How lifespan associated genes modulate aging changes: lessons from analysis of longitudinal data

Abstract

Background and Objective: The influence of genes on human lifespan is mediated by biological processes that characterize body's functioning. The age trajectories of these processes contain important information about mechanisms linking aging, health, and lifespan. The objective of this paper is to investigate regularities of aging changes in different groups of individuals, including individuals with different genetic background, as well as their connections with health and lifespan. Data and Method: To reach this objective we used longitudinal data on four physiological variables, information about health and lifespan collected in the Framingham Heart Study (FHS), data on longevity alleles detected in earlier study, as well as methods of statistical modeling. Results: We found that phenotypes of exceptional longevity and health are linked to distinct types of changes in physiological indices during aging. We also found that components of aging changes differ in groups of individuals with different genetic background. Conclusions: These results suggest that factors responsible for exceptional longevity and health are not necessary the same, and that postponing aging changes is associated with extreme longevity. The genetic factors which increase lifespan are associated with physiological changes typical of healthy and long-living individuals, smaller mortality risks from cancer and CVD and better estimates of adaptive capacity in statistical modeling. This indicates that extreme longevity and health related traits are likely to be less heterogeneous phenotypes than lifespan, and studying these phenotypes separately from lifespan may provide additional information about mechanisms of human aging and its relation to chronic diseases and lifespan.

Aging in the Context of Cohort Evolution and Mortality Selection

“…Some biomarkers may be positively related to aging, and others may be negatively associated with aging; moreover, the relationship between biomarkers and the aging process may be heterogeneous across individuals and may also vary over the life course (Yashin 2013). Furthermore, nonmonotonic age patterns of biomarkers (e.g., body mass index, which may rise and fall over the life course) introduce additional challenges for using biomarkers to measure biological aging (Yashin et al 2013). Other important biomarkers of aging are unknown or cannot be measured (Piantanelli et al 2001).…”

Predicting all‐cause mortality from basic physiology in the Framingham Heart Study

“…More recently, Yashin and colleagues have sought to use longitudinal data to gain insight into the overall aging process and the relationship between physiological change over time and mortality risk (e.g., Yashin et al ., 2013). In particular, modeling physiological change as a dynamic system allowed mortality risk to be successfully modeled as a function of the difference between an individuals' current physiological state and the ideal state for an individual of that age (Yashin et al ., 2007; Arbeev et al ., 2011).…”

Molecular Gerontology

“…Genetic linkage studies for longevity and several other studies showing an association between human longevity and single nucleotide polymorphisms (SNPs) in a variety of genes in various biological pathways, including heat shock response, mitochondrial functions, immune response, cholesterol metabolism, and others (Singh et al, 2004;Rattan and Singh, 2009;Yashin et al, 2012;Yashin et al, 2013). An analysis of the various functions of the genes associated with aging and longevity shows that these genes cover a wide range of biochemical pathways, such as energy metabolism, kinases, kinase receptors, transcription factors, DNA helicases, membrane glucosidases, GTP-binding protein coupled receptors, chaperones, and cell cycle check point pathways.…”