Distinct metabolic and vascular effects of dietary triglycerides and cholesterol in atherosclerotic and diabetic mouse models
Bibliographic record
Abstract
Cholesterol and triglyceride-rich Western diets are typically associated with an increased occurrence of type 2 diabetes and vascular diseases. This study aimed to assess the relative impact of dietary cholesterol and triglycerides on glucose tolerance, insulin sensitivity, atherosclerotic plaque formation, and endothelial function. C57BL6 wild-type (C57) mice were compared with atherosclerotic LDLr(-/-) ApoB(100/100) (LRKOB100) and atherosclerotic/diabetic IGF-II × LDLr(-/-) ApoB(100/100) (LRKOB100/IGF) mice. Each group was fed either a standard chow diet, a 0.2% cholesterol diet, a high-fat diet (HFD), or a high-fat 0.2% cholesterol diet for 6 mo. The triglyceride-rich HFD increased body weight, glucose intolerance, and insulin resistance but did not alter endothelial function or atherosclerotic plaque formation. Dietary cholesterol, however, increased plaque formation in LRKOB100 and LRKOB100/IGF animals and decreased endothelial function regardless of genotype. However, cholesterol was not associated with an increase of insulin resistance in LRKOB100 and LRKOB100/IGF mice and, unexpectedly, was even found to reduce the insulin-resistant effect of dietary triglycerides in these animals. Our data indicate that dietary triglycerides and cholesterol have distinct metabolic and vascular effects in obese atherogenic mouse models resulting in dissociation between the impairment of glucose homeostasis and the development of atherosclerosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".