Dataset Information

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

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.

SUBMITTER: Frye MA

PROVIDER: S-EPMC4478187 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Frye Mark A MA McElroy Susan L SL Fuentes Manuel M Sutor Bruce B Schak Kathryn M KM Galardy Christine W CW Palmer Brian A BA Prieto Miguel L ML Kung Simon S Sola Christopher L CL Ryu Euijung E Veldic Marin M Geske Jennifer J Cuellar-Barboza Alfredo A Seymour Lisa R LR Mori Nicole N Crowe Scott S Rummans Teresa A TA Biernacka Joanna M JM

International journal of bipolar disorders 20150624 1

<h4>Background</h4>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.<h4>Methods</h4>Patients were recruited for the Mayo Clinic Bipolar Biobank beginning in July 2009. The Structured Clinical Interview for DSM-IV was used to confirm bi ...[more]

PMID: 26105627

Similar Datasets

Project description:BackgroundPatients with bipolar spectrum disorders (BSDs) exhibit an increased risk of Parkinson's disease (PD).ObjectiveThe aim is to investigate whether a previous diagnosis of BSDs influences the phenotype of PD.MethodsOf 2660 PD patients followed for at least 6 years (6-27), 250 (BSD-PD) had BSDs, 6-20 years before PD diagnosis; 48%-43% had a PD or BSD family history, and 34 carried glucocerebrosidase (GBA) and Parkin (PRKN) mutations. The cohort was split into a subset of 213 BSD-PD patients, compared with 426 matched PD patients without BSDs, and a subset of 34 BSD-PD and 79 PD patients carrying GBA or PRKN mutations. Carriers of mutations absent in BSD-PD patients and of synuclein triplication were excluded. Structured clinical interviews and mood disorder questionnaires assessed BSDs. Linear mixed models evaluated the assessment scales over time. Thirteen BSD-PD patients underwent subthalamic nucleus deep brain stimulation (STN-DBS) and were compared with 27 matched STN-DBS-treated PD patients.ResultsCompared to PD patients, BSD-PD showed (1) higher frequency of family history of PD (odds ratio [OR] 3.31; 2.32-4.71) and BSDs (OR 6.20; 4.11-9.35) 5); (2) higher incidence of impulse control disorders (hazard ratio [HR] 5.95, 3.89-9.09); (3) higher frequency of functional disorders occurring before PD therapy (HR, 5.67, 3.95-8.15); (4) earlier occurrence of delusions or mild dementia (HR, 7.70, 5.55-10.69; HR, 1.43, 1.16-1.75); and (5) earlier mortality (1.48; 1.11-1.97). Genetic BSD-PD subjects exhibited clinical features indistinguishable from nongenetic BSD-PD subjects. STN-DBS-treated BSD-PD patients showed no improvements in quality of life compared to the control group.ConclusionsBSDs as a prodrome to PD unfavorably shape their course and are associated with detrimental neuropsychiatric features and treatment outcomes. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Project description:There are currently no biological tests that differentiate patients with bipolar disorder (BPD) from healthy controls. While there is evidence that peripheral gene expression differences between patients and controls can be utilized as biomarkers for psychiatric illness, it is unclear whether current use or residual effects of antipsychotic and mood stabilizer medication drives much of the differential transcription. We therefore tested whether expression changes in first-episode, never-medicated BPD patients, can contribute to a biological classifier that is less influenced by medication and could potentially form a practicable biomarker assay for BPD. We employed microarray technology to measure global leukocyte gene expression in first-episode (n=3) and currently medicated BPD patients (n=26), and matched healthy controls (n=25). Following an initial feature selection of the microarray data, we developed a cross-validated 10-gene model that was able to correctly predict the diagnostic group of the training sample (26 medicated patients and 12 controls), with 89% sensitivity and 75% specificity (p<0.001). The 10-gene predictor was further explored via testing on an independent cohort consisting of three pairs of monozygotic twins discordant for BPD, plus the original enrichment sample cohort (the three never-medicated BPD patients and 13 matched control subjects), and a sample of experimental replicates (n=34). 83% of the independent test sample was correctly predicted, with a sensitivity of 67% and specificity of 100% (although this result did not reach statistical significance). Additionally, 88% of sample diagnostic classes were classified correctly for both the enrichment (p=0.015) and the replicate samples (p<0.001). We have developed a peripheral gene expression biomarker profile, that can classify healthy controls from patients with BPD receiving antipsychotic or mood stabilizing medication, which has both high sensitivity and specificity. Moreover, assay of three first-episode patients who had never received such medications, to first enrich the expression dataset for disease-related genes independent of medication effects, and then to test the 10-gene predictor, validates the peripheral biomarker approach for BPD.

Project description:Bipolar disorder (BD) is a heritable mood disorder characterized by episodes of mania and depression. Although genomewide association studies (GWAS) have successfully identified genetic loci contributing to BD risk, sample size has become a rate-limiting obstacle to genetic discovery. Electronic health records (EHRs) represent a vast but relatively untapped resource for high-throughput phenotyping. As part of the International Cohort Collection for Bipolar Disorder (ICCBD), we previously validated automated EHR-based phenotyping algorithms for BD against in-person diagnostic interviews (Castro et al. Am J Psychiatry 172:363-372, 2015). Here, we establish the genetic validity of these phenotypes by determining their genetic correlation with traditionally ascertained samples. Case and control algorithms were derived from structured and narrative text in the Partners Healthcare system comprising more than 4.6 million patients over 20 years. Genomewide genotype data for 3330 BD cases and 3952 controls of European ancestry were used to estimate SNP-based heritability (h2g) and genetic correlation (rg) between EHR-based phenotype definitions and traditionally ascertained BD cases in GWAS by the ICCBD and Psychiatric Genomics Consortium (PGC) using LD score regression. We evaluated BD cases identified using 4 EHR-based algorithms: an NLP-based algorithm (95-NLP) and three rule-based algorithms using codified EHR with decreasing levels of stringency-"coded-strict", "coded-broad", and "coded-broad based on a single clinical encounter" (coded-broad-SV). The analytic sample comprised 862 95-NLP, 1968 coded-strict, 2581 coded-broad, 408 coded-broad-SV BD cases, and 3 952 controls. The estimated h2g were 0.24 (p?=?0.015), 0.09 (p?=?0.064), 0.13 (p?=?0.003), 0.00 (p?=?0.591) for 95-NLP, coded-strict, coded-broad and coded-broad-SV BD, respectively. The h2g for all EHR-based cases combined except coded-broad-SV (excluded due to 0 h2g) was 0.12 (p?=?0.004). These h2g were lower or similar to the h2g observed by the ICCBD?+?PGCBD (0.23, p?=?3.17E-80, total N?=?33,181). However, the rg between ICCBD?+?PGCBD and the EHR-based cases were high for 95-NLP (0.66, p?=?3.69?×?10-5), coded-strict (1.00, p?=?2.40?×?10-4), and coded-broad (0.74, p?=?8.11?×?10-7). The rg between EHR-based BD definitions ranged from 0.90 to 0.98. These results provide the first genetic validation of automated EHR-based phenotyping for BD and suggest that this approach identifies cases that are highly genetically correlated with those ascertained through conventional methods. High throughput phenotyping using the large data resources available in EHRs represents a viable method for accelerating psychiatric genetic research.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets