Effectiveness of aerobic exercise intervention on cardiovascular disease risk in female breast cancer: a systematic review with meta-analyses
Bibliographic record
Abstract
Cardiovascular disease (CVD) has become the leading cause of competitive mortality in female breast cancer (BC). Regular aerobic exercise (AE) has been widely accepted as an effective intervention to reduce cardiovascular risk in a variety of different clinical conditions. This study is aimed at evaluating the efficacy and safety of AE on cardiovascular risk factors in female BC and assessing the quality of the synthesized evidence. We searched five English databases (Cochrane Library, PubMed, Embase, Scopus, and Web of Science) from inception to January 2023. Randomized controlled trials (RCTs) and cohort trials studying the effects of AE intervention on cardiovascular disease risk in female breast cancer were included. We used Stata 16 for data synthesis, Risk of Bias 2, and the Newcastle–Ottawa Scale for methodological quality evaluation and assessed the certainty of the synthesized evidence in the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. Forty RCTs and 6 cohort trials involving 44,877 BC patients showed AE reduced the incidence of CVD events by 29.4% [risk ratio (RR) = 0.706, 95% confidence interval (CI) (0.659, 0.757), low certainty] and coronary artery disease events by 36% [RR = 0.640, 95% CI (0.561, 0.729), low certainty]. AE improved LVEF, and reduced weight and hip circumference. The subgroup analysis results showed that nonlinear AE increased VO 2 max by 5.354 ml·kg·min −1 [mean difference (MD) = 5.354, 95% CI (2.645, 8.062), very low certainty] and reduced fat mass by 4.256 kg [MD = 4.256, 95% CI (-3.839, -0.094), very low certainty]. While linear AE reduced low-density lipoprotein cholesterol (LDL-C) by 8.534 mg/dL [MD = -8.534, 95% CI (-15.511, -1.557), low certainty]. The sensitivity analysis results showed that each trial did not affect the impact index of the highly heterogeneous outcomes. Our study indicates that AE has a positive effect in reducing cardiovascular risk factors. The individualization principle of AE deserves more attention in the future. This will provide new ideas to reduce CVD events and improve the quality of life in female BC patients. However, further research on AE in female BC should take into account long-term and well-designed administration to draw definitive conclusions.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.041 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".