T-cell subset abnormalities predict progression along the Inflammatory Arthritis disease continuum: implications for management
| dc.contributor.author | Frederique Ponchel | |
| dc.contributor.author | Agata N. Burska | |
| dc.contributor.author | Laura Hunt | |
| dc.contributor.author | Hanna Gul | |
| dc.contributor.author | Thibault Rabin | |
| dc.contributor.author | Rekha Parmar | |
| dc.contributor.author | Maya H. Buch | |
| dc.contributor.author | Philip G. Conaghan | |
| dc.contributor.author | Paul Emery | |
| dc.date.accessioned | 2025-08-20T10:36:37Z | |
| dc.date.available | 2025-08-20T10:36:37Z | |
| dc.date.issued | 2020-01-01 | |
| dc.description.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. | en |
| dc.identifier.citation | Ponchel, F.; Burska, A.N.; Hunt, L.; Gul, H.; Rabin, T.; Parmar, R.; Buch, M.H.; Conaghan, P.G.; Emery, P. (2020). Sci. Rep., 10:3669. DOI:10.1038/s41598-020-60314-w | en |
| dc.identifier.doi | 10.1038/s41598-020-60314-w | |
| dc.identifier.uri | https://doi.org/10.1038/s41598-020-60314-w | |
| dc.identifier.uri | https://nur.nu.edu.kz/handle/123456789/9684 | |
| dc.language.iso | en | |
| dc.publisher | Scientific Reports (Nature) | |
| dc.relation.ispartof | Scientific Reports | en |
| dc.source | Scientific Reports, 10, 3669, (2020) | en |
| dc.subject | T-cell subsets | en |
| dc.subject | disease progression | en |
| dc.subject | inflammatory arthritis continuum | en |
| dc.subject | risk-score | en |
| dc.subject | type of access: open access | en |
| dc.title | T-cell subset abnormalities predict progression along the Inflammatory Arthritis disease continuum: implications for management | en |
| dc.type | Journal Article | en |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- T-cell subset abnormalities predict progression along the Inflammatory Arthritis disease continuum implications for management.pdf
- Size:
- 1.52 MB
- Format:
- Adobe Portable Document Format