Diabetes Adversely Affects Macrophages During Atherosclerotic Plaque Regression in Mice
Bibliographic record
Abstract
OBJECTIVE: Patients with diabetes have increased cardiovascular risk. Atherosclerosis in these patients is often associated with increased plaque macrophages and dyslipidemia. We hypothesized that diabetic atherosclerosis involves processes that impair favorable effects of lipid reduction on plaque macrophages. RESEARCH DESIGN AND METHODS: Reversa mice are LDL receptor-deficient mice that develop atherosclerosis. Their elevated plasma LDL levels are lowered after conditional knockout of the gene encoding microsomal triglyceride transfer protein. We examined the morphologic and molecular changes in atherosclerotic plaques in control and streptozotocin-induced diabetic Reversa mice after LDL lowering. Bone marrow-derived macrophages were also used to study changes mediated by hyperglycemia. RESULTS: Reversa mice were fed a western diet for 16 weeks to develop plaques (baseline). Four weeks after lipid normalization, control (nondiabetic) mice had reduced plasma cholesterol (-77%), plaque cholesterol (-53%), and plaque cells positive for macrophage marker CD68+ (-73%), but increased plaque collagen (+116%) compared with baseline mice. Diabetic mice had similarly reduced plasma cholesterol, but collagen content increased by only 34% compared with baseline; compared with control mice, there were lower reductions in plaque cholesterol (-30%) and CD68+ cells (-41%). Diabetic (vs. control) plaque CD68+ cells also exhibited more oxidant stress and inflammatory gene expression and less polarization toward the anti-inflammatory M2 macrophage state. Many of the findings in vivo were recapitulated by hyperglycemia in mouse bone marrow-derived macrophages. CONCLUSIONS: Diabetes hindered plaque regression in atherosclerotic mice (based on CD68+ plaque content) and favorable changes in plaque macrophage characteristics after the reduction of elevated plasma LDL.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".