X chromosome dose and sex bias in autoimmune diseases: increased 47,XXX in systemic lupus erythematosus and Sjögren’s syndrome (BA4P.211)
Bibliographic record
Abstract
Abstract Mechanism for female predominance in autoimmunity is unknown. We suspected an X chromosome dose effect and predicted if so, triple X (47,XXX, 1 in ~1,000 live female births) would be increased in female predominant diseases (systemic lupus erythematosus [SLE], primary Sjögren’s syndrome [SS], primary biliary cirrhosis [PBC] and rheumatoid arthritis [RA])compared to diseases without female predominance (sarcoidosis, granulomatosis with polyangiitis [GPA]) and healthy controls. We used single nucleotide polymorphism (SNP) arrays to identify 47,XXX and fluorescent in situ hybridization, or q-PCR to confirm when possible. 47,XXX was found in 7 of 2,948 SLE and 3 of 1,053 SS female patients, but in none of the 4,822 female controls (OR≥21.31, 95% CI: 2.36-∞, p=0.001 and OR≥22.95, 95% CI: 1.89-∞, p=0.006, respectively). One 47,XXX was present for every ~421 SLE women and ~351 SS women. In addition, we identified one 47,XXX from each of 1,159 women with PBC and 943 with sarcoidosis. No 47,XXX was identified among 453 women with RA or 247 with GPA. In conclusion, 47,XXX was present in excess among SLE and SS subjects as predicted by X chromosome dose effect. These estimated prevalence of SLE and SS with 47,XXX being respectively ~2.4 and ~2.8 times higher than in women with 46,XX and ~24 and ~39 times higher than in men with 46,XY. There was no increase of 47,XXX is other female-biased diseases, suggesting multiple pathways to such a bias in autoimmunity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".