Update of clinical aspects of knee osteoarthritis: data mining from NMR-based metabolomics of synovial fluid

Abstract

Introduction: Knee osteoarthritis (OA) is a prevalent disease which leads to progressive disability. There is increasing evidence of the association between OA with metabolic syndrome. Metabolomics emerges as a promising tool for investigation of this connection. The goal of this study was to correlate the metabolic profile of synovial fluid of patients with and without knee osteoarthritis with clinical factors related to the development of metabolic syndrome (MetS) based on the reanalysis of a patient’s database from our previous study.

Methods: Patients were divided in two groups: without osteoarthritis (OA), who underwent knee arthroscopy (n = 8; K-L Grade 0) and with knee OA (KOA), who underwent total knee arthroplasty surgery (n = 26, K-L Grades 3 and 4). From a database of synovial fluid metabolomic analysis by nuclear magnetic resonance, clinical data were collected from medical records, including age, sex, height and weight, and fasting blood glucose levels. Then, multivariate analysis was performed to identify the possibility of distinguishing different subgroups of patients based on clinical factors potentially associated with osteoarthritis and MetS.

Results: Metabolic analysis was able to differentiate patients without osteoarthritis from those with osteoarthritis, based on metabolic profile, with glycerol being the increased metabolite in the group of patients with osteoarthritis. However, metabolomics was not able to classify patients into subgroups according to blood glucose ranges, body mass index and by age.

Conclusion: In a population with osteoarthritis, metabolic analysis evidences a slightly different profile in the analysis by age and BMI, or age and glycaemia.

Keywords: Metabolomics; Metabolic syndrome; Osteoarthritis; Synovial fluid.