NG00185 – DNA methylation in saliva, buffy coat and PBMC samples in CATSLife

The TranslAGE research project aims to evaluate and optimize DNA methylation (DNAm)-based aging biomarkers for geroscience clinical trials. We will benchmark existing biomarkers to identify those that are: (1) responsive to aging interventions, (2) prognostic for age-related diseases and outcomes, and (3) stable over time without intervention. This will guide the development of next-generation biomarkers. Study Design: We will analyze DNAm and phenotypic data from the Health and Retirement Study (HRS), evaluating how various DNAm biomarkers relate to mortality, disease, and physical and cognitive function. All pipelines and variable names will be harmonized with other major studies, including the Baltimore Longitudinal Study of Aging (BLSA) and Framingham Heart Study, enabling cross-study comparison. Analysis Plan: 1. Data Harmonization: Core variables (age, sex, death, follow-up time) will be standardized using a common data dictionary. 2. Biomarker Computation: Multiple DNAm biomarkers will be calculated using https://github.com/HigginsChenLab/methylCIPHER and Z-scored for comparability. 3. Model Fitting: Cox models for mortality and disease onset, Logistic regression for incident disease, Linear models for continuous traits like cognition, BMI, gait speed. 4. Performance Evaluation: Predictive performance will be assessed using hazard ratios, concordance index (C-index), and AUCs. Biomarkers will be ranked across domains, and visualizations will summarize associations. 5. Cross-Study Integration: Summary statistics will be compared with those from external cohorts, intervention studies (e.g., effect sizes), and test-retest reliability datasets (e.g., intraclass correlation) 6. Composite Development: Top biomarkers with strong prognostic power, responsiveness, and stability will be combined into a composite score. Novel mortality, morbidity, and frailty predictors will be trained in external datasets and validated in HRS. Planned collaborations: Sofiya Milman at Albert Einstein College of Medicine (relating DNAm biomarkers to frailty), Jessica Lasky-Su at Brigham and Women’s Hospital (relating DNAm proxies of serum proteins and metabolites to aging outcomes).

The TranslAGE knowledgebase aims to improve how we measure biological aging, helping scientists evaluate new treatments that target the aging process. We will use DNA methylation from these and other studies to identify biomarkers that predict future health, respond to anti-aging therapies, and remain stable over time. We will calculate many different biological age scores from blood samples and test how well each one predicts outcomes like memory, physical ability, disease, and mortality. We will combine this information with results from other studies to identify the best scores that not only predict future age-related disease, but are also responsive to treatments intended to prevent future disease. We will also use insights from this study to develop better biological age scores that may eventually be used to assess whether interventions are successfully modifying the aging process to promote healthier, longer lives.

This project will develop DNA methylation (DNAm)-based surrogates of clinical, functional, metabolomic, proteomic and other molecular biomarkers, and integrate them into interpretable physiological system- and disease-specific predictors of biological ageing. The objectives are to improve prediction of mortality, frailty, disability, cognitive decline, ADRD/dementia and other age-related diseases, and to identify pathways underlying multisystem ageing. The study will use an observational cohort design. HRS will serve as the primary training dataset because it links DNAm profiles with clinical, functional, genetic and longitudinal health data. Other DNAm datasets will be used as external validation cohorts to assess generalisability across populations and study settings. Analyses will include epigenome-wide association and differential methylation analyses to identify CpGs associated with target biomarkers and ageing-related phenotypes. Penalised regression, primarily elastic net with nested cross-validation, will be used to train DNAm surrogate models. Other machine-learning methods may be explored where appropriate. DNAm surrogates will be grouped into physiological systems or disease domains using biological annotation, clustering and model-based approaches. The resulting DNAm proxies and integrated scores will be evaluated in association with mortality, multimorbidity, frailty, disability, functional performance, cognitive performance, cognitive decline, ADRD/dementia and other age-related diseases. Where genotype data are available, genome-wide variants and polygenic risk scores will be assessed in relation to DNAm surrogates and ageing-related phenotypes to evaluate genetic contributions to biological ageing and disease susceptibility. Restricted data will be analysed only within approved secure environments. Exported results will be aggregate only, such as regression coefficients, hazard ratios, odds ratios, summary statistics and model performance metrics. No individual-level data, direct identifiers or small-cell outputs will be exported. Planned collaborators include Prof Andrea B. Maier and researchers at the Academy for Healthy Longevity, NUS.

Ageing affects many parts of the body, but current biological ageing tests do not fully capture this complexity. This project aims to develop new DNA methylation-based measures that reflect different aspects of health, including clinical markers, physical function, metabolism and age-related diseases. DNA methylation is a chemical mark on DNA that can provide information about biological ageing. The Health and Retirement Study will be used as the main dataset to develop these measures because it includes DNA methylation data linked with long-term health information. Other DNA methylation datasets will be used to test whether the measures work well in different populations. The goal is to create more accurate and interpretable tools for studying ageing, predicting risks such as frailty, disability, dementia and mortality, and understanding how different body systems age together. Findings will be reported only as summary results, with no release of individual-level participant data.

Objective: Utilize ADSP data sets to identify genes & specific genetic variants that confer risk for or protection from Alzheimer disease. Aim 1: Using combined WGS/WES across the ADSP Discovery, Disc-Ext, and FUS Phases, including single nucleotide variants, small insertion/deletions, and structural variants. We will: Aim 1a. Perform whole genome single variant and rare variant case/control association analyses of AD using ADSP and other available data; Aim 1b. Target protective variant identification via association analysis using selected controls within the ADSP data and performing meta analysis across association results based on selected controls from non-ADSP data sets. Aim 1c. Perform endophenotype analyses including cognitive function measures, hippocampal volume and circulation beta-amyloid ADSP data in subjects for which these measures are available. Meta analysis will be conducted across ADSP and non-ADSP analysis results. Aim 2: To leverage ethnically-diverse and admixed populations to identify AD variants we will: Aim 2a. Estimate and account for global and local ancestry in all analyses; Aim 2b. Perform admixture mapping in samples of admixed ancestry; and Aim 2c. Perform ethnicity-specific and trans-ethnic meta-analyses. Aim 3: To identify putative therapeutic targets through functional characterization of genes and networks via bioinformatics, integrative omics analyses. We will: Aim 3a. Annotate variants with their functional consequences using bioinformatic tools and publicly available “omics” data. Aim 3b. Prioritize results, group variants with shared function, and identify key genes functionally related to AD via weighted association analyses and network approaches. Analyses will be performed in coordination with the following PIs. Coordination will involve sharing expertise, analysis plans or analysis results. No individual level data will be shared across institutions. Philip De Jager, Columbia University; Eric Boerwinkle & Myriam Fornage, UT Health Science Center at Houston; Sudha Seshadri, UT Health Science Center at San Antonio; Ellen Wijsman, University of Washington; William Salerno, Baylor College of Medicine.

This proposal seeks to analyze existing genetic sequencing data generated as part of the Alzheimer’s Disease Sequencing Project (ADSP) including the ADSP Follow-up Study (FUS) with the goal of identifying genes and specific changes within those genes that either confer risk for Alzheimer’s disease or provide protection from Alzheimer’s disease. Analytic challenges include analysis of whole genome sequencing data, appropriately accounting for population structure across European ancestry, Hispanic, and African American participants, and interpreting results in the context of other genomic data available.