Combination of Soy Isoflavones, 8-Prenylnaringenin and Melatonin Improves Hot Flashes and Health-Related Quality of Life Outcomes in Postmenopausal Women: Flavie Study
Bibliographic record
Abstract
Objectives: This study aims to investigate the effects of a combination of soy isoflavones, 8-prenylnaringenin (8-PN), and melatonin in postmenopausal women suffering from moderate-to-severe hot flashes (HFs).Methods: A multicenter, prospective, open-label study enrolled 44 postmenopausal women suffering from moderate-to-severe HFs (≥ 5 daily or ≥ 35 weekly) to receive 54.4 mg standardized soy isoflavones (including 24.5 mg genistein and 16.3 mg daidzein), 100 µg 8-PN, and 1 mg melatonin once daily for 12 weeks.The primary clinical outcomes included changes in health-related quality of life (HRQoL) scores (Menopause-Specific QoL questionnaire [MENQoL] and Cervantes Scale) and HFs following 4 and 12 weeks of treatment.Other analyses included treatment adherence, acceptability, tolerability, and safety.Results: All of the four domains of MENQoL questionnaire significantly improved at 4 weeks (P < 0.05) and 12 weeks (P < 0.001), affecting significantly the vasomotor, psychosocial, and physical spheres (41.2%, 26.3%, and 25.0%; 12 weeks improvements, respectively).Similarly, in the menopause (39.3%) and psychic (51.7%) domains (both P < 0.05 at 12 weeks), the global score of the Cervantes Scale significantly increased at 4 weeks (18.6%) and 12 weeks (35.4%).Accordingly, moderate-to-severe HFs significantly decreased at 4 weeks compared to baseline (41.7% reduction) and further reduced at 12 weeks (76.5%), including the total number of episodes.Conclusions: Food supplements containing soy isoflavones, 8-PN, and melatonin showed an early and progressive benefit for reducing clinically significant HFs and for improving HRQoL across all domains, favorably affecting postmenopausal women's overall well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".