Abstract

Background: Biobanks can accelerate research by providing researchers with samples and data. However, hospital-based recruitment as a source for controls may create bias as who comes to the hospital may be different from the broader population.Methods: In an effort to broadly improve the quality of research studies and reduce costs and challenges associated with recruitment and sample collection, a group of diverse researchers at Cincinnati Children's Hospital Medical Center led an institution-supported initiative to create a population representative pediatric “Greater Cincinnati Childhood Cohort (GCC).” Participants completed a detailed survey, underwent a brief physician-led physical exam, and provided blood, urine, and hair samples. DNA underwent high-throughput genotyping.Results: In total, 1,020 children ages 3–18 years living in the 7 county Greater Cincinnati Metropolitan region were recruited. Racial composition of the cohort was 84% non-Hispanic white, 15% non-Hispanic black, and 2% other race or Hispanic. Participants exhibited marked demographic and disease burden differences by race. Overall, the cohort was broadly used resulting in publications, grants and patents; yet, it did not meet the needs of all potential researchers.Conclusions: Learning from both the strengths and weaknesses, we propose leveraging a community-based participatory research framework for future broad use biobanking efforts.

Highlights

  • Biobanks allow storage of biological specimens and corresponding data for biomedical research, omics studies [1]

  • Inclusion criteria were as follows: participants were between the ages of 3 and 18 years at the time of enrollment living in Greater Cincinnati [Hamilton (OH), Clermont (OH), Butler (OH), Warren (OH), Boone (KY), Kenton (KY), Campbell (KY)]

  • Racial distribution of the Greater Cincinnati Childhood Cohort (GCC) were similar to Greater Cincinnati Metropolitan area and the United States (Supplementary Figure 1)

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Summary

Introduction

Biobanks allow storage of biological specimens and corresponding data for biomedical research, omics studies [1]. While providing convenient access to human samples, sample utility can be variable. Biobanks relying on residual sample may have limited sample types and accompanying data. Participants in hospital-based biobanks may not be population representative. These differences introduce possible confounding [2, 3]. Biobanks can accelerate research by providing researchers with samples and data. Hospital-based recruitment as a source for controls may create bias as who comes to the hospital may be different from the broader population

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