Biological ageing is a process of cumulative degeneration of organs, cells, and molecules. It is an important risk factor of a range of adverse health events. Proteomic and metabolomic biomarkers are predictive of risk of age-related phenotypes, such as diabetes, cancers, and mortality. Recent developments in technology have enabled more precise estimation of biological age using omics data, which can improve prediction of disease risks.
Subjective aspects of health, collected via questionnaires, play an irreplaceable role in health assessments and potentially in estimating ageing progress beyond more objective factors such as sociodemographic and lifestyle factors, medical conditions, and biomarkers. For example, self-rated health is a subjective summative marker that captures an individual’s perception of their overall physical and mental health and predicts disease risk independently of other known risk factors. There is increasing evidence of interactions between subjective and objective markers of health. Considering both objective and subjective variables could enable more precise biological age estimations.
In this project, we aim to improve risk prediction of ageing-related health outcomes using objective molecular-level and subjective questionnaire-based data by:
i) Developing biological ageing markers with proteomic, metabolomic, and / or biochemistry data using machine learning methods with training and validation, and assessing their roles in disease and mortality risk predictions; and
ii) Assessing the interactions between subjective and objective indicators of health or ageing and their contribution to the prediction of ageing-related adverse health events.
This project will enhance our understanding of the role of ageing biomarkers in predicting adverse health events and the molecular underpinnings of subjective health, and will highlight the utility of both objective and subjective health assessments as indicators of disease risk.