Development of a bipolar disorder biobank: differential phenotyping for subsequent biomarker analyses

Mark A. Frye*, Susan L. McElroy, Manuel Fuentes, Bruce Sutor, Kathryn M. Schak, Christine W. Galardy, Brian A. Palmer, Miguel L. Prieto, Simon Kung, Christopher L. Sola, Euijung Ryu, Marin Veldic, Jennifer Geske, Alfredo Cuellar-Barboza, Lisa R. Seymour, Nicole Mori, Scott Crowe, Teresa A. Rummans, Joanna M. Biernacka

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


Background: We aimed to establish a bipolar disorder biobank to serve as a resource for clinical and biomarker studies of disease risk and treatment response. Here, we describe the aims, design, infrastructure, and research uses of the biobank, along with demographics and clinical features of the first participants enrolled. Methods: Patients were recruited for the Mayo Clinic Bipolar Biobank beginning in July 2009. The Structured Clinical Interview for DSM-IV was used to confirm bipolar diagnosis. The Bipolar Biobank Clinical Questionnaire and Participant Questionnaire were designed to collect detailed demographic and clinical data, including clinical course of illness measures that would delineate differential phenotypes for subsequent analyses. Blood specimens were obtained from participants, and various aliquots were stored for future research. Results: As of September 2014, 1363 participants have been enrolled in the bipolar biobank. Among these first participants, 69.0 % had a diagnosis of bipolar disorder type I. The group was 60.2 % women and predominantly white (90.6 %), with a mean (SD) age of 42.6 (14.9) years. Clinical phenotypes of the group included history of psychosis (42.3 %), suicide attempt (32.5 %), addiction to alcohol (39.1 %), addiction to nicotine (39.8 %), obesity (42.9 %), antidepressant-induced mania (31.7 %), tardive dyskinesia (3.2 %), and history of drug-related serious rash (5.7 %). Conclusions: Quantifying phenotypic patterns of illness beyond bipolar subtype can provide more detailed clinical disease characteristics for biomarker research, including genomic-risk studies. Future research can harness clinically useful biomarkers using state-of-the-art research technology to help stage disease burden and better individualize treatment selection for patients with bipolar disorder.

Original languageEnglish
Article number14
JournalInternational Journal of Bipolar Disorders
Issue number1
StatePublished - 30 Dec 2015

Bibliographical note

Funding Information:
Funding for the study was provided by the Marriott Foundation. The foundation had no further role in the study design, analysis or interpretation of the data, in the writing of the report, or in the decision to submit the paper for publication.

Funding Information:
Dr. Frye has received grant support from Assurex Health, Myriad, Pfizer, National Institute of Mental Health (RO1 MH079261), National Institute of Alcohol Abuse and Alcoholism (P20AA017830), Mayo Foundation; has been a consultant to Janssen Global Services, LLC, Mitsubishi Tanabe Pharma Corporation, Myriad, Sunovion, and Teva Pharmaceuticals; has received CME/Travel Support/presentation from CME Outfitters Inc. and Sunovian; Mayo Clinic has a financial interest in AssureRx and the technology referenced in this publication/presentation.

Funding Information:
Dr. Prieto has received honoraria for speaker activities and development of educational presentations from GlaxoSmithKline, has received travel support from GlaxoSmithKline, Lilly, Lundbeck, and Pharmavita, has received research support from the Marriott Foundation and from Mayo Foundation for Medical Education and Research and has received scholarships from the Government of Chile.

Publisher Copyright:
© 2015, Frye et al.


  • Biobank
  • Bipolar disorder
  • Phenotype


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