Associations between Aerobic Fitness and Estrogen Metabolites in Premenopausal Women
Bibliographic record
Abstract
Chronic physical activity may alter estrogen metabolism, a proposed biomarker of breast cancer risk, by causing a shift toward higher 2-OHE1 and lower 16α-OHE1 levels. Purpose: To investigate the association between an objective indicator of chronic exercise, aerobic fitness, and 2-OHE1 and 16α-OHE1 in premenopausal women. Methods: Women with high aerobic fitness (N = 17; V̇O2max ≥ 48 mL·kg·min−1) were compared with women with average aerobic fitness (N = 13; V̇O2max ≤ 40 mL·kg·min−1) in terms of 2-OHE1 and 16α-OHE1 profiles. Participants were healthy, regularly menstruating, Caucasian women, aged 20–42 yr, with a normal body mass index (BMI) of 18–24, not using pharmacologic contraceptives. We measured height, weight, sum of four skinfolds, and maximal aerobic fitness (V̇O2max), using an incremental cycle ergometer test. Urine samples were collected during the follicular and luteal phase of the menstrual cycle. Results: There were no statistically significant differences between average and highly fit women for 2-OHE1, 16α-OHE1, or the 2:16α-OHE1 ratio in either the follicular or luteal phase. However, the high-fitness group showed a trend toward a higher luteal 2:16α-OHE1 (P = 0.20). In ancillary analyses, a higher sum of skinfolds was associated with significantly higher luteal 16-OHE1 levels (r = 0.39, P = 0.03) and lower luteal phase 2:16 OHE ratio (r = −0.41, P = 0.02). Higher BMI was associated with lower follicular phase 2-OHE1 (r = −0.37, P = 0.04) and lower follicular 2:16 OHE1 ratio (r = −40, P = 0.03). Conclusion: This exploratory study is the first to investigate the association between aerobic fitness and estrogen metabolites in premenopausal women using metabolic parameters. We observed no statistically significant association between aerobic fitness and 2-OHE1 and 16α-OHE1, but found that body composition was associated with 2-OHE1 and 16α-OHE1 levels.
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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 | 0.001 |
| 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) | 0.001 | 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".