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

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

Author McIntyre, Roger S. Google Scholar
Cha, Danielle S. Google Scholar
Jerrell, Jeanette M. Google Scholar
Swardfager, Walter Google Scholar
Kim, Rachael D. Google Scholar
Costa, Leonardo G. Google Scholar
Baskaran, Anusha Google Scholar
Soczynska, Joanna K. Google Scholar
Woldeyohannes, Hanna O. Google Scholar
Mansur, Rodrigo B. Autor UNIFESP Google Scholar
Brietzke, Elisa Autor UNIFESP Google Scholar
Powell, Alissa M. Google Scholar
Gallaugher, Ashley Google Scholar
Kudlow, Paul Google Scholar
Kaidanovich-Beilin, Oksana Google Scholar
Alsuwaidan, Mohammad Google Scholar
Institution Univ Toronto
Univ Hlth Network
Univ S Carolina
Sunnybrook Res Inst
Univ Fed Rio Grande do Sul
Queens Univ
Universidade Federal de São Paulo (UNIFESP)
Mt Sinai Hosp
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.
Keywords Big Data
bipolar disorder
integrated profiles
prediction prodrome
Language English
Sponsor Stanley Medical Research Institute
National Alliance for Research on Schizophrenia and Depression
National Institute of Mental Health
Bristol-Myers Squibb
Eli Lilly Co.
National Institute of Health
Neuropsychopharmacology Research Group
Sunnybrook Research Institute
Toronto Rehabilitation Institute
Heart and Stroke Foundation Centre for Stroke Recovery
Eli Lilly Fellowship Award
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Date 2014-08-01
Published in Bipolar Disorders. Hoboken: Wiley-Blackwell, v. 16, n. 5, p. 531-547, 2014.
ISSN 1398-5647 (Sherpa/Romeo, impact factor)
Publisher Wiley-Blackwell
Extent 531-547
Access rights Closed access
Type Review
Web of Science ID WOS:000340381500008

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