The interleukin-6, serotonin transporter, and monoamine oxidase A genes and endurance performance during the South African Ironman Triathlon
Bibliographic record
Abstract
Previous studies have identified an association of genetic variants believed to alter physiological and biochemical processes locally within the skeletal muscle and therefore performance in the Ironman triathlon. There is growing evidence that the serotonergic system and circulating interleukin (IL)-6 levels are also involved in mediating endurance capacity. Investigators have demonstrated that recombinant human IL-6 administration and serotonergic neurotransmission manipulation, with 5-hydroxytryptamine transporter (5-HTT) and monoamine oxidase A (MAO-A) inhibitors, prior to exercise, can alter running performance, consistent with a central governor hypothesis. The aim of this study was to investigate possible associations of functional polymorphisms within the IL-6, 5-HTT, and MAO-A genes with endurance performance of Ironman triathletes. Four hundred sixty-eight male Caucasian triathletes who completed the 2000 and (or) 2001 South African Ironman Triathlon and 200 healthy Caucasian male controls were genotyped for the -174 IL-6 G/C, 5-HTT 40 base pair (bp) insertion-deletion and 30 bp variable number of tandem repeats (VNTR) MAO-A gene polymorphisms. There were no significant differences in the relative genotype distributions within the IL-6 (p = 0.636), 5-HTT (p = 0.659), and MOA-A (p = 0.227) polymorphisms when the fastest-fnishing, middle-finishing, and slowest-finishing triathletes, as well as the control groups, were compared. There were no direct associations between the IL-6 -174 G/C, 5-HTT 44 bp insertion-deletion, and MAO-A 30 bp VNTR gene polymorphisms and endurance performance in the 2000 and (or) 2001 South African Ironman Triathlons. The neurogenetic basis of the central governor requires further investigation.
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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".