Obesity and muscle‐macrophage crosstalk in humans and mice: A systematic review
Bibliographic record
Abstract
Obesity is associated with the production of inflammatory cytokines that are implicated in insulin resistance (IR), and if not addressed, can lead to type 2 diabetes (T2D). The role of the immune system in skeletal muscle (SM) inflammation and insulin sensitivity is not yet well characterized. As SM IR is an important determinant of glycaemia, it is critical that the muscle-immune phenotype is mapped to help design interventions to target T2D. This systematic review synthesized the evidence for SM macrophage content and phenotype in humans and murine models of obesity, and the association of muscle macrophage content and phenotype with IR. Results were synthesized narratively, as we were unable to conduct a meta-analysis. We included 28 studies (n=10 human, n=18 murine), and all studies detected macrophage markers in SM. Macrophage content was positively associated with IR. In humans and mice, there was variability in muscle macrophage content and phenotype in obesity. Overall certainty in the evidence was low due to heterogeneity in detection methods and incompleteness of data reporting. Macrophages are detected in human and murine SM in obesity and a positive association between macrophage content and IR is noted; however, the standardization of markers, detection methods, and reporting of study details is warranted to accurately characterize macrophages and improve the potential for creating specific and targeted immune-based therapies in obesity.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".