Soy isoflavones and exercise to improve physical capacity in postmenopausal women
Bibliographic record
Abstract
AIM: In postmenopause, ovarian decline along with sedentary lifestyle could contribute to the loss of lean body mass (LBM) and muscle strength. This study aimed to verify whether exercise and isoflavones could have additive effects on muscle quality, muscle mass index, relative strength and physical capacity in overweight sedentary postmenopausal women. METHOD: We recruited 70 overweight-to-obese (body mass index 32.2±4.8 kg/m(2)) postmenopausal women (59±5 years old) to participate in a 6-month clinical study combining isoflavones (70 mg/day) and exercise (resistance and aerobic training) treatments. Subjects were divided into four groups: (1) placebo (n =15), (2) isoflavones (n =15), (3) exercise and placebo (n =20), and (4) exercise and isoflavone (n =20). Principal outcome variables included maximal muscle strength (1RM) at the leg press and the bench press, muscle mass index, muscle quality in the legs and relative strength. RESULTS: After 6 months of training, exercise produced 49% and 23% increases, respectively, in leg press and bench press 1RM (p ≤0.01). Leg relative strength and muscle quality increased by more than 50% (both p <0.01), while muscle mass index increased by 7% (p <0.05) in both exercise groups only. CONCLUSION: Exercise training can improve muscle tissue strength, function and quality in sedentary postmenopausal women. Isoflavones, irrespective of exercise, did not produce changes in these variables. From a clinical perspective, these results suggest that overweight women could reduce the risks of mobility impairments, even in the absence of weight loss, by following a sound exercise intervention that includes both resistance and aerobic training at a high intensity.
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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 | 0.000 |
| 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".