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

Jason S Rockel; Mehdi Layeghifard; Y Raja Rampersaud; Anthony V Perruccio; Nizar N Mahomed; J Roderick Davey; Khalid Syed; Rajiv Gandhi; Mohit Kapoor

doi:10.1016/j.ocarto.2022.100258

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

Osteoarthr Cartil Open. 2022 Apr 2;4(3):100258. doi: 10.1016/j.ocarto.2022.100258. eCollection 2022 Sep.

Authors

Jason S Rockel^{1

2}, Mehdi Layeghifard^{1

3}, Y Raja Rampersaud^{1

2

4}, Anthony V Perruccio^{1

2

4

5}, Nizar N Mahomed^{1

2

4}, J Roderick Davey^{1

2

4}, Khalid Syed^{1

2

4}, Rajiv Gandhi^{1

2

4}, Mohit Kapoor^{1

2

4

6}

Affiliations

¹ Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada.
² Krembil Research Institute, University Health Network, Toronto, ON, Canada.
³ Program of Genetics & Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada.
⁴ Department of Surgery, Faculty of Medicine, University of Toronto, ON, Canada.
⁵ Institute of Health Policy, Management & Evaluation, Dalla Lana School of Public Health, University of Toronto, Canada.
⁶ Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada.

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/m², mean = 26.25 kg/m²), 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.

Keywords: Cross-sectional; Knee osteoarthritis; Metabolomics; Pathway enrichment; Signature.