Oral hormonal therapy with ethinylestradiol–levonorgestrel improves insulin resistance, obesity, and glycogen synthase kinase-3 independent of circulating mineralocorticoid in estrogen-deficient rats
Bibliographic record
Abstract
Estrogen deficiency has been associated with increased incidence of cardiovascular diseases , and recent clinical trials of standard formulations of hormonal therapies have not demonstrated consistent beneficial effects. Estrogen-progestin therapy has been used as exogenous estrogen to normalize depressed estrogen level during menopause. Ovariectomized rodents mimic an estrogen-deficient state in that they develop cardiometabolic dysfunction, including insulin resistance (IR). We therefore hypothesized that hormonal therapy with combined oral contraceptive steroids, ethinylestradiol-levonorgestrel (EEL), improves IR, obesity, and glycogen synthase kinase-3 (GSK-3) through reduction of circulating mineralocorticoid in ovariectomized rats. Twelve-week-old female Wistar rats were divided into 4 groups: sham-operated (SHM) and ovariectomized (OVX) rats were treated with or without EEL (1.0 μg ethinylestradiol and 5.0 μg levonorgestrel) daily for 8 weeks. Results showed that OVX or SHM + EEL treated rats had increased HOMA-IR (homeostatic model assessment of IR), 1 h postload glucose, HOMA-β, triglycerides (TG), total cholesterol (TC), TC/HDL cholesterol, TG/HDL cholesterol, plasma insulin, GSK-3, corticosterone, and aldosterone. On the other hand, OVX + EEL treatment ameliorated all these effects except that of aldosterone. Taken together, the results demonstrate that oral hormonal replacement with EEL improves IR and pancreatic β-cell function and suppresses GSK-3 and glucocorticoid independent of circulating aldosterone, suggesting a positive cardiometabolic effect of oral EEL therapy in estrogen-deficient rats.
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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 | 0.000 |
| 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".