Insulin increases macrophage triglyceride accumulation under diabetic conditions through the down regulation of hormone sensitive lipase and adipose triglyceride lipase
Bibliographic record
Abstract
Diabetes mellitus (DM) is a major risk factor for the development of atherosclerosis, and high-serum levels of insulin are strongly associated with type 2 DM. Atherosclerosis is characterized by lipid-laden macrophage foam cell formations, which contain substantial amount of cholesterol and triglycerides (TG). This study analyzed for the first time, the effects of insulin on TG metabolism in macrophages under normal and diabetic conditions. Mouse peritoneal macrophages from C57BL6 mice were cultured under normal (5 mM) or high (diabetic condition, 25 mM) glucose concentration, with or without insulin, followed by the assessment of TGs metabolism in these cells. Under diabetic condition, insulin increased TG accumulation in macrophages by 100%, decreased cellular TG degradation by 21%, and increased C-reactive protein levels in macrophages by 83%. Insulin decreased hormone-sensitive lipase mRNA and protein expression by 28 and 60%, respectively, and adipose TG lipase (ATGL) protein expression by 36%, with no significant reduction in ATGL mRNA levels. The inhibition of insulin-mediated phosphorylation, and the addition of cyclic adenosine 3'5'-monoposphate, abolished the insulin-mediated inhibition of TGs degradation in cells. Insulin increases macrophage TGs accumulation only under diabetic conditions, suggesting that impaired glycemic control in diabetic patients treated with insulin may contribute to foam cell formations and enhanced inflammation in macrophages.
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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 | 0.000 | 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 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".