Patterns of Natural Aging and the Role of Senescence Registry

Overview

This is a registry to identify changes in the expression of aging-related biomarkers, changes in functional performance, and/or changes in quality-of-life across the aging spectrum in 250 participants ≥ 25 years of age that will be conducted by the University of North Carolina. The primary purpose of this registry is to measure biomarkers of aging/senescence and build computational models of aging in order to better understand the role of senescence in aging-related functional decline and differences between aging in a general population vs cohorts enriched for aging related disease (cancer, heart disease). Aging biomarker data in cohorts with cancer and heart disease has already been collected; the current study will enroll participants into the cohort of aging in the general population (Aging Cohort). Over the past century, life expectancy has increased by 30 years. With that gain has come a dramatic rise in age-related disease and an urgent need to understand, prevent, and treat these conditions. While age-related diseases have diverse phenotypes, there is increasing recognition of common biological underpinnings with cellular senescence as the nexus linking subcellular changes due to epigenetic changes, DNA damage, and mitochondria dysfunction with a decline in health due to multi-morbidity. The molecular changes that shift one's aging trajectory from a 'healthy' state to a 'disease' state are poorly understood; however, there is increasing evidence that senescence plays a key role in this shift. Computational models of natural aging and aging related disease are important tools in understanding the phenomenon of senescence, its regulation and dynamics, and its role in physiological or pathological processes during human aging. These findings will serve as pilot data for future analysis of cellular senescence, as measured by p16INK4 (hereafter referred to as p16) expression, and aging in other cohorts and begin to establish comparisons between p16 and other potentially clinically relevant aging biomarkers such as DNA methylation and plasma proteomics.

The primary purpose of this registry is to collect aging biomarker data in a general population across the entire lifespan (Aging Cohort). The investigators will then build several computational models of aging in order to better understand senescence in the context of general aging or specific aging associate diseases. As a secondary goal, The investigators will determine the association between biomarkers of aging and health characteristics (defined through blood chemistry panels and specific functional measures). Central to these computational models is the measurement of a biomarker of aging/senescence, p16INK4 (hereafter referred to as p16) in peripheral blood T cells. The primary aim of this registry is to determine if p16 mRNA expression measured in T cells reflects total organismal senescent load. The secondary aim is to determine if senescence dynamics are affected by age-associated chronic diseases. The exploratory aim is to determine the ability of computational models of senescence to predict clinical and functional status. Active participation in the study will end at the completion of the study questionnaires and assessments, but The investigators will ask permission to recontact participants at a later date for longitudinal sample collection. Despite the prominence of senescence as an aging mechanism and target of geroscience-based therapies, little is known about the dynamics of senescent cells in humans. Understanding the process of senescence, including its regulation, dynamics, and contribution to physiologic or pathologic processes, is essential for an understanding of aging. In this registry, the investigators will assess the contribution of senescence, co-morbidities, functional performance, and quality of life to aging. The investigators will determine the ability of these factors to aid in the development of a new stochastic model that will be formulated specifically in the context of T-cell turnover and senescence, thereby providing a cell level description of p16 accumulation and a mechanistic model with parameters characterizing relevant physiological processes. The development of this new model will thus enable a quantitative, predictive, mechanistic study of the role of senescence in aging at both organismal and cellular levels