POS0353 DNA METHYLOME ALTERATIONS AS POTENTIAL DIAGNOSTIC AND PROGNOSTIC BIOMARKERS IN PATIENTS WITH RHEUMATOID ARTHRITIS

• Published in: Annals of the rheumatic diseases Vol. 82; no. Suppl 1; p. 426
• Main Authors: Martín-Núlez, G. M., Mucientes, A., Lisbona, J. M., Ruiz-Limon, P., Redondo, R., Manrique Arija, S., Ureña, I., Cano Garcia, L., Moreno-Indias, I., Mena-Vázquez, N., Fernandez-Nebro, A.
• Format: Journal Article
• Language: English
• Published: Kidlington BMJ Publishing Group Ltd and European League Against Rheumatism 01.06.2023; Elsevier B.V; Elsevier Limited
• Subjects: Biomarkers; Citrulline; CpG islands; Cytosine; DNA methylation; Environmental factors; Epigenetics; Genetics/Epigenetics; Genomes; Inflammation; Lung diseases; Phenotypes; Pseudogenes; Regression analysis; Rheumatoid arthritis; Rheumatoid factor; Scientific Abstracts; Biomarkers; Rheumatoid arthritis; Genetics/Epigenetics
• ISSN: 0003-4967; 1468-2060
• DOI: 10.1136/annrheumdis-2023-eular.3575

BackgroundThe etiology of rheumatoid arthritis (RA) is not entirely known. Epigenetic modifications could be the link between genetic and environmental factors related to the appearance and evolution of RA.ObjectivesTo identify differential DNA methylation patterns throughout the entire genome in patients with RA versus healthy controls, as well as epigenetic changes that may predict greater disease severity.MethodsCross-sectional study of a prospective cohort, in which 64 subjects were studied: 16 severe RA, 16 non-severe RA and 32 healthy controls. The severity phenotype was defined according to an average of moderate-high inflammatory activity defined by an accumulated Disease activity score (DAS28-ESR) ≥3.2, positivity for rheumatoid factor (RF) and anti-citrullinated peptide antibodies (ACPA), as well as higher Collinsela aerofaciens levels (OTU ≥0.15) [1,2]. DNA methylation was determined using Infinium Methylation EPIC BeadChip (Illumina, San Diego, CA, USA). The methylation level of each cytosine was expressed as β-value. ANOVA and bivariate analysis were performed to compare between groups of subjects. Multivariate logistic regression models were performed to identify factors associated with RA and severe RA phenotype.ResultsThe majority (75%) were female with a mean age of 57.6 ± 9.4 years. The patients with RA, in comparison with the controls, had higher smoking habits (62.5% vs. 40.7%; p=0.015) and obesity (53.1% vs. 28.1%; p=0.012). Among patients, subjects with severe RA compared with non-severe had a higher mean DAS28-ESR (3.9 ± 0.6 vs. 3.3 ± 0.2 mg/l, p=0.001), and higher abundance in Collinsela (median (IQR), 0.3 (0.1-1.7) vs. 0.1 (0.0-0.4), p=0.003); as well as a higher frequency of erosions (93.8% vs. 50.0%; p=0.006), elevated ACPA (68.8% vs. 31.1%; p=0.034) and treatment with biological therapy (56.3% vs. 12.5%, p=0.009). Regarding the methylation analysis, those CpGs related to genes or pseudogenes, with a minimum β value change of ± 0.10 between groups and p value ≤ 0.01 were selected (Figure 1). The described CpG sites located in differentially methylated regions, together with other CpGs, were proposed as possible biomarkers (Table 1). In addition, cg06166490 was also considered, since it is located in a differentially methylated region with an adjusted p-value <0.00001. Of these CpGs, 5 CpG sites were associated with the presence of RA, and 2 CpG sites were associated with disease severity (Table 1).ConclusionDNA methylation level at specific CpG sites is associated with RA patients. The present study identified epigenome marks related to RA and possible disease severity, which warrant further investigation and could be useful in the diagnosis and management of the disease.References[1]Ruiz-Limón P, Mena-Vázquez N, Moreno-Indias I, et al. Collinsella is associated with cumulative inflammatory burden in an established rheumatoid arthritis cohort. Biomed Pharmacother. 2022 Sep;153:113518.[2]Mena-Vázquez N, Rojas-Gimenez M, Fuego-Varela C, et al. Safety and Effectiveness of Abatacept in a Prospective Cohort of Patients with Rheumatoid Arthritis-Associated Interstitial Lung Disease. Biomedicines. 2022 Jun;10[7].Table 1.CpG sites selected as possible potential biomarkersID ProbeRegionGene symbolMultivariate Logistic RegressionModel 1 (R2=0,618)cg16474696PromoterMRI1OR: 1.04, 95%IC (1.00-1.07), p=0.034cg15741931PromoterUBAP2LOR: 1.12, 95%IC (1.04-1.21), p=0.003cg06508795BodyDCCOR: 1.05, 95%IC (1.00-1.07), p=0.012cg05510714BodyKYNUOR: 0.94, 95%IC (0.89-0.98), p=0.026cg06166490PromoterHoxa2OR: 1.23, 95%IC (1.07-1.41), p=0.003Model 2 (R2=0,381)cg08586441BodyTECOR: 1.07, 95%IC (1.00-1.15), p=0.037cg14435720BodyMIR126cg19405177BodyPLEC1cg09497409PromoterLASS4OR: 1.17, 95%IC (1.02-1.35), p=0.019cg25251562PromoterALLCModel 1: Dependent variable: Patients (1) vs. Controls (0). Model 2: Dependent variable: severe RA (1) vs. Non-severe RA (0). Age and sex were variables included in both models.AcknowledgementsThis work was supported by FIS Grant PI18/00824 (Instituto Carlos III, Fondos FEDER). “Ayuda de Garantía Juvenil 2020” of the University of Malaga, Spain (SNGJ5Y6-12). Redes de Investigación Cooperativa Orientadas a Resultados en Salud (RICORS) (RD21/0002/0037).Disclosure of InterestsNone Declared.