Simultaneous angiotensin receptor blockade and glucagon‐like peptide‐1 receptor activation ameliorate albuminuria in obese insulin‐resistant rats
Bibliographic record
Abstract
Abstract Insulin resistance increases renal oxidant production by upregulating NADPH oxidase 4 (Nox4) expression contributing to oxidative damage and ultimately albuminuria. Inhibition of the renin‐angiotensin system (RAS) and activation of glucagon‐like peptide‐1 (GLP‐1) receptor signalling may reverse this effect. However, whether angiotensin receptor type 1 (AT1) blockade and GLP‐1 receptor activation improve oxidative damage and albuminuria through different mechanisms is not known. Using insulin‐resistant Otsuka Long‐Evans Tokushima Fatty (OLETF) rats, we tested the hypothesis that simultaneous blockade of AT1 and activation of GLP‐1r additively decrease oxidative damage and urinary albumin excretion (UalbV) in the following groups: (a) untreated, lean LETO (n = 7), (b) untreated, obese OLETF (n = 9), (c) OLETF + angiotensin receptor blocker (ARB; 10 mg olmesartan/kg/d; n = 9), (d) OLETF + GLP‐1 mimetic (EXE; 10 µg exenatide/kg/d; n = 7) and (e) OLETF + ARB +exenatide (Combo; n = 6). Mean kidney Nox4 protein expression and nitrotyrosine (NT) levels were 30% and 46% greater, respectively, in OLETF compared with LETO. Conversely, Nox4 protein expression and NT were reduced to LETO levels in ARB and EXE, and Combo reduced Nox4, NT and 4‐hydroxy‐2‐nonenal levels by 21%, 27% and 27%, respectively. At baseline, UalbV was nearly double in OLETF compared with LETO and increased to nearly 10‐fold greater levels by the end of the study. Whereas ARB (45%) and EXE (55%) individually reduced UalbV, the combination completely ameliorated the albuminuria. Collectively, these data suggest that AT1 blockade and GLP‐1 receptor activation reduce renal oxidative damage similarly during insulin resistance, whereas targeting both signalling pathways provides added benefit in restoring and/or further ameliorating albuminuria in a model of diet‐induced obesity.
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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 | 0.000 | 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".