Vagotomy improves brown adipose tissue morphology and reduces steatosis in obese mice
Bibliographic record
Abstract
The abolishment of vagal abdominal afferents and efferent inputs through vagotomy has been shown to prevent obesity. However, it is unknown whether such a strategy, performed after obesity installation, may treat or ameliorate obesity and its comorbidities. Here, we aimed to verify the effects of subdiaphragmatic vagotomy on obesity and metabolic dysfunction-associated steatotic liver disease (MASLD) in obese mice that continued to be fed an obesogenic diet after the operation. Obesity was induced in male C57Bl/6 mice by ingestion of a high-fat diet (HFD). Afterward, obese (OB) mice were randomly submitted to Sham (OB-Sham group) or subdiaphragmatic vagotomy (OB-Vag group) and continued to be fed a HFD for 8 weeks. Vagotomy led to reductions in body weight, without modifying food intake in OB-Vag mice. While these rodents showed no modifications in subcutaneous fat accumulation, they exhibited higher abdominal adiposity. In contrast, the weight of the interscapular brown adipose tissue (BAT) was lower in OB-Vag mice, with brown adipocytes in its parenchyma showing reduced size and fewer lipid vacuoles, resembling the high-thermogenic adipocyte type. This effect was partially explained by increased gene expressions of Prdm16, Pgc-1α, and Dio2, key factors maintaining BAT identity and function. Furthermore, subdiaphragmatic vagotomy enhanced glucose tolerance, insulin sensitivity, improved serum and hepatic lipids levels, and ameliorated MASLD in OB-Vag mice. Vagotomy performed after obesity induction improved BAT function and insulin sensitivity, which may partly contribute to alleviating MASLD in OB mice that continued to consume an obesogenic diet.
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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 | 0.001 |
| 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".