Relation of HLA-B27, Tumor Necrosis Factor-α Promoter Gene Polymorphisms, and T Cell Cytokine Production in Ankylosing Spondylitis — A Comprehensive Genotype-Phenotype Analysis from an Observational Cohort
Bibliographic record
Abstract
OBJECTIVE: In a pilot study, a distinct T cell cytokine pattern associated with HLA-B27 status and a tumor necrosis factor-α (TNF-α) promoter gene polymorphism was found at -308 (TNF-308). The objective of our study was to assess these associations in a different cohort of patients with ankylosing spondylitis (AS) and to evaluate any effect on clinical measurements. METHODS: Peripheral T cell cytokine production of patients with AS (n = 121) from the German Spondyloarthritis Inception Cohort was assessed by flow cytometry and correlated with HLA-B27, TNF-238, and TNF-308, and with clinical measurements. RESULTS: In HLA-B27-positive, anti-TNF-naive patients with AS, the percentages of TNF-α-producing (5.02%) and interleukin 10-producing (0.31%) CD8+ cells were significantly lower in comparison to HLA-B27-negative patients (9.52%, p = 0.048, and 0.46%, p = 0.037, respectively). A nonsignificant trend was found for a lower production of TNF-α by CD4+ and interferon-γ by both CD4+ and CD8+ T cells, as compared to HLA-B27-negative patients with AS (p > 0.05 for all comparisons). The A allele at TNF-308 was associated with a lower percentage of TNF-α-producing CD4+ T cells. No significant correlations were found between clinical or radiological measurements and cytokine production or with TNF-α promoter gene polymorphisms. CONCLUSION: Modulation of T cell cytokines by HLA-B27 might play a role in AS pathogenesis in B27-positive individuals. No conclusive data were obtained for the TNF-308 polymorphism on cytokine production, and no effect of cytokines or genetic polymorphisms on clinical manifestations was observed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".