Physical Activity and Postmenopausal Breast Cancer: Proposed Biologic Mechanisms and Areas for Future Research
Bibliographic record
Abstract
Convincing evidence now supports a probable preventive role for physical activity in postmenopausal breast cancer. The mechanisms by which long-term physical activity affect risk, however, remain unclear. The aims of this review were to propose a biological model whereby long-term physical activity lowers postmenopausal breast cancer risk and to highlight gaps in the epidemiologic literature. To address the second aim, we summarized epidemiologic literature on 10 proposed biomarkers, namely, body mass index (BMI), estrogens, androgens, sex hormone binding globulin, leptin, adiponectin, markers of insulin resistance, tumor necrosis factor-alpha, interleukin-6, and C-reactive protein, in relation to postmenopausal breast cancer risk and physical activity, respectively. Associations were deemed "convincing," "probable," "possible," or "hypothesized" using set criteria. Our proposed biological model illustrated the co-occurrence of overweight/obesity, insulin resistance, and chronic inflammation influencing cancer risk through interrelated mechanisms. The most convincing epidemiologic evidence supported associations between postmenopausal breast cancer risk and BMI, estrogens, and androgens, respectively. In relation to physical activity, associations were most convincing for BMI, estrone, insulin resistance, and C-reactive protein. Only BMI and estrone were convincingly (or probably) associated with both postmenopausal breast cancer risk and physical activity. There is a need for prospective cohort studies relating the proposed biomarkers to cancer risk and for long-term exercise randomized controlled trials comparing biomarker changes over time, specifically in postmenopausal women. Future etiologic studies should consider interactions among biomarkers, whereas exercise trials should explore exercise effects independently of weight loss, different exercise prescriptions, and effects on central adiposity.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".