Study Population: The UK Biobank cohort consists of 500,000 participants aged 40-69 at baseline, with extensive follow-up for health outcomes. Data Availability:Exposome: Data on environmental exposures (e.g., air pollution, smoking, diet, physical activity, occupational exposures) from questionnaires, external databases, and biomonitoring. Genome: Genotype data for over 800,000 SNPs, including imputed variants, allowing the construction of Polygenic Risk Scores (PRS) for chronic diseases. Metabolome: Blood and urine metabolomics data measured by NMR spectroscopy and mass spectrometry, providing thousands of metabolites that reflect both endogenous processes and external exposures. Chronic Diseases Studied: Focus on diseases with high public health impact: cardiovascular diseases (e.g., heart disease, stroke), type 2 diabetes, cancers (e.g., breast, colorectal cancer), and respiratory diseases.
Statistical Analysis: Fit generalized linear models (GLMs) to assess genome-exposome interactions (e.g., PRS × air pollution) on disease outcomes. Conduct metabolome-exposome interaction analysis, for example, assessing how the effect of diet on disease risk is mediated by metabolites (e.g., fatty acids). Use generalized estimating equations (GEE) to account for repeated measures and clustered data when modeling joint effects of the exposome, genome, and metabolome on chronic diseases. Assess interactions of PRS with environmental exposures (e.g., physical activity, pollutants). Implement elastic net regularization for feature selection from high-dimensional metabolomics and environmental data. Use random forest or deep learning models to explore non-linear interactions between the genome, exposome, and metabolome. Apply Latent Class Analysis (LCA) or Latent Growth Mixture Models (LGMM) to identify distinct clusters of individuals based on their exposome and metabolome profiles, and analyze their association with chronic disease risk.
