The Insulin-like Growth Factor Axis, Adipokines, Physical Activity, and Obesity in Relation to Breast Cancer Incidence and Recurrence
Bibliographic record
Abstract
BACKGROUND: Obesity, a risk factor for the development of postmenopausal breast cancer and certain other cancer types, has also been associated with poorer response to cancer therapy and cancer recurrence. The insulin-like growth factor (IGF) axis also influences cancer risk. METHODS: In this commentary, we consider the literature on IGF and its binding proteins and the risk of breast cancer, along with effects of obesity, adipokines, and insulin resistance on breast cancer, and the potential for lifestyle interventions to address weight gain and physical inactivity among at-risk women. RESULTS: Greater body fatness is associated with a higher risk of postmenopausal breast cancer. The association may be explained, in part, by hyperinsulinemia and alterations in adipokines and estrogens. Nutrition, energy balance, and levels of physical activity are determinants of IGF bioactivity. Alterations in the IGF axis can increase cancer risk and progression. Results from epidemiologic studies indicate that higher circulating levels of IGF-I are associated with an increased risk of breast cancer. CONCLUSIONS: Intervention studies are needed to determine how to sustain the positive effects of exercise over time and to identify the optimal mode, intensity, frequency, duration, and timing of exercise for breast cancer survivors, including important subgroups of survivors such as African American and Hispanic women. Future epidemiologic studies of the relationships between the IGF axis and breast cancer should include adequate numbers of African American women, Hispanic women, and other minority women who have been underrepresented in studies completed to date.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".