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

Osteoarthritis and Cartilage Open (Sep 2022)

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

Affiliations

Jason S. Rockel: Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada; Krembil Research Institute, University Health Network, Toronto, ON, Canada; Corresponding author. Jason S. Rockel Schroeder Arthritis Institute, Krembil Research Institute, University Health Network 60 Leonard Avenue, Toronto, Ontario, M5T 0S8, Canada.
Mehdi Layeghifard: Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada; Program of Genetics & Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
Y. Raja Rampersaud: Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada; Krembil Research Institute, University Health Network, Toronto, ON, Canada; Department of Surgery, Faculty of Medicine, University of Toronto, ON, Canada
Anthony V. Perruccio: Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada; Krembil Research Institute, University Health Network, Toronto, ON, Canada; Department of Surgery, Faculty of Medicine, University of Toronto, ON, Canada; Institute of Health Policy, Management & Evaluation, Dalla Lana School of Public Health, University of Toronto, Canada
Nizar N. Mahomed: Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada; Krembil Research Institute, University Health Network, Toronto, ON, Canada; Department of Surgery, Faculty of Medicine, University of Toronto, ON, Canada
J. Roderick Davey: Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada; Krembil Research Institute, University Health Network, Toronto, ON, Canada; Department of Surgery, Faculty of Medicine, University of Toronto, ON, Canada
Khalid Syed: Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada; Krembil Research Institute, University Health Network, Toronto, ON, Canada; Department of Surgery, Faculty of Medicine, University of Toronto, ON, Canada
Rajiv Gandhi: Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada; Krembil Research Institute, University Health Network, Toronto, ON, Canada; Department of Surgery, Faculty of Medicine, University of Toronto, ON, Canada
Mohit Kapoor: Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada; Krembil Research Institute, University Health Network, Toronto, ON, Canada; Department of Surgery, Faculty of Medicine, University of Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada

Journal volume & issue: Vol. 4, no. 3
p. 100258

Abstract

Read online

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.

Published in Osteoarthritis and Cartilage Open

ISSN: 2665-9131 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Medicine: Internal medicine: Specialties of internal medicine: Diseases of the musculoskeletal system
Website: https://www.journals.elsevier.com/osteoarthritis-and-cartilage-open/

About the journal

Abstract

Keywords