Antihypertensive treatment with hydrochlorothiazide-hydralazine combination aggravates medial vascular calcification in CKD rats with mineral bone disorder
Bibliographic record
Abstract
Background: Arterial stiffness and medial vascular calcification, leading to isolated systolic blood pressure (BP), are major cardiovascular risk factors in patients with chronic kidney disease (CKD) and mineral bone disorders (MBD). The impact of BP on MBD-induced medial vascular calcification in CKD remains uncertain. We investigated whether BP reduction improves arterial stiffness and medial vascular calcification in a rat model of CKD-MBD. Methods: receptor antagonist losartan, and (2) the combination of the thiazide diuretic hydrochlorothiazide and the direct vasodilator hydralazine (HCTZ/HY). After 5 weeks, mean BP (MBP), pulse pressure (PP), and pulse wave velocity (PWV) were determined. Vascular calcification was assessed in the thoracic aorta. Results: While MBP was similar in CKD-MBD and control CKD rats, PP and PWV were increased in CKD-MBD rats. The heightened arterial stiffness in CKD-MBD rats was associated with diffused medial calcification along the thoracic aorta. Although both losartan and HCTZ/HY reduced MBP in CKD-MBD rats, losartan did not affect PP and PWV nor medial vascular calcification, whereas HCTZ/HY, unexpectedly, further increased arterial stiffness and medial vascular calcification. Conclusion: In the rat model of CKD-MBD, antihypertensive treatment with losartan did not affect arterial stiffness or medial vascular calcification. However, HCTZ/HY treatment aggravated arterial stiffness and vascular calcification despite a similar reduction of MBP, suggesting a blood pressure-independent mechanism for vascular calcification.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".