**Research questions**: What multi-omics signatures drive aging-related comorbidities? Can machine learning and Mendelian randomization (MR) validate causal biomarkers for these comorbidities to guide therapeutic development?
**Objectives**: 1. Define aging comorbidity phenotypes using UKB clinical/follow-up data; 2. Integrate genomic/transcriptomic/proteomic data to screen candidate biomarkers; 3. Apply MR to confirm causal targets; 4. Develop and validate a machine learning-based predictive model for aging comorbidities.
**Scientific rationale**: Aging comorbidities pose a critical global health burden, with complex molecular mechanisms yet to be fully elucidated. UKB’s large, phenotypically detailed cohort enables high-power multi-omics analyses, addressing limitations of small-scale studies. Machine learning excels at decoding complex genotype-phenotype relationships, while MR mitigates confounding to ensure reliable causal inference-key for identifying actionable therapeutic targets. This work fills gaps in precision geriatric medicine by linking molecular signatures to clinical outcomes, ultimately supporting the development of targeted interventions and predictive tools to reduce disease burden.
