CAG Repeat Polymorphism in the Androgen Receptor Gene in Women with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Rheumatoid arthritis (RA) is the most common chronic, autoimmune, inflammatory disease, with a genetic and hormonal background. The prevalence of women among patients with RA suggests the important role of sex hormones in the pathogenesis of RA. We examined the association between CAG repeat polymorphism in the androgen receptor (AR) gene and susceptibility to RA and its clinical features in white women. METHODS: The study groups consisted of 325 female patients with RA and 238 female controls. CAG repeat polymorphism was determined using polymerase chain reaction and subsequent fragment analysis by capillary electrophoresis. RESULTS: The number of CAG repeats in patients did not differ from that of controls (22.1 ± 2.9 vs 21.9 ± 2.9, respectively; p = 0.26), but the presence of articular erosions was associated with a lower number of repeats in the shorter allele of patients with RA (20.4 ± 2.2 vs 21.2 ± 2.4; p = 0.031). When alleles with < 22 CAG were classified as short (S) and those with ≥ 22 CAG as long (L), the age at diagnosis of RA was lower in women with S-S genotype in comparison to combined S-L + L-L genotypes (43.0 ± 14.6 yrs vs 47.6 ± 12.5 yrs; p = 0.021). In patients with the L-L genotype, the frequency of erosive disease (OR 0.45, 95% CI 0.25-0.80, p = 0.0085) and extraarticular manifestations (OR 0.50, 95% CI 0.26-0.98, p = 0.047) was lower in comparison to carriers of the S allele. In multivariate analysis, the L-L genotype was an independent factor associated with a lower risk of erosions (OR 0.44, 95% CI 0.22-0.90, p = 0.024). CONCLUSION: The results suggest the association of short AR (CAG)(n) alleles with earlier onset and a more aggressive course of RA.
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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 | 0.000 |
| 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".