Aerobic training reduces immune cell recruitment and cytokine levels in adipose tissue in obese mice
Bibliographic record
Abstract
Obesity is associated with an energy imbalance that results from excessive energy intake, low diet quality, and a sedentary lifestyle. The increased consumption of a high-refined carbohydrate (HC) diet is strongly related to higher adiposity and low-grade inflammation. Aerobic training is a well-known nonpharmacological intervention to treat obesity and metabolic disturbances. However, the mechanisms through which aerobic training ameliorates the low-grade inflammation induced by an HC diet should be further investigated. Our hypothesis herein was that aerobic training would decrease the recruitment of leukocytes in adipose tissue, thereby reducing the levels of cytokines and improving metabolism in mice fed an HC diet. Male Balb/c mice were assigned to the following groups: control diet/nontrained (C-NT), control diet/trained (C-T), high-refined carbohydrate diet/nontrained (HC-NT), and high-refined carbohydrate diet/trained (HC-T). Mice were submitted to moderate-intensity training sessions that consisted of running 60 min per day for 8 weeks. An intravital microscopy technique was performed in vivo in anesthetized mice to visualize the microvasculature of the adipose tissue. The HC diet induced obesity and increased the influx of immune cells into the adipose tissue. In contrast, HC-T mice presented a lower adiposity and adipocyte area. Furthermore, relative to HC-NT mice, HC-T mice showed increased resting energy expenditure, decreased recruitment of immune cells in the adipose tissue, reduced cytokine levels, and ameliorated hyperglycemia and fatty liver deposition. Collectively, our data enhance understanding about the anti-inflammatory effect of aerobic training and shed light on the adipose tissue-mediated mechanisms by which training promotes a healthier metabolic profile.
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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 | 0.001 | 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".