Stage related metabolic profile of the synovial fluid in patients with acute flares of knee osteoarthritis.

Background and aim: Osteoarthritis (OA) is the most common joint condition and the leading cause of pain and disability in elderly patients. Currently, there is no biomarker available for the early diagnosis of OA, and limited data is available regarding the molecular basis of progression for OA. For this reason, this study aimed to identify the metabolomic profile of early and late OA using high-performance liquid chromatography coupled with untargeted mass spectrometry (LC-MS). Methods: 31 patients with knee OA and joint effusion were enrolled. Based on Kellgren/Laurence scale, 12 patients were classified as early OA (eOA) and 19 as late OA (lOA). The synovial fluid (SF) was collected and characterized by untargeted LC-MS. Only the metabolites identified in more than 25% of each group were kept for further analysis. Principal component analysis (PCA) enabled the unsupervised clustering of the eOA and lOA groups. Further, for classification, the best three principal components (PCs) were used as input for two machine learning algorithms (random forest and naïve Bayes), which were trained to discriminate between the eOA and lOA groups. Results: 43 metabolites were identified in both eOA and lOA, but after selecting the metabolites present in at least 25% of the patients in each group, the metabolomics analysis yielded a panel of only nine metabolites: four metabolites related to phospholipids (phosphatidylcholine 20:0/18:2 and 18:0/20:2, sphingomyelin, and ceramide), three metabolites belonging to purine metabolites (inosine 5'-phosphate, adenosine thiamine diphosphate, and diadenosine 5',5'-diphosphate), one metabolite was a gonadal steroid hormone (estrone 3-sulfate), and one metabolite represented by heme, with all but ceramide (d18:1/20:0) being enriched in the lOA group. By using as features the best three PCs (PC2, PC8 and PC9), random forest and naïve Bayes machine learning algorithms yielded a classification accuracy of 0.81 and 0.78, respectively. Conclusion: Our LC-MS analysis of SF from patients with eOA and lOA indicates stage-dependent differences, lOA being associated with a perturbed metabolome of phospholipids, purine metabolites, gonadal steroid hormones (estrone 3-sulfate) and a heme molecule. Specific questions need to be answered regarding the biosynthesis and function of these metabolites in osteoarthritic joints, with the aim of developing new relevant biomarkers and therapeutic strategies.