Ageing represents the most important risk factor for chronic diseases. While everybody ages the pace of biological ageing diverges broadly amongst individuals with the same chronological age due to inter-individual differences in genetic and environmental factors. This is because the ‘real’ pace of ageing on an individual level is dictated by the biological age, a synthetic highly informative measure encapsulating genetic factors, endogenous/exogenous stressors (‘Entropy’) and anti-stressor buffers (‘Resilience’). A robust proxy of biological ageing represents an urgent yet unmet endeavour to effectively tackle the soaring epidemic of age-related diseases in western societies.
Our research aims to (i) deliver a personalised proxy of biological age by applying cutting-edge supervised machine-learning to extract multi-parametric multi-organ magnetic resonance imaging (MRI) datasets and formulate high dimensional regression models to derive the whole-body MRI ageotype as a proxy of biological ageing; (ii) explore the genetic signature of the whole-body MRI ageotype by genome-wide-association study and formulate Mendelian Randomisation analysis to assess the causal link between this ageotype and development of age-related diseases; (iii) to carry out a phenome-wide association study (PheWAS) to relate the whole-body MRI ageotype and organ-specific MRI ageotype to multidimensional non-imaging age-based phenotypes.
Our research will deliver a robust and comprehensive proxy of biological age to empower clinical decision making and help to transform the public health strategies for tackling age-related diseases. Additionally, it will inform about potential biological pathways underpinning the transition from normative (physiological) ageing to chronic disease fostering research of novel anti-ageing interventions.
