Rheumatoid arthritis (RA) and osteoarthritis (OA) are inflammatory joint diseases, characterized by pain and structural damage. Besides prostaglandins, usually targeted by non-steroidal anti-inflammatory drugs, other lipids, including fatty acids, phospholipids and other bioactive lipid mediators derived from fatty acids could also contribute to RA and OA. In this review, we present evidence for the role of fatty acids and derivatives in RA and OA by summarizing findings related to their presence in serum and synovial fluid, as well as their association with clinical characteristics and effects on RA and OA tissues in vitro. Finally, a more direct evidence for their role in RA and OA derived from intervention studies in humans or mouse models of disease is summarized. Based on the presented data, we present a research agenda, in which some key unresolved questions regarding the role of lipids in RA and OA are formulated.
Background:Lipidomics analysis has become a valuable technology for understanding patho-physiological mechanisms and may aid the identification of biomarkers of therapeutic responsiveness.Objectives:To explore the use of lipidomics for prediction of prednisolone treatment response in patients with inflammatory hand osteoarthritis.Methods:The Hand Osteoarthritis Prednisolone Efficacy (HOPE) study is a blinded, randomized placebo-controlled trial, that investigated the effect of prednisolone treatment in patients with painful, inflammatory hand OA, fulfilling the American College of Rheumatology criteria. The present analyses comprised only patients randomized to daily 10 mg prednisolone treatment for six weeks. Response to prednisolone treatment was defined according to the OARSI-OMERACT responder criteria at six weeks. Baseline blood samples were obtained non-fasted. Lipid species were quantified in erythrocytes with the LipidyzerTM platform (Sciex). After pre-processing of the data, 286 lipids species were available for further analyses (nmol/mL). In addition, we used an in-house LC-MS/MS platform to analyse oxylipins in plasma, identifying 25 oxylipins (area ratios). Elastic net regularized regression was used to predict prednisolone treatment response. A 10-fold cross-validation (CV) was performed for selection of the optimal tuning parameters based on the smallest CV mean prediction error. First, a model was fit with commonly assessed patient characteristics and patient reported outcomes, measured at baseline (model 1). Second, we fitted model 2 by adding the LipidyzerTM platform lipids to model 1. Third, we fitted model 3 by adding the oxylipins to model 1. The discriminatory accuracy of the model was estimated by receiver operating characteristic (ROC) analyses. The area under the curve (AUC) and corresponding 95% confidence intervals (CI) were calculated using 1,000 bootstrap replications.Results:Among the 40 patients included, 31 (78%) fulfilled the OARSI-OMERACT responder criteria. From the included general patient characteristics (Table 1), elastic net selected baseline hand function as only predictor of treatment response, with an AUC of 0.78 (95% CI 0.60;0.96) (Figure 1). In model 2, we added the 286 LipidyzerTM platform variables to model 1. In addition to hand function, two lipids were selected: diacylglycerol(DAG)(16:0/16:0) and phosphatidylethanolamine(PE)(O-18:0/20:4), which improved the discriminatory accuracy to an AUC of 0.92 (0.83;1.02). Lastly, model 3 was fit with patient characteristics as well as oxylipins, resulting in selection of AUSCAN function and three oxylipin predictors: 9-hydroxy-octadecatrienoic acid (HOTrE), 5-hydroxy-eicosapentaenoic acid (HEPE) and 10-hydroxy-docosahexaenoic acid (HDHA), with an AUC of 0.85 (0.69;1.02).Conclusion:The patients’ lipid profile improved the discriminative accuracy of the prediction of prednisolone treatment response in patients with inflammatory hand osteoarthritis compared to prediction by commonly measured patient characteristics alone. This exploratory study suggests that lipidomics is a promising field for biomarker discovery for prediction of anti-inflammatory treatment response.Table 1.Baseline characteristicsAll prednisolone treatedn = 40Respondersn = 31 (78%)Non-respondersn = 9 (23%)General characteristicsAge, year62.4 (9.3)62.9 (9.4)60.8 (9.4)Sex, % women858489BMI, kg/m227.4 (4.4)27.8 (4.2)26.2 (5.0)Education, % high464256Disease duration6.7 (7.1)7.2 (7.4)4.9 (5.8)Erosive OA, %717456Kellgren-Lawrence sum score, 0-12035.1 (16.4)34.1 (16.5)37.5 (14.7)Ultrasound synovitis sum score, 0-9016.2 (6.6)15.5 (6.4)18.7 (7.2)VAS global assessment, 0-10052.3 (20.6)54.2 (16.8)45.6 (30.8)AUSCAN pain, 0-2011.0 (3.3)11.3 (2.4)10 (5.4)AUSCAN function, 0-3617.7 (7.6)19.6 (6.6)11 (7.5)Numbers represent mean (SD) unless otherwise specified. AUSCAN = Australian/Canadian Hand Osteoarthritis Index, BMI = body mass index, VAS = visual analogue scaleDisclosure of Interests:Marieke Loef: None declared, Tariq Faquih: None declared, Johannes von Hegedus: None declared, Mohan Ghorasaini: None declared, Andreea Ioan-Facsinay: None declared, Féline Kroon: None declared, Martin Giera Shareholder of: Pfizer, Consultant of: Boehringer Ingelheim Pharma, Margreet Kloppenburg: None declared.
Background:Lipidomics analysis has become a valuable technology for understanding patho-physiological mechanisms and the identification of candidate biomarkers in rheumatic musculoskeletal disorders. Variability in within-subject repeated measurements may lead to bias towards the null when estimating the association between biomarkers and a disease or treatment. Hence, information regarding the stability of the metabolite levels over time is essential.Objectives:We aimed to assess the lipid composition and biological reproducibility of lipid measurements in plasma and erythrocytes.Methods:Plasma and erythrocyte samples from 42 osteoarthritis patients (77% women, mean age 65 years, mean BMI 27 kg/m2), obtained non-fasted at baseline and six weeks, were used for the quantitative measurement of up to 1000 lipid species across 13 lipid classes with the LipidyzerTM platform in nmol/mL. Data was processed based on the relative standard deviation of quality controls, taking batch effects into account. Intraclass correlation coefficients (ICCs) and corresponding 95% confidence intervals (CI) were calculated to investigate the variability of the lipid concentrations between timepoints. The ICC distribution of lipid metabolites in plasma and erythrocytes were compared using two-sided paired Wilcoxon tests.Results:We measured 778 lipids in plasma, compared to 916 lipids in erythrocytes. After data processing, the analyses included 630 lipids in plasma, and 286 in erythrocytes. From these, 243 lipids overlapped between sample types. Major differences were observed between the sample types in the number of lipids per lipid class and the total concentration of the lipids within a class. Triacylglycerols (TAG) and cholesteryl esters (CE) were more abundant in plasma. Conversely, phosphatidylethanolamines (PE), sphingomyelins (SM) and ceramides (CER) were less abundant in plasma compared to erythrocytes (table 1). In plasma 78% of lipid measurements were good to excellently reproduced, with an overall median ICC 0.69. Compared to plasma, a considerably lower amount (35%) of lipids were well reproduced in erythrocytes. Median reproducibility of lipids in erythrocytes was 0.51. Figure 1 shows the ICC score distribution in plasma with erythrocytes, with a significantly better reproducibility in plasma (p-value<0.001). However, while overall reproducibility was better in plasma, this was not observed for all lipid classes. At class-level, reproducibility in plasma was superior for TAGs and CEs, while CERs, DAGs, (L)PEs and SMs showed better reproducibility in erythrocytes.Table 1.Number of individual lipids per class and class concentrations in plasma and erythrocytesPlasmaErythrocytesNumber of lipid speciesClass concentration (nmol/mL)Number of lipid speciesClass concentration (nmol/mL)Triacylglycerols4821579.4 (1064.9-3195.2)1346.5 (5.6-9.4)Diacylglycerols913.3 (8.4-22.2)105.8 (4.7-6.2)Free fatty acids20745.3 (552.0-1202.9)20486.9 (379.2-669.2)Cholesteryl esters244571.6 (4065.1-5521.3)51.2 (0.9-1.7)Phosphatidylcholines314013.7 (3203.1-4661.6)423899.2 (3723.0-4296.6)Phosphatidylethanolamines26156.2 (120.9-180.3)423954.6 (3721.9-4323.3)Lysophosphatidylcholines9385.9 (335.6-442.9)7119.8 (109.7-168.9)Lysophosphatidylethanolamines24.2 (3.5-4.9)48.6 (6.8-9.7)Sphingomyelins121204.6 (1037.0-1351.9)82695.8 (2434.8-2815.6)Ceramides614.1 (11.9-17.4)7163.0 (133.3-186.4)Dihydroceramides21.0 (0.8-1.3)11.8 (1.4-2.1)Hexosylceramides55.1 (4.7-5.9)45.6 (5.0-7.4)Lactosylceramides23.4 (2.7-3.8)223.8 (20.6-33.5)Numbers represent median (interquartile range) unless otherwise specified. Data represents baseline measurements.Conclusion:In plasma biological reproducibility was good for most lipid measurements. Although overall reproducibility was better in plasma compared to erythrocytes, notable differences were observed at individual- and lipid class-level that may favour the use of a particular sample type.Disclosure of Interests:Marieke Loef: None declared, Johannes von Hegedus: None declared, Mohan Ghorasaini: None declared, Féline Kroon: None declared, Martin Giera Shareholder of: Pfizer, Consultant of: Boehringer Ingelheim Pharma, Andreea Ioan-Facsinay: None declared, Margreet Kloppenburg: None declared
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