Endurance interval training in obese mice reduces muscle inflammation and macrophage content independently of weight loss
Bibliographic record
Abstract
Obesity is associated with chronic low-grade inflammation that involves infiltration of macrophages into metabolic organs such as skeletal muscle. Exercise enhances skeletal muscle insulin sensitivity independently of weight loss; but its role in regulating muscle inflammation is not fully understood. We hypothesized that exercise training would inhibit skeletal muscle inflammation and alter macrophage infiltration into muscle independently of weight loss. Wild type C57BL/6 male mice were fed a chow diet or a high-fat diet (HFD, 45% calories fat) for 6 weeks. Then, mice maintained on the HFD either remained sedentary (HFD Sed) or exercised (HFD Ex) on a treadmill for another 6 weeks. The exercise training protocol involved conducting intervals of 2 min in duration followed by 2 min of rest for 60 min thrice weekly. Chow-fed control mice remained sedentary for the entire 12 weeks. Muscle cytokine and macrophage gene expression analysis were conducted using qRT-PCR, and muscle macrophage content was also measured using immunohistochemistry. Muscle cytokine protein content was quantified using a cytokine array. The HFD increased adiposity and weight gain compared to chow-fed controls. HFD Sed and HFD Ex mice had similar body mass as well as total and visceral adiposity. However, despite similar adiposity, exercise reduced inflammation and muscle macrophage infiltration. We conclude that Endurance exercise training modulates the immune-metabolic crosstalk in obesity independently of weight loss, and may have potential benefits in reducing obesity-related muscle inflammation.
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) | 0.001 | 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) | 0.002 | 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".