The mayo clinic biobank: a building block for individualized medicine.
ABSTRACT To report the design and implementation of the first 3 years of enrollment of the Mayo Clinic Biobank.
Preparations for this biobank began with a 4-day Deliberative Community Engagement with local residents to obtain community input into the design and governance of the biobank. Recruitment, which began in April 2009, is ongoing, with a target goal of 50,000. Any Mayo Clinic patient who is 18 years or older, able to consent, and a US resident is eligible to participate. Each participant completes a health history questionnaire, provides a blood sample, and allows access to existing tissue specimens and all data from their Mayo Clinic electronic medical record. A community advisory board provides ongoing advice and guidance on complex decisions.
After 3 years of recruitment, 21,736 individuals have enrolled. Fifty-eight percent (12,498) of participants are female and 95% (20,541) of European ancestry. Median participant age is 62 years. Seventy-four percent (16,171) live in Minnesota, with 42% (9157) from Olmsted County, where the Mayo Clinic in Rochester, Minnesota, is located. The 5 most commonly self-reported conditions are hyperlipidemia (8979, 41%), hypertension (8174, 38%), osteoarthritis (6448, 30%), any cancer (6224, 29%), and gastroesophageal reflux disease (5669, 26%). Among patients with self-reported cancer, the 5 most common types are nonmelanoma skin cancer (2950, 14%), prostate cancer (1107, 12% in men), breast cancer (941, 4%), melanoma (692, 3%), and cervical cancer (240, 2% in women). Fifty-six percent (12,115) of participants have at least 15 years of electronic medical record history. To date, more than 60 projects and more than 69,000 samples have been approved for use.
The Mayo Clinic Biobank has quickly been established as a valuable resource for researchers.
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ABSTRACT: To evaluate the participants in the Mayo Clinic Biobank for their representativeness to the entire Employee and Community Health program (ECH) primary care population with regard to hospital utilization. Participants enrolled in the Mayo Clinic Biobank from April 1, 2009, to December 31, 2010, were linked to the ECH population. These individuals were categorized into risk tiers (0-4) on the basis of the number of health conditions present as of December 31, 2010. Outcomes were ascertained through December 31, 2011. Hazard ratios (HRs) and 95% CIs for risk of hospitalization, emergency department (ED) visits, and for risk of hospitalization and emergency department (ED) visits were estimated. The 8927 Biobank participants were part of ECH (N=84,872). Compared with the entire ECH population, the Biobank-ECH participants were more likely to be female (64.3% vs 54.6%), older (median age, 58 years vs 47 years), and categorized to tier 0 (6.4% vs 24.0%). There were strong positive associations between tier (tier 4 vs combined tiers 0 and 1) and risk of hospitalization (HR, 5.8; 95% CI, 4.6-7.5) and ED visits (HR, 5.4; 95% CI, 4.2-6.8) among Biobank-ECH participants. Similar associations for risk of hospitalization (HR, 8.5; 95% CI, 7.8-9.3) and ED visits (HR, 6.9; 95% CI, 6.4-7.5) were observed for the entire ECH population. Although the Biobank-ECH participants were older and had more chronic conditions compared with the overall ECH population, the associations of risk tier with utilization outcomes were similar, supporting the use of the Biobank participants to assess biomarkers for health care outcomes in the primary care setting.Mayo Clinic Proceedings 09/2013; 88(9):963-9. DOI:10.1016/j.mayocp.2013.06.015 · 5.81 Impact Factor
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ABSTRACT: To report the design and implementation of the Right Drug, Right Dose, Right Time-Using Genomic Data to Individualize Treatment protocol that was developed to test the concept that prescribers can deliver genome-guided therapy at the point of care by using preemptive pharmacogenomics (PGx) data and clinical decision support (CDS) integrated into the electronic medical record (EMR). We used a multivariate prediction model to identify patients with a high risk of initiating statin therapy within 3 years. The model was used to target a study cohort most likely to benefit from preemptive PGx testing among the Mayo Clinic Biobank participants, with a recruitment goal of 1000 patients. We used a Cox proportional hazards model with variables selected through the Lasso shrinkage method. An operational CDS model was adapted to implement PGx rules within the EMR. The prediction model included age, sex, race, and 6 chronic diseases categorized by the Clinical Classifications Software for International Classification of Diseases, Ninth Revision codes (dyslipidemia, diabetes, peripheral atherosclerosis, disease of the blood-forming organs, coronary atherosclerosis and other heart diseases, and hypertension). Of the 2000 Biobank participants invited, 1013 (51%) provided blood samples, 256 (13%) declined participation, 555 (28%) did not respond, and 176 (9%) consented but did not provide a blood sample within the recruitment window (October 4, 2012, through March 20, 2013). Preemptive PGx testing included CYP2D6 genotyping and targeted sequencing of 84 PGx genes. Synchronous real-time CDS was integrated into the EMR and flagged potential patient-specific drug-gene interactions and provided therapeutic guidance. This translational project provides an opportunity to begin to evaluate the impact of preemptive sequencing and EMR-driven genome-guided therapy. These interventions will improve understanding and implementation of genomic data in clinical practice.Mayo Clinic Proceedings 01/2014; 89(1):25-33. DOI:10.1016/j.mayocp.2013.10.021 · 5.81 Impact Factor
Article: Biobanks and personalized medicine[Show abstract] [Hide abstract]
ABSTRACT: We provide a mini-review of how biobanks can support clinical genetics in the era of personalized medicine. We discuss types of biobanks, including disease specific and general biobanks not focused on one disease. We present considerations in setting up a biobank, including consenting and governance, biospecimens, risk factor and related data, informatics, and linkage to electronic health records for phenotyping. We also discuss the uses of biobanks and ongoing considerations, including genotype driven recruitment, investigations of gene-environment associations, and the re-use of data generated from studies. Finally, we present a brief discussion of some of the unresolved issues, such as return of research results and sustaining biobanks over time. In summary, carefully designed biobanks can provide critical research and infrastructure support for clinical genetics in the era of personalized medicine.Clinical Genetics 03/2014; 86(1). DOI:10.1111/cge.12370 · 3.65 Impact Factor