17β-Estradiol Supplementation Decreases Glucose Rate of Appearance and Disappearance with No Effect on Glycogen Utilization during Moderate Intensity Exercise in Men
Bibliographic record
Abstract
CONTEXT AND OBJECTIVE: Women use less carbohydrate during endurance exercise, as compared with men. In rodents, 17beta-estradiol (E2) supplementation robustly increases lipid use and lowers muscle and liver glycogen use during exercise. E2 supplementation has been found to influence substrate selection by decreasing glucose rate of appearance (Ra), disappearance (Rd), and metabolic clearance rate during exercise in humans; however, neither a change in total carbohydrate use nor a sparing of muscle glycogen was demonstrated. SUBJECTS AND METHODS: We investigated the effect of 8 d of E2 (2 mg/d) supplementation on glucose turnover and net muscle glycogen use in 11 men using a randomized, double-blind, placebo-controlled, crossover design. Subjects underwent primed constant infusion of [6,6-(2)H]glucose, and muscle biopsies were taken before and after 90 min of cycling at 65% maximal oxygen uptake. RESULTS: E2 supplementation decreased the respiratory exchange ratio (P = 0.03) and glucose Ra and Rd (both P = 0.04) during exercise, as compared with placebo. E2 supplementation lowered proglycogen (P < 0.05) and total glycogen (P = 0.04) concentration, as compared with placebo; however, there was no effect of E2 on net muscle glycogen use during exercise. CONCLUSIONS: These findings show that E2 supplementation alters fuel selection in exercising men by increasing lipid use and reducing carbohydrate use, glucose Ra (primarily liver glucose production), and Rd (primarily muscle glucose uptake). Furthermore, E2 reduces the basal level of total muscle glycogen, particularly the proglycogen form.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".