Introduction:
Multimorbidity, defined as the coexistence of two or more chronic conditions in an individual, presents a growing challenge to healthcare systems, influencing patient outcomes and healthcare strategies. Our proposed study aims to leverage the comprehensive data resource of the UK Biobank (UKB) to investigate the intricate biomarker profiles associated with multimorbidity, employing cutting-edge data-mining techniques such as association rules (AR) analysis and differential expression analysis.
Problem Statement:
Multimorbidity leads to significantly worsened health outcomes and increased healthcare use and costs. However, the biomarker profiles that signal the onset and progression of multiple chronic diseases are poorly understood, particularly in terms of their combined effects over the life course. This knowledge gap is a pressing issue, as it hampers the development of targeted interventions and predictive models essential for early diagnosis and personalised treatment strategies. Our study aims to address this urgent need to understand biomarker profiles in multimorbidity.
Importance of the Study:
This study can generate pilot data and pave the way for larger UKRI-MRC funding. It aims to identify novel biomarker clusters and their trajectories in individuals with multimorbidity using a large, richly characterised cohort. By understanding these profiles, we can better predict disease progression, tailor interventions, and ultimately improve the management and outcomes of patients with multiple chronic conditions. This study can significantly advance our understanding of multimorbidity and open doors for future collaboration and funding opportunities.
