Genotype scores in energy and iron-metabolising genes are higher in elite endurance athletes than in nonathlete controls
Bibliographic record
Abstract
Information about the association of energy and iron-metabolising genes with endurance performance is scarce. The objective of this investigation was to compare the frequencies of polymorphic variations of genes involved in energy generation and iron metabolism in elite endurance athletes versus nonathlete controls. Genotype frequencies in 123 male elite endurance athletes (75 professional road cyclists and 48 elite endurance runners) and 122 male nonathlete participants were compared by assessing 4 genetic polymorphisms: AMPD1 c.34C/T (rs17602729), PPARGC1A c.1444G/A (rs8192678) HFE H63D c.187C/G (rs1799945) and HFE C282Y c.845G/A (rs1800562). A weighted genotype score (w-TGS; from 0 to 100 arbitrary units (a.u.)) was calculated by assigning a corresponding weight to each polymorphism. In the nonathlete population, the mean w-TGS value was lower (39.962 ± 14.654 a.u.) than in the group of elite endurance athletes (53.344 ± 17.053 a.u). The binary logistic regression analysis showed that participants with a w-TGS > 38.975 a.u had an odds ratio of 1.481 (95% confidence interval: 1.244–1.762; p < 0.001) for achieving elite athlete status. The genotypic distribution of polymorphic variations involved in energy generation and iron metabolism was different in elite endurance athletes vs. controls. Thus, an optimal genetic profile in these genes might contribute to physical endurance in athlete status. Novelty Genetic profile in energy generation and iron-metabolising genes in elite endurance athletes is different than that of nonathletes. There is an implication of an “optimal” genetic profile in the selected genes favouring endurance sporting performance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".