Research question: Osteoarthritis is a highly prevalent degenerative joint disease, affecting approximately 15% of the global population, with more than 500 million individuals diagnosed per year. In advanced stages, osteoarthritis can lead to disability, imposing a substantial medical burden on society. However, there are currently no ideal biological interventions available for its treatment.
Objective: To address this issue, the current project aims to adopt an integrated approach by combining genomic, metabolomic, and comprehensive epidemiological data from databases to investigate the risk factors associated with osteoarthritis and its severity. Additionally, we are committed to leveraging machine learning and other advanced methods to identify novel biomarkers, thereby uncovering new therapeutic targets for osteoarthritis.
Scientific rationale: Previous large-scale studies have provided extensive evidence on risk factors for the onset and progression of osteoarthritis. Recent research has increasingly focused on investigating more microscopic biomarkers, such as multi-omics variables including proteomics, metabolomics, and genomics. These approaches offer stronger evidence for the associations between exposure factors and the disease, as well as reliable therapeutic targets. To achieve this goal, our project will integrate comprehensive epidemiological data with multi-omics data (proteomics, metabolomics, and genomics) to investigate risk factors and biomarkers. On one hand, this will provide more robust evidence for traditional clinical risk factors; on the other hand, machine learning methods and multi-omics data will be leveraged to uncover novel biomarkers. This approach aims to identify potential biological targets for treatment and offer new insights into the causal network connecting macroscopic and microscopic factors in osteoarthritis.
