Tumor Necrosis Factor–α Promoter −308/238 Polymorphism Association with Less Severe Disease in Ankylosing Spondylitis is Unrelated to Serum TNF-α and Does Not Predict TNF Inhibitor Response
Bibliographic record
Abstract
OBJECTIVE: Despite the clinical efficacy of tumor necrosis factor inhibitors (TNFi), the manner in which TNF-α contributes to disease in patients with ankylosing spondylitis (AS) remains unresolved. We investigated the relationship between TNF-α gene promoter region polymorphism, serum TNF-α levels, and clinical phenotype. METHODS: We did a cross-sectional and longitudinal cohort study in TNFi-naive patients with AS (n = 335). Clinical data and biological samples were collected during a research visit with genotyping for TNF-α -238 A/G and -308 A/G performed by Taqman RT-PCR and TNF levels determined by sandwich ELISA. Longitudinal TNF levels were monitored in unselected patients (n = 61). RESULTS: TNF-α -308 GA/AA genotype was present in 14% and TNF-α -238 GA/AA genotype in 1% of patients. TNF-α -308 GA/AA genotype was associated with a reduced risk of uveitis and better spinal function, while TNF-α -238 GA/AA genotype was associated with later age of onset and lower erythrocyte sedimentation rate (ESR). Serum TNF-α level was lower in patients with AS (151 pg/ml) than in controls (263 pg/ml), because more patients with AS had undetectable serum TNF-α (66 vs 25%, p < 0.001). TNFi treatment did not influence serum TNF-α. There was no effect of TNF-α -308/-238 or HLA-B27 genotype on serum TNF-α or subsequent initiation of TNFi. CONCLUSION: TNF-α -238 or -308 GA/AA genotypes in patients with AS are associated with signs of less severe disease. Serum TNF-α is, however, undetectable in two-thirds of patients with AS and is not influenced by TNF-α promoter genotype or TNFi therapy. These data suggest a more significant role for TNF-α at local sites of inflammation in AS than through systemic effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".