Sergei Likhodii

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.

Lysophosphatidylcholines to phosphatidylcholines ratio predicts advanced knee osteoarthritis

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.

Hyperglycemia-related advanced glycation end-products is associated with the altered phosphatidylcholine metabolism in osteoarthritis patients with diabetes

To test whether type 2 diabetic patients have an elevated level of advanced glycation end-products (AGEs) and responsible for altered phosphatidylcholine metabolism, which we recently found to be associated with osteoarthritis (OA) and diabetes mellitus (DM), synovial fluid (SF) and plasma samples were collected from OA patients with and without DM. Hyperglycemia-related AGEs including methylglyoxal (MG), free methylglyoxal-derived hydroimidazolone (MG-H1), and protein bound N-(Carboxymethyl)lysine (CML) and N-(Carboxyethyl)lysine (CEL) levels were measured in both SF and plasma samples using liquid chromatography coupled tandem mass spectrometry methodology. The correlation between these AGEs and phosphatidylcholine acyl-alkyl C34:3 (PC ae C34:3) and C36:3 (PC ae C36:3) were examined. Eighty four patients with knee OA, including 46 with DM and 38 without DM, were included in the study. There was no significant difference in plasma levels of MG, MG-H1, CML, and CEL between OA patients with and without DM. However, the levels of MG and MG-H1, but not CML and CEL in SF were significantly higher in OA patients with DM than in those without (all p ≤0.04). This association strengthened after adjustment for age, body mass index (BMI), sex and hexose level (p<0.02). Moreover, the levels of MG-H1 in SF was negatively and significantly correlated with PC ae C34:3 (ρ = -0.34; p = 0.02) and PC ae C36:3 (ρ = -0.39; P = 0.03) after the adjustment of age, BMI, sex and hexose level. Our data indicated that the production of non-protein bound AGEs was increased within the OA-affected joint of DM patients. This is associated with changes in phosphatidylcholine metabolism and might be responsible for the observed epidemiological association between OA and DM.

A classification modeling approach for determining metabolite signatures in osteoarthritis

Multiple factors can help predict knee osteoarthritis (OA) patients from healthy individuals, including age, sex, and BMI, and possibly metabolite levels. Using plasma from individuals with primary OA undergoing total knee replacement and healthy volunteers, we measured lysophosphatidylcholine (lysoPC) and phosphatidylcholine (PC) analogues by metabolomics. Populations were stratified on demographic factors and lysoPC and PC analogue signatures were determined by univariate receiver-operator curve (AUC) analysis. Using signatures, multivariate classification modeling was performed using various algorithms to select the most consistent method as measured by AUC differences between resampled training and test sets. Lists of metabolites indicative of OA [AUC > 0.5] were identified for each stratum. The signature from males age > 50 years old encompassed the majority of identified metabolites, suggesting lysoPCs and PCs are dominant indicators of OA in older males. Principal component regression with logistic regression was the most consistent multivariate classification algorithm tested. Using this algorithm, classification of older males had fair power to classify OA patients from healthy individuals. Thus, individual levels of lysoPC and PC analogues may be indicative of individuals with OA in older populations, particularly males. Our metabolite signature modeling method is likely to increase classification power in validation cohorts.

THU0022 Hyperglycemia-Related Advanced Glycation End-Products Is Associated with The Altered Phosphytidylcholine Metabolism in Osteoarthritis Patients

Background Accumulating evidence suggests an independent association between osteoarthritis (OA) and type 2 diabetes mellitus (DM). Our recent work [Zhang, et al. Metabolomics, 2015] found phosphatidylcholine acyl-alkyl C34:3 (PC ae C34:3) and phosphatidylcholine acyl-alkyl C36:3 (PC ae C36:3) were associated with both OA and DM. Both synovial and plasma concentrations of these two metabolites were reduced in knee OA and DM patients, and OA patients with DM had lowest concentration of these two metabolites, suggesting the altered unsaturated phosphatidylcholine metabolism may be responsible for the association between OA and DM. Objectives We hypothesized hyperglycemia-related production of advanced glycation end-products (AGEs) was involved in the altered phosphatidylcholine metabolism in OA patients and tested this hypothesis in the current study. Methods Synovial fluid and plasma samples were collected from OA patients with and without DM. Hyperglycemia-related AGEs including methylglyoxal (MG) and methylglyoxal-derived hydroimidazolone (MG-H1) levels were measured in both synovial fluid and plasma samples using UPLC/MS method. The correlation between MG, MG-H1, and PC ae C34:3 and PC ae C36:3 were examined. Results 84 knee OA patients, including 46 with DM and 38 without DM, were included in the study. We did not find a significant difference in plasma MG-H1 concentration between OA with and without DM. However, we found that log transformed synovial concentrations of MG-H1 in the groups of OA with diabetes were 2.56±0.27 ng/ml which was significantly higher than that in the group of OA without diabetes (2.39 ±0.25 ng/ml, P=0.012). Similarly, synovial concentration of MG was 2.05±0.11 ng/ml in the group of OA with diabetes which was significantly higher than 1.99±0.11 ng/ml in the group of OA without diabetes (P=0.046). The significance remained after adjusting the age, BMI and sex. The correlation between MG-H1 and PC ae C34:3, MG-H1 and PC ae C36:3, MG and PC aeC34:3, and MG and PC ae C36:3 were -0.15, -0.32, -0.23 and -0.06, respectively. Conclusions We demonstrated that both MG-H1 and MG concentrations in synovial fluid were elevated in OA patients with DM and associated with the levels of PC ae C34:3 and PC ae C36:3, suggesting that hyperglycemia-related AGEs may be responsible for the altered phosphotidylcholine metabolism in OA. References Weidong Zhang, Guang Sun, Sergei Likhodii, Erfan Aref-Eshghi, Patricia E. Harper, Edward Randell, Roger Green, Glynn Martin, Andrew Furey, Proton Rahman, Guangju Zhai. Metabolomic analysis of human synovial fluid and plasma reveals that phosphatidylcholine metabolism is associated with both osteoarthritis and diabetes mellitus. Metabolomics (2016) 12:24. DOI 10.1007/s11306–015–0937-x Acknowledgement We thank all the study participants who made this study possible, and all the staff who helped us in the collection of samples. The study was funded by Canadian Institutes of Health Research (CIHR), Newfoundland & Labrador RDC, and Memorial University. Disclosure of Interest None declared