AT1 receptor blockade alters nutritional and biometric development in obesity-resistant and obesity-prone rats submitted to a high fat diet
Bibliographic record
Abstract
Obesity is a chronic metabolic condition with important public health implications associated with numerous co-morbidities including cardiovascular disease, insulin resistance, and hypertension. The renin angiotensin system (RAS), best known for its involvement in cardiovascular control and body fluid homeostasis has, more recently, been implicated in regulation of energy balance. Interference with the RAS (genetically or pharmacologically) has been shown to influence body weight gain. In this study we investigated the effects of systemic AT1 receptor blockade using losartan on ingestive behaviors and weight gain in diet induced obese (DIO) rats. Prior to losartan administration (30 mg/kg/day) body weight gain remained constant within the DIO animals (3.6 ± 0.3 g/day, n = 8), diet resistant (DR) animals (2.1 ± 0.6 g/day, n = 8) and in the age-matched chow fed control (CHOW) animals (2.8 ± 0.3 g/day, n = 8), Losartan administration abolished body weight gain in animals fed a high fat diet (DIO: -0.4 ± 0.7 g/day, n = 8; and DR: -0.8 ± 0.3 g/day, n = 8) while chow fed animals continued to gain weight (2.2 ± 0.3 g/day, n = 8) as they had previously to oral administration of losartan. This decrease in daily body weight gain was accompanied by a decrease in food intake in the HFD fed animals. Following the removal of losartan, both the DIO and DR animals again showed daily increases in body weight gain and food intake which were similar to control values. Our data demonstrate that oral losartan administration attenuates body weight gain in animals fed a HFD whether the animal is obese (DIO) or not DR while having no effect on body weight gain in age-matched chow fed animals suggesting a protective effect of losartan against body weight gain while on a HFD.
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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 | 0.001 |
| 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".