Angiotensin II type 2 receptor promotes adipocyte differentiation and restores adipocyte size in high-fat/high-fructose diet-induced insulin resistance in rats
Bibliographic record
Abstract
This study was aimed at establishing whether specific activation of angiotensin II (ANG II) type 2 receptor (AT2R) modulates adipocyte differentiation and function. In primary cultures of subcutaneous (SC) and retroperitoneal (RET) preadipocytes, both AT2R and AT1R were expressed at the mRNA and protein level. Cells were stimulated with ANG II or the AT2R agonist C21/M24, alone or in the presence of the AT1R antagonist losartan or the AT2R antagonist PD123,319. During differentiation, C21/M24 increased PPARγ expression in both RET and SC preadipocytes while the number of small lipid droplets and lipid accumulation solely increased in SC preadipocytes. In mature adipocytes, C21/M24 decreased the mean size of large lipid droplets. Upon abolishment of AT2R expression using AT2R-targeted shRNAs, expressions of AT2R, aP2, and PPARγ remained very low, and cells were unable to differentiate. In Wistar rats fed a 6-wk high-fat/high-fructose (HFHF) diet, a significant shift toward larger adipocytes was observed in RET and SC adipose tissue depots. C21/M24 treatments for 6 wk restored normal adipocyte size distribution in both these tissue depots. Moreover, C21/M24 and losartan decreased hyperinsulinemia and improved insulin sensitivity impaired by HFHF diet. A strong correlation between adipocyte size area and glucose infusion rate during euglycemic-hyperinsulinemic clamp was observed. These results indicate that AT2R is involved in early adipocyte differentiation, while in mature adipocytes and in a model of insulin resistance AT2R activation restores normal adipocyte morphology and improves insulin sensitivity.
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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 | 0.000 |
| 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 | 0.001 |
| 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".