Patricia E. Harper

Classification of osteoarthritis phenotypes by metabolomics analysis

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. Clares Mercy Hospital and Health Science Centre General Hospital in St. Johns, 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.

Metabolomic analysis of human plasma reveals that arginine is depleted in knee osteoarthritis patients

OBJECTIVE To identify novel biomarker(s) for knee osteoarthritis (OA) using a metabolomics approach. METHOD We utilized a two-stage case-control study design. Plasma samples were collected from knee OA patients and healthy controls after 8-h fasting and metabolically profiled using a targeted metabolomics assay kit. Linear regression was used to identify novel metabolic markers for OA. Receiver operating characteristic (ROC) analysis was used to examine diagnostic values. Gene expression analysis was performed on human cartilage to explore the potential mechanism for the novel OA marker(s). RESULTS Sixty-four knee OA patients and 45 controls were included in the discovery stage and 72 knee OA patients and 76 age and sex matched controls were included in the validation stage. We identified and confirmed six metabolites that were significantly associated with knee OA, of which arginine was the most significant metabolite (P < 3.5 × 10(-13)) with knee OA patients having on average 69 μM lower than that in controls. ROC analysis showed that arginine had the greatest diagnostic value with area under the curve (AUC) of 0.984. The optimal cutoff of arginine concentration was 57 μM with 98.3% sensitivity and 89% specificity. The depletion of arginine in OA patients was most likely due to the over activity of arginine to ornithine pathway, leading to imbalance between cartilage repair and degradation. CONCLUSION Arginine is significantly depleted in refractory knee OA patients. Further studies within a longitudinal setting are required to examine whether arginine can predict early OA changes.

Relationship between blood plasma and synovial fluid metabolite concentrations in patients with osteoarthritis.

Objective. To investigate the relationship between plasma and synovial fluid (SF) metabolite concentrations in patients with osteoarthritis (OA). Methods. Blood plasma and SF samples were collected from patients with primary knee OA undergoing total knee arthroplasty. Metabolic profiling was performed by electrospray ionization tandem mass spectrometry using the AbsoluteIDQ kit. The profiling yielded 168 metabolite concentrations. Correlation analysis between SF and plasma metabolite concentrations was done on absolute concentrations as well as metabolite concentration ratios using Spearman’s rank correlation (ρ) method. Results. A total of 69 patients with knee OA were included, 30 men and 39 women, with an average age of 66 ± 8 years. For the absolute metabolite concentrations, the average ρ was 0.23 ± 0.13. Only 8 out of 168 metabolite concentrations had a ρ ≥ 0.45, with a p value ≤ 2.98 × 10−4, statistically significant after correcting multiple testing with the Bonferroni method. For the metabolite ratios (n = 28,056), the average ρ was 0.29 ± 0.20. There were 4018 metabolite ratios with a ρ ≥ 0.52 and a p value ≤ 1.78 × 10−6, significant after correcting multiple testing. Sex-separate analyses found no difference in ρ between men and women. Similarly, there was no difference in ρ between people younger and older than 65 years. Conclusion. Correlation between blood plasma and SF metabolite concentrations are modest. Metabolite ratios, which are considered proxies for enzymatic reaction rates and have higher correlations, should be considered when using blood plasma as a surrogate of SF in OA biomarker identification.

SMAD3 Is Upregulated in Human Osteoarthritic Cartilage Independent of the Promoter DNA Methylation

Objective. To compare SMAD3 gene expression between human osteoarthritic and healthy cartilage and to examine whether expression is regulated by the promoter DNA methylation of the gene. Methods. Human cartilage samples were collected from patients undergoing total hip/knee joint replacement surgery due to primary osteoarthritis (OA), and from patients with hip fractures as controls. DNA/RNA was extracted from the cartilage tissues. Real-time quantitative PCR was performed to measure gene expression, and Sequenom EpiTyper was used to assay DNA methylation. Mann-Whitney test was used to compare the methylation and expression levels between OA cases and controls. Spearman rank correlation coefficient was calculated to examine the association between the methylation and gene expression. Results. A total of 58 patients with OA (36 women, 22 men; mean age 64 ± 9 yrs) and 55 controls (43 women, 12 men; mean age 79 ± 10 yrs) were studied. SMAD3 expression was on average 83% higher in OA cartilage than in controls (p = 0.0005). No difference was observed for DNA methylation levels in the SMAD3 promoter region between OA cases and controls. No correlation was found between SMAD3 expression and promoter DNA methylation. Conclusion. Our study demonstrates that SMAD3 is significantly overexpressed in OA. This overexpression cannot be explained by DNA methylation in the promoter region. The results suggest that the transforming growth factor-β/SMAD3 pathway may be overactivated in OA cartilage and has potential in developing targeted therapies for OA.