Adipose tissue (P)RR regulates insulin sensitivity, fat mass and body weight
Bibliographic record
Abstract
OBJECTIVE: We previously demonstrated that the handle-region peptide, a prorenin/renin receptor [(P)RR] blocker, reduces body weight and fat mass and may improve insulin sensitivity in high-fat fed mice. We hypothesized that knocking out the adipose tissue (P)RR gene would prevent weight gain and insulin resistance. METHODS: An adipose tissue-specific (P)RR knockout (KO) mouse was created by Cre-loxP technology using AP2-Cre recombinase mice. Because the (P)RR gene is located on the X chromosome, hemizygous males were complete KO and had a more pronounced phenotype on a normal diet (ND) diet compared to heterozygous KO females. Therefore, we challenged the female mice with a high-fat diet (HFD) to uncover certain phenotypes. Mice were maintained on either diet for 9 weeks. RESULTS: KO mice had lower body weights compared to wild-types (WT). Only hemizygous male KO mice presented with lower total fat mass, higher total lean mass as well as smaller adipocytes compared to WT mice. Although food intake was similar between genotypes, locomotor activity during the active period was increased in both male and female KO mice. Interestingly, only male KO mice had increased O2 consumption and CO2 production during the entire 24-hour period, suggesting an increased basal metabolic rate. Although glycemia during a glucose tolerance test was similar, KO males as well as HFD-fed females had lower plasma insulin and C-peptide levels compared to WT mice, suggesting improved insulin sensitivity. Remarkably, all KO animals exhibited higher circulating adiponectin levels, suggesting that this phenotype can occur even in the absence of a significant reduction in adipose tissue weight, as observed in females and, thus, may be a specific effect related to the (P)RR. CONCLUSIONS: (P)RR may be an important therapeutic target for the treatment of obesity and its associated complications such as type 2 diabetes.
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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) | 0.000 | 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".