Beige fat is dispensable for the metabolic benefits associated with myostatin deletion
Bibliographic record
Abstract
OBJECTIVE: Increasing muscle mass and activating beige fat both have great potential for ameliorating obesity and its comorbidities. Myostatin null mice have increased skeletal muscle mass and are protected from obesity and its sequelae. Deletion of myostatin has also been suggested to result in the activation of beige adipocytes, thermogenic fat cells with anti-obesity and anti-diabetes properties. It is not known whether beige fat activation contributes to the protection from obesity in myostatin null mice. METHODS: To investigate the role of beige fat activation in the metabolic benefits associated with myostatin deletion, we crossed myostatin null mice to adipocyte-specific PRDM16 knockout mice. We analyzed this new mouse model using molecular profiling, whole mount three-dimensional tissue imaging, tissue respiration, and glucose and insulin tolerance tests in models of diet-induced obesity. RESULTS: Here, we report that PRDM16 is required for the activation of beige fat in the absence of myostatin. However, we show in both male and female mice that beige fat activation is dispensable for the protection from obesity, glucose intolerance, insulin resistance, and hepatic steatosis mediated by myostatin deletion. CONCLUSION: These findings demonstrate that increasing muscle mass can compensate for the inactivation of beige fat and raise the possibility of targeting muscle mass as a therapeutic approach to offset the deleterious effects of adipose tissue dysfunction in obesity and metabolic syndrome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".