Alagebrium attenuates acute methylglyoxal‐induced glucose intolerance in Sprague‐Dawley rats
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Alagebrium is a breaker of cross-links in advanced glycation endproducts. However, the acute effects of alagebrium on methylglyoxal (MG), a major precursor of advanced glycation endproducts have not been reported. MG is a highly reactive endogenous metabolite, and its levels are elevated in diabetic patients. We investigated whether alagebrium attenuated the acute effects of exogenous MG on plasma MG levels, glucose tolerance and distribution of administered MG in different organs in Sprague-Dawley rats. EXPERIMENTAL APPROACH: We measured MG levels (by HPLC), glucose tolerance, adipose tissue glucose uptake, GLUT4, insulin receptor and insulin receptor substrate 1 (IRS-1) protein expression, and phosporylated IRS-1 in rats treated with MG at doses of either 17.25 mg*kg(-1) i.p. (MG-17 i.p.) or 50 mg*kg(-1) i.v. (MG-50 i.v.) with or without alagebrium, 100 mg*kg(-1) i.p. KEY RESULTS: Alagebrium attenuated the increased MG levels in the plasma, aorta, heart, kidney, liver, lung and urine after MG administration. In MG-treated rats, glucose tolerance was impaired, plasma insulin levels were higher and insulin-stimulated glucose uptake by adipose tissue was reduced, relative to the corresponding control groups. In rats treated with MG-50 i.v., GLUT4 protein expression and IRS-1 tyrosine phosphorylation were decreased. Alagebrium pretreatment attenuated these effects of MG. In an in vitro assay, alagebrium reduced the amount of detectable MG. CONCLUSIONS AND IMPLICATIONS: Alagebrium acutely attenuated MG-induced glucose intolerance, suggesting a possible preventive role for alagebrium against the harmful effects of MG.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".