RESUMEN
Objective: There is an urgent need for novel biomarkers to improve the early diagnosis of rheumatoid arthritis (ERA). Current serum biomarkers used in the management of ERA, including rheumatoid factor and anti-cyclic citrullinated peptide (ACPA), show limited specificity and sensitivity. Here, we used metabolomics to uncover new serum biomarkers of ERA. Methods: We applied an untargeted metabolomics approach including gas chromatography time-of-flight mass spectrometry in serum samples from an ERA cohort (n=32) and healthy controls (n=19). Metabolite set enrichment analysis was performed to explore potentially important biological pathways. Partial least squares discriminant analysis and variable importance in projection analysis were performed to construct an ERA biomarker panel. Results: Significant differences in the content of 11/81 serum metabolites were identified in patients with ERA. Receiver operating characteristic (ROC) analysis showed that a panel of only three metabolites (glyceric acid, lactic acid, and 3-hydroxisovaleric acid) could correctly classify 96.7% of patients with ERA, with an area under the ROC curve of 0.963 and with 94.4% specificity and 93.5% sensitivity, outperforming ACPA-based diagnosis by 2.9% and, thus, improving the preclinical detection of ERA. Aminoacyl-tRNA biosynthesis and serine, glycine, and phenylalanine metabolism were the most significant dysregulated pathways in patients with ERA. Conclusion: A metabolomics serum-based biomarker panel composed of glyceric acid, lactic acid, and 3-hydroxisovaleric acid offers potential for the early clinical diagnosis of RA.
Asunto(s)
Artritis Reumatoide , Humanos , Artritis Reumatoide/diagnóstico , Ácidos Glicéricos , Biomarcadores , Ácido LácticoRESUMEN
Background: The etiology of rheumatoid arthritis (RA) remains poorly understood. Early and accurate diagnosis still difficult to achieve. Inflammatory related molecules released into the circulation such cytokines and exosome-derived microRNAs (exomiRNAs) could be good candidates for early diagnosis of autoimmune diseases. We sought to discover a serum biomarker panel for the early detection of RA based on exomiRNAs and inflammatory markers. Methods: A 179 miRNAs-microarray panel was analyzed in a pilot study (4 early RA and 4 controls). Validation of deregulated exomiRNAs was performed in a larger cohort (24 patients with early RA and 24 controls). miRNet software was used to predict exomiRNA gene-targets interactions. Potentially altered pathways were analyzed by Reactome pathway database search. STRING database was used to predict protein-protein interaction networks. Enzyme-linked immunosorbent assay was used to measure serum levels of sTWEAK and sCD163. Signature biomarker candidates were statistical analyzed. Results: We detected 11 differentially expressed exomiRNAs in early RA pilot study. Validation analysis revealed that 6/11 exomiRNAs showed strong agreement with the pilot microarray data (exomiR-144-3p, -25-3p, -15a-5p, -451a, -107 and -185-5p). sTWEAK and sCD163 biomarkers were significantly elevated in the serum of patients with early RA. Receiver operating characteristic (ROC) analysis showed that the best panel to diagnose early RA contained exomiR-451a, exomiR-25-3p and sTWEAK, and could correctly classify 95.6% of patients, with an area under the ROC curve of 0.983 and with 100% specificity and 85.7% sensitivity. The YWHAB gene was identified as a common target of the putative miRNA-regulated pathways. Conclusion: A novel serum biomarker panel composed of exomiR-451a, exomiR-25-3p and serum levels of sTWEAK may have use in the early clinical diagnosis of RA. A new predicted exomiRNA-target gene YHWAB has been identified and may have a relevant role in the development of RA.