Development of a XXXX biobank: differential phenotyping for subsequent biomarker analyses.

@entity1 @entity162 @entity28 @entity1910 @entity1239 @entity910 @entity716 @entity9022 @entity285 @entity221
BACKGROUND: We aimed to establish a @entity162 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 @entity1 enrolled. METHODS: @entity1 were recruited for the Mayo Clinic Bipolar Biobank beginning in July 2009. The Structured Clinical Interview for DSM-IV was used to confirm @entity162 . The Bipolar Biobank Clinical Questionnaire and @entity1 Questionnaire were designed to collect detailed demographic and clinical data, including clinical @entity221 measures that would delineate differential phenotypes for subsequent analyses. Blood specimens were obtained from @entity1 , and various aliquots were stored for future research. RESULTS: As of September 2014, 1363 @entity1 have been enrolled in the bipolar biobank. Among these first @entity1 , 69.0  % had a diagnosis of @entity162 type I. The group was 60.2  % @entity1 and predominantly white (90.6  %), with a mean (SD) age of 42.6 (14.9) years. Clinical phenotypes of the group included history of @entity716 (42.3  %), suicide attempt (32.5  %), addiction to @entity285 (39.1  %), addiction to @entity1239 (39.8  %), @entity28 (42.9  %), @entity9022 -induced @entity162 (31.7  %), @entity910 (3.2  %), and history of drug-related serious @entity1910 (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 @entity1 with @entity162 .
