ANALYTICAL CONSIDERATIONS OF DEVELOPING A PHENOTYPIC AGING MEASURE: THE CONCEPTUAL FRAMEWORK MUST COME FIRST!. (8th November 2019)

• Record Type: Journal Article
• Authors: Shardell, Michelle; Shardell, Michelle; Kuo, Pei-Lun; Schrack, Jennifer; Simonsick, Eleanor M; Resnick, Susan M; Levine, Morgan E; Ferrucci, luigi
• Is Part Of: Innovation in aging. Volume 3(2019)Supplement 1
• Page Start: S579
• Page End: S580
• Publication Date: 2019-11-08
• Subjects: Aging -- Periodicals; Gerontology -- Periodicals; 612.67
• DOI: 10.1093/geroni/igz038.2148
• Languages: English
• ISSNs: 2399-5300

Abstract

Abstract: We propose a latent structural model framework where phenotypic aging is a latent variable influenced by chronological age, genes and environment. Within this framework, phenotypic age influences aging-related outcomes and is reflected by latent domains of aging (body composition, energetics, homeostasis, and neural functioning) reflected by biomarkers. First, we validate the framework by selecting age-associated domain-specific biomarkers and assessing internal consistency and convergent construct validity (Cronbach's alpha). Using data from the Baltimore Longitudinal Study of Aging, within-domain Cronbach's alphas ranged from 0.80 to 0.92, supporting convergent construct validity. Second, we evaluate two broad methods for combining biomarkers into one phenotypic age measure customized to different objectives: 1) confirmatory factor analysis of chronological age-adjusted biomarkers to create a measure to identify pleiotropic genetic and environmental mechanisms, and 2) machine-learning methods to create a measure optimizing predictive and concurrent criterion validity. This framework will enable evaluation of candidate biological mechanisms of aging.