T-cell subset abnormalities predict progression along the Inflammatory Arthritis disease continuum: implications for management.
Frederique PonchelAgata N BurskaLaura HuntHanna GulThibault RabinRekha ParmarMaya H BuchPhilip Gerard ConaghanPaul EmeryPublished in: Scientific reports (2020)
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.
Keyphrases
- rheumatoid arthritis
- disease activity
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- peritoneal dialysis
- systemic lupus erythematosus
- prognostic factors
- transcription factor
- emergency department
- interstitial lung disease
- stem cells
- single cell
- high throughput
- metabolic syndrome
- patient reported outcomes
- peripheral blood
- machine learning
- systemic sclerosis
- deep learning
- diabetic rats
- mesenchymal stem cells
- drug induced
- high resolution
- patient reported
- electronic health record
- combination therapy