Characterization of ¹H NMR plasma glycoproteins as a new strategy to identify inflammatory patterns in rheumatoid arthritis.

Identifier:  imarina:5251774

Handle: https://hdl.handle.net/20.500.11797/imarina5251774

Authors:  Fuertes-Martin, Rocio; Taverner, Delia; Vallve, Joan-Carles; Paredes, Silvia; Masana, Lluis; Correig Blanchar, Xavier; Amigo Grau, Nuria

Abstract:
Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease associated with a high index of morbidity and mortality from cardiovascular diseases. We used 1H NMR to characterize the plasma glycoprotein and lipoprotein profiles of a cohort of patients with RA (n = 210) versus healthy individuals (n = 203) to associate them with the RA disease and its severity. Using 1H NMR, we developed a line-shape method to characterize the two peaks associated with glycoproteins (GlycA and GlycB) and its derived variables: areas of GlycB (Area GlycB) and GlycA (Area GlycA), shape factors of these two peaks (H/W = height/width), and the distance between them (Distance GlycB-GlycA). We also used the advanced lipoprotein test Liposcale (CE) to characterize the lipoprotein subclasses. The standard lipid panel and traditional inflammatory markers such as C-reactive protein, the erythrocyte sedimentation rate, fibrinogen, the rheumatoid factor, anticitrullinated peptide antibodies, and the DAS28 index have also been determined. RA patients presented a significant 10.65% increase in the GlycA associated area compared with the control group (p = 2.21 × 10-10). They also presented significantly higher H/W GlycA and GlycB ratios than the control population (H/W GlycB p = 7.88 × 10-8; H/W GlycA p = 5.61 × 10-8). The prediction model that uses the traditional inflammatory variables and the 1H NMR-derived parameters presented an AUC that was almost 10% higher than the model that only uses the traditional inflammatory variables (from 0.7 to 0.79 AUC). We have demonstrated that GlycA and GlycB variables derived from 1H NMR, along with classic inflammatory parameters, help to improve the classification of individuals with high RA disease activity.