ACE2 exerts anti-obesity effect via stimulating brown adipose tissue and induction of browning in white adipose tissue
Bibliographic record
Abstract
The angiotensin II (ANG II)-ANG II type 1 receptor (AT1R) axis is a key player in the pathophysiology of obesity. Angiotensin-converting enzyme 2 (ACE2) counteracts the ANG II/AT1R axis via converting ANG II to angiotensin 1–7 (Ang 1–7), which is known to have an anti-obesity effect. In this study, we hypothesized that ACE2 exerts a strong anti-obesity effect by increasing Ang 1–7 levels. We injected intraperitoneally recombinant human ACE2 (rhACE2, 2.0 mg·kg−1·day−1) for 28 days to high-fat diet (HFD)-induced obesity mice. rhACE2 treatment decreased body weight and improved glucose metabolism. Furthermore, rhACE2 increased oxygen consumption and upregulated thermogenesis in HFD-fed mice. In the rhACE2 treatment group, brown adipose tissue (BAT) mass increased, accompanied with ameliorated insulin signaling and increased protein levels of uncoupling protein-1 (UCP-1) and PRD1-BF1-RIZ1 homologous domain containing 16. Importantly, subcutaneous white adipose tissue (sWAT) mass decreased, concomitant with browning, which was established by the increase of UCP-1 expression. The browning is the result of increased H3K27 acetylation via the downregulation of histone deacetylase 3 and increased H3K9 acetylation via upregulation of GCN5 and P300/CBP-associated factor. These results suggest that rhACE2 exerts anti-obesity effects by stimulating BAT and inducing browning in sWAT. ACE2 and the Ang 1–7 axis represent a potential therapeutic approach to prevent the development of 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) | 0.000 | 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 | 0.001 |
| 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".