The proposed research seeks to identify core biomarkers and pharmacological targets in cardiovascular diseases (CVD) and their related comorbidities, with a specific focus on circadian rhythm influences. By accessing different timepoints within the UK Biobank data, the study will explore how circadian variations affect CVD risk factors, molecular signatures, and treatment outcomes. Advanced statistical and machine learning methods will be applied to identify genetic variants, molecular pathways, and pharmacological targets. The research aims to uncover insights into personalized treatment strategies, considering both genetic and circadian influences, to improve prevention and management of CVD and related comorbidities such as diabetes and hypertension. The results will contribute to public health by revealing actionable insights into disease mechanisms and therapeutic options.
