Advanced glycation end products potentiate the stimulatory effect of glucose on macrophage lipoprotein lipase expression
Bibliographic record
Abstract
Lipoprotein lipase (LPL) secreted by macrophages in the arterial wall promotes atherosclerosis. We have shown that macrophages of patients with type 2 diabetes overproduce LPL and that metabolic factors, including glucose, stimulate macrophage LPL secretion. In this study, we determined the effect of advanced glycation end products (AGEs) on LPL expression by macrophages cultured in a high-glucose environment and the molecular mechanisms underlying this effect. Our results demonstrate that AGEs potentiate the stimulatory effect of high glucose on murine and human macrophage LPL gene expression and secretion. Induction of macrophage LPL mRNA levels by AGEs was identical to that elicited by physiologically relevant modified albumin and was inhibited by anti-AGE receptor as well as by antioxidants. Treatment of macrophages with AGEs resulted in protein kinase C (PKC) and mitogen-activated protein kinase (MAPK) activation. Inhibition of these kinases abolished the effect of AGEs on LPL mRNA levels. Finally, exposure of macrophages to AGEs increased the binding of nuclear proteins to the activated protein-1 consensus sequence of the LPL promoter. This effect was inhibited by PKC and MAPK inhibitors. These results demonstrate for the first time that AGEs potentiate the stimulatory effect of high glucose on macrophage LPL expression. This effect appears to involve oxidative stress and PKC/MAPK activation.
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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".