Diabetes Adversely Affects Macrophages During Atherosclerotic Plaque Regression in Mice
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 it