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Development of a bipolar disorder biobank: differential phenotyping for subsequent biomarker analyses
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 fi...
Autores principales: | , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4478187/ https://www.ncbi.nlm.nih.gov/pubmed/26105627 http://dx.doi.org/10.1186/s40345-015-0030-4 |
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author | Frye, Mark A McElroy, Susan L Fuentes, Manuel Sutor, Bruce Schak, Kathryn M Galardy, Christine W Palmer, Brian A Prieto, Miguel L Kung, Simon Sola, Christopher L Ryu, Euijung Veldic, Marin Geske, Jennifer Cuellar-Barboza, Alfredo Seymour, Lisa R Mori, Nicole Crowe, Scott Rummans, Teresa A Biernacka, Joanna M |
author_facet | Frye, Mark A McElroy, Susan L Fuentes, Manuel Sutor, Bruce Schak, Kathryn M Galardy, Christine W Palmer, Brian A Prieto, Miguel L Kung, Simon Sola, Christopher L Ryu, Euijung Veldic, Marin Geske, Jennifer Cuellar-Barboza, Alfredo Seymour, Lisa R Mori, Nicole Crowe, Scott Rummans, Teresa A Biernacka, Joanna M |
author_sort | Frye, Mark A |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4478187 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-44781872015-06-25 Development of a bipolar disorder biobank: differential phenotyping for subsequent biomarker analyses Frye, Mark A McElroy, Susan L Fuentes, Manuel Sutor, Bruce Schak, Kathryn M Galardy, Christine W Palmer, Brian A Prieto, Miguel L Kung, Simon Sola, Christopher L Ryu, Euijung Veldic, Marin Geske, Jennifer Cuellar-Barboza, Alfredo Seymour, Lisa R Mori, Nicole Crowe, Scott Rummans, Teresa A Biernacka, Joanna M Int J Bipolar Disord Research 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. Springer Berlin Heidelberg 2015-06-24 /pmc/articles/PMC4478187/ /pubmed/26105627 http://dx.doi.org/10.1186/s40345-015-0030-4 Text en © Frye et al. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. |
spellingShingle | Research Frye, Mark A McElroy, Susan L Fuentes, Manuel Sutor, Bruce Schak, Kathryn M Galardy, Christine W Palmer, Brian A Prieto, Miguel L Kung, Simon Sola, Christopher L Ryu, Euijung Veldic, Marin Geske, Jennifer Cuellar-Barboza, Alfredo Seymour, Lisa R Mori, Nicole Crowe, Scott Rummans, Teresa A Biernacka, Joanna M Development of a bipolar disorder biobank: differential phenotyping for subsequent biomarker analyses |
title | Development of a bipolar disorder biobank: differential phenotyping for subsequent biomarker analyses |
title_full | Development of a bipolar disorder biobank: differential phenotyping for subsequent biomarker analyses |
title_fullStr | Development of a bipolar disorder biobank: differential phenotyping for subsequent biomarker analyses |
title_full_unstemmed | Development of a bipolar disorder biobank: differential phenotyping for subsequent biomarker analyses |
title_short | Development of a bipolar disorder biobank: differential phenotyping for subsequent biomarker analyses |
title_sort | development of a bipolar disorder biobank: differential phenotyping for subsequent biomarker analyses |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4478187/ https://www.ncbi.nlm.nih.gov/pubmed/26105627 http://dx.doi.org/10.1186/s40345-015-0030-4 |
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