiatry and Pharmacology

      University of Toronto

      University Health Network

      399 Bathurst Street

      Toronto

      ON M5T 2S8

      Canada

      Fax: 416-603-5368

      E-mail: roger.mcintyre@uhn.on.ca

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  • Danielle S Cha,

    1. Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
    2. Institute of Medical Science, University of Toronto, Toronto, ON, Canada
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  • Jeanette M Jerrell,

    1. Department of Neuropsychiatry, University of South Carolina, School of Medicine, Columbia, SC, USA
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  • Walter Swardfager,

    1. Sunnybrook Research Institute, Toronto, ON, Canada
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  • Rachael D Kim,

    1. Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
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  • Leonardo G Costa,

    1. Psychiatry Resident's Hospital de Clinicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
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  • Anusha Baskaran,

    1. Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
    2. Centre for Neuroscience Studies, Queen's University, Kingston, Canada
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  • Joanna K Soczynska,

    1. Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
    2. Institute of Medical Science, University of Toronto, Toronto, ON, Canada
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  • Hanna O Woldeyohannes,

    1. Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
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  • Rodrigo B Mansur,

    1. Program for Recognition and Intervention in Individuals in At-risk Mental States, Department of Psychiatry, Universidade Federal de São Paulo, São Paulo
    2. Interdisciplinary Laboratory of Clinical Neurosciences, Department of Psychiatry, Universidade Federal de São Paulo, São Paulo, Brazil
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  • Elisa Brietzke,

    1. Program for Recognition and Intervention in Individuals in At-risk Mental States, Department of Psychiatry, Universidade Federal de São Paulo, São Paulo
    2. Interdisciplinary Laboratory of Clinical Neurosciences, Department of Psychiatry, Universidade Federal de São Paulo, São Paulo, Brazil
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  • Alissa M Powell,

    1. Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
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  • Ashley Gallaugher,

    1. Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
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  • Paul Kudlow,

    1. Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
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  • Oksana Kaidanovich-Beilin,

    1. Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
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  • Mohammad Alsuwaidan

    1. Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
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Abstract

Objective

To provide a strategic framework for the prevention of bipolar disorder (BD) that incorporates a ‘Big Data’ approach to risk assessment for BD.

Methods

Computerized databases (e.g., Pubmed, PsychInfo, and MedlinePlus) were used to access English-language articles published between 1966 and 2012 with the search terms bipolar disorder, prodrome, ‘Big Data’, and biomarkers cross-referenced with genomics/genetics, transcriptomics, proteomics, metabolomics, inflammation, oxidative stress, neurotrophic factors, cytokines, cognition, neurocognition, and neuroimaging. Papers were selected from the initial search if the primary outcome(s) of interest was (were) categorized in any of the following domains: (i) ‘omics’ (e.g., genomics), (ii) molecular, (iii) neuroimaging, and (iv) neurocognitive.

Results

The current strategic approach to identifying individuals at risk for BD, with an emphasis on phenotypic information and family history, has insufficient predictive validity and is clinically inadequate. The heterogeneous clinical presentation of BD, as well as its pathoetiological complexity, suggests that it is unlikely that a single biomarker (or an exclusive biomarker approach) will sufficiently augment currently inadequate phenotypic-centric prediction models. We propose a ‘Big Data’- bioinformatics approach that integrates vast and complex phenotypic, anamnestic, behavioral, family, and personal ‘omics’ profiling. Bioinformatic processing approaches, utilizing cloud- and grid-enabled computing, are now capable of analyzing data on the order of tera-, peta-, and exabytes, providing hitherto unheard of opportunities to fundamentally revolutionize how psychiatric disorders are predicted, prevented, and treated. High-throughput networks dedicated to research on, and the treatment of, BD, integrating both adult and younger populations, will be essential to sufficiently enroll adequate samples of individuals across the neurodevelopmental trajectory in studies to enable the characterization and prevention of this heterogeneous disorder.

Conclusions

Advances in bioinformatics using a ‘Big Data’ approach provide an opportunity for novel insights regarding the pathoetiology of BD. The coordinated integration of research centers, inclusive of mixed-age populations, is a promising strategic direction for advancing this line of neuropsychiatric research.

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