Juvenile Idiopathic Arthritis (JIA) is an autoimmune condition characterised by flares of joint inflammation. However, no reliable biomarker exists to predict the erratic disease course. Normally, regulatory T cells (Tregs) maintain tolerance, with altered Tregs associated with autoimmunity. Treg signatures have shown promise in monitoring other conditions, therefore a Treg gene/protein signature could offer novel biomarker potential for predicting disease activity in JIA. Machine learning on our nanoString Treg 48-gene signature on peripheral blood (PB) Tregs generated a model to distinguish active JIA (active joint count, AJC≥1) Tregs from healthy controls (HC, AUC = 0.9875 on test data). Biomarker scores from this model successfully differentiated inactive (AJC = 0) from active JIA PB Tregs. Moreover, scores correlated with clinical activity scores (cJADAS), and discriminated subclinical disease (AJC = 0, cJADAS≥0.5) from remission (cJADAS<
0.5). To investigate altered protein expression as a surrogate measure for Treg fitness in JIA, we utilised spectral flow cytometry and unbiased clustering analysis. Three Treg clusters were of interest in active JIA PB, including TIGIT