The term "undifferentiated arthritis (UA)" is used to refer to all cases of arthritis that do not fit a specific diagnosis. A significant percentage of UA patients progress to rheumatoid arthritis (RA), others to a different definite rheumatic disease, and the rest undergo spontaneous remission. Therapeutic intervention in patients with UA can delay or halt disease progression and its long-term consequences. It is therefore of inherent interest to identify those UA patients with a high probability of progressing to RA who would benefit from early appropriate therapy. This study was undertaken to investigate whether alterations in the DNA methylation profiles of immune cells may provide information on the genetically or environmentally determined status of patients and potentially discriminate between disease subtypes.
We performed DNA methylation profiling of a UA patient cohort, in which progression to RA occurred for a significant proportion of the patients.
We found differential DNA methylation in UA patients compared to healthy controls. Most importantly, our analysis identified a DNA methylation signature characteristic of those UA cases that differentiated to RA. We demonstrated that the methylome of peripheral mononuclear cells can be used to anticipate the evolution of UA to RA, and that this methylome is associated with a number of inflammatory pathways and transcription factors. Finally, we designed a machine learning strategy for DNA methylation-based classification that predicts the differentiation of UA toward RA.
Our findings indicate that DNA methylation profiling provides a good predictor of UA-to-RA progression to anticipate targeted treatments and improve clinical management.