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

Frye MA, McElroy SL, Fuentes M, Sutor B, Schak KM, Galardy CW, Palmer BA, Prieto ML, Kung S, Sola CL, Ryu E, Veldic M, Geske J, Cuellar-Barboza A, Seymour LR, Mori N, Crowe S, Rummans TA, Biernacka JM - Int J Bipolar Disord (2015)

Bottom Line: 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 %).Quantifying phenotypic patterns of illness beyond bipolar subtype can provide more detailed clinical disease characteristics for biomarker research, including genomic-risk studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry and Psychology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA, mfrye@mayo.edu.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

Body mass index of the participants (n = 1257)
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Fig1: Body mass index of the participants (n = 1257)

Mentions: Medical comorbid conditions were substantial. The total mean (SD) CIRS was 4.1 (3.6) with subsection mean scores as follows: cardiac, 1.2 (0.6); hypertension, 1.4 (0.7); vascular, 1.2 (0.5); respiratory, 1.4 (0.7); eyes, ears, nose, throat, 1.4 (0.7); lower gastrointestinal, 1.3 (0.6); upper gastrointestinal, 1.4 (0.7); hepatic, 1.1 (0.3); renal, 1.1 (0.4); other genitourinary, 1.3 (0.6); musculoskeletal, 1.5 (0.8); neurologic, 1.6 (0.8); and endocrine-metabolic, 1.5 (0.8). A high medical burden (total score ≥4) was present in 47.7 % of participants. A total of 539 participants (42.9 % of 1257 with data available) met the criteria for obesity, with a BMI of 30 kg/m2 or higher (Fig. 1).Fig. 1


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

Frye MA, McElroy SL, Fuentes M, Sutor B, Schak KM, Galardy CW, Palmer BA, Prieto ML, Kung S, Sola CL, Ryu E, Veldic M, Geske J, Cuellar-Barboza A, Seymour LR, Mori N, Crowe S, Rummans TA, Biernacka JM - Int J Bipolar Disord (2015)

Body mass index of the participants (n = 1257)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4478187&req=5

Fig1: Body mass index of the participants (n = 1257)
Mentions: Medical comorbid conditions were substantial. The total mean (SD) CIRS was 4.1 (3.6) with subsection mean scores as follows: cardiac, 1.2 (0.6); hypertension, 1.4 (0.7); vascular, 1.2 (0.5); respiratory, 1.4 (0.7); eyes, ears, nose, throat, 1.4 (0.7); lower gastrointestinal, 1.3 (0.6); upper gastrointestinal, 1.4 (0.7); hepatic, 1.1 (0.3); renal, 1.1 (0.4); other genitourinary, 1.3 (0.6); musculoskeletal, 1.5 (0.8); neurologic, 1.6 (0.8); and endocrine-metabolic, 1.5 (0.8). A high medical burden (total score ≥4) was present in 47.7 % of participants. A total of 539 participants (42.9 % of 1257 with data available) met the criteria for obesity, with a BMI of 30 kg/m2 or higher (Fig. 1).Fig. 1

Bottom Line: 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 %).Quantifying phenotypic patterns of illness beyond bipolar subtype can provide more detailed clinical disease characteristics for biomarker research, including genomic-risk studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry and Psychology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA, mfrye@mayo.edu.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus