No effect of short-term 17β-estradiol supplementation in healthy men on systemic inflammatory responses to exercise
Bibliographic record
Abstract
Sex-based differences in inflammatory responses to exercise may be mediated by estrogen through increased muscle membrane stability and/or inhibited cytokine production. In this study, in vivo effects of estrogen on systemic inflammation-related responses to exercise were assessed in healthy men. In a double-blind, placebo-controlled, crossover design, 11 men cycled for 90 min at 65% Vo2 max after 8 days of 17beta-estradiol supplementation (ES; 2 mg/day) or placebo (PL; glucose polymer). After a 2-wk washout, exercise was repeated after 8 days on the alternate treatment. Blood was collected pre- and postexercise to determine IL-6, soluble intercellular adhesion molecule-1 (sICAM-1), neutrophil counts, and cortisol. Preexercise serum was assayed for sex hormones. ES increased estradiol (133+/-71 to 840+/-633 pmol/l, P=0.005) and reduced testosterone (19.9+/-3.7 to 16.1+/-3.9 nmol/l, P=0.007). Exercise increased cortisol (P=0.02), IL-6 (P<0.001) and neutrophil counts (P<0.001) with no influence on sICAM-1 (P=0.34) and no effect of ES on these changes. Postexercise IL-6 and neutrophil counts were correlated (r=0.58, P=0.005); postexercise IL-6 and cortisol (r=0.18, P=0.43) and postexercise cortisol and neutrophil counts (r=0.06, P=0.78) were not. Postexercise sICAM-1 was not correlated with the above variables (P>or=0.79). In conclusion, 8 days of ES in healthy men did not influence systemic inflammation-related responses to acute exercise. Future studies should investigate 17beta-estradiol effects on IL-6 production and neutrophil infiltration within skeletal muscle during and after exercise.
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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) | 0.001 | 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 | 0.001 | 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".