Soy and the exercise-induced inflammatory response in postmenopausal women
Bibliographic record
Abstract
Aging is associated with increasing inflammation and oxidative stress in the body, both of which can have negative health effects. Successful attenuation of such processes with dietary countermeasures has major public health implications. Soy foods, as a source of high-quality protein and isoflavones, may improve such indices, although the effects in healthy postmenopausal women are not well delineated. A single-blind, randomized controlled trial was conducted in 31 postmenopausal women who were assigned to consume 3 servings of soy (n = 16) or dairy (n = 15) milk per day for 4 weeks. Parameters of systemic inflammation (tumor necrosis factor-alpha (TNF-alpha), interleukin-1beta (IL-1beta), and interleukin-6 (IL-6)) and the oxidative defense system (superoxide dismutase (SOD), glutathione peroxidase, cyclooxygenase-2) were measured post supplementation, before and after an eccentric exercise bout performed to elicit an inflammatory response. A significant group-by-time effect for plasma TNF-alpha was observed (p = 0.02), with values in the dairy group increased post supplementation and then decreasing into the postexercise period. Additionally, significant time effects were observed for plasma SOD (p < 0.0001) and IL-6 (p < 0.0001) in the postexercise period. Overall results from our study do not support the notion that 4 weeks of daily soy milk ingestion can attenuate systemic elevations in markers of inflammation or oxidative defense. However, data do suggest that the downhill-running protocol utilized in this study can be effective in altering systemic markers of inflammation and oxidative defense enzyme activity, and that the ingestion of soy may help prevent fluctuations in plasma TNF-alpha.
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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 | 0.001 | 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".