Effect of combination treatment of S–amlodipine with peroxisome proliferator-activated receptor agonists on metabolic and cardiovascular parameters in Zucker fa/fa rats
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes is a complex metabolic disorder characterized by hyperglycemia, impaired glucose tolerance and insulin resistance associated with dyslipidemia and hypertension. The available drugs are not sufficiently efficacious in reducing cardiovascular risk and restoring normal glucose metabolism associated with type 2 diabetes as a mono- or a combination therapy. The present study examined the combined effects of an antihypertensive (S-Amlodipine) and an insulin-sensitizing agent, peroxisome proliferator-activated receptor (PPAR) agonists (Pioglitazone and Ragaglitazar), on cardiovascular risk factors in aged diabetic and insulin-resistant Zucker fa/fa rats. METHODS: Following combination treatment for 14 days, blood pressure (BP), serum glucose, total cholesterol and triglycerides were measured. Aortic ring study was conducted to determine the effect of combination treatments on phenylephrine-induced vasoconstriction and acetylcholine (Ach)-induced vasorelaxation. RESULTS: In combination, S-Amlodipine and Pioglitazone significantly reduced blood glucose (115.1 ± 6.6 vs. 81.7 ± 4.2), BP (184.4 ± 5.0 vs. 155.1 ± 5.0), serum triglycerides (362.5 ± 47.5 vs. 211.1 ± 23.7) and glucose intolerance when compared with vehicle treated Zucker fa/fa rats. Similar results were observed with the combination of S-Amlodipine and Ragaglitazar (Triglycerides, 362.5 ± 47.5 vs. 252.34 ± 27.86; BP, 184.4 ± 5.0 vs. 159.0 ± 8.0) except for serum glucose. ACh-induced vasorelaxation in aortic rings was also superior with both of the combinations compared to individual treatment. Furthermore, there was less body weight gain and food intake with S-Amlodipine and Pioglitazone combination in Zucker fa/fa rats. S-Amlodipine itself caused significant reduction in glucose (115.1 ± 6.6 vs. 89.7 ± 2.7) and BP (184.4 ± 5.0 vs. 156.1 ± 4.0) with improvement in insulin sensitivity observed through oral glucose tolerance test. CONCLUSIONS: The results suggest that a combination of PPAR agonists and S-Amlodipine has partial benefits in improving the cardiovascular risk factors such as reduction in triglyceride levels, associated with chronic type 2 diabetes, and therefore may be utilized as an approach for addressing some of these devastating metabolic syndrome complications.
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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 | 0.001 |
| 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".