Differences in Exercise-Linked Biomarkers between Premenopausal and Postmenopausal Middle-Aged Females
Bibliographic record
Abstract
While the exercise-induced responses of circulated biomarkers related to inflammation and brain health are well documented in humans, little is known about the effect of menopausal status on these responses. This study compared the responses of inflammatory cytokines and brain-derived neurotrophic factor (BDNF) to high-intensity exercise between pre- and postmenopausal middle-aged females. Eight premenopausal (44 ± 3 years) and seven postmenopausal (57 ± 2 years) females performed a high-intensity interval training (HIIT) session consisting of 10 × 1 min running intervals (90% maximum heart rate) separated by 1 min passive recovery intervals. Blood samples were collected at baseline (fasted), pre-exercise (postprandial), and at 0, 30, and 90 min post-HIIT and analyzed for interleukin (IL-6) and 10 (IL-10), tumour necrosis factor-alpha (TNF-α), and BDNF. IL-6 significantly increased from pre-exercise to 0 min post-HIIT in postmenopausal (+40%, p = 0.01) and to 30 min post-HIIT in premenopausal females (+60%, p = 0.02). IL-6 remained elevated at 90 min post-HIIT in premenopausal (+104%, p = 0.05) and to a higher degree in postmenopausal females (+385%, p < 0.001). IL-10 showed no response. TNF-α increased from pre- to 0 min post-HIIT (+10%, p = 0.05), then decreased to below pre-exercise at 30 min (−10%, p = 0.02) and 90 min (−5%, p = 0.04) in both groups. BDNF increased immediately post-HIIT in premenopausal (+60%, p < 0.001) but not postmenopausal females. The differences in IL-6 and BDNF responses to HIIT between pre- and postmenopausal females provide evidence of the role of female reproductive hormones in the regulation of these exercise-induced responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".