Skewed X Chromosome Inactivation and Female Preponderance in Autoimmune Thyroid Disease: An Association Study and Meta-Analysis
Bibliographic record
Abstract
CONTEXT: A number of small data sets have suggested a potential role for skewed X chromosome activation (XCI), away from the expected 50:50 parent of origin ratio, as an explanation for the strong female preponderance seen in the common autoimmune thyroid diseases (AITD), Graves' disease (GD), and Hashimoto's thyroiditis (HT). OBJECTIVE: The objective of the study was to confirm a role for XCI skewing as a potential explanation for the strong female preponderance seen in AITD. DESIGN: The design of the study was to screen XCI in the largest GD, HT, and control case-control cohort and family cohort to date and undertake a meta-analysis of previous AITD XCI reports. SETTING: The study was conducted at a research laboratory. PATIENTS: Three hundred and nine GD, 490 HT, and 325 female UK Caucasians controls, 273 UK Caucasian GD families, and a meta-analysis of 454 GD, 673 HT, and 643 female Caucasian controls were included in the study. MAIN OUTCOME MEASURES: Case-control and family-based association studies and meta-analysis were measured. RESULTS: Skewed XCI was observed with GD [odds ratio (OR) 2.17 [95% confidence interval (CI) 1.43-3.30], P=2.1×10(-4)] and a trend toward skewing with HT (P=.08) compared with the control cohort. A meta-analysis of our UK data and that of four previous non-UK Caucasian studies confirmed significant skewing of XCI with GD [OR 2.54 (95% CI 1.58-4.10), P=1.0×10(-4), I2=30.2%] and HT [OR 2.40 (95% CI 1.10-5.26), P=.03, I2=74.3%]. CONCLUSIONS: Convincing evidence exists to support a role for skewed XCI in female subjects with AITD, which may, in part, explain the strong female preponderance observed in this disease.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.026 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".