HFE Genotype and Endurance Performance in Competitive Male Athletes
Bibliographic record
Abstract
INTRODUCTION: Hereditary hemochromatosis can cause individuals to absorb too much iron from their diet. Higher tissue iron content, below the threshold of toxicity, may enhance oxygen carrying capacity and offer a competitive advantage. Single nucleotide polymorphisms (SNP) in the homeostatic iron regulator (HFE) gene have been shown to modify iron metabolism and can be used to predict an individual's risk of hemochromatosis. Several studies have shown that HFE genotypes are associated with elite endurance athlete status; however, no studies have examined whether HFE genotypes are associated with endurance performance. PURPOSE: The objectives of this study were to determine whether there was an association between HFE risk genotypes (rs1800562 and rs1799945) and endurance performance in a 10-km cycling time trial as well as maximal oxygen uptake (V˙O2peak), an indicator of aerobic capacity. METHODS: Competitive male athletes (n = 100; age = 25 ± 4 yr) completed a 10-km cycling time trial. DNA was isolated from saliva and genotyped for the rs1800562 (C282Y) and rs1799945 (H63D) SNP in HFE. Athletes were classified as low risk (n = 88) or medium/high risk (n = 11) based on their HFE genotype for both SNP using an algorithm. ANCOVA was conducted to compare outcome variables between both groups. RESULTS: Individuals with the medium- or high-risk genotype were ~8% (1.3 min) faster than those with the low-risk genotype (17.0 ± 0.8 vs 18.3 ± 0.3 min, P = 0.05). V˙O2peak was ~17% (7.9 mL·kg-1⋅min-1) higher in individuals with the medium- or high-risk genotype compared with those with the low-risk genotype (54.6 ± 3.2 vs 46.7 ± 1.0 mL·kg-1⋅min-1, P = 0.003). CONCLUSION: Our findings show that HFE risk genotypes are associated with improved endurance performance and increased V˙O2peak in male athletes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".