Long-term Effects of Moderate versus High Durations of Aerobic Exercise on Biomarkers of Breast Cancer Risk: Follow-up to a Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Background: The optimal lifestyle for breast cancer prevention over the long term is unclear. We aimed to determine whether or not the amount of exercise prescribed in a year-long exercise intervention influences breast cancer biomarker levels 1 year later. Methods: We conducted a 24-month follow-up study (2012–2014) to the Breast Cancer and Exercise Trial in Alberta (BETA), a 12-month, two-armed (1:1), two-center randomized controlled trial of exercise in 400 cancer-free, postmenopausal women. The exercise prescription was moderate–vigorous aerobic exercise, 5 days/week (3 days/week supervised) for 30 minutes/session (MODERATE) or 60 minutes/session (HIGH). Participants were asked not to change their usual diet. We used linear mixed models to compare biomarker concentrations (C-reactive protein, insulin, glucose, HOMA-IR, estrone, sex hormone binding globulin, total estradiol, and free estradiol) over time (0, 12, and 24 months) by group (MODERATE, HIGH), using group–time interactions. Results: After 12 months of no intervention, 24-month fasting blood samples were available for 84.0% and 82.5% of MODERATE and HIGH groups, respectively (n = 333/400). We found no evidence that 0 to 24– or 12 to 24–month biomarker changes differed significantly between randomized groups (HIGH:MODERATE ratio of mean biomarker change ranged from 0.97 to 1.06, P values >0.05 for all). We found more favorable biomarker profiles among participants who experienced greater than the median fat loss during the trial. Conclusions: Prescribing aerobic exercise for 300 versus 150 minutes/week for 12 months to inactive, postmenopausal women had no effects on longer-term biomarkers. Impact: Exercise may lead to larger improvements in breast cancer biomarkers after intervention among women who also experience fat loss with exercise.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".