Atherosclerosis and the Hypercholesterolemic AGE–RAGE Axis
Bibliographic record
Abstract
Background Interaction of advanced glycation end products (AGE) with the receptor for advanced glycation end products (RAGE) has been implicated in the pathogenesis of atherosclerosis. Soluble receptors for advanced glycation end products (sRAGE) act as a decoy for AGE by competing with RAGE and suppressing developing atherosclerosis. Hypercholesterolemia and the oxidative stress are known factors involved in atherosclerosis. High-density lipoprotein cholesterol (HDL-C) is known to exert a protective effect against the development of atherosclerosis. We hypothesize that hypercholesterolemia-induced atherosclerosis may be mediated through the AGE-RAGE axis. Objectives Two objectives to be determined are: (1) if hypercholesterolemia is positively correlated with serum AGE, AGE/sRAGE, and malondialdehyde (MDA: a marker for oxidative stress) and (2) if the protective effect of HDL-C is positively associated with serum sRAGE and negatively correlated with the levels of AGE and AGE/sRAGE. Methods Measurement of serum lipid levels from 100 patients allowed the separation into two groups (hypercholesterolemic and normocholesterolemic). Measurements of serum levels of AGE, sRAGE, and MDA were performed. Results Serum levels of sRAGE were lower, while the levels of AGE and AGE/sRAGE were higher in hypercholesterolemic subjects as compared with normocholesterolemic subjects. sRAGE levels are positively correlated with HDL, while they are negatively correlated with low-density lipoprotein, triglycerides, total cholesterol, and MDA in hypercholesterolemic subjects. Conclusions Hypercholesterolemia is positively correlated with serum AGE, AGE/sRAGE, and MDA. The effect of HDL-C may be due to increases in sRAGE and decreases in the levels of AGE and AGE/sRAGE. Hypercholesterolemia-induced atherosclerosis may be mediated through the AGE-RAGE axis; however, more research must be conducted.
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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 | 0.000 |
| 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".