Physiological processes are carried out by multiple interacting systems, or networks. With aging, these networks lose the ability to function properly, causing dysregulation?network interactions and connectivity degrade as nodes are no longer able to respond to signals from one another. As a result, individual variations in the rate of aging give rise to differences in disease onset, functioning decline, and life expectancy. It has been suggested that the only way to extend healthy lifespan in humans is by slowing the aging process. Thus, identifying the gene networks and environmental components (and their interactions) which alter the rate of aging is essential for improving the health of the population. The goal of the proposed project is to use systems medicine approaches to identify genetic variants, molecular signals, and social/behavioral factors associated with differences in the rate of aging. More specifically, during the K99 phase, the aim of this project is to identify network-based genetic signatures for human healthspan. To accomplish this research goal, the K99 phase will involve training in biomedical sciences, high- dimensional omics data analysis, network analysis, and social genomics. This training will take place at the UCLA David Geffen School of Medicine in the departments of statistical genetics and biostatistics, under the mentorship of Dr. Steve Horvath (bioinformatics, quantitative genetics, network analysis), Dr. Steve Cole (social genomics, computational modeling, biochemical analyses, molecular genetics), and Dr. Rita Effros (biology of aging, immune function, telomere biology). Building on the training during the K99, the goal of the R00 phase will be to: 1) Model transcriptional drift (dysregulation) in humans and test whether it is associated with aging- related disease prevalence or social factors; and 2) Identify age-related genome-wide methylation changes (in blood) that mediate the association between morbidity and socioeconomic status. Aging is a highly complex and multidimensional process, which is influenced by factors ranging from gene variants to social/political policies. This complexity, has presented a challenge for researchers whose goals are to uncover the regulators of biological aging. This proposal presents a truly novel approach for both modeling the aging process and examining factors which alter it. The studies performed during the K99 and R00 phases will advance our understanding of how gene networks and social factors influence the pace of human aging. Additionally, this project will identify molecular signals of aging and transcriptional drift that accumulate with time, and which subsequently give rise to morbidity and mortality. Understanding the molecular and environmental regulators of biological aging is essential for developing effective interventions that slow the aging process and increase healthy life expectancy. This knowledge will also improve risk assessment in relation to multiple-aging related conditions, and aid primary prevention strategies by identifying at-risk groups.
The only way to significantly increase healthy life expectancy for the human population is to slow the aging process. The ability to accomplish this is dependent upon researchers? understanding of the factors which regulate the pace of aging. The proposed research combines novel network modeling techniques with high- dimensional omics and population-level data to 1) identify the intricate molecular and environmental interactions that influence the human aging process, and 2) develop a model to quantify age-related dysregulation, or transcriptional drift, in humans.
| Levine, Morgan E; Crimmins, Eileen M (2018) Is 60 the New 50? Examining Changes in Biological Age Over the Past Two Decades. Demography 55:387-402 |
