Patterns of Aging-Related changes on the way to 100: An Approach to studying aging, mortality, and longevity from longitudinal data

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2012-12-01

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Abstract

The objective of this paper is to investigate dynamic properties of age trajectories of physiological indices and their effects on mortality risk and longevity using longitudinal data on more than 5,000 individuals collected in biennial examinations of the Framingham Heart Study (FHS) original cohort during about 50 subsequent years of follow-up. We first performed empirical analyses of the FHS longitudinal data. We evaluated average age trajectories of indices describing physiological states for different groups of individuals and established their connections with mortality risk. These indices include body mass index, diastolic blood pressure, pulse pressure, pulse rate, level of blood glucose, hematocrit, and serum cholesterol. To be able to investigate dynamic mechanisms responsible for changes in the aging human organisms using available longitudinal data, we further developed a stochastic process model of human mortality and aging, by including in it the notions of "physiological norms," "allostatic adaptation and allostatic load," "stress resistance," and other characteristics associated with the internal process of aging and the effects of external disturbances. In this model, the persistent deviation of physiological indices from their normal values contributes to an increase in morbidity and mortality risks. We used the stochastic process model in the statistical analyses of longitudinal FHS data. We found that different indices have different average age patterns and different dynamic properties. We also found that age trajectories of long-lived individuals differ from those of the shorter-lived members of the FHS original cohort for both sexes. Using methods of statistical modeling, we evaluated "normal" age trajectories of physiological indices and the dynamic effects of allostatic adaptation. The model allows for evaluating average patterns of aging-related decline in stress resistance. This effect is captured by the narrowing of the U-shaped mortality risk (considered a function of physiological state) with age. We showed that individual indices and their rates of change with age, as well as other measures of individual variability, manifested during the life course are important contributors to mortality risks. The advantages and limitations of the approach are discussed.

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10.1080/10920277.2012.10597640

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Yashin, AI, KG Arbeev, SV Ukraintseva, I Akushevich and A Kulminski (2012). Patterns of Aging-Related changes on the way to 100: An Approach to studying aging, mortality, and longevity from longitudinal data. North American Actuarial Journal, 16(4). pp. 403–433. 10.1080/10920277.2012.10597640 Retrieved from https://hdl.handle.net/10161/14846.

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Scholars@Duke

Yashin

Anatoli I. Yashin

Research Professor in the Social Science Research Institute
Arbeev

Konstantin Arbeev

Associate Research Professor in the Social Science Research Institute

Konstantin G. Arbeev received the M.S. degree in Applied Mathematics from Moscow State University (branch in Ulyanovsk, Russia) in 1995 and the Ph.D. degree in Mathematics and Physics (specialization in Theoretical Foundations of Mathematical Modeling, Numerical Methods and Programming) from Ulyanovsk State University (Russia) in 1999. He was a post-doctoral fellow in Max Planck Institute for Demographic Research in Rostock (Germany) before moving to Duke University in 2004 to work as a Research Scientist and a Senior Research Scientist in the Department of Sociology and the Social Science Research Institute (SSRI).  He is currently an Associate Research Professor in SSRI. Dr. Arbeev's major research interests are related to three interconnected fields of biodemography, biostatistics and genetic epidemiology as pertains to research on aging. The focus of his research is on discovering genetic and non-genetic factors that can affect the process of aging and determine longevity and healthy lifespan. He is interested in both methodological advances in this research area as well as their practical applications to analyses of large-scale longitudinal studies with phenotypic, genetic and, recently, genomic information. Dr. Arbeev authored and co-authored more than 150 peer-reviewed publications in these areas.

Ukraintseva

Svetlana Ukraintseva

Research Professor in the Social Science Research Institute

Dr. Ukraintseva studies causes of human aging and related decline in resilience, to identify genetic and other factors responsible for the increase in mortality risk with age eventually limiting longevity. She explores complex relationships, including trade-offs, between physiological aging-changes and risks of major diseases (with emphasis on Alzheimer’s and cancer), as well as survival, to find new genetic and other targets for anti-aging interventions and disease prevention. She also investigates possibilities of repurposing of existing vaccines and treatments for AD prevention and interventions into the aging. For this, Dr. Ukraintseva and her team use data from several large human studies containing rich genetic and phenotypic information (including longitudinal measurements) on thousands of individuals. Dr. Ukraintseva is a PI and Key Investigator on several NIH funded grants, and has more than 130 peer-reviewed publications, including in major journals such as Nature Reviews, Stroke, European Journal of Human Genetics, and some other.

Akushevich

Igor Akushevich

Research Professor in the Social Science Research Institute
Kulminski

Alexander Kulminski

Research Professor in the Social Science Research Institute

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