Identification of a differential metabolite-based signature in patients with late-stage knee osteoarthritis. Issue 3 (September 2022)

• Record Type: Journal Article
• Authors: Rockel, Jason S.; Layeghifard, Mehdi; Rampersaud, Y. Raja; Perruccio, Anthony V.; Mahomed, Nizar N.; Davey, J. Roderick; Syed, Khalid; Gandhi, Rajiv; Kapoor, Mohit
• Is Part Of: Osteoarthritis and cartilage open. Volume 4:Issue 3(2022)
• Publication Date: 2022-09
• Subjects: Knee osteoarthritis -- Cross-sectional -- Metabolomics -- Signature -- Pathway enrichment; Osteoarthritis -- Periodicals; Cartilage -- Periodicals; 616.7223005
• DOI: 10.1016/j.ocarto.2022.100258
• Languages: English
• ISSNs: 2665-9131

Abstract

Abstract: 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.