The proposed research aims to investigate the correlation between Alzheimer’s disease-specific biomarkers and those associated with other major diseases, including cancer and neurodegenerative disorders, to identify shared pathological pathways and optimal diagnostic timing. Using data from a cohort of 10,000-15,000 participants from the UK Biobank, the study will employ a comprehensive analysis of biomarker data, including protein levels, reported genetic markers, and patient records.
The research will utilise statistical models, such as multivariate regression and principal component analysis, to identify significant relationships between Alzheimer’s biomarkers and those of other diseases. Additionally, machine learning models, including clustering algorithms and random forests, will be applied to uncover common biomarker profiles across diseases and determine the most relevant clinical timepoints for biomarker assessment. Time-series and survival analysis will be conducted to correlate biomarker expression with disease onset and progression.
The goal is to refine diagnostic strategies by identifying shared biomarker signatures and optimal assessment timings, potentially leading to earlier detection and more personalised treatment strategies. The findings from this research could significantly impact public health by improving diagnostic practices and outcomes for patients with multi-morbidity.
