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T cell subsets: an immunological biomarker to predict progression to clinical arthritis in ACPA-positive individuals

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 iden...

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Autores principales: Hunt, L, Hensor, E M, Nam, J, Burska, A N, Parmar, R, Emery, P, Ponchel, F
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036223/
https://www.ncbi.nlm.nih.gov/pubmed/27613874
http://dx.doi.org/10.1136/annrheumdis-2015-207991
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author Hunt, L
Hensor, E M
Nam, J
Burska, A N
Parmar, R
Emery, P
Ponchel, F
author_facet Hunt, L
Hensor, E M
Nam, J
Burska, A N
Parmar, R
Emery, P
Ponchel, F
author_sort Hunt, L
collection PubMed
description 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. Naïve, 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 naïve (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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spelling pubmed-50362232016-10-17 T cell subsets: an immunological biomarker to predict progression to clinical arthritis in ACPA-positive individuals Hunt, L Hensor, E M Nam, J Burska, A N Parmar, R Emery, P Ponchel, F Ann Rheum Dis Basic and Translational Research 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. Naïve, 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 naïve (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. BMJ Publishing Group 2016-10 2015-12-01 /pmc/articles/PMC5036223/ /pubmed/27613874 http://dx.doi.org/10.1136/annrheumdis-2015-207991 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/ This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/
spellingShingle Basic and Translational Research
Hunt, L
Hensor, E M
Nam, J
Burska, A N
Parmar, R
Emery, P
Ponchel, F
T cell subsets: an immunological biomarker to predict progression to clinical arthritis in ACPA-positive individuals
title T cell subsets: an immunological biomarker to predict progression to clinical arthritis in ACPA-positive individuals
title_full T cell subsets: an immunological biomarker to predict progression to clinical arthritis in ACPA-positive individuals
title_fullStr T cell subsets: an immunological biomarker to predict progression to clinical arthritis in ACPA-positive individuals
title_full_unstemmed T cell subsets: an immunological biomarker to predict progression to clinical arthritis in ACPA-positive individuals
title_short T cell subsets: an immunological biomarker to predict progression to clinical arthritis in ACPA-positive individuals
title_sort t cell subsets: an immunological biomarker to predict progression to clinical arthritis in acpa-positive individuals
topic Basic and Translational Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036223/
https://www.ncbi.nlm.nih.gov/pubmed/27613874
http://dx.doi.org/10.1136/annrheumdis-2015-207991
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