Pro‐Inflammatory macrophages increase in skeletal muscle of high fat‐Fed mice and correlate with metabolic risk markers in humans
Bibliographic record
Abstract
OBJECTIVE: In obesity, immune cells infiltrate adipose tissue. Skeletal muscle is the major tissue of insulin-dependent glucose disposal, and indices of muscle inflammation arise during obesity, but whether and which immune cells increase in muscle remain unclear. METHODS: Immune cell presence in quadriceps muscle of wild type mice fed high-fat diet (HFD) was studied for 3 days to 10 weeks, in CCL2-KO mice fed HFD for 1 week, and in human muscle. Leukocyte presence was assessed by gene expression of lineage markers, cyto/chemokines and receptors; immunohistochemistry; and flow cytometry. RESULTS: After 1 week HFD, concomitantly with glucose intolerance, muscle gene expression of Ly6b, Emr1 (F4/80), Tnf, Ccl2, and Ccr2 rose, as did pro- and anti-inflammatory markers Itgax (CD11c) and Mgl2. CD11c+ proinflammatory macrophages in muscle increased by 76%. After 10 weeks HFD, macrophages in muscle increased by 47%. Quadriceps from CCL2-KO mice on HFD did not gain macrophages and maintained insulin sensitivity. Muscle of obese, glucose-intolerant humans showed elevated CD68 (macrophage marker) and ITGAX, correlating with poor glucose disposal and adiposity. CONCLUSION: Mouse and human skeletal muscles gain a distinct population of inflammatory macrophages upon HFD or obesity, linked to insulin resistance in humans and CCL2 availability in mice.
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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 | 0.001 | 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 | 0.000 |
| 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".