Obesity and altered glucose metabolism impact HDL composition in CETP transgenic mice: a role for ovarian hormones
Bibliographic record
Abstract
Mechanisms underlying changes in HDL composition caused by obesity are poorly defined, partly because mice lack expression of cholesteryl ester transfer protein (CETP), which shuttles triglyceride and cholesteryl ester between lipoproteins. Because menopause is associated with weight gain, altered glucose metabolism, and changes in HDL, we tested the effect of feeding a high-fat diet (HFD) and ovariectomy (OVX) on glucose metabolism and HDL composition in CETP transgenic mice. After OVX, female CETP-expressing mice had accelerated weight gain with HFD-feeding and impaired glucose tolerance by hyperglycemic clamp techniques, compared with OVX mice fed a low-fat diet (LFD). Sham-operated mice (SHAM) did not show HFD-induced weight gain and had less glucose intolerance than OVX mice. Using shotgun HDL proteomics, HFD-feeding in OVX mice had a large effect on HDL composition, including increased levels of apoA2, apoA4, apoC2, and apoC3, proteins involved in TG metabolism. These changes were associated with decreased hepatic expression of SR-B1, ABCA1, and LDL receptor, proteins involved in modulating the lipid content of HDL. In SHAM mice, there were minimal changes in HDL composition with HFD feeding. These studies suggest that the absence of ovarian hormones negatively influences the response to high-fat feeding in terms of glucose tolerance and HDL composition. CETP-expressing mice may represent a useful model to define how metabolic changes affect HDL composition and function.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".