This research aims to explore cardiometabolic diseases by leveraging large-scale longitudinal health records. The focus is on understanding the associations, causal relationships, and modeling genetic, environmental, lifestyle, and clinical factors influencing the onset, progression, outcomes, and treatment effectiveness for these diseases in real-world settings. Association Analysis will identify associations between various factors and cardiometabolic disease outcomes; Causal Inference analysis will use techniques like Mendelian randomization and propensity score matching to infer causal relationships; Modeling analysis will develop predictive models incorporating genetic, environmental, lifestyle, and clinical factors, using machine learning and traditional statistical approaches.
The plan includes Exploratory Data Analysis to identify trends, hypothesis testing to evaluate the influence of specific factors, and model validation using cross-validation techniques. The research aims to identify key determinants of cardiometabolic diseases, understand causal pathways, and develop predictive models that could guide personalized treatment strategies.
