Prohibitin-induced obesity leads to anovulation and polycystic ovary in mice
Bibliographic record
Abstract
Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder and the most common cause of female infertility. However, its etiology and underlying mechanisms remain unclear. Here we report that a transgenic obese mouse (Mito-Ob) developed by overexpressing prohibitin in adipocytes develops polycystic ovaries. Initially, the female Mito-Ob mice were equally fertile to their wild-type littermates. The Mito-Ob mice began to gain weight after puberty, became significantly obese between 3-6 months of age, and ∼25% of them had become infertile by 9 months of age. Despite obesity, female Mito-Ob mice maintained glucose homeostasis and insulin sensitivity similar to their wild-type littermates. Mito-Ob mice showed morphologically distinct polycystic ovaries and elevated estradiol, but normal testosterone and insulin levels. Histological analysis of the ovaries showed signs of impaired follicular dynamics, such as preantral follicular arrest and reduced number, or absence, of corpus luteum. The ovaries of the infertile Mito-Ob mice were closely surrounded by periovarian adipose tissue, suggesting a potential role in anovulation. Collectively, these data suggest that elevated estradiol and obesity per se might lead to anovulation and polycystic ovaries independent of hyperinsulinemia and hyperandrogenism. As obesity often coexists with other abnormalities known to be involved in the development of PCOS such as insulin resistance, compensatory hyperinsulinemia and hyperandrogenism, the precise role of these factors in PCOS remains unclear. Mito-Ob mice provide an opportunity to study the effects of obesity on anovulation and ovarian cyst formation independent of the major drivers of obesity-linked PCOS.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".