The effect of exercise on blood concentrations of angiogenesis markers in older adults: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Physical exercise has positive impacts on health and can improve angiogenesis, which is impaired during aging, but the underlying mechanisms of benefit are unclear. This meta-analysis and systematic review investigated the effects of exercise on several peripheral angiogenesis markers in older adults to better understand the relationship between exercise and angiogenesis. Methods MEDLINE, Embase, and Cochrane CENTRAL were searched for original, peer-reviewed reports of peripheral concentrations of angiogenesis markers before and after exercise interventions in older adults (> 50 years). The risk of bias was assessed with standardized criteria. Standardized mean differences (SMD) with 95% confidence intervals (CIs) were calculated from random-effects models. Publication bias was assessed with Egger’s test, funnel plots, and trim-and-fill. A priori subgroup analyses and meta-regressions were performed to investigate heterogeneity where possible. Results Of the 44 articles included in the review, 38 were included in meta-analyses for five proteins. Vascular endothelial growth factor (VEGF) was found to be higher after exercise (SMD[95%CI] = 0.18[0.03, 0.34], p = 0.02), and e-selectin (CD62E) was found to be lower after exercise (SMD[95%CI]= -0.72[-1.42, -0.03], p = 0.04). Endostatin (SMD[95%CI] = 0.28[-0.56, 1.11], p = 0.5), fibroblast growth factor 2 (SMD[95%CI] = 0.03[-0.18, 0.23], p = 0.8), and matrix metallopeptidase-9 (SMD[95%CI] = -0.26[-0.97, 0.45], p = 0.5) levels did not change after exercise. Conclusions Of the five angiogenesis blood markers evaluated in this meta-analysis, only VEGF and CD62E changed with exercise. Although more studies are needed, changes in angiogenesis markers may explain the beneficial effects of exercise on angiogenesis and health in older adults.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.004 | 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".