Effect of Soy Isoflavones Supplementation on Adiponectin Levels in Postmenopausal Women
Bibliographic record
Abstract
Decreased adiponectin levels has been demonstrated in postmenopausal (PMP) women. Soy isoflavones, as an herbal product have been shown to increase adiponectin level but the results are inconclusive and inconsistent. The present study reassessed the data on the impact of soy isoflavones supplementation on adiponectin levels in PMP women through a meta-analysis. A systematic search was performed in the databases of PubMed, Web of science, Scopus and the Cochrane library. The literature search identified 830 studies with duplicates. Out of those, 80 were screened for title and abstract and 12 articles were ultimately selected for the analysis. Meta-regression and subgroup analyses, based on the moderator variables such as treatment duration, dose of soy isoflavones and BMI were performed. The quality of the studies was evaluated using the Grading of Recommendation Assessment, Development and Evaluation (GRADE) approach. The results revealed that soy isoflavones supplementation significantly increased the circulating level of adiponectin in PMP women (SMD: 0.36 µg/mL; 95% CI (0.05 to 0.66); P= 0.02). No publication bias was observed using Begg's (P = 0.38) and Egger's (P = 0.07) tests. Sensitivity analysis indicated the results were completely powerful and stable. Moreover, Meta-regression and subgroup analyses indicated a significant increase of adiponectin levels in subgroups of dose > 50 mg and treatment duration less or equal 3 months. Our findings showed significantly increase in adiponectin levels after isoflavones-supplemented soy consumption in postmenopausal women, who received dose > 50 mg of soy isoflavones in treatment duration ≤ 3 months.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".