Exercise training and vascular function in postmenopausal individuals: a systematic review and meta-analysis
Bibliographic record
Abstract
IMPORTANCE: Cardiovascular disease (CVD) is a leading cause of morbidity and mortality for menopausal individuals. Flow-mediated dilation (FMD), a surrogate marker of CVD, improves with aerobic exercise training in healthy and nonhealthy cohorts. However, systematic evaluation and precise estimate of this effect for menopausal individuals are unknown. OBJECTIVE: We conducted a systematic review with meta-analysis to evaluate the influence of exercise training on FMD in postmenopausal individuals. EVIDENCE REVIEW: Studies were identified from systematic search of major electronic databases (PubMed, ScienceDirect, and Cochrane Library) from inception to February 2021. Healthy, postmenopausal individuals were included, following an aerobic exercise intervention assessing FMD. A random-effects meta-analysis was used to calculate a pooled effect size (mean difference [MD]) with 95% confidence interval. Heterogeneity was assessed using I2 statistics. Metaregression was used to assess the association between changes in FMD and physical characteristics (eg, blood pressure, age, baseline FMD) and intervention details (metabolic equivalents and change in maximal oxygen uptake [∆V˙ o2max ]). For variables that significantly correlated, a multiple metaregression model was used to assess the accounted variance in between-study ∆FMD%. Study quality was assessed using the National Heart, Lung, and Blood Institute assessment tool. FINDINGS: Nine studies, including 11 interventions (6 controlled interventions and 5 pre-post interventions; N = 182), with age range of 52 ± 4 to 64 ± 7 years underwent quantitative pooling of data. Exercise training significantly improved ∆FMD% (MD, 0.99; 95% confidence interval, 0.46-1.52; P < 0.001). Between-study heterogeneity was large and statistically significant ( I2 = 93.8%, P < 0.001). Post hoc analysis based on study design identified significant heterogeneity in the MD in ∆FMD% between controlled and pre-post study interventions ( P < 0.05). According to multiple metaregression, diastolic and systolic blood pressure and ∆V˙ o2max significantly predicted ∆FMD% ( Q = 15.74, df = 3, P < 0.01, R2 = 0.72). CONCLUSIONS AND RELEVANCE: Aerobic exercise training improves FMD for postmenopausal individuals, and this observation was greater among controlled versus pre-post interventions. A higher resting blood pressure and the greatest ∆V˙ o2max yielded the largest improvements in FMD.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| 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".