Decreased expression of γ-carboxylase in diabetes-associated arterial stiffness: impact on matrix Gla protein
Bibliographic record
Abstract
AIMS: Arterial stiffness is accelerated in type 1 diabetic patients. Medial artery calcification (MAC) contributes to the development of arterial stiffness. Vitamin K oxidoreductase (VKOR) reduces the vitamin K required by γ-carboxylase to activate matrix γ-carboxyglutamic acid (Gla) protein (MGP), an inhibitor of vascular calcification. This study aimed to evaluate the hypothesis that diabetes reduces the γ-carboxylation of MGP in the aortic wall, leading to increased vascular calcification, and the role of γ-carboxylase and VKOR in this γ-carboxylation deficit. METHODS AND RESULTS: Type 1 diabetes was induced in male Wistar rats with a single ip injection of streptozotocin. Augmentation of arterial stiffness in diabetic rats was shown by a 44% increase in aortic pulse wave velocity. Aortic and femoral calcification were increased by 26 and 56%, respectively. γ-Carboxylated MGP (cMGP, active) was reduced by 36% and the aortic expression of γ-carboxylase was reduced by 58%. Expression of γ-carboxylase correlated with cMGP (r= 0.59) and aortic calcification (r = -0.57). VKOR aortic expression and activity were not modified by diabetes. Vitamin K plasma concentrations were increased by 191% in diabetic rats. In ex vivo experiments with aortic rings, vitamin K supplementation prevented the glucose-induced decrease in γ-carboxylase expression. CONCLUSION: Our results suggest that reduced cMGP, through an impaired expression of γ-carboxylase, is involved in the early development of MAC in diabetes, and therefore, in the acceleration of arterial stiffness. A defect in vitamin K uptake by target cells could also be involved.
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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 | 0.001 |
| 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".