Additive effects of isoflavones and exercise training on inflammatory cytokines and body composition in overweight and obese postmenopausal women
Bibliographic record
Abstract
OBJECTIVE: Isoflavones and exercise have been shown to affect C-reactive protein (CRP) and body composition and to act synergistically on trunk and total fat mass (FM), glucose metabolism, and lean body mass in postmenopausal women with a body mass index higher than 25 kg/m. We hypothesized that exercise and isoflavone supplementation (Ex + ISO) could reduce inflammation in the same subpopulation of women. The objective of this study was to investigate if 6 months of mixed exercise combined with isoflavones could have greater effects on specific inflammatory markers than exercise alone in overweight or obese postmenopausal women. METHODS: Thirty-four postmenopausal women aged 50 to 70 years were randomly assigned to exercise and placebo (Ex + PLA; n = 15) or Ex + ISO (n = 19). At baseline and after 6 months, waist circumference, hip circumference, total FM, trunk FM, leg FM, and muscle mass index (MMI; = total fat free mass [kg] / height [m]) were assessed (dual-energy x-ray absorptiometry). Inflammatory markers (CRP, tumor necrosis factor-α [TNF-α], and interleukin-6) were obtained by enzyme-linked immunosorbent assay. T tests were used to compare groups at baseline. RESULTS: The Ex + PLA group showed significant changes in MMI (+0.33 kg/m, P ≤ 0.009) and FM compartments (waist circumference, -5.13 cm; % FM, -1.31%; P ≤ 0.001), whereas inflammation remained unchanged. However, the Ex + ISO group showed significant changes in total FM (-1.70 kg, P < 0.0001), FM compartments (hip circumference [-2.51 cm, P = 0.019], leg FM [-1.16 kg, P = 0.037], and trunk FM [-0.72 kg, P = 0.006]), MMI (+0.39 kg, P = 0.011), and inflammation (CRP, -1.14 mg/L, P = 0.029; TNF-α, +0.29 pg/mL, P = 0.010). CONCLUSIONS: Despite an increase in TNF-α, the use of isoflavones-when body weight remains stable-seems to enhance the beneficial effects of mixed-exercise training on body composition and CRP in overweight or obese postmenopausal women.
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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".