PS-BPB01-8: IMPACT OF DIFFERENT DIURETIC TYPES ON ARTERIAL STIFFNESS AND CALCIFICATION IN A RAT MODEL OF CHRONIC KIDNEY DISEASE
Bibliographic record
Abstract
Introduction: We reported that antihypertensive therapy with a hydrochlorothiazide (HCTZ)-based regimen aggravates arterial stiffness and vascular calcification in a rat model of chronic kidney disease (CKD) with mineral and bone disorder (MBD). In this study, we investigated whether the aggravation of arterial stiffness and vascular calcification in CKD-MBD rats is induced by HCTZ and whether these abnormalities could also occur with other types of diuretic currently used in clinic. Methods: In rats with renal mass ablation-induced CKD, MBD was generated by a Ca/P-rich diet and calcitriol supplementation. The animals were divided into 4 groups; 1) CKD-MBD control; 2) CKD-MBD + HCTZ (thiazide diuretic, 5 mg/kg/d); 3) CKD-MBD + Chlorthalidone (thiazide-like diuretic, 5 mg/kg/d), and; 4) CKD-MBD + Furosemide (loop diuretics, 10 mg/kg/d). At week 6, hemodynamic parameters including systolic and mean blood pressure (SBP and MBP), pulse pressure (PP) and pulse wave velocity (PWV) were determined. The thoracic aorta was harvested to assess vascular calcification. Results: SBP was reduced by all types of diuretic in CKD-MBD rats. Contrary to chlortalidone nor furosemide, HCTZ treatment led to a reduction of MBP, but an increase in PP and PWV in CKD-MBD rats (p < 0.05). As expected from these hemodynamic changes, medial calcification in the thoracic aorta was significantly greater in CKD-MBD rats treated with HCTZ as compared to all the other groups of rats (p < 0.05). Conclusion: In rats with CKD-MBD, antihypertensive therapy with the thiazide diuretic HCTZ, but not other types of diuretic, exacerbated arterial stiffness and vascular calcification despite a reduction in SBP. The deleterious effect of HCTZ in CKD-MBD rats may have major clinical impact as this diuretic is widely use in patients with CKD that often develop MBD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".