Unravelling Age-Related Multimorbidity through Biomarker Insights

About the Project

Our study aims to advance ageing research using artificial intelligence (AI) to analyse UK Biobank (UKB) health data. We will explore how clusters of inflammatory biomarkers are linked to multiple long-term conditions (MLTCs), such as heart disease, respiratory issues, and diabetes. By identifying key biomarkers that signal the early onset of these conditions, we hope to improve prevention, enhance quality of life, and reduce mortality rates.

Age is the primary risk factor for MLTCs, but not everyone ages similarly. Our research will compare biomarker profiles in age-matched groups with and without MLTCs, examining how these profiles change over time. This will help us understand the role of chronic inflammation in disease development.

We will use AI to uncover biomarker clusters and investigate their relationship with disease profiles. Our team includes epidemiology, data science, AI, geriatrics, and machine learning experts. This work will provide valuable insights into ageing and health, potentially leading to innovative clinical applications and easing the burden on healthcare systems.

The project will be managed through a collaborative approach involving regular meetings and progress reviews with both the university and supervisor/s. The student will be based at the University of Bath.