Background and Objectives ACPA+ individuals with non-specific musculoskeletal symptoms are at high risk of developing rheumatoid arthritis (RA). We previously demonstrated dys-regulation of T-cell subsets with loss of naïve and regulatory T-cells (Treg) in early disease. The aim of the current study is to demonstrate the predictive value of T-cell subset analysis for progression towards symptom onset in ACPA+ individuals.
Materials and Methods 84 ACPA+ individuals without clinical synovitis at recruitment were followed. 95 healthy controls (HC) provided a reference group. At baseline T-cell subset analyses were performed using 6-colour flowcytometry for naïve T-cells (CD4+ CD45RB + CD45RA+ CD62L+), Treg (CD4+ CD25highFoxp3 + CD127low) and inflammation related cells (IRC: CD4+ CD45RB + CD45RA+ CD62L-). The relationship between naïve cell frequency and age was established in HC and used to age-correct values in ACPA+ . ROC curve analysis was used to identify 2 T-cell cut-offs predicting progression to IA at any time; one which maximised the Youden index (sensitivity + specificity-1), and one which prioritised specificity over sensitivity.
Results 42/84 (50%) of patients developed clinical synovitis within a median follow-up of 6.0 months (range 1 week-46 months). For age-corrected naïve T-cells area under the ROC curve (AUC) was 0.67 (95% CI 0.55, 0.79; n = 84, p = 0.007), for IRC 0.70 (0.59, 0.81; n = 81, p = 0.002) and for Treg 0.67 (0.53, 0.80; n = 65, p = 0.021). For each of the three subsets, the Youden index cut-off correctly classified >65% of patients (Table 1). Cut-offs prioritising specificity were identified which did not greatly reduce overall classification success. The confidence intervals for these estimates remain wide and our sample size may still be limited for running such analysis.
Conclusions T-cell dys-regulation in ACPA+ individuals with non-specific musculoskeletal pain may be useful in predicting progression to inflammatory arthritis. Multivariable modelling in larger cohorts is needed to quantify the utility of T-cell subsets in predicting progression to IA.