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Current BARU Research Projects

Title:Genetics of Changes in Population Pyramids: Implications for Health Forecasting
Sponsor:National Institutes of Health / National Institute on Aging
Grant #:R01-AG046860Link to:NIH Project RePORTER
Summary
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.

Title:Relationships among Genetic Regulators of Aging Health and Lifespan
Sponsor:National Institutes of Health / National Institute on Aging
Grant #:P01-AG043352Link to:NIH Project RePORTER
Summary
Following the strategic directions proposed by the National Institute on Aging, an overall objective of this research project is to identify genetic and non-genetic factors and mechanisms which can promote long and healthy life in humans on the basis of better understanding the relationships among regulators of aging, risks, of major diseases and related traits, and lifespan. This objective will be reached on the basis of integrative analyses of genome-wide SNP genotyping data and longitudinal data on life course processes in human organisms. The research in this program project will be performed in three subprojects, supported by the (A) Administrative, and (B) Data Management/Analytic Cores. We will use traditional and advanced methodologies of genetic analyses and statistical modeling, and methods of systems biology, which will be built on knowledge accumulated in the fields of aging, health, and lifespan incorporated into the integrative statistical platform. The methodological concept of the POI stands to advance paradigms of current GWAS and future association studies, using next generation sequencing, by bringing state-of-the-art methods to analyzing traits of late life that breaks new ground in the area of life-course genetics. The project will address three Specific Aims. Aim 1. Conduct comprehensive association analyses using genome-wide SNP genotyping data to identify pleiotropic and specific genetic underpinnings of lifespan, risks of major diseases, health related traits, and physiological aging changes in human body. Aim 2. Conduct analysis of up-to-date information on biological effects of pleiotropic and specific genes for SNPs discovered in Aim 1 to dissect their roles in molecular pathways, and biological processes and functions. Aim 3. Perform dynamic integration of genetic effects revealed in Aims 1 and 2 into the life course processes in individuals by combining methods of systems biology and advanced statistical modeling.

Title:Data Management/Analytical Core
Sponsor:National Institutes of Health / National Institute on Aging
Project Leader:Konstantin Arbeev, PhD
Grant #:P01-AG043352Link to:NIH Project RePORTER
Summary
This sub-project is designed: (i) to manage seven large-scale longitudinal datasets with phenotypic and genotyping information including the Framingham Heart Study (FHS), the Atherosclerosis Risk in Communities (ARIC) Study, the Cardiovascular Health Study (CHS), the Multi-Ethnic Study of Atherosclerosis (MESA), the Late Onset Alzheimer’s Disease Family Study (LOADFS), the Health and Retirement Sun/ey (HRS), and the Long Life Family Study (LLFS) selected to cover major health problems of the elderly and comprehensively characterize aging-related processes and survival, (ii) to prepare genotyping and phenotypic information from these studies for each subproject, and (iii) to provide a basis for barrier-free integrative analyses of statistical and biological nature. As such, this core will assist all subprojects in both routine and highly significant aspects. Core B has four Specific Aims: Aim 1. Download and manage longitudinal datasets; Aim 2. Construct phenotypes; Aim 3. Conduct integrative analyses using advanced models; Aim 4. Dissect biological role of genes for allelic variants with systemic effects. Aims 1 and 2 are designed to aggregate routine efforts associated with pre-processing these data to increase synergy by: (i) ensuring a unified access to the data and diminishing the role of study-specific biases when processing the data in each subproject and (ii) reducing the burden of routine data-processing procedures on researchers in subprojects. Aim 3 stands to provide a barrier-free basis for integrating genetic effects discovered in each subproject into the life course processes in aging individuals. This role will be achieved by dropping barriers imposed by specifics of different phenotypes on the basis of a common methodological platform that is a key to enhancing synergism among the subprojects. Aim 4 will combine statistical inferences on genetic underpinnings of healthspan with previously obtained biological information to provide insights on biologically meaningful mechanisms of the systemic nature underlying healthspan in aging individuals.

Title:Genes and Non-Genetic Factors Affecting Lifespan: Effects on Health and Aging
Sponsor:National Institutes of Health / National Institute on Aging
Project Leader:Anatoliy Yashin, PhD
Grant #:P01-AG043352Link to:NIH Project RePORTER
Summary
The objective of Project 1 is to significantly improve our understanding of the roles of genetic and non-genetic factors in regulation of “longevity-related” traits, which include: (i) lifespan; (ii) free of selected diseases lifespan; (iii) duration of life with diseases; and (iv) cause of death, as well as to investigate relation of these factors to processes of aging and disease development in human body. This objective will be reached by performing comprehensive analyses of available data on genome-wide SNP genotyping, non-genetic data on fixed covariates, longitudinal data on aging-related changes in physiological state, changes in health status associated with diseases such as cancer, CHD, diabetes, asthma, Alzheimer’s disease, and stroke, available from several large sets of longitudinal and cross-sectional human data (FHS, ARIC, CHS, MESA, LOADFS, and HRS). Specific aims: 1. Using state-of-the-art methods of GWAS, identify genetic and non-genetic factors having positive and negative associations with longevity-related traits defined above. 2. Identify subsets of SNP genetic variants showing pleiotropic (antagonistic and non-antagonistic) effects on two or more traits investigated in Project 1 and, more general in this P01, as well as the age-specific genetic effects on these traits. 3. Validate research findings obtained in Aims 1 and 2: (i) by replicating them in independent populations; (ii) by investigating functional properties of SNP-related genes from sets selected in Aim 2, and their roles in cellular pathways and metabolic processes involved in regulation of longevity-related traits. 4. Evaluate polygenic influence (of groups of genes) on longevity traits, including linear (additive) and non-linear (epistatic) effects. 5. Evaluate dynamic properties of mechanisms connecting longevity traits studied in this project with aging- and disease-related traits by analyzing available longitudinal data using extended versions of stochastic process model of human aging, health and mortality with coefficients depending on genetic and non-genetic covariates. The results of these analyses will facilitate development of personalized prevention and will significantly contribute to improvement of population health. RELEVANCE (See instructions): The results of these analyses will improve our understanding of mechanisms of aging related changes and their influence on health and survival outcomes. The new knowledge produced in the course of work on this project will facilitate development of personalized preventive and treatment strategies which contribute to improvement of population health in the U.S. and other developed countries.

Title:Genes and Other Factors Affecting Aging Changes: Effects on Health and Lifespan
Sponsor:National Institutes of Health / National Institute on Aging
Project Leader:Svetlana Ukraintseva, PhD
Grant #:P01-AG043352Link to:NIH Project RePORTER
Summary
The objective of Project 2 is to significantly improve our understanding of complex genetic regulation of human aging, and investigate relevance of genetic factors influencing physiological aging changes in body to risks of major diseases and to longevity. To address this objective, we will identify genetic variants which individually and jointly influence markers of physiological aging specified in this project, using sets of longitudinal human data available through dbGaP. A special emphasis of the research will be on pleiotropic effects (both antagonistic and non-antagonistic) of a genotype on different traits, and on one trait at different ages. Specific Aims: 1) Evaluate individual and additive polygenic influence of SNPs on markers of physiological aging. For this we will conduct a hypothesis-free GWAS of aging phenotypes specified in this project and evaluate individual and joint (additive) effects of selected genetic variants on the aging phenotypes, using polygenic risk scores; 2) Investigate pleiotropic (both antagonistic and non-antagonistic) effects of individual SNPs and polygenic scores evaluated in Aim 1 on aging traits specified in this project, and on health and longevity traits evaluated in two other projects. We will test hypotheses about the pleiotropic (including trade-offs)influence of a genotype on: (i) different traits; and (ii) one trait at different ages. 3) Investigate functional relationships among genes/regulatory elements linked to SNPs detected in Aim1. For this, we will use online resources and tools for SNPs/genes/proteins annotating, gene-to-function and pathway analysis, to specify biological functions most relevant to the detected genes, and investigate their involvement in known aging pathways and networks. 4) Evaluate epistatic genetic influence on markers of physiological aging. We will select subsets of SNPs for their: a) pleiotropic effects on aging phenotypes detected in Aim 2; b) relation to genes involved in similar biological functions in Aim 3; c) involvement in aging-related pathways. For these subsets of SNPs, we will evaluate epistatic genetic effects on aging phenotypes. Results of this study will significantly improve our understanding of genetic regulation of aging, and its role in health and longevity.

Title:Genes and Other Factors Affecting Health Traits: Effects on Aging and Lifespan
Sponsor:National Institutes of Health / National Institute on Aging
Project Leader:Alexander Kulminski, PhD
Grant #:P01-AG043352Link to:NIH Project RePORTER
Summary
Project 3: An emerging problem for societies in developed countries is extending years of healthy life. Specialists in biology and genetics argue that major breakthrough in the field can be achieved by revealing genetic variants which can be involved in regulation of health in late life. A puzzling problem is that traits of late life are not the result of direct evolutionary selection that reinforces the role of life-course-related processes in an organism in heterogeneous environment which contribute to a complex spectrum of actions of genes on traits of late life. Analysis of the role of this complexity in genetic effects is not in mainstream of genome-wide association studies (GWAS). The objective of this subproject is to identify genetic underpinnings of major human diseases and related traits of late life using state-of-the-art methods addressing the role of life-course-related processes in an organism shaping genetic associations in heterogeneous environment and to dynamically integrate the revealed allelic variants into the individuals’ health-related changes during life course. This challenging goal requires a rich data on the life course health-related processes assessed in the Framingham Heart Study (FHS), the Atherosclerosis Risk in Communities Study (ARIC), the Cardiovascular Health Study (CHS), the Multi-Ethnic Study of Atherosclerosis (MESA), the Late Onset Alzheimer’s Disease Family Study (LOADFS), the Health and Retirement Survey (HRS), and the Long Life Family Study (LLFS). Aim 1. Reveal genetic variants associated with quantitative phenotypes of physiological health given time-repeated observations for the same individuals using the FHS, ARIC, CHS, and MESA. Aim 2. Reveal genetic variants associated with risks of major human diseases using the FHS, ARIC, CHS, MESA, HRS, and LOADFS. Aim 3. Elucidate the role of the revealed allelic variants in lifespan and conduct dynamic integration. Aim 4. Dissect biological role of genes for the revealed SNPs and construct integrated variants. RELEVANCE (See instructions): This project directly addresses concerns which are of inherent relevance to public health including concern on genetic predisposition to medical complications and side-effects as a result of complex nature of gene action on health traits and concern on whether such genes can be considered as early life targets for preventive interventions as a result of their important role in health changes during the individuals’ life course.

Title:Genetic Modulations of Morbidity Compression: A Population-Based Study
Sponsor:National Institutes of Health
Grant #:R56-AG047402Link to:NIH Project RePORTER (Parent R01)
Summary
Human longevity steadily increased over the past century. There is great uncertainty, however, regarding the extent to which this was accompanied by the compression of morbidity. Given current dramatic increases in health care costs, especially costs during the last 6 months of life, this question is of profound importance to society. The National Long Term Care Survey (NLTCS) is a Medicare-based sample of the U.S. population aged 65+ initiated in 1982 with longitudinal follow-up in 1984, 1989, 1994, 1999, and 2004, with complete linkage to Medicare claims and vital statistics data for 1982-2009, and for eligible participants to Medicaid files (i.e., the 2004-2007 MAX files), supplemented with the 1999-2009 MDS and OASIS files. The 1982-1994 NLTCS produced the first reported major improvements of functional health as assessed by Activities of Daily Living (ADL) and Instrumental ADL (IADL) scores within the NLTCS population. The 1999 NLTCS substantially expanded its scope by selecting 1,877 participants and 869 siblings for supplemental data collection in 2000- 2002, yielding 639 blood (participants only) and 2,078 buccal swab (participants and siblings) samples for genetic studies. Moreover, of the 1,808 NLTCS participants contributing to the biospecimen sample, 1,345 were alive at the 2004 NLTCS and 1,183 were re-assessed. A total of 1,031 participants were age 85+ initially (429) or attained age 85 during the 2000-2010 follow-up period (602), with a total of 717 still alive as of the cutoff date in 2010. The biospecimen sample currently provides 13,303 person-years of follow-up data, with 5,098 person-years above age 85. We propose to utilize this unique population-based sample, its associated functional data, linked Medicare/Medicaid claims data, and DNA samples, to quantitate variable degrees of the compression of morbidity and to test the hypothesis that constitutional genetic factors contribute to the modulation of these differential ratios of healthspans/lifespans. We will first address the connections between longevity, co-morbidity, functional health (ADL/IADL), and declines of physiological and cognitive functions (Aim 1). We will then conduct SNP array analysis of 639 blood and 2,078 buccal swab samples to obtain and assess a wide range of genetic information (Aim 2). We will assess associations of phenotypes of long healthy life with candidate polymorphisms within two highly relevant coupled gene networks-Insulin/IGF1 signaling (incl. FOXO3A and IGFR) and mTOR pathways-linked to aging and longevity across different species (Aim 3). Then, given published associations of genome-wide heterozygosity with cardiovascular health (Campbell et al., 2007), we will seek such associations with degrees of morbidity compression (Aim 4). Also, given the scientific value of the project data, we will release additional years of de-identified CMS data (Aim 5) and de- identified versions of the genotypic/phenotypic data derived from our SNP array analysis (Aim 6), using CMS, NIA, and Duke IRB approved protocols. We will replicate/validate our results using data from the Health and Retirement Study (HRS), employing comparable phenotypic and genotypic measures and linked CMS data.

Title:Life Course and Genetic and Non-genetic Factors in Health and Survival
Sponsor:National Institutes of Health / National Institute on Aging
Grant #:R01-AG047310Link to:NIH Project RePORTER
Summary
An emerging problem for societies in developed countries is extending years of healthy life. Specialists in biology and genetics argue that major breakthrough in the field can be achieved by revealing genetic variants which can be involved in regulation of phenotypes characterizing health, wellbeing and survival in late life. A puzzling problem is that phenotypes in late life are not the result of direct evolutionary selection that reinforces the role of the life course processes shaping genetic effects on health, wellbeing and survival within and across generations. Analysis of the role of the life course in genetic effects s not in mainstream of standard strategies of genome-wide association studies. The objective of this proposal is to identify systemic contribution of genetic and non-genetic factors to health, wellbeing, and survival over the life course by revealing associations of genetic factors with physiological and behavioral endophenotypes (EPs), by identifying their roles in risks of morbidity (cardiovascular disease (CVD) and cancer), disability, mortality, and mortality attributed to CVD and cancer, by elucidating the role of life-course related processes in shaping genetic effects on the risk outcomes, and by integrating the effects of genetic factors with the lie course in men and women within and across generations. To achieve this goal, we will use rich data on individuals from different generations longitudinally followed in the Framingham Heart Study, Health and Retirement Study linked with Medicare data, the Coronary Artery Risk Development in Young Adults study, and the Long Life Family Study. The following Specific Aims will be addressed. Aim 1. Construction of phenotypes. Aim 2. Identification of genetic associations with EPs. Aim 3. Identification of genetic associations with risks of morbidity, disability, and mortality. Aim 4. Elucidating systemic role of the revealed genetic variants in health, wellbeing, and survival and conducting dynamic integration. Aim 5. Dissecting biological role of genes for the revealed SNPs.

Title:Predictors of Severity in Alzheimer’s Disease
Sponsor:National Institutes of Health/National Institute on Aging via subcontract from Columbia University College of Physicians and Surgeons, New York, NY
Grant #:R01 AG007370
Summary
The objective of this project is to further the understanding of Alzheimer’s disease progression in order to develop algorithms to predict the length of time from disease onset to major disease outcomes in individual patients with Alzheimer’s disease (AD).

Title:Actuarial Certification for Medicaid Upper Payment Limit for the Program of All-Inclusive Care for the Elderly (PACE)
Sponsor:State of Colorado via subcontract from Hause Actuarial Solutions, Inc., Overland Park, KS
Grant #:No identifying number [ Durington (PI)]
Summary
The objective of this project is to compare Medicaid expenditures on participants enrolling in PACE with Medicaid expenditures on long-term care (LTC) entrants to the Elderly, Blind, and Disabled (EBD) waiver program and nursing facilities (NFs) in the PACE market areas in Colorado. The Grade of Membership (GoM) model will be used to determine the amounts that would have been paid by Medicaid for the PACE participants if they were not enrolled in the PACE program.


Title:Demography of Sex Differences in Health and Survival (Zeng Yi-Comp Rev)
Sponsor:National Institute on Aging
Grant #:P01-AG031719Link to:NIH Project RePORTER
Summary
The three projects of our PPG examine male-female health-survival paradox by comparing numerous life tables, social, behavioral and other environmental factors in various human and nonhuman populations, but none of them deal with the effects of genetic variants and GxE interactions. As reviewer recommended “Down the road, results from genetic demography will likely become important to the study of differential health and mortality by sex (P.10, PPG summary statement), This competing revision project focusing on sex differences in the effects of genetics & GxE interaction on cognition (emphasizing prevention of Alzheimer disease), mental health and survival would make significant contributions to expand our PPG and the research field.