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 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".