The influence of sex, menstrual cycle phase, estrogen, obesity, and endurance training on carbohydrate and lipid metabolism at rest and during moderate-intensity endurance exercise
Bibliographic record
Abstract
Carbohydrate (CHO) and fat constitute the main fuels used during endurance exercise. The purpose of this thesis was to determine how sex, menstrual cycle phase, estrogen, obesity, and endurance training (ET) influence substrate metabolism. In study 1, men (n = 11) and women (n = 13) underwent primed constant infusion of [6,6-2H] glucose with muscle biopsies taken before and after a 90 min bike ride at 65% VO2 peak. Luteal-phase women had lower muscle glycogen utilization compared with follicular-phase women. Women had lower respiratory exchange ratio (RER) and plasma glucose utilization than men. In study 2, men (n = 17) and women (n = 19) had muscle biopsies taken before and after a 90 min bike ride at 65% VO2 peak. Women had greater intramyocellular lipid (IMCL) content and lower RER during exercise, with no effect of sex on net IMCL utilization. In study 3, Men (n = 11) were treated with 17-β-estradiol (E2, 2 mg·d–1, 8 d) and placebo and underwent primed constant infusion of [6,6–2H] glucose with muscle biopsies taken before and after a 90 min bike ride at 65% VO2 peak. E2 lowered RER and plasma glucose oxidation during exercise, with no effect on muscle glycogen utilization. In study 4, lean (n = 12) and obese (n = 11) women underwent 12 weeks of ET with muscle biopsies taken before and after training. Obese women were insulin resistant compared with lean women. ET increased oxidative capacity and β-oxidation capacity, as well as mitochondrial content. ET redistributed IMCL from the subsarcolemmal region to the intermyofibrillar region of the muscle fibre. These studies demonstrate that substrate metabolism is affected by many factors in health and disease and the ability to manipulate these factors is of importance to athletes wishing to improve performance, as well as to obese individuals attempting to normalize an altered metabolic state.
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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) | 0.001 | 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".