Loss of Ovarian Estrogens Causes Only Mild Deterioration of Glucose Homeostasis in Female ZDF Rats Preventable by Voluntary Running Exercise
Bibliographic record
Abstract
Studies on normoglycemic ovariectomized Sprague-Dawley rats have provided insights about the effects of estrogen deficiency on insulin resistance in lean individuals. It is not completely clear if subjects with pre-established obesity and insulin resistance are at greater risk of developing type 2 diabetes when ovarian estrogens are no longer secreted, and if physical activity can protect against this susceptibility. Contrasting with their male counterparts, obese and insulin resistant female ZDF (Zucker diabetic fatty) rats do not become hyperglycemic when fed a standard diet. The aim of the study was to evaluate the hypothesis that withdrawal of ovarian estrogens in insulin resistant female ZDF rats would trigger overt hyperglycemia, provided they remain physically inactive. Female ZDF rats underwent either an ovariectomy (OVX) or a simulated surgery (SHAM). Thereafter, OVX rats engaged either in voluntary wheel cage running (OVX-Active), or like the Sham rats, remained sedentary (OVX-Sed) for 6 weeks. Fasting glycemia, insulinemia, and glucose tolerance were not altered in OVX-Sed as compared to SHAM-Sed rats. However, OVX-Sed rats showed altered liver triglyceride and glycogen contents, increased pancreatic insulin content and reduced insulin-stimulated muscle pAKT as compared to SHAM-Sed rats. Physical activity in OVX rats lowered fasting glucose and insulin levels, improved glucose tolerance and insulin-stimulated skeletal muscle glucose uptake as compared to OVX-Sed rats. OVX-induced alterations in pancreatic insulin content and liver glycogen and triglyceride contents were significantly improved by physical activity. Loss of ovarian estrogens did not cause overt hyperglycemia in insulin-resistant female ZDF rats. Physical activity improved glucose homeostasis despite estrogen deficiency.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".