TY - JOUR
T1 - T cell subsets
T2 - An immunological biomarker to predict progression to clinical arthritis in ACPA-positive individuals
AU - Hunt, L.
AU - Hensor, E. M.
AU - Nam, J.
AU - Burska, A. N.
AU - Parmar, R.
AU - Emery, P.
AU - Ponchel, F.
N1 - Funding Information:
This study presents independent research supported by the National Institute for Health Research (NIHR). This work has been partly supported by a European Union funded FP7-integrated project Masterswitch No. 223404 and the IMI funded project BeTheCure No 115142-2.
Funding Information:
Funding This study presents independent research supported by the National Institute for Health Research (NIHR). This work has been partly supported by a European Union funded FP7-integrated project Masterswitch No. 223404 and the IMI funded project BeTheCure No 115142-2. Competing interests None declared. Patient consent Obtained.
PY - 2015/12/1
Y1 - 2015/12/1
N2 - Objectives Anticitrullinated protein antibody (ACPA)+ individuals with non-specific musculoskeletal symptoms are at risk of inflammatory arthritis (IA). This study aims to demonstrate the predictive value of T cell subset quantification for progression towards IA and compare it with previously identified clinical predictors of progression. Methods 103 ACPA+ individuals without clinical synovitis were observed 3-monthly for 12 months and then as clinically indicated. The end point was the development of IA. Na?ve, regulatory T cells (Treg) and inflammation related cells (IRCs) were quantified by flow cytometry. Areas under the ROC curve (AUC) were calculated. Adjusted logistic regressions and Cox proportional hazards models for time to progression to IA were constructed. Results Compared with healthy controls (age adjusted where appropriate), ACPA+ individuals demonstrated reduced na?ve (22.1% of subjects) and Treg (35.8%) frequencies and elevated IRC (29.5%). Of the 103 subjects, 48(46.6%) progressed. Individually, T cell subsets were weakly predictive (AUC between 0.63 and 0.66), although the presence of 2 T cell abnormalities had high specificity. Three models were compared: model-1 used T cell subsets only, model-2 used previously published clinical parameters, model-3 combined clinical data and T cell data. Model-3 performed the best (AUC 0.79 (95% CI 0.70 to 0.89)) compared with model-1 (0.75 (0.65 to 0.86)) and particularly with model-2 (0.62 (0.54 to 0.76)) demonstrating the added value of T cell subsets. Time to progression differed significantly between high-risk, moderate-risk and low-risk groups from model-3 (p=0.001, median 15.4 months, 25.8 months and 63.4 months, respectively). Conclusions T cell subset dysregulation in ACPA+ individuals predates the onset of IA, predicts the risk and faster progression to IA, with added value over previously published clinical predictors of progression.
AB - Objectives Anticitrullinated protein antibody (ACPA)+ individuals with non-specific musculoskeletal symptoms are at risk of inflammatory arthritis (IA). This study aims to demonstrate the predictive value of T cell subset quantification for progression towards IA and compare it with previously identified clinical predictors of progression. Methods 103 ACPA+ individuals without clinical synovitis were observed 3-monthly for 12 months and then as clinically indicated. The end point was the development of IA. Na?ve, regulatory T cells (Treg) and inflammation related cells (IRCs) were quantified by flow cytometry. Areas under the ROC curve (AUC) were calculated. Adjusted logistic regressions and Cox proportional hazards models for time to progression to IA were constructed. Results Compared with healthy controls (age adjusted where appropriate), ACPA+ individuals demonstrated reduced na?ve (22.1% of subjects) and Treg (35.8%) frequencies and elevated IRC (29.5%). Of the 103 subjects, 48(46.6%) progressed. Individually, T cell subsets were weakly predictive (AUC between 0.63 and 0.66), although the presence of 2 T cell abnormalities had high specificity. Three models were compared: model-1 used T cell subsets only, model-2 used previously published clinical parameters, model-3 combined clinical data and T cell data. Model-3 performed the best (AUC 0.79 (95% CI 0.70 to 0.89)) compared with model-1 (0.75 (0.65 to 0.86)) and particularly with model-2 (0.62 (0.54 to 0.76)) demonstrating the added value of T cell subsets. Time to progression differed significantly between high-risk, moderate-risk and low-risk groups from model-3 (p=0.001, median 15.4 months, 25.8 months and 63.4 months, respectively). Conclusions T cell subset dysregulation in ACPA+ individuals predates the onset of IA, predicts the risk and faster progression to IA, with added value over previously published clinical predictors of progression.
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U2 - 10.1136/annrheumdis-2015-207991
DO - 10.1136/annrheumdis-2015-207991
M3 - Article
C2 - 27613874
AN - SCOPUS:84954286489
SN - 0003-4967
VL - 75
SP - 1884
EP - 1889
JO - Annals of the Rheumatic Diseases
JF - Annals of the Rheumatic Diseases
IS - 10
ER -