| Title: | Whole-body MRI-based ageotype as a holistic proxy of biological ageing: a transformative approach to age-related diseases |
| Lead Institution: | King's College London |
| Principal investigator: | Dr Pier-Giorgio Masci |
Application 82189
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About
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.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 16745 | A streamlined CMR-derived machine-learning model for estimating cardiovascular biological age: development and validation in the UK-biobank and multi-ethnic study of atherosclerosis | Pier-Giorgio Masci (+17) | 2025 | European Heart Journal - Cardiovascular Imaging |
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