Research Questions:
– Can ML effectively predict the health consequences of alcohol consumption using molecular and clinical biomarkers?
– How do ML-inferred relationships compare to causal estimates from Mendelian Randomisation?
– What are the advantages and limitations of ML vs. MR in identifying causal effects of alcohol consumption?
– Can a hybrid approach integrating ML and MR improve the accuracy of causal inference in dietary and lifestyle research?
Research Objectives:
Primary Objectives
– Develop ML models to predict health outcomes (e.g., liver disease, cardiovascular risk) based on alcohol consumption and other lifestyle factors.
– Compare ML-derived causal inferences with MR-based findings.
– Evaluate the strengths and weaknesses of ML vs. MR in detecting causal effects.
Secondary Objectives
– Identify key biomarkers influenced by alcohol using feature importance techniques (e.g., SHAP analysis).
– Investigate the impact of confounding variables on ML predictions.
– Explore the integration of ML and MR for improved causal inference in alcohol-related research.
Why This Study?
– Public Health Relevance: Alcohol consumption contributes to major global disease burdens. Identifying causal pathways can inform preventive strategies and personalized interventions.
– Limitations of MR: MR is a robust causal inference method but assumes no pleiotropy and valid genetic instruments, which may not always hold.
– Advantages of ML: ML can model complex, nonlinear interactions between alcohol intake, biomarkers, and health outcomes.
– Novel Contribution: This study is the first to directly compare ML-based causal inference with MR in the context of alcohol-related health consequences.