T-cell subset abnormalities predict progression along the Inflammatory Arthritis disease continuum: implications for management

Abstract

The presence of a disease continuum in inflammatory arthritis (IA) is a recognised concept, with distinct stages from at-risk stage (presence of anti citrullinated-peptide autoantibody) to diagnosis of rheumatoid arthritis (RA), including therapy-induced remission. Despite T-cell dysregulation being a key feature of RA, there are few reports of T-cell phenotyping along the IA-continuum. We investigated the disturbances of naïve, regulatory and inflammation related cell (IRC) CD4+ T-cell subsets in 705 individuals across the IA-continuum, developing a simple risk-score (summing presence/absence of a risk-associated with a subset) to predict progression from one stage to the next. In 158 at-risk individuals, the 3 subsets had individual association with progression to IA and the risk-score was highly predictive (p < 0.0001). In evolving IA patients, 219/294 developed RA; the risk-score included naïve and/or Treg and predicted progression (p < 0.0001). In 120 untreated RA patients, the risk-score for predicting treatment-induced remission using naïve T-cells had an odds ratio of 15.4 (p < 0.0001). In RA patients in treatment-induced remission, a score using naïve T-cells predicted disease flare (p < 0.0001). Evaluating the risk of progression using naïve CD4+ T-cells was predictive of progression along the whole IA-continuum. This should allow identification of individuals at high-risk of progression, permitting targeted therapy for improved outcomes.

Overview of Dual-Acting Drug Methotrexate in Different Neurological Diseases, Autoimmune Pathologies and Cancers

“…Self-activation of the memory T cells causes constant amplification of the disease. These lymphocytes release cytokines as IFN-γ and IL-2, which further activate monocytes and macrophages releasing subsequent IL-1, TNF-α and growth factors [93][94][95][96][97][98].…”

Toward Individualized Prediction of Response to Methotrexate in Early Rheumatoid Arthritis: A Pharmacogenomics‐Driven Machine Learning Approach

“…Relatively modest predictive importance of sociodemographic and clinical predictors in our study is in line with some earlier reports and reflects on clinical reality, wherein clinicians are unable to individualize methotrexate therapy in current practice. For example, in a study from the UK, baseline higher naive T cell frequency was found to be predictive of early RA remission at 6 months of first therapy with methotrexate, regardless of demographic, clinical, or other immunologic parameters, and it was proposed that naive CD4+ T cells can be predictive of progression of inflammatory arthritis (35,36). Taken together with our results, this suggests the role of assayable plasma biomarkers (e.g., genomics) relating to the immune system that prove to be vital in individualizing methotrexate therapy in treating early RA.…”

Bridging Insights From Lymph Node and Synovium Studies in Early Rheumatoid Arthritis

“…The origin of T cells in the synovium and how and where they have been activated is still largely unknown. Part of them may reflect activated resident T cells but probably the majority have been activated in peripheral lymphoid organs after which they then migrate to the synovium, as changes in the T cell compartment can be found in peripheral blood of early RA and RA-risk individuals (7,45,46). As previously highlighted, our work on LN-derived T cells suggest that there may be increased Tfh cells in RA patients (10) as well as a possibly exhausted population in RA-risk individuals (19,21) which are both characterized by increased PD1 expression.…”