Effects of Exercise on Insulin, IGF Axis, Adipocytokines, and Inflammatory Markers in Breast Cancer Survivors: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background: Insulin, IGF axis, adiponectin, and inflammatory markers are associated with breast cancer. Given that physical activity improves prognosis of breast cancer survivors, we investigated the effects of exercise on these markers as potential mediators between physical activity and breast cancer. Methods: PubMed, EMBASE, CENTRAL, CINAHL, and SportDiscus were searched up to December 3, 2015, to identify randomized controlled trials (RCT) that investigated the effect of exercise on insulin, IGF axis, and cytokines in breast cancer survivors. Weighted mean difference (WMD) was calculated using either fixed- or random-effects models on the basis of the heterogeneity of the studies. Results: A total of 18 studies involving 681 breast cancer survivors were included, and these numbers were reduced for individual biomarker analyses. We found that exercise significantly reduced fasting insulin levels [WMD, −3.46 μU/mL; 95% confidence interval (CI), −5.97 to −0.95; P = 0.007]. Furthermore, potentially meaningful but statistically nonsignificant changes were observed in insulin resistance (WMD, −0.73; 95% CI, −0.54 to 0.13; P = 0.23), adiponectin (WMD, 1.17 μg/mL; 95% CI, −0.87 to 3.20; P = 0.26), and C-reactive protein (WMD, −1.10 mg/L; 95% CI, −2.39 to 0.20; P = 0.10). Subgroup analyses showed that fasting insulin levels were significantly more impacted in studies in which intervention participants experienced a weight reduction (WMD, −7.10 μU/mL; 95% CI, −10.31 to −3.90; P < 0.001). Conclusions: Exercise reduces fasting insulin levels in breast cancer survivors. This may be due to exercise-induced reductions in body weight. Impact: Practitioners and clinicians may better help breast cancer prognosis be improved through exercise, anticipating physiological effects on cancer. Cancer Epidemiol Biomarkers Prev; 26(3); 355–65. ©2016 AACR.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.027 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".