Degenerative musculoskeletal diseases, including osteoporosis, osteoarthritis, degenerative spinal disease, and sarcopenia, are age-related conditions that primarily affect the elderly. As the population ages, the incidence, prevalence and socio-economic impact of degenerative musculoskeletal diseases are increasing, making them a major public health concern. However, current knowledge of degenerative musculoskeletal diseases remains limited, resulting in imperfect early diagnosis and clinical management of these complex conditions. To address this gap, this study aims to identify robust biomarkers, stratify patients at different risk levels and discover potential therapeutics that can aid in the early detection, accurate diagnosis and effective treatment of degenerative musculoskeletal diseases.
Various pathological changes are involved in the degenerative musculoskeletal diseases, including long-term chronic inflammation, extracellular matrix degradation, and ectopic bone formation. These changes alter circulating proteins, blood metabolome, and even radiological signs of the musculoskeletal system, providing unique opportunities to uncover novel biomarkers and improve our understanding of the etiology of these diseases. By integrating the wealth of genetics, metabolomics, proteomics, radiomics and comprehensive epidemiological data, we are able to connect the molecular phenotypes with clinical and disease phenotypes to identify novel biomarkers and therapeutic targets through multidimensional analyses. Our methods will include conventional biostatistical approaches, modern explainable machine learning algorithms, Mendelian randomization, polygenic risk score analysis, and network-based causal modelling. Analyzing multi-omics and epidemiological information will enable us to identify new risk factors and potential biomarkers, as well as the complex causal relationships underlying degenerative musculoskeletal diseases.
