The overall objective of the proposed research is to significantly improve quality of health forecasting for the US elderly. This objective will be reached by constructing a set of new health predicting models having different levels of complexity, evaluating quality of their predictions, and using verified models to predict future prevalence of cancer, coronary heart disease (CHD), stroke, diabetes, and Alzheimer's disease (AD) under different scenarios. The models will use information about factors affecting health and survival available in five datasets including the Framingham Heart Study (FHS), Health and Retirement study merged with Medicare files (HRS-M), National Long Term Care Survey linked to Medicare records (NLTCS-M), the Surveillance, the Epidemiology and End Results data merged with Medicare records (SEER-M), and the 5% Medicare (5%-M) file. The most sophisticated models will use information about genetic and non-genetic factors, and take pleiotropic, polygenic, and age-specific effects of genes on health and survival, as well as dynamic mechanisms of aging related changes, into account. The following specific aims will be addressed: 1. Predict age patterns of prevalence for cancer, CHD, stroke, diabetes, and AD for years 2020, 2025, 2030, and 2035 using models having different levels of complexity constructed using data from SEER-M, and 5%-M files, NLTCS-M and HRS-M (without genetic data) for males and females under different scenarios.2. Identify sets of genetic variants showing individual and pleiotropic associations with health and survival traits in the FHS and HRS-M data using candidate genomic regions enriched for pleiotropic genetic effects on health traits. Identify genes related to selected genetic variants and evaluate their roles in metabolic and signaling pathways and disease networks. Construct polygenic score indices and evaluate their influence on health and survival traits. 3. Predict age patterns of prevalence for the same diseases and time horizons as in Aim 1, however applying advanced modeling approaches incorporating the genetic information about pleiotropic, polygenic and age-specific effects of genetic variants on health and survival and using different scenarios. Test the quality of health predictions using subsets of available data. Use verified models in health forecasting for time horizons specified above. 4. Predict age patterns of prevalence of diseases listed above using extended multistate health and mortality models by considering risks of health transitions as functions of genetic factors, as well as observed covariates and physiological variables. For these purposes, evaluate risks of transitions and their time trends for subsequent birth cohorts using FHS and HRS-M data. Test quality of health predictions using subsets of available data. Use verified models in health forecasting under different scenarios. Compare results of health predictions using different models constructed in this project, as well as models available in the literature. Make recommendations concerning the proper use of data and models in health forecasting for time horizons specified above.

Public Health Relevance

The result of these analyses will clarify roles of genetic mechanisms in forming health and longevity traits, reduce uncertainty in health forecasting, and contribute to improvement of functioning of health care system which will result in improvement of population health in the US elderly.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Research Project (R01)
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Special Emphasis Panel (ZRG1-PSE-H (57))
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King, Jonathan W
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Duke University
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Yashin, Anatoliy I; Arbeev, Konstantin G; Arbeeva, Liubov S et al. (2016) How the effects of aging and stresses of life are integrated in mortality rates: insights for genetic studies of human health and longevity. Biogerontology 17:89-107
Yashin, Anatoliy I; Zhbannikov, Ilya; Arbeeva, Liubov et al. (2016) Pure and Confounded Effects of Causal SNPs on Longevity: Insights for Proper Interpretation of Research Findings in GWAS of Populations with Different Genetic Structures. Front Genet 7:188
Ukraintseva, Svetlana; Yashin, Anatoliy; Arbeev, Konstantin et al. (2016) Puzzling role of genetic risk factors in human longevity: ""risk alleles"" as pro-longevity variants. Biogerontology 17:109-27
Kulminski, Alexander M; Loika, Yury; Culminskaya, Irina et al. (2016) Explicating heterogeneity of complex traits has strong potential for improving GWAS efficiency. Sci Rep 6:35390
Yashin, Anatoliy I; Arbeev, Konstantin G; Wu, Deqing et al. (2016) How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data. N Am Actuar J 20:201-232
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Arbeev, Konstantin G; Ukraintseva, Svetlana V; Yashin, Anatoliy I (2016) Dynamics of biomarkers in relation to aging and mortality. Mech Ageing Dev 156:42-54
Kulminski, Alexander M; Culminskaya, Irina; Arbeev, Konstantin G et al. (2015) Birth Cohort, Age, and Sex Strongly Modulate Effects of Lipid Risk Alleles Identified in Genome-Wide Association Studies. PLoS One 10:e0136319
Kravchenko, Julia; Berry, Mark; Arbeev, Konstantin et al. (2015) Cardiovascular comorbidities and survival of lung cancer patients: Medicare data based analysis. Lung Cancer 88:85-93
Yashin, Anatoliy I; Wu, Deqing; Arbeeva, Liubov S et al. (2015) Genetics of aging, health, and survival: dynamic regulation of human longevity related traits. Front Genet 6:122

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