Development and validation of improved epigenetic markers of biological ageing

Objectives
In this project, we aim to develop DNA methylation (DNAm)-based markers that capture additional aspects of biological ageing and ultimately improve disease risk prediction.
We will use genome-wide blood DNA methylation data and phenotypic / clinical data from the Health Retirement Study (HRS), including objective and subjective health indicators, to:
i) Carry out epigenome-wide association studies (EWAS) of subjective health and ageing-related biomarkers;
ii) Use machine learning methods to develop and validate DNA methylation-based markers of biological ageing and assess their contribution to disease risk prediction.

Study design
The project will use multiple prospective cohorts to implement a discovery-validation analytical strategy, and assess the associations of novel methylation-based markers with ageing-related phenotypes including self-reported health variables and disease / mortality risk.

Analysis plan
1) Carry out EWAS of self-reported health variables (e.g. physical and psychological functioning) and ageing-related biomarkers to uncover the molecular mechanisms underlying subjective and objective health.
2) Apply machine learning methods to multiple large-scale datasets using a discovery and validation strategy to generate novel, improved epigenetic markers of ageing.
3) Asses the association of these novel epigenetic markers of ageing with major health-related variables, including lifestyle-related variables (e.g. smoking, alcohol consumption, diet), clinical markers (e.g. CRP, blood pressure, gait speed, grip strength), and risk of disease (e.g. cancers, cardiovascular, Alzheimer’s Disease).

Age and ageing are fundamental risk factors of many chronic diseases. Biomarkers of ageing using large-scale molecular data are increasingly being developed and used for disease risk prediction and health assessment.
The aim of this project is to develop and validate novel ageing biomarkers using genome-wide blood DNA methylation data. We will further the capacity of these markers to predict ageing-related health traits such as functional decline, cancers, cardiovascular diseases, dementia, and mortality. Our ultimate goal is to improve biological age estimation and disease risk prediction by using molecular-level data that can be obtained from one blood draw.