NG00153 – Health and Retirement Study (HRS) DNA Methylation

Epigenetic changes play a role in the biological pathways associated with psychosocial and cognitive outcomes. Little research, however, has been devoted to understanding whether differences in methylation for one psychosocial phenotype also accounts for differential methylation of other phenotypes. The objective of the proposed research is to estimate the associations between epigenetic modifications (i.e., DNA methylation) and behavioral and psychosocial risk factors (e.g., depressive symptomatology, loneliness, low physical activity) previously shown to predict cognitive decline in older adulthood. Our principal analyses include epigenome-wide association studies of the principal phenotypes of interest to quantify differential methylation at specific CpG sites for various phenotypes of Health and Retirement Study participants who were enrolled in the 2016 Venous Blood Study. We subsequently will estimate correlations between the beta weights generated from these EWASs to quantify whether differences in methylation underlying psychosocial and cognitive phenotypes covary. Follow-up analyses will include gene-enrichment analyses to identify pathways that contain genes that are overexpressed for two phenotypes (e.g., loneliness and cognition). In addition, we will construct DNA methylation age acceleration measures using principal components methodologies that improve the reliability of DNAm age variables. The phenotypic characteristics proposed for the present study are broad and include self-report measures of current and previous smoking use, frequency of social interactions, loneliness, depressive symptomatology, self-reported physical activity, subjective memory rating, and cognitive ability measures (e.g., immediate and delayed word recall, backward counting, serial 7s, verbal fluency, vocabulary, quantitative reasoning, and verbal reasoning).

The general purpose of the study is to identify epigenetic correlates of behavioral risk factors and cognitive decline in older adults. For example, our planned analyses will investigate epigenome-wide associations with loneliness, depressive symptomatology, self-reported physical activity, and cognition. Further, we will identify overlapping CpG sites between behavioral risk factors and cognition in older adulthood, and use gene ontology to identify the differentially expressed genes that are enriched in biological processes relevant to disease risk and aging.

Overview. In this NIAGADS application, we seek access to DNA methylation (DNAm) data for the US Health and Retirement Study (HRS) to (1) investigate how environmental and behavioral factors in middle and later life, including physical and social environmental conditions, work and retirement, family structure, and life events affect the pace of biological aging ; and (2) to develop, validate, and refine DNAm-based indices of the pace of biological aging. These activities will establish the sensitivity of metrics of biological aging to social determinants of health and furnish validated metrics of biological aging that can be deployed to evaluate the impacts of interventions targeting the biology of aging (R01AG061378), social determinants of health that we hypothesize indirectly impact the biology of aging (R01AG087158, R01AG073402) and early-life exposures that we hypothesize hasten pace of aging across the lifespan (R01AG066887).Analysis Plan. The program of analysis for this project will include a range of activities. We will process and normalize DNAm data following methods established in our prior work. The first step will be to merge DNAm data obtained from NIAGADS with phenotypic data obtained from the HRS. We will then (1) compute indices of the pace and progress of biological aging defined in other studies, many of which are not yet available as data products from the HRS; (2) test how these indices of biological aging are related to social and physical environmental factors (e.g. socioeconomic status and social mobility, family structure, air pollution, neighborhood social environment) and investigate the extent to which they mediate environmental impacts on incidence of chronic disease, disability, and mortality as outlined in in our previous work; (3) test how these indices relate to phenotypes of aging-related decline in functional capacities and organ system integrity defined in Balachandran et al. 2024; (4) conduct discovery analysis to determine DNAm correlates and signatures of pace of aging phenotypes defined in Balachandran et al. 2024 along with related phenotypes we are currently in the process of developing.

Life-course social science links early-life social disadvantage is how social disadvantage is biologically embedded, leading to social inequalities. A hypothesis is that social disadvantage actually hastens aging. While everyone ages chronologically at the same rate, biological changes with aging may proceed faster for some than others. These changes are thought to be a root cause of disease/disability and an intervention target to extend healthy lifespan. A knowledge gap is whether social disadvantage hastens aging-related biological changes. If so, it would open opportunity to join forces between biomedical research developing interventions to slow aging and social science research to address social inequalities. Our work and that of others indicates the pace and progress of biological aging can be measured from DNA methylation. In this project, we will investigate how social determinants of health affect trajectories of biological aging with the goal of informing programs and policies to dismantle health disparities and build healthy longevity for all.

Our project will rely on phenotype and genotype data from the Health and Retirement Study (HRS), a nationally representative longitudinal study of the older adult population in the U.S. This is an on-going study. Data we have been using beginning in 2016 are from an approved application through dbGaP, from 15,507 HRS participants and include single nucleotide polymorphism (SNP) data on just under 2.5 million markers, imputed data on approximately 21 million DNA variants, and phenotype data on disease incidence and prevalence, functioning, biomarkers, mortality, and environmental and behavioral covariates. Our request from NIAGADS would provide us with an additional genetic sample to what we have been using, for the additional data on 3,409 participations (yielding N=18,916 total with harmonized genetic data through NIAGADS) as well as the DNA methylation data for the sub-sample (N=4,018). We will also seek validation of genetic findings in a study from the HRS International Family of Studies cohort, the LASI-DAD. Data usage will not create additional risk to participants. Aims of the project are to (1) Identify genetic networks and pathways that influence human aging, disease, functioning, and longevity; (2) Develop predictive models of aging-related health outcomes using information from gene networks; and (3) Examine how social and environmental conditions interact with genes within these aging-related gene networks. We will implement statistical models to test for associations between genetic variants and the same phenotype data. In moving forward with the additional samples, we will use the HRS genome-wide data to examine genetic signatures of healthspan, lifespan, and cognitive aging. Using these genetic signatures, we plan to (i) run pathway enrichment analysis to identify influential biological pathways, (ii) use them for predictive modeling of morbidity/mortality risk and cognitive aging, and (iii) incorporate information from social and behavioral data to examine GxE interactions. The overall goal of the project is to identify mechanistic gene and environment networks that contribute to aging acceleration or deceleration.

Non-Technical Summary: Aging is the largest risk factor for morbidity and mortality. Previous research using animal models or case-control studies of centenarians have suggested that variations in the pace of aging may be partially explained by genetic and genomic differences. However, few genetic regulators of human lifespan and healthspan have been identified. Furthermore, there is reason to suggest that the pace of aging may be a polygenic trait, for which multiple genes form complex networks that collectively influence aging and longevity phenotypes. These complex genetic networks may further interact with exogenous factors causing variation to arise in health outcomes under diverse environments. The goal of this project is to use advantaged statistical modeling techniques to understand how gene-gene and gene-environment interactions influence longevity and aging-related conditions.

The goal of this study is to identify new genes and mutations that cause or increase risk for Alzheimer disease (AD), as well as protective factors. Individuals and families were selected from the Knight-ADRC (Washington University) and the NIA-LOAD study. Only families with at least three first-degree affected individuals were included. Families with pathogenic variants in the known AD or FTD genes, or in which APOE4 segregated with disease were excluded. At least two cases and one control were selected per family. Cases had an age at onset (AAO) after 65 yo and controls had a larger age at last assessment than the latest AAO within the family. Whole exome (WES) and whole genome sequencing (WGS) was generated for 1,235 individuals (285 families) that together with data from our collaborators and the ADSP family-based cohort (3,449 individuals and 757 families) will provide enough statistical power to identify new genes for AD. Dr. Tanzi (Harvard Medical School) will provide WGS from 400 families from the NIMH Alzheimer disease genetics initiative study. We will perform single variant and gene-based analyses to identify genes and variants that increase risk for disease in AD families. Single variant analysis will consist of a combination of association and segregation analyses. We will run family-based gene-based methods to identify genes that show and overall enrichment of variants in AD cases. We will also look for protective and modifier variants. To do this we will identify families loaded with AD cases, that also include individuals with a high burden of known risk variants but that do not develop the disease (escapees). We will use the sequence data and the family structure to identify variants that segregate with the escapee phenotype. The most promising variants and genes will be replicated in independent datasets (ADSP case-control, ADNI, Knight-ADRC, NIA-LOAD ). We will perform single variant and gene-based analyses to replicate the initial findings, and survival analysis to replicate the protective variants. We will select the most promising variants/genes for functional studies

Family-based approaches led to the identification of disease-causing Alzheimer’s Disease (AD) variants in the genes encoding APP, PSEN1 and PSEN2. The identification of these genes led to the A?-cascade hypothesis and to the development of drugs that target this pathway. Recently, we have identified rare coding variants in TREM2, ABCA7, PLD3 and SORL1 with large effect sizes for risk for AD, confirming that rare coding variants play a role in the etiology of AD. In this proposal, we will identify rare risk and protective alleles using sequence data from families densely affected by AD. We hypothesize that these families are enriched for genetic risk factors. We already have sequence data from 695 families (2,462 individuals), that combined with the ADSP and the NIMH dataset will lead to a dataset of more than 1,042 families (4,684 individuals). Our preliminary results support the flexibility of this approach and strongly suggest that protective and risk variants with large effect size will be found, which will lead to a better understanding of the biology of the disease.

We are studying the effects of rare (minor allele frequency < 5%) genetic variants on the risk of developing late-onset Alzheimer’s Disease (AD). We are interested in variants that have a protective effect in subjects who are at an increased genetic risk, or variants that lead to multiple dementias. Our aim is to identify any genetic variants that are present in the “case” group but not the “AD control” groups for both types of variants. The raw data we receive will be annotated to identify SNP locations and frequencies using existing databases such as 1,000 Genomes. We will filter the data based on genetic models such as compounded heterozygosity, recessive and dominant models to identify different types of variants.

Current genetic understanding of Alzheimer’s Disease (AD) does not fully explain its heritability. The APOE4 allele is a well-established risk factor for the development of Alzheimer’s Disease (AD). However, some individuals who carry APOE4 remain cognitively healthy until advanced ages. Additionally, the cause of mixed dementia pathology development in individuals remains largely unexplained. We aim to identify genetic factors associated with these “protected” and mixed pathology phenotypes.

The proposed study aims to investigate cognitive aging disparities among various racial and ethnic groups, focusing on the role of DNA methylation (DNAm) in mediating these differences. This research addresses the public health challenge posed by higher rates of cognitive decline and dementia in African Americans, Hispanics/Latinos, and Native Americans compared to non-Hispanic Whites. The study hypothesizes that socioeconomic factors (SES) such as education, and occupation, influence DNAm patterns, which in turn affect cognitive health. The study design involves a retrospective analysis of longitudinal data from the Health and Retirement Study (HRS), supplemented by DNAm data. The analysis plan includes developing a predictive algorithm for cognitive age using existing DNAm data, examining the association of SES with DNAm markers, and exploring whether these markers mediate racial/ethnic disparities in cognitive decline rates. The algorithm will be developed by regressing cognitive change slopes against DNAm parameters using elastic net linear regression, resulting in a DNAm biomarker indicative of cognitive age (DNAmCogAge). This biomarker's predictive power for future dementia will be evaluated using multilevel linear mixed models, focusing on its correlation with cognitive decline rates and the influence of race/ethnicity. In addition to the quantitative analysis, the study incorporates a qualitative component through the development of a questionnaire aimed at understanding community perspectives, particularly among older racial and ethnic minorities. This questionnaire will assess knowledge, perceived barriers, and attitudes towards epigenetic biomarkers and dementia risk, informing future efforts to develop DNAm biomarkers of cognitive age and enhance health knowledge in these communities. This study aligns with NIAGADS's mission by utilizing genetic analysis data to uncover mechanisms underlying cognitive aging disparities, bridging scientific advancements with community perspectives to foster a more holistic understanding of dementia risk factors.

Our research focuses on understanding why certain racial and ethnic groups experience faster cognitive decline and higher dementia rates. We're exploring how life experiences, particularly socio-economic factors like education and income, might affect brain health through DNA changes. Using data from previous studies, we aim to develop a new way to measure 'cognitive age' based on these DNA changes. This will help us see if and how these changes link to cognitive decline. Additionally, we are creating a survey to understand community views on this topic, especially among older adults from diverse backgrounds. Our goal is to combine scientific findings with community insights, making our research relevant and beneficial for everyone. This study could lead to better ways of predicting and understanding dementia risk, particularly in underrepresented communities

Objectives: Biological aging has been quantified in DNA-methylation samples of older adults and applied as so-called “methylation profile scores” (MPSs) in separate target samples, including children. This research indicates that biological aging is affected by childhood environments and shows overlap with developmental processes. Because the MPSs were computed using algorithms developed in adults, these studies indicate a molecular link between childhood environments, development, and adult biological aging. Yet, if MPSs can be used to connect development and aging, previous research has only traveled one way, deriving MPSs developed in adults and applying them to samples of children. Researchers have not yet probed if MPSs that reflect childhood development are associated with physical and psychological aging later in life. Study design: Here we examine whether MPSs quantifying childhood socioeconomic contexts and developmental processes – computed in adults– are related to their adult health. We will use published algorithms developed in child methylation samples and apply them to methylation data from the HRS. We focus on MPSs of socioeconomic contexts, age, gestational age, birthweight, and pubertal timing measured in children and adolescents. We will test whether these MPSs computed in adults are related to their health, mortality, retrospective reports of childhood poverty, as well as their MPSs of biological aging. Analysis plan: In preregistered multiple regressions analyses, we will test for associations of MPSs of childhood exposures and development with physical health (BMI, menopause, functional limitations, multimorbidity, mortality), mental health (cognitive impairment, depression), childhood poverty index, and biological aging (PhenoAge, GrimAge and DunedinPACE). Sex and cell composition will be included as covariate controls, and we will test whether associations hold after accounting for substance use, racial identity, and adult SES.

Biological aging has been quantified in DNA-methylation samples of older adults and applied as so-called “methylation profile scores” (MPSs) in separate target samples, including samples of children. Research shows that biological aging is affected by childhood environments and shows overlap with “developmental processes” (e.g., puberty, cognition). Because the MPSs were computed using algorithms developed in adults, these studies indicate a molecular link between childhood environments, development, and adult biological aging. Researchers have not yet probed if MPSs that reflect exposures in childhood or child development are associated with physical and psychological aging later in life. Here we examine whether MPSs quantifying childhood socioeconomic contexts and child developmental processes – computed in adults from the Health and Retirement study – are related to their adult physical and psychological health. By applying MPSs developed in child samples to older adults, we can potentially unravel how early-life factors contribute to later-life health and aging.

Objectives The goal of this study is to perform statistical and bioinformatics analyses and identify DNA methylation changes associated with cognitive data and dementia outcomes collected by the Health and Retirement Study. Our specific aims are: (1) Identify blood DNAm associated with aging, AD diagnosis and longitudinal cognitive outcomes (2) Validate DNAm-based prediction models for identifying subjects with high risk for dementia.Study design. The Health and Retirement Study (HRS) is a longitudinal panel studyAnalysis plan To identify blood DNAm associated with LOAD diagnosis and longitudinal cognitive outcomes, we will fit a mixed effects model with LOAD diagnosis (or longitudinal outcome) as the dependent variable, CpG methylation as the main independent variable, along with age, sex, batch type, estimated blood cell type proportions as covariates. In addition, we will apply our DNAm-based prediction models for LOAD to the Health and Retirement Study data and evaluate the prediction model by computing AUC (area under ROC curve).Phenotypic characteristics. We will evaluate the association of DNA methylation variants with dementia diagnosis and chronological age, adjusting for covariate variables race/ethnicity, APOE genotype, baseline MMSE at time of blood draw, age, sex, education, smoking history.

This study aims to explore how DNA methylation changes are associated dementia outcomes in older adults using data from the Health and Retirement Study. We will identify specific DNA methylation patterns associated with aging and Alzheimer's dementia, while also validating prediction models to identify individuals at high risk for dementia.

Our goal is to determine the joint epigenetic and environmental contributions to ADRD risk that underlie these health disparities. Using existing epigenetic and genetic data, well-characterized dementia phenotypes, and diverse risk factor data, we will analyze a population representative, multi-ethnic aging sample from the Health and Retirement Study (HRS). We aim to (1) test the associations between DNA methylation and dementia phenotypes (prevalent, 8-year incident), stratified by race/ethnicity and test for effect modification by ADRD disparity-related factors (educational attainment, sex, urban/rural); (2) identify associations between longitudinal measures of modifiable risk factors for ADRD and DNA methylation, stratified by race/ethnicity and test for effect modification or mediation by ADRD disparity-related factors; and finally, (3) identify genetic polymorphisms controlling DNA methylation and whether these are enriched in dementia outcomes to evaluate the role of DNA methylation in disease development. This study will likely impact the field of Alzheimer’s research and contribute to public health because it will a) establish the relevance of DNA methylation on ADRD in multiple race/ethnicities; b) elucidate important biological epigenetic mechanisms; c) determine the combined and individual epigenetic-environment interplay contributions to ADRD; and d) consider the effects of sex, educational attainment, race/ethnicity, younger age groups, and urban/rural status in the same study where comparisons of relative contribution to risk can be made. Here, we have the opportunity to simultaneously and substantially improve our understanding of the genetic and environmental etiologic contributions to health disparities in ADRD.

The overall purpose of this proposal is to identify modifiable risk factors for Alzheimer’s disease and related dementias that influence DNA methylation and dementia status among groups at increased risk for dementia including women, minorities, rural inhabitants, and those with low educational attainment. Results from this proposal may provide an opportunity to identify epigenetic components that contribute to the prevalence and risk of dementia that could lead to a mechanistic understanding or targeted interventions that may substantially decrease the burden of Alzheimer’s disease and related dementias in the US population

Moderate to severe impairments in cognitive functioning are a primary hallmark of Alzheimer's disease (AD) and Alzheimer's Disease Related Dementias (ADRD), a class of disorders affecting ~30% of the population by age 90. Currently, scientists hypothesize that the AD/ADRD disease process begins decades prior to the low functioning observed at the time of diagnosis. The ideal study design to gain insight into liability in prodromal and preclinical stages of AD/ADRD would involve collecting data on a wide range of measures from a large group of participants across the entirety of the life course. However, this data collection strategy includes major pragmatic barriers. Longitudinal study designs that might identify prospective risk factors of later life disease onset carry high participant and financial costs and take decades to produce conclusive results. Because of these limitations, much of the literature has been left to speculate in a piecemeal fashion on what characterizes the AD/ADRD prodromal period. However, research into these prodromal and preclinical periods holds significant promise for improving prevention and intervention efforts by identifying at-risk individuals and those who are at an earlier, and likely more intervenable, stage of disease.We will analyze the links between genotypes and phenotypes to investigate the onset times of risk factors for AD/ADRD across the life course using data from the HRS. Our insights will focus on pinpointing specific periods when these outcomes manifest as individuals age. We hypothesize that genetic data and structural equation modeling can help identify the specific times when Alzheimer's risk factors emerge as individuals age throughout their lives. Our team proposes to leverage the Genomic Structural Equation Modeling (Genomic SEM) framework to identify genetic risk pathways to AD/ADRD across the life course using existing data from large epidemiological studies that index different age ranges. Results from this study will add previously unseen levels of precision to our understanding of when genetic risk for AD/ADRD emerges across the life course and which specific risk factors index its onset.\

We will analyze the links between genotypes and phenotypes to investigate the onset times of risk factors for Alzheimer's and Dementia (AD) across the life course using data from the Health and Retirement Study (HRS). Our insights will focus on pinpointing specific periods when these outcomes manifest as individuals age. We hypothesize that genetic data and structural equation modeling can help identify the specific times when Alzheimer's risk factors emerge as individuals age throughout their lives.