Low Levels of Serum Soluble Receptors for Advanced Glycation End Products, Biomarkers for Disease State: Myth or Reality
Bibliographic record
Abstract
Advanced glycation end products (AGEs) interact with the receptor for AGEs (RAGE) on the membrane and induce deleterious effects via activation of nuclear factor kappa-B, and increased oxidative stress and inflammatory mediators. AGEs also combine with circulating soluble receptors (endogenous secretory RAGE [esRAGE] and soluble receptor for RAGE [sRAGE]) and sequester RAGE ligands and act as a cytoprotective agent. esRAGE is secreted from the cells and is a spliced variant of RAGE. The sRAGE on the other hand is proteolytically cleaved from cell surface receptor via matrix metalloproteinase (MMPs). sRAGE is elevated in type 1 and type 2 diabetes and in patients with decreased renal function. Serum levels of sRAGE are reduced in diseases including coronary artery disease, atherosclerosis, essential hypertension, chronic obstructive lung disease, heart failure, and hypercholesterolemia. Serum levels of AGEs are elevated in patients with coronary artery disease and atherosclerosis. However, the increases in serum AGEs are very high in patients with diabetes and renal disease. There is a positive correlation between serum levels of AGEs and RAGE and sRAGE. The elevated levels of sRAGE in patients with diabetes and impaired renal function may be due to increased levels of MMPs. AGEs increase in the expression and production of MMPs, which would increase the cleavage of sRAGE from cell surface. In conclusion, low level of serum sRAGE is a good biomarker for disease other than diabetes and renal disease. A unified formula that takes into consideration of AGEs, sRAGE, and esRAGE such as AGE/sRAGE or AGEs/esRAGE would be better biomarker than sRAGE or esRAGE for all AGE-RAGE-associated diseases including diabetes and renal disease.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".