This 3-year study investigates the systemic interactions driving multimorbidities in endocrine-metabolic disorders (EMDs) through a novel multi-omics framework. The research addresses five interconnected questions: (1) How does genetic susceptibility influence comorbidity patterns? (2) What environmental factors exacerbate disease progression? (3) How do circulating metabolites mediate organ crosstalk? (4) Do epigenetic mechanisms regulate multimorbidity risks? and (5) Can machine learning optimize personalized risk stratification? Objectives integrate genomic (GWAS), metabolomic (NMR-based 2923 metabolites), and imaging (100k FDG-PET scans) data from the full UK Biobank cohort (N!500k) to identify core genetic pathways via GWAS-PheWAS and MAGMA enrichment analysis, validated by Mendelian randomization (MR-Egger). Environmental exposures will be quantified using geospatial analytics and mixed-effects Cox models, while XGBoost algorithms will discover early microvascular biomarkers by integrating metabolites (GDF15/NT-proBNP) and FDG-PET texture features (GLCM). Epigenetic validation will employ 450K Illumina arrays and bidirectional MR-Egger to exclude pleiotropy. The project develops an open-source toolkit (GitHub) with SHAP interpretability and FHIR-EHR integration. Scientific rationale derives from UK Biobank’s longitudinal data and multi-omics integration, addressing gaps in shared biological pathways for EMDs-a critical frontier given the global burden of multimorbidity. Expected contributions include revealing “gene-environment-epigenome” networks for therapeutic targeting, deploying scalable tools to reduce hospitalizations , and informing WHO guidelines on air pollution thresholds. Ethical compliance is ensured via UK Biobank’s RAP platform and GDPR adherence, aligning with the Biobank’s mission to advance translational research and tackle multimorbidity-a growing public health crisis exacerbated by aging populations and environmental change.
