A15194 INVOLVEMENT OF ENDOTHELIN AND ANGIOTENSIN II IN CHRONIC KIDNEY DISEASE-RELATED ARTERIAL STIFFNESS
Bibliographic record
Abstract
Objectives: Increased risk of cardiovascular disease in patients with chronic kidney disease (CKD) has been related to vascular stiffness and isolated systolic hypertension. Using a rat model of CKD with mineral imbalance, we reported that endothelin (ET)A receptor blockade reduced vascular stiffness and hypertension revealing a role for endothelin. This study was designed to investigate whether the protective effect of ETA receptor blockade on vascular stiffness is independent of blood pressure reduction. Methods: CKD was induced in Wistar rats by renal mass ablation and mineral imbalance by a calcium/phosphate-rich diet and vitamin D supplementation (Ca/P/VitD) which increased vascular stiffness. CKD rats given Ca/P/VitD were treated either with the ETA receptor antagonist atrasentan (10 mg/kg/d), the angiotensin AT1 receptor antagonist losartan (25 mg/kg/d) or the combination of hydrochlorothiazide/hydralazine (Hy/Hy; 100 mg/L and 25 mg/L, respectively) for 5 weeks. Hemodynamic parameters were determined by vessels catheterisation in anesthetised animals. Results: Systolic blood pressure and carotid-femoral pulse wave velocity were higher in CKD+Ca/P/VitD rats as compared to CKD control (p < 0.05). Treatment of CKD+Ca/P/VitD rats with atrasentan, losartan and Hy/Hy reduced mean, diastolic and systolic blood pressure (p < 0.05). However, treatment with atrasentan and losartan, but not Hy/Hy, reduced carotid-femoral pulse wave velocity (p < 0.05). Conclusion: The protective effect of ETA and AT1 receptor blockade on vascular stiffness is independent of the blood pressure lowering effect in CKD rats with mineral imbalance. This study reveals a role for both endothéline and angiotensin II in CKD-related vascular stiffness.
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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) | 0.007 | 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".