Joint Analysis of Health Histories, Physiological State, and Survival

Mechanisms of Ageing and Development

Ukraintseva

Yashin

2016

Self Cite

Contemporary longitudinal studies collect repeated measurements of biomarkers allowing one to analyze their dynamics in relation to mortality, morbidity, or other health-related outcomes. Rich and diverse data collected in such studies provide opportunities to investigate how various socioeconomic, demographic, behavioral and other variables can interact with biological and genetic factors to produce differential rates of aging in individuals. In this paper, we review some recent publications investigating dynamics of biomarkers in relation to mortality, which use single biomarkers as well as cumulative measures combining information from multiple biomarkers. We also discuss the analytical approach, the stochastic process models, which conceptualizes several aging-related mechanisms in the structure of the model and allows evaluating “hidden” characteristics of aging-related changes indirectly from available longitudinal data on biomarkers and follow-up on mortality or onset of diseases taking into account other relevant factors (both genetic and non-genetic). We also discuss an extension of the approach, which considers ranges of “optimal values” of biomarkers rather than a single optimal value as in the original model. We discuss practical applications of the approach to single biomarkers and cumulative measures highlighting that the potential of applications to cumulative measures is still largely underused.

Section: Stochastic Process Model: Unifying Framework For Analysesmentioning

confidence: 99%

Dynamics of biomarkers in relation to aging and mortality

Mechanisms of Ageing and Development

Ukraintseva

Yashin

2016

Self Cite

“…The SPM for analyzing hidden components of aging has been developed and validated in the studies of subsets of the longitudinal data (Arbeev et al 2009; Arbeev et al 2011; Arbeev et al 2012; Yashin et al 2011; Yashin et al 2011; Yashin et al 2007; Yashin et al 2008; Yashin et al 2012; Yashin et al 2010; Yashin et al 2007; Yashin et al 2007; Yashin et al 2008; Yashin et al 2012; Yashin et al 2013; Yashin et al 2009). The use of the genetic version of such a model (GenSPM) (Arbeev et al 2009) allows us to synthesize all of the components and the outcomes, and to evaluate how genetic effects on aging and longevity traits are mediated by physiological variables and the key biomarkers of aging.…”

Section: Introductionmentioning

confidence: 99%

How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data

Yashin

North American Actuarial Journal

et al. 2016

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Background and Objective To clarify mechanisms of genetic regulation of human aging and longevity traits, a number of genome-wide association studies (GWAS) of these traits have been performed. However, the results of these analyses did not meet expectations of the researchers. Most detected genetic associations have not reached a genome-wide level of statistical significance, and suffered from the lack of replication in the studies of independent populations. The reasons for slow progress in this research area include low efficiency of statistical methods used in data analyses, genetic heterogeneity of aging and longevity related traits, possibility of pleiotropic (e.g., age dependent) effects of genetic variants on such traits, underestimation of the effects of (i) mortality selection in genetically heterogeneous cohorts, (ii) external factors and differences in genetic backgrounds of individuals in the populations under study, the weakness of conceptual biological framework that does not fully account for above mentioned factors. One more limitation of conducted studies is that they did not fully realize the potential of longitudinal data that allow for evaluating how genetic influences on life span are mediated by physiological variables and other biomarkers during the life course. The objective of this paper is to address these issues. Data and Methods We performed GWAS of human life span using different subsets of data from the original Framingham Heart Study cohort corresponding to different quality control (QC) procedures and used one subset of selected genetic variants for further analyses. We used simulation study to show that approach to combining data improves the quality of GWAS. We used FHS longitudinal data to compare average age trajectories of physiological variables in carriers and non-carriers of selected genetic variants. We used stochastic process model of human mortality and aging to investigate genetic influence on hidden biomarkers of aging and on dynamic interaction between aging and longevity. We investigated properties of genes related to selected variants and their roles in signaling and metabolic pathways. Results We showed that the use of different QC procedures results in different sets of genetic variants associated with life span. We selected 24 genetic variants negatively associated with life span. We showed that the joint analyses of genetic data at the time of bio-specimen collection and follow up data substantially improved significance of associations of selected 24 SNPs with life span. We also showed that aging related changes in physiological variables and in hidden biomarkers of aging differ for the groups of carriers and non-carriers of selected variants. Conclusions . The results of these analyses demonstrated benefits of using biodemographic models and methods in genetic association studies of these traits. Our findings showed that the absence of a large number of genetic variants with deleterious effects may make substantial contribution to exceptional longevit...

“…Comprehensive analyses of such data can be performed using the new integrative mortality models [29].…”

Section: Introductionmentioning

confidence: 99%

Resistance to Stresses and Reliability of Biological Systems: Insights for Genetic Studies of Human Aging, Health, and Longevity

Yashin¹,

2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SM

Arbeeva

et al. 2016

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Abstract-Connection between stress resistance and longevity in biological organisms is widely discussed and confirmed experimentally. Much less is known about the roles of genetic and non-genetic factors in regulation of such connection. Earlier studies emphasized that mechanism that realizes such connection involves interplay between processes of individual aging and external challenges. As a result of such interplay the parameters of the Gompertz mortality curve are negatively correlated. Such correlation has been also observed in the process of survival improvement in developed part of the world during the first part of the last century. The mortality decline was mainly due to favorable changes in external and living conditions as well as progress in health care. Surprisingly, similar pattern of survival changes is observed in the groups of individuals ranked with respect to the number of "longevity" alleles carried by individuals. We showed that this phenomenon can be interpreted as an increase in resistance to stresses and showed that similar effect is observed in reliability of technical systems when redundancy of their components increases. The availability of longitudinal data for genotyped individuals opens unique opportunity to address more sophisticated questions about roles of genetic and non-genetic factors in connection between aging, stress resistance and longevity in humans. For this purpose the dynamic model of human mortality and aging is used. We show how such model can be used in genetic analyses of fundamental processes of interaction between genetic and non-genetic factors to influence human longevity.