AB0151 Characterization of the plasmatic glycoprotein and lipoprotein profiles of systemic lupus erythematosus by nuclear magnetic resonance spectroscopy. (12th June 2018)

• Record Type: Journal Article
• Authors: Julià, A.; Lopez-Longo, F.J.; Perez Venegas, J.J.; Olive, A.; Andreu, J.L.; Aguirre-Zamorano, M.A.; Vela, P.; Nolla, J.M.; Marenco de la Fuente, J.L.; Zea, A.; Pego-Reigosa, J.M.; Freire, M.; Diez, E.; Rodriguez, E.; Carreira, P.; Blanco, R.; Martinez Taboada, V.; Lopez Lasanta, M.; Lopez, M.; Fernandez-Nebro, A.; Marsal, S.
• Is Part Of: Annals of the rheumatic diseases. Volume 77(2018)Supplement 2
• Page Start: 1266
• Page End: 1266
• Publication Date: 2018-06-12
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2018-eular.3500
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: Systemic Lupus Erythematosus (SLE) is an autoimmune disease associated with systemic inflammatory processes. Chronic inflammation alters the of the number and type of non-cellular elements, including lipoproteins and glycoproteins. Objectives: Here we investigate the plasmatic lipoprotein and glycoprotein profiles associated with SLE. Methods: Plasma samples were obtained from 50 healthy control individuals and 50 SLE patients. All patients fulfilled the ACR criteria for classification of SLE. 1 H-NMR was used to determine the lipoprotein and glycoprotein profiles from cases and controls. For the lipoprotein profiles, VLDL, LDL, IDL and HDL concentration, lipid composition and particle properties were quantified. The glycoprotein profile was decomposed in four reproducible peak regions LMW, Glyc-B, Glyc-A, Glyc-Lipid and baseline, and quantified for concentration, position and aggregation state. Association analysis to disease was performed using logistic regression fitting for age, gender and body mass index. Classifiers were built using recursive-partition algorithm and classification accuracy was evaluated using receiver operating curves. Results: We found a significant perturbation of the lipoprotein profile in SLE patients compared to controls. A statistically significant (P<0.05) reduction of cholesterol-LDL and HDL lipoproteins as well as an increase in triglyceride VLDL was found in SLE. Particle number for LDLs, were found to be reduced compared to controls (P<0.05), and mostly due to lower quantities of large LDL particles (P<0.005). The glycoprotein profile was also significantly altered for several NMR measures. The most significant changes were related to the reduction of the width and area of the Glyc-Lipid peak in SLE patients (P<0.005 and P<0.05, respectively). Using the plasma molecular patterns to predict the presence of SLE, we found a significant classifier for the lipoprotein pattern (ROC AUC(95%CI)=0.85(0.78–0.93)), and an optimal classifier for the glycoprotein pattern (ROC AUC(95%CI)=0.90(0.84–0.97)) Conclusions: SLE is characterized by perturbations of the plasmatic lipoprotein and glycoprotein profiles. The increase of triglyceride VLDL in patients compared to controls, could explain the increased risk for cardiovascular disease observed in SLE. Our results also show that SLE patients have a significantly different glycoprotein profile compared to controls. In both cases, plasmatic 1 H-NMR profiling could be a valuable source of informative biomarkers for SLE. Disclosure of Interest: None declared