Expression of macrophage genes within skeletal muscle correlates inversely with adiposity and insulin resistance in humans
Bibliographic record
Abstract
Local inflammation in obese adipose tissue has been shown to contribute to insulin resistance; however, the role of macrophage infiltration within skeletal muscle is still debatable. This study aimed to evaluate the association of skeletal muscle macrophage gene expression with adiposity levels and insulin sensitivity in obese patients. Twenty-two nondiabetic obese patients and 23 healthy lean controls were included. Obese patients underwent a 3-month weight loss intervention. Macrophage gene expression in skeletal muscle (quantitative real-time polymerase chain reaction), body composition (dual-energy X-ray absorptiometry), and insulin sensitivity (homeostatic model assessment (HOMA) and oral glucose tolerance test) were compared between groups and their associations were analyzed. To validate skeletal muscle findings, we repeated the analyses with macrophage gene expression in adipose tissue. Expression levels of macrophage genes (CD68, CD11b, CD206, CD16, CD40, and CD163) were lower in skeletal muscle tissue of obese versus lean participants. Macrophage gene expression was also found to be inversely associated with adiposity, fasting insulin, and HOMA (r = -0.4 ∼ -0.6, p < 0.05), as well as positively associated with insulin sensitivity (r = 0.4 ∼ 0.8, p < 0.05). On the other hand, adipose tissue macrophage gene expression showed higher levels in obese versus lean participants, presenting a positive association with adiposity levels. Macrophage gene expression, in both skeletal and adipose tissue samples, was only minimally affected by the weight loss intervention. In contrast with the established positive relationship between adiposity and macrophage gene expression, an unexpected inverse correlation between these 2 variables was observed in skeletal muscle tissue. Additionally, muscle macrophage gene expression was inversely correlated with insulin resistance.
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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) | 0.001 | 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".