Glycation of LDL by Methylglyoxal Increases Arterial Atherogenicity
Bibliographic record
Abstract
OBJECTIVE: To study whether modification of LDL by methylglyoxal (MG), a potent arginine-directed glycating agent that is increased in diabetes, is associated with increased atherogenicity. RESEARCH DESIGN AND METHODS: Human LDL was isolated and modified by MG in vitro to minimal extent (MG(min)-LDL) as occurs in vivo. Atherogenic characteristics of MG(min)-LDL were characterized: particle size, proteoglycan-binding, susceptibility to aggregation, LDL and non-LDL receptor-binding, and aortal deposition. The major site of modification of apolipoprotein B100 (apoB100) modification was investigated by mass spectrometric peptide mapping. RESULTS: MG(min)-LDL contained 1.6 molar equivalents of MG modification-mostly hydroimidazolone-as found in vivo. MG(min)-LDL had decreased particle size, increased binding to proteoglycans, and increased aggregation in vitro. Cell culture studies showed that MG(min)-LDL was bound by the LDL receptor but not by the scavenger receptor and had increased binding affinity for cell surface heparan sulfate-containing proteoglycan. Radiotracer studies in rats showed that MG(min)-LDL had a similar fractional clearance rate in plasma to unmodified LDL but increased partitioning onto the aortal wall. Mass spectrometry peptide mapping identified arginine-18 as the hotspot site of apoB100 modification in MG(min)-LDL. A computed structural model predicted that MG modification of apoB100 induces distortion, increasing exposure of the N-terminal proteoglycan-binding domain on the surface of LDL. This likely mediates particle remodeling and increases proteoglycan binding. CONCLUSIONS: MG modification of LDL forms small, dense LDL with increased atherogenicity that provides a new route to atherogenic LDL and may explain the escalation of cardiovascular risk in diabetes and the cardioprotective effect of metformin.
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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".