Characteristics and utilisation of the Mayo Clinic Biobank, a clinic-based prospective collection in the USA: cohort profile

Olson, Janet E.; Ryu, Euijung; Hathcock, Matthew; Bublitz, Joshua T.; Takahashi, Paul Y.; Bielinski, Suzette J.; Sauver, Jennifer L. St.; Meagher, Karen M.; Thibodeau, Stephen N.; Cicek, Mine S.; Cerhan, James R.

doi:10.1136/bmjopen-2019-032707

Cited by 39 publications

(35 citation statements)

References 27 publications

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“…This is a retrospective cohort study. All participants in this study were enrolled in the RIGHT expansion study as described previously by Bielinski et al 1 Briefly, adult subjects were selected from the Mayo Clinic Biobank (MCB) 25 for use of the Mayo Clinic for their healthcare and offered the opportunity to receive pharmacogenomic testing plus deposition of key results in the Mayo Clinic electronic health record (EHR). A total of 18,199 adult subjects were invited and 10,085 enrolled.…”

Section: Methodsmentioning

confidence: 99%

No Association Between Pharmacogenomics Variants and Hospital and Emergency Department Utilization: A Mayo Clinic Biobank Retrospective Study

Takahashi

Ryu

Bielinski

et al. 2021

PGPM

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Background The use of pharmacogenomics data is increasing in clinical practice. However, it is unknown if pharmacogenomics data can be used more broadly to predict outcomes like hospitalization or emergency department (ED) visit. We aim to determine the association between selected pharmacogenomics phenotypes and hospital utilization outcomes (hospitalization and ED visits). Methods This cohort study utilized 10,078 patients from the Mayo Clinic Biobank in the RIGHT protocol with sequence and interpreted phenotypes for 10 selected pharmacogenes including CYP2D6, CYP2C9, CYP2C19, CYP3A5, HLA B 5701, HLA B 5702, HLA B 5801, TPMT, SLCO1B1 , and DPYD . The primary outcome was hospitalization with ED visits as a secondary outcome. We used Cox proportional hazards model to test the association between each pharmacogenomics phenotype and the risk of the outcomes. Results During the follow-up period (median [in years] = 7.3), 13% (n=1354) and 8% (n=813) of the subjects experienced hospitalization and ED visits, respectively. Compared to subjects who did not experience hospitalization, hospitalized patients were older (median age [in years]: 67 vs 65), poorer self-rated health (15% vs 4.7% for fair/poor), and higher disease burden (median number of chronic conditions: 7 vs 4) at baseline. There was no association of hospitalization with any of the pharmacogenomics phenotypes. The pharmacogenomics phenotypes were not associated with disease burden, a well-established risk factor for hospital utilization outcomes. Similar findings were observed for patients with ED visits during the follow-up period. Conclusion We found no association of 10 well-established pharmacogenomics phenotypes with either hospitalization or ED visits in this relatively large biobank population and outside the context of specific drug use related to these genes. Traditional risk factors for hospitalization like age and self-rated health were much more likely to predict hospitalization and/or ED visits than this pharmacogenomics information.

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Section: Methodsmentioning

confidence: 99%

No Association Between Pharmacogenomics Variants and Hospital and Emergency Department Utilization: A Mayo Clinic Biobank Retrospective Study

Takahashi

Ryu

Bielinski

et al. 2021

PGPM

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“…Minimal eligibility criteria were required for the MCB: Mayo Clinic Patients who were 18 or older, current residents of the United States, and able to provide informed consent. Appointments were selected largely from departments that provided primary care (1). The initial recruitment phase identified Mayo Clinic patients through the clinic appointment database in Rochester, MN (MCR) starting in April, 2009.…”

Section: Particpants and Methodsmentioning

confidence: 99%

Characteristics Associated With Recruitment and Re-contact in Mayo Clinic Biobank

Hathcock

Kirt

Ryu

et al. 2020

Front. Public Health

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Objective: To better understand the characteristics associated with a participant's willingness to consent to the Mayo Clinic Biobank (MCB) and examine factors associated with willingness to participate in follow-up studies embedded within MCB that require re-contact and participant approval. Participants and Methods:Consent rates were compared across patient demographics to the MCB. Rates of participation to follow-up studies were also compared across demographics and request types.Results: Among 272,102 Mayo Clinic patients invited to the MCB, 48,314 (19%) consented across the three recruitment sites within 90 days of initial invitation. A significant age by gender interaction was identified, showing young males consent at a lower rate than young females and older males consent at a higher rate than older females. Over the recruitment time frame of 2009-2015, there was a significant decrease in consent rates (decline of 2.5%/year). Of the 57,041 consented MCB participants, 33,487 participants (59%) have been invited to participate in follow-up studies via re-contact. Follow-up studies of the MCB may require participants to provide additional samples, complete questionnaires, and/or release their identity to a research team. MCB participants have been invited to enroll in a median of two studies (IQR: 1-3). Seventy-one percent of participants consented to at least one follow-up study, with individual follow-up study consent rates ranging from 14 to 87% depending on study type, with a median consent rate of 61% (IQR: 47-70%). Studies requesting return of a questionnaire had the highest participation rates. White participants, older participants, and participants with some college or a degree were significantly more likely to participate to follow-up studies, while there was no association with gender.Conclusion: Consent rates among younger and non-white patients were lower than in older, white patients. However, we also found that participation rates among those Hathcock et al.Recruitment and Re-contact in Biobanking already enrolled in the biobank were much higher than those seen in new recruitment efforts, external to an existing biobank. We thus demonstrate an important way that biobanks can advance precision medicine goals: through provision of populations from which studies can draw participants for future studies.

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“…The membership of the Community Advisory Board reflects the sociocultural characteristics of the [Mayo Clinic] Biobank population. 14 In November 2017,…”

Section: Ethicsmentioning

confidence: 99%

“…Biobank contributor demographics include higher educational attainment and less ethnic diversity than the population in the surrounding upper Midwest community. 14 Cohorts with greater racial, ethnic, educational, and socioeconomic diversity might reveal other comprehension findings, including use of different terminology to make sense of their PGx results. Participation in the [Mayo Clinic] biobank might have given focus group participants greater familiarity with genetic research, affecting valuations.…”

Section: Strength Limitations and Future Directionsmentioning

confidence: 99%

Lay understandings of drug‐gene interactions: The right medication, the right dose, at the right time, but what are the right words?

Meagher

Finn

Curtis

et al. 2021

Clinical Translational Sci

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As pharmacogenomic (PGx) testing increases in popularity, lay concepts of druggene interactions set the stage for shared decision making in precision medicine.Few studies explore what recipients of PGx results think is happening in their bodies when a drug-gene interaction is discovered. To characterize biobank participants' understanding of PGx research results, we conducted a focus group study, which took place after PGx variants conferring increased risk of dihydropyrimidine dehydrogenase (DPD) deficiency were disclosed to biobank contributors. DPD deficiency confers an increased risk of adverse reaction to commonly used cancer chemotherapeutics. Ten focus groups were conducted, ranging from two to eight participants. Fifty-four individuals participated in focus groups. A framework approach was used for descriptive and explanatory analysis. Descriptive themes included participants' efforts to make sense of PGx findings as they related to: (1) health implications, (2) drugs, and (3) genetics. Explanatory analysis supplied a functional framework of how participant word choices can perform different purposes in PGx communication. Results bear three main implications for PGx research-related disclosure. First, participants' use of various terms suggest participants generally understanding their PGx results, including how positive PGx results differ from positive disease susceptibility genetic results. Second, PGx disclosure in biobanking can involve participant conflation of drug-gene interactions with allergies or other types of medical reactions. Third, the functional framework suggests a need to move beyond a deficit model of genetic literacy in PGx communication. Together, findings provide an initial evidence base for supporting bidirectional expert-recipient PGx results communication. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? Pharmacogenomic (PGx) testing is often the hallmark of personalized medicine, offering the ability to tailor prescriptions to specific patients. Although prior | 723 LAY UNDERSTANDINGS OF DRUG-GENE INTERACTIONS biobank community advisory board (CAB) members in Jacksonville, FL, and Rochester, MN, reviewed an initial draft of a proposal to disclose PGx results to CAB feedback informed revised. Revised disclosure materials are published elsewhere. 12 How to cite this article: Meagher KM, Stuttgen Finn K, Curtis SH, et al. Lay understandings of drug-gene interactions: The right medication, the right dose, at the right time, but what are the right words?

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Characteristics and utilisation of the Mayo Clinic Biobank, a clinic-based prospective collection in the USA: cohort profile

Cited by 39 publications

References 27 publications

No Association Between Pharmacogenomics Variants and Hospital and Emergency Department Utilization: A Mayo Clinic Biobank Retrospective Study

No Association Between Pharmacogenomics Variants and Hospital and Emergency Department Utilization: A Mayo Clinic Biobank Retrospective Study

Characteristics Associated With Recruitment and Re-contact in Mayo Clinic Biobank

Lay understandings of drug‐gene interactions: The right medication, the right dose, at the right time, but what are the right words?

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