Mean corpuscular volume in <i>HFE</i> p.C282Y/p.H63D compound heterozygotes with high iron phenotypes: clinical and laboratory associations
Bibliographic record
Abstract
Abstract Background Variables that influence mean corpuscular volume (MCV) in HFE p.C282Y (rs1800562)/p.H63D (rs1799945) compound heterozygotes are inadequately defined. Methods We retrospectively studied self-reported non-Hispanic white adult compound heterozygotes with transferrin saturation (TS) >50% and serum ferritin (SF) >300 μg/L (men) or TS >45% and SF >200 μg/L (women) who participated in primary care-based screening. In post-screening evaluations, we excluded participants with anemia, pregnancy, or medication use that increases MCV. We defined heavy alcohol intake as >28 g/d men and >14 g/d women. We determined associations of MCV with 11 clinical and laboratory variables. Results There were 74 participants (37 men, 37 women) of mean age 59±12 (SD) y. Mean screening TS and SF were 65±13% and 529±169 µg/L (men) and 59±14% and 376±195 µg/L (women). Post-screening values did not differ significantly. Mean MCV was 95.7±4.0 fL. There was a negative correlation of MCV with body mass index (p=0.0488) and positive correlations of MCV with age (p=0.0098), daily heme iron intake (p=0.0333), and daily alcohol intake (p=0.0113). Mean MCVs of 19 participants with and 55 without heavy alcohol intake were 97.8±3.8 g/d and 95.0±3.9 g/d, respectively; p=0.0074). Linear regression on MCV confirmed positive associations with age (p=0.0064) and daily alcohol intake (p=0.0151). MCV was not significantly associated with sex, diabetes, daily intakes of non-heme and supplemental iron, swollen or tender 2nd/3rd metacarpophalangeal joints, TS, or SF. Conclusion MCV in HFE p.C282Y/p.H63D compound heterozygotes with high iron phenotypes is positively associated with age and daily alcohol intake, after adjustment for other variables.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".