Leveraging Proteomics and Proteogenomics for Understanding Osteoporosis and Other Musculoskeletal Diseases

Osteoporosis and musculoskeletal diseases, including osteoarthritis and sarcopenia, contribute substantially to global morbidity and healthcare costs. This review explores how proteogenomics integrates genomic and proteomic data to refine disease classification, identify causal pathways, and accelerate biomarker and drug target discovery.

Large-scale proteomic studies, including UK Biobank-based research, have identified circulating proteins associated with musculoskeletal disease risk and progression. Proteomic risk models outperform conventional clinical metrics in predicting outcomes. Genomics, proteomics, and proteogenomics have facilitated the identification of causal markers through methods such as genome-wide association studies, effector gene mapping, and proteome-wide Mendelian randomization. Pathways implicated in disease mechanisms include extracellular matrix remodeling (e.g., COL6A3, COL9A1), metabolic regulation (e.g., IGFBP2, GDF15), inflammatory processes (e.g., TNF family ligands, CXCL17), and sex-hormone-related signaling (e.g., FSHB, SHBG). While these biological processes contribute across osteoporosis, osteoarthritis, and sarcopenia, distinct proteins have also been linked to disease-specific pathophysiology, offering potential therapeutic targets.

Genomics, proteomics, and proteogenomics refine our understanding of musculoskeletal conditions and hold strong potential for improving early diagnosis, enhancing risk stratification, and advancing precision treatments.