Absence of CC Chemokine Ligand 2 Does Not Limit Obesity-Associated Infiltration of Macrophages Into Adipose Tissue
Bibliographic record
Abstract
Macrophage recruitment to adipose tissue in obesity contributes to enhanced adipose tissue inflammatory activity and thus may underlie obesity-associated metabolic dysfunction. Obese adipose tissue exhibits increases in CC chemokine ligand 2 (CCL2, or monocyte chemoattractant protein-1), an important macrophage-recruiting factor. We therefore hypothesized that elevated CCL2 may contribute to obesity-associated adipose tissue macrophage recruitment. Male 6-week-old CCL2(-/-) and wild-type mice (n = 11-14 per group) were fed standard and high-fat diets until 34 weeks of age. At 12-16 and 25-29 weeks of age, blood was collected for plasma glucose and hormone measurements, and glucose tolerance and insulin tolerance tests were performed. Adipose tissue was collected at 34 weeks for analysis of macrophage infiltration. Surprisingly, CCL2(-/-) mice on high-fat diet showed no reductions in adipose tissue macrophages. CCL2(-/-) mice on standard and high-fat diet were also glucose intolerant and had mildly increased plasma glucose and decreased serum adiponectin levels compared with wild-type mice. On high-fat diet, CCL2(-/-) mice also gained slightly more weight and were hyperinsulinemic compared with wild-type mice. Because macrophage levels were unchanged in CCL2(-/-) mice, the phenotype appears to be caused by lack of CCL2 itself. The fact that metabolic function was altered in CCL2(-/-) mice, despite no changes in adipose tissue macrophage levels, suggests that CCL2 has effects on metabolism that are independent of its macrophage-recruiting capabilities. Importantly, we conclude that CCL2 is not critical for adipose tissue macrophage recruitment. The dominant factor for recruiting macrophages in adipose tissue during obesity therefore remains to be identified.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".