Identification of a differential metabolite-based signature in patients with late-stage knee osteoarthritis

OBJECTIVE: Multiple disease phenotypes have been identified in knee osteoarthritis (OA) patients based on anthropometric, sociodemographic and clinical factors; however, differential systemic metabolite-based signatures in OA patients are not well understood. We sought to identify differential plasma metabolome signatures in a cross-sectional sample of late-stage knee OA patients. METHODS: Plasma from 214 (56.5% female; mean age ​= ​67.58 years) non-diabetic, non-obese (BMI <30 ​kg/m(2), mean ​= ​26.25 ​kg/m(2)), radiographic KL 3/4 primary knee OA patients was analyzed by metabolomics. Patients with post-traumatic OA and rheumatoid arthritis were excluded. Hierarchical clustering was used to identify patient clusters based on metabolite levels. A refined metabolite signature differentiating patient clusters was determined based on ≥ 10% difference, significance by FDR-adjusted t-test (q-value < 0.05), and random forests importance score ≥1, and analyzed by AUROC. Bioinformatics analysis was used to identify genes linked to ≥2 annotated metabolites. Associated enriched pathways (q ​< ​0.05) were determined. RESULTS: Two patient clusters were determined based on the levels of 151 metabolites identified. Metabolite signature refinement found 24 metabolites could accurately predict cluster classification within the sample (AUC ​= ​0.921). Fifty-six genes were linked to at least 2 ​KEGG annotated metabolites. Pathway analysis found 26/56 genes were linked to enriched pathways including tRNA acylation and B-vitamin metabolism. CONCLUSION: This study demonstrates systemic metabolites can classify a cross-sectional cohort of OA patients into distinct clusters. Links between metabolites, genes and pathways can help determine biological differences between OA patients, potentially improving precision medicine and decision-making.