Re-evaluating the utility of iron indices in hereditary hemochromatosis genotyping: A retrospective study
Bibliographic record
Abstract
INTRODUCTION: Hereditary hemochromatosis (HH), associated with C282Y or H63D mutations in the HFE gene, is the commonest genetic disorder in Canada. The majority of HH cases are attributable to C282Y homozygosity which can precipitate iron overload and organ damage, but with low penetrance. Elevated transferrin saturation (TSat) and ferritin levels are key biochemical indicators of iron overload in C282Y homozygotes. This retrospective study examined TSat and ferritin levels as predictors of C282Y homozygosity in genotyped patients. METHODS: This study included 23,432 individuals from Maritime provinces who underwent HFE genotyping from 2009 to 2022. Those with available biomarkers (TSat, ferritin, ALT) were included in the study sample. C282Y and H63D variants were identified based on HFE genotying. Median values for each biomarker were compared across genotypes and their diagnostic performance in predicting C282Y homozygosity evaluated using ROC analysis. RESULTS: 1241 individuals (5.3 %) showed C282Y homozygosity, marking the largest North American study cohort. C282Y homozygotes showed significantly higher median TSat and ferritin levels than wildtypes. TSat showed the best diagnostic performance in detecting C282Y homozygosity (AUC = 0.82, 95 % CI: 0.78-0.85), outperforming ferritin (AUC = 0.54, 95 % CI: 0.50-0.58) and ALT (AUC = 0.59, 95 % CI: 0.56-0.63). TSat thresholds of 32 % (females) and 35 % (males) had a 90 % sensitivity for C282Y homozygosity. Using thresholds of TSat ≤46 % and ferritin ≤370 µg/L (females), and TSat ≤49 % and ferritin ≤703 µg/L (males) reduced the need for genotyping by up to 50 % without missing significant biochemical iron overload cases. Implementing this strategy across 23,432 tests could save $1,701,163 and potentially reduce unnecessary downstream management. CONCLUSION: Our study suggests significant efficiency savings by implementing an algorithm to reduce unnecessary HFE genotyping and alleviate unwarranted genetic testing anxiety.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".