A predictive model for progression to clinical arthritis in at-risk individuals with arthralgia based on lymphocyte subsets and ACPA.

BACKGROUND: The presence of autoantibodies against citrullinated proteins (ACPA) significantly increases the risk of developing rheumatoid arthritis (RA). Dysregulation of lymphocyte subpopulations was previously described in RA.

OBJECTIVES: To propose the predictive model for progression to clinical arthritis based on peripheral lymphocyte subsets and ACPA in individuals who are at risk of RA.

METHODS: Our study included 207 at-risk individuals defined by the presence of arthralgias and either additional ACPA positivity or meeting the EULAR definition for clinically suspect arthralgia. For the construction of predictive models, 153 individuals with symptom duration ≥12 months who have not yet progressed to arthritis were included. The lymphocyte subsets were evaluated using flow cytometry and anti-CCP using ELISA.

RESULTS: Out of all individuals with arthralgia, 41 progressed to arthritis. A logistic regression model with baseline peripheral blood lymphocyte subpopulations and ACPA as predictors was constructed. The resulting predictive model showed that high anti-CCP IgG, higher percentage of CD4+ T cells, and lower percentage of T and NK cells increased the probability of arthritis development. Moreover, the proposed classification decision tree showed, that individuals having both high anti-CCP IgG and low NK cells have the highest risk of developing arthritis.

CONCLUSIONS: We propose a predictive model based on baseline levels of lymphocyte subpopulations and ACPA to identify individuals with arthralgia with the highest risk of progression to clinical arthritis. The final model includes T cells and NK cells, which are involved in the pathogenesis of RA. This preliminary model requires further validation in larger at-risk cohorts.