HFE gene mutation is a risk factor for tissue iron accumulation in hemodialysis patients
Bibliographic record
Abstract
INTRODUCTION: HFE gene mutations are responsible from iron overload in general population. Studies in hemodialysis patients investigated the effect of presence of HFE gene mutations on serum ferritin and transferrin saturation (TSAT) with conflicting results. However effect of HFE mutations on iron overload in hemodialysis patients was not previously extensively studied. METHODS: 36 hemodialysis patients (age 51.3 ± 15.6, (18/18) male/female) and 44 healthy control subjects included in this cross sectional study. Hemoglobin, ferritin, TSAT in the preceding 2 years were recorded. Iron and erythropoietin (EPO) administered during this period were calculated. Iron accumulation in heart and liver was detected by MRI. Relationship between HFE gene mutation, hemoglobin, iron parameters and EPO doses, and tissue iron accumulation were determined. FINDINGS: Iron overload was detected in nine (25%) patients. Hemoglobin, iron parameters, weekly EPO doses, and monthly iron doses of patients with and without iron overload were similar. There was no difference between control group and hemodialysis patients with respect to the prevalence of HFE gene mutations. Iron overload was detected in five of eight patients who had HFE gene mutations, but iron overload was present in 4 of 28 patients who had no mutations (P = 0.01). Hemoglobin, iron parameters, erythropoietin, and iron doses were similar in patients with and without gene mutations. HFE gene mutations remained the main determinant of iron overload after multivariate logistic regression analysis (P = 0.02; OR, 11.6). DISCUSSION: Serum iron parameters were not adequate to detect iron overload and HFE gene mutation was found to be an important risk factor for iron accumulation.
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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 | 0.000 | 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) | 0.002 | 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".