This study aims to elucidate the role of skeletal muscle in systemic diseases and aging by integrating multi-omics data to identify key molecular signatures and modifiable risk factors. We will investigate the dynamic interactions between muscle structural features (mass, fat infiltration), molecular profiles (proteomics, metabolomics), and systemic conditions such as cardiovascular disease, metabolic syndrome, and frailty. Using advanced imaging (MRI-derived muscle metrics) and multi-omics data (Olink proteomics, Nightingale metabolomics), we will characterize muscle-specific biomarkers and their associations with disease progression and aging trajectories. Machine learning approaches will be employed to develop predictive models for early identification of high-risk individuals, while Mendelian randomization will clarify causal relationships between muscle traits and disease outcomes. The study will also explore how lifestyle factors (physical activity, nutrition) modulate these associations, providing a foundation for targeted interventions. By bridging muscle biology with clinical phenotypes, this research will advance precision medicine approaches for aging-related musculoskeletal and systemic disorders, offering novel diagnostic tools and therapeutic strategies.
