Associations Between the <i>HFE</i> Genotypes and Iron Status Markers in an Apparently Healthy Population
Bibliographic record
Abstract
Background: The ethnic Danish population has high frequencies of the hemochromatosis gene (HFE gene) variants H63D and C282Y and clinical hemochromatosis is quite common. The objective of the study was to examine the impact of the combination of the HFE variants wildtype (wt), H63D, and C282Y on body iron status markers in a population of 2,613 apparently healthy ethnic Danish men and women. Methods: An epidemiological population study was performed in Copenhagen County comprising a randomly selected population consisting of 1,342 men and 1,271 women. Blood samples were drawn in the morning in the fasting state for analysis of HFE genotypes and hematological iron status markers (hemoglobin, serum iron, serum transferrin, transferrin saturation, and serum ferritin). Results: The HFE gene variants, H63D and C282Y, in any combination as well as in any combination with the HFE wt genotype, had a significant influence on iron status markers, compared with the wt/wt genotype. When arranged according to their relative frequency in the population, individuals with the six different HFE genotypes, i.e. wt/wt, H63D/wt, C282Y/wt, H63D/H63D, C282Y/H63D, and C282Y/C282Y, displayed gradually increasing levels of serum iron, decreasing levels of serum transferrin, and increasing levels of transferrin saturation in both genders. In men, ferritin increased gradually according to the HFE genotype. In women, ferritin levels were by and large independent of the HFE genotypes. Conclusions: Using the iron status markers in HFE wt/wt individuals as a baseline, any other combination of the HFE genotypes had a significant impact on iron status. The C282Y/C282Y genotype was associated with the highest impact on iron status markers including serum ferritin and thus body iron overload. Due to physiological iron losses with menstruations and pregnancies, women are partly protected against significant body iron overload, regardless of the HFE genotype.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".