Consumption of soya isoflavones improved polycystic ovary syndrome-associated metabolic disorders in a rat model
Bibliographic record
Abstract
Abstract Polycystic ovary syndrome is associated with increased risks for certain metabolic disorders such as insulin resistance, non-alcoholic fatty liver disease and suppressed ovarian follicular development. This study aimed to examine whether soya isoflavones (ISF) mitigate these polycystic ovary syndrome-associated metabolic disorders in a rat model. Weanling Sprague-Dawley female rats were randomly divided into six groups and were treated with either 0 or 83 µg/d dihydrotestosterone (DHT) to induce polycystic ovary syndrome and fed diets containing 0, 0·5, or 1 g ISF/kg diet for 8 weeks. DHT treatment increased food intake, body weight gain (P < 0·001), percentage of primordial follicles (60 % v. 50·9 %, P < 0·05) and accumulation of lipid droplets in the livers. It also elevated serum total cholesterol, free cholesterol, TAG, NEFA and leptin and hepatic total cholesterol and NEFA. Additionally, DHT treatment reduced the percentage of primary follicles (13·8 % v. 30·2 %, P < 0·05), ovary weight and length (P < 0·001), as well as insulin sensitivity (P < 0·01) compared with the Control. ISF intake at 1 g/kg reduced body weight gain, serum total cholesterol, free cholesterol, NEFA, leptin and hepatic TAG and DHT-induced insulin resistance (P < 0·01). ISF intake at both levels decreased DHT-induced lipid droplet accumulation in the livers and changes in the percentages of primordial and primary follicles. Dietary soya ISF alleviated DHT-induced body weight gain, insulin resistance and hepatic lipid droplet accumulation, as well as suppressed ovarian follicular development. This suggests that the consumption of soya foods or ISF supplements may be beneficial for individuals with polycystic ovary syndrome, mitigating the associated metabolic disorders such as diabetes and non-alcoholic fatty liver disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".