Effect of oestrogen and progesterone on hypoxic exercise ventilation and respiratory muscle recruitment: considerations of the menstrual cycle and post-menopause
Bibliographic record
Abstract
We tested the hypothesis that high oestrogen [E2] and progesterone [P] increase minute ventilation (V̇E) and work of breathing (WB) during hypoxic exercise in the midluteal (ML) compared to the early follicular (EF) phase of the menstrual cycle, and in post-menopausal females using or not using hormone replacement therapies (HRT; non-HRT). Young (YF: n=11, 24±4y) and older females (OF non-HRT: n=10, 58±4y; HRT: n=7, 59±2y) completed pulmonary function and a graded cycle test to determine maximal oxygen consumption (V̇O2max: YF=51±6; OF non-HRT=45±10; HRT=35±8ml/kg/min). On experimental days, serum [P] and [E2] were measured prior to insertion of gastric and oesophageal balloon catheters, and 5 min of cycling at 70% peak power (two trials ea. in normoxia and hypoxia, FIO2=0.15). Ribcage (RMRC) and abdominal (RMAB) respiratory muscle (RM) recruitment were determined with optoelectronic plethysmography. OF were tested at any time, and YF were tested in the EF ([P]=1.1±1.6ng/ml, p<0.001; [E2]=197.7±134.8pg/ml, p=0.002) and ML phase ([P]=29.6±15.2ng/ml; [E2]=514.8±249.8pg/ml). EF and ML hypoxic exercise V̇E (EF=86±18; ML=90±21L/min, p=0.315), WB (EF=132±62; ML=123±56J/min, p=0.151) and RM recruitment (RMRC & RMAB=∆1±0%) were similar. There was a moderate correlation between hypoxic exercise ∆V̇E/V̇O2 and ∆[P] (r=0.685, p=0.029). A significant interaction of condition and HRT was present in OF (hypoxic V̇E non-HRT=83.2±18.5, HRT=66.9±12.3L/min, p=0.006). Our findings suggest high [P] increases V̇E relative to low [P] during hypoxic exercise in YF. Contrary to our hypothesis, in OF HRT use may blunt hypoxic V̇E (funding:NSERC).
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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".