Biodemographic Analyses of Longitudinal Data on Aging, Health, and Longevity: Recent Advances and Future Perspectives

Interplay between stress-related genes may influence Alzheimer's disease development: The results of genetic interaction analyses of human data

Emerging evidence from experimental and clinical research suggests that stress-related genes may play key roles in AD development. The fact that genome-wide association studies were not able to detect a contribution of such genes to AD indicates the possibility that these genes may influence AD non-linearly, through interactions of their products. In this paper, we selected two stress-related genes (GCN2/EIF2AK4 and APP) based on recent findings from experimental studies which suggest that the interplay between these genes might influence AD in humans. To test this hypothesis, we evaluated the effects of interactions between SNPs in these two genes on AD occurrence, using the Health and Retirement Study data on white indidividuals. We found several interacting SNP-pairs whose associations with AD remained statistically significant after correction for multiple testing. These findings emphasize the importance of nonlinear mechanisms of polygenic AD regulation that cannot be detected in traditional association studies. To estimate collective effects of multiple interacting SNP-pairs on AD, we constructed a new composite index, called Interaction Polygenic Risk Score, and showed that its association with AD is highly statistically significant. These results open a new avenue in the analyses of mechanisms of complex multigenic AD regulation.

Roles of interacting stress-related genes in lifespan regulation: insights for translating experimental findings to humans

AIM

Experimental studies provided numerous evidence that caloric/dietary restriction may improve health and increase the lifespan of laboratory animals, and that the interplay among molecules that sense cellular stress signals and those regulating cell survival can play a crucial role in cell response to nutritional stressors. However, it is unclear whether the interplay among corresponding genes also plays a role in human health and lifespan.

METHODS

Literature about roles of cellular stressors have been reviewed, such as amino acid deprivation, and the integrated stress response (ISR) pathway in health and aging. Single nucleotide polymorphisms (SNPs) in two candidate genes (GCN2/EIF2AK4 and CHOP/DDIT3) that are closely involved in the cellular stress response to amino acid starvation, have been selected using information from experimental studies. Associations of these SNPs and their interactions with human survival in the Health and Retirement Study data have been estimated. The impact of collective associations of multiple interacting SNP pairs on survival has been evaluated, using a recently developed composite index: the SNP-specific Interaction Polygenic Risk Score (SIPRS).

RESULTS

Significant interactions have been found between SNPs from GCN2/EIF2AK4 and CHOP/DDI3T genes that were associated with survival 85+ compared to survival between ages 75 and 85 in the total sample (males and females combined) and in females only. This may reflect sex differences in genetic regulation of the human lifespan. Highly statistically significant associations of SIPRS [constructed for the rs16970024 (GCN2/EIF2AK4) and rs697221 (CHOP/DDIT3)] with survival in both sexes also been found in this study.

CONCLUSION

Identifying associations of the genetic interactions with human survival is an important step in translating the knowledge from experimental to human aging research. Significant associations of multiple SNPxSNP interactions in ISR genes with survival to the oldest old age that have been found in this study, can help uncover mechanisms of multifactorial regulation of human lifespan and its heterogeneity.

Dynamics of biomarkers in relation to aging and mortality

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 socio-economic, 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.

Joint Analyses of Longitudinal and Time-to-Event Data in Research on Aging: Implications for Predicting Health and Survival

Longitudinal data on aging, health, and longevity provide a wealth of information to investigate different aspects of the processes of aging and development of diseases leading to death. Statistical methods aimed at analyses of time-to-event data jointly with longitudinal measurements became known as the "joint models" (JM). An important point to consider in analyses of such data in the context of studies on aging, health, and longevity is how to incorporate knowledge and theories about mechanisms and regularities of aging-related changes that accumulate in the research field into respective analytic approaches. In the absence of specific observations of longitudinal dynamics of relevant biomarkers manifesting such mechanisms and regularities, traditional approaches have a rather limited utility to estimate respective parameters that can be meaningfully interpreted from the biological point of view. A conceptual analytic framework for these purposes, the stochastic process model of aging (SPM), has been recently developed in the biodemographic literature. It incorporates available knowledge about mechanisms of aging-related changes, which may be hidden in the individual longitudinal trajectories of physiological variables and this allows for analyzing their indirect impact on risks of diseases and death. Despite, essentially, serving similar purposes, JM and SPM developed in parallel in different disciplines with very limited cross-referencing. Although there were several publications separately reviewing these two approaches, there were no publications presenting both these approaches in some detail. Here, we overview both approaches jointly and provide some new modifications of SPM. We discuss the use of stochastic processes to capture biological variation and heterogeneity in longitudinal patterns and important and promising (but still largely underused) applications of JM and SPM to predictions of individual and population mortality and health-related outcomes.