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)
Summary
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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