Transforming growth factor- 1 is associated with kidney damage in patients with essential hypertension: renoprotective effect of ACE inhibitor and/or angiotensin II receptor blocker
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that transforming growth factor-beta1 (TGF-beta(1)) is associated with target organ damage in hypertension. This study aimed to investigate the relationship between TGF-beta(1) levels and kidney damage and renoprotective effects of angiotensin-converting enzyme inhibitor and/or angiotensin II type 1 receptor blocker in patients with essential hypertension (EH). METHODS: A total of 156 patients with EH were enrolled and grouped according to albumin-to-creatinine ratio (ACR). Of these, 90 patients with EH underwent a 12-week antihypertensive trial with administration of benazepril, valsartan or both. Serum TGF-beta(1), plasma angiotensin (Ang) II and urinary albumin were quantified by immunoassays. RESULTS: Serum TGF-beta1, plasma Ang II and ACR were highly elevated in patients with EH (P < 0.01). There was a positive correlation between serum TGF-beta1 levels and ACR (r = 0.53, P < 0.01). Significant decreases in TGF beta1 and ACR were obtained in all groups at the end of 12-week antihypertensive therapy compared to the baseline values, with the combined group to a greater extent (P < 0.01). Plasma Ang II levels were significantly decreased in the benazepril group but increased in the valsartan group (P < 0.05) while no significant change was observed in the combined group. CONCLUSIONS: TGF-beta(1) is highly elevated and strongly associated with urinary albumin excretion in patients with EH. Treatment with benazepril or valsartan attenuates serum TGF-beta(1) levels and microalbuminuria with the combined therapy receiving the greater effect. TGF-beta(1) could be a potential surrogate marker in monitoring the development and progression of kidney damage in EH.
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 | 0.001 |
| 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 | 0.000 |
| 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".