Abstract 215: Glycosphingolipids Contribute to Pro-atherogenic Pathways in the Pathogenesis of Hyperglycemia-induced Atherosclerosis
Bibliographic record
Abstract
Three out of every four people with diabetes will die of cardiovascular disease. However, the molecular mechanisms by which diabetes promotes atherosclerosis are not clear. In this study, comprehensive metabolomic techniques were used to investigate the molecular mechanisms by which hyperglycemia promotes accelerated atherogenesis in three distinct disease mouse models. Normoglycemic apolipoprotein E-deficient mice served as the atherosclerotic control. Hyperglycemia was induced by multiple low-dose streptozotocin injections or by introducing a point mutation in one copy of the insulin-2 gene. Glucosamine-supplemented mice, which experience accelerated atherosclerosis to a similar extent as the hyperglycemia-induced models, without alterations in the levels of glucose or insulin, were also included in the analysis. Mice with accelerated atherosclerosis showed distinct metabolomic profiles compared to the controls. We detected 6369 metabolite features in the plasma of each mouse. Second-order analysis of pair comparisons between each disease model and the control resulted in 62 commonly altered features (p<0.05). Identification of shared metabolites revealed alterations in glycerophospholipid and sphingolipid metabolisms, and pro-atherogenic processes including inflammation and oxidative stress. Post-multivariate and pathway analyses indicated glycosphingolipid metabolism is the most significantly altered pathway. Glycosphingolipid metabolites induced oxidative stress and inflammation in cultured human vascular cells including macrophages, endothelial and smooth muscle cells. Treatment with a known antioxidant, α-tocopherol, reduced oxidative stress and inflammation induced by glycosphingolipids. Our findings suggest that the glycosphingolipid pathway contributes to pro-atherogenic pathways in the pathogenesis of hyperglycemia-induced atherosclerosis, making it a potential therapeutic target to block or slow atherogenesis in diabetic patients.
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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) | 0.000 | 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 | 0.000 |
| 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".