X Chromosome Dose and Sex Bias in Autoimmune Diseases: Increased Prevalence of 47,XXX in Systemic Lupus Erythematosus and Sjögren's Syndrome
Bibliographic record
Abstract
OBJECTIVE: More than 80% of autoimmune disease predominantly affects females, but the mechanism for this female bias is poorly understood. We suspected that an X chromosome dose effect accounts for this, and we undertook this study to test our hypothesis that trisomy X (47,XXX; occurring in ∼1 in 1,000 live female births) would be increased in patients with female-predominant diseases (systemic lupus erythematosus [SLE], primary Sjögren's syndrome [SS], primary biliary cirrhosis, and rheumatoid arthritis [RA]) compared to patients with diseases without female predominance (sarcoidosis) and compared to controls. METHODS: All subjects in this study were female. We identified subjects with 47,XXX using aggregate data from single-nucleotide polymorphism arrays, and, when possible, we confirmed the presence of 47,XXX using fluorescence in situ hybridization or quantitative polymerase chain reaction. RESULTS: We found 47,XXX in 7 of 2,826 SLE patients and in 3 of 1,033 SS patients, but in only 2 of 7,074 controls (odds ratio in the SLE and primary SS groups 8.78 [95% confidence interval 1.67-86.79], P = 0.003 and odds ratio 10.29 [95% confidence interval 1.18-123.47], P = 0.02, respectively). One in 404 women with SLE and 1 in 344 women with SS had 47,XXX. There was an excess of 47,XXX among SLE and SS patients. CONCLUSION: The estimated prevalence of SLE and SS in women with 47,XXX was ∼2.5 and ∼2.9 times higher, respectively, than that in women with 46,XX and ∼25 and ∼41 times higher, respectively, than that in men with 46,XY. No statistically significant increase of 47,XXX was observed in other female-biased diseases (primary biliary cirrhosis or RA), supporting the idea of multiple pathways to sex 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 | 0.003 |
| 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 | 0.001 | 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".