title: How to optimize the use of biobanks from population-based cohorts in aging research.
abstract: In epidemiological cohorts, there is an increased interest for the implementation of biobanks. The potential role of biological determinants of diseases needs to be investigated before the onset of the event of interest in order to limit the problems encountered when examining biological determinants in classical case-control studies. Biobank is now a very sophisticated system that consists of a programmed storage of biological material and related data. Our aim in this paper is to document how biobank constitution is useful for studying biological determinants of aging and to give some indications on methodological issues that can be helpful to optimize the constitution and use of biobanks in aging cohorts. Optimization of sampling through two-phase designs (nested case control or case-cohort studies) allows better efficiency. These elements are, for most of them, not specific to aging populations but are useful more generally for the epidemiology of chronic diseases. Our purpose will be illustrated with some examples and results obtained in an ongoing aging cohort, the Three-City Study.
authors.affiliation: Inserm U1061, Hôpital La Colombière, 34093, Montpellier Cedex 5, France. claudine.berr@inserm.fr.|Montpellier University, 34000, Montpellier, France. claudine.berr@inserm.fr.|Department of Neurology, Memory Research and Resources Center, Hospital Gui de Chauliac, Montpellier University, 34295, Montpellier, France. claudine.berr@inserm.fr.
authors.forename: C
authors.identifier: http://orcid.org/0000-0001-5254-7655
authors.initials: C
authors.lastname: Berr
authors.affiliation: Montpellier University, 34000, Montpellier, France.|Department of Neurology, Memory Research and Resources Center, Hospital Gui de Chauliac, Montpellier University, 34295, Montpellier, France.|Inserm U1183, Saint Eloi Hospital, 34295, Montpellier, France.
authors.forename: A
authors.initials: A
authors.lastname: Gabelle
authors.affiliation: Institut Pasteur de Lille Laboratoire d'Analyse Génomique Inserm-U1167, 59019, Lille, France.
authors.forename: N
authors.initials: N
authors.lastname: Fievet
authors.affiliation: Inserm, Population-Based Epidemiologic Cohorts Unit, UMS 011, 94807, Villejuif, France.|Versailles St-Quentin University, UMS 011, 94807, Villejuif, France.
authors.forename: M
authors.initials: M
authors.lastname: Goldberg
authors.affiliation: Inserm, Population-Based Epidemiologic Cohorts Unit, UMS 011, 94807, Villejuif, France.|Versailles St-Quentin University, UMS 011, 94807, Villejuif, France.
authors.forename: M
authors.initials: M
authors.lastname: Zins
authors.affiliation: Inserm U1061, Hôpital La Colombière, 34093, Montpellier Cedex 5, France.|Montpellier University, 34000, Montpellier, France.
authors.forename: I
authors.initials: I
authors.lastname: Carriere
mesh_terms: D017677:Age Distribution; D000375:Aging; D035843:Biomedical Research / Q000379:methods; D015331:Cohort Studies; D015897:Comorbidity; D057225:Data Mining / Q000379:methods; D016208:Databases, Factual; D003704:Dementia / Q000175:diagnosis / Q000453:epidemiology*; D004812:Epidemiologic Methods; D005260:Female; D005602:France / Q000453:epidemiology; D006801:Humans; D008297:Male; D015995:Prevalence; D018570:Risk Assessment / Q000379:methods; D014015:Tissue Banks; D014652:Vascular Diseases / Q000175:diagnosis / Q000453:epidemiology*
keywords: Aging; Biobank; Cohort; Sampling