Objectives To identify metabolic markers that can classify patients with osteoarthritis (OA) into subgroups. Design A case-only study design was utilised. Participants Patients were recruited from those who underwent total knee or hip replacement surgery due to primary OA between November 2011 and December 2013 in St. Clare's Mercy Hospital and Health Science Centre General Hospital in St. John's, capital of Newfoundland and Labrador (NL), Canada. 38 men and 42 women were included in the study. The mean age was 65.2±8.7 years. Outcome measures Synovial fluid samples were collected at the time of their joint surgeries. Metabolic profiling was performed on the synovial fluid samples by the targeted metabolomics approach, and various analytic methods were utilised to identify metabolic markers for classifying subgroups of patients with OA. Potential confounders such as age, sex, body mass index (BMI) and comorbidities were considered in the analysis. Results Two distinct patient groups, A and B, were clearly identified in the 80 patients with OA. Patients in group A had a significantly higher concentration on 37 of 39 acylcarnitines, but the free carnitine was significantly lower in their synovial fluids than in those of patients in group B. The latter group was further subdivided into two subgroups, that is, B1 and B2. The corresponding metabolites that contributed to the grouping were 86 metabolites including 75 glycerophospholipids (6 lysophosphatidylcholines, 69 phosphatidylcholines), 9 sphingolipids, 1 biogenic amine and 1 acylcarnitine. The grouping was not associated with any known confounders including age, sex, BMI and comorbidities. The possible biological processes involved in these clusters are carnitine, lipid and collagen metabolism, respectively. Conclusions The study demonstrated that OA consists of metabolically distinct subgroups. Identification of these distinct subgroups will help to unravel the pathogenesis and develop targeted therapies for OA.

Objective: Systemic inflammatory factors have been implicated in symptomatic hand osteoarthritis (OA). Gut microbiome dysbiosis promotes systemic inflammation. The aim of this study was to examine the association between the gut microbiome and the presence of symptomatic hand OA in a population‐based study.Methods: Study participants were subjects of the Xiangya Osteoarthritis Study, a community‐based observational study conducted in the Hunan Province of China. Symptomatic hand OA was defined as the presence of both symptoms and radiographic OA in the same hand. The gut microbiome was analyzed using 16S ribosomal RNA gene sequencing in stool samples. We examined the relation of α‐diversity, β‐diversity, relative abundance of taxa, and potential bacterial functional pathways to symptomatic hand OA.Results: A total of 1,388 participants (mean age 61.3 years, 57.4% women) were included in the study, of whom 72 had symptomatic hand OA (prevalence of symptomatic hand OA 5.2%). Beta‐diversity of the gut microbiome, but not α‐diversity, was significantly associated with the presence of symptomatic hand OA (P = 0.003). Higher relative abundance of the genera Bilophila and Desulfovibrio as well as lower relative abundance of the genus Roseburia was associated with symptomatic hand OA. Most functional pathways (i.e., those annotated in the KEGG Ortholog hierarchy) that were observed to be altered in participants with symptomatic hand OA belonged to the amino acid, carbohydrate, and lipid metabolic pathways.Conclusion: This large, population‐based study provides the first evidence that alterations in the composition of the gut microbiome were observed among study participants who had symptomatic hand OA, and a low relative abundance of Roseburia but high relative abundance of Bilophila and Desulfovibrio at the genus level were associated with prevalent symptomatic hand OA. These findings may help investigators understand the role of the microbiome in the development of symptomatic hand OA and could contribute to potential translational opportunities.

Objective. To identify novel biomarker(s) for predicting advanced knee OA. Methods. Study participants were derived from the Newfoundland Osteoarthritis Study and the Tasmania Older Adult Cohort Study. All knee OA cases were patients who underwent total knee replacement (TKR) due to primary OA. Metabolic profiling was performed on fasting plasma. Four thousand and eighteen plasma metabolite ratios that were highly correlated with that in SF in our previous study were generated as surrogates for joint metabolism. Results. The discovery cohort included 64 TKR cases and 45 controls and the replication cohorts included a cross-sectional cohort of 72 TKR cases and 76 controls and a longitudinal cohort of 158 subjects, of whom 36 underwent TKR during the 10-year follow-up period. We confirmed the previously reported association of the branched chain amino acids to histidine ratio with advanced knee OA (P = 9.3 × 10 ⁻⁷ ) and identified a novel metabolic marker—the lysophosphatidylcholines (lysoPCs) to phosphatidylcholines (PCs) ratio—that was associated with advanced knee OA (P = 1.5 × 10 ⁻⁷ ) after adjustment for age, sex and BMI. When the subjects of the longitudinal cohort were categorized into two groups based on the optimal cut-off of the ratio of 0.09, we found the subjects with the ratio ⩾0.09 were 2.3 times more likely to undergo TKR than those with the ratio <0.09 during the 10-year follow-up (95% CI: 1.2, 4.3, P = 0.02). Conclusion. We identified the ratio of lysoPCs to PCs as a novel metabolic marker for predicting advanced knee OA. Further studies are required to examine whether this ratio can predict early OA change.

Sir Archibald Edward Garrod, who pioneered the field of inborn errors of metabolism and first elucidated the biochemical basis of alkaptonuria over 100 years ago, suggested that inborn errors of metabolism were “merely extreme examples of variations of chemical behavior which are probably everywhere present in minor degrees, just as no two individuals of a species are absolutely identical in bodily structure neither are their chemical processes carried out on exactly the same lines”, and that this “chemical individuality [confers] predisposition to and immunities from various mishaps which are spoken of as diseases”. Indeed, with advances in analytical biochemistry, especially the development of metabolomics in the post-genomic era, emerging data have been demonstrating that the levels of many metabolites do show substantial interindividual variation, and some of which are likely to be associated with common diseases, such as osteoarthritis (OA). Much work has been reported in the literature on the metabolomics of OA in recent years. In this narrative review, we provided an overview of the identified alteration of metabolic pathways in OA and discussed the role of those identified metabolites and related pathways in OA diagnosis, prognosis, and treatment.