Palmitate- and lipopolysaccharide-activated macrophages evoke contrasting insulin responses in muscle cells
Bibliographic record
Abstract
Factors secreted by macrophages contribute to whole body insulin resistance, acting in part on adipose tissue. Muscle is the major tissue for glucose disposal, but how macrophage-derived factors impact skeletal muscle glucose uptake is unknown, or whether the macrophage environment influences this response. We hypothesized that conditioned medium from macrophages pretreated with palmitate or LPS would directly affect insulin action and glucose uptake in muscle cells. L6-GLUT4myc myoblasts were exposed to conditioned medium from RAW 264.7 macrophages pretreated with palmitate or LPS. Conditioned medium from palmitate-treated RAW 264.7 macrophages inhibited myoblast insulin-stimulated glucose uptake, GLUT4 translocation, and Akt phosphorylation while activating JNK p38 MAPK, decreasing IkappaBalpha, and elevating inflammation markers. Surprisingly, and opposite to its effects on adipose cells, conditioned medium from LPS-treated macrophages stimulated myoblast insulin-stimulated glucose uptake, GLUT4 translocation, and Akt phosphorylation without affecting stress kinases or inflammation indexes. This medium had markedly elevated IL-10 levels, and IL-10, alone, potentiated insulin action in myoblasts and partly reversed the insulin resistance imparted by medium from palmitate-treated macrophages. IL-10 neutralizing antibodies blunted the positive influence of LPS macrophage-conditioned medium. We conclude that myoblasts and adipocytes respond differently to cytokines. Furthermore, depending on their environment, macrophages negatively or positively influence muscle cells. Macrophages exposed to palmitate produce a mixture of proinflammatory cytokines that reduce insulin action in muscle cells; conversely, LPS-activated macrophages increase insulin action, likely via IL-10. Macrophages may be an integral element in glucose homeostasis in vivo, relaying effects of circulating factors to skeletal muscle.
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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 | 0.000 | 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".