Population‐based study of the relationship between mutations in the hemochromatosis (<i>HFE</i>) gene and arthritis
Bibliographic record
Abstract
BACKGROUND AND AIM: Mutations in the hemochromatosis (HFE) gene are carried by one in three individuals of British Isles descent and may result in increased iron stores. These increased iron stores could potentially induce or exacerbate diseases, such as arthritis, in which iron has a role in pathogenesis. Although arthritis is a well-known association of clinically overt hereditary hemochromatosis, controversy surrounds the role of mutations in the HFE gene as risk factors for arthritis. The aim of the present study was to determine whether mutations in the HFE gene are associated with an increased prevalence of arthritis. METHODS: A population-based study was conducted in Busselton, Western Australia, of the prevalence of arthritis in 1372 individuals of British Isles descent. Participants completed a questionnaire and general physical examination. Analysis for C282Y and H63D HFE mutations was undertaken. Unadjusted and adjusted odds ratios (OR) were calculated for the relationship between HFE mutations and the prevalence of self-reported, doctor-diagnosed arthritis. RESULTS: There was no association between the presence of HFE mutations and the prevalence of self-reported, doctor-diagnosed arthritis (C282Y/wild type (WT) adjusted OR = 1.041 (95% confidence interval (CI) 0.68-1.61), H63D/WT OR = 0.76 (95% CI 0.53-1.08), C282Y/C282Y OR = 0.39 (95% CI 0.04-3.63), C282Y/H 63D OR = 0.808 (95% CI 0.27-2.42), H63D/H63D OR = 0.419 (95% CI 0.13-1.36)). Overall adjusted OR for arthritis in participants with one or more HFE mutations was 0.81 (95% CI 0.61-1.09). CONCLUSIONS: Mutations of the HFE gene are not risk factors for arthritis in populations of British Isles descent.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".