Chronic kidney dysfunction is a common age-related condition and is increasingly recognised as a systemic disorder affecting multiple organs beyond the kidney. Growing evidence suggests that impaired renal function may accelerate biological ageing through chronic inflammation, metabolic dysregulation, vascular injury and immune dysfunction. However, the contribution of kidney dysfunction to brain ageing and other systemic ageing phenotypes, as well as the underlying genetic and molecular mechanisms, remains incompletely understood.
The primary objective of this project is to investigate the association between kidney function and ageing-related phenotypes, with a particular focus on brain ageing, using large-scale multi-modal data from the UK Biobank. Kidney function will be characterised using estimated glomerular filtration rate (eGFR), albuminuria and longitudinal kidney function trajectories. Brain ageing will be assessed using structural and diffusion MRI-derived measures, together with machine-learning-based brain age prediction models to estimate brain age gap as an index of accelerated or resilient brain ageing.
Secondary objectives include examining systemic inflammation, immune-related biomarkers, circulating proteins and metabolic traits as potential mediators linking kidney dysfunction to ageing phenotypes. Where available, plasma proteomic and metabolomic data will be integrated to characterise molecular pathways involved in kidney-related ageing processes. Genetic analyses, including genome-wide association studies, polygenic risk scores and selected analyses of rare variants from exome or genome sequencing data, will be used to explore shared genetic architecture between kidney function and ageing-related traits.
Advanced statistical modelling, causal inference approaches such as Mendelian randomisation, and machine-learning methods will be applied to integrate imaging, genetic, molecular and clinical data. By leveraging the breadth of UK Biobank d
