Exercise Training is More Effective Than The Ability of 17‐β‐Estradiol to Reverse Glucose Intolerance in Ovariectomized (OVX) Rats
Bibliographic record
Abstract
Introduction Loss of estrogen in females increases the risk for insulin resistance. Hormone replacement therapy (HRT) is often prescribed to treat estrogen deficiency but has several detrimental side effects. Exercise is suggested as an HRT substitute since it improves insulin sensitivity, but the efficacy of exercise vs. HRT on glucose tolerance is unknown. The purpose of this study was to compare exercise training to 17‐β‐estradiol (E2) supplementation on restoring glucose tolerance (GT) in ovariectomized (OVX) rats. Methods 30 OVX and 20 SHAM rats consumed a phytoestrogen free diet, ad libitum . Glucose tolerance tests (GTT) were performed at 10 weeks. Blood samples were drawn to assess serum insulin. Rats were randomly assigned to one of five 4 week treatment groups: SHAM sedentary (sed) or exercise (ex; 60 min, 5x/wk), OVX sed, ex or E2 (28 ug/kg/day). GTTs and blood sampling were repeated at 15 weeks. Glucose uptake in soleus and EDL muscle was assessed, and basal and maximal insulin‐stimulated EDL, soleus, adipose and liver samples were frozen for analysis. Results OVX rats were glucose intolerant at 10 weeks relative to SHAMs. After treatment, GT in OVX ex rats was entirely recovered, but only partially in OVX E2 rats. Insulin secretion at 15 min. during the 15 week GTT was significantly higher in OVX sed vs. OVX E2 or ex groups; however maximal insulin‐stimulated glucose uptake in soleus or EDL was unaffected. Conclusions Exercise restores GT in OVX rats more effectively than E2. Whole‐body improvements are not reflected by alterations in muscle maximal insulin response. This suggests adipose or liver as primary effectors of E2 or exercise treatment in OVX rats. Supported by NSERC
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".