A roadmap to build a phenotypic metric of ageing: insights from the Baltimore Longitudinal Study of Aging

Abstract

Over the past three decades, considerable effort has been dedicated to quantifying the pace of ageing yet identifying the most essential metrics of ageing remains challenging due to lack of comprehensive measurements and heterogeneity of the ageing processes. Most of the previously proposed metrics of ageing have been emerged from cross‐sectional associations with chronological age and predictive accuracy of mortality, thus lacking a conceptual model of functional or phenotypic domains. Further, such models may be biased by selective attrition and are unable to address underlying biological constructs contributing to functional markers of age‐related decline. Using longitudinal data from the Baltimore Longitudinal Study of Aging (BLSA), we propose a conceptual framework to identify metrics of ageing that may capture the hierarchical and temporal relationships between functional ageing, phenotypic ageing and biological ageing based on four hypothesized domains: body composition, energy regulation, homeostatic mechanisms and neurodegeneration/neuroplasticity. We explored the longitudinal trajectories of key variables within these phenotypes using linear mixed‐effects models and more than 10 years of data. Understanding the longitudinal trajectories across these domains in the BLSA provides a reference for researchers, informs future refinement of the phenotypic ageing framework and establishes a solid foundation for future models of biological ageing.

Changes in Body Composition, Energy Expenditure, and Energy Intake during Four Years of University—A Follow-Up Study

“…Collecting data over several timepoints in longitudinal studies better captures the true trajectories of these variables. Along these lines, the study by Kuo et al [ 39 ], using data from the Baltimore Longitudinal Study of Aging, demonstrated that assessing body composition with increasing age using a combination of a few measures in a linear fashion can be problematic as this approach assumes that change is constant over time when it is quite heterogenous. That is, some manifestations of aging accelerate/decelerate at different rates and may even change direction over time.…”

Longitudinal Changes in Resting Metabolic Rates with Aging Are Accelerated by Diseases

“…The visit frequency depends on age: visits occur every 4 years until the subject reaches age 60, biannually from the age of 60 to 79, and yearly thereafter. A description of the sample and selection criteria has been previously reported [ 3 , 13 ].…”

“…As such, RMR can be understood as the “cost of living”, i.e., the energetic cost of maintaining all physiological processes that preserve homeostatic equilibrium and cognitive alertness and sets the stage for all activities of life. RMR is affected by changes in body size, with greater RMR associated with larger body size, especially large lean body mass [ 2 , 3 ]. RMR is widely determined by the most metabolically active tissues, such as muscle, heart, brain, and liver, and, as the function and metabolic activity of these organs and tissues decline with aging, RMR also declines with aging [ 4 ].…”

Identifying Biomarkers for Biological Age: Geroscience and the Icfsr Task Force

“…Using these data, the investigative team recently proposed a roadmap to build a phenotypic metric of aging, which by systematically characterizing the continuum of aging in an individual, could advance understanding of the kinetics of aging, as well as discovery and development of effective interventions. The framework encompasses four domains: body composition, energetics, homeostatic mechanisms, and neurodegeneration/neuroplasticity ( 16 ).…”