<i>IL10</i> GGC Haplotype Is Positively and HLA-<i>DQA1*</i>05<i>-DQB1*</i>02 Is Negatively Associated with Radiographic Progression in Undifferentiated Arthritis
Bibliographic record
Abstract
OBJECTIVE: In rheumatoid arthritis (RA), many genetic markers, such as the shared-epitope (SE) alleles, are described in association with radiographic progression, but limited data are available on undifferentiated arthritis (UA). We investigated whether single-nucleotide polymorphisms (SNP) and haplotypes in immune response genes and HLA class II alleles are associated with radiographic progression in patients with early UA. METHODS: Progression of radiographic damage was determined in white Dutch patients with early UA after 2 years of followup. Severe progression was defined as an increase in Sharp/van der Heijde Score > or = 5 points after 2 years of followup. The remainder was classified as mild. These SNP were genotyped by Taqman technology: tumor necrosis factor (TNF) -1031, -863, -857, -308, -238; lymphotoxin-alpha (LTA) +368, +252; interleukin 10 (IL10) -2849, -1082, -819; IL1A -889, IL1B -31, +3953; and IL1RN +2018. Carriage of SE alleles and HLA-DQA1*05-DQB1*02 haplotype was established. These markers were analyzed in relation to radiographic progression. RESULTS: Forty-eight out of 151 patients with early UA had severe radiographic progression. Severe radiographic progression was associated with an increased carrier frequency of SE alleles (OR 5.12, 95% CI 2.0-13.1, p < 0.001) and IL10 GGC haplotype (OR 2.8, 95% CI 1.4-5.8, p = 0.003). Mild radiographic progression was associated with the HLA-DQA1*05-DQB1*02 haplotype (OR 0.3, 95% CI, 0.1-0.8, p = 0.013) and with allele TNF -308A (OR 0.4, 95% CI, 0.2-0.9, p = 0.02). CONCLUSION: The SE and the IL10 GGC haplotype are associated with severe progression of radiographic damage, in contrast to the DQA1*05-DQB1*02 haplotype and the TNF -308A allele, which are associated with mild radiographic progression in early UA.
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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 | 0.002 |
| 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) | 0.004 | 0.001 |
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".