Effect of the APOE Polymorphism and Age Trajectories of Physiological Variables on Mortality: Application of Genetic Stochastic Process Model of Aging

How are APOE4, changes in body weight, and longevity related? Insights from a causal mediation analysis

The ε4 allele of the APOE gene (APOE4) is known for its negative association with human longevity; however, the mechanism is unclear. APOE4 is also linked to changes in body weight, and the latter changes were associated with survival in some studies. Here, we explore the role of aging changes in weight in the connection between APOE4 and longevity using the causal mediation analysis (CMA) approach to uncover the mechanisms of genetic associations. Using the Health and Retirement Study (HRS) data, we tested a hypothesis of whether the association of APOE4 with reduced survival to age 85+ is mediated by key characteristics of age trajectories of weight, such as the age at reaching peak values and the slope of the decline in weight afterward. Mediation effects were evaluated by the total effect (TE), natural indirect effect, and percentage mediated. The controlled direct effect and natural direct effect are also reported. The CMA results suggest that APOE4 carriers have 19%-22% (TE p = 0.020-0.039) lower chances of surviving to age 85 and beyond, in part, because they reach peak values of weight at younger ages, and their weight declines faster afterward compared to non-carriers. This finding is in line with the idea that the detrimental effect of APOE4 on longevity is, in part, related to the accelerated physical aging of ε4 carriers.

Understanding Alzheimer's disease in the context of aging: Findings from applications of stochastic process models to the Health and Retirement Study

There is growing literature on applications of biodemographic models, including stochastic process models (SPM), to studying regularities of age dynamics of biological variables in relation to aging and disease development. Alzheimer's disease (AD) is especially good candidate for SPM applications because age is a major risk factor for this heterogeneous complex trait. However, such applications are largely lacking. This paper starts filling this gap and applies SPM to data on onset of AD and longitudinal trajectories of body mass index (BMI) constructed from the Health and Retirement Study surveys and Medicare-linked data. We found that APOE e4 carriers are less robust to deviations of trajectories of BMI from the optimal levels compared to non-carriers. We also observed age-related decline in adaptive response (resilience) related to deviations of BMI from optimal levels as well as APOE- and age-dependence in other components related to variability of BMI around the mean allostatic values and accumulation of allostatic load. SPM applications thus allow revealing novel connections between age, genetic factors and longitudinal trajectories of risk factors in the context of AD and aging creating new opportunities for understanding AD development, forecasting trends in AD incidence and prevalence in populations, and studying disparities in those.

External Validation of the Core Obesity Model to Assess the Cost-Effectiveness of Weight Management Interventions

BACKGROUND

For economic models to be considered fit for purpose, it is vital that their outputs can be interpreted with confidence by clinicians, budget holders and other stakeholders. Consequently, thorough validation of models should be carried out to enhance confidence in their predictions. Here, we present results of external dependent and independent validations of the Core Obesity Model (COM), which was developed to assess the cost-effectiveness of weight management interventions.

OBJECTIVE

The aim was to assess the external validity of the COM (version 6.1), in line with best practice guidance from the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.

METHODS

For validation, suitable sources and outcomes were identified, and used to populate the COM with relevant inputs to allow prediction of study outcomes. Study characteristics were entered into the COM to replicate either the studies used to develop the model (dependent validation) or those not included in the model (independent validation). The concordance between predicted and observed outcomes was then assessed using established statistical methods and generation of mean error estimates.

RESULTS

For most outcomes, the predictions of the COM showed good linear correlation with observed outcomes, as evidenced by the high coefficients of determination (R² values). The independent validation revealed a degree of underestimation in predictions of cardiovascular (CV) disease and mortality, and type 2 diabetes.

CONCLUSION

The predictions generated by the risk equations used in the COM showed good concordance both with the studies used to develop the model and with studies not included in the model. In particular, the concordance observed in the external dependent validation suggests that the COM accurately predicts obesity-related event rates observed in the studies used to develop the model. However, the impact of existing CV risk, as well as mortality, is a key area for future refinement of the COM. Our results should increase confidence in the estimates derived from the COM and reduce uncertainty associated with analyses using this model.

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

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 longevity. These effects are dynamically mediated by a number of physiological variables and hidden biomarkers of aging. The results of these research demonstrated benefits of using integrative statistical models of mortality risks in genetic studies of human aging and longevity.

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.

Optimal Versus Realized Trajectories of Physiological Dysregulation in Aging and Their Relation to Sex-Specific Mortality Risk

While longitudinal changes in biomarker levels and their impact on health have been characterized for individual markers, little is known about how overall marker profiles may change during aging and affect mortality risk. We implemented the recently developed measure of physiological dysregulation based on the statistical distance of biomarker profiles in the framework of the stochastic process model of aging, using data on blood pressure, heart rate, cholesterol, glucose, hematocrit, body mass index, and mortality in the Framingham original cohort. This allowed us to evaluate how physiological dysregulation is related to different aging-related characteristics such as decline in stress resistance and adaptive capacity (which typically are not observed in the data and thus can be analyzed only indirectly), and, ultimately, to estimate how such dynamic relationships increase mortality risk with age. We found that physiological dysregulation increases with age; that increased dysregulation is associated with increased mortality, and increasingly so with age; and that, in most but not all cases, there is a decreasing ability to return quickly to baseline physiological state with age. We also revealed substantial sex differences in these processes, with women becoming dysregulated more quickly but with men showing a much greater sensitivity to dysregulation in terms of mortality risk.

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

Biodemography became one of the most innovative and fastest growing areas in demography. This progress is fueled by the growing variability and amount of relevant data available for analyses as well as by methodological developments allowing for addressing new research questions using new approaches that can better utilize the potential of these data. In this review paper, we summarize recent methodological advances in biodemography and their diverse practical applications. Three major topics are covered: (1) computational approaches to reconstruction of age patterns of incidence of geriatric diseases and other characteristics such as recovery rates at the population level using Medicare claims data; (2) methodological advances in genetic and genomic biodemography and applications to research on genetic determinants of longevity and health; and (3) biodemographic models for joint analyses of time-to-event data and longitudinal measurements of biomarkers collected in longitudinal studies on aging. We discuss how such data and methodology can be used in a comprehensive prediction model for joint analyses of incomplete datasets that take into account the wide spectrum of factors affecting health and mortality transitions including genetic factors and hidden mechanisms of aging-related changes in physiological variables in their dynamic connection with health and survival.

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.

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

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.