Hereditary hemochromatosis gene (<i>HFE</i>) variants are associated with birth weight and childhood leukemia risk
Bibliographic record
Abstract
BACKGROUND: Our original studies reported an association between the iron-metabolism gene HFE and risk of childhood acute lymphoblastic leukemia (ALL), and a birth weight association in ALL. Through its effect on cell proliferation, iron is involved in both fetal development and cancer. We hypothesize that HFE links higher infant birth weight with leukemia risk and that maternal HFE genotype modifies this association. PROCEDURE: Nine hundred ninety-five infants and their mothers from the North Cumbria Community Genetics Project, and 163 incident childhood ALL cases from the Newcastle Haematology Biobank were genotyped for HFE, HAMP, TFRC variants and 21 genomic control loci. Cord blood iron levels were measured in 217 control infants. RESULTS: Three HFE variants showed correlations with birth weight with a gene-dosage relationship in males (gender effect). The association was stronger in homozygotes for TFRC S142G and when the mother was positive for any HFE variant (maternal effect). The genotypes expected to increase fetal iron levels correlated with birth weight in males and their association with ALL was stronger in females who, we postulate, could not offset iron excess by increasing their weight. CONCLUSIONS: Certain materno-fetal genotype combinations that increase fetal iron exposure showed associations with higher birth weight in males and somewhat higher ALL risk in females. Gender-specific use of iron during fetal growth may lead to this dichotomy in birth weight change. Only the materno-fetal genotype combinations that increase iron levels most extremely correlated with birth weight and ALL risk in males.
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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 | 0.001 |
| 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) | 0.003 | 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".