Exploring antidiabetic drug targets as potential disease-modifying agents in osteoarthritis
Kai Fu, Shucheng Si, Xinzhong Jin, Yan Zhang, Vicky Duong, Qianying Cai, Guangyi Li, Win Min Oo, Xianyou Zheng, Cindy G. Boer, Yuqing Zhang, Xiaojuan Wei, Changqing Zhang, Youshui Gao, David J. Hunter
Abstract
BACKGROUND: Osteoarthritis is a leading cause of disability, and disease-modifying osteoarthritis drugs (DMOADs) could represent a pivotal advancement in treatment. Identifying the potential of antidiabetic medications as DMOADs could impact patient care significantly. METHODS: We designed a comprehensive analysis pipeline involving two-sample Mendelian Randomization (MR) (genetic proxies for antidiabetic drug targets), summary-based MR (SMR) (for mRNA), and colocalisation (for drug-target genes) to assess their causal relationship with 12 osteoarthritis phenotypes. Summary statistics from the largest genome-wide association meta-analysis (GWAS) of osteoarthritis and gene expression data from the eQTLGen consortium were utilised. FINDINGS: ) osteoarthritis. Metformin and GLP1-RA, targeting GPD1 and GLP1R respectively, were associated with reduced risk of knee and finger osteoarthritis. In the SMR analyses, gene expression of KCNJ11, GANAB, ABCA1, and GSTP1, targeted by antidiabetic drugs, was significantly linked to at least one osteoarthritis phenotype and was replicated across at least two gene expression datasets. Additionally, increased KCNJ11 expression was related to decreased osteoarthritis risk and co-localised with at least one osteoarthritis phenotype. INTERPRETATION: Our findings suggest a potential therapeutic role for antidiabetic drugs in treating osteoarthritis. The results indicate that certain antidiabetic drug targets may modify disease progression, with implications for developing targeted DMOADs. FUNDING: This study was funded by the Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant (2022), the Shanghai Municipal Health Commission Health Industry Clinical Research Project (Grant No. 20224Y0139), Beijing Natural Science Foundation (Grant No. 7244458), and the Postdoctoral Fellowship Program (Grade C) of China Postdoctoral Science Foundation (Grant No. GZC20230130).