Impact of Menstrual Cycle and Oral Contraceptives on Haematological and Inflammatory Biomarkers in Highly Trained Female Athletes
Bibliographic record
Abstract
Haematological and inflammatory biomarkers play an important role in athlete performance and health, with some of them used in the fight against doping. However, little is known about how they are modulated by sex hormone fluctuations in highly trained female athletes. We therefore measured the haematological parameters monitored in the athlete biological passport (ABP) as well as erythropoietin, serum markers of iron and inflammatory statuses (iron, ferritin, transferrin, transferrin saturation, albumin, creatinine, total protein, interleukin-6 and TNF-alpha) in 20 highly trained female athletes: 10 with normal menstrual cycle (NMC) during the early follicular and mid-luteal phases and 10 using a combined oral contraceptive (COC, i.e., ethinyloestradiol and levonorgestrel) during active and inactive hormone intake. Body composition, leptin and lipid profile (total cholesterol, HDL, LDL and triglycerides) were determined in parallel. No changes were observed throughout NMC phases. Irrespective of active/inactive pill intake, COC use increased transferrin, triglycerides as well as reticulocyte count (p < 0.05) and decreased interleukin-6 (p < 0.05), with no significant changes in the other parameters studied. In conclusion, given our results across NMC phases in highly trained athletes, it seems warranted to investigate whether intense physical training would mitigate the impact of endogenous sex hormones on body composition and haematological and inflammatory parameters. In addition, further studies are needed to determine the extent of the changes induced by COCs on these blood biomarkers in elite female athletes when subjected to extreme environments such as intensive training or competition in humid heat, cold and/or hypoxia or when using other medications in parallel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".