Effect of exercise training on diastolic function in metabolic syndrome
Bibliographic record
Abstract
It has been reported that metabolic syndrome (MetS) impairs left ventricular (LV) diastolic function. The objective of this study was to determine whether exercise training can improve LV diastolic function in individuals with MetS. Twenty-eight individuals with MetS (9 males, aged 60 ± 5 years) underwent a 1-year combined endurance and resistance exercise training program; maximal aerobic capacity (V̇O2max), blood pressure, blood markers, and LV diastolic function were measured at weeks 0, 12, 24, and 52 throughout the training. Pulsed wave Doppler echocardiography across the mitral valve was used to assess peak early flow velocity (E) and peak atrial flow velocity (A) to determine the E/A ratio. Individuals with MetS had a reversed E/A ratio, suggesting impaired LV relaxation, the first stage of LV diastolic dysfunction. Exercise training reduced systolic blood pressure (SBP) (129 ± 14 to 120 ± 12 mm Hg; p < 0.01) and increased V̇O2max (29.2 ± 6.3 to 33.4 ± 6.5 mL·kg(-1)·min(-1); p < 0.01) and high-density lipoprotein cholesterol (1.04 ± 0.21 to 1.12 ± 0.25 mmol·L(-1); p = 0.02), but did not improve LV diastolic function. Individuals with an E/A ratio <1 at the start of training had a tendency toward an increased E/A ratio (p = 0.12) accompanied by significant decreases in SBP and increases in V̇O2max with exercise training. Combined resistance and aerobic exercise training improved cardiometabolic health but did not improve the impaired LV diastolic function of individuals with MetS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".