title: Advancing biomarker research: utilizing 'Big Data' approaches for the characterization and prevention of bipolar disorder.
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.
authors: Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Department of Pharmacology, University of Toronto, Toronto, ON, Canada; Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
authors: Roger S
authors: RS
authors: McIntyre
authors: Danielle S
authors: DS
authors: Cha
authors: Jeanette M
authors: JM
authors: Jerrell
authors: Walter
authors: W
authors: Swardfager
authors: Rachael D
authors: RD
authors: Kim
authors: Leonardo G
authors: LG
authors: Costa
authors: Anusha
authors: A
authors: Baskaran
authors: Joanna K
authors: JK
authors: Soczynska
authors: Hanna O
authors: HO
authors: Woldeyohannes
authors: Rodrigo B
authors: RB
authors: Mansur
authors: Elisa
authors: E
authors: Brietzke
authors: Alissa M
authors: AM
authors: Powell
authors: Ashley
authors: A
authors: Gallaugher
authors: Paul
authors: P
authors: Kudlow
authors: Oksana
authors: O
authors: Kaidanovich-Beilin
authors: Mohammad
authors: M
authors: Alsuwaidan
mesh_terms: D015415:Biomarkers; D035843:Biomedical Research; D001714:Bipolar Disorder / Q000175:diagnosis / Q000517:prevention & control / Q000523:psychology; D016208:Databases, Factual / Q000706:statistics & numerical data*; D006801:Humans
chemical_list: D015415:Biomarkers
keywords: Big Data; biomarkers; bipolar disorder; integrated profiles; prediction; prodrome