Evidence Supporting a Role for Endoplasmic Reticulum Stress in the Development of Atherosclerosis in a Hyperglycaemic Mouse Model
Bibliographic record
Abstract
We previously observed a correlation between elevated levels of vascular endoplasmic reticulum (ER) stress and accelerated atherosclerotic plaque development in chronically hyperglycemic apolipoprotein-deficient (ApoE(-/-)) mice. We hypothesize that ER stress plays a causative role in diabetic atherogenesis. Here we examine the temporal relation between the onset of hyperglycemia, glucosamine accumulation in the vessel wall, ER stress, and the development of atherosclerosis. We demonstrate, by using streptozotocin-induced hyperglycemic ApoE(-/-) mice, that conditions of hyperglycemia increase intracellular glucosamine levels and endothelial ER stress levels in the endothelium before the onset of atherosclerosis. At 15 weeks of age, hyperglycemic mice have significantly larger atherosclerotic lesions (0.120 +/- 0.023 vs. 0.065 +/- 0.021 mm2; p = 0.001) relative to normoglycemic mice. Significantly, hyperglycemia-associated accelerated atherosclerosis is observed before the onset of dyslipidemias, suggesting that leveled glucose is sufficient to promote atherogenesis independently. Diagnostic markers of elevated ER-stress levels are increased in macrophage-derived foam cells in early and advanced atherosclerotic lesions. Dietary supplementation with valproate, a small branched-chain fatty acid that interferes with ER-stress signaling, significantly attenuates accelerated atherogenesis in this model. Together, these data are consistent with a causative role for hyperglycemia-associated ER stress in the development and progression of diabetic 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".