POS1087\u2005USING LIPIDOMICS TO PREDICT PREDNISOLONE TREATMENT RESPONSE IN PATIENTS WITH INFLAMMATORY HAND OSTEOARTHRITIS: THE HOPE STUDY

Abstract

Lipidomics analysis has become a valuable technology for understanding patho-physiological mechanisms and may aid the identification of biomarkers of therapeutic responsiveness.To explore the use of lipidomics for prediction of prednisolone treatment response in patients with inflammatory hand osteoarthritis.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\u2009mg 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.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).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 scaleMarieke 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.