Vegan vs. omnivore diets paradox: A whole-metagenomic approach for defining metabolic networks during the race in ultra-marathoners- a before and after study design
Bibliographic record
Abstract
BACKGROUND: The effect of vegan diets on metabolic processes in the body is still controversial in ultra endurance athletes. The study aims to determine gut microbiome adaptation to extreme exercise according to vegan or omnivore diet consumed in ultra-marathoners. We also seek to evaluate long-term vegan diets' effects on redox homeostasis, and muscle fatigue, and assess energy availability. METHODS: Seventy participants will be assigned to the study, including 35 vegan ultra-marathoners and 35 omnivores competing in the Sri-Chinmoy ultra marathon race. Research data will be collected from the participants at four steps (three visits to the research laboratory and the race day) throughout the study. At the first visit (seven days before the race), fecal samples, and anthropometric measurements will be collected. Body composition will be measured using DXA. Participants will be informed about keeping detailed food records and will be asked to record their diet data and activity logs during the entire study period. At second visit, maximum oxygen consumption will be measured on treadmill. On race day, blood samples will be collected immediately before, and 0. min, 2 hours, and 24 hours after the race. Body weight will be measured before and after the race. The blood and fecal samples will be stored at -80 C until analysis. Plasma malondialdehyde, reactive oxygen metabolites, total antioxidant capacity, Heatshockprotein-70, and serum Orosomucoid-1 will be analyzed in blood samples. Fecal samples will be analyzed with shotgun metagenomic analysis and interpreted using bioinformatics pipeline (HumanN2). Statistical tests will be analyzed using SPSS version 23.0 and R Software. DISCUSSION: Study findings will determine the effects of the vegan diet on sports performance, revealing the multiple interactions between host and gut microbiome at the whole metagenomic level. Additionally, results will show the possible adaptation throughout the race by analyzing blood and fecal samples. Furthermore, by assessing energy availability and determining host-metabolite crosstalk for ultra-endurance athletes, possible nutritional deficiencies can be identified. Thus, advanced nutritional strategies can be developed based on metabolic needs. TRIAL REGISTRATION: Current controlled trials, ISRCTN registry 69541705. Registered on 8 December 2019.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".