Do Advanced Glycation End Products and Its Receptor Play a Role in Pathophysiology of Hypertension?
Bibliographic record
Abstract
There is a close relationship between arterial stiffness and blood pressure. The studies suggest that the advanced glycation end products (AGEs) and its cell receptor (RAGE) are involved in the arterial stiffness in two ways: changes in arterial structure and vascular function. Plasma levels of AGEs and expression of RAGE are elevated, while the levels of soluble RAGE (sRAGE) and endogenous secretory RAGE (esRAGE) are lowered in patients with hypertension (HTN). There is a positive correlation between plasma levels of AGEs and arterial stiffness, and an inverse association between arterial stiffness/HTN, and serum levels of sRAGE and esRAGE. Various measures can reduce the levels of AGEs and expression of RAGE, and elevate sRAGE. Arterial stiffness and blood pressure could be reduced by lowering the serum levels of AGEs, and increasing the levels of sRAGE. Levels of AGEs can be lowered by reducing the consumption of AGE-rich diet, short duration of cooking in moist heat at low temperature, and cessation of cigarette smoking. Drugs such as aminoguanidine, vitamins, angiotensin-converting enzyme (ACE) inhibitors, angiotensin-II receptor blockers, statins, and metformin inhibit AGE formation. Alagebrium, an AGE breakers reduces levels of AGEs. Clinical trials with some drugs tend to reduce stiffness. Systemic administration of sRAGE has beneficial effect in animal studies. In conclusion, AGE-RAGE axis is involved in arterial stiffness and HTN. The studies suggest that inhibition of AGEs formation, reduction of AGE consumption, blockade of AGE-RAGE interaction, suppression of RAGE expression, and exogenous administration of sRAGE may be novel therapeutic strategies for treatment of arterial stiffness and HTN.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".