Aerobic exercise and DNA methylation in postmenopausal women: An ancillary analysis of the Alberta Physical Activity and Breast Cancer Prevention (ALPHA) Trial
Bibliographic record
Abstract
Physical activity is associated with a lower risk of breast, colon, and endometrial cancer. Epigenetic mechanisms such as changes in DNA methylation may help to explain these protective effects. We assessed the impact of a one year aerobic exercise intervention on DNA methylation biomarkers believed to play a role in carcinogenesis. The Alberta Physical Activity and Breast Cancer Prevention (ALPHA) Trial was a two-armed randomized controlled trial in 320 healthy, inactive, postmenopausal women with no history of cancer. In an ancillary analysis, frozen blood samples (n = 256) were reassessed for levels of DNA methylation within LINE-1 and Alu repeats as well as within the promoter regions of APC, BRCA1, RASSF1, and hTERT genes. Differences between the exercise and control arm at 12-months, after adjusting for baseline values, were estimated within an intent-to-treat and per-protocol analysis using linear regression. No significant differences in DNA methylation between the exercise and control arms were observed. In an exploratory analysis, we found that the prospective change in estimated VO2max was negatively associated with RASSF1 methylation in a dose-response manner (p-trend = 0.04). A year-long aerobic exercise intervention does not affect LINE-1, Alu, APC, BRCA1, RASSF1, or hTERT methylation in healthy, inactive, postmenopausal women. Changes in DNA methylation within these genomic regions may not mediate the association between physical activity and cancer in healthy postmenopausal women. Additional research is needed to validate our findings with RASSF1 methylation. TRIAL REGISTRATION: ClinicalTrials.gov NCT00522262.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".