With global populations now living longer than ever, it is critical to identify those at risk of accelerated ageing using early biomarkers which stratify or potentially modify the risk of frailty progression. Thus, emerging research is focusing on how epigenetic signatures can provide biological insights into frailty – or even predict future frailty progression across the population. The Irish Longitudinal Study on Ageing (TILDA) have developed a cross-sectional epigenetic biomarker of frailty, but the metric still requires assessment as a functional biomarker. It remains unknown to what extent epigenetic frailty is associated with adverse health outcomes or classical risk factors. Furthermore, external validation is still required.
This project will utilise 10-year health data from TILDA (n = 500) to assess epigenetic frailty using classical risk factor data, such as smoking, diet, and exercise, and adverse health outcome data, such as falls, disability, and mortality. To perform this analysis, classical statistical analyses such as binomial/linear regression modelling will be performed, as well as more complex, cutting-edge machine learning frameworks of feature selection, such as Elastic Net Regularization. A well as internal validation in the TILDA cohort, this study will perform external validation of epigenetic frailty, using publicly available epigenetic datasets from older adults (aged 50+, n = 1,000+), and develop a pipeline to facilitate the stratification of frailty risk using epigenetic data. In doing so, this study will define the associations between epigenetic frailty and classical predictors of frailty, allow comparison of the metric to classical, objective frailty measures, and evaluate the performance of the metric in predicting adverse health outcomes. This study will potentially generate a validated pipeline of analysis by which epigenetic data can be used to stratify frailty risk in independent populations, informing future analysis of frailty and risk stratification in the population.
