Browsing by Author "Arbeev, K"

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    Age-Associated Disorders As A Proxy Measure Of Biological Age: Findings From the NLTCS Data
    (2017-06-07) Kulminski, A; Yashin, A; Ukraintseva, S; Akushevich, I; Arbeev, K; Land, K; Manton, K
    Background: The relative contribution of different aging-associated processes to the age phenotype may differ among individuals, creating variability in aging manifestations among age-peers. Capturing this variability can significantly advance understanding the aging and mortality. An index of age-associated health disorders (deficits), called a "frailty index" (FI), appears to be a promising characteristic of such processes. In this study we address the connections of the FI with age focusing on disabled individuals who might be at excessive risk of frailty. Methods: The National Long Term Care Survey (NLTCS) assessed health and functioning of the U.S. elderly in 1982, 1984, 1989, 1994, and 1999. Detailed information for our sample was assessed from about 26,700 interviews. The individual FI is defined as a proportion of deficits for a given person. We perform cross-sectional empirical analysis of the FI age-patterns. Results: FI in the NLTCS exhibits accelerated (quadratic) increase with age. Deficits might accumulate faster among the elderly who, at younger ages, had a low mean FI ("healthy" group) than a high FI ("disabled" group). Age-patterns for "healthy" and "disabled" groups converge at advanced ages. The rate of deficit accumulation is sex-sensitive. Convergence of the (sex-specific) FI for "healthy" and "disabled" groups in later ages determines biological age limits, associated with given levels of health-maintenance in the society, which correspond to 109.4 years for females and 92.5 years for males. Conclusions: The FI can be employed as a measure of biological age and population heterogeneity for modeling aging processes and mortality in elderly individuals.
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    Frailty Index as a Major Indicator of Aging Processes and Mortality in Elderly: Results From Analyses of the National Long Term Care Survey Data
    (2017-06-07) Kulminski, A; Yashin, A; Akushevich, I; Ukraintseva, S; Land, K; Arbeev, K; Manton, K
    To better understand mortality change with age capturing the variability in individuals' rates of aging, we performed comprehensive analysis of statistical properties of a cumulative index of age-associated disorders (deficits), called a "frailty index" (FI). This index is calculated as the proportion of the health deficits in an individual. It is found, first, that frequency, time-to-death, mortality-rate, and relative-risk-of-death exhibit remarkably similar FI- and age- patterns. Second, the FI, on the one hand, and mortality rate and relative risk, on the other hand, also exhibit similar age patterns with accelerated increase up to oldest-old ages and with subsequent deceleration and even decline. Third, distribution of the FI with time-to-death is sharper than that of age with time-to-death. These and related findings support the conclusion that the FI can describe aging processes and population heterogeneity. We also discuss the ability of the FI to capture physiological processes underlying aging both on individual and population levels.
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    Multidimensional Stochastic Process Model and its applications to analysis of longitudinal data with genetic information
    (ACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, 2016-10-02) Zhbannikov, I; Arbeev, K; Yashin, A
    Copyright 2016 ACM.Stochastic Process Model has many applications in analysis of longitudinal biodemographic data. In general, such data contain various physiological variables (sometimes known as covariates or physiological indices). Longitudinal data can also contain genetic information available for all or a part of participants. Taking advantage from both genetic and non-genetic information can provide future insights into a broad range of processes describing aging-related changes in the organism. In this work, we implemented a multi-dimensional Genetic Stochastic Process Model (GenSPM) in newly developed software tool, an R-package stpm (available from CRAN: https://cran.rproject.org/web/packages/stpm), which allows researchers performing such kind of analysis.
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    PHYSIOLOGICAL DYSREGULATION AS PROMISING MEASURE OF ROBUSTNESS AND RESILIENCE IN AGING STUDIES
    (GERONTOLOGIST, 2016-11) Arbeev, K; Ukraintseva, S; Bagley, O; Duan, M; Arbeeva, L; Zhbannikov, I; Cohen, A; Yashin, AI
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    Theory of partitioning of disease prevalence and mortality in observational data.
    (Theor Popul Biol, 2017-04) Akushevich, I; Yashkin, AP; Kravchenko, J; Fang, F; Arbeev, K; Sloan, F; Yashin, AI
    In this study, we present a new theory of partitioning of disease prevalence and incidence-based mortality and demonstrate how this theory practically works for analyses of Medicare data. In the theory, the prevalence of a disease and incidence-based mortality are modeled in terms of disease incidence and survival after diagnosis supplemented by information on disease prevalence at the initial age and year available in a dataset. Partitioning of the trends of prevalence and mortality is calculated with minimal assumptions. The resulting expressions for the components of the trends are given by continuous functions of data. The estimator is consistent and stable. The developed methodology is applied for data on type 2 diabetes using individual records from a nationally representative 5% sample of Medicare beneficiaries age 65+. Numerical estimates show excellent concordance between empirical estimates and theoretical predictions. Evaluated partitioning model showed that both prevalence and mortality increase with time. The primary driving factors of the observed prevalence increase are improved survival and increased prevalence at age 65. The increase in diabetes-related mortality is driven by increased prevalence and unobserved trends in time-periods and age-groups outside of the range of the data used in the study. Finally, the properties of the new estimator, possible statistical and systematical uncertainties, and future practical applications of this methodology in epidemiology, demography, public health and health forecasting are discussed.