This project aims to develop and validate multi-biomarker models of biological ageing and disease risk using the UK Biobank resource. Chronological age does not fully capture inter-individual differences in physiological decline or disease susceptibility. We hypothesise that combinations of routinely measured blood biomarkers, physical measures, lifestyle factors, and selected imaging-derived phenotypes can be used to construct robust indicators of biological age and organ-specific health status.
The primary research questions are: (1) Which combinations of biomarkers and physiological measures best predict all-cause mortality and major disease outcomes? (2) Can organ-system-specific biomarker panels be derived to estimate differential ageing trajectories (e.g., cardiovascular, metabolic, renal, hepatic, and inflammatory ageing)? (3) How do lifestyle, socioeconomic, and clinical factors modulate the gap between biological and chronological age?
The objectives are to: (i) build and validate machine learning models to estimate biological age using multi-modal UK Biobank data; (ii) derive organ-specific ageing and risk scores; (iii) evaluate their associations with prospective health outcomes including cardiovascular disease, diabetes, cancer, and mortality; and (iv) assess their potential utility for risk stratification and preventive health research.
The scientific rationale is that early detection of accelerated biological ageing and subclinical organ dysfunction may enable improved disease prevention strategies. UK Biobank’s scale, longitudinal follow-up, and depth of phenotyping provide a unique opportunity to construct and validate such models in a large, well-characterised population. The results of this research are intended to advance population health research, improve understanding of ageing heterogeneity, and support the development of precision prevention approaches.