Estrogen Treatment After Ovariectomy Protects Against Fatty Liver and May Improve Pathway-Selective Insulin Resistance
Bibliographic record
Abstract
Pathway-selective insulin resistance where insulin fails to suppress hepatic glucose production but promotes liver fat storage may underlie glucose and lipid abnormalities after menopause. We tested the mechanisms by which estrogen treatment may alter the impact of a high-fat diet (HFD) when given at the time of ovariectomy (OVX) in mice. Female C57BL/6J mice underwent sham operation, OVX, or OVX with estradiol (E2) treatment and were fed an HFD. Hyperinsulinemic-euglycemic clamps were used to assess insulin sensitivity, tracer incorporation into hepatic lipids, and liver triglyceride export. OVX mice had increased adiposity that was prevented with E2 at the time of OVX. E2 treatment increased insulin sensitivity with OVX and HFD. In sham and OVX mice, HFD feeding induced fatty liver, and insulin reduced hepatic apoB100 and liver triglyceride export. E2 treatment reduced liver lipid deposition and prevented the decrease in liver triglyceride export during hyperinsulinemia. In mice lacking the liver estrogen receptor α, E2 after OVX limited adiposity but failed to improve insulin sensitivity, to limit liver lipid deposition, and to prevent insulin suppression of liver triglyceride export. In conclusion, estrogen treatment may reverse aspects of pathway-selective insulin resistance by promoting insulin action on glucose metabolism but limiting hepatic lipid deposition.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".